The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Program of The Twelfth International Symposium on

ARTIFICIAL LIFE AND ROBOTICS(AROB 12th '07)

January 25-27, 2007
B-Con Plaza, Beppu, Oita, Japan,

Editors:©ISAROB 2007

Masanori Sugisaka and Hiroshi Tanaka

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

THE TWELFTH INTERNATIONAL SYMPOSIUM

ONARTIFICIAL LIFE AND ROBOTICS(AROB 12th '07)

ORGANIZED BY
Organizing Committee of International Symposium on Artificial Life and Robotics(Department of Electrical and Electronic Engineering, Oita University, Japan)

OPERATED BY
Santa Fe Institute (SFI, USA)The Robotics Society of Japan (RSJ, Japan)
The Institute of Electrical Engineers of Japan (IEEJ, Japan)
Institute of Control, Automation and Systems Engineers (ICASE, Korea)
Chinese Association for Artificial Intelligence (CAAI, P. R. China)

CO-OPERATED BY
The Institute of Systems, Control and Information Engineers (ISCIE)The Institute of Electronics, Information and Communication Engineers (IEICE)The Institute of Electrical and Electronics Engineers, Inc.(IEEE Japan Council)
Japan Robot Association (JARA)The Society of Instrument and Control Engineers (SICE, Japan)

SPONSORED BY
Japan Society for the Promotion of Science (JSPS)
Mitsubishi Electric Corporation Advanced Technology R&D Center
Oita Gas Co., Ltd.
ME System Co., Ltd.
Sanwa Shurui Co., Ltd.

SUPPORTED BY
Kyushu Bureau of Economy, Trade and Industry, Ministry of Economy, Trade and Industry
Oita Prefecture
Oita City
Beppu City
The Federation of Oita Prefecture Chambers of Commerce and Industry
The Oita Prefectural Organization of the Industrial Groups
Kyodo News
JIJI PRESS
Oita Godo Shimbunsya
The Asahi shimbun Co.
The Mainichi Newspapers Co.
The Yomiuri Shimbun Seibuhonsya
The Nishinippon Newspaper Co.
The NIKKAN KOGYO SHIMBUM. LTD.
NHK Oita Station
Oita Broadcasting System©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Television Oita System corporation
Oita Asahi Broadcasting Co., Ltd.

ADVISORY COMMITTE CHAIRMAN
F. Harashima (Tokyo Denki University, Japan)

GENERAL CHAIRMAN
M. Sugisaka (Oita University, Japan)

PROGRAM CHAIRMAN
H. Tanaka (Tokyo Medical & Dental University, Japan)

VICE-CHAIRMAN
J. J. Lee (KAIST, Korea)
J. L. Casti (Technical University of Vienna, Austria)
Y. G. Zhang (Academia Sinica, P. R. China)

ADVISORY COMMITTEE
T. Fukuda (Nagoya University, Japan)
F. Harashima (Tokyo Denki University, Japan)
H. Kimura (RIKEN, Japan)
M. Tomizuka (University of California Berkeley, USA)

INTERNATIONAL ORGANIZING COMMITTEE
K. Aihara (The University of Tokyo, Japan)
C. Barrett (Los Alamos National Laboratory, USA)M. Bedau (Reed College, USA)
J. L. Casti (Technical University of Vienna,Austria)
M. Eaton (University of Limerick, Ireland)
J. M. Epstein (The Brookings Institution, USA)T. Fukuda (Nagoya University, Japan)
H. Hashimoto (The University of Tokyo, Japan)
D. J. G. James (Coventry University, UK)
K. Kyuma (Mitsubishi Electric Corporation, Advanced Technology
R&D Center, Japan)
C. G. Langton (Santa Fe Institute, USA)M. H. Lee (Pusan National University, Korea)
J. J. Lee (KAIST, Korea)
S. Rasmussen (Santa Fe Institute, USA)T. S. Ray (University of Oklahoma, USA)
M. Sugisaka (Oita University, Japan) (Chairman)
C. Taylor (University of California-Los Angeles, USA)W. R. Wells (University of Nevada-Las Vegas, USA)Y. G. Zhang (Academia Sinica, P. R. China)©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

INTERNATIONAL PROGRAM COMMITTEE
K. Abe (Nihon University, Japan)
K. Aihara (The University of Tokyo, Japan) (Co-chairman)
T. Arita (Nagoya University, Japan)
M. Bedau (Reed College, USA)R. Belew (University of California-San Diego, USA)
S- M. Chen (National Taiwan University of Science and Technology, Taiwan)
Y. Y. Fan (University of California-Davis, USA)S. H. Han (Kyungnam University, Korea)
H. Hashimoto (The University of Tokyo, Japan)(Co-chairman)
N. Homma (Tohoku University, Japan)
H. Inooka (Tohoku University, Japan)
S. Ishikawa (Kyushu Institute of Technology, Japan)
T. Ishimatsu (Nagasaki University, Japan)
T. Iwamoto (Mitsubishi Electric Corporation, Advanced
Technology R&D Center, Japan)
J. Johnson (The Open University, UK)
O. Katai (Kyoto University, Japan)
M. Kono (University of Miyazaki, Japan)
H. Kawamura (Hokkaido University, Japan)
S. Kumagai (Osaka University, Japan)
J. M. Lee (Pusan National University, Korea)
H. H. Lund (University of Southern Denmark, Denmark)M. Minami (Fukui University, Japan)
T. Nagashima (Muroran Institute of Technology, Japan)
M. Nakamura (Saga University, Japan)
K. Nakano (The University of Electro-Communications, Japan)
K. Naitoh (Wasada University, Japan)
J. Nishii (Yamaguchi University, Japan)
W. Nistico (University of Dortmund, Germany)
S. Omatu (Osaka Prefecture University, Japan)
K. Onishi (Niigata University, Japan)
M. Okamoto (Kyushu University, Japan)
M. Osano (Aizu University, Japan)
M. Oswald (Vienna University of Technology, Austria)
R. Pfeifer (University of Zurich-Irchel, Switzerland)
T. S. Ray (University of Oklahoma, USA) (Co-chairman)
S. Sagara (Kyushu Institute of Technology, Japan)
P. Sapaty (National Academy of Sciences of Ukraine, Ukraine)
T. Sawaragi (Kyoto University, Japan)
T. Shibata (AIST, Japan)
K. Shimohara (Doshisha University, Japan)
K- B. Sim (Chung-Ang University, Korea)
K. Sugawara (Tohoku Gakuin University, Japan)
M. Sugisaka (Oita University, Japan)
H. Suzuki (The University of Tokyo, Japan)
H. Tanaka (Tokyo Medical & Dental University, Japan) (Chairman)
M. Tanaka-Yamawaki (Tottori University, Japan)
K. Tsuchiya (Kyoto University, Japan)
K. Uosaki (Osaka University, Japan)
H. Umeo (Osaka Electro-Communication University, Japan)
K. Watanabe (Saga University, Japan)
T. Yamamoto (University of Ryukyus, Japan)©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

H. Yanagimoto (Osaka Prefecture University, Japan)
M. Yano (Tohoku University, Japan)
Y. Yin (The Beijing University of Science and Technology, P. R. China)
M. Yokota (Fukuoka Institute of Technology, Japan)

LOCAL ARRANGEMENT COMMITTEE
S. Sagara (Kyushu Institute of Technology, Japan)
M. Rizon (Northern Malaysia University College of Engineering, Malaysia)
M. Sugisaka (Oita University, Japan) (Chairman)
J. Wang (Oita University, Japan)
X. Wang (Niihama National College of Technology Japan)
F. Dai (Matsue National College of Technology, Japan)
M. Ito (Konan University, Japan)
R. Mamat (Terengganu Advanced Technical Institute, Malaysia)

HISTORY
This symposium was founded in 1996 by the support of Science and International Affairs
Bureau, Ministry of Education, Culture, Sports, Science and Technology, Japanese
Government. Since then, this symposium has been held every year at B-Con Plaza, Beppu,
Oita, Japan except in Oita, Japan (AROB 5th ’00) and in Tokyo, Japan (AROB 6th ’01). The
twelfths symposium will be held on 25-27 January, 2007, at B-Con Plaza, Beppu, Oita, Japan.
This symposium invites you all to discuss development of new technologies concerning
Artificial Life and Robotics based on simulation and hardware in the twenty first century.

OBJECTIVE
The objective of this symposium is the development of new technologies for artificial life androbotics which have been recently born in Japan and are expected to be applied in various
fields. This symposium will discuss new results in the field of artificial life and robotics.

TOPICS
Artificial brain research
Artificial intelligence
Artificial life
Artificial living
Artificial mind research
Bioinformatics
Brain science
Chaos
Cognitive science
Complexity
Computer graphics
Evolutionary computations
DNA computing
Fuzzy control
Genetic algorithms
Human-machine cooperative systems
Human-welfare robotics
Innovative computations
Intelligent control and modeling©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Micromachines
Micro-robot world cup soccer tournament
Mobile vehicles
Molecular biology
Multi-agent systems
Nano-biology
Nano-robotics
Neural networks
Neurocomputers
Neurocomputing technologies and its application for hardware
Pattern recognition
Robotics
Robust virtual engineering
Virtual reality
OthersCOPYRIGHTS
Accepted papers will be published in the proceeding of AROB and some of high quality
papers in the proceeding will be requested to re-submit their papers for the consideration of
publication in an international journal ARTIFICIAL LIFE AND ROBOTICS and APPLIED
MATHEMATICS AND COMPUTATION. All correspondence related to the symposium
should be addressed to AROB Secretariat.

Dept. of Electrical and Electronic Engineering,
Oita University
700 Dannoharu, Oita 870-1192, JAPAN
TEL：+81-97-554-7841, FAX：+81-97-554-7818
E-MAIL arobsecr@cc.oita-u.ac.jp
Home Page http://arob.cc.oita-u.ac.jp©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

MESSAGES

Fumio Harashima
Advisory Committee Chairman of AROB

Fumio Harashima
Advisory Committe Chairman(President, Tokyo Denki
University)

The science and technology (S&T) on Artificial Life and Robotics is newlyborn recently. This new S&T provides human being with happiness. Researchis heart and desire of human being and the S&T is going toward clarifying
human mind and heart. Artificial Life and Robotics provides us with a strong
tool to achieve our objective.
I would like to congratulate researchers who work in the fields on Artificial
Life and Robotics.

Masanori Sugisaka
General Chairman of AROB

Masanori Sugisaka
General Chairman(Professor, Oita University)©ISAROB 2007

It is my great honor to invite you all to the Twelfth International
Symposium on Artificial Life and Robotics (AROB 12th ’07).
The symposiums from the first (1996) to the Eleventh (2006) wereorganized by Oita University under the sponsorship of the Science andTechnology Policy Bureau, the Ministry of Education, Science, Sports, andCulture (Monbusho), presently, the Ministry of Education, Culture, Sports,
Science, and Technology (Monkasho), Japanese Government, The
Commemorative Organization for the Japan World Exposition (’70), Air
Force Office of Scientific Research, Asian Office of Aerospace Research andDevelopment (AFOSR/AOARD), USA and co-operated by Santa Fe
Institute (USA), RSJ, IEEJ, ICASE (Korea), CAAI (P. R. China), ISCIE,IEICE, IEEE (Japan Council), JARA , and SICE.
I would like to express my sincere thanks to Monkasho, The
Commemorative Organization for the Japan World Exposition (’70) andscientific societies for their repeated support.
This Twelfth symposium is sponsored by Japan Society for the Promotionof Science (JSPS) and Japanese companies (Mitsubishi Electric Corporation
Advanced Technology R&D Center, Oita Gas Co., Ltd., ME System
Co.,Ltd., Sanwa Shurui Co., Ltd.). I would like to express special thanks for
JSPS and the companies stated above.
The symposium invites you to discuss the development of newtechnologies in the 21st century concerning Artificial Life and Robotics,
based on simulation and hardware.
We hope that AROB 12th ’07 will facilitate the establishment of aninternational joint research institute on Artificial Life and Robotics in future.
I hope that you will obtain fruitful results from exchange of ideas between
researchers during the symposium.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Hiroshi Tanaka
Program Chairman of AROB

Hiroshi Tanaka
Program Chairman(Professor, Tokyo Medical
and Dental University)©ISAROB 2007

On behalf of the program committee, it is my great pleasure and honor toinvite you all to the Twelfth International Symposium on Artificial Life andRobotics (AROB 12th ’07). This symposium is made possible owing to thecooperation of Oita University and Santa Fe Institute. We are also debt toJapanese academic associations such as SICE, RSJ, and several privatecompanies. I would like to express my sincere thanks to all of those who
make this symposium possible.
As is needless to say, the Alife and biologically-inspired Robotics
approach now attracts wide interests as a new paradigm of science andengineering. Take an example in the field of bioscience, the accomplishmentof HGP (Human Genome Project) and subsequent post-genomic
comprehensive “Omics data” such as transcriptome, proteome andmetabolome, bring about vast amount of bio-information. However, as aplenty of omics data becomes available, it becomes sincerely recognized thatthe framework by which these omics data can be understood to make awhole picture of life is critically necessary. Thus, in the post-genomic era,
Alife approach is expected to give one of new alternative ideas to integrate
this vast amount of bio-data.
This example shows the Alife approach is very promising and becomeswidely accepted as a paradigm of next generation of life science. We hope
this symposium becomes a forum for exchange of the ideas of the attendants
from various fields who are interested in the future possibility of complex
systems approach.
I am looking forward to meeting you in Beppu, Oita.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Y. G. Zhang
Vice Chairman of AROB

Y. G. Zhang
Vice Chairman(Professor, Academia Sinica)©ISAROB 2007

Dear all friends and all participants,
Here I would like to show my warm welcome to you all attending the
Twelfth International Symposium on Artificial Life and Robotics(AROB ’07). As you know, this annual symposium was founded in 1996 by
the support of Science and International Affairs Bureau, Ministry of
Education, Science, Sports, and Culture (presently, Ministry of Education,
Culture, Sports, Science and Technology) of Japan. Since then AROB is
gradually become worldwide famous international symposium. Now
AROB is already not an “academic baby”, but “academic teenage”, and
AROB also owns an international journal, named the “Journal of Artificial
Life and Robotics” published by Springer in last nine years.
The objective of AROB is aimed to develop new technologies for
Artificial Life and Robotics which have been born recently. The important
devotion of AROB is not only to pay attention to the development of theory
on Artificial Life, but also expected to apply the principle to various fields,
especially, the combination of both the Artificial Life and Robotics
together. So far there are only few international symposiums or conference
on artificial life, however, AROB is the only one that to explore the newgeneration of Robotics in far-sight with artificial life principle. This is avery great and difficult career, and need continuous and consistent efforts
of more and more scientists and engineers. All participants to AROB are
the part of team and we are devoting to the same target.
Beppu, the place of held most AROB symposium, is very charming cityin Japan, she has variety of hot spring (jigoku), beautiful bay and colorful
mountains. Her phantasmagoric visual change in various season attract
many tourists. I hope all of you enjoy and appreciate her.
Finally, I would like to show my great thanks to all people who are
working for this AROB’07, including all staffs of AROB Lab, secretariat,
and students, the successful holding of AROB symposium is dependent on
the contributions of you all.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Ju-Jang Lee
Vice Chairman of AROB

Ju-Jang Lee
Vice Chairman(Professor, KAIST)©ISAROB 2007

The Twelfth International Symposium on Artificial Life and Robotics(AROB) will be held in Beppu, Oita, JAPAN from Jan. 25th to 27th, 2007.
This year's Symposium will be held amidst the high expectations of theincreasingly important role of the new interdisciplinary paradigm of scienceand engineering represented by the field of artificial life and robotics that
continuously attracts wide interests among scientist, researchers, andengineers around the globe.
Since the time of the very first AROB meeting in 1996, each year,
distinguished researchers and technologists from around the world arelooking forward to attending and to meeting at AROB. AROB is becoming
the annual excellent forum that represents a unique opportunity for theacademic and industrial communities to meet and assess the latest
developments in this fast growing artificial life and robotics field. AROB
enables them to address new challenges and share solutions, discuss research
directions for the future, exchange views and ideas, view the results ofapplied research, present and discuss the latest development of newtechnologies and relevant applications.
In addition, AROB offers the opportunity of hearing the opinions of wellknown leading experts in the field through the keynote sessions, provides thebases for regional and international collaborative research, and enables toforesee the future evolution of new scientific paradigms and theories
contributed by the field of artificial life and robotics and associated researcharea. The twenty-first century will become the century of artificial life andintelligent machines in support of humankind and AROB is contributing
through wide technical topics of interest that support this direction.
It is a great for me as the Vice Chairman of the 12th AROB 2007 to
welcome everyone to this important event. Also, I would like to extend myspecial thanks to all the authors and speakers for contributing their researchworks, the participants, and the organizing team of the 12th AROB.
Looking forward to meeting you at the 12th AROB in Beppu-Oita andwishing you all the best.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

TIME TABLE
RoomA
1/24

13:00

(Wed)

17:00

RoomB

RoomC

RoomD

Registration (Registration Desk)
Welcome Party (at Hanabishi Hotel)

1/25

8:00

(Thur)

8:40

Registration (Registration Desk)
GS 22 ( 6 )OS 2 ( 6 )OS 14 ( 6 )Chair M. Zhao

Chair M. Kono

Chair S-H. Han

10:10
10:30

Coffee Break
Ceremony
Plenary Talk
PT1 Y. G. Zhang
Chair M. Nakamura

11:40
Lunch
12:40
GS 23 ( 5 )OS 6 ( 4 )

GS4 ( 3 )Chair M. Eaton

Chair S. Omatu

Chair G. Cui

will end at 13:40

13:55
14:05

will end at 13:25
Coffee BreakGS 24 ( 4 )GS 2 ( 4 )

GS5 ( 5)Chair I. H. Suh

Chair M. Nakamura

Chair V. Trifa

GS 20 ( 4 )GS 3 ( 4 )Chair D. Ai

Chair P. Sapaty

will end at 15:20
15:05

GS 15 ( 4 )Chair S. Yaacob

16:05

GS 21 ( 5 )OS 1 ( 5 )will end at 16:20

Chair K. Ikeda

Chair O. Sato

GS 16 ( 4 )Chair D. H. Kim

17:20
GS: General Session

OS: Organized Session

GS1 Artificial Brain Research & Brain Science
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life-Ⅰ
GS5 Artificial Life-Ⅱ& Artificial Living
GS6 Bioinformatics & Chaos
GS7 Cognitive Science & Evolutionary Computations
GS8 Complexity
GS9 Computer Graphics-Ⅰ
GS10 Computer Graphics-Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms
GS13 Innovative Computations-Ⅰ
GS14 Innovative Computations-Ⅱ& Human- Machine Cooperative Systems
GS15 Intelligent Control and Modeling-I
GS16 Intelligent Control and Modeling-Ⅱ
GS17 Intelligent Control and Modeling-Ⅲ
GS18 Mobile Vehicles-Ⅰ
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-Ⅰ©ISAROB 2007

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GS21 Neural Networks-Ⅱ& Neurocomputers
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Robotics-Ⅳ
GS26 Robotics-Ⅴ& Micro-Robot World Cup Soccer Tournament
GS27 Robust Virtual Engineering & Virtual Reality
OS1 Intelligent Systems and Robotics
OS2 Automata and Control
OS3 Models for Neural Networks and Brains
OS4 Methods for Complex Systems: Feature Identification and Control
OS5 Fundamental Research on Recognition and Control
OS6 Intelligent Networks
OS7 Financial Market and Related Topics
OS8 Intuitive Human-System Interaction
OS9 Biomimetic Machines and Robots
OS10 Soft Robotics
OS11 Intelligent Robot Control and Sensors
OS12 Bio-inspired Approach and Application
OS13 Membrane Computing. From Calls to Computers and Back
OS14 Robot Control and Application

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

RoomA
1/26

8:00

(Fri)

8:40

RoomB

RoomC

RoomD

Registration (Registration Desk)
OS 10

(4)

Chair H. Kinjo

GS 11

(4)

GS 12 ( 7 )Chair K. B. Sim

Chair S. Sagara

9:40
Poster Session
PS1, PS2, PS3, PS4
10:25

Coffee Break

10:40

Plenary Talk
PT2 H. Tsumura
Chair M. Sugisaka

11:30
12:30

Lunch
GS 25 ( 5 )GS 18

Chair Y. Wang

Chair H. Hashimoto

Chair H. Suzuki

GS 26 (6 )GS 19

GS 17 ( 6 )Chair K. Uosaki

Chair J. Wang

(6)

OS 3

(6)

will end at 13:45
14:00

(6)

Chair A. Kanasugi

will end at 15:15
15:30
15:40

Coffee BreakGS 9 ( 3 )OS13 ( 4 )OS 4 ( 6 )Chair S. Ishikawa

Chair M. Oswald

Chair T. Kohno

will end at 16:25
16:40

Invited Talks session

GS 10 ( 4 )Chair H. Furutani

will end at 17:25

IT1

H. H. Lund

IT2

L. Pagliarini

Chair M. Rizon
will end at 17:30

18:10
20:10
GS: General Session

GS 6 ( 3 )Chair K. Naitoh
will end at 17:55

AROB Award Ceremony (Chair K. Watanabe )Banquet – Hotel Shiragiku (Chair S. Sagara)...... Welcome Address T. Fukuda / J. J. Lee
OS: Organized Session

GS1 Artificial Brain Research & Brain Science
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life-Ⅰ
GS5 Artificial Life-Ⅱ& Artificial Living
GS6 Bioinformatics & Chaos
GS7 Cognitive Science & Evolutionary Computations
GS8 Complexity
GS9 Computer Graphics-Ⅰ
GS10 Computer Graphics-Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms
GS13 Innovative Computations-Ⅰ
GS14 Innovative Computations-Ⅱ& Human- Machine Cooperative Systems
GS15 Intelligent Control and Modeling-I
GS16 Intelligent Control and Modeling-Ⅱ
GS17 Intelligent Control and Modeling-Ⅲ
GS18 Mobile Vehicles-Ⅰ
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-Ⅰ©ISAROB 2007

will end at 17:10

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GS21 Neural Networks-Ⅱ& Neurocomputers
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Robotics-Ⅳ
GS26 Robotics-Ⅴ& Micro-Robot World Cup Soccer Tournament
GS27 Robust Virtual Engineering & Virtual Reality
OS1 Intelligent Systems and Robotics
OS2 Automata and Control
OS3 Models for Neural Networks and Brains
OS4 Methods for Complex Systems: Feature Identification and Control
OS5 Fundamental Research on Recognition and Control
OS6 Intelligent Networks
OS7 Financial Market and Related Topics
OS8 Intuitive Human-System Interaction
OS9 Biomimetic Machines and Robots
OS10 Soft Robotics
OS11 Intelligent Robot Control and Sensors
OS12 Bio-inspired Approach and Application
OS13 Membrane Computing. From Calls to Computers and Back
OS14 Robot Control and Application

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

RoomA
1/27

8:00

(Sat)

8:40

RoomC

RoomD

Registration (Registration Desk)
OS 5

(3)

Chair F. Dai
9:25

10:40

RoomB

Invited Talks session

GS 8 ( 3 )IT3 B. Pillai

Chair Y. Ishida

Chair J. J. Lee
OS 12 ( 5 )GS 1 ( 5 )OS 8 ( 5 )Chair I. Yoshihara

Chair X. Wang

Chair M. Yokota
Coffee Break

10:50

Plenary Talk
PT3 Y. I. Cho
Chair Y. G. Zhang

11:40
Lunch

12:40
GS 13

(4)

Chair T. Arita

GS 7

GS 27 ( 3 )(4)Chair T. Kondo

Chair J. Du
will end at 13:25

13:40
GS 14

(3)

Chair K. Yamasaki

OS 9

OS 11 ( 6 )(6)Chair K. Watanabe

Chair Y. I. Cho

will end at 14:25
OS 7
15:10

will end at 14:55(3)

Chair M. Tanaka- Y
Farewell Party (Room B)

16:10
GS: General Session

OS: Organized Session

GS1 Artificial Brain Research & Brain Science
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life-Ⅰ
GS5 Artificial Life-Ⅱ& Artificial Living
GS6 Bioinformatics & Chaos
GS7 Cognitive Science & Evolutionary Computations
GS8 Complexity
GS9 Computer Graphics-Ⅰ
GS10 Computer Graphics-Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms
GS13 Innovative Computations-Ⅰ
GS14 Innovative Computations-Ⅱ& Human -Machine Cooperative Systems
GS15 Intelligent Control and Modeling-I
GS16 Intelligent Control and Modeling-Ⅱ
GS17 Intelligent Control and Modeling-Ⅲ
GS18 Mobile Vehicles-Ⅰ
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-Ⅰ©ISAROB 2007

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GS21 Neural Networks-Ⅱ& Neurocomputers
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Robotics-Ⅳ
GS26 Robotics-Ⅴ& Micro-Robot World Cup Soccer Tournament
GS27 Robust Virtual Engineering & Virtual Reality
OS1 Intelligent Systems and Robotics
OS2 Automata and Control
OS3 Models for Neural Networks and Brains
OS4 Methods for Complex Systems: Feature Identification and Control
OS5 Fundamental Research on Recognition and Control
OS6 Intelligent Networks
OS7 Financial Market and Related Topics
OS8 Intuitive Human-System Interaction
OS9 Biomimetic Machines and Robots
OS10 Soft Robotics
OS11 Intelligent Robot Control and Sensors
OS12 Bio-inspired Approach and Application
OS13 Membrane Computing. From Calls to Computers and Back
OS14 Robot Control and Application

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

TECHNICAL PAPER INDEXJanuary 25 (Thursday)
Room D
10:50~11:40 Plenary Talk
Chair M. Nakamura (Saga University, Japan)
PT1 K(Knowledge)-Net: Building up and its dynamics
Y. G. Zhang (Academia Sinica, P. R. China)

January 26 (Friday)
Room B
16:40~17:30 Invited Talks Session
Chair M. Rizon (Northern Malaysia University College of Engineering(KUKUM), Malaysia)
IT1 Robomusic
H. H. Lund (University of Southern Denmark, Denmark)IT2

Polymorphic intelligence
L. Pagliarini (University of Southern Denmark, Denmark)Room D
10:40~11:30 Plenary Talk
Chair M. Sugisaka （Oita University, Japan）
PT2 A computer simulation in surgery for human hip joint
H. Tsumura (Oita University, Japan)

January 27 (Saturday)
Room B
8:40~9:25 Invited Talks Session
Chair J. J. Lee （Korea Advanced Institute of Science and the Technology, Korea）
IT3 Artificial intelligent inserts in healthcare systems
B. Pillai（Helsinki University of Technology, Finland）

Room D
10:50~11:40 Plenary Talk
Chair Y. G. Zhang (Academic Sinica, P. R. China)
PT3 Intelligent multi agent application system in AI system
Y. I. Cho (The University of Suwon, Korea)©ISAROB 2007

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January 25 (Thursday)
8:00~Registration

Room A
8:40~10:10 GS22 Robotics-Ⅰ
Chair: M. Zhao (Tsinghua University, P. R. China)
GS22-１ Hybrid tracking control of eye-in-hand robotic manipulators
W- C. Chang, C- W. Cho (National Taipei University of Technology, Taiwan)
GS22-2 Robust adaptive sliding-mode fuzzy-neural-network model-following position controlof PMSM servo drives for robotic applications
F. F. M. El-Sousy (Electronics Research Institute, Egypt)
GS22-3 Multi-agent robotics: towards energy autonomy
T. D. Ngo, H. Raposo, H. Schioler (Aalborg University, Denmark)GS22-4 Realization of a visuo-motor system based on multiple self-organizing maps in a 3D space
R. Uehara, N. Okada, E. Kondo (Kyushu University, Japan)
M. Han (TOYOTA AUTO BODY Co., LTD., Japan)
GS22-5 Development of an autonomous mobile system for an autonomous robot in an indoor
environment
T. Uewaki, E. Hayashi (Kyushu Institute of technology, Japan)
GS22-6 A study on improvement of the surveillance system of an indoor robot
T. Takaya (Richo Software Inc., Japan)
H. Kawamura, Y. Minagawa M. Yamamoto, A. Ohuchi (Hokkaido University, Japan)

12:40~13:55 GS23 Robotics-Ⅱ
Chair: M. Eaton (University of Limerich, Ireland)
GS23-1 Human face detection with neural networks and the direct algorithm
Y. Z. Chang, G. T. Hung (Chang Gung University, Taiwan)
S. T. Lee (Chang Gung Memorial Hospital, Taiwan)
GS23-2 High shock disturbance rejection of single-link robot arm with a disturbance observer
S. H. Kang, C. S. Kim, M. H. Lee (Pusan National University, Korea)
GS23-3 Learning landing control of indoor blimp robot for autonomous energy recharging
Y. Minagawa, H. Kawamura, M. Yamamoto (Hokkaido University, Japan)
T. Takaya (Richo Software Inc., Japan)
A. Ohuchi (Hokkaido University, Japan)
GS23-4 User-recognition system for an autonomous robot in human living environments
Y. Hane, E. Hayashi (Kyushu Institute of technology, Japan)©ISAROB 2007

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GS23-5 Development of a robot following human using color information
K. Tsugimura, J. K. Tan, S. Ishikawa (Kyushu Institute of Technology, Japan)

14:05~15:05 GS24 Robotics-Ⅲ
Chair: I. H. Suh (Hanyang University, Korea)
GS24-1 Development of a self-care robot
H. Henta, E. Hayashi (Kyushu Institute of Technology, Japan)
GS24-2 Adaptive control of a looper-like robot based on the CPG-Actor-Critic method
K. Makino (Nara Institute of Science and Technology, Japan)
Y. Nakamura (Osaka university, Japan)
T. Shibata, S. Ishii (Nara Institute of Science and Technology, Japan)
GS24-3 Explorations in evolutionary humanoid robotics
M. Eaton (University of Limerick, Ireland)
GS24-4 Vision based control for line following blimp robot
R. Nishioka, H. Kawamura, M. Yamamoto (Hokkaido University, Japan)
T. Takaya (Richo Software Inc., Japan)
A. Ohuchi (Hokkaido University, Japan)

15:05~16:05 GS20 Neural Networks-Ⅰ
Chair: D. Ai (University of Science and Technology Beijing, P. R. China)
GS20-1 Learning properties of recurrent neural network with parametric biases
K. Ikeda, K. Narita (Kyoto University, Japan)
GS20-2 Three dimensional medical image recognition of the brain by the feedback
GMDH-type neural network self-selecting optimum neural network architecture
T. Kondo, J. Ueno (The University of Tokushima, Japan)
GS20-3 Applying neural networks for diagnosis of cancer relapse
K. J. Mackin (Tokyo University of Information Sciences, Japan)
GS20-4 Learning how, what, and whether to communicate: emergence of protocommunicationin reinforcement learning agents
T. Sato, E. Uchida, K. Doya (IRP OIST, Japan)

16:05~17:20 GS21 Neural Networks-Ⅱ & Neurocomputers
Chair: K. Ikeda (Kyoto University, Japan)
GS21-1 A neural network model of the olfactory system of mice: computer simulation of an
attention behavior of mice for some components in an odor
Z. Soh, T. Tsuji, N. Takiguchi (Hiroshima University, Japan)
H. Ohtake (Osaka University, Japan)
GS21-2 Graphical analysis of time-series data from waste incinerator using self-organizing
map
T. Yamaguchi, K. J. Mackin (Tokyo University of information Sciences, Japan)©ISAROB 2007

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GS21-3 Design of a real time evaluation system for multiple neuro-biological signals
T. Zhang (Tsinghua University, P. R. China)
M. Nakamura, T. Sugi (Saga University, Japan)
H. Shibasaki (National Institutes of Health, USA)
GS21-4 Feedback GMDH-type neural network algorithm self-selecting optimum neuralnetwork architecture
T. Kondo, J. Ueno (The University of Tokushima, Japan)
GS21-5 Remarks on adaptive type neural network direct controller with separate learning ruleof each layer
T. Yamada (Ibaraki University, Japan)

Room B
8:40~10:10 OS2 Automata and Control
Chair: M. Kono (University of Miyazaki, Japan)
Co-Chair: M. Yokomichi (University of Miyazaki, Japan)
OS2-1 Three-dimensional parallel turing machines
T. Ito, M. Sakamoto, Y. Nagamizu, K. Iihoshi, N. Tomozoe,
H. Furutani, M. Kono, S. Ikeda (University of Miyazaki, Japan)
T. Tamaki (Ube National College of Technology, Japan）
K. Inoue (Yamaguchi University, Japan)
OS2-2 Remarks on recognizability of topological components by three-dimensional automata
Y. Nagamizu, M. Sakamoto, T. Ito, K. Iihoshi, N. Tomozoe, H. Furutani,
M. Kono, S. Ikeda (University of Miyazaki, Japan)
T. Tamaki (Ube National College of Technology, Japan)
K. Inoue (Yamaguchi University, Japan)
OS2-3 Leaf-size hierarchy of four-dimensional alternating turing machines
K. Iihoshi, M. Sakamoto, T. Ito, N. Tomozoe, Y. Nagamizu, H. Furutani,
M. Kono, S. Ikeda (University of Miyazaki, Japan)
T. Tamaki (Ube National College of Technology, Japan)
K. Inoue (Yamaguchi University, Japan)
OS2-4 A relationship between the accepting powers of alternating finite automata andnondeterministic on-line tessellation acceptors on four-dimensional input tapes
N. Tomozoe, M. Sakamoto, K. Iihoshi, Y. Nagamizu, T. Ito, H. Furutani,
M. Kono, S. Ikeda (University of Miyazaki, Japan)
T. Tamaki (Ube National College of Technology, Japan)
K. Inoue (Yamaguchi University, Japan)
OS2-5 Guaranteed cost control of discrete time system with performance index including
cross term
N. Takahashi, M. Kono, O. Sato (University of Miyazaki, Japan)
K. Hiranuma (Tokyo University of Mercantile Marine, Japan)©ISAROB 2007

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OS2-6 An LMI approach to observer-based guaranteed cost control
M. Miyachi, M. Ishitobi (Kumamoto University, Japan)
N. Takahashi, M. Kono (University of Miyazaki, Japan)

12:40~13:40 OS6 Intelligent Networks
Chair: S. Omatu (Osaka Prefecture University, Japan)
Co-Chair: H. Ido (Niihama National College of Technology, Japan)
OS6-1 Isomorphic structure of graphs with the maximum number of trees
H. Ido, Y. Ohama (Niihama National College of Technology, Japan)
S. Omatu (Osaka Prefecture University, Japan)
OS6-2 Relations between network reliability and number of trees of graph
H. Ido (Niihama National College of Technology, Japan)
S. Omatu (Osaka Prefecture University, Japan)
OS6-3 Acoustic signals separation of mechanical devices using the ICA with band-pass filters
S. Omatu (Osaka Prefecture University, Japan)
OS6-4 Reliability of bank note classifier by neural networks
S. Omatu (Osaka Prefecture University, Japan)

14:05~15:05 GS2 Artificial Intelligence-Ⅰ
Chair: M. Nakamura (Saga University, Japan)
GS2-1 A new concept of flexible organization for distributed robotized systems
P. Sapaty, A. Morozov (National Academy of Sciences, Ukraine)
M. Sugisaka (Oita University, Japan)
R. Finkelstein (Robotic Technology Inc., USA)D. Lambert (Defence Science and Technology Organisation, Australia)
GS2-2 Information display system using active projector in intelligent space-Integration of distributed devices based on RT-middleware
Y. Toshima (The University of Tokyo, Japan)
N. Ando (National Institute of Advanced Industrial Science and Technology (AIST),
Japan)
H. Hashimoto (The University of Tokyo, Japan)
GS2-3 Elementary developmental process of intentional agency:
Artificial construction of gaze alternation in communicative eye gaze by infants
T. Konno, T. Hashimoto (Japan Advanced Institute of Science and Technology, Japan)
GS2-4 The research of data mining for quantitative association rules and algorithm fornumerical attribute
J. Du (Beijing University of Posts and Telecommunications, P. R. China)
W. R. Jie (University of Science and Technology, P. R. China)(Paper Only)
GS2-5 Skill-based manipulation and error recovery in maintenance tasks
A. Nakamura, K. Kitagaki (National Institute of Advanced Industrial Science andTechnology (AIST), Japan)©ISAROB 2007

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15:20~16:20 GS3 Artificial Intelligence-Ⅱ
Chair: P. Sapaty (National Academy of Science, Ukraine)GS3-1 TV program recommender using user’s preference
J. Kim, S. Cheon, J. Kim, S. Kim (Pusan National University, Korea)
GS3-2 A multi-labeled classification method based on error-correcting output coding
T. Yamashita, T. Takenouchi, S. Ishii (Nara Institute of Science and Technology, Japan)
GS3-3 A control method of an electric vehicle
M. Sugisaka, H. Tanaka, M. Hara (Oita University, Japan)
GS3-4 The influence for human boredom in interaction by mutual prediction
M. Shimamoto, T. Shiose, H. Kawakami, O. Katai (Kyoto University, Japan)

16:20~17:35 OS1 Intelligent Systems and Robotics
Chair: O. Sato (University of Miyazaki, Japan)
Co-Chair: A. Date (University of Miyazaki, Japan)
OS１-1 A property of associative memory model with replacing units
A. Date (University of Miyazaki, Japan)
K. Kurata (University of the Ryukyus, Japan)
OS１-2 Force control of 6-DOF pneumatic joystick
T. Moriki (Kyushu Institute of Technology, Japan)
Y. Tanaka (Kitakyushu National College of Technology, Japan)
Y. Tanaka (Hosei University, Japan)
I. Yokomichi, Y. Hitaka, J. Ishii, (Kitakyushu National College of Technology, Japan)
C. Wada (Kyushu Institute of Technology, Japan)
OS１-3 Force analysis of the 6DOF parallel manipulators
Y. Hitaka, Y. Tanaka, J. Ishii (Kitakyushu National College of Technology, Japan)
OS１-4 Braille block recognition for an autonomous wheelchair
M. Tabuse (Kyoto Prefectural University, Japan)
Y. Takehara (NTT Date Sanyo System Corp., Japan)
OS１-5 Throwing motion of manipulator with passive revolute joint
A. Sato (Miyakonojo National College of Technology, Japan)
O. Sato, N. Takahashi, M. Kono (University of Miyazaki, Japan)

Room C
8:40~10:10 OS14 Robot Control and Application
Chair: S-H. Han (Kyungnam University, Korea)
OS14-1 A study on real time intelligent control of a three fingers hand system
X- T. Le, S- B. Oh, W- S. Lee, W- I. Kim, S- H. Han (Kyungnam University, Korea)©ISAROB 2007

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OS14-2 A Study on design of multifingered robot hand with 15 D.O.F and applications
X- T. Le, S- B. Oh, H. B. Shin, Y. K. Kim, S- H. Han (Kyungnam University, Korea)
OS14-3 Accuracy improvement of a 5-axis hybrid machine tool
J- W. Kim, H- Y. Kim, H- S. Kim (Kyungnam University, Korea)
OS14-4 The design of automatic grease lubricator type of the cylindrical cam piston pump
W. Choi, J. Yang, H. S. Koo, D. H. Wang, S- H. Lee (Kyungnam
University, Korea)
OS14-5 Non-contact 3-D surface profiler using optical fiber
Y. K. Kwon, S. H. Kim, E. C. Heo, B. C. Kim (Kyungnam University, Korea)
OS14-6 Fault tolerant control of magnetic actuators
U. J. Na, J. S. Won, D. D. Lee, C. W. Ju (Kyungnam University, Korea)

12:40~13:25 GS4 Artificial Life-Ⅰ
Chair: G. Cui (Zhengzhou University of Light Industry, P. R. China)
GS4-1 Automated wildlife monitoring using self-configuring sensor networks deployed innatural habitats
V. Trifa (ATR HRCN, Japan)
L. Girod (MIT, USA)
T. Collier, D. Blumstein, C. Taylor (UCLA, USA)
GS4-2 Analysis of circuit breakers using artificial stock market
S. Kobayashi, T. Hashimoto (Japan Advanced Institute of Science and Technology, Japan)
GS4-3 Interactive musical editing system to support human errors and offer personal
preferences for an automatic piano : Method of searching for similar phrases with DP
matching and inferring performance expression
Y. Hikisaka, E. Hayashi (Kyushu Institute of Technology, Japan)

14:05~15:20 GS5 Artificial Life-Ⅱ& Artificial Living
Chair: V. Trifa (ATR HRCN, Japan)
GS5-1 Self-reproduction on 1-bit communication cellular automata
R. Takebayashi, H.Umeo (University of Osaka Electro-Communication, Japan)
GS5-2 On the abundance of energy sources and evolution of collective swarm in
auto-constructive artificial life
A. R. Adzni, J. Teo, A. Saudi (Universiti Malaysia Sabah, Malaysia)
GS5-3 Interactive musical editing system to support human errors and offer personal
preferences for an automatic piano –information system about a musical signY. Chigoi, E. Hayashi (Kyushu Institute of Technology, Japan)
GS5-4 Cognitive modeling of artificial fish learning and memory
D. Ai, X. Ban (University of Science and Technology Beijing, P. R. China)
S. Zhang (Ocean University of China, P. R. China)
W. Wang (Shandong Agriculture Mechanism Administration, P. R. China)©ISAROB 2007

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GS5-5 Design of robotic behavior that imitates animal consciousness
N. Goto, E. Hayashi (Kyushu Institute of Technology, Japan)

15:20~16:20 GS15 Intelligent Control and Modeling-I
Chair: S. Yaacob (Northern Malaysia University college of Engineering,
Malaysia)
GS15-1 Design and implementation of a use friendly JAVA based GUI software formaneuvering mobile robot
T. C. Manjunath (Indian Institute of Technology Bombay, India)
GS15-2 The effect of occasional rational decision on the cooperative relationship between
groups
T. Ohdaira, H. Ohashi (University of Tokyo, Japan)
GS15-3 Intelligent motor control using advanced bacterial foraging combined with immune
algorithm
Y. D. Kim, D. H. Kim (Hanbat National University, Korea)
GS15-4 Unmanned navigation of container transporter using U-SAT (Ultrasonic satellite
system)K. S. Lee, S. Y. Kim, T. Y. Shin, J. M. Lee, M. H. Lee (Pusan National University,
Korea)

16:20~17:20 GS16 Intelligent Control and Modeling-Ⅱ
Chair: D. H. Kim (Hanbat National University, Korea)
GS16-1 Spreader pose control using dual-electric compasses
S. Han, H. Jeong, J. Lee (Pusan National University, Korea)
GS16-2 P2P sensor data mining system for school temperature measurement system
A. Niimi, H. Shimada, R. Goto, M. Wada, K. Ito, O. Konishi (Future University–Hakodate, Japan)
GS16-3 A probabilistic modeling of MOSAIC learning
S. Osaga, J. Hirayama, T. Takenouchi, S. Ishii (Nara Institute of Science andTechnology, Japan)
GS16-4 Iterative learning control for linear time-variant continuous systems based on
two-dimensional system theory
D. Meng, Y. Jia (Beihang University, P. R. China)

January 26 (Friday)
8:00~ Registration

Room A©ISAROB 2007

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8:40~9:40 OS10 Soft Robotics
Chair: H. Kinjo (University of the Ryukyus, Japan）
Co-Chair: K. Kurata (University of the Ryukyus, Japan)
OS10-1 Solution searching for multi-variable optimization problems by GA with momentum
offspring
H. Kinjo, S. D. Chau, M. Maeshiro, K. Nakazono, T. Yamamoto (University of the
Ryukyus, Japan)
OS10-2 Improvement of training method for dynamic neural network
K. Nakazono (University of the Ryukyus, Japan)
K. Ohnishi (Keio University, Japan)
H. Kinjo, T. Yamamoto (University of the Ryukyus, Japan)
OS10-3 A learning model of head-direction cells and grid cells by VQ layers connected via
anti-Hebbian synapses
N. Oshiro, K. Kurata, T. Yamamoto (University of the Ryukyus, Japan)
OS10-4 Color system for skin color extraction
N. Toma (University of the Ryukyus, Japan)
E. Tamaki (University of Tsukuba, Japan)

12:30~13:45 GS25 Robotics -Ⅳ
Chair: Y. Wang (Hunan University, P. R. China)
GS25-1 Cooperative manipulation of a floating object by some space robots-Application of a tracking control method using transpose of generalized jacobianmatrixS. Sagara (Kyushu Institute of Technology, Japan)
Y. Taira (National Fisheries University, Japan)
GS25-2 Improvement of the algorithm for the search of periodic gaits of a passive dynamic
walkerX. Su, M. Zhao, M. Wang (Tsinghua University, P. R. China)
GS25-3 Clothes manipulation by inchworm robot grippers
K. Salleh, H. Seki, Y. Kamiya, M. Hikizu (Kanazawa University, Japan)
GS25-4 Development of an obstacle recognition system for autonomous robots in indoor
environments
H. Mizoguchi, E. Hayashi (Kyushu Institute of Technology, Japan)
GS25-5 The autonomous control of a robot arm based on contact with an object
M. Shimono, E. Hayashi (Kyushu Institute of Technology, Japan)

13:45~15:15 GS26 Robotics -Ⅴ & Micro- Robot World Cup Soccer Tournament
Chair : K. Uosaki (Fukui University of Technology, Japan)
GS26-1 Real-time target detection using infrared camera
M. Sugisaka, E. R. M. Faizal (Oita University, Japan)©ISAROB 2007

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GS26-2 Digital type disturbance compensation control of a floating underwater robot with 2link manipulator
T. Yatoh, S. Sagara, M. Tamura (Kyushu Institute of Technology, Japan)
GS26-3 Simulating crowd motion with shape preference and fuzzy rules
J- Y. Chang, T- Y. Li (National Chengchi University, Taiwan)
GS26-4 Development of an autonomous driving personal robot “Development of arm’s
mechanism and control program”
M. Tone, E. Hayashi (Kyushu Institute of Technology, Japan)
GS26-5 Knowledge evolution in a dynamic environment of robocup simulation
T. Nakashima, H. Ishibuchi (Osaka Prefecture University, Japan)
M. Mii (University of Hyogo, Japan)
GS26-6 Control of autonomous mobile robot through environment recognition withphotoelectric Sensor
T. Kurita, N. Abe, K. Tanaka (Kyushu Institute of Technology, Japan),
H. Taki (Wakayama University, Japan)
S. He (Eastman Kodak Company, USA)

15:40~16:25 GS9 Computer Graphics-Ⅰ
Chair: S. Ishikawa (Kyushu Institute of technology, Japan)
GS9-1 An interactive system for creating a 3-D graphical road map
K. Tou (Kyushu Institute of technology, Japan)
T. Irie (GEO Technical Laboratory, Japan)
J. K. Tan, S. Ishikawa (Kyushu Institute of technology, Japan)
GS9-2 Interactive denoising using non linear filter in wavelet domain
M. Hariharan, Paulraj MP, S. Yaacob, M. Rizon (Northern Malaysia University College
of Engineering (KUKUM), Malaysia)
M. Sugisaka, D. Hazry (Oita University, Japan)
GS9-3 Computer aided diagnosis system for pulmonary nodules using hierarchical feature
extraction
K. Takei, N. Homma, T. Ishibashi, M. Sakai, M. Yoshizawa (Tohoku University, Japan)
K. Abe (Nihon University, Japan)(Paper only)
GS9-4 Segmentation and tracking of cells in the image sequences
T. Kubik (Wroclaw University of Technology, Poland)
M. Sugisaka (Oita University, Japan)

16:25~17:25 GS10 Computer Graphics-Ⅱ
Chair: H. Furutani (University of Miyazaki, Japan)
GS10-1 Level set methods and auto-relation for detection of objects
X. Wang (Niihama National College of Technology, Oita University, Japan)
M. Sugisaka (Oita University, Japan)©ISAROB 2007

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GS10-2 Motion trace in real-time processing
E. Gobaru, M. Hara, M. Sugisaka (Oita University, Japan)
GS10-3 On the application of lip features in classifying human emotions
M. Karthigayan (Northern Malaysia University College of Engineering (KUKUM),
Malaysia)
M. Rizon (Northern Malaysia University College of Engineering (KUKUM),
Malaysia, Oita University, Japan)
S. Yaacob, R. Nagarajan (Northern Malaysia University College of Engineering(KUKUM), Malaysia)
M. Sugisaka (Oita University, Waseda University, Japan)
M. Rozalian (TATI, Malaysia)
D. Hazry (Oita University, Japan)
GS10-4 Face localization for facial features extraction using symmetrical filter and linear
hough transform
H. Arof (University Malaya, Malaysia)
F. Ahmad (International University College of Technology Twintech, Malaysia)
N. M. Shah (University Malaya, Malaysia)

Room B
8:40~9:40 GS11 Fuzzy Control
Chair: S. Sagara (Kyushu Institute of Technology, Japan)
GS11-1 Imitation of human action intelligence for the environment of desktop teleoperation
T. Shang (Shenyang University of Technology, P. R. China)
S. Wang (Kochi University of Technology, Japan)
GS11-2 A modified K-means clustering method for microarray data classification
D. Park, Y. Kim, S. Kim, C-H. Lee (Pusan National University, Korea)
GS11-3 A cost-based fuzzy system for pattern classification with class importance
T. Nakashima, Y. Yokota, H. Ishibuchi (Osaka Prefecture University, Japan)
G. Schaefer (Aston University, UK)
GS11-4 A verification of normalization results using variable clustering methods in cDNA
microarray data
G. Beak, Y. Kim, J-Y. Kim, S. Kim, C-H. Lee (Pusan National University, Korea)

9:40~10:40 Poster Session & Coffee BreakPS1 Improvement of color images halftoning with simulated annealing
K. Tanaka, R. Isoi (Meiji University, Japan)
PS2 Optimization of area traffic control parameter using a GA
H. Sasaki, M. Shinohara, K. Shoji, F. Toyama, J. Miyamichi (Utsunomiya University,
Japan)©ISAROB 2007

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PS3 Three dimensional medical image recognition of the lungs using artificial neural network
T. Matsuki, T. Kondo, J. Ueno (University of Tokushima, Japan)
PS4 A design of rough set processor for knowledge discovery
M. Matsumoto, A. Kanasugi (Tokyo Denki University, Japan)

12:30~14:00 GS18 Mobile Vehicles -Ⅰ
Chair: H. Hashimoto (The University of Tokyo, Japan)
GS18-1 An outdoor autonomously moving robot using a distributed image processing system
H. Goto, N. Abe (Kyushu Institute of Technology, Japan)
H. Taki (Wakayama University, Japan)
S. He (Eastman Kodak Company, USA)
GS18-2 Lateral control design of an autonomous land vehicle using RTK-DGPS
H. G. Park, J. H. Ryu, S. T. Hwang, M. H. Lee (Pusan National University, Korea)
GS18-3 Navigation system for a mobile robot using an omni-directional camera
T. Mochizuki, E. Hayashi (Kyushu Institute of Technology, Japan)
GS18-4 Adaptation of a distributed controller depending on morphology
N. Labhart, S. Miyashita (University of Zurich, Switzerland)
GS18-5 On-line variational PCA for adaptive visual tracking
T. Date, T. Bando, T. Shibata, S. Ishii (Nara Institute of Science and Technology,
Japan)
GS18-6 The development of an indoor positioning system using incident angle detection of
infrared emitters
S. T. Hwang, J. H. Ryu, C. H. Lee, M. H. Lee (Pusan National University, Korea)

14:00~15:30 GS19 Mobile Vehicles -Ⅱ
Chair: J. Wang (Oita University, Japan)
GS19-1 An efficient localization algorithm in the RFID sensor space for mobile robot
localization
H. Lim, J. M. Lee (Pusan National University, Korea)
GS19-2 Development of an autonomous mobile robot
K. Niimoto, E. Hayashi (Kyushu Institute of Technology, Japan)
GS19-3 Linux-based real time monitoring system of mobile robots
S. Cho (Pusan National University, Korea)
I.O. Lee (Ninety system, Korea)
J. M. Lee (Pusan National University, Korea)
GS19-4 Ontological representation of vision-based 3D spatio-temporal context for mobile
robot application
G. H. Lim, J. Chung, G. G. Ryu, J. B. Kim, S. H. Lee, S. Lee, I. H. Suh (Hanyang
University, Korea)
J. H. Choi, Y. T. Park (Soongsil University, Korea)©ISAROB 2007

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GS19-5 Cortical current sources for processing visual target motion revealed by MEG and
fMRI
D. Kawawaki, T. Shibata, Y. Fujiwara (Nara Institute of Science and Technology,
Japan)
O. Yamashita, M. Sato, M. Kawato (ATR Computational NeuroScience Laboratories,
Japan)
GS19-6 Robust tracking control based on neural network for nonholonomic mobile robot
Y. Wang, J. Peng (Hunan University, P. R. China)

15:40~16:40 OS13 Membrane Computing. From Calls to Computers and Back
Chair: M. Oswald (Vienna University of Technology, Austria)
Co-Chair: Y. Suzuki (Nagoya University, Japan)
OS13-1 Parallel software architectures analysis for implementing P systems
L. Fernandez, F. Arroyo, I. Garcia, A. Gutierrez (Universidad Politecnica de of Madrid,
Spain)
OS13-2 An architecture for attacking the bottleneck communication in P systems
A. Tejedor, L. Fernandez, F. Arroyo, G. Bravo (Universidad Politecnica de of Madrid,
Spain)
OS13-3 Dynamics of an abstract chemical system with few molecular
Y. Suzuki (Nagoya University, Japan)
OS13-4 Spiking neural P systems with inhibitory axons
R. Freund, M. Oswald (Vienna University of Technology, Austria)

Room C
8:40~10:25 GS12 Genetic Algorithms
Chair : K. B. Sim (Chung-Ang University, Korea)
GS12-1 Genetic algorithms for buffer size and work stations capacity in serial-parallel
production lines
A. Q. Jaber, R. Ramli, H. Yamamoto (Gifu University, Japan)
GS12-2 Implementation of a GA driven programming training support system
E. Nunohiro, K. Matsushita, K. J. Mackin, M. Ohshiro, K. Yamasaki (Tokyo
University of Information Sciences, Japan)
GS12-3 Computer simulation of an augmented automatic choosing control designed by
Hamiltonian and absolute anti-windup measure
T. Nawata (Kumamoto National College of Technology, Japan)
H. Takata (Kagoshima University, Japan)
GS12-4 Estimation of the source-filter model via GA-like algorithm for acoustical feature
extraction
M. Ihara, S. Maeda, S. Ishii (Nara Institute of Science and Technology, Japan)©ISAROB 2007

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GS12-5 A network analysis of simple genetic algorithms
H. Funaya, K. Ikeda (Kyoto University, Japan)
GS12-6 Function-discovery-system by the evolutional strategy using the search-accumulation
M. Saito, S. Serikawa (Kyushu Institute of Technology, Japan)
GS12-7 Development of satellite image searching using distributed genetic algorithm withnormalized correlation
K. Matsushita, K. Katayama, K. Mackin, E. Nunohiro (Tokyo University ofInformation Sciences, Japan)(Paper only)
GS12-8 Job-shop scheduling problems based on immune ant colony optimization
M- L. Sui, Y- I. Zhang (PLA University of Science and Technology, P. R. China)

12:30~14:00 OS3 Models for Neural Networks and Brains
Chair: H. Suzuki (The University of Tokyo, Japan)
Co-Chair: Y. Hirata (The University of Tokyo, Japan)
OS3-1 Features of action potential initiation in cortical neurons with multiple internal states
M. Matsuo (Aihara Complexity Modelling Project, ERATO, JST, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS3-2 Signal processing with spikes
M. Gutmann (The University of Tokyo, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS3-3 A mathematical model of planning in the prefrontal cortex
M. Oku (The University of Tokyo, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS3-4 Pattern recognization in chaotic neural networks
G. He (Aihara Complexity Modelling Project, ERATO, JST, The University of Tokyo,
Japan, Zhejiang University, P. R. China)
L. Chen (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Osaka Sangyo University, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS3-5 Bistability of synchronous and desynchronous dynamics in a network with gap
junctionsY. Katori, Y. Hirata (Aihara Complexity Modelling Project, ERATO, JST, Japan)
H. Suzuki (The University of Tokyo, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)©ISAROB 2007

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OS3-6 Digital spiking silicon neuron: concept and behaviors in GJ-coupled network
T. Kohno, K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The
University of Tokyo, Japan)

14:00 ~15:30 GS17 Intelligent Control and Modeling-Ⅲ
Chair: A. Kanasugi (Tokyo Denki University, Japan)
GS17-1 Hexagon-based Q-learning for object search with multiple robots
H- C. Yang. H- D. Kim, K- B. Sim (Chung-Ang University, Korea)
GS17-2 Motion generation of pneumatic artificial muscle
S. Ichikawa, M. Hara, M. Sugisaka (Oita University, Japan)
GS17-3 An antibody-based computing: An application to stable marriage problem
Y. Ishida (Toyohashi University of Technology, Japan)
GS17-4 Network route design of public transport system with network evolution
T. Majima (National Maritime Research Inst., Japan)
K. Takadama (University of Electro-Communications, Japan)
D. Watanabe (National Maritime Research Inst., Japan)
M. Katuhara (Hokkaido Intellect Tank, Japan)
GS17-5 Robot multiple tasks performance and neural complexity
G. Capi (Fukuoka Institute of Technology, Japan)
K. Bode (Polytechnic University of Tirana, Albania)
GS17-6 Motion control of biped robot by using simulation
K. Kabata, M. Hara, M. Sugisaka (Oita University, Japan)

15:40~17:10 OS4 Methods for Complex Systems: Feature Identification andControl
Chair: T. Kohno (The University of Tokyo, Japan)
Co-Chair: S. Horai (Aihara Complexity Modelling Project, ERATO, JST, Japan)
OS4-1 A new technique for adjusting the learning rate of RPEM algorithm automatically
X. Zhao (Aihara Complexity Modelling Project, ERATO, JST, The University of Tokyo,
Japan)
Y. Cheung (Hong Kong Baptist University, P. R. China)
L. Chen (Aihara Complexity Modelling Project, ERATO, JST, The University of Tokyo,
Osaka Sangyo University, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS4-2 Fitting a predictive model with a priori information
Application in intermittent androgen suppression for prostate cancer
Y. Hirata (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)©ISAROB 2007

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OS4-3 Effect of facility closure in the SEIR epidemic model
H. Maeda (The University of Tokyo, Japan)
Y. Ohkusa (Infectious Disease Surveillance Center, National Institute of Infectuious
Diseases, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS4-4 A systematic approach to analysis of robustness in oscillatory networks
R. Wang (Aihara Complexity Modelling Project, ERATO, JST, Japan)
L. Chen (Osaka Sangyo University, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS4-5 Phase synchronization of limit cycle oscillators in a fluctuating environment
K. Hashimoto (Aihara Complexity Modelling Project, ERATO, JST, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS4-6 Mathematical modeling of frog’s calling behaviors and its possible application to
artificial life and robotics
I. Aihara, H. Kitahata, K. Yoshikawa (Kyoto University, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)

17:10~17:55 GS6 Bioinformatics & Chaos
Chair: K. Naitoh (Waseda University, Japan)
GS6-1 Local cause of coherence in Boolean networks
C. Oosawa (Kyushu Institute of Technology, Japan)
K. Takemoto (Kyoto University, Japan)
S. Matsumoto (Kyushu Institute of Technology, Japan)
M. A. Savageau (University of California, USA)
GS6-2 Continuous modeling of biomolecular systems based on process calculus
Y. Pu, X. Dong (Shanghai Jiao Tong University, P. R. China)
GS6-3 Modeling chaos neural networks for classification of EEG signals
T. Q. D. Khoa, M. Nakagawa (Nagaoka University of Technology, Japan)

January 27 (Saturday)
8:00~ Registration

Room A
8:40~9:25 OS5 Fundamental Research on Recognition and Control
Chair: F. Dai (Matsue National College of Technology, Japan)
OS5-1 Fundamental research on polymer material as artificial muscle
T. Hanamoto, Y. Neba, Y. Fujihara, J. Hayashida (Matsue National College of©ISAROB 2007

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Technology, Japan)
OS5-2 Fundamental research on face recognition by genetic algorithm
F. Dai, T. Adachi, Y. Fujihara (Matsue National College of Technology, Japan)
H. Zhao (Zhengzhou University, P. R. China)
OS5-3 The characteristics of Mckibben muscle based on the pneumatic experiment system
H. Zhao, M. Sugisaka (Oita University, Japan)
F. Dai (Matue National College of Technology, Japan)

9:25~10:40 OS12 Bio-inspired Approach and Application
Chair: I. Yoshihara (University of Miyazaki, Japan)
Co-Chair: M. Yasunaga (University of Tsukuba, Japan)
OS12-1 Effect of using partial solutions in creating new candidate solutions with EDA andACO schemes
S. Tsutsui (Hannan University, Japan)
OS12-2 Stochastic analysis of schema distribution in OneMax problem
H. Furutani, S. Katayama, M. Sakamoto (University of Miyazaki, Japan)
OS12-3 Identification of exon-intron boundaries by Integration of base-oriented genetic
programming and statistical heuristics
K. Yamamori (University of Miyazaki, Japan)
Y. Fujita (Mitsubishi Space Software, Co., Japan)
I. Yoshihara, M. Aikawa (University of Miyazaki, Japan)
OS12-4 Variable-length-segmental-transmission-line and its design guidelines
N. Koizumi (University of Miyazaki, Japan)
K. Hayashi, M. Yasunaga (University of Tsukuba, Japan)
K. Yamamori, I. Yoshihara (University of Miyazaki, Japan)
OS12-5 A reconfigurable-VLSI-based double-lens tracking-camera
M. Yasunaga, N. Aibe, N, Y. Yamaguchi (University of Tsukuba, Japan)
Y. Yamamoto (Yamamoto System Design Inc, Japan)
T. Awano (Bethel inc., Japan)
I. Yoshihara (University of Miyazaki, Japan)

12:40~13:40 GS13 Innovative Computations-Ⅰ
Chair: T. Arita (Nagoya University, Japan)
GS13-1 Fault detection by evolution strategies based particle filters
K. Uosaki (Fukui University of Technology, Japan)
T. Hatanaka (Osaka University, Japan)
GS13-2 A synergetic particle swarm optimization algorithm-DHPSO
J. Guo (Huazhong University of Science and Technology, P. R. China)
G. Cui (Zhengzhou University of Light Industry, P. R. China)©ISAROB 2007

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GS13-3 Inevitability of bio-molecules
K. Naitoh (Waseda University, Japan)
GS13-4 Calculation of 3-D nonnegative outer product expansion by the power method and its
application to digital signal processing
C. Okuma, J. Murakami, N. Yamamoto (Kumamoto National College of Technology,
Japan)

13:40~14:25 GS14 Innovative Computations-Ⅱ& Human- Machine Cooperative
Systems
Chair: K. Yamasaki (Tokyo University of Information Science, Japan)
GS14-1 Adaptive knowledge base for Japanese-to-Braille translation
S. Ono, T. Yamasaki, S. Nakayama (Kagoshima University, Japan)
GS14-2 Language evolution and the baldwin effectY. Watanabe, R. Suzuki, T. Arita (Nagoya University, Japan)
GS14-3 A distributed algorithm of group robots applied to maze searching
T. Onitsuka, O. Araki (Tokyo University of Science, Japan)
T. Tamura (TOSHIBA Corporation, Japan)

14:25~15:10 OS7 Financial Market and Related Topics
Chair: M. Tanaka- Yamawaki (Tottori University, Japan)
Co-Chair: A. Sato (Kyoto University, Japan)
OS7-1 Interaction of agents in financial markets and informational method to quantify it
A. Sato (Kyoto University, Japan)
OS7-2 Trend predictions of tick-wise stock prices by means of technical indices selected bygenetic Algorithm
S. Tokuoka, M. Tanaka-Yamawaki (Tottori University, Japan)
OS7-3 Effective indices to characterize short sequences of human random generations
M. Mishima, M. Tanaka-Yamawaki (Tottori University, Japan)

Room B
9:25~10:40 GS1 Artificial Brain Research & Brain Science
Chair: X. Wang (East China University of Science and Technology, P. R. China)
GS1-1 Intelligent management of distributed dynamic sensor networks
P. Sapaty (National Academy of Sciences, Ukraine)
M. Sugisaka (Oita University, Japan)
J. Delgado-Frias (WSU, USA)
J. Filipe (INSTICC, Portugal)
N. Mirenkov (Aizu University, Japan)
GS1-2 Brain enhancement attempt based on visual recognition
Y. Jiang, S. Wang (Kochi University of Technology, Japan)©ISAROB 2007

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GS1-3 Designing brain machine interfaces for rehabilitation: a study
C. R. Hema, Y. Sazali, A. Hamid Adom, R. Nagarajan, M. P. Paulraj, M. Rizon(Northern Malaysia University College of Engineering (KUKUM), Malaysia)
GS1-4 Soft-hard memory structure for cognitive systems
L. Xiong, N. Zhao, C. Zhang (Tsinghua University, P. R. China)
GS1-5 Artificial realization of decision making for sleep stage of EEG contaminated withartifacts: conditional probability of knowledge-base of expert visual inspection
B. Wang (Saga University, Japan)
X. Wang (East China University of Science and Technology, P. R. China)

12:40~13:40 GS7 Cognitive Science & Evolutionary Computations
Chair: T. Kondo (The University of Tokushima, Japan)
GS7-1 A study on quantum-inspired evolutionary algorithm based on pair swap
T. Imabeppu, S. Nakayama, S. Ono (Kagoshima University, Japan)
GS7-2 Epidemic models and a self-repairing network with a simple lattice
Y. Aoki, Y. Ishida (Toyohashi University of Technology, Japan)
GS7-3 The novel feature selection algorithm for emotion recognition
H- D. Kim, K- B. Sim (Chung-Ang University, Korea)
Y. I. Cho (The University of Suwon, Korea)
GS7-4 A trial to improve the intelligibility of spontaneous concatenative speech synthesis
K. Fujii (Kumamoto National College of Technology, Japan)

13:40~15:10 OS9 Biomimetic Machines and Robots
Chair: K. Watanabe (Saga University, Japan)
Co-Chair: K. Izumi (Saga University, Japan)
OS9-1 Feature based estimation for mapping robot environments using fuzzy kalman filter
C. D. Pathiranage, K. Watanabe, K. Izumi (Saga University, Japan)
OS9-2 Kansei with behavioral patterns for human-robot interaction in ubiquitousenvironments
J. C. Balasuriya, K. Kamohara (Saga University, Japan)
C. A. Marasinghe (University of Aizu, Japan)
K. Watanabe, K. Izumi (Saga University, Japan)
OS9-3 Improvement of group performance of job distributed mobile robots by emotionallybiased control system
S. C. Banik, K. Watanane, K. Izumi (Saga University, Japan)
OS9-4 Feed rate control using fuzzy reasoning for NC machine tools
F. Nagata, K. Kuribayashi (Tokyo University of Science, Yamaguchi, Japan)
Y. Kusumoto (Fukuoka Industrial Technology Center, Japan)
K. Watanabe (Saga University, Japan)©ISAROB 2007

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OS9-5 Control of three-link underactuated manipulators using a switching method of fuzzyenergy regions
K. Ichida, K. Izumi, K. Watanabe, N. Uchida (Saga University, Japan)
OS9-6 Central pattern generators based on Matsuoka Oscillators for the locomotion of bipedrobotsG. L. Liu, M. K. Habib, K. Watanabe, K. Izumi (Saga University, Japan)

Room C
8:40~9:25 GS8 Complexity
Chair: Y. Ishida (Toyohashi University of Technology, Japan)
GS8-1 Networks on earth from the climate date
K. Yamasaki, K. J. Mackin, M. Ohshiro, K. Matusita, E. Nunohiro (Tokyo University ofInformation Sciences, Japan)
GS8-2 An approach to the learning curves of an incremental SVM
T. Yamasaki, K. Ikeda (Kyoto University, Japan)
GS8-3 An experimental study on geometric support vector machines
Y. Nomura, T. Yamasaki, K. Ikeda (Kyoto University, Japan)

9:25~10:40 OS8 Intuitive Human-System Interaction
Chair: M. Yokota (Fukuoka Institute of Technology, Japan)
Co-Chair: T. Oka (Fukuoka Institute of Technology, Japan)
OS8-1 Towards integrated multimedia understanding for intuitive human-system interaction
M. Yokota (Fukuoka Institute of Technology, Japan)
OS8-2 Designing a multi-modal language for directing multipurpose home robots
T. Oka, M. Yokota (Fukuoka Institute of Technology, Japan)
OS8-3 Tele-immersive collaborative virtual environment for intuitive interpretation
N. Kukimoto (Tohwa University, Japan)
Y. Ebara, K. Koyamada (Kyoto University, Japan)
OS8-4 A next generation video streaming system for intuitive remote interaction
K. Sugita, N. Nakamura, S. Baba, M. Yokota (Fukuoka Institute of Technology, Japan)
OS8-5 Face recognition across illumination
S. Aly, A. Sagheer (Kyushu University, Japan)
N. Tsuruta (Fukuoka University, Japan)
R. Taniguchi (Kyushu University, Japan)

12:40~13:25 GS27 Robust Virtual Engineering & Virtual Reality
Chair: J. Du (Beijing University of Rosts and Telecommunications, P. R. China)
GS27-1 Construction of super-micro sense of force feedback and visual for elastic body
T. Sonoda, E. Hayashi (Kyushu Institute of Technology, Japan)©ISAROB 2007

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GS27-2 A system allowing concurrent design and implementation of both virtual and realrobotsT. Oomori, N. Abe, K. Tanaka (Kyushu Institute of Technology, Japan)
GS27-3 Real-time interactive dialog system between human and virtual agent
S. Uchino, N. Abe (Kyushu Institute of Technology, Japan)
H. Taki (Wakayama University, Japan)
S. He (Eastman Kodak Company, USA)

13:25~14:55 OS11 Intelligent Robot Control and Sensors
Chair: Y. I. Cho (The University of Suwon, Korea)
Co-Chair: J. J. Lee (Korea Advanced Institute of Science and Technology, Korea)
OS11-1 Design of the tactile sensor element using microbending optical fiber sensors
J- S. Heo, C- H. Han, J. J. Lee (Korea Advanced Institute of Science and Technology,
Korea)
OS11-2 A localization of mobile robot based on ultra-sonic sensor using dynamic obstacles
B- S. Choi, J. J. Lee (Korea Advanced Institute of Science and Technology, Korea)
OS11-3 On the periodic sequence of a discrete sliding mode control system for a single-linkrobot arm
S- H. Son (Agency for Defense Development, Korea)
K- B. Park (Korea University, Korea)
OS11-4 Stable adaptive neural control for a nonlinear robot system in the presence of actuator
failures and uncertainties
J- H. Shin (Dong-eui University, Korea)
K- H. Seo, M- S. Kim, J. J. Lee (Korea Advanced Institute of Science and Technology,
Korea)
W- H. Kim, M- N. Lee (Dong-eui University, Korea)
OS11-5 Intelligent automatic community grouping system by multiagents
Y. I. Cho (The University of Suwon, Korea)
OS11-6 PID controller design using double helix structured DNA algorithms with recovery
function
J. J. Kim, J. J. Lee (EE&CS Korea Advanced Institute of Science and Technology,
Korea)©ISAROB 2007

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K(Knowledge)-net: Building up and its dynamics
Y. G. Zhang(Institute of Systems Science, Academia Sinica, Beijing, China, 100080, yzhang@iss.ac.cn)

Masanori Sugisaka(Dept. of Electronic and Electric Engineering, Oita University, Japan, msugi@cc.oita-u.edu.jp)

Lu Tang(Institute of Systems, Academia Sinica, Beijing, China, 100080)

Abstract: The essence of intelligence is to possess

1. Introduction

certain abilities that to obtain knowledge , to use
knowledge and to operate knowledge. So, the

Human brain has two main functions:knowledge in our brain exists in isolated and1) Control Body’s movement;

accumulated form, but it has certain dynamic

2) Learn knowledge and to form intelligencestructure to ensure the emergence of this kind of

Artificial Brain research has similar purposes:

abilities. Based on the understanding to real process

1) To control the complex movements of robots;of learning knowledge by human being, in this paper2) To learn knowledge and make emergence of

we discussed how to make a model to describe theintelligence in a computer or some other machines

dynamic

which can. Then human tries to equip complex

structureofknowledge.

The

most

knowledge of ours is leaned by using of natural
language, we introduce the notion of semantic

robots by this kind of artificial brain finally.
So-called “intelligence” means the abilities:

knowledge and model its growing up process by a

To learn knowledge;

network, we named it as K-net. It is a dynamic

To use knowledge; andnetwork with two main dynamics: one is added new

To operate knowledge.

knowledge, the other is to aggregate knowledge
existed in the network with some probability. Under

Obviously, “ intelligence ” depends on theexpression of knowledge and its structure in brain.

these very natural conditions we found that originally

Our research aims to discuss the principle tothe network is a random simple net and then somedesign an artificial brain. Observing the relation ofcharacteristics

appearedknowledge it is easy to understand that knowledge

gradually when more new knowledge s be added andhas a network form. This network is a dynamic

aggregated. More interesting phenomena is the

network. Depending on the Piagent’s theory weappearance of random hierarchical structure, is that

proposed two main dynamics: one is a newmeans emergence?

knowledge is added into this network; the other isofcomplexnetworkthat knowledge are combined based on the similarity.

Keyword:networks;

semantic
small-world;

knowledge;
complexscale-free; hierarchicalorganization

In our research we have found that the knowledge inhuman brain formed a complex network, we call it“K-net”, in which small word characteristic andalmost hierarchical structure appeared. The model ofknowledge we proposed in this paper is a kind ofdesign of artificial brain.

©ISAROB 2007

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2. Structure of semantic knowledge

At beginning, baby learned only few words, forexample he/she learns some nouns, verbs and some

2.1 The elements of semantic knowledge

adjectives, because the classes of knowledge is very

At beginning child learn knowledge fromlimited, so when he/she learned more and more the“mother” and their perception, we say it isknowledge in the same class become denser andpresentative knowledge, or semantic knowledge.

denser, not only the amount of knowledge arise, but

Baby learned more and more. However, the sort ofalso links between the knowledge appeared, this time
this kind of links made the all knowledge formed asimple network. Maybe there are several small
simple networks. These small networks are theoriginal seeds for evolution later.

2.3 The dynamic process
knowledge evolutionofsemantic

Jean Piaget (1896 - 1980 )knowledge is very limited, so the number ofknowledge in some sort become more dense.

Thefamouspsychologist Jean Piaget

Comparing with the abstract thinking, semantic

had done research on the(presentative) knowledge is concrete, perceptional,

process

primitive,

evolution, he pointed out

elementary,

andevolable.

Abstractknowledge is evolved knowledge from semantic(presentative)knowledge,

itisusuallynotperceptional, it is complex and logic.

that

of
the

knowledgechangeofknowledge has several ways, two of which are more
important, they are Accommodation（顺化） andSemantic knowledge can be expressed in several

Assimilation ( 同 化 ). Assimilation of knowledge

level layers. For example, “apple” is only a noun,means new knowledge is added into the existed

but it is a real presentative knowledge. “eat” is only

structure, or that means one learned a new

a verb, but it is still a real presentative knowledge.

knowledge and it is adapted into the semantic

We say it is senmantic knowledge in the “fragment”

knowledge network. Accommodation of knowledge

level. The basic semantic knowledge are grouped asmeans that certain knowledge in the semantic

several layers, such thatknowledge network has been absorbed or combined“fragment”,

with others, usually a new concept appeared and is“very simple”,

incorporated. These two processes presented and“simple”,

appeared continually in our brain. In fact, he/she“usual”,

learn new knowledge continually and put them into

“complex”,

adequate position of semantic network, it make the“very complex”, etc.

network enlarged; also he/she work. Due to these

Any relation in those groups will combine somedynamics the network become a dynamic network

schemes, this combination is very similar to theand presents some complexities.

structure of a sentence but it is not a sentence.

3. Some Concepts of Complex Networks

We say an element of Semantic Knowledge= “sentence” + mapping

The most interesting features of complex
networks are the small-world and scale-free. The

2.2 To form simple network©ISAROB 2007

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small-world

cause such a structure [13]. It displays a hierarchicalnetworks are clustering coefficient C and the averageand coarse-grained similarity. This intrinsic hierarchy

length of shortest path L.

can be characterized in a quantitative manner. The

statisticalquantities

Regularcharacterizing

networks

have

high

clustering

clustering coefficient of a node with k links follows

coefficient and large average length of shortest path,−1

C (k )

k . This type of structure

opposite to random networks which have low

the scaling law

clustering coefficient and small average length of

could give an explanation to the feature ofshortest

small-world in many scale-free networks.

path.

Between

these

two

extremes

somewhere, the clustering coefficient is almost as

Degree correlation coefficient r could distinguishhigh as that of a regular network while the averageassortative

length of shortest path is almost as small as that of aassortative networks, nodes with many connections

random network with the same number of nodes andtend to be connected to other nodes with manyedges.

asconnections. It was found that social networks are“small-world” for it is similar to the small worldoften assortative while biological networks are often

phenomenon. The average length of shortest path ofdisassortative [14]. r could be measured byThistype ofnetworks is called

small world networks increase slowly with the total
number of its nodes: L

and

M
r=

ln( N )

M

The study of scale-free networks concerns−1−1

disassortative

networks.

1∑ j k − [ M ∑ 2 ( j + k )]
−1

i

i

1

i

i

i

2

i

1

∑ 2 ( j + k ) − [ M ∑ 2 ( j + k )]
i

2

2

i

i

−1

i

In

i

2

i

behavior in the probability distribution of degree, the,where ji, ki are the degrees of the vertices at the ends

possible number of links at a random chosen node inof the ith edge, with i = 1 . . .M.

the networks. Unlike the Poisson degree distribution
distribution of degree follows a power law,

4．The dynamics 1 of K-net(K-net growing model)

P(k ) ∝ k −γ , where k is the degree of nodes andWe consider the first dynamic that to add a new

P(k) is the probability of the degree of an arbitrary

knowledge (a new node) to the existed K-net. The

node equals k. In such a network most nodes haveprinciple to add a new node is to choose a node J in

only a few connections and few nodes have very

the K-net which has the best conditional probability

for random networks, in a scale-free network, thelarge number of neighbors

proportional to the connection degree[9].

It has been discovered recently that aggregation
and regeneration of nodes can also leads to the powerlaw distribution of degree[11-12]. Kim and his

Pi (t ) =ki (t )∑ j∈N k j (t )cooperators propose a network model in which nodes

Our connection is not to link the node J directly, but

can merge with one of their neighbors and new nodes

to the m (m<M) neighbors of node J randomly. This

been added to the network to maintain the number ofmeans the new node has metaphor relation with node

nodes [11]. Another model proposed by Alava andJ. see the Fig.1a and 1b. The new one is green one

Dorogovtsev permit to aggregate nodes which are

and node J is the red one, the 5 neighbors of J is blue.

selected at random[12]. Those mechanisms give us

new suggestions on how could scale-free networks
emerge.
Different from BA model networks, some real
scale-free networks have hierarchical structures. A
model with network duplication mechanism could©ISAROB 2007

3

Fig. 1a

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 2bFig. 3awhere m=2.
The evolution process is in Fig.2a and 2b.

Fig. 3b6. The meta-process when these two
dynamics together

n=30
Fig. 2aBased on the theory of J. Piagent The two dynamics
of semantic knowledge will appear randomly andcontinuously, he say that is the equilibrium. We
design the K-net has these two dynamics together
with certain probability. We found the result still leadto a small word feature in K-net. The evolutionary

n=180

process is shown in the following simulation. There

Fig. 2b
The result shows us that K-net presents the feature of

we assume M=2, without loss of generality, andsmall word.

probability P=0.5. The evolutionary process shows in
Fig.4

5. The dynamics 2 of K-net(K-net aggregation model)
We consider another dynamics of K-net now,

n=100

aggregation of nodes, that means two nodes are
combined as one node, or two very related
knowledge are aggregate a concept. This is very

Fig. 4aimportant process to knowledge evolution, either forthe emergence of intelligence. The criterion of
aggregation depends on the similarity between thetwo knowledge (nodes). It is defined asn=150,λe (t )Pe (t ) =∑ e ∈E (t ) λe (t )iFig. 4b

i

j

j

where

λe = S ( a, b) =i

1
1 + k a - kb

The two nodes which has the highest similarity

n=200

will be combined as one node. See Fig. 3a and 3b.
This single process cannot go through to the end, it
has to be run with the growing process.

©ISAROB 2007

4

Fig. 4c

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
0

10

N=214
13
N=2
N=212
N=211
best fitting−1

P ( k ) : Probability

10

−2

10

−γ

P(k)∼kγ =1.36

−3

10

−4

10

−5

10

0

10

1

2

10

10

3

10

4

10

k : Degree

Fig. 4d The link degree presents power low
More interested things is this evolution appears
the almost hierarchical structure, maybe we could
explain it as a emergence of intelligence.

Summary
In this paper we introduce the semantic
knowledge, defined the elements of semantic
knowledge, the building up K-net. Also, we haveintroduce two kind of dynamics for K-net and somesimulations have been done, the results presented
K-net evolutionarily become a complex network andpossess the feature of scale-free and small word. The
almost hierarchical structure could be explained as
the emergence of intelligence. This model could be aconstructive model for artificial brain.

References[1] A.M. Collins, E.F. Loftus, “A Spreading-activation
Theory of Semantic Processing”, Psych. Rev., 1975, 82(6):pp. 407-428.
[2] C. Fellbaum, WordNet, an electronic lexical database,
MIT Press, Cambridge, 1998.
[3] Z-D. Dong, HowNet2000, http://www.keenage.com,
2000.
[4] R. Ferrer, R.V. Solé, “The Small World of Human
Language”, Proc. Roy. Soc. London B, 2001, 268(1482):
pp. 2261-2265.
[5] S.N. Dorogovtsev, J.F.F. Mendes, “Language as an
Evolving Word Web”, Proc. Roy. Soc. London B, 2001,
268(1485): pp. 2603-2606.
[6] M. Sigman, G.A. Cecchi, “Global Organization of the
Wordnet Lexicon”, Proc. Natl. Acad. Sci., 2002,99(3): pp.
1742-1747.
[7] M. Steyvers, J.B. Tenenbaum, “The Large-scale
Structure of Semantic Networks: Statistical Analyses and a
Model of Semantic Growth”, Cognitive Science, 2005,
29(1): pp.41-78.
[8] A.E. Motter, A.P.S. de Moura, Y-C. Lai, P. Dasgupta,“Topology of the Conceptual Network of Language”, Phy.
Rev. E, 2002, 65: 065102.
[9] A-L. Barabási, R. Albert, “Emergence of Scaling in
Random Networks”, Science, 1999, 286: pp. 509-512.

©ISAROB 20075[10] D.J. Watts, S.H. Strogatz, “Collective Dynamics ofSmall-world Networks”, Nature, 1998, 393: pp. 440-442.
[11] E. Ravasz, A-L. Barabási, “Hierarchical Organizationin Complex Networks”, Phys. Rev. E, 2003, 67: 026112.
[12] M.E.J. Newman, “Assortative mixing in networks”,
Phys. Rev. Lett., 2002, 89: 208701.
[13] P. Érdi, J. Szentagothai, “Neural connectivities:
between determinism and randomness”, In Dynamics ofMacrosystems, J.P.Aubin, D. Saari & K. Sigmund (eds.),
pp. 21-29. Springer, 1985.
[14] J.M.J. Murre, D.P.F. Sturdy, “The connectivity of thebrain: multi-level quantitative analysis”, Biol. Cybernet.,
1995, 73, pp. 529–545.
[15] V.M. Eguíluz, D.R. Chialvo, G. Cecchi, M. Baliki,
A.V. Apkarian, “Scale-free brain functional networks”,
Phys. Rev. Lett., 2005, 94, 018102,[16] B.J. Kim, “Geographical Coarsegraining of Complex
Networks”, Phys. Rev. Lett., 2004, 93, 168701.
[17] J.M. Beggs, D.J. Plenz, “Neuronal avalanches in
neocortical circuits”, J. Neurosci., 2003, 23, 11167.
[18] D.R. Chialvo, “Critical brain networks”, Physica A,
2004, 340, pp. 756-765.
[19] O. Sporns, “Network analysis, complexity, and brain
function”, Complexity, 2002, 8 (1), pp. 56-60.
[20] R. Quian Quiroga, L. Reddy, G. Kreiman, C. Koch, I.
Fried, “Invariant visual representation by single neurons in.
the human brain”, Nature, 2005, 435, pp. 1102-1107.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

RoboMusic
Henrik Hautop Lund

Martin Ottesen

Maersk Mc-Kinney Moller Institute
University of Southern Denmark, Campusvej 55, 5230 Odense M., Denmark
hhl@mip.sdu.dk funkstardeluxe@funkstardeluxe.com
www.adaptronics.dk
www.funkstardeluxe.com

Abstract
Based on principles from modern artificial intelligenceand robotics, we developed the RoboMusic concept. In
RoboMusic we use a number of robotic devices as
instruments, and the tunes are composed as a behaviourbased system. The music artist composes a baseline
behaviour of the robotic instruments and composes the
behavioural response to interaction by human musicians.
The music artist is transformed from a composer of staticmusic tunes to a developer of robot behaviours –behaviours that are expressed by the robotic system asmusic pieces. Music compositions are transformed to
become robotic behaviours as in a behaviour-based
system. A RoboMusic concert is performed with robotic
instruments, and changes the concept of live concerts byinviting the audience to interact with the band’s
instruments themselves and thereby guide the live
performance of the music themselves.

in artificial intelligence such as ambient intelligence andubiquitous and pervasive computing. In many cases,
however, we observe that developments in such subfields seems to build upon (parts of) the generaldefinition of robotics. We use the following definition ofa robot:
A robot is defined to be a programmablemachine that by its interaction with thesurrounding environment autonomously can
perform a variety of tasks, and its behaviour isdifferent from a computer program by theinteraction with the environment through sensors
and actuators.
According to the definition, we may develop robotic
instruments being programmable instruments that by
their interaction with the surrounding through sensors
and actuators can be used for playing a variety of tunes.

Introduction
Where robotic technology was used mainly in heavy
industries in the past, the last decade has shown awidening of the use of robotic technology into theprofessional and private service sector, including theentertainment sector. For instance, inspired by Brooks’
early development of behaviour-based systems, iRobot
has developed vacuum cleaning and floor washing robots
for the home market, and Wow-Wee has developed toy
robots based upon Tilden’s bio-inspired bottom up
approach to robotics. We have ourselves developed the
behaviour-based approach for interactive robot
entertainment such as RoboCup Junior [7], I-BLOCKS
play in hospitals and developing countries [5], andinteractive playgrounds [4].

Related work

Here, we will be looking at the possibility of using the
behaviour-based robotic approach to develop a new genreof music that we term RoboMusic. In this and the abovementioned robot service and entertainment applications,the robot technology is merging with other developments

Also, a number of researchers have worked withmultimodal interactive environments, including musicalenvironment. Antonio Camurri [2] developed amultimodal environment, in which users were allowed tocommunicate by means of full-body movement, singing©ISAROB 2007

6

Robotics and AI has been used previously to develop
music by other researchers and musicians. Most notably,
Gil Weinberg [11] and his research group at Georgia
Tech have developed a number of robot instruments,
such as the Haile drumming robot and other instruments
like the beat bugs and squeezables. The robotic drummer
Haile is an impressive robotic design made to listen to
two live percussionists, analyze their drumming in realtime, and use the product of this analysis to play back in
an improvisational manner [11], e.g. as shown at live
performances at the Robots at Play Festival in Odense,
Denmark in September 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007or playing. In the multimodal environment, the users
could get feedback from the environment in real time interms of sound, music, visual media, and actuators, i.e.
movement of semi-autonomous mobile systems including
mobile scenography, on-stage robots behaving as actors
or players, possibly equipped with music and multimedia
output.
Other interactive environments were developed withfocus on musical composition with novel interactive
instruments, such as the Bubble Bumble and MusicalDesk by Zhou et al. [12].
Also, interactive computer music researchers and artists
such as Wayne Siegel have explored the possibility ofcreating real-time interaction with music pieces, as
exemplified with Wayne Siegel’s “Music for Wind” and“Movement Study” performances in which wind speed
and direction control the music and dancer movement
control the music [10].
Other artists and researchers have explored similar
concepts, developments and performances withinteractive computer music and movement control ofsound. The work that we present here on RoboMusic
distinguishes itself from such related work in theinspiration from and exploitation of different strands ofrobotics to create intuitive interaction with robotic
instruments for both audience and composer, and tocreate musical compositions as robotic behaviours asknown from the behaviour-based robotics.
In many cases of related work, a well instructed
professional dancer or musician has to control theinteractive devices, and the professional musician ordancer has to do so in a precise way in order to create amusical response which is pleasant for the audience. On
the other hand, the RoboMusic is taking advantage of thework in human-robot interaction to create a seamless
interface for any non-expert human user to make pleasant
run-time manipulations of musical pieces.

RoboMusic
RoboMusic defines a novel genre of music. In
RoboMusic, music is composed using robotic
instruments, music is recorded based on playing robotic
instruments, and concerts are performed with robotic
instruments.
According to the definition above, a robot is defined to
be a programmable machine that by its interaction with©ISAROB 20077

the surrounding environment autonomously can perform
a variety of tasks, and its behaviour is different from acomputer program by the interaction with theenvironment through sensors and actuators. Hence, arobotic instrument is programmable instrument that by its
interaction with the surrounding through sensors andactuators can be used for playing a variety of tunes.
Through communication, robotic instruments can be used
together to orchestra an ensemble. If left untouched byhuman (or environmental) interaction, the robotic
instrument will behave with its own performancecomposed by the music artist. When a human or otherenvironmental subject interacts with a robotic instrument,
the instrument may change performance from its normal
autonomous behaviour.
The artistic and technological challenge of the musicartist is to compose baseline behaviour of the robotic
instruments and compose the behavioural response tointeraction by human musicians. The music artist istransformed from a composer of static music tunes to adeveloper of robot behaviours – behaviours that are
expressed by the robotic system as music pieces. Music
compositions are transformed from being static tobecome dynamic; music compositions are transformed
from being static nodes to become robotic behaviours.
A RoboMusic concert is performed with robotic
instruments, and changes the concept of live concerts byinviting the audience to interact with the band’s
instruments themselves and thereby guide the live
performance of the music themselves. The audience isactively engaged in the performance of the music of theirconcert, and their interaction with the robotic instruments
guide the robotic behaviour and thereby creates a unique
live concert performance that change from concert toconcert depending on the behaviour of the audience.
Each RoboMusic concert is a unique live performance.
The music artist has composed the baseline, and theaudience is manipulating the robotic instruments to allow
the robotic behaviour to change, and thereby the musictune to diverge. For the audience, the concert form haschanged from passive listening to active participation inplaying the concert.

Behaviour-based RoboMusic composition
Behaviour-based robotics [1] can be used to create easy
access and manipulation of complex technology by nonexpert users, and therefore becomes an appealing
technology for creating RoboMusic, in which bothmusicians and audience with no robotics/technology

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

knowledge should be able to manipulate the robotic
instruments in an easy and straightforward manner.
Interaction between human and traditional robotic
systems can be problematic, as the traditional robotic
systems often perform repetitive actions in a restrictedenvironment, whereas human interaction often ischaracterised by novel ways of interactions, which
creates an ever changing environment (which should be
the case with RoboMusic live concerts). Therefore,
traditional robotic system methods and technologies areoften difficult to apply in situations and applications
where the interaction plays a major role. So wedeveloped a new kind of user-guided behaviour-based
robotics (e.g. [3]) and applied this method to many
robotic systems, such as manipulative robotic
technologies, mobile and humanoid robots, etc. in order
to investigate how non-expert users could develop theirown complex robot behaviours within very short time(e.g. 30-60 minutes) with no prior knowledge to the robot
technology. For instance, the robotic building block
concept allows us to develop ‘programming by building’,
which, for instance, allow African school children and
African hospitalised children with no a prior knowledge
whatsoever about IT, robotics and technology to develop
their own electronic artefacts [5].
The behaviour-based robotics approach (and in general
the embodied artificial intelligence approach [8]) puts
emphasis on placing the robot/system in the real, physicalenvironment and utilise the characteristics of the realworld in the development of the intelligent system. The
resulting control systems provide a close loop between
environmental stimuli and actuation in the environment
through the use of primitive behaviours executed inparallel and coordinated to provide the overall behaviourof the system. So, the overall behaviour of the system
becomes the emergent effect of the interaction with theenvironment and the coordination of the primitive
behaviours. The task of the system designer becomes todesign the correct primitive behaviours and to set up theprimitive behaviours in the right manner to allow thedesired, overall behaviour to emerge as the interplay
between the primitive behaviours. With the same
primitive behaviours, the designer may be given theopportunity to design many different overall behavioursof the system, depending on the designer’s selection andset up of the primitive behaviours [4].
Therefore, in RoboMusic, the design challenge becomesto create primitive robotic behaviours and to coordinate
these primitive behaviours in order for the music piece toemerge as the coordination of primitive behaviours.
Thereby, a music composition emerge from the way the©ISAROB 2007

8composer, musicians or audience interact with the robotic
instruments that provide the primitive behaviours.

Figure 1. The behaviour based robotics approach.
Each robotic instrument is used to trigger a particularprimitive behaviour (Behaviour 1, Behaviour 2, … ,Behaviour N on Fig. 1) dependent on the interaction(Sense on Fig. 1) with the instrument(s). In RoboMusic,
the primitive behaviours can be anything from a volume
or a cut-off to a small sequence of tones. The music
composer designs the way in which the primitive
behaviours that are triggered should interact with eachother (Coordinate Act on Fig. 1).
Hence, as is the case when designing behaviour based
robots such as mobile robots, the robot designer (in thiscase the music composer) designs the primitive
behaviours and the coordination scheme. And, as is thecase with user-guided behaviour based robotics, if nonexpert users (e.g. live concert audience) are supposed tomanipulate and become creative with the systems, it is
crucial that the designer (music composer) createsprimitives on a fairly high abstraction level that allows
the non-expert user to understand and have positive
feedback from the human-robot interaction within a very
short time frame (seconds in the case of live music
concerts).
Therefore, we initially created understandable robotic
instruments and primitives. The instruments used to play
the music include interactive mats that measure touch,rolling pins that measure rotational acceleration, andlight&sound cylinders that measure distance (of aperson/hand). Such features as pressure, rotational
acceleration and distance are used to trigger primitive
behaviours which include variations in resonance, cutoff, volume and pan of musical tracks in the musicalcomposition.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Experimental set-up and test
For the first RoboMusic concert (Robo[rave] in Odense,
Denmark on 15/9/2006) and for the recording of the
Funkstar De Luxe album “No Man’s Planet”, wedeveloped three kinds of robotic instruments, primitive
behaviours and coordination.

For the particular concert, 10 robotic instruments wereused: 2 RollingPins, 2 Light&Sound cylinders and 6
Rehab Tiles. The Rehab Tiles were divided into two
main groups with the same ID number. This implies that
3 tiles mimic each other. This is mainly done forrobustness of the system. There is not implemented any
additional transmission reliability or robustness otherthan the default mechanisms in the Xbee protocol (which
may be necessary in future concert environments). The
setup used for the Funkstar De Luxe concert is illustrated
in Figure 3. The instruments can be activated ordeactivated by MIDI control commands. This can bedone from any MIDI device connected to the MCB that
respects the implemented protocol.
The command control sequence is illustrated in Fig. 4
where a MIDI device 1 activates an instrument by sending
a command on a MIDI channel. Furthermore, the MIDI
Controller Box defines control numbers for input andoutput (e.g. 24 and 54 respectively).

Figure 2. Two Rehab Tiles and a RollingPin used as
robotic instruments.
The system we made for this purpose is composed bythree elements:
1. Device with MIDI in/out features (E.g. PC with
Cubase or Keyboard)
2. MIDI controller box (MCB)
3. Robotic instruments
The robotic instruments communicate with radio
communication (Xbee) to the “base station” where thecoordination is taking place (see Fig. 3 and Fig. 4).
Figure 4. Control flow diagram.
The robotic instruments were developed by modifying
modular robotic tools designed specifically for the
purpose of supporting dynamic configurations of theenvironment, initially utilized in physiotherapy withcardiac patients and in dementia treatment [6]. We usedinspiration from modular robotics to develop threedifferent kinds of tools, namely the Rehab Tiles, theLight&Sound Cylinders and the RollingPins. According
to the robotic building block concept used in somemodular robotic work [4, 5, 6], a building block needs tohave a physical expression and should be able to process
and communicate with its surrounding environment,
where the communication with the surrounding
Figure 3. Concert set-up.
1

In this particular case the MIDI device is a Laptop
running Cubase©ISAROB 2007

9

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

environment can be through communication toneighbouring building blocks and/or through sensing or
actuation.

the following possibilities for receiving input from ahuman player:
1.

The Light&Sound Cylinders and RollingPins were
initially developed for the non pharmacologicaltherapeutic treatment and were designed to allow very
easy and understandable physical operation by dementia
affected patients and therapists (see [6] for details), andlikewise the Rehab Tiles were initially developed forphysiotherapeutic rehabilitation, e.g. of cardiac patients,
with an easy interface for both therapists and patients.

2.

3.

Light&Sound Cylinders contain a ultrasoundsensor that measures the distance to a reflection,e.g. from a hand moving up and down over theLight&Sound Cylinder
RollingPins contain an accelerometer and agyroscope to measure angle and rotational
speed, e.g. when a human player is rolling thepinRehab Tiles contain a force sensitive resistor tomeasure the force of pressure, e.g. when ahuman player puts a hand or foot on the tile

And all three kinds of instruments produce output in theform of coloured light in different patterns and radio
communication of primitive behaviours to the MIDI
controller box for further coordination (see fig. 3).
In general, it is possible to view the standard musicalcomposition as a baseline robotic behaviour, and theinteraction with the robotic instruments as triggering
deviations from the baseline behaviour. Therefore, thetask of the music composer is to create the “standard”
behaviour when there is no interaction with the robotic
instruments (i.e. the normal “static” music piece), theprimitive behaviours of the individual robotic
instruments and the coordination that defines theinfluence of the human players on the “standard”
behaviour.
Figure 5. Light&Sound Cylinder, RollingPins and Rehab
Tile.
In all cases, the modular robotic units were modified to
become robotic instruments by allowing them to use
Xbee radio communication to the MIDI Controller Box,see figure 3,4. Hence, the musical composition can radio
communicate with the robotic instruments and for
instance control the robotic instruments to light up in
different colours, patterns and intensities according to thekind of human interaction which may be possible ordesirable at a given point in time during the music
composition. For instance, the musical composition (orthe composer) may send a signal to a robotic instrument
to blink in red, which may be understood by a humanplayer as the signal that it is possible to increase avolume by interacting with the instrument.
When a robotic instrument receives an input from ahuman player, it will trigger a primitive behaviour. The
three kinds of robotic instruments developed here have

©ISAROB 200710

In the case of the first RoboMusic live concert withpresentation of the Funkstar De Luxe album “No Man’s
Planet”, we (i.e. the artist Funkstar De Luxe) designed
when and how the audience could interact with therobotic instruments, the form and limits of primitive
behaviours, and how they would be coordinated.
To keep things simple for the first live concert, therobotic instruments would trigger primitive behaviours
being volume control, cut-off, pan, and resonance – but
only when the robotic instruments were active, as
signalled in the standard musical composition withdifferent MIDI signals that would actuate the robotic
instruments to turn on and off their lights in different
colours and patterns. In those intervals, the audience wereallowed to interact with robotic instruments, and theinteraction pattern would define parameters in theprimitive behaviours, e.g. the volume of a particularchannel in the musical composition.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Conclusion
We developed the concept of RoboMusic based upon theuse of modern artificial intelligence and behaviour-based
robotics to allow live music to emerge as thecoordination of primitive behaviours. We thereby
transform the role of the music composer to become acomposer of robotic/music behaviours. In order to test
the concept, we developed three kinds of simple robotic
instruments, and tested the system with the developmentof the Funkstar De Luxe album “No Man’s Planet” andthe inaugural live concert when the audience was invitedon stage to perform together with Funkstar De Luxe byinteracting with the robotic instruments.
Figure 6. The RoboMusic live concert set-up, with
Funkstar De Luxe and his control station in the center,and the robotic instruments on the left and right side of
the stage.
The art of the music composer is to design the primitive
behaviours and their limits (in time and form), so that thehuman player (e.g. audience) interactions with the robotic
instruments provide deviations from the standard musicpiece in a manner that is at the same time improvisationaland controllable – i.e. improvisational in the sense thatthe human player (audience) should be able to create aunique version of the music piece, and controllable in thesense that the deviations provided by the coordination of
the primitive behaviours should in all cases manipulate
the standard music piece in a manner that it is guaranteed
to provide a pleasant music piece according to the art ofthe composer.
The first RoboMusic live concert was played as part of
the Robo[rave] concert during the Robots at Play Festival(www.robotsatplay.dk) in Odense, Denmark on
15/9/2006 – a concert that also featured percussion musicby Gil Weinberg, Scott Driscoll and their Haile
drumming robot. The RoboMusic performance was theinauguration live concert of the Funkstar De Luxe “No
Man’s Planet” album, and audience were allowed to enteron both the left side and right side of the stage where therobotic instruments in the form of RollingPins,
Light&Sound Cylinders, and Rehab Tiles were placed(see figure 6). Documentation of the effect of theaudience interaction with the robotic instruments and themusic can be seen and heard on the concert video
available on [9].
The same RoboMusic system was used to record several
tracks on the Funkstar De Luxe album “No Man’s
Planet”.

©ISAROB 200711

Acknowledgements
R. Beck, A. Derahkshan, F. Hammer, C. Isaksen, T.
Klitbo, J. Nielsen, L. Pagliarini and Entertainment
Robotics (www.e-robot.dk) collaborated on developing
the concept and technology.

References[1] R. A. Brooks. A robust layered control system for amobile robot. IEEE Journal of Robotics and Automation,2(1):14--23, 1986.
[2] A. Camurri, P. Ferrentino. Interactive environments
for music and multimedia. Multimedia Systems, 7:1, 3247, 1999.
[3] H. H. Lund. Modern Artificial Intelligence for
Human-Robot Interaction. Proceedings of the IEEE
journal, 92:11, 1821-1838, 2004
[4] H. H. Lund, T. Klitbo, and C. Jessen. Playware
Technology for Physically Activating Play, Artificial
Life and Robotics Journal, 9:4, 165-174, 2005
[5] H. H. Lund and P. Marti. Designing Manipulative
Technologies for Children with Different Abilities.
Artificial Life and Robotics Journal, 9:4, 175-187, 2005
[6] H. H. Lund, P. Marti, A. Derakhshan, R. Beck, T.
Klitbo, and J. Nielsen. Modular Robotics for Novel Tools
in Dementia Treatment. Proceedings of the 15th IEEE Int.
Symposium on Robot and Human Interactive
Communication (RO-MAN 2006), IEEE Press, ISBN: 14244-0565-3, 2006.
[7] H. H. Lund and L. Pagliarini. RoboCup Jr. with
LEGO Mindstorms. In Proceedings of Int. Conf. On
Robotics and Automation (ICRA2000), IEEE Press, NJ,
2000.
[8] R. Pfeifer and C. Scheier. Understanding Intelligence.
MIT Press, Cambridge, MA, 1999.
[9] RoboMusic web-site (checked 17/12/2006):

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

http://www.e-robot.dk/robomusic.html[10] W. Siegel. The Challenges of Interactive Dance - anoverview and case study. Computer Music Journal, vol.
22, no. 4, 1998.
[11] G. Weinberg, S. Driscoll, T. Thatcher. Jam’aa – A
Percussion Ensemble for Human and Robotic Players.
ACM International Conference on Computer Graphics
and Interactive Techniques (SIGGRAPH 2006), Boston,MA, 2006.
[12] Z. Zhou, A. D. Cheok, W. Liu, X. Chen, F. Farbiz,
X. Yang, M. Haller. Multisensory Musical Entertainment
Systems. IEEE MultiMedia, 11:3, 88-101, 2004.

Professor Henrik Hautop Lund is known for his workin robotics and modern artificial intelligence. His teamdeveloped the ATRON shape-shifting robots, which hepresented to the emperor of Japan, HM Queen of
Denmark, etc. His team has developed playware with thenovel intelligent playgrounds, and has won the RoboCup
Humanoids Free Style World Championship 2002.
Professor Henrik Hautop Lund is engaged also in thedevelopment of intelligent artefacts, I-BLOCKS, for theteaching of creativity in Africa, where his team were oneof the driving forces behind the first science park in East
Africa in Iringa, Tanzania. He is member of the Danish
national research council, and numerous international
committees. He has produced more than 100 scientificpublications in robotics and modern artificial
intelligence.
Funkstar De Luxe (Martin Ottesen) became world
famous for his remix of Bob Marley’s Sun Is Shining.
The remix sold more than one million copies and won
several awards including a World Music Award for the
best-selling reggae single of 2000. Besides releasing two
albums resulting in several gold plates Funkstar De Luxe
has been touring most of the world performing as a DJ
and musician. The list of artists remixed is long andincludes Grace Jones, Bob Dylan, Mary J Blige andBarry White to name a few. With a characteristic touch in
his remixes and original songs Funkstar De Luxe’s
influence on electronic dance music is significant. With
his new album No Man’s Planet he continues to pushback boundaries of creating and performing electronic
music.

©ISAROB 2007

12

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Polymorphic Intelligence
Luigi Pagliarini
Maersk Mc-Kinney Moller Institute
University of Southern Denmark, Campusvej 55, 5230 Odense M., Denmark
luigi@mip.sdu.dk
www.adaptronics.dk

Abstract
The need of defining a multiple, multifaceted andPolymorphic Intelligence concept comes up as a possible
answer to many ‘false’ paradigms and philosophical andconceptual orientations that, recently, pervade many
research fields, as psychology, pedagogy, literature, art,
and, of course, science, technology and A.I.
Indeed, in this exact moment of human history, it
becomes necessary to clarify with a strong theoreticalconstruct in which relationships are machines andhumans, without compromises. We need to free ourthoughts from ambiguities and face a new definition ofmind and intelligence.
The first step to take consists in burring the idea of
human-machine interaction or interaction design –basically these two terms are out of times – to move on
and start thinking of a human-machine interrelation. It
has become necessary to abandon the scheme that theconstructive(and,symmetrically,
destructive)
intelligence is an exclusive prerogative of the humans(or, more in general, biological), to fully recognize,
admit, and capacitate ourselves that artefacts are able tocreate, to express a real collaborative and/or competitive
force and to produce ideation, inspiration and tocontribute to the wealth of ideas that are about to takepart of our own world, and our own existences.

rename it as “everything”. Of course, this overalltendency is not as good as we would expect since it isleading to a neutral and senseless portrait of what, on the
opposite,should be the core-business of ourphilosophical and technological research.
Further, and more specifically, in the robotics and AI
fields the inheritance of old paradigms, ideas andapproaches seems to be strangling the upcoming needs
for a new definition of human-machine interaction basedat the moment on a sort of human-machine extraneity.
Unfortunately, these ideas originated from few, but very
popular, scientific, philosophic and artistic theoreticalconstructs has been simply prejudicing and polluting theentire domain of human thought and the way we’ve been
thinking about intelligent artefacts in the last century.

Introduction
Being conscious of our own final goals is one of the mostimportant rule to follow to achieve good results while
conceiving and implementing ideas. On the opposite, it
is our feeling that for much too long A.I. researchers andexperts when focusing on ‘how to make a machine
intelligent’ or ‘intelligent as humans and other life forms
are’ have been keeping in their mind the “wrong” target.
It is not a case, indeed, that the word intelligence, itself,
has gained dozens additional meanings and has had toincorporate so many extra aspects (either in psychology,
literature and science) that today one could almost©ISAROB 2007

13

Figure 1. One of the first editions of the famous Isaac
Asimov’s I, Robot [2].
Amongst all, the most dangerous and mining were thosetheories popping out of the Turing’s [1], Asimov’s [2],Orwell’s [3] manuscripts where, paradoxically, thedistance between humans and machines is thought asabsolute. Their notion of machines (and therefore
machine-intelligence) is of a somehow isolated external

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

device, while the relationship they thought we could
build is extrinsic, either physically and mentally. In our
opinion, such an idea is to be demolished because
generates a conceptual and structural approach tomachine-thinking disintegrated to what is to beconsidered the human specie evolution. Indeed, since
years now, humans and machines share the same spaces
either physical and geographical, or cerebral and virtual
and, in other words, machines are an integrated part ofour Ego or, at least, part of our own world and everyday
life.
To overcome the old way of thinking modern artifacts it
seems to be necessary to step back to old schemata
typical of Oriental, Indian or Native American culturesand integrate them with most recent western
psychological and philosophical theories like thosepostulated by G. Bateson [4] in “Steps to an Ecology ofMind”, or by J. Gibson [5] in “The ecological approach
to visual perception”, theories that can be seen as thebases for “Ecological Psychology” [6] or ”Environmental
Psychology” [7]. In short, the idea that lays behind these
theories is that the whole world takes part in our own
computational brain, and intellectual potentialities, and is
an important component of our motor and sensory
systems. If so, it would become essential for those who
deal with AI and Robotics to inherit the Bateson’s [4]principle for which the “blind man stick is part of his
sensory-motor system of his brain associative areas, andof his mind”, and facing the idea that artefacts might beintegral part of our abilities of elaboration, besides
perception and action. Of course, this has been true for
centuries but, recently, also thanks to the IT
sophistications and worldwide information digitalization,is becoming unequivocal.
Indeed, while modern Psychotechnologies [8]differentiate from the traditional ones - motor (e.g.
bicycle) and sensory (e.g. telescope) – and accordinglywith the famous De Kerckhove [9] classification, include
radio, television (i.e. connectivity) and, overall,computers and Internet (i.e. interconnectivity) it is to be
noticed that nowadays many automations, more than an
integrative processes, are pervasive ones or, evenfurther, are substituting human cognitive process, even at
high-level, like for example creativity and problem
solving.
Now, although all of that has become truth it seems that
we are not fully conscious of the changes that are taking
place, and while we easily understand that the mobile
phones agenda are replacing part of our long-term
memory functionality we find it harder to catch how thefamous “cut&paste” or “undo” or “T9text input” are©ISAROB 200714changing the way we write and, therefore, think andcommunicate. In short, the symbolic system we are
inheriting by the electronic culture is affecting our minds
and is likely to engrave up to the revolution of our entiresemeiotic system.
In addition, elements like hypertexts, global searches,internet maps, GPS, wearable computers, autonomous
robotics, and so on represent an increasing number offunctions the biological brain is enriched with and, inparallel, delegating to machines. In other words, these
artifacts are the witnesses of the idea of restructuring towhich the same brain is pushed to, while they also
represent the increasing level of “dependency” the human
intelligence is destining to machine themselves. One
practical example might be seen in the recent growth ofsuch disciplines as Psychogeography [10] (i.e.: how tocreate geographical maps linked to humans emotional
experiences) or tools like Brain Training [11].
Essentially, intelligence as doubled its evolution speed
and hugely enlarged its domains. That’s happening
because, besides the natural genetic evolution,
intelligence is evolving as definition as well. This is due
to both the power the new technological achievements toimprove our ability of self observation (and selfconsciousness), and to the fact that our minds arethemselves influenced by the advent of “intelligent”
artifacts. Indeed, as G. Rizzolati pointed out with isMirror Neurons [13] theory humans mostly learn by
imitation. Of course, we all - computer scientists, AI andRobotics experts – use that knowledge to apply it tomachine learning, but we must notice that by doing so wehave initiated a never ending loop in which learning andteaching is somehow simultaneous (in terms of a society
extended to intelligent machines). These facts, are taking
us straight to the first forms of hybridized intelligences.
Last but not the least, intelligence evolves in a new
dimension since the latest artificial apparatuses havestarted creating intelligence, thinking and metacognition,
themselves.
To summarize, if on one side it is very easy to predictthat - as affirmed by the lucid Alexander Chislenko’s
analysis [12] - “the ability of future machines to directly
share experiences and knowledge with each other willlead to evolution of intelligence from relatively isolated
individual minds to highly interconnected structural
entities” and that “the development of a network ofcommunicating mobile and stationary devices may beseen as a natural continuation of biological andtechnological processes leading to a community ofintentionally designed and globally interconnectedstructures”, on the other hand, what is much harder toThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

comprehend to us is that the human brain is notextraneous to all of that but, on the contrary, it gets
deeply influenced by A.I. in action. Part of that is what
we can call the Polymorphic Intelligence.

2.

3.

A robot must obey orders given it by humanbeings except where such orders would conflictwith the First Law.
A robot must protect its own existence as long as
such protection does not conflict with the First or
Second Law.

Polymorphic Intelligence
As far as we know, humans are the most “intelligent”
organisms since their brain functions are complex andsophisticated at the very same time. Indeed, when in
Frames of Mind [14] the psychologist and neurologist
Howard Gardner tried to define intelligence he came out
with seven different substructures:

So, while artists were anticipating crucial philosophicalgoals for the future human-machine relationship, on theother side, many different scientists were defining
practical objectives. Amongst them a special mention
goes to A. Touring who tried to define through thefamous Turing Test [1] the meaning of A.I.
1.

1.
2.

3.
4.

5.

6.

7.

Linguistic intelligence (sensitivity to spoken andwritten language);
Logical-mathematical intelligence (the capacity toanalyze
problems
logically,
carry
out
mathematical operations, and investigate issues
scientifically);
Musical intelligence (encompasses the capacity torecognize and compose and perform music);
Bodily-kinesthetic intelligence (the potential ofusing one's whole body or parts of the body tosolve problems);
Spatial intelligence (the potential to recognize anduse the patterns of wide space and more confinedareas);
Interpersonal intelligence (the capacity tounderstand the intentions, motivations and desiresof other people);
Intrapersonal intelligence (the capacity tounderstand oneself, to appreciate one's feelings,
fears and motivations).

Certainly, also thanks to such a refined biologicalevolution of their computational functions it has become
possible to human beings to reach an high level of social
and technological evolution that, only recently, is flowing
to such a stage that might be defined as the intelligent
machines age. Amongst us few great artists (e.g. W.
Shelley [15]; G. Orwell [16]; P.K. Dick [17]) , many
years ago, envisioned we were about to get to this pointand consequently depicted a possible scenario to try to
prevent the moral and the ethical decay of our societies
and specie. In particular, Asimov [2] who tried to define
the three famous A.I. constrains:
1.

A robot may not injure a human being or, throughinaction, allow a human being to come to harm.

©ISAROB 200715

A human judge engages in a natural language
conversation with two other parties, one a humanand the other a machine; if the judge cannot
reliably tell which is which, then the machine issaid to pass the test.

For what came later all these conceptual paradigms werevery significant for any further theoretical development
and worked as lighthouses for thinking about AI.
Despite that, as it often happens in the history of ideas,
what was a fundamental and inspiring landmark in thepast might represent an obstacle for further evolutions,and, most likely, either Asimov’s and Turing’s (and otherauthors) principles today are still being followed too
much and believed, while they actually seem to be fullyout of time.

Figure 2. The Atron modules [20].
Things have changed because, since then, the definitionof AI itself has changed. Indeed, it is clear how the
Touring Test has been surmounted and AI is moving
towards the idea of Collective Intelligence - e.g. Swarm

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007[18], Boids [19] – as well as that robotics is moving awayfrom the basic concept of mono-shaped body structure
and the ‘prison’ of its canonical aspect – e.g. Atron [20],RoboMusic [26].
Even more, A.I. has started opening to such problems
like interfacing humans, hence taking us to a
Polymorphic Intelligence state where Artificial
Intelligences deeply interacts with biological ones. Thisis occurring at all levels. In virtual worlds (SecondLife[21], Gazira Babeli [22] and Marco Cadioli [23]) in realworld (MipTiles [24], I-BLOCKS [25], RoboMusic[26]), and in mixed realities (Stelarc [27]; Talkers [28];Ambient Addition [29]).

are a bit more aleatory or, at least, less predictable and,even more important, are completely different from what
we have been dealing with in the past, since theinteractive procedure moves from a one way to abidirectional intelligence flow.
Indeed, what we will call here the Imitational
Intelligence, a factor, neglected by the Howard theory[14] but indirectly consecrated by the Rizzolati’s recent
discovery, seems to be a crucial issue that must be taken
into consideration and that will play a large role in future
human-machine theories. Theories that, inevitably so,
will lead us towards a new conceptualization of themeaning of Intelligence as a domain hybridized bymachine and therefore Polymorphic.

Conclusion
When looking at all ideas and definitions of AI andRobotics of the last centuries it becomes evident that
there is something wrong regarding the philosophicalapproach that has been developed in the so calledmachine (or android, or cyborg, or robot) thinking. What
seems to be missing is the idea of feedback that machine
intelligence impose to biological intelligence therefore
creating brand new forms of intelligence (either naturaland artificial) that we define as Polymorphic Intelligenceand that might be leading the way we will approach AI in
the next future.
Figure 3. Stelarc [27]. ExoSkeleton

Acknowledgements

In other words, what is happening is that we cannot point
at A.I. as the result of a single, linear artificial processbut, on the opposite, the new picture tells us of amultidimensional non-linear process which is difficult tohandle and, more or less, impossible to fully control.
Things get even more complex when, instead of the old
fashioned
interactivity
(i.e.
the
switchonactivate/deactivate rule) we insatiate a run-time multiinteractive dynamic (i.e. interrelation) with a single‘specie’ of AI artefacts or even “worse” a multitude ofthem, simultaneously. Obvious enough, the outcome is a
scenario where the Asimov’s laws largely looses sense
since machines themselves are loosely controllable (i.e.
often dealing with non-linear maths and non-complete
problems) and largely interconnected and therefore nondirectly responsible of the general system outputs. In thisperspective, we both need to renew our methods andmove from the idea of Human-Machine Interaction (orInteraction Design) to the concept of Human-MachineInterrelation where the basic principles of interactivity

All the Adaptronics Group and, in particular, Prof.
Henrik H. Lund and Vibeke Nielsen for the great support
given in my Danish research activities. Sergio Lombardo
for the precious conversations and the support of myresearches in Art Psychology. Salvatore Iaconesi for the
feedbacks and the English revision of the manuscript.
All the Artificialia staff for the valuable research madealong the years on the topic.

©ISAROB 200716

References[1] S. Harnad (2004) The Annotation Game: On Turing(1950) on Computing, Machinery, and Intelligence, in
Epstein, Robert and Peters, Grace, Eds. The Turing Test
Sourcebook: Philosophical and Methodological Issues in
the Quest for the Thinking Computer. Kluwer[2] I. Asimov, (1950). I, Robot. New York: Doubleday &Company[3] G. Orwell (1949). Nineteen-Eighty-Four. London:
Secker & Warburg. (later ed. ISBN 0-451-52493-4)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[4] G. Bateson, (2000), Steps to an Ecology of Mind,
Chicago: University of Chicago Press[5] J. J. Gibson (1979), The Ecological Approach to
Visual Perception, Boston: Houghton-Mifflin.
[6] R. G. Barker, (1968), Ecological Psychology:
Concepts and methods for studying the environment of
human behavior, Stanford University Press, Palo Alto,CA.
[7] P. Bell, T. Greene, J. Fisher, & A. Baum (1996).
Environmental Psychology. Ft Worth: Harcourt Brace.
[8] M. Cardaci (2004) Psicotecnologie e
cognizione. In: Immagini della emergente società inRete. S.Martelli e S. Gaglio (a cura di) Franco
Angeli (pp.169-171).
[9] D. de Kerckhove (1991), Leonardo, Vol. 24, No.2,
Connectivity: Art and Interactive Telecommunications,pp. 131-135.
[10] M. Coverley (2006). Psychogeography. Pocket
Essentials.
[11] Dr. Kawashima (2006) Brain Training: How Old Is
Your Brain? Nintendo DS.
[12] A. Chislenko (1996) Networking in the Mind Age
http://www.lucifer.com/~sasha/mindage.html[13] G. Rizzolati (2005), The mirror neuron system andits function in humans. Anat. Embryol., 210(5-6):419-21.
[14] H. Gardner (1993) Frames Of Mind. The Theory Of
Multiple Intelligences.
[15] M. W. Shelley (1918), Frankenstein.
[16] G. Orwell (1948), 1984.
[17] P.K. Dick (1968), Do Androids Dream of Electric
Sheep?
[18] V. Grimm, (1999). Swarm http://www.swarm.org[19] C. W. Reynolds (1987) Flocks, Herds, and Schools:
A Distributed Behavioral Model, in Computer Graphics,
21(4) SIGGRAPH '87 Conference Proceedings. p. 25-34.
[20] H. H. Lund, R. Beck, L. Dalgaard, (2005). "SelfReconfigurable Robots with ATRON Modules" In
Proceedings of 3rd International Symposium on
Autonomous Minirobots for Research and Edutainment(AMiRE 2005), Springer-Verlag, Fukui, 2005.
[21] SecondLife, http://www.secondlife.com;[22] Gazira Babeli, http://www.gazirababeli.com;[23] Marco Cadioli, http://www.internetlandscape.it/[24] H. H. Lund, C. Jessen, K. Moeller, T. Klitbo, (2005)"Playware - Intelligent technology for children's play",
short position paper to appear in Workshop on Metapolis
and Urban Life, UbiComp 2005.
[25] Nielsen, J., and Lund, H. H. (2004) "Building Blocks
with Spiking Neural Networks", Submitted to European
Symposium on Artificial Neural Networks (ESANN'2004),
2004.
[26] H.H. Lund, M. Ottesen, (2006). RoboMusic,
http://www.e-robot.dk/robomusic.html©ISAROB 200717[27] Stelarc. http://www.stelarc.va.com.au/[28] Talkers. http://www.artisopensource.net/talkers/[29] N. Vawter (2006), Ambient Addition
http://web.media.mit.edu/~nvawter/thesis/
Professor Luigi Pagliarini is psychologist, multimedia
and software designer, expert in robotics, AI and
Artificial Life, and artist. He is currently Associate
Professor at Maersk Institute, University of Southern
Denmark; Professor of Perception Theory andPsychology of Shape at the Academy of Fine Arts ofBari, Italy; Director of the Pescara Electronic Artists
Meeting; President of the Cultural Association
Artificialia; Art Director of Ecoteca Café; Member of theInternational Committee RoboCup Junior; Board
Committee of “Rivista di Psicologia dell’Arte” (Journalof Psychology of Art); Member of EvoNet and Executive
Member of EvoMusArt; Partner Consultant ofEntertainment Robotics; Partner Consultant of the Visual
Emotion - Video Productions. He has published indifferent international books, journals, congresses andconferences proceedings and has been rewarded withinternational prizes more than once. He has exhibited his
work in different museums and institutions all over theworld. Luigi Pagliarini has worked for many different
institutes and universities as teacher or researcher and, as
consultant, with many enterprises and multinational
factories. His work has often been reported on many
international newspapers, magazines and televisions.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Computer Simulation in Surgery for Human Hip Joint
H. Tsumura
Department of Orthopaedic Surgery, Faculty of Medicine
Oita Univesity
1-1 Idaigaoka Hasama-machi Yufu-shi Oita 879-5593, Japandevised a computer simulator to support preoperativeplanning.

Abstract
For patients who have early signs of hip
joint disease resulting from structural abnormality,
various surgeries for correcting abnormal stress
distribution can be useful to prevent the progressionof the disease. However, it is difficult to confirm theoptimal procedure of surgeries. To deal with thisproblem, we devised a computer program to support
preoperative planning. Hip images obtained bycomputed tomography were loaded into our program,and a three-dimensional voxel model was created.
Then the pressure distribution of hip joint was
analyzed with a rigid-body spring analysis(computational non-linear mechanical analysis). Thissystem has a module for performing virtual surgeries.
This program allows the hip joint mechanics to beevaluated easily, so that the advantages anddisadvantages of various surgical methods could be
examined biomechanically prior to surgery. However,this system has several problems that should be solvedin the near future.

1

2This computer simulator is composed of twoparts (with three programs): Data Preparation partand Mechanical Analysis /Virtual Osteotomy part. Allof these programs were written by one of the authors
with Visual C++ (Microsoft, Redmond, WA).
Images of the hip joint were obtained withcomputed tomography. Image data from each slice
were stored as a 320 x 320 or 512 x 512 matrix andthen were loaded into our program. A threshold was
set to distinguish the bones from the other tissues. The
program reconstructed automatically the pelvis andthe femur as a voxel model (Fig. 1). The joint surface
could not be determined automatically by this system,so joint lines were delineated using a mouse and thejoint surface was reconstructed as a polygonal model.
Figure 2 shows views of the femoral head and thejoint surface.
Fig. 1Introduction

For patients who have early signs of hip joint
disease resulting from structural abnormality, various
surgeries for correcting abnormal stress distribution
can be useful to prevent the progression of the disease.
To correct this condition, periacetabular osteotomy isfrequently performed [1, 2]. This osteotomy involves
cutting of the bone around the circumference of theacetabulum and moving the osteotomized acetabular
fragment to increase the coverage of the femoral head.
It is believed that these procedures have a soundtheoretical basis, but it is difficult to confirm theoptimal transposition of the osteotomized acetabular
fragment. Conventionally, plain X-ray films havebeen used for preoperative planning. Recently,
three-dimensional computed tomography has become
available for assessment of the hip joint
preoperatively [3]. As mentioned previously, the aimof acetabular osteotomy is to improve mechanicalcondition of the hip joint. Therefore, it is desirable toinclude mechanical assessment of the hip joint in the
preoperative planning. To deal with this problem, we©ISAROB 2007

18

Methods

Fig. 2

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

For mechanical analysis, the joint contactpressure distribution was calculated using therigid-body spring method of Kawai (computational
non-linear mechanical analysis) [4]. The articularsurface is assumed to consist of numerous small
triangular planes. Each plane has one normal spring
and two shear springs. These springs connect
components of the joint articulation system that are
assumed to be rigid elements.
Formulation of a rigid-body spring model
In addition to the global coordinate system (X, Y,
Z), a local coordinate system (x, y, z) was also
considered on every contact triangle (Fig. 3). Two
rigid elements (G1, G2) contact at triangle ABC. An
arbitrary point P (XP, YP, ZP) on triangle ABC moves
to P1 and P2 after loading (Fig. 4). Because of therigid body assumption, the displacement vector, UP, ofpoint P on the contact surface of bodies 1 and 2
moving to P1 and P2 can be expressed as a function of
the displacement of the selected reference point UG :UP=[Q]UG(1)
where
UP=[UP1,VP1,WP1;UP2,VP2,WP2]t
UG=[UG1,VG1,W G1,θ G1,φG1,χG1;UG2,VG2,W G2,θ G2,φG2,χG2]t
and the transformation matrix

Q
1 0 0
0 1 0
0 0 1
1 0 0
0 1 0
0 0 1

0
(Zp ZG1)(Zp ZG1)
0(Yp YG1) ( Xp XG1)0
(Zp ZG2)(Zp ZG2)
0(Yp YG2) (Xp XG2)

(Yp YG1)( Xp XG1)0(Yp YG2)( Xp XG2)
0

where (XP, YP, ZP), (XG1, Y G1, Z G1) and (XG2, Y G2, Z
G2) are the coordinates of point P, reference point G1
and reference point G2, respectively, based on the
global coordinate system before deformation.
For each contact area, transformation from theglobal coordinate, UP, to the local coordinate, up, wasachieved through the transformation matrix R :uP=[R]UP (2)
where

R

19

0

0

0

l 2 m 2 n2l 3 m 3 n3
0 0
0
0 0
0
0 0
0

0 0
0 0
l 1 m1l 2 m2
l3 m3

0
0
n1
n2
n3

and l1, m1, n1, l2, m2, n2, l3, m3 and n3 are directional
cosines between (x,X), (x,Y), (x,Z), (y,X), (y,Y), (y,Z),(z,X), (z,Y) and (z,Z), respectively.
The relative displacement between the matchedpoints P1 and P2 was :δ=[δsx,δsy,δn]t=[M]uP
(3)
where

1 0 0 1 0 0
0M

1 0 0 1 00 0

1 0 0 0and δsx,δsy and δn represent the components
tangential and normal to the contact surface.
Combining equations (1),(2), and (3), therelative displacement vector δ is expressed asfollows :δ= [M][R][Q]UG=[B]UG
Denoting the stiffness constants of normal and shear
springs by ksx,ksy and kn ,the stored strain energy, W,
is :

W
where

1
δ ｔ D δ dA
2 A

1t
UG t B D B dAUG
A2

ksx 0 00 ksy 0D

0 0 knand integration is performed on the contact area.
According to the principle of minimum
potential energy, the external force, F, which is related
to the member stiffness matrix, [K], was calculated
as follows :F©ISAROB 2007

l 1 m1 n1

W
UG

A

B

t

D B dA U G

K UG

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 3

Osteotomy simulation
Osteotomy simulation is conducted by cutting
the bone and by moving the acetabular fragment on
the computer. In this software, the cut surface can bechosen among the flat plane, cylindrical surface andspherical surface. The osteotomized fragment of thebone can be rotated and/or translated as needed.
In the simulation of the rotational osteotomy ofacetabulum, the first step is to choose the acetabulum
as a target bone, and the second step is to choose thespherical surface as a cut plane. The third step is to set
the cut plane at the osteotomy site and cut theacetabulum (Fig. 3), while the final step is to movethe osteotomized acetabular fragment and the articularsurface. In this study, the diameter of the cut plane
was 40mm and the center of the cut plane was set atthe center of the inscribed sphere to the acetabulum.
To determine the optimal transposition of theacetabular fragment, a peak pressure was calculated
for every 5 degrees of rotation of the acetabular
fragment.

Fig. 4

3. Results
Figure 5 shows an original contactpressure distribution of the example case thatwas shown in Fig. 1and 2.
Fig. 5
Algorithm
At the joint contact surface, tensile stress cannot
be transmitted. If tensile force is applied to the hip
joint, the acetabulum and femur easily become
separated. The rigid-body spring model can easily
simulate these mechanical characteristics. After the
initial stiffness equation was solved to determine thestress distribution, inadmissible solutions for thespring, such as tension on the joint surface, were
removed from the model by subtracting their member
stiffness matrices from the global stiffness matrix andthen repeating the process until no inadmissible
stresses were detected.
Modeling of Hip Joint
The reference point for the pelvis was set as thecenter of the body, and the reference point for the
femur was the center of the femoral head. All
displacements of the femur were constrained and theload was applied to the acetabulum. The applied load
was based on the hip contact force reported by
Bergmann [5] during the one-legged stance phase of
the normal walking cycle at 3km/h walking velocity.
Amount of force was 3.3 times the body weight andthe components of loading vector are backward: 0.11,
inward: 0.44, downward: 0.89.

©ISAROB 200720

Virtual osteotomy was performed in this patient.
The osteotomy is shown in Figure 6. The osteotomy
plane for the pelvic bone was spherical with a 40-mm
radius. The acetabular fragment was rotated 25
degrees anteriorly and 15 degrees laterally. This
increased coverage of the femoral head caused
decrease in the peak pressure and the pressure
distribution became less unequal (Fig. 7).
The peak pressure was predicted to decrease to
2.28 MPa from a peak pressure before operation of
4.03 MPa.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 6

osteotomy for preoperative planning [3]. Their
program could only show the relationship between thearticular surface of the acetabulum and the femoral
head three-dimensionally. Our simulator program is
more advanced and also incorporates a mechanicalanalysis module to predict the results of osteotomy [6,7].
In the actual operation, osteotomy cannot be
performed exactly as planned at the present time.
However, computer navigation systems and robotics
are continuously improving. If this program will belinked to computer navigation system, it will become
more useful to determine the most appropriate
treatment method for coxarthrosis with acetabular
dysplasia.

Fig. 7

References
1.
2.

3.

Figure 8 shows the peak pressures that were
calculated for every 5 degrees of rotation of theacetabular fragment. The peak pressure was lowest
with 25 degrees of anterior and 15 degrees of lateralrotation. However, low peak pressure zone spreadfrom 15 to 25 degrees anterior and from 15 to 20
degrees of lateral rotation.

4.
5.
6.

Fig. 8
7.

4. Discussions
Klaue et al. reported a computer program that could
simulate the coverage and the congruency of pelvic©ISAROB 2007

21

Ninomiya S, Tagawa H. Rotational acetabularosteotomy for the dysplastic hip. J Bone Joint
Surg Am 1984; 66:430-6.
Nozawa M, Shitoto K, Matsuda K, et al.
Rotational acetabular osteotomy for acetabular
dysplasia. A follow-up for more than ten years. J
Bone Joint Surg Br 2002; 84: 59-65.
Klaue K, Wallin A, Ganz R. CT evaluation ofcoverage and congruency of the hip priorosteotomy. Clin Orthop 1988; 232: 15-25.
Kawai T, Toi Y. A new element in discrete
analysis of plane strain problems. Seisan Kenkyu
1977; 29: 204-7.
Bergmann G, Graichen F, Rohlmann A. Hip joint
loading during walking and running, measured in
two patients. J Biomech 1993; 26: 969-90.
Tsumura H, Miura H, Iwamoto Y.
Three-dimensional pressure distribution of
the human hip joint: comparison between
normal hips and dysplastic hips. Fukuoka
Acta Med 1998; 89: 109-18.
Tsumura H, Kaku N, Ikeda S, Torisu T. A
computersimulationofrotational
acetabular osteotomy for dysplastic hip
joint: does the optimal transposition of theacetabular fragment exist? J Orthop Sci.
2005;10:145-51.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ARTIFICIAL INTELLIGENT INSERTS IN HEALTHCARE SYSTEMSBALAN PILLAIPROFESSOR, D.Sc. (TECHNOLOGY)KISBED ORG, Teknillinen korkeakoulu [Helsinki University of Technology], Espoo, Finland

Abstract
The presentation is here to make a technical walkthrough to the most recent initiatives in healthcaretechnology INSERTS predominantly in European
Union Countries. We may penetrate on bio-materials
and Implants.
Early adopters of new technologies require bothinnovative product partners and clinical teams. With
this in mind, a group of leading-edge researchers,
forward-thinking clinical experts, and innovative
product partners have formed the Healthy Aims
Consortium. They have chosen a range of implants anddiagnostic equipment to demonstrate how key Microand nano-systems technology components andprocesses can be developed and integrated into new
products. To rapidly demonstrate technology
integration, some products have been chosen which canbe designed, fabricated and prepared for clinical trials
within few years. We reveal here the Functional
Electrical Stimulation (FES) implant, an implant intracranial pressure sensor system, retina and cochlear
implants, and glaucoma, sphincter, and inertial sensors.
Within this short speech, we could merely walkthrough the state-of-the-art; confirm technically the
emerging need of engineering and human-bodyscience-fusion; into the next-level of cross-culturalscientific body that could achieve the future“connected” smart systems, and or are rolling at the bit
stream technology trajectory and the “free-sky”.

1.1 A study [1] analyzes the $ 906 million US market
for nanotechnology in health care products. It presents
historical demand data for 1999 and 2004, andforecasts to 2009, 2014 and 2020 by material andproduct (e.g., nanoparticles, nanoarrays, nanotubes,
dendrimers); by application (e.g., pharmaceuticals,diagnostic products, medical supplies and devices); andby indication (e.g., cancer, central nervous system
conditions, infectious and viral conditions). The study
also considers market environment trends andindicators, details industry structure and profiles 73
industry participants including Elan Drug Delivery,
NUCRYST Pharmaceuticals, Quantum Dots, BristolMyers Squibb, Dow Chemical, and GlaxoSmithKline1.
1.2 This speech is not anticipated to be a large sum of
paper supplies; except to show that a great number ofstudies that are not made any efforts to get there a roadmap on medical technology or policy; not to mention
about the drivers and trend in medically used chemicals
to human-body systems. The “Committee for Future,”consistently offered the plot with a sundry scenario on
political-cum-financial prospective. A paper [Elina
Savola, et.al. (2005)] produced last year in Finland;showing the global evaluation methods in medical
practices and its footings. To land a real-touch-roadmap of the future; one needs much more time andground-to-earth studies, which, of course, is notexpected due to either of the both or available monies.

1 INTRODUCTION
The health care is a local and at the same time global
conspiracy subject to handle. Mr. Bill Clinton, last

©ISAROB 2007

President of the United States of America [2000],
introduced the Medicare Bill that could not
successfully passed as final Law enforcement at the
Senate in the Capital Hill, at Washington, D.C.,US, andstill it is hanging with Mr. George W. Bush’s cabinet,
who lives his Offices as President at 2008. It is
obviously a never-ending process on healthcare; now
and tomorrow and no one wants to make any
compromises to decision! Demand for nanotechnology
in health care products will jump 48% annually
through 2009, led by improved cancer and central
nervous system therapies; based on solubilization
technologies. Diagnostic tests focused on nanoarrays
and quantum dots, and imaging agents conducted onsuperparamagnetic iron oxide nanoparticles; wouldalso
see
stronggrowth.

22

1 Notes extracted from the publication Source of NIST,
Gaithersburg, MD, USA. [2005].

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1.3 The Finnish government funding agency, [TEKES],
beside the other European Union Countries; haveconducted a “map-out project” on competitiveness and
Healthcare. See figure below:

Technical System Boundaries
Entertainment
And
Experience
Processes

Safety ProductsIntelligent Building• Automation of processing which is very difficult /expensive by “conventional” means– Offers a key to substantially larger, valuable market(e.g. Cochlear)• Enables novel sensors (e.g. glaucoma, sphincterclosure)
3 ELECTRODES AND SENSORS

Traffic andLogistics Processes

3.1 In a generic thinking, electrodes and sensors arevery sensitive in their surveillance due to the following
reasons: Electrodes are; key to active implants –EnergyMobileVehicleAcive parts(RFID, LTCC,
Optical Elements)

Telecom
Telecom

Combination ofCompetence

Telecom
MicroSensors

Well-being
FutureCustomerRequirements

Wireless
Automation
Distant Services,
Hospital
Systems”HealthCare”

LIIM A PROJECT

Micro sensors
and actuators
Microsystems and Automation Systems

ModernManufacturing
Industry

They provide the interface to the body tissue
Applications range from short term <1day toentire life implants
Active electrodes are being developed toallow dense arrays with minimal wiring
Electrode is integrated to multiplex
component

Pulp&
Paper

Service and Product System

Functional
Requirements

Customer Process

Automobile
Industry

ICE 2006, Jari Tam me la

The figure [Salminen. V, 2005] shows above the broadboundaries that interleaving with several emerging
technologies; and endless entities.

3.2 The lifecycle is downscaled to meet eminent
processes at the body system; and electrodes andsensors are therefore needed to be (see table) Long term
(electrodes)

2 ARTIFICIAL INTELLIGENTS IN HUMANBODY-SYSTEMS

Macro Micro

2.1 The present era of Internet, people around the globe
are searching ways to be happy and long living
healthily. The legal instrument in some extent
preventing or doing so; this is due to rejection ofembryonic inserts or use. This is also a moral issue; touse or insert artificial intelligent into the human-bodysystem [Pillai, B (2006). The implants; we now
consider mainly for the teeth-based levels. While ago,
we revolutionized with immense success; the Internet,
wireless connectivity, handheld devices, and cell
phones. Now “the-time” to-go; for the human healthissues with; smart systems implants that could help an
extended comfortable life to the elderly, chronic orotherwise sick and handicapped human-beings. We
owe many thanks to our elderly citizens of the world
who dedicated their expensive life to us now to flourish.
We should now consider helping them to survive in
guiding again to an extended success in healthy-life.

(array)Musclestimulation
Cochlear Retina

Short term
(sensors)
SphincterclosureGlaucoma

Table: Shows the long-term and short term
applications.
3.2 Let us take a quick at another smart device, that is;
Retinal stimulator • This requires a dense array of electrodes• This can only be made by microfabrication
techniques. See the image –2.2 Apparently we bump into several challenging
obstacles; they are the following:• Microfabrication technology• Range of electrode and sensor capability for current
and future applications• Challenge is to work with suitably biocompatible
materials• Dense arrays that require microfabrication– New markets / devices (e.g. retinal)©ISAROB 2007

3.3 This is a fantastic stimulator. It has proven working
perfectly though the microfabrication techniques are
extremely high. In this application the electrodestructure is very thin (10µm) and;

23

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

• The electrode tips are formed as truncated pyramids,• This improves the contact to the tissue.
3.4 See also a couple of other images below –4 BIOMATERIALS & FUNCTIONALINTERFACES
4.1 Biomaterials and Functional Interfaces - exists toaddress the need for achieving optimal interface
between implanted devices and the host tissues. The
active implants being developed within the Healthy
project present considerable additional
Aims 2challenges over current devices owing to theintegration of active electronics and multi-electrodearrays on flexible substrates. One partner, QMUL, isdeveloping biocompatible, flexible, and impermeable
device coating materials to the product partners'
specifications and evaluating their performance using
cell-culture and other models of biocompatibility. The
other partner, INEX, is developing physical andbiological surface treatments for electrodes that
improve coupling with the target cells (primarily nerve
cells) to eliminate much of the loss of efficiency withtime currently seen in many implanted electrodes. Thisis particularly important for the very small electrodes
making up the complex stimulation arrays being
developed within the project. A development roadmap
has been produced and tasks allocated in consultation
with others. Activity is now being focused on threematerials developments at QMUL; polyurethane,
primarily for the urethral device, modified silicones forthe other devices and the application of diamond-likecarbon to impart an impermeable yet biocompatible
coating. QMUL have also established detailed
biocompatibility test strategies in partnership with the
product partners. Test methodologies have beendeveloped and initial tests have been completed on key
materials. At INEX studies have focused on
topographic surface modifications in relation to the
behavior of mixed populations of cells. In addition a
sophisticated and systematic approach to chemical andbiomolecular surface fictionalization has highlighted
the importance of specific chemical groups in theadhesion of cells to surfaces.

2
European Union Initiative, funded by the FP 6
Framework Program, and is being continued unto 2010
and beyond.

©ISAROB 200724

4.2 It is mentioned earlier that the artificial intelligent
inserts; are no longer an imaginary thinking; virtually
they are already here. Ubiquitous and ambient system
integration needs more accurate performance inexperimenting; though it is a time-consuming effort
that could yield the next generation healthcaretechnology for sure. At the beginning this presentation
started in saying that the technical walk includes theglaucoma sensor development. This disease is affecting
more than seventy million human-being around theglobe. Leonard, et al [2006] reported in Dissemination
Day at EPF de Lausanne, Switzerland that glaucoma
sensor develops story and its successes. Human-being
lost their retina cells due to excessive intraocularpressure (IOP) and also natural phenomenon thatcauses a gradual and irreversible loosing their vision
leading to complete blindness, when not treatedproperly and timely. An image presented below thevision problem -4.3 The future wireless contact lens would be like theone shown below. This implant would create a newgeneration technology to sustain the health and sight.

5 THE IMLANTS
5.1
Latestimplantable
telemetricpressuremeasurements are aiming to monitor quality andfunctionality of therapeutic implants inserted; bysimply invasive procedures. In order to reduce, thehospital stay of patients to a minimum number of days;
implantable sensors for therapy control, integrated into
commercial Tele-healthcare applications; provide costsaving option in monitoring, and allowing the patient tocarry on participating their everyday life normally. In awireless hybrid sensor system approach (Fig below) as
described by T Eggers et al (5), the basic components
are –The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007oa long-term stable absolute capacitive pressure
sensor,oan ultra-low power interface circuitry,oa radio-frequency transponder for bidirectional communication to an outside bodyarea network unit, andoAt this point, my personal greetings and thanks go to
two eminent professors, who got dirtied their hands inarranging this Conference successful here. They arenamely Professor Masanori Sugisaka, Department ofElectrical Engineering of Oita University, Japan, andProfessor Ju-Jang Lee, Korea Advanced Institute of
Science and Technology, Daejeon, South Korea.
References[1] National Institute of Sciences and Technology,
NIST, Gaithersburg, Maryland, USAa micro coil for inductive data and energy
transmission to the implant.

[2005]: Nanotechnology Trends and Markets – Source
search unpublished documents.
[2] Vesa Salminen (2005): LIIMA Project, publishedby TEKES, Finland[3] Savola, E, et.al [2005]: Evaluation Methods ofMedical Practices, Published by the Finnish Parliament,
Suomen Eduskunta, Helsinki, Finland
Fig: Block diagram of an implantable telemetricpressure measurement system.
5.2 To avoid the lifetime of the system being limited tothe capacity of a battery; contemporary systems use apassive, inductively coupling method; to provide theimplant with power from outside. However, at thesame time transfer the data over a sufficiently longtransmission distance; to an external reader unit.
Consequently ultra low power consumption on part ofthe implant is essential. The encapsulation of theimplant has to provide hermetically sealing in order toassure a driftfree and highly reliable system; over theentire lifetime of the implant. An additional nanotype
coating ensures highly biocompatible performance of
the implant. The latest three-dimensional micro
packaging technologies facilitate the development offlexible devices; like integrated pressure sensor
systems that meet the elevated demands of modern
surgical insertion procedures; and could stay stable andreliable inside the human body over a long period oftime.
6 Summary
6.1 The next generation technology scenarios are
already set; for connecting into human-body-science,and information technology. This is not a trap but
value-added well-being technology and permanent
approach to better and happy life; the life time
available or provided by the nature.
6.2 Let me conclude this presentation here with thanks
to you all.
Acknowledgement©ISAROB 2007

25

[4] Pillai, B [2006]: A squat scenario on healthcaretechnology globally: Roadmap for Finland, A
proprietary document presented to LIIMA Project for
TEKES, Finland.
[5] Eggers, T, et.al [2000]: MEMS2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Intelligent Multi Agent Application System in AI System
Young Im Cho
Dept. of Computer Science, The Suwon University
San 2-2, Wau-ri, Bongdam-eup, Hwaseong, Gyeonggi-do, KOREA, 445-743
e-mail : ycho@suwon.ac.kr

Abstract
In this paper, I will present about the intelligent
multi agent application system in AI. The
research trend of multi agent is changed from
centralized computing environment to distributed
computing environment. Also, the research ofmulti agent can be changed to mobile
environment. Initially, the study of multi agent isfrom the research of the human modeling.
Therefore, I will present a brief the concept ofmobile multiagent, and present some application
areas of mobile multi agents, especially inbioinformatics, control, and information retrieval
etc. And finally I will present the research themeof multiagent in AI.

I. Introduction
Artificial Intelligence Artificial Intelligence(AI) is the area of computer science focusing on
creating machines that can engage on behaviors
that humans consider intelligent. The ability tocreate intelligent machines has intrigued humans
since ancient times, and today with the advent of
the computer and 50 years of research into AI
programming techniques, the dream of smart
machines is becoming a reality. Researchers are
creating systems which can mimic human thought,
understand speech, beat the best human chessplayer, and countless other feats never before
possible.
Recent developments of the internet andnetwork technologies evoke the technical changeof the data processing from a conventional
centralized and local processing system to the
distributed processing system. The research about
this network and the various approaches havebeen studied in order to efficiently manage mutual
operations in such a network environment.
Many studies have been actively carried out ina distributed processing environment by using
agent systems for efficient network management.
An agent system has the following characteristics:
multi agents in the distributed environments
promote efficiency by solving one problem
through any cooperation. Each agent manages the
problem by dividing a common work into thenumber of agents, or each agent manages it
independently, and then they solve the problemsby analyzing the results. In addition, it has some

©ISAROB 200726

advantages such that intelligent agents reflecting
the tendency of users make no limitation of
movement in a network, and it remarkably
decreases the network traffic [1].
The study of multiagent systems focuses on
systems in which many intelligent agents interact
with each other in distributed environment. The
agents are considered to be autonomous entities,
such as software programs or robots. Theirinteractions can be either cooperative or selfish.
That is, the agents can share a common goal (e.g.
an ant colony), or they can pursue their own
interests (as in the free market economy).
Multiagent system researchers develop
communications languages, interaction protocols,
and agent architectures that facilitate thedevelopment of multiagent systems. For example,
a multiagent system researcher can tell you how
to program each ant in a colony in order to getthem all to bring food to the nest in the mostefficient manner, or how to set up rules so that agroup of selfish agents will work together toaccomplish a given task. Multiagent system
researchers draw on ideas from many disciplines
outside of AI, including biology, sociology,
economics, organization and management science,complex systems, and philosophy.
In this paper, I will explain the distributed
multiagent system which is a modern AI approachand their applications as an example case.
This paper is composed of the followings.
Multiagent digital library is explained in chapter 2,
and the application areas in multiagent are
explained in chapter 3, and finally I will conclude
this research in chapter 4.

2. Multiagent Digital Library
2.1 Distributed Agent Framework
There are so many application areas of agentsin the real world. One of these areas is a digital
library system. The digital library is called anelectronic library or a virtual library. This is alibrary developed to replace the conventional
library, in order to serve information fromdatabases on the web to users, according to thedevelopment of computers and the related fields.
However, there are several problems in the
searching of data of the existing digital libraries.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

First, as the searching method is one dimensionaland distinguishes the existence of the searching
keyword from the database, the result is very
simple. Second, the results may contain
unnecessary information under a condition thatwas not given the prior information about the user.
Thirdly, whenever a client connects to the servers,
he has to receive the certification and be under thedominant power of the influence of network.
To overcome such problems, a new platform ofmobile multi agents for a personal digital libraryis necessary.
For the user's preference, some modular clientsare applied to a neural network. A multi agent
platform and a mobile agent platform are
combined to develop a new mobile multi agent
platform so as to decrease a network burden. Also,a new negotiation algorithm and a schedulingalgorithm are activated for the effectiveness of
PDLS.
DECAF is a kind of operating system including
agent communication, planning, scheduling,
monitoring, coordination, diagnosis, and learning
among agents[2]. DECAF makes a socket
program by itself, and presents some building
blocks which makes messages and communicates
between agents. Therefore, users or programmers
can produce agents without having someknowledge about API approaches. Also, users or
programmers do not need to make thecommunication codes directly to communicate
among agents. Agent systems have beendeveloped using various languages and platforms,
and they are classified into so many types bypurpose. In DECAF, many agents’ tasks are
divided by both GPGP (Generalized Partial
Global Planning) and TAEMS (Task Analysis
Environment
Modeling
andSimulation)
algorithms.
GPGP is for improving of PGP which acts as acoordination algorithm of multiagents [3]. The
first advantage of GPGP is that it reduces thesystem overhead which occurs by overlapping
interaction among agents. And the second
advantage of GPGP is that it is independent fromsome specific domain areas. Therefore, GPGP canmake heterogeneous multi agents system having
different functions. User’s requirements can be
decomposed by GPGP, and be structured by
TAEMS [4]. User’s requirements can be
decomposed by GPGP, and be structured by
TAEMS [4]. The root task can be decomposed
into subtasks, and the subtasks can be
decomposed into methods. The leaf node acts as amethod which means actually acted elements.
Voyager [5] is a distributed mobile agent’s
framework for developing agent’s applications,©ISAROB 200727

whereas DECAF is a non-mobile agent’s
framework. Voyager is an interactive frameworkwith Java programming. Also, Voyager canactivate any Java class in remote sites, and it
makes use of network bandwidths effectively.

Figure 1. DECAF

2.2 The Concept of Agent-based Digital
Library
A digital library serves a lot of information online [6,7]. The advantages of digital libraries areuser friendly, on-site service and accessibility.
However, in case of not having standardized
platform, the search of heterogeneous informationfrom digital libraries may be hard, as well as
impossible. If it does not have or learn about theuser’s information, unnecessary or uselessinformation will appear in the searched resultsfrom the digital library. Each agent can access
DBMS and search documents according to theuser profile. And then each agent categorizes thesearched results.
The proposed system PDLS based on a new
multi mobile agent platform. The system
combines a mobile system and a distributed
processing system to make an optimization of
behaviors in a distributed environment. To
establish a distributed environment, DECAF isused, and to activate a mobile framework,
Voyager is used here.

Figure 2. PDLS Structure

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

As shown in Figure 2, PDLS has two modules
and two databases. The monitoring agent module
is composed of Voyager and DECAF, and it
monitors the agents’ movements and controls
their executions. When the user’s requirements
are transferred to the PDLS, the monitoring agent
module checks whether the servers are available
or not. After that, it makes some agents, andpasses them to the servers. The searched results
are saved in a temporary repository. They arefiltered by negotiation agents, and the final results
are saved in the result repository.
User query

User query search in DB

Searched query?
By KQML message

Using KQML
massage query to
Negotiation Agent

Negotiation Agent
Search user DB

Yes

Save userDB library
update user profile

Searched query byanother agentsNo
Non-negotiation Tasks

YesNo

MMA delete

User satisfy?

Print to user by AM GUI

Save totemporal tableCalculate content scoresby MMA

Send content
information to
Information Collection
Agent

Figure 3. Negotiation Algorithm among
Multiagent
The construction of the initial user’s profile isconstructed by the user’s first input information.
According to the user’s searched results, PDLS
endows the user’s keywords to weight values, andupdates user’s profile information by SOM (selforganizing map) network in real time [8].
SOM is an unsupervised neural network. In thispaper, 2-layered SOM network is used here.

2.3 Simulation Results
The scenario is the followings. User can login
by user interface. The monitoring agent(MA) is
activated, and MA check the current connected
remote digital library. At the same time, a user
profile is read from database. The queries
according to users are sent to PDLS. MA makes asearch agent 1and registers it in ANS. The search
agent 1 is sent to the remote library. The agent 1
is activated by receive the parameters from PDLS.
The results from the agent 1 with informationsuch as name and index and abstract of the remotelibrary are sent to PDLS. And then they are sent
to the negotiation agent(NA) for negotiation. At
the same time, they inform to the MA and ANS.
MA is clustered using SOM network according to
the received results. The results by the user profile
can be shown in the remote plane. user can moveto his local library after receiving his necessary
results. The user profile’s update is activated.

©ISAROB 2007

28

As time passed, the searching times between
the proposed PDLS and the traditional clientserver model PDLS showed a faster search time
as well as a much safer search than the clientserver model. The result showed that as thenumbers of servers were increased, the searchingtime was decreased in PDLS.

3. Multiagent Heath System
3.1 TMA(Tissue Mineral Analysis)
TMA[9] is a very popular method in hairmineral analysis for health care professionals in
medical centers in over 48 countries[10,11]. The
assessment of the relative relationship between
nutritional minerals and toxic elements in hair isvery important for determining adequacy,
deficiencies, or the unbalance of nutritional
minerals in the body.
There are some problems in the TMA method.
The TMA method, which uses a TMA database,applies a pattern matching method in producing
outputs when the mineral ratios and the propertiesof a patient are entered into the database forrequesting. This pattern matching method may beeffective if the database of TMA has a large-scaledata, but it may produce inaccurate results if thedatabase of TMA consists of a small number ofdata. Secondly, because there are insufficient
health information databases for TMA in KOREA,
it could not get the effective medical information.
Also, it can cause some problems in the reliabilityof the TMA results, since the TMA results are
based on the database of western health andmineral standards.
For these reasons, I propose the first Intelligent
Medical Health Information System (IHIS). The
IHIS uses a new TMA method. In the TMA
method, a new multiple fuzzy rule base is
proposed for intelligent inference. A multi-levelstatistical analysis is used to reduce some errors instatistical analysis, so that a new TMA database isdeveloped and some medical data inferred fromthe TMA database. Also, this system can produce
Korean reports about both patients’ reports anddoctors’ reports as well. The effectiveness of the
proposed method is verified through a series ofsimulation using some real clinical data.

3.2 IHIS Overview
The proposed Intelligent Health Information
System(IHIS) is composed of 3 modules as shownin Figure 4: TMA statistical analysis module,
TMA health information system module, and theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Domain Expert
Knowledge andMedical Data

Doctor and Patient
Reports

about characteristics variables such as Ca / P ,too. Figure 5 show the classifications of metabolic
type using class distributions by adopting somecharacteristics variables.
1.5

intelligent inference system module. After
finishing the statistical analysis module, TMA
database can be developed and it can receive
feedback with real medical data for verification.
Users

F4

TMA Health Information
System module

AI techniques
Intelligent inference
Diagnosis, prediction

Intelligent Inference
System module

F3

F1

0.5

Statistical Analysis
moduleClinical data collection
Mineral data collection

1.0Classification of type andmineral ratio
Analysis of mineral andDiseaseNormal Distribution

F2

Multi level fuzzy rule baseConstruction of TMA database

English-Korean translation
Development of expert system
Medical information service

S4
S2
S3

Figure 4. System Overview-2

In the Statistical Analysis module, the 8 type of
metabolic types such as 4 slow metabolic types
and 4 fast metabolic types, and mineral ratios areclassified by a decision tree classifier[12,13]. The
decision tree classifier can be adopted for thefollowing reasons. The first step of the classifier
makes use of the combination of thecharacteristics that is a maximum mean value
among classes. However, the combination ofcharacteristics does not represent an optimalclassification among classes. Therefore, several
repeating classifications between two types ofdecisions should be adopted so that it can bedecided into input patterns correctly.
In the classification process of input patterns by
a decision tree classifier, four types of slow
metabolic types {S1 , S 2 , S 3 , S 4 } and four types
of fast metabolic types {F1 , F2 , F3 , F4 } areclassified in root node on the basis of a mineral
ratio of Ca / P ( = 2.63) . And in the second step, thesub slow metabolic types {S1 , S 2 } and{S 3 , S 4 } are classified on the basis of a mineral
ratio Ca / K (= 4.2) , and the sub fast types{F1 , F2 } and {F3 , F4 } are classified on the basisof a mineral ratio Na / Mg (= 4.0) . And in the
final step, {S1} {S 2 } {S 3 } {S 4 } are finallyclassified on the basis of a mineral
ratio Na / Mg (= 4.0) .
Likewise, {F1} {F2 } {F3 } {F4 } are finallyclassified on the basis of a mineral ratio of
Ca / K (= 4.2) , too. In this paper, I have classified
the basic metabolic types and the correlation ofnutritional minerals and toxic minerals bycorrelation analysis among minerals overcoefficient of correlation 3.0.
The metabolic type classification shows thesame results when using class type distributions©ISAROB 2007

S1

0.0

Classification of 8 mineral types
by decision tree classifier

29

0

2

4

6

8

10

12

pointsFigure 5. Metabolic Type Classification by
Mineral Ratio of Ca / P

3.3 TMA IHIS Development Steps
I propose a new multiple fuzzy rule basesystem based on a statistical analysis method,which is designed for an intelligent inference
from a lot of uncertain health information data.
The purpose of the multiple fuzzy rule basesystem is following. In case that the complicateddecisions by a lot of minerals are necessary, a rulebase may be complicated in the conventional
fuzzy rule base system. Therefore the rule baseshould be distributed so that it can be found,searched, and inferred by some multi-level
relations among rules in the multiple fuzzy rulebase.
Due to this, IHIS is composed of multi leveltree structures which represent relations between
the conclusion parts of a rule and the conditionalparts of other rules, so that IHIS is appropriate forrepresenting more complicated fuzzy rules.
The reason why the fuzzy theory is adopted as
an uncertain handling method in IHIS, is that a
fuzzy theory is appropriate for inference as well
as representing the clinical minerals than any
other uncertain handling techniques [9, 10]. The
implemental steps of the multiple fuzzy rule baseare the followings.
<Step 1> Classification of Metabolic Types andDevelopment of a Fuzzy Rule base
The <Step 1> is an initial implementation stepof a multiple fuzzy rule base on the basis of thecorrelation analysis of minerals by which defines
fast metabolic types and slow metabolic types. To
implement the multiple fuzzy rule base in thispaper, I analyzed the following mineral types:
nutritional mineral 30 types, toxic mineral 8 types,
the relations between nutritional minerals andThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

toxic minerals, the ratios of nutritional minerals,
the ratios of toxic minerals, the tendencies
between all types of diseases and minerals.
The linguistic values which are in the mineral
ratio’s analysis, such as ‘high’, ‘low’, and‘acceptable’, can be represented by fuzzy
membership functions as shown in Figure 8. In
Figure 8, X axis represents mineral ratios and Y
axis represents fuzzy membership function values.
These values are obtained by a statistical analysis
method using a decision tree classifier.

Figure 6. Fuzzy Membership Functions ofMineral Ratios
Not only those above minerals ratios but also
all of the minerals such as 15 types of nutrient
minerals ( Ca, Na, Mg ) and 8 types of toxic
minerals ( Hg, Pb ) and other 15 types of minerals
can be represented by fuzzy membershipfunctions as shown in Figure 4.
<Step 2> Development of relations between
endocrine glands and minerals by measuring
the amounts and ratios of minerals
The <Step 2> is a refinement step of themultiple fuzzy rule base that implemented by<Step 1>. And also it implements a database
about correlations between endocrine glands andminerals. The results of this analysis, shown in
Figure 5, are used in designing the multiple fuzzy
rule base. For example, if some mineral ratios are
increased,suchasCa / P, Ca / Mg
Ca / Na , Ca / K , Ca / Fe , then parathyroid isincreased. However, if Fe / Cu is increased, thenparathyroid is decreased. If Na / Mg , Fe / Cu are
increased, then thyroid is increased. If
Zn / Mg , Fe / Cu are increased, then progesterone
is increased. If Ca / P, Ca / K are increased, thenthyroid is decreased. If Na / K , Ca / K are
increased, then progesterone is decreased. If
Ca / P, Ca / Mg.Ca / K , Ca / Fe, Ca / Na are increased,©ISAROB 2007

30

then

pancreasisincreased.

However,if

Zn / Mg , Fe / Cu are increased, then pancreas isdecreased.
If
Ca / Mg , Ca / P, Ca / K , Ca / Na, Ca / Fe, Na / K
are increased, then Estrogen is increased.
However, if Zn / Cu , Fe / Cu are increased, thenestrogen is decreased. If Zn / Cu , Fe / Cu are
increased, then progesterone is increased.
However, if Na / K , Ca / K are increased, thenprogesterone is decreased. The other results ofstatistical analysis are used to implement themultiple fuzzy rule base.
<Step 3> Paragraph Analysis by Mineral
Distribution
Patient and doctor’s report have so many
paragraphs explaining a patient’s examination by
TMA. Since a doctor’s report contains extra
information plus the contents of a patient’s report,
the doctor can explain the patient’s examination to
the patient very effectively. Patient and doctor’sreport have so many paragraphs explaining apatient’s examination by TMA. Since a doctor’sreport contains extra information plus the contents
of a patient’s report, the doctor can explain the
results of the patient’s examination to the patientvery effectively.
For the implementation of the refined fuzzy
rule base, I analyzed the paragraphs explaining apatient’s examination according to mineral
distribution.
The rule type of the proposed multiple fuzzy
rule base and the rule type of conventional simple
fuzzy rule base are very different. In the multiple
fuzzy rule base, only 3 propositions (fuzzy
membership functions) are in antecedent part of arule to make a multilevel inference effectiveness.
Also, because of a hierarchical structure in themultiple fuzzy rule base, the system can infer the
next relative rules from the multiple fuzzy rulebase. However, in a conventional simple fuzzy
rule base, there are so many propositions being
represented by fuzzy membership functions in
antecedent part of a rule. Even though it can be
represented by small sized propositions, it isnecessary to have some intelligent techniques,
such as time scheduling or optimality, as well assome intelligent fuzzy inference techniques,because the medical minerals are very huge aswell as complex, and they have so many relations
among them.

3.4 Implementation Step
The format of the multiple fuzzy rule base is as

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

follows. The antecedent part of a rule is composedof mineral ratios, and the conclusion part of a rule
is composed of both metabolic types and mineral
ratios entering antecedent of the rules. In thispaper, rules in a fuzzy rule base which has been
implemented so far are more than 1000 cases.
.
<Format of Fuzzy Rule base>Rule 1: IF X 1 is A1 and Y1 is B1 and…… Z 1 is C1 THEN K1 is R1
Rule 2: IF X 2 is A2 and Y2 is B2 and…… Z 2 is C 2 THEN K 2 is R2……….
Rule n: IF X n is An and Yn is Bn and…… Z n is C n THEN K n is Rn

though the IHIS has small-sized database with
about 2000 cases, it could successfully find out
the desired results by using the statisticalclassification and the intelligent multiple fuzzy
rule bases. If it is compared with the simple fuzzy
rule basis method, the proposed multiple fuzzy
rule basis method has increased the effectivenessof the IHIS where the size of the sample is about
2000 cases.
FUZZ_INFER( )……………………………

Rule i

Rule j

input

xj

yj

zj

Condition part

……

input

Kj

xi

yi

zi

yk

zk

……

……

Ki

conclusion partRule kWhere,

X 1 , Y1 , Z 1 , K1 : mineral, mineral ratios,

endocrine gland etc. A1 , B1 , C1 , R1 : fuzzysets( high, low, medium etc.)
In this paper, the multiple fuzzy rule basemakes use of TAEMS in order to be an effective
fuzzy inference using data gained from multilevel statistical analysis.
Each rule in the fuzzy rule base has aninterrelationship like Figure 7. The fuzzy
implications that are composed of fuzzy
antecedent and conclusion parts of a rule havesome interrelationships which send to otherantecedent parts of rules in the fuzzy rule base.
The reason is that TMA mineral data is classified
by the complicated interrelationship and not
simple mineral data.
To infer the ID paragraph, the three rules
should be interconnected as shown in Figure 7.
The conclusion part of ‘THEN’ such as ' S1 ' ,' Ca / K is low' , ' Na / Mg is high' , are entered
to the antecedent part of a Rule #2. And therelative ID paragraphs are inferred by multilevel
scheduling process using TAEMS. In the IHIS
system, each conclusion part of rules is repeatedly
finding the antecedent parts of the upper level
rules, and finally it finds the appropriate ID
paragraphs of the conclusion part of rules. The
inferred relative ID paragraphs are as follows.
Therefore, according to the inferred ID
paragraphs by mineral amounts and ratios, thetranslated paragraphs in Korean are inferred fromthe TMA database, and they finally served topatients and doctors as a format of reports.
Since USA-TMA consists of simple structured
RDB with approximately 0.35 million cases, it
could be carried out by the inference of somerequired data via just pattern matching. Even

©ISAROB 2007

31

xk

Kk

입력
Data Set(Input Facts)

Input Facts

Input_Array[50]={Ca, Mg,Na,K,Cu,Zn,P,Fe,Mn,Cr,Se,B,Co,Ge,Mo,S,V,As,Be,Hg,Cd,Pb,Al,Sb,…Zr}
Mid_Output_Array[20]={Ca/P,Na/K,Ca/K,Zn/Cu,Na/Mg,Ca/Mg,Fe/Cu,Ca/Pb,Fe/Pb,Fe/Hg,…S/Pb}

Figure 7. Interrelationship of Rules inMultiple Fuzzy Rule Base
Rule #2:

IF S1 andS1 (Slow Type #1)
pass

Ca/K is low and

Ca/K is low
pass

Na/Mg is high THEN

Na/Mg is highpass

13002368

Inferred Sentence ID

Rule #21:

IF Ca/P is high and Na/Mg is low and Ca/K is high THEN S1 (Slow Type #1)

Rule #31:
Rule #32:

IF Na/K is low and Zn/Cu is high and Fe/Cu is high THEN Progesteron is highIF Progesteron is high and Throid is high THEN Ca/K is low

Rule #41:

IF Adrenal Cotex(Catabolic) is high and Adrenal Cortex(Anabolic) is high
THEN Na/Mg is high…………

Figure 8. Inference Process in Multiple
Fuzzy Rule Base
<ID: 13002312 English Paragraph>“Magnesium deficiency has been shown to be
associated with decreased antibody production.
Published studies have revealed that thelymphocytes, which are the body’s defence against
foreign invaders, are inhibited when there is a
deficiency of Magnesium”
The average processing speed of the proposedsystem means, the rate of searching the literal
sentence matching with the mineral data of TMA,
at which must be inserted in the initial menu of
the IHIS. While the processing speed of thesimple fuzzy rule basis method is considerably
slow due to frequent comparison between input
data of rules, the proposed multiple fuzzy rulebasis method has been increased 5 times by

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

decreasing the number of comparison of input
data of rules and reinserting the resulting data.
In order to measure the accuracy of output in
the IHIS, I inspected the matching sentences
between the resultant documents (set ofparagraphs) from the proposed IHIS system andthose from TMA in the US for about 2000 casesof domestic patients’ clinical data occurred from
July 2001 to February 2002. For 50 sentences of a
result document per patient, the simple fuzzy rulebase’s method resulted in 30 matched sentences inaverage, but the multiple fuzzy rule base’s method
resulted in 46 matched sentences. The accuracy ofthe proposed multiple fuzzy rule base’s method
has been increased by 32% than that of the simple
fuzzy rule base’s method. Presently, the total
number of result sentence saved in the database of
the IHIS is approximately 700 per eachmetabolism and mineral. Consequently, theperformance of the inference by the proposed
multiple fuzzy rule basis method, having only
2000 cases, is exceedingly better than that of theconventional method with 0.35 million data.
I have analyzed the degree of satisfaction,
which means the ratio of the degree of theapplication and the efficiency of the IHIS. Usually,
the proper time required for a patient is threeminutes. Here, I set the basic processing time to 3
minutes and counted the over time for evaluating
the degree of the customer’s satisfaction.

4. Conclusions
In this paper, I will present about the intelligent
multi agent application system in AI. The
research trend of multi agent is changed from
centralized computing environment to distributed
computing environment. Also, the research ofmulti agent can be changed to mobile
environment. Initially, the study of multi agent isfrom the research of the human modeling.
Therefore, I will present a brief the concept ofmobile multiagent, and present some application
areas of mobile multi agents, especially inbioinformatics, control, and information retrieval
etc.
In this paper, I proposed a Personal Digital
Library System. PDLS is designed based on a
new mobile multi agent platform using Voyager
and DECAF agent framework. The new platform
is a hybrid system of a mobile and a distributed
system in order to achieve an optimality in
distributed. Also, I have developed the new TMA
System, named IHIS.
In the future, PDLS needs to be compensated inorder to be activated in the real world. Also, IHIS©ISAROB 200732

will be constructed the eastern style medical
database not western style database as time passed.
Also, I anticipate the system could be served on
the World Wide Web very soon.

Acknowledgements
This paper is carried out by supporting of
KOSEF(KOrea
ScienceandEngineering
Foundation) during 2003 to 2005 as an Eminent
Woman Science Program.

References[1] David C., Colin H., Aaron Kershenbaum,
Mobile Agents: Are They a Good Idea?, 1995
[2] John R. Graham, Keith S. Decker, Towards
Distributed, Environment Centered Agent
Framework. Appearing in Intelligent Agents IV,
Agent Theories, Architectures, and Languages
Springer-Verlag, 2000, Nicholas Jennings,
Yves Lesperance, Editors[3] Keith S. Decker, Victor R. Lessor
Generalizing the partial global algorithm.
Intelligent Cooperative Information Systems,
vol.2, no.2, pp.319~346. 1992
[4] Keith S. Decker, Task Environment Centered
Simulation, 1996
[5] ObjectSpace, Voyager Core Technology 2.0
User Guide, ObjectSpace, 1998
[6] Sánchez, AGS: Introducing Agents as
Services Provided by Digital Libraries, 1997[7] Jonas Holmstrom, A framework for
Personalized Library Services, October 2002
[8] Kohonen, Self Organizing Feature Map, 1995
[9] Watts, D.L., Trace Elements and Other
Essential Nutrients-Clinical Application of
Tissue Mineral Analysis, 2nd Writer’s
BLOCK edition, 1997.
[10] Baumgartner, W.A., and Hill,V.A., and Blahd,
W.H., “Hair Analysis for Drugs of Abuse”,
Journal of Forensic Science, vol.34, no.6,
1989.
[11] Vir etal, “Serum and hair concentrations of
Copper During Pregnancy”, American
Journal of Clinical Nutrition, vol.34, 1981.
[12] Jang, S.I. etal., “Research of hair minerals ina diabetic”, Medical news, .vol.45, no.5, 2002.
[13] Safavian, S.R., and Landgrebe, D., “A surveyof decision tree classifier methodology”,
IEEE Transactions on System, Man, andCybernetics, vol.21, no.3, pp. 660-674, 2001.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Hybrid Tracking Control of Eye-in-Hand Robotic Manipulators
Wen-Chung Chang and Chih-Wei Cho
Department of Electrical Engineering
National Taipei University of Technology
Taipei 106, Taiwan, R.O.C.
Abstract
Classical force and vision-based tracking control approaches typically require expensive six-axis force sensors in addition to vision sensors. In this article, a new
approach is presented which only requires a single-axis
force senor together with a laser cross projector in addition to a vision sensor. The key idea is to estimate
the direction normal to an unknown 3-D surface byprojecting and reconstructing a laser cross on it. The
proposed autonomous task is to drive the end-eﬀectorof a 6-DOF manipulator to a visually determined trajectory and continue tracking the trajectory in desiredpose, contact force, and speed. The proposed hybrid
force and vision-based control approach is successfully
validated in a real task environment by performing experiments with an industrial robotic manipulator on
an arbitrary-shaped acrylic ﬁber 3-D object.

mated. This normal vector is computed based on thereconstruction of the projected laser cross onto the unknown surface provided that the ﬁxed transformation
between the coordinate systems of the camera and thelaser projector is known a priori.
The conﬁguration of the proposed autonomous system is shown in Figure 1, where the system includes arobot arm, an unknown surface, a laser cross projector, a PC, and a CCD camera. The problem of in-

Laser crossprojector
CCD
cameraTrajectory

1-axisforce sensor

1

Unknown 3-D surface

Introduction
Robot

Vision is becoming a popular sensor in robot control since it can extract extensive information without
contacting with the environment. Based on available
visual information, appropriate feedback lows can besynthesized for variety of control applications [1, 2].
Xiao et al. [3], proposed force and position control ofa robotic manipulator in Cartesian space with an expensive 6-axis force sensor and a CCD video camera.
In [4], an autonomous dexterous planar robotic systemcapable of tracking unknown contours has been presented and validated in experiments. This system canguarantee task precision employing only a single-axis
force sensor and an imprecisely calibrated CCD camera. In this research, force and pose tracking control
can be further performed on 3-D unknown surfaces bya low-cost single-axis force sensor, a CCD camera, anda laser cross projector. In order to control the pose of
the end-eﬀector with respect to an unknown 3-D surface, the direction normal to the tangential plane at
the contacting point, a normal vector, must be esti-©ISAROB 2007

33

Robot controller

PC

Figure 1: System conﬁguration.

Mux

Hybrid task
encoding

Controller

Manipulator

Single-axis
force sensor
CCD camera

Unknown
3-D surface

Figure 2: Hybrid control structure.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

terest is to simultaneously control the normal contact
force and the pose of a six-degree-of-freedom robotic
manipulator on the unknown 3-D surface. The goalof the autonomous task is to drive the end-eﬀector to
the trajectory and continue tracking the trajectory indesired pose and contact force. The proposed hybrid
control structure is illustrated in Figure 2.

2

Normal vectors on 3-D surface

2.1

Detection of laser crosson the uα-plane and the vβ-plane as illustrated in Figure 4, the image coordinate of the laser cross center[ uc vc ]T are calculated by setting α = 0 and β = 0in (1). To determine the two curves and the precise
center of laser cross, four regions are partitioned by
the previously detected laser cross center [ uc vc ]T .
The curve in quadrant I and quadrant III can be estimated by least square algorithm. Similarly, the othercurve in quadrant II and quadrant IV can also be estimated.

2.2

As the color of the laser cross is diﬀerent from thesurrounding environment, the laser cross could be detected by color ﬁltering and connected components labelling [5]. A typical observed image from a CCD camera and the corresponding image processing results canbe seen in Figure 3. In order to locate the two curves

Figure 3: Laser cross on image plane.
that the laser cross is composed of, one could ﬁrst
identify the corresponding two sets of pixels and apply
least square algorithm to determine their equations inimage space. An eﬀective way of computing the center of the cross for correctly allocating sets of pixels to
the two curves is as follows. Let [ u v ]T be the 2-D
coordinates in the image plane, α denote the distance
between the edges of laser cross at ﬁxed u, and β denote the distance between the edges of laser cross at
ﬁxed v as illustrated in Figure 4. By determining theReconstruction of normal vectors

To determine the directional vector normal to the
3D unknown surface, the image projection of lasercross onto the unknown surface is used to estimate
the normal vector. Firstly, calculate the transformation matrix between the camera and the laser projector, and then the transformation matrix between thelaser projector and the robot. Finally, reconstruct the
projected laser cross on the surface in laser projector frame by the known transformation between thecamera and the laser projector as shown in Figure 5.
Speciﬁcally, when projecting a laser line to form theyz-plane in the laser projector frame, one can reconstruct a 3-D laser point m̄ with Im̄ as the 3-D coordinate of its projection on the image space by setting the
x-component of m̄ to be zero in the following equality.
m̄ − c = k(Im̄ − c)

Similarly, one can reconstruct a 3-D laser point by
setting the y-component of m̄ to be zero in (2) if alaser line is projected to form the xz-plane. Based on
m̄

m̄

u
v

(2)

I

α

β
α

β
uc

u

vc

v

x
y
Laser cross projector

Figure 4: Laser cross curves on image plane{left}, uαplane{middle}, and vβ-plane{right}.
equation of the curves
α = g1 (u) and β = g2 (v)©ISAROB 2007

c

z

(1)

34

CCD camera

Figure 5: Reconstruction of laser cross curves.
the reconstructed laser cross in Cartesian space, one
can compute the normal vector n at the laser crosscenter, which is the cross product of the two tangential
lines at the laser cross center.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

Hybrid control design

There are three subtasks for the hybrid task including position, orientation, and force control tasks.

3.1

Position control

l = Ir̄ ∗ − c, f denotes the contact force, and withsuitably deﬁned positive numbers k1 and δ
0 f > δ; if contacting
k1 (f ) =.
(10)
k1 f ≤ δ; otherwise
In the light of (5) and (9), it follows that

The purpose of this subtask is to control the robot toreach the desired target position. The task can be divided into two parts. The ﬁrst part is an image-based
set-point control task. The second part is a contacting control task. Based on the perspective projectivemodel G of the camera, the image coordinate Ir of apoint feature r ∈ 3 can be computed as⎤⎡
iT (r−c)γT(r−c) ⎦
I
(3)
r = G(r) = ⎣ kj T (r−c)γ kT (r−c)
where γ is the focal length and c is the optical center ofthe camera, and iT , j T , and kT are the row vectors of
the rotation matrix from the base frame to the camera
frame. The encoded error for this task can be deﬁned
as(4)
e1 = Ir − Ir∗ė1 = −e1 .

Therefore, the end-eﬀector tip continues reaching thelaser cross center while e1 → 0 exponentially.

3.2

where

⎡
J 1 (r) = ⎣fkT (r−c)fkT (r−c)




(5)

T

 ⎤

T

 ⎦

iT − ki T (r−c)
kT
(r−c)

j T − kj T (r−c)
kT
(r−c)

(6)

The image-based position control law capable of driving e1 to zero can be chosen as follows.
+ṙ = − (J 1 ◦ L(θ)) e1(7)

where L denotes the forward kinematic function andthe superscript + denotes the pseudo-inverse. Since
one cannot perform 3D positioning task with only
single-camera vision, a contacting control law is considered which together with the image-based feedback
law can guarantee precise positioning at the target
point r ∗ on the 3-D trajectory. Let P ∈ 3×3 , theprojector onto the null space of J 1 , be deﬁned as
P = I − J+1 J 1.

(8)

Consider the feedback connection ṙ = ṙ p , where+ṙ p = − (J 1 ◦ L(θ)) e1 + k1 (f )P l

©ISAROB 2007

(9)

35

Orientation control

Based on the reconstructed normal vector n and theorientation of the end-eﬀector, denoted by z, one candeﬁne the encoded error for the orientation control
subtask as(12)e2 = z(θ) × n = h(θ)
where θ denotes the joint position vector and z canbe computed based the forward kinematics of the manipulator. Diﬀerentiate (12) with respect to time, itfollows that

where r and r ∗ are the positions of the end-eﬀector tip
and the laser cross center respectively. Diﬀerentiate(4) with respect to time, it follows thatė1 = J 1 (r)ṙ

(11)

ė2 =

∂h(θ)
θ̇ = J 2 (θ)θ̇
∂θ

(13)

The following joint control law when placed in the
feedback loop, θ̇ = θ̇ o , is capable of driving z to alignwith n.
θ̇ o = −J 2 (θ)+ e2
(14)

3.3

Force control

This subtask is to maintain the required contact force
along the normal direction to the unknown surface.
Let fd denote the desired contact force and the force
encoded error be deﬁned as
ef = f − fd .

(15)

Consider the feedback connection ṙ = ṙ f , whereṙ f = kf ef n, kf > 0.

(16)

Then, the joint velocity command for the force control
task can be synthesized as follows.

ṙ f
−1
θ̇ f = J m (θ)
(17)
03×1
where J m (θ) ∈ 6×6 is the mechanical Jacobian of
the manipulator.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.4

Hybrid control

The encoded error for the pose control task can thus
be deﬁned as

e1
e=
(18)
e2
Diﬀerentiate (18) with respect to time, it follow thatė = M (θ)θ̇
where



M (θ) =

(19)

(J 1 ◦ L(θ)) J m1 (θ)
J 2 (θ); J m1 (θ) =

∂L(θ)
.
∂θ

With the understanding that the force control law does
not disturb the pose control law, the hybrid control law
in the joint space capable of driving e and ef to zero
exponentially can thus be deﬁned as follows.
θ̇ = −M + (θ)e + θ̇f

4

(20)

Experiments
u

30

Pixel

20

10

v

References

−30

−40
0

1 2
10

20

30

3
40

50

60

70

80

Time(s)
50

y

30
40

Pixel

25

Bit

10
5

30

20

10

0

0

−10

−5
−10

[1] Gregory D. Hager and Wen-Chung Chang and
A. Stephen Morse, “Robot hand-eye coordination based on stereo vision,” IEEE Control Syst.
Mag.,Vol. 15, pp. 30-39, 1995.

60

15

0

1 2
10

20

30

40

50

Time(s)

3
60

−20
70

80

0

x
z
1 2
10

20

30

3
40

50

60

70

80

Time(s)Figure 6: Experimental setup{top left}, position tracking error{top right}, force tracking error{bottom left},and orientation tracking error{bottom right} .
The experimental system is composed of a 6-DOF
Mitsubishi robot RV-1A, a JAI CV-S3200 camera, alaser cross projector, a P4-2.4G computer, and an
arbitrary-shaped acrylic ﬁber 3-D object as illustrated
in Figure 6. Successive stages in the autonomous hybrid tracking control are as follows. In Stage 1, thetool tip is driven to the laser cross center with orientation normal to the unknown surface by employing the©ISAROB 2007

This paper presents a seemingly novel approach toautonomously control the pose of a robotic manipulator with respect to an unknown 3-D surface. In particular, the end-eﬀector can be driven to a set-point
and trajectory on the surface while kept normal to thesurface. A low-cost single-axis force sensor together
with a CCD camera and a laser cross projector are required to perform such control tasks. Compared withexisting approaches, the proposed system is not only
eﬀective but also less expensive.

0

−10

−20

20

Conclusion

This research is supported by the National Science
Council of Taiwan, R.O.C. under grants NSC 94-2213E-027-006 and NSC 95-2221-E-027-049.

40

35

5

Acknowledgements

50

40

proposed pose control law. In Stage 2, force control isapplied while maintaining the desired pose. In Stage
3, the tool is controlled to follow the moving laser crossin desired pose and contact force. The tracking errors
for position, orientation, and force in these stages areshown in Figure 6. In these experiments, the proposed
hybrid control approach has demonstrated satisfactory
performance on tracking control.

36[2] Seth A. Hutchinson and Gregory D. Hager and Peter I. Corke, “A Tutorial on Visual Servo Control,”IEEE Trans. Robot. Automat.,Vol. 12, pp. 651-670,
1996.
[3] Di Xiao and B.K. Ghosh and Ning Xi and T. J.
Tarn, “Sensor-Based Hybrid Position/Force Control of a Robot Manipulator in an Uncalibrated
Environment,” IEEE Transactions on Control Systems Technology,Vol. 8, pp. 635-645, 2000.
[4] Wen-Chung Chang, “Hybrid Force and VisionBased Contour Following of Planar Robots,” Journal of Intelligent and Robotic Systems,Vol. 47, pp.
215-237, 2006.
[5] I. Pitas, Digital Image Processing Algorithms andApplications, Wiley, 2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Robust Adaptive Sliding-Mode Fuzzy-Neural-Network Model-Following Position
Control of PMSM Servo Drives for Robotic Applications
Fayez F. M. El-Sousy
Electronics Research Institute (ERI), Power Electronics & Energy Conversion Department
Al-Tahrir Street, Dokki, Cairo, Egypt
fayez@eri.sci.eg

Abstract
In this paper, an intelligent sliding-mode controller(SMC) for achieving favorable decoupling control and highprecision position tracking performance of permanentmagnet synchronous motor (PMSM) servo drives is
proposed. The intelligent position controller consists of a
SMC in the position feed-back loop in addition to an online trained fuzzy-neural-network model-following
controller (FNNMFC) in the feed-forward loop. The
intelligent position controller combines the merits of the
SMC with robust characteristics and the FNNMFC with online learning ability for periodic command tracking of a
PMSM servo drive. The theoretical analyses of the SMC
are described with a second order switching surface whichis insensitive to parameters uncertainties and external load
disturbances. The FNNMFC generates an adaptive control
signal which is added to the SMC output to attain robustmodel-following characteristics under different operating
conditions regardless of parameter uncertainties and load
disturbances. The results of simulations confirm that the
proposed SMC with FNNMFC grants robust performanceand precise response to the reference model regardless ofload disturbances and PMSM parameter uncertainties.
Keywords: PMSM Servo Drives, Sliding-Mode Control,
Fuzzy-Neural-Network, Model Following Control

1 Introduction
The permanent–magnet synchronous motor (PMSM)
servo drives play a vitally important role in highperformance motion-control applications such as industrial
robots and machine tools. Utilizing the field-oriented
control (FOC) technique simplifies the dynamic model ofthe PMSM and control scheme [1–10]. In recent years, thevariable structure control (VSC) with sliding-mode or
sliding-mode control (SMC) has received much attention tocontrol PMSM servo drives because it can offer good
properties such as insensitivity to parameter variations,external load disturbance and fast dynamic response. The
first step of SMC design is to select a sliding surface thatmodels the desired closed-loop performance in statevariable space. Then, the controller is designed such thatthe system state trajectories are forced toward the sliding
surface. The sensitivity of the controlled system to©ISAROB 2007

37

uncertainties exists only in the reaching phase. Thus, thesystem dynamic in the reaching phase is still influenced byuncertainties. To keep robustness in the whole sliding-mode
control system, several researchers have focused on
eliminating the effects of uncertainties [3–6]. Intelligent
control techniques in many researches have been developed
to improve the performance of the PMSM servo drives. The
concept of incorporating fuzzy logic into a neural network
has grown into a popular research topic. The fuzzy neuralnetwork (FNN) possesses both their advantages; it
combines the capability of fuzzy reasoning in handling
uncertain information and the capability of artificial neuralnetworks in learning from process. However, the adaptive
control schemes of PMSM servo drives that incorporate thetechniques of FNNs have also grown rapidly [7–10].
The aim of this paper is to design a proposed intelligent
SMC for PMSM servo drive system. The intelligent
position controller consists of a SMC in the position feedback loop in addition to an on-line trained FNNMFC in the
feed-forward loop. First, the SMC is designed and applied
to the control of the rotor position of the PMSM servo drive.
In the sliding-mode control system, when the sliding mode
occurs, the servo drive system dynamic behaves as a robust
state feedback control system. Then, a proposed on-line
trained FNNMFC system is designed in addition to the
SMC to improve the dynamic performance and to preservea favorable model-following characteristics under various
operating conditions of the servo drive system. In the
proposed FNNMFC, the error between the reference modeland the PMSM servo drive system output is used to train
the connective weights and membership functions of theFNN. The output of the FNNMFC is added to the SMC
output to compensate the error between the reference modeland the PMSM servo drive system output under parameter
uncertainties and external load disturbances. The dynamic
performance of the PMSM servo drive system has beenstudied under load changes and parameter uncertainties.
The simulation results are given to demonstrate theeffectiveness of the proposed robust non-linear intelligent
sliding-mode controller.

2 Robust Intelligent Sliding-Mode Control
2.1. Problem Formulation
To solve the problems of uncertainties, an intelligent
SMC is proposed to increase the robustness of the FOC-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

PMSM servo drive system. The proposed intelligent robust
position controller combining the SMC and the FNNMFC.
Utilizing FOC, the proposed intelligent PMSM servo drive
can be simplified to the control system block diagram asshown in Fig. 1. The adaptive control law is designed as:
rc
r*
r*
i qs
(t ) = U SMC (t ) + U FNNMFC (t ) = i qs
(t ) + δi qs
(t )

(1)

The q-axis current command, iqsr* , is generated from the
SMC and δiqsr* is the adaptive control signal generated fromthe proposed FNNMFC to automatically compensate theperformance degradation due to load disturbances andPMSM parameter uncertainties. The inputs to the SMC are
the position error, eθ , and the rotor speed ω r to construct
the sliding surface S(t) and the sliding-mode control law to
get the q-axis current command, U SMC = iqsr * . While theinputs to the FNNMFC are the error between the reference
model and actual rotor position, eθmf , and the rate of changeof the rotor position (speed), Kθ θ&r . Those signals are used
to train the connective weights and membership functionsof FNNMFC. The output of the FNNMFC is the adaptive
control signal, U FNNMFC = δiqsr * .
eθmf = (θ rmf − θ r ) , θ&r = k θ dθ r / dt

(2)

2.2 Sliding-Mode Position Controller
The sliding-mode control of the PMSM servo drive
system is shown in Fig. 1. By considering the dynamics
with parameter variations, disturbance load andunpredictable uncertainties will give:

θ&&r (t ) = Amnθ&r (t ) + B mn .U (t ) + L(t )(3)

L(t ) = ∆Amθ&r (t ) + ∆B m .U (t ) + ( Dmn + ∆Dm )T L

(4)

where ∆Am, ∆Bm and ∆Dm are the uncertainties due tomechanical parameters Jm and βm and L(t) is called the
lumped parameter uncertainty and is defined as:
The bound of the lumped parameter uncertainty is
assumed to be L (t ) ≤ K f . The objective is to design acontrol law so that the rotor position of the PMSM can
track any desired command. To achieve this control
objective, we can define the error functioneθ (t ) = (θ r (t ) − θ r* (t )) . The sliding surface can be defined as a
PID performance measure with the positive constants K1, K2
and K3 that are chosen based on the desired system response.
t

S (t ) = K 1eθ (t ) + K 2 e&θ (t ) + K 3 ∫ eθ (τ )dτ(5)0

In (5), with K2=1, differentiating S(t) with respect to timeand using the error position function eθ (t ) , the tracking
control law can be derived from (3-5), USMC(t), so that therotor position, θ r , remaining on the sliding surface, S(t), for©ISAROB 200738

all t>0. The sliding-mode control objective is given by (6).
The first term in (6) describes the desired system
performance, the second term is a torque estimator which isable to compensate for the nonlinear effect in the PMSM
model, while the third term keeps the PMSM servo drive
system dynamics on the sliding surface, S(t)=0 for all thetime. To keep the trajectory in the sliding surface, theselection of the control gain , Kf, is very important due to its
significant effect on the magnitude of the lumped parameter
uncertainties of the PMSM servo drive system and hence its
performance. The incorrect selection of this control gain
will yield to the deviation from the sliding surface andcausing chattering phenomena. To solve this problem anadaptive control signal, U FNNMFC = δiqsr * , is generated fromthe proposed on-line trained FNNMFC to compensate forthe error in the control effort of the sliding-mode position
controller, U SMC = iqsr * .
r*U SMC (t ) = i qs
= B m−1 [(θ&&r* (t ) − K 1e&θ (t ) − K 3 eθ (t ))− Amθ&r (t ) − K f sgn( S (t ))](6)

2.3 Fuzzy-Neural-Network Position Controller
The online trained FNNMFC for a high-performance
PMSM servo drive system integrates the ideas of the fuzzy
logic controller and neural network structure into an
intelligent control system.

(A) Architecture of the FNNMFC: The architecture detailsof the proposed four-layers FNNMFC, the signal
propagation and the basic function in each layer are given
in [7]. Nodes in the input layer represent input linguistic
variables. Nodes in the membership layer act as themembership functions. All the nodes in the rule layer for a
fuzzy rule base. In the proposed FNNMFC, an input layer(the i layer), a membership layer (the j layer), a rule layer(the k layer) and an output layer (the o layer) are two, six,nine and one respectively [8-9]. The nodes in layer 1transmit the input signals to the next layer. Each nodecorresponds to one input variable. The input variables are
the error signal, eθmf , and the rate of change of the rotor
position (speed), Kθ θ&r = Kθ ωr . The output of the FNNMFC
is given by the following adaptive control signal.
r*
U FNNMFC (t ) = δi qs
(t )

(7)

(B) On-Line Training Signal Analysis for FNNMFC: The
details of the on-line training signal analysis for FNNMFC
will be given in the full paper. The selection of parameters
for the weights and membership functions has aconsiderable effect on the network performance. The
connecting weights between rule layer and output layer are
adjusted on-line in addition to the weights and themembership functions. To describe the on-line learningalgorithm of the FNNMFC using the supervised gradientdescent method, the energy function is chosen as in (8).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Grm (s )θ rmf

On-Line Training
Algorithmeθmf

FNNMFC
Fuzzy Neural Network
Model-Following
Controller

r*
δiqs

kθ d / dt

θr

SMC

θ r*

eθ
∫

Sliding
SurfaceS

Sliding
Mode
Control law

r*
i qs

rc
iqs

r
i qs

G clc ( s )

Te

Kt

TL

K mω
(s + β m / J m )

1
s

PMSM

d/dt

ωr

θr
Fig. 1. Configuration of the proposed intelligent sliding-mode FNNMFC for a vector controlled PMSM servo drive system
The learning algorithm based on the backpropagation
method is described in [10]. To overcome the problem of
uncertainties of the PMSM due to parameter variations andto increase the on-line learning rate of the network
parameters, a control law is proposed as in (9).
Eθ =

1 mf
1
(θ r − θ r ) 2 = (eθmf ) 222

δ o4 = eθmf + K θ θ&r

(8)
(9)

3 Simulation Results
To investigate the effectiveness of the proposed
intelligent sliding-mode position controller, five cases withparameter uncertainties are considered. The PMSM used inthis drive system is a three-phase type, 1 hp, four poles, 208
V, 60 Hz, 1800 rpm and has the parameters, voltage
constant= 0.314 V.s/rad, Rs=1.5 Ω, Lss=Ld=Lq=0.05 H,
Jm=0.003 kg.m2, βm=0.0009 N.m/rad/sec.

Case 1: 1.0×(Rs and Ls), 1.0×(Jm and βm), 1.00×λm,
Case 2: 0.5×(Rs and Ls), 0.5×(Jm and βm), 1.00×λm
Case 3: 1.5×(Rs and Ls), 3.0×(Jm and βm), 1.00×λm
Case 4: 1.0×(Rs and Ls), 1.0×(Jm and βm), 0.85×λm
Case 5: 1.0×(Rs and Ls), 1.0×(Jm and βm), 1.25×λm
The dynamic performance of the drive system due toreference model of 2π rad under subsequent loading of 03.6 N.m is predicted as illustrated in Fig. 2 at Case 1. The
disturbance rejection capabilities have been checked when aload of 3.6 N.m is applied to the shaft at t = 1.5 s andremoved after a period of 1.625 s. These Figures clearly
illustrate favorable tracking responses and robust
characteristics in command tracking and load regulation
performance are realized for both controllers. The sliding©ISAROB 200739

motion characteristics without reaching phase are obvious
by determining the sliding-surface shown in Fig. 2.
Improvement of the control performance by augmenting
the proposed FNNMFC to the SMC shown in Fig. 1 can beobserved from the obtained results in command tracking
and load regulation characteristics. The simulation resultsof the dynamic response for both position controllers underparameter variations are plotted in Figs. 3-4. The position
response, speed response and the load regulation
performance of the servo drive system with the SMC and
SMC with FNNMFC are shown in Figs. 3-4 under the casesof PMSM parameter uncertainties. Fig. 3 illustrates the
position and speed tracking responses for both position
controllers. At the same conditions, the load regulation
performance and torque current responses are given in Fig.
4. The results shown in these Figures clearly indicate that as
the uncertainties of the PMSM parameters occurred, theresponses deviate insignificantly from the nominal case forboth SMC and SMC with FNNMFC but the SMC with
FNNMFC confirms the correct operation and slightly
influenced by full load conditions under the five cases of
PMSM uncertainties. The proposed intelligent controller
quickly returns the rotor position to the reference model
under full load condition with a recovery time of 0.05s anda maximum dip of 0.053 rad while the SMC provides aslow response for the reference model under full loadcondition with a long recovery time of about 0.5s and alarge dipping in the rotor position of about of 0.28 radunder the five cases of PMSM uncertainties. Therefore,robust control characteristics under occurrence of
uncertainties can be clearly observed utilizing both theproposed position controllers. An obvious model-following
error (MFE) due to the SMC reaches to 0.15 rad while theMFE due to SMC with FNNMFC is about 0.05 rad. Also,
good model-following and tracking responses using SMC
with FNNMFC at all cases of parameters uncertainties are

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

observed from these results, and the resulting regulation
performances are also much better, in both position dip andrecovery time, than those obtained by the SMC. Therefore,

the SMC and SMC with FNNMFC are more suitable tocontrol the rotor position of the PMSM servo drives
considering the existence of parameters uncertainties.

(a) Dynamic response using SMC(b) Dynamic response using SMC with FNNMFC
Fig. 2. Dynamic response for a reference model of 2π rad and loading of 3.6 N.m of the servo drive system for both position
controllers(a) Model-following response using SMC(b) Model-following response using SMC with FNNMFC
Fig. 3. The model-following position and speed responses of the servo drive system under parameter variations for both
position controllers©ISAROB 2007

40

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(a) Load regulation performance using SMC(b) Load regulation performance using SMC with FNNMFC
Fig. 4. The load regulation performance of the servo drive system under parameter variations for both position controllers

4 SummaryThis paper proposed a robust intelligent and adaptive
position controller for PMSM servo drive system underfield orientation control which guarantees the robustness inthe presence of parameter uncertainties. The proposedrobust position controllers (SMC and SMC with FNNMFC)
consists of a feed-back SMC in addition to an on-line
trained FNNMFC. The theoretical bases and the stability
analyses of the proposed SMC and SMC with FNNMFC
control systems were described in details. An adaptive
control signal is generated from the proposed FNNMFC
and was added to the SMC output to preserve good modelfollowing response under various operating conditions.
Simulation results have shown that the proposed SMC and
SMC with FNNMFC grant robust model-following tracking
response and good regulation characteristics in the presenceof PMSM parameter uncertainties and external load
disturbance. Finally, the major contributions of this paper
are the successful development of the intelligent SMC
system, in which a FNNMFC is utilized to compensate theuncertainty bound in the SMC system on-line and thesuccessful application of the proposed SMC with FNNMFC
system methodology to control the rotor position of the
PMSM with the existence of parameters uncertainties.

References
[1]
[2]
[3]

Ned Mohan, Advanced Electric Drives: Analysis,control, and Modeling using Simulink, MNPERE
Press, USA, 2001.
V. I. Utkin, “Sliding mode control design principles
and applications to electric drives,” IEEE Trans. Ind.
Electron., Vol. 40, pp. 23–36, Feb. 1993.
I. C. Baik, K. H. Kim, and M. J. Youn, “Robust
nonlinear speed control of PM synchronous motor©ISAROB 2007

41

using adaptive and sliding mode control techniques,”
Proc. IEE—Elect. Power Applicat., Vol. 145, No. 4,
pp. 369–376, 1998.
[4] Z. H. Akpolat, G. M. Asher, and J. C. Clare, “Apractical approach to the design of robust speed
controllers for machine drives,” IEEE Trans. Ind.
Electron., Vol. 47, pp. 315–324, April 2000.
[5] S. K. Chung, J. H. Lee, J. S. Ko, and M. J. Youn,“Robust speed control of brushless direct drive motorusing integral variable structure control,” Proc. Inst.
Elect. Eng., Vol. 142. No. 6, pp. 361–370, 1995.
[6] Franck Betin, Daniel Pinchon, and Gérard-André
Capolino, “A Time-Varying Sliding Surface for
Robust Position Control of a DC Motor Drive,” IEEE
Transactions on Industrial Electronics, Vol. 49, No.
2, pp. 462-472, April 2002.
[7] Fayez F. M. El-Sousy, “A Vector-Controlled PMSM
Drive with a Continually On-Line Learning Hybrid
Neural-Network Model-Following Speed Controller,”
The Korean Institute of Power Electronics (KIPE),
Journal of Power Electronics (JPE), Vol. 5, No. 2, pp.
197-210, April 2005.
[8] Fayez F. M. El-Sousy, “Intelligent Model-Following
Position Control for PMSM Servo Drives,” 6th
WSEAS International Conference on Neural
Networks, Lisbon, Portugal, pp. 230-238, June 16-18,
2005.
[9] Faa-Jeng Lin, and Chih-Hong Lin, “A PermanentMagnet Synchronous Motor Servo Drive Using SelfConstructing Fuzzy Neural Network Controller,”IEEE Trans. on Energy Conversion, Vol. 19, No. 1,
pp. 66–72, March 2004.
[10] F. J. Lin, W. J. Huang, and R. J. Wai, “A supervisory
fuzzy neural network control system for tracking
periodic inputs,” IEEE Trans. Fuzzy Syst., Vol. 7, pp.
41–52, Feb. 1999.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Multi-Agent Robotics: Towards Energy Autonomy
Trung Dung Ngo

Hector Raposo, Henrik Schiøler

Center for Embedded Software Systems (CISS)
Aalborg University
Fr. Bajersvej 7B, 9220 Aalborg East, Denmark

Control & Automation Department
Aalborg University
Fr. Bajersvej 7C, 9220 Aalborg East, Denmark

dungnt@cs.aau.dk{hrve05, henrik}@control.aau.dk

Abstract

constraints of remaining energy and related distances. We
implement the simulation in accordance with agent
characteristics, interaction, organization and change ofenvironment in order to suggest course of our real multirobot
implementation. Finally, the paper is concluded in section 4.

In this paper, we aim to propose a novel trend in multi-agent
robotics: energy autonomy. A definition of energy autonomy isdeveloped from an original concept “potential energy” that isunder constraints of remaining energy capacity and related
distance among robotic agents. Towards energy autonomy, we
initially present a simulation of multi-agent robotic system inwhich each robot is capable of exchanging energy cells toother robots. Our simulation points out that: (1) each robot isable to not only act as an autonomous agent but also interact
with the others to be beyond the individual capabilities; (2) To
adapt change of the environment, each robot is situated as an
adaptive agent in a network or a cluster of the neighboring
robots to lead to a state of energy autonomy. Finally, based on
discovery of the simulation we adjust rules for our realmultirobot system.

1. Introduction
A multi-agent system (MAS) is a system composed of anumber of agents that are collectively capable of reachinggoals that are difficult to achieve by an individual agent ormonolithic system [6]. In a MAS there is no centralized
control, information is distributed, computation is
asynchronous, therefore each agent is incompletely capable ofsolving a problem [8], it needs assistant from other agents tosolve its own problem or helps other agents with their problem
appropriately. Adaptiveness of an agent is depending on thecharacteristic of such an agent, its surrounding environment,
its capabilities of interaction with other agents or environmentand capabilities of organization in a network or a cluster.
Therefore, mapping MAS to multirobot system, anautonomous robot is able to exhibit as an autonomous agent in
the MAS - robotic agent. However, a robotic agent is aware of
as a physical robot in fact, thus the robot exists if it is capableof solving energy resource.
In the paper, we propose a multi-agent robotic system inwhich each robotic agent is a mobile agent and is equippedwith a special mechanism of energy exchange, so that it iscapable of fairly collecting and carrying energy cells,
rechargeable batteries to share with others. The paper isorganized as: we briefly describe our models of single robotic
agent and their cooperation in section 2. In section 3 we
perform our simulation of robotic agent behaviors under©ISAROB 2007

42

2. Model of Multi-Agent Robotic System
Because Matlab software supports a lot of graphicallibraries to easily execute mathematical computation, or
generate graphs and charts, we are using the tool to implement
our multirobot system.

2.1 Model of Single Robotic Agent Robot
We focus on the energetic autonomy of a multi-agent
robotic system so we are going to simplify robotic agent as
points moving on scanlines in the Euclidean space. Therefore,
distance relation among robotic agents is based on Manhattan
space. Because of difficulty of modeling a general equation of
power consumption in robotics we are temporally using the
Peurket’s discharging function C = Ι κ t where k is supportedby the battery manufacturer.

Fig1: Model of single robot

In the simulation, a robotic agent is initialized with 800
energy unit (eu) and it consumes a specific amount of energy
using Peukert’s equation for each step so we propose 4 statesof a robotic agent: (State 1): a robotic agent has to go tomother to take full energy if its energy is less than 200 eu andby default, it has higher priority to go to the mother station;

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

(State 2): a robotic agent is able to exchange 100 eu to otherrobotic agent if its energy amount is more than 500 eu; (State3): a robotic agent will stop to wait for other robot coming tocharge 100 eu if its energy is less than 100 eu and it is
impossible to go to the mother charging station; and (State 0):
otherwise, a robotic agent is autonomously free to go
anywhere to consume energy.

2.2 Model of Robot Coordination
To facilitate our approaching to battery exchange, we
implement a coordination algorithm for the multi-agent robotic
system based on two phases: path planning and battery
exchange. Briefly, each robotic agent has a own battery

effect to behavior of robotic agents in the entire network.
Third, we demonstrate that an robotic agent is able to beenergetically autonomous if it is able to exchange energy withother robotic agents or the mother agent.
In the simulation setup shown in figure 3, we present
significant information of the system. It shows four graphicalwindows (left to right): animation of robotic motion, potential
energy of the mother agent and the robotic agent, the energy
state of the robotic agents, and the task of the robotic agents.
That is, the animation window shows us the motion planningof the robotic agents; the potential energy window presents theremaining energy of the robotic agents (height of the
pyramids) and their capability to share energy with the others(projected contour of the pyramids); the window of the state ofFig 3: Graphical simulation setup

exchange supervisor. The supervisor collects input data of therobots: current coordinate (X,Y) and current state of energy
STATE; deals with such updating data; issue output
commands: NEXT STATE of energy, goal coordinate (Xgoal,
Ygoal). To be more detailed, algorithm of the battery
exchange executes infinite loops of comparison of energy
states and current positions among the robotic agents as wellas the robotic agent with the mother to issue commands what
the robotic agent should do (goal of the robot). Meanwhilepath planner is to guide the robotic agent to reach the directedgoal and updating the next position of the robotic agents,
which is used as a feedback for the battery exchange algorithm
to compute the next states of the robotic agents.

energy realizes the energy chart of the robotic agentsaccording to the instant; and the task window reports operatingtasks of the robotic agents. We also generate a register of thestates of the robotic agents in every experiment:

Fig 2: Model of multi-agent robotic system

3. Simulation
We present early simulation results of our objectives. First,
we emphasize potential energy and its constraints amongrobotic agents, and the robotic agent with the mother agent.
Second, we examine how interaction and environment can©ISAROB 200743

Fig 4: An example of a register of 5 robotic agents in 2000 running steps

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.1 Potential Energy
In this section we emphasize the potential energy of robotic
agents under constraints of remaining energy and related
distance. As described in figure 5, the potential energy can bedivided into two meanings: Height of the pyramids (H) is to
imply the remaining capacity of an agent while Contour of the
pyramids (C) is to determine how wide the robotic agent iscapable of distributing energy to the others. Actually,
considering a robotic agent in state of low energy, if its
pyramid peak, H is fully covered by the another pyramid, itmeans it is now in the space where the other robotic agent isable to share energy, also we can say the agent is inside thepotential energy of other agents, and it will be in rechargeablestate. Otherwise, the robotic agent must be waiting until theother robotic agents are moving close to it. Further, interferingwave of Contour of the pyramids also tells us more about the
distance and the energy relationship among the robotic agent,and these with the mother agent.

The section is to present effectiveness to energy in a multiagent robotic system. First we experiment with increasing thenumber of agents in the same field. Figure 6 shows that if thenumber of agents increases (left to right) from 2 to 5, thenumber of battery exchange between robots also raises from 0
to 4. It is indispensable that the battery exchange between
agents is proportional to the number of agents and the robotic
agent being far away from the mother agent need moreassistance than the closer ones. Thereby, we have to take anumber of robotic agents into account when we deploy them ina specific environment.

Fig 6: A comparison of the increasing number of robotic agents

Fig 5: Potential Energy of robotic agentsThe blue lines in figure 6 show the energy and related
distance constraints between the robotic agents, or between the
robotic agents and the mother agent. Moreover, creating theblue line is based on the gradient of the potential energy
between the robotic agents, or the mother agent. The slope ofthe blue lines shows us the reciprocal energy effects and thelength of the projected blue lines to the corresponding
distance.
Indeed, figure 5 is to perform that density of the potential
energy is stronger when the number of agents is increasing (a):2 agents, b): 3 agents, c): 4 agents, d) 5 agents) in the same
field. We discuss that a robotic agent in need of energy has
more opportunity to be recharged if the density of the potential
energy in the field raises up, but it is more hard to reach otherrobotic agents since the number of physical interaction
increases. Thereby, we have to estimate the balance of thenumber of robots and the mother location to deploy such amulti-agent robotic system.

3.2 Effective Elements in Multi-Agent Robotic
System©ISAROB 2007

44

Consequently, keeping a number of 5 robotic agents, we try
to change the location of the mother agent in the same field.
First, we locate the mother agent in the center of field. The
result of 100 experiments shows that the total number of thebattery exchange is almost the same. That is, the overall
energy consumption is only depending on the size of the field.
However, in the distribution seen in figure 7, since we movethe mother to one corner of the field, robotic agents must comeback at a more narrow area thereby, they are preferable to be
charged by the other agents than the mother agent (red-line).

Fig 7: A comparison of the different location of the mother agent

3.3 Energy Autonomy
In fact, if a mobile agent wants to act, it will face theproblem of providing food as animals. Most of mobile robots
are using rechargeable batteries as a power source. Thereby, it
has only autonomous behavior in the duration of the batterylife, e.g. free moving around to explore, free searching a way

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

to carry heavy objects, or searching to rescue humans after
earthquake. A robot is able to be absolutely autonomous if it
achieves state of energy autonomy.
The section shows that the robotic agents are able to selforganize in order to achieve complete autonomy, without
human intervention if they are able to coordinate energy
sharing with the other robotic agents. The objective also meansthat each robotic agent is able to be energetically autonomousor assist others to be completely autonomous.

complete autonomy if they are able to achieve energy
autonomy.

4. Conclusion & Future Works
This paper issues the concept of the potential energy inmulti-agent robotic system and how to make the system to becomplete autonomy. In a multi-agent robotic system, eachrobotic agent is a mobile agent, so it has own potential energy.
This is the reason why it is able to be self-sustained withenergy if it is capable of sharing energy. To deploy robotic
agents in a specific environment, we have to solve the problem
under constraint of effective elements as mentioned in section
2. However, to facilitate the process, we proposed the concept
Potential Energy and its elements in order to analysis andsetup a multi-agent robotic system in section 1.
Next, based on initial results of the simulation, we are going
to experiment with our testbed of the real multirobot system.
First, to simplify high requirements of localization andapproaching, our mobile robots are also following the lines ina grid so the robot is able to know its current location. In thisway it is rather easy to approach to contact to the motherstation, or the other robot in order to exchange batteries. We
will improve the techniques of sensor fusion for such robots toremove the grid, so our robots can be more sociable. They canbe applied for home, office or manufacturer environments.

References
Fig 8: Energy autonomy of 5 robotic agent in time scale

Figure 8 shows an experiment of 5 robotic agents executing
in 2000 steps. On the second column, we see that the potential
energy of the mother agent can always cover the field. That is,
every robotic agent can be globally covered by the motheragent (M) so it can be refilled if it is able to come to themother agent. However, the potential energy of the robotic
agents (A, B, C, D, E) is much lower so it is capable ofcovering a local vicinity. Because the robotic agent is amoving agent, it can be sub-mother agent for the other agentsin its local vicinity. Therefore, every agent can be a first-aid
unit in the case of emergency in which other agents have notenough energy to go back to M, or desire to finish their duty ina short time before coming back to M. Additionally, referringto the fourth column, we see that at the instant 1201, A isgoing to (->) share 100 eu with C, and C stops working tosave energy and waiting (<~) for A. At the instant 1341, C stillneeds energy again since C has already consumed 100 eu, B isindicated to give battery for C. Therefore, at the instant 1691,
we can easily be aware that C has already come back to M inorder to be fully replenished and is now working with anamount of 700 eu approximately. Due to the assistance of A
and B, C can survive to continue its duty, instead of being
stuck on the field. As a result, robotic agents can have

©ISAROB 200745

[1] Chris Melhuish and Masao Kubo (2004). Collective
Energy Distribution: Maintaining the Energy balance inDistributed Autonomous Robots, in the proceedings of 7thInternational Symposium on Distributed Autonomous
Robotic Systems, June 23-25, 2004 Toulouse, France,
p261-70,2004.
[2] Gregory Dudek, Michael Jenkin, Evangelos Milioes, andDavid Wilkes. A Taxonomy for Multi-agent Robotics,
Autonomous Robots, vol. 3. pp. 375-397,1996[3] Yongguo Mei, Yung-Hsiang Lu, C.S. George Lee and Y.
Charlie Hu Energy-Efficient Motion Planning for Mobile
Robots, International Conference on Robotics andAutomation 2004.
[4] Ngo. T.D, Schioler. H, An Approach to Sociable Robots
through Self-distributed Energy, in proceedings ofIEEE/R SJ Internnational Conference on Intelligent
Robots and Systems, Beijing, China, Oct 2006.
[5] N.R. Jennings, K. Sycara, and M.Wooldridge. A
Roadmap of Agent Research and Development. Journal ofAutonomous Agents and Multi-Agents Systems, 1:7-38,
1998.
[6] E.H. Durfee, and V.R.Lesser, Negociating Task
Decomposition and Allocation using Partial Global
Planning. In Distributed Artificial Intelligence, vol.2,
pages 229-244, 1989.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Realization of a visuo-motor system based on multiple self-organizing maps in a 3D
space
Ryunosuke UEHARA†

Min HAN‡

Nobuhiro OKADA†

Eiji KONDO††

Department of Intelligent Machinery and System
Graduate School of Engineering, Kyushu University
744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

Abstract
This paper describes a visuo-motor system which
realizes a feasible collusion avoidance of a manipulator in an unknown environment. In order to handle spaces occluded by obstacles, we adapt the plural cameras system and multiple self-organizing maps
(SOMs).Each self-organizing map is directly connectedto the camera system and trained to perform motion
control, by which the joint angle of the manipulator
are determined. In our visuo-motor system, neither
any priori knowledge about the manipulator nor thecamera parameters are required. In addition, the system is robust to change in its geometry. Simulation result shows that the proposed learning method ensuresthat the manipulator moves smoothly and consistently
in whole workspace even using multiple maps. In thispaper, we validated the proposed approach by an experiment and confirmed that proposed method realizes collision avoidance for the visio-motor system in
a 3D space. Thus, we presented simulation result thatthe system overcomes the collusion problem in cluttered environments and variety of obstacle shapes by
increasing the number of cameras and self-organizing
maps.

1

introduction

Collusion avoidance is a basic problem that a robot
handles its end-effector avoiding obstacles cluttered in
an environment performing the primary task for autonomous or industrial robots. To plan a path avoiding collision in an intricate environment, two contrasting approaches have been studied.
First, the high-level path planning is to find globallya collision avoidance path in the configuration space.
One example of a global method is PRM (Probabilistic Roadmap Method) that computes a cell decomposition of the free space and uses a search graph basedon this decomposition [1]. RRT (Rapidly-exploring©ISAROB 2007

46‡

TOYOTA AUTO BODY CO., LTD.
100,Kanayama Ichiriyama-cho
Kariya,Aichi 448-8666, Japan

Random Trees) is a roadmap method where a set of
canonical paths is used to cover the components ofthe free space, and the planning task is reduced todetermine a connection to the canonical paths [2, 3].
However, in these approaches, an exact, known andstatic environment model is required. In addition, thecalculation time grows exponentially with the geometry complexity and the number of degree of freedom(DOF).
Therefore, local path planning techniques are potentially more efficient in robot motion planning whenthe environment is unknown of only partially known.
An efficient local path planning method is the potential field method which has been widely used in collision avoidance [4]. In this method, a potential function is defined in the free space, based on an attraction
component from the goal point and a repulsion component from the obstacle boundaries, and the planning
process becomes to a determination of the global minima of the potential function using a greedy and local
search.
Alternatively, obstacle avoidance can be solved online by a robot controller at the low-level, which is focused on the problem of controlling a redundant robot
so that the end-effector tracks a given path in theworkspace as closely as possible and simultaneously
ensures that the links avoid obstacles. Reasoned asabove, such techniques naturally depend on the use ofdifferent control frameworks [5, 6].
On the other hand, some methods tried to integrate a task planning and a motion control, motivatedby Khatibs work [7]. This work and a few other integrated architectures [8] have utilized methods basedon potential fields in their reactive control algorithms,
while their planning and interface techniques differ [9].
The SOMs also can be used for path-planning or trajectory formation tasks [10]. After the mapping has
been established, a path is generated from any initial
position to a given target, e.g., to guide an end-effectorof a robot manipulator in the presence of obstacles

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

within the workspace. Using the TRN model [11],showed that a locally optimized path can be determined by minimizing the Euclidean distance from thecurrent position to a given target position. However, acollision check was necessary in the path planning, andthe proposed method was only investigated by using a
non-redundant manipulator.
In our studies, we integrate the path planning of theend-effector and SOMs to achieve collision avoidance.
The SOMs are learned to perform motion control, by
which joint angles of the manipulator are determined.
The learning promises to make the manipulator reachtargets precisely with obstacle-free poses. The path
planning system plans a collision-free path for the endeffector from an initial point to a target point in theimage spaces. The proposed collision avoidance approach differs from others in: (1) The system only
needs to plan a collision-free path for the end-effector;
the computational cost of the path planning does notincrease exponentially even for a high dimensional redundant manipulator. (2) The obstacle-free poses ofthe manipulator are achieved in the learning of theSOMs, so collision checking is not necessary in whole
path planning process.

2

visuo-motor system

Visuo-motor system which we propose is illustrated
in Fig.1. The system contains:
Top CameravutSide Camera
Top Camea's Image

WorkspaceutSide Cameas' Imagev, ut
Side Camera

Side Camera

The self-organizing maps

As shown in Fig.2, each self-organizing map is consisted of neurons, which are distributed in the image spaces of the camera. Each neuron Ni has 4parameters.
For ξi , refer to our previous study.

Ji : Jacobi matrix from the joint space to the image
spaces.
θi : Joint angle of the manipulator at Wi .

Self-organizing
Mapsξi : The gradiant vector.

v : Position vectors of the end-effector
ut : Position vectorw of target
ǰout: Joint angle vectors of the manipulator

Fig. 1: Simulation model
1. A 4-degree of freedom redundant manipulator
moving in a 3D space.
2. The plural CCD cameras.
3. Multiple related self-organizing maps.

©ISAROB 2007

2.1

Wi : position of the neuron in two image spaces.

vv
ut

CCD cameras are used to acquire information about
obstacles and to recognize the position of the target,
the location of the end-effector and the pose of themanipulator while learning. Based on visual information provides by cameras, each SOM learns projections that convert the image vectors of targets in theimage spaces into joint angles vectors of the manipulator. The manipulator is ordered by a set of joint
angle commands θout which are outputs of SOMs.
In our previous works, the system could not deal
with spaces occluded by obstacles. In this paper, aredundant camera system is introduced to overcome
the occlusion problem [13]. One camera observes theworkspace from the top and other cameras are arranged from the sides. Because the valid workspaceis increased obviously by adding the redundant camera, multiple related SOMs are employed in our system. As shown in Fig.1, the projections of a target
point ut in side cameras and top camera are (ui , vi )and (ut , vt ) respectively. A pair of image coordinatesof side camera (ui , vi ) and top camera (ut , vt ) is combined into a 4 dimensional vector (ui , vi , ut , vt ) which
is used as the point if one. In the same way, a pair of
the other side camera and top camera is combined into
the input of another map. Since the valid workspacesof each map and camera are different, maps are usedalternately. Besides the number of joint, no further
information about the manipulator and cameras will
be used in our visuo-motor system.

47

When a target ut is given in the workspace, an appropriate map is chosen based on which cameras can see
the target. In the chosen map, the neuron which wi
is the nearest to the projection of the target is chosen. The joint angles θout , which conduct end-effector
to the target, are calculated obeying following linear
equation. Although the transformation from the image spaces to the joint angle spaces is not a linear projection for a redundant manipulator, the domain of aneuron is small enough to use the linear projection as

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

: Neuron

Image spaceut

Top CamerautSide Camera

Fig. 2: Self-Organizing Map
an approximation of the non-linear projection. In theactual system, weighted sum of outputs from plural
neurons around the target is used instead of the linear
equation.
P
gn (θ + J † (ut − W ))P
(1)
θout =
gn
Where,J † is a pseudoinverse matrix of J. gn is theweight defined by the following equation.
(exp(−n/λ) for exp(−n/λ) > ²
gn =0
for exp(−n/λ) ≤ ²(2)

Where, n is the order of the neuron determined according to the distance between the neuron and thetarget. It has a large value for the neuron that is nearto the target, and has a small value for the neuron
that is far from the target. The symbol λ and ² arevalue to define neuron numbers that can affect θout .

3
3.1

for a common visible target, they learn with influenceeach other. In this case, one of SOMs is chosen to
determine the joint angles θout of the manipulator forthe target. The manipulator is driven by the θout , andeach camera obtains end-effector position v in each
SOM respectively. Then, each SOM corrects its parameters by using ut , θout and v. This learning procedure results in that in the end of learning the neuronsof plural SOMs possess the similar value if θout , ξanddifferent W, J for the target given in the common visible space.
Thus, the outputs from either SOMs will ensure themanipulator has the same pose. This means: while atarget is given in common visible parts, the outputs
from either SOMs do not result in a change of the manipulator pose. In addition, the assignment of similar
joint angles to adjacent target point is, in fact, oneof the main features of the learning algorithm of the
SOM. By the construction of a map between inputs
in the image space and the neural net, learning algorithm makes sure that adjacent target points always
activate adjacent neuron in the network. The learning
forces adjacent neurons to adapt their output towards
similar values.
Therefore, at the end of the learning phase the output values will very smoothly from a neuron to anotherneuron. Both features bring about a continuous andsmooth transformation from the input image spaces oftarget points to the out put space of joint angle sets.
According to such a learning algorithm, SOMs guarantee smooth and consistent movements of the manipulator in whole workspace. For detials about evaluation
function, learning algorithm and update of the parameter, refer to our previous paper [13].
TargetProcess
Learning procedure
Organizing Map

of

the

Self-

In our system, plural SOMs are employed. If they
are learned separately, their outputs are different evenfor the same target. This will result in that the manipulator moves inconsistently when it is driven fromvalid workspace of one map to another. In the learning algorithm, the problem has to be solved effectively. The problem also can be described as: the algorithm should guarantee that the manipulator moves
smoothly and consistently in whole workspace no matter which SOM outputs joint angles for it.
The learning procedures illustrated in Fig.3. Whena target position ut is presented randomly in theworkspace, each camera sees the target. If a camera
can see the target, the SOM which is connected to thecamera will learn. While more than two SOMs learn©ISAROB 2007

48

Visible inSide Camera 1

Visible in Top Camera

W1, J1, ǰ1, Ƕ1
W2, J2, ǰ2, Ƕ2
W3, J3, ǰ3, Ƕ3Visible inSide Camera 2

Visible inSide Camera 3

Fig. 3: Relation of Plural Self-Organizing Maps

3.2

Path planningIn our system, collision avoidance is based on theidea, While projected path does not interfere with pro-The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

jected obstacle, the path in 3 dimensional space can
avoid the obstacle. Here collision avoidance is realized
in the image spaces. It is not required to reconstruct
a 3D model. Consequently collision avoidance in theimage spaces by combining a SOM and a path planning system. The SOM determine joint angles of manipulator so that the end-effector of the manipulator
reaches a target position given in the image spaces
arbitrarily, and also ensures the manipulator taking
obstacle-free poses. Here, the path planning system
adapts Laplaces potential method to plan a collusionfree path for the end-effector from an initial to a goal
position without plagued into local minima.
Since the manipulator can drive the end-effector toa target point given in the image spaces with obstaclefree poses under control of the SOMs, the path planning system only needs to plan a path for the endeffector of the manipulator. Accordingly, it is not necessary to pay attention to the collision between obstacles and links in the process of driving the end-effector
along the planed path. It is different from most of existing algorithm, which are intended to work in configuration space. Our system always plans paths in 2D
spaces, so the computational cost of planning does notincrease exponentially for a high dimensional redundant manipulator. For procedure of the path planningand path planning by Laplaces potential method, refer
to our previous study [12].

4Simulation and experimental result
video signal
CCD camreras

Redundant Manipulator: LED

Digital input / Output Board

Obstacle
Drive Unit

Image Processing Boardǰout

LED controlv, utMonitorSelf-Organizing Map
Host Computer

Fig. 4: Outline of experimental system
A photograph and the outline of our system are respectively shown in Fig.4 and Fig.5. The manipulator
is Mitsubishi Manipulator RV-1A. It is driven by using the operating signal θout which is received through©ISAROB 2007

49

Fig. 5: Our experimental systema Drive unit. The CCD cameras see the manipulator and the outputs from cameras are lead to a frame
memory in the host computer. The size of the frame
memory is 640[pixel] × 480[pixel]. The SOMs are composed in a host computer. LEDs, which are lighted by
the host computer, are set on joints so that the system
can find the position of the end-effector and links ofthe manipulator in camera images. Cameras are also
used to get shapes of obstacles.
At first, by simulation, we will show that our system
can output joint angles using 3 CCD cameras and 2 related SOMs so that the end-effector of the manipulator
reaches to the target with obstacle-free poses, and thatthe system accomplish collision avoidance using proposed approach. Then, we will confirm reproductivityof the system to experiment in actual environment.
Secondly, we will expend the system to increase
CCD cameras and SOMs and show that the system
makes the manipulator take obstacle-free poses andaccomplish collision avoidance.
Simulation results were shown in Fig.6 and Fig.7.
In the simulation, a SOM consisted 240 neurons wasused, and 15000 targets were given for learning. There
was one obstacle. Fig.6 shows the target positions andthe poses of the manipulator, when all cameras cansee the target after learning. In the figure, we can seethat the manipulator takes obstacle-free poses and theend-effector reaches targets correctly. The average error of the end-effector position was 1.57[pixel]. Fig.7
illustrates planed path from initial to goal point andmanipulator poses driven by the SOMs without stopping at local minima. The average error along planed
path was 3.06 [pixel].
An experimental result is shown in Fig.8 and Fig.9.
To shorten the learning time and confirm robustnessof the system, the SOMs of above simulation were

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[pixel][pixel]: target ut: end-effector v: target ut
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Fig. 7: Output of collusion avoidance by simulation

Fig. 9: Output of collusion avoidance by experimentused as initial state by experiment. The number of
re-learning time was 1500 and the time required process was about 16,500[sec]. Similarly with Fig.6, Fig.8
shows target positions and poses of the manipulator
after re-learning. The average error of the end-effector
position was 1.57[pixel], and it was about 2[mm] in thereal environment. Also, Fig.9 illustrates the planed
path and the manipulator poses. The average of error
along planed path was 2.75[pixel].
By the simulation and experiments, we have verified that the system can make the manipulator takeobstacle-free poses and practice collision avoidance.
Next, simulation results, which are leaded from theextended system, are shown in Fig.10 and Fig.11. In
the simulation, a SOM consisted 240 neurons was used,and 30000 targets were given for learning. There wasone obstacle. Fig.10 shows the target positions andthe poses of manipulator, when all camera can see thetarget after learning. In the figure, we can see that manipulator takes obstacle-free poses and the end-effector
reaches the targets correctly. The average error of theend-effector was 1.74[pixel]. Fig.11 illustrates planed

path from an initial to a goal point and the manipulator poses driven by SOMs without stopping at local
minima. The average of error along planed path was
2.56 [pixel].

©ISAROB 200750

5Conclusion

In this paper, we validated the proposed method approach by real experiment and confirm the efficiencyof the proposed method and robustness of the system.
Then, we extended the system by increasing number ofcamera and SOM and showed the efficiency of the system. Advantages of this approach are (1) By employing multiple SOMs alternately, the system overcomes
the occlusion problems in cluttered environment, (2)In our visuo-motor system, neither any priori knowledge about the manipulator nor the camera parameters are required.
In the present system, when a target point is put
under obstacles and the top camera can not see it,
SOMs can not learn joint angles because of dependence

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

: target ut[pixel][pixel]

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600
[pixel]0: target ut: end-effector v[8] O.Brock and L.E. Kavraki, “Decomposition-nased
motion planning: A framework for real-time motion planning in high-dimensional configuration
spaces,” In Proc. ICRA, vol.2, pp. 1469-1474, 2001
[9] K.H. Low, W.K. Leow and M.H. Ang, Jr., “Integrated planning and cotrol of mobile robot with
self-organizing neural network,” In Proc. ICRA,
2002[10] S.X. Yang and M. Meng, “Neural network approaches to dynaic collision-free trajectory generation,” IEEE Trans. Systems Man Cybernet, B
31(3), pp. 302-318, 2001

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Fig. 11: Output of collusion avoidance in extended
system by simulationon the top camera. Therefore, we have to extend thesystem and make each camera independent.
A part of this study is financially supported byElectro-Mechanic Technology Advancing Foundation,
Japan.

References[1] S.M LaValle, “Rapidly-exploring random trees: A
new tool for path planning,”Technical Report 9811,Computer Science Dept., Iowa State University,
1998[2] J.F. Canny, “The complexity of robot mootion
planning,” MIT Press, 1988
[3] R. Bohlin and L.E. Kavraki, “Path Planning using
lazy PRM,” In Proc.ICRA, pp. 521-538, 2000.

©ISAROB 2007

[5] K.Glass, R. Colbaugh, D. Lim and H. Seraji,“Real-time collision avoidance for redundant manipulators,” IEEE Trans. On Robot. and Automat,
vol.11, no.3, pp-. 448-457, June, 1995

[7] O.Khatib, S. Quinlan and D. Williams, “Robot
planning and control,” Robotics and Autonomous
System, 21: 249-261, 1997(b) left image

300[4] O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. of Robotics
Reseach,5(1): 90-98, 1986

[6] H. Seraji and R. Colbaugh, “Improved configuration control for redundant robots,” J. of Robot.
Syst., vol.7, no.6, pp-. 897-928, 1990(c) Reverse image[pixel]0200: end-effector v

51[11] M. Zeller, R. Sharma and K. Schulten, “Motion
planning of a pneumatic robot using a neural network,” IEEE Control System Mag, 17, pp. 89-98,
1997[12] N. Okada, K. Minamoto and E, Kondo, “Collision avoidance for a visuo-motor system with a redundant manipulator using a sef-organizing map,”
ISATP, pp. 104-109, 2001[13] M. Han, N. Okada and E. Kondo, “Coorinationof an Uncalibrated 3-D Visuo-Motor System Basedon Multiple Self-Organizing Maps,” JSME International Jounal, Series C, vol.49, No.1, pp. 230239, 2006

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of an autonomous mobile system
for an autonomous robot in an indoor environment
Toshifumi Uewaki
Eiji Hayashi
Department of Mechanical Information Science and Technology
Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka Prefecture, Japan
Key Words: Personal robot, Autonomous driving, Image Processing

Abstract
We are developing an autonomous robot that canperform work in a human living environment. To movesafely in a human environment, a robot should be able
to recognize changes in the environment. Thus, wehave developed a mobile system for autonomous
robots with an obstacle-detection function and ahuman-following function. This system uses image
information from a CCD camera and image processing.
The obstacle-detection function, which is one of thefunctions of this system, detects obstacles for safe
move, while the human-following function allows therobot to follow a person using information fromimages.
In this paper, we explain in detail theimage-processing algorithm used with both the
obstacle-detection function and the human-following
function. Furthermore, we experimentally evaluate thesystem and discuss its problems based on the results.

1. Introduction
Autonomous robots are expected to provide
various services in human living environments in the
future. It is important that such robots can move safely
in an environment with human activity and cancommunicate with humans. Therefore, we aredeveloping an autonomous robot that can perform
work in a human living environment and possesses
these abilities.
Our robot has a drive mechanism of two front
wheels and one back wheel. The front wheels areattached to the motor, which operates the wheels oneither side independently, while the back wheel is acastor wheel. This method has the advantage of being
able to negotiate a far smaller turn than that of apassenger car’s steering system, for instance. DC
servo motors are used for the robot’s drive mechanism,
and position control and speed control are achieved by
the control system of the drive mechanism. The robot
also has two arms and hands and sensors; these devices©ISAROB 2007

52

Fig. 1

Robot appearance

enable the robot to respond to various demands. An
installed wireless LAN provides remote control forhumans. All devices are controlled by a PC, and leadbatteries supply electric power. [1], [2], [3]In order to acquire a distance perspective for theautonomous driving robot, we developed anautonomous mobile system. This system creatively
uses two functions based on the presence of an
obstacle or human in the first image. The
obstacle-detection system is used when there are no
humans in the image; the human-following function isused when there are. The obstacle-detection functionuses image differences found during the robot’s
motion, by which obstacles can be quickly detected.
After detection, the position of the obstacle iscalculated using the principle of triangulation. From
this result, the suitable operation of the robot isdecided. In the human-following function, the locationof the human user is first determined from the location
information acquired before the robot runs. Afteracquisition, this function uses the color distribution of
the area specified as a human image as a model tomatch. A similar area of the present image is soughtusing this model color distribution. Thus, if the robot
can recognize a human, it can be made to follow thehuman.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Obstacle-detection function

two images of low resolution. As a result, the change
in the appearance of the background is removed. The
surrounding area of the moving obstacle appears as an
output image.

2.1 Outline
The robot being developed in this laboratory moves
by using the finite-space map stored in the database. If
an unknown obstacle not existing on the map appears
during this motion, the robot cannot avoid thisobstacle without an obstacle-detecting function, suchas the one we developed. In section 2, we explain andexperiment on this function.

Step4. Obstacle area presumption
A histogram of the image differences value is madefor the vertical and horizontal axes of the image. The
moving obstacle area is presumed based on thishistogram.

2.2 Method for obstacle detection

Step5. Output of position information
The distance from the moving obstacle area
specified in the image to the bottom of the obstacle inreal space is calculated by the following equation:

In this section 2.2, we explain the method for the
obstacle detection. The flow for obstacle detection isshown in Fig 2.

L=

hf
,
f sin θ + y i cos θ

where f is the focal length, θ is the depression
angle, and y i is the y-coordinate in the image.

Fig.3 Result of obstacle detection
Fig.2 Flow for obstacle detection

2.3 Experiment
Step1. Image capture
The PC in the robot captures two CCD camera
images at short intervals of time for the
post-processing algorithm.
Step2. Image resolution change
The mean value of an arbitrary block in the image
is found, and the pixels in the block are replaced by themean value.
Step3. Image differences
The wall and an obstacle in the image that doesn’t
actually move seem to have moved due to the
movement of the robot. Thus, a moving obstacle and astatic obstacle cannot be distinguished by simple
image differences. Thus, we subtract each pixel of the©ISAROB 2007

53

We performed an experiment to evaluate theperformance of the function. The robot was made to
run on an indoor flat passage at the speed of 0.5m/s.
The function detected when an obstacle approached
and when an obstacle appeared suddenly from the side.
Moreover, the angle of depression of the camera was
set to 30 degrees. Fig. 4 shows the result of the
obstacle’s approach. Fig. 5 shows the result of the
obstacle’s sudden appearance from the side.
As a result of the experiment in the two situations,the obstacle was almost exactly detected using thefunction. Thus, the effectiveness of the system at the
speeds used in this experiment was shown.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig.4 Approach of an obstacle

Fig.6 Creating a histogram
Fig.5 Sudden appearance of an obstacle from the side

3. Human-following function
3.1 Outline
To move safely in a human environment, a robot
should be able to recognize the external environment.
This environment recognition must combine
self-positional presumption, obstacle recognition,route selection, etc. It is difficult for the robot tocorrectly recognize the environment given thelimitations in its memory and calculation capacities.
Therefore, when a person is walking forward, therobot can be made to follow the human. The amountsof memory and calculation required decrease because
the robot simply has to recognize the human. In
addition, it is possible to move safely because therobot can follow the route of the human, whorecognizes and understands the environment. In
section 3, we explain this human-following function.

Next, in order to seek a similar area in image weanalyze correlation between created histogram of
human feature and histogram of arbitrary area ofpresent image. Two normalized histograms areoverlapped, and the minimum value of the pair of thevalue is taken. The value in which all these minimum
values were added is set as a similarity. If thissimilarity is high overlapping of two histograms
increase as shown upper in upper of fig6. In contrast, if
this similarity is low overlapping of two histograms
decrease as shown in bottom of fig.7. The area where
this similarity is the highest in the image is judged to
be an area where the human exists.

3.2 Method for human following
The human who is the target of tracking is not a
constant shape, but rather, the change in shape is great.
Thus, the tracking of a human using the shape feature
is difficult. Consequently, in order to acquire thehuman feature, this function uses color distribution.
First, the area where the human exists in the image isselected. After the area is selected, a histogram in
RGB space is created for this area. Fig. 6 shows theappearance of the image by which the histogram iscreated. The RGB space is divided into 8×8×8. The
memory required for a more detailed RGB space, forinstance, 256×256×256, is huge.

©ISAROB 2007

54

Fig.7 Comparing histograms
After seeking the similar area, the distance iscalculated using the y-coordinate at the bottom of thearea specified as a human. The speed of the robot ischanged in proportion to this distance, and the humanis followed.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.3 Experiment
We experimented of human tracking in the image
for the evaluation of the function.

In order to acquire a human-following function, weused color distribution. However, this function has notbeen mounted on the robot yet. Thus, we mount on the
robot this function and we want to test in real space theperformance of the human-following function.
Our next subject of study is to develop a system foraction planning.

References[1] Toru Azuma, Yoshihiro Takenaga, Eiji Hayashi,
Development of a self-driven personal robot、Artificial
Life and Robotics(AROB 8th ’03)、pp.1-3、Oita, Japan,
2003[2] Eiji Hayashi, Keisuke Ikeda, Development of asystem for self-driving by an autonomous robot,
proceeding of 10th international symposium on
artificial life and robotics, Vol.10th, pp.4, Beppu, Oita,
Japan, 2005

Fig. 8 Tracking images
The tracking image for every fifth frame is shownin Fig. 8. The human was tracked over 30 frames. The
similarity of the histogram during the pursuit of theperson was maintained at 70% or more. As a result, thearea where the human existed was able to be presumed
with considerable accuracy. Furthermore, the distanceto the bottom of the obstacle was able to be calculatedwith considerable accuracy.

4. Conclusions
We have proposed a mobile system forautonomous robots with an obstacle-detection
function and a human-following function. We wereable to show the effectiveness of both the
obstacle-detection function and the human-following
functions.
The problem of the obstacle-detection system is toreflect the obstacle data detected in a finite space map
so that the robot can select a safe route that avoids an
unknown moving obstacle. However, changes in the
speed of the robot or increases in the background’s
complexity impairs this detection. Thus, we want toimprove the accuracy of the obstacle-detection by
combination with other method.

©ISAROB 200755

[3] Takuma Umeno, Eiji Hayashi, Navigation system
for an autonomous robot using an ocellus camera inindoor environment, proceedings of the 11th
international symposium on artificial life and robotics,
Vol.11th, pp.229, Beppu, Oita, Japan, 2006

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A study on improvement of the surveillance system of an Indoor blimp robot
T.Takaya

H.Kawamura

Business Strategy Group

Graduate School of Information

Business Strategy & Control Office

Science and Technology

Sapporo, Hokkaido.

Hokkaido University

060-0807

Sapporo, Hokkaido,060-0814

Y.Minagawa

M.Yamamoto

Graduate School of Information

Graduate School of Information

Science and Technology

Science and Technology

Hokkaido University

Hokkaido University

Sapporo, Hokkaido,060-0814

Sapporo, Hokkaido,060-0814

A.Ohuhi
Graduate School of Information
Science and Technology
Hokkaido University
Sapporo, Hokkaido,060-0814

Abstract
Blimp robots are attractive as indoor flying robots
because it can float in the air, land safely with low
energy, and stay in motion for a long time comparedwith other flying robots. However, controlling of
blimp robots for surveillance is difficult, because it
has nonlinear characteristics, is influenced by air
streams and is easy to be influenced of inertia.
Therefore, it is not easy to control blimp robot only by
PID control and to carry out position control.
However, since PID control can change the control
characteristic easily by adjustment of a parameter, it isthought possible to use for rough control of operation
Therefore, when performing control of blimp robot of
operation, PID control performs rough operation. And
application of blimp robot is considered to round andsurveillance by using the more precise control methodfor detailed control of operation near the surveillance
point.
By this research, in order that the control
instructions from the outside may make operation
there is nothing independently possible, having©ISAROB 2007

56

improved the certainty of round surveillance is shownby developing blimp robot which carried all the partsfor control and in which autonomous control ispossible. Using learning control near the surveillance
point and using PID control between surveillance
points.

1 introduction
As compared with other flight move objects,
floating and movement are possible for Blimp robot
with low energy. Therefore, it is observed as a flightrobot for indoor. Recently, researches have studied
ways to make the best use of the features of blimp
robot and how to apply it to indoor applications, suchas situation, the area and house that received thecalamity survey [1], indoor security [2].
Blimp robot will be wanted direct motion when
flying, example through narrow spaces for security
and round an indoor surveillance. The thrust unit of anindoor blimp robot must be designed high mobility.
And, blimp robot has to operate in the limited range.
the entire blimp robot must be kept small. Buoyancy
decreases by miniaturizing the airframe. In designing

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
a blimp robot, one must considering the weight of thecontroller unit, thruster unit, and the sensor unit on theairframe. Characteristic of blimp robots is influenced
of inertia; establishing original control architecturecompares the other robots.
Based on the flight navigation of insects [5], by
position detection using beacons [6], and based on
camera pictures [8] are researched about the method ofcontrolling Blimp robot. In addition, investigation in
outdoor environment has been conducted using
cooperative operation with ground robots [4], an
original method is proposed by each research. The
research on the round system that uses the PID control
is performed. [10]2.3 Blimp robot and PID controller
Figure 2 showed appearance of blimp robot. The
balloon is a column type, and the diameter is 0.94m,
and 0.80m in height for enough to obtain buoyancy [8].
And, it has a controller and driving part under theballoon.

0.94[m]0.83[m]2 ExperimentThis chapter explains the experimental environment
and the experiment blimp robot to show that blimp
robot accurately reaches the target surveillance point.

2.1 Experimental environment
Experiment environment is an inside of a building.
And the blue and red circle are arranged with a diameterof 0.50m at intervals of 0.75m to the floor at the space
for an experiment. As experiment space, it is referred toas over 3m at width, depth, and height for experiments in
Blimp robot.

2.2

Fig.2 Appearance of developed Balloon Robot

Experiment operation"S" was made a starting point, and the operation of
Blimp robot in the actual experiment was set for thestraight line to move in order of P1~P7. In thisexperiment, P1~P7 is made a relay point, and it makes
it to surveillance point.
1．When the camera installation part of blimp robot
enters the space of 50cm in the radius for the point
where P1~P7 is observed, it switches to the learning
control[11].
2．It rotates when is in the space of 30cm in the radius
for five seconds for each surveillance points of P1~P7.
3．After rotations are made, it moves to the next point.

Fig.3 Abstraction of the Developed Blimp Robot
In this research, the camera of the resolution of
160×144pixel is installed and the RGB 16bit color
image to be able to acquire the location information byrecognizing the landmark in the experimental
environment is position acquired.μT-Engine calculates
information and the image data processing from thesensor, and taken image and sensor value are processed⊿T=0.3 sec or less of the sampling duration of thepropeller motor control.
Fig.3 shows the block diagram of developed blimp

Fig.1 Round condition and PID parameters©ISAROB 2007

57

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
robot,μ T-Engine board (M32104) controls the DC
motor driver of the RBTMC one-board microcomputer
board by the serial port after doing the processing of
sensor information and the calculation concerning thedecision of the control output, and six propellers(ch0~ch5) are driven. Thrust was able to be generated
axially each for a direct movement in the direction of
the desire and the propeller was arranged. Propeller
motor ch4 and ch5 that intersected, united the midair
carbon pipe, arranged the propeller motor of ch0~ch3that generated thrust x axis and y axis, and generated
thrust z axis were arranged.
The variables manipulated for translation androtational motion in our PID controller were decided bydeviations based on the relative distance from the x, y,
and z axes and the relative yaw angle. The relative
distances ex, ey, and ez [cm] are from the blimp to thetarget. The relative angle eθ [rad] is the angle from theyaw angle of the blimp to that of the target. The
manipulated variables mx(t), my(t), and mz(t) [g] fortranslational motion, and mθ(t) [g] for rotational motion
at time t, are calculated ase (t) − ex (t − ∆T)mx (t) = Kpxex (t) + KIx ∑ex (t)∆T + KDx x∆T(1)e (t ) − ey (t − ∆T )my (t ) = K pyey (t ) + KIy ∑ey (t )∆T + KDy y∆T(2)e (t) − ez (t − ∆T )mz (t) = Kpzez (t) + KIz ∑ez (t)∆T + KDz z∆T(3)e (t) − eθ (t − ∆T)mθ (t) = Kpθ eθ (t) + KIθ ∑eθ (t)∆T + KDθ θ∆T(4)positive rotation and the reverse rotation are
individually possible. By these devices, controllingsuch a propeller motor, it becomes possible to generate
an independent thrust and blimp robot is freely
movable to x, y, and z axis.

3 Result
The result of doing Experimental operation in theexperimental environment is shown by using developed
blimp robot.
The parameters are adjusted for each basic
motion.

Fig.4 Three-dimensional orbit result of blimp

where KP is the proportional gain, KI, is the integral
gain, and KD is the derivative gain. The parameters areadjusted for each basic motion.
mx(t)–mθ(t) generated for propellers ch0–ch5 aredetermined by using m, my, mx, and m0 as follows:(5)m0 (t ) = my (t ) + mθ (t )m1(t) = mx (t ) + mθ (t )(6)m2 (t ) = my (t ) − mθ (t )(7)m3 (t) = mx (t ) − mθ (t)(8)

m4 (t) = mz (t)(9)

m5 (t) = mz (t)(10)m0(t)–m5(t) are continuation values that arecalculated by equations (5) through (10). However, themotor drive developed by this research cannot control acontinuous rotational speed. As for the drive of the
motor, only a binary control of ON-OFF is possible.
The drive of the motor is adjusted and thrust is adjusted
according to the time width for that. As for the motor, a©ISAROB 2007

58

Fig.5 The orbit of X-Y plane of blimp
Figure 4 showed three dimension orbit of blimp
robot. The orbit of X-Y plane was shown for Figure 5.
A straight line orbit lacks stability because it hasreceived the influence of inertia. The control thatmoves between the target points is done by the PID
control now. However, the appearance in which theorbit is corrected is shown toward the target point.
Especially, it is shown that it is operation to whichadjacent to the target point is done as it is a setting.
Figure 6.7.8 showed the transition of the time of theorbit in x, y, and z each axis. In each axis, blimp
independently corrects the orbit, and does the
movement operation to the target point. And, an
individual axis and the orbit result shows, it isunderstood that the influence of inertia is large.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4 Conclusion
The rotation operation in the PID control and thesurveillance point (each target point) was able to beachieved. However, there is a problem that the disorderof the orbit by the influence of inertia is caused in otherside. And, the error margin of keeping position in thesurveillance point is large.
The orbit is stabilized and it will be necessary to
improve keeping position with surveillance point in the
future. It is scheduled to change the PID control from a
positional control to the speed control and to try to thisimprovement.

References
Fig.6 Transition at time in x axis[1]T.Yabuuchi,M.O nosato, “Developmentof A Small
Airship for Surveying Damaged Buildings”, SICE SI
2002 Kobe, Vol. 3, pp. 17-18,2002(in Japanese)[2]A.Hosoi, Masanori Sugimoto, “An Autonomous
Security System Using Multi-Flying Robots”, The 18th
Annual Conference of the Japanese Society for Artificial
Intelligence, 2C1-04,2004(in Japanese)[3]Hidenori Kawamura et al.,”Development ofEntertainment Indoor Blimp Robot based on Hovering
Control”, Entertainment Computing 2004, pp. 69-74,
2004 (in Japanese)[4]A. Elfes et al., “Air-Ground Robotics Ensembles for
Cooperative Applications, Concepts and Preliminary
Results”, Proceedings of the International Conference on
Field and Service Robotics, pp. 75-80,1999[5]F. Iida, “Biologically inspired visual odometer fornavigation of a flying robot”, Robotics and Autonomous
Systems 44, pp. 201-208, 2003
[6]C. Melhuish, J. Welsby, “Gradient Ascent with agroup of Minimalist Real Robots: Implementing
Secondary Swarming”, Proceedings of 2002 IEEE
InternationalConference on
Systems, Man andCybernetics, pp. 509-514,2002[7]J. Kobayashi et al., “Flight Control of Blimp basedon CCD Camera Images”, Proceedings of International
Symposium on
Advanced Control of Industrial
Processes, pp. 269-274 ,2002[8]H . Kawamura et al., “Motion design for Indoor
Blimp Robot with PID Controller”, Journal of Robotics
and Mechatronics, Vol. 17,No. 5, pp. 500-508, 2005[9]S. Zwaan et al., Visual Station Keeping for Floating R
obots in Unstructured Environments, Robotics and Autonomous Systems 39, pp. 145-155,2002[10]T . Takaya et al, ”Motion Control in Three
Dimensional
Round System of Blimp Robot”,
SICEI
CASE
2006(CD-ROM),2006[11]Y.Minagawa et al, ”Learning Docking Control ofAutonomous Recharging for Indoor Blimp Robot”, Fazzy Sytem Symposium,pp689-692, 2006

Fig.7 Transition at time in y axis

Fig.8 Transition at time in z axis

©ISAROB 2007

59

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Human Face Detection with Neural Networks and the DIRECT Algorithm
Yau-Zen Chang

Kao-Ting Hung

Shih-Tseng Lee

Medical Augmented Reality Research and Development center
Department of Mechanical Engineering
Chang Gung University, Tao-Yuan 33302, Taiwan

zen@mail.cgu.edu.tw

kattin@st.cgu.edu.tw

ABSTRACT
Based on Rowley’s approach [2], this paper aims
at proposing a new architecture that uses a specificoptimization technique, the DIRECT (DIviding
RECTangle) algorithm, to improve the efficiency of facedetection in images. The system consists of two mainparts: a neural network-based face detection arbitrator
and a search strategy based on an integer-handling
DIRECT algorithm. By the architecture, the number ofarbitration is dramatically reduced, and human faces, if
they are present in an image, are not restricted topredetermined
resolutions
andaspectratios.
Experimental results show that the proposed architectureis efficient in terms of both speed and robustness.
INDEX TERMS: face detection, neural network, image
processing, Image pattern recognition, DIRECT
algorithm

1. INTRODUCTION
Human face detection from complex backgroundand in different positions of an image is an important
step for surveillance and intelligent human-machineinterface.
Rowley’s approaches [2] [3] are typical imagebased face detection methods in which upright andfrontal views of human face are detected using artificial
neural networks (ANN). Owing to its conceptual
simplicity, the approach of [2] has been followed bymany researchers, such as [4]-[6] which use ANNs. In
our previous work [11], we adopted both the image andfeature-based techniques to design two ANNs to estimate
the position as well as planar orientation of faces.
However, the approach of [2] is restricted to a
fixed training and classification template, which is of
20×20 pixels. And the original image must be re-sampled
at several preset resolutions due to the so-called image©ISAROB 2007

60

joe1181o@adm.cgmh.org.tw

pyramid arrangement. Furthermore, the ANN must checkall the extracted windows in all the sampled images forthe potential existence of human face. These restrictions
can lead to prolonged processing time and overlook of
faces that are difficult to identify in these resolutions andaspect ratios.
With these shortages of [2] in mind, this paper
aims at proposing a new architecture that uses a specificoptimization technique, the DIRECT (DIviding
RECTangle) algorithm, to improve the efficiency of facedetection in images.

2. THE INTEGER DIRECT ALGORITHM
We propose to apply the DIRECT algorithm [13]to improve the face detection architecture described in
Section 2. The name of the optimization algorithm is anabbreviation of DIviding RECTangles, describing a
specific manipulation procedure. Developed by Jones etal. in 1993, the procedure is deterministic, guaranteed toconverge, and has a very fast convergence rate without
trapping in local extremes [14]-[16].
In this work, the images are of finite resolution,
and we need an integer version of the DIRECT algorithm.
There are two parts to be considered: center definition
and trisecting manipulation. The center point between
two integers, say a to b, is defined as floor( (a+b)/2 ),where output of the floor function is the greatest possible
integer.
In order to divide boxes into 3 parts, the trisected
widths are calculated according to the following equation:⎛ (b − a + 1) ⎞Δ = floor ⎜⎟3⎠⎝
The above equation would lead to two specificconditions of Δ:⎧ Child L : [a, a + Δ − 1]⎪
If Δ ≥ 1 , ⎨Child C : [a + Δ, b − Δ ]⎪ Child : [b − Δ + 1, b]
R⎩The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

⎧ChildC = a
If Δ = 0 , ⎨⎩Child R = b
where ChildC, ChildL and ChildR denote the center, left,
and right decedents after participation, respectively. Thatis, when Δ≧1, the box will be divided into 3 parts:
ChildL ranges from a to a＋Δ－1 with width Δ, ChildC
ranges from a＋Δ to b－Δ with width b－a－2Δ＋1, andChildR ranges from b－Δ＋1 to b with width Δ. When Δ＝0, the box is divided into 2 parts: ChildC and ChildR,
each is of unit length.

3. PROPOSED FACE DETECTIONMETHODThe strategy takes the symmetrical property ofhuman face into consideration. As Fig. 2 (a) illustrates,
we segment the face image into 8 by 8 grids, this would
result in 64 nodes from N0,0 to N8,8. Note that only theintensities at the node are used, thus no resize operationis required, and the template images are not required tobe square.
7 8
5 6
4
2 3

0 1
0
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4
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6
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N
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ace
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ens

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3

ime
XD

n
nsio

age
of Im

ce
Spa

(b)
Fig. 2 (a) Location of the 49 nodes. (b) Distribution of these 49
normalized values.

Fig. 1 The proposed face detection architecture

4.1. Neural network-based face arbitrator
The training and testing images for ANN in [2] arebased on a fixed window (20×20 pixels). This approach
not only restricts its applicability to face images withdifferent aspect ratios, but also requires thecomputationally intensive resizing operation.
Based on these observations, we propose amoment-based feature extraction strategy that canalleviate the restrictions on aspect ratio and resolution.

©ISAROB 2007

1,1

N

Normalized Y Dim
momentOur system consists of two main parts: a neuralnetwork-based face detection arbitrator and a searchstrategy based on an integer-handling DIRECT algorithm.
As shown in Fig. 1, the position, size, and aspect ratio of
the windows to crop sub-images for face detection are
based on the DIRECT algorithm. After cropping andpreprocessing, the feature is extracted at predetermined
49 locations. The feature values are then sent to the ANN
to decide how close they represent a human face.
In other words, the face detection problem isconverted into an optimization problem to find out thebest position and size of the cropping window that
contains a human face.

61

Based on the first moment values of these nodes, 49
quantities are obtained:
M i , j = I ( pi , j )× pi , j − pC , i = 1 ~ 7, j = 1 ~ 7
where pi,j denotes the position of node Ni,j, pC are theposition of central node of this grid map, i.e., N4,4, andI(pi,j) represents the intensity of node Ni,j. Fig. 9 (b)shows distribution of these 49 normalized values
corresponding to the example image. Obviously, thecentral moment would be smallest, and the distribution
has a symmetrical characteristic along horizontal axis.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The ANN uses these 49 values as inputs to judge for the
existence of human face. There are two output neurons,
and the ANN is trained such that the output is [1, 0]
when a human face is present, and [0, 1] when none.
There are at least four variables to be decided in
the face detection problem: the X and Y coordinates, x
and y, width, w, and height, h. These variables are
summarized in to an unknown vector:

Input test image

Define the parameters of optimization

DIRECT algorithm

θ = {x, y, w, h}.

The ranges of these variables are defined within thefollowing boundaries:
1 ≤ x ≤ width of the image
1 ≤ y ≤ height of the image .
24 ≤ w ≤ 90
24 ≤ h ≤ 90
The problem is then formulated as finding the parameter
vector, θ , by the DIRECT algorithm, such that thecorresponding output of the ANN approximates theoutput of the arbitrator when a human face is present,
which is the value [1, 0] in our current design. Fig. 3
shows the flow chart of the face detection architectureusing the integer-handling DIRECT algorithm
In general, DIRECT algorithm proposes a
searching strategy to obtain all possible solution. We
extract all solution according to a specific threshold, i.e.
if the values of objective function less than thepredefined threshold, the corresponding solution would
be considered.

Generate θ: (x, y, w, h)Crop the region according to θ

Arbitrate the region by ANN

no
Reach the criterion
yesCease searching
Fig. 3 Flow chart of the face detection architecture using the
integer-handling DIRECT algorithm

5. EXPERIMENTAL RESULTS
22©ISAROB 2007

62

20
18
16

Size Index

We randomly selected 340 frontal-view face
images and 306 non-face images from the MIT-CBCL
face recognition database, CMU face database, and ourown database to train the ANN. Besides, we prepare 138
images contains no face in order to improve therobustness of the ANN using the so-called “bootstrap”
method [12].
The network is of the two-layered Multi-layered
Perceptron (MLP) architecture with 20 neurons in thefirst hidden layer and 15 neurons in the second hidden
layer. A standard back-propagation algorithm [17] wasused to train this 49-20-10-2 network for 500 cycles. Fig.
4 shows the positions in the search space visited by the
integer-handling DIRECT algorithm. Note that, for a 3dimensional illustration, only the X and Y coordinates, x
and y, and width, w are shown.

14
12
10
8
6
4
2

Y a 100
xis
o

f Im

50

ag
eS

0

pa
ce

0

20

40

60

X axis

80

100

120

e Spaceof Imag

Fig. 4 Positions in the search space visited by the integerhandling DIRECT algorithm

Based on 50 detection results, the system based on the
DIRECT algorithm can efficiently locate the human facewithin 1000 arbitrations using the ANN. As acomparison, when a typical 600 by 400 image isencountered, the number of arbitration in the approach of[2] can easily exceed the amount of 300,000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Also found in the experiments, the DIRECT
algorithm could cause repetitive detection. This problem
can be easily solved by integrating all the neighboring
positions into one by, for instance, the fuzzy c-mean
method. Furthermore, we can integrate other image
processing techniques, such as the skin color
segmentation, to reduce the search area to further reducethe computing effort.

6. CONCLUSION
Based on the architecture of [2], we proposed a
new human face detection architecture using the integerhandling DIRECT algorithm. The newly designed faceclassifier is computationally efficient and invariant toimage size. Furthermore, the architecture is robust interms of correctness, since the aspect ratio of face images
is not faxed.
As for future work, we will address the issues ofrotated faces and side-view faces. Skin color
segmentation may also be included to provide further
acceleration.

ACKNOWLEDGEMENT
This study is supported by Technology Development
Program for Academia (TDPA) of the Ministry of
Economic Affairs, R.O.C., under contract no. 94-EC-17A-19-S1-035.

REFERENCES[1] E. Hjelmås and B. K. Low, “Face Detection: A Survey,”
Computer Vision and Image Understanding, Vol. 83, pp. 236274, 2001.
[2] H. A. Rowley, S. Baluja and T. Kanade, “Neural Networkbased Face Detection,” IEEE Transaction Pattern Analysis andMachine Intelligence, Vol. 20, pp. 23-38, January, 1998.
[3] H. A. Rowley, S. Baluja and T. Kanade, “Rotation Invariant
Neural Network-based Face Detection,” Proceedings ofComputer Vision and Pattern Recognition, pp. 38-44,
1998.

©ISAROB 2007

63

[4] C. Garcia, M. Delakis, “Convolutional Face Finder: A
Neural Architecture for Fast and Robust Face Detection”, IEEE
Transaction Pattern Analysis and Machine Intelligence, Vol.
26, No. 11, pp. 1408-1423, 2004.
[5] K. Curran, X. Li, and N. M. Caughley, “The Use of Neural
Networks in Real-time Face Detection,” Journal of Computer
Science, Vol. 1(1), pp. 47-62, 2005.
[6] P. Hartono, S. Hashimoto, “A Robust Face Detector
Algorithm Utilizing Neural Networks and Partial Template
Matching,” Proceedings of the SPIE, Vol. 5603, pp. 119-127,
2004.
[7] S. Birchfield, “Elliptical head tracking using intensity
gradients and color histograms,” Proceedings of the IEEE
Computer Society Conference on Computer Vision and Pattern
Recognition, pp.232-237, 1998.
[8] C.C. Han, H.Y. Liao, G.J. Yu, and L.H. Chen, “Fast facedetection via morphology-based pre-processing,” Pattern
Recognition, Vol.33, pp. 1701-1712, 2000.
[9] C.H. Lin and K.C. Fan, “Triangle -based approach to thedetection of human face,” Pattern Recognition, Vol.34,
pp.1271-1284, 2001.
[10] R.L. Hsu, ”Face detection in color images,” IEEE
Transaction on Pattern Analysis and Machine Intelligence,
Vol.24, pp.696-706, 2002.
[11] Y. Z. Chang and G. T. Hung, “Detection of Face Pose with
Neural Networks,” International Symposium on Artificial Lifeand Robotics, B-Con Plaza, Beppu, Oita, Japan, January 23-25,
2006.
[12] K. K. Sung, “Learning and Example Selection for Object
and Pattern Detection,” PhD Thesis, MIT AI Lab, January 1996.
[13] D. R. Jones, C. D. Perttunen, and B. E. Stuckman,“Lipschitzian optimization without the Lipschitz constant,”
Journal of Optimization Theory and Application, Vol. 79(1), pp.
157-181, 1993.
[14] C. A. Floudas, and P. M. Pardalos, Encyclopedia of
Optimization, Kluwer Academic Publishers, London, 2001.
[15] H. Zhu and D. B. Bogy, “DIRECT Algorithm, and Its
Application to Slider Air-Bearing Surface Optimization,” IEEE
Transactions on Magnetics, Vol. 38, No. 5, September, 2002.
[16] H. Zhu and D. B. Bogy, “Hard disc drive air bearing
design: modified DIRECT algorithm and its application toslider air bearing surface optimization,” Tribology International,Vol. 37, pp. 193-201, 2004.
[17] F. M. Ham, I. Kostanic, Principles of Neurocomputing for
Science & Engineering, McGraw-Hill, 2001.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

High Shock Disturbance Rejectionof Single-Link Robot Arm with a Disturbance Observer
Seong Ho Kang and Chang Sup Kim

Man Hyung Lee

Department of Mechanical andIntelligent Systems Engineering
Pusan National University,
Busan, 609-735, KOREA

School of Mechanical Engineering,
Pusan National University,
Busan, 609-735, KOREA

Abstract
In this paper, we propose a disturbance observer
system for an instrument that is affected by regular
shock. The disturbance observer uses the position
error signal and a nominal model of the plant tocreate an estimation of the disturbance. The
disturbance observer algorithm is designed to
enforce robust input/output behavior by cancelingthe effects of disturbances and modeling errors. We
apply to the filter using the binomial filter design
method. The design of disturbance observer using
the methods improves the performance ofdisturbance rejection. The effectiveness of the
disturbance observer in rejecting high shock
disturbances is demonstrated in simulation.

1

Introduction

Recently, robot motion control has been theimportant technology. It has been expected thatrobots help us, human beings, or that robots workin various fields instead of us. Especially, robots
have been expected to work in the heavy
environment where human beings can not stand, orto be active in the medical and welfare fields. In
order to respond to the above expectations,teleoperation has been researched as one of therobot motion control methods[1].
Position controller is one of the important parts inrobot motion control system. It mainly functions to
accept command signals from the process control
computer and feed-back signals from load, andoutput control signals to motor velocity control
system so as to drive the mechanical transmission
to ensure that the load tracks its target timely andprecisely. It remains as a hard nut many people aretrying to crack that restrict requirements are placedon input signal smoothing, motional disturbance, andsystem precisition, stability and quick response, as
well the parameters are difficult to mach with eachother.
The currently available position controls©ISAROB 200764

mostly adopt PID control mode. PID control iseffective for simple process control but limited forthe targets whose parameters vary in large range orare remarkably non-linear[3]. To meet theseadvanced requirements, various kinds of controllers
have been proposed. Disturbance observer isadaptive robust controller.
In this paper, we propose a disturbance observer
system for an instrument that is affected by regular
shock. The regular disturbances cause a position
error of the machine. Research about reducing theposition error by the disturbance has been madeprogress. But, it is insensibility to high shock
regular vibration. So we research possible toimplement a disturbance observer (DOB) in thesystems that has high shock regular vibration.
This research suggest a disturbance observer for
position control of a BLDC motor that is used toposition control of a single-link robot arm. The
system model of the motor is calculated using
ADAMS. And, the parameters of the disturbancesignals are 9000N of high shock and 11Hz of
regular shock. Fig. 1 show disturbance signal.

2

Selection of Disturbance Observer

Among these kinds of robust motion control
methods with two-loop structure, it can be said thatthe most popular method is the disturbance-observer(DOB)-based control. The disturbance observer uses

Fig.1 DisturbanceThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the position error signal and a nominal model ofthe plant to create an estimation of the disturbance.
The estimation is then used to compensate for the
disturbance effects.
Using the low-pass filter    and the inverse of
a nominal model, the DOB estimates the disturbanceand the estimate signal is utilized as a disturbance
cancellation input. Hence, the DOB makes thesystem's behavior between control input and plant
output robust in the presence of uncertainties anddisturbances. Fig. 2 shows the structure of the
DOB. From the block diagram in Fig. 2, the plant   output  can be expressed in terms of thereference control input   , the external disturbance , and the measurement noise                 
 

(1)

where,                      
, nominal model     of DOB. Below the cutoff
frequency of   ,    ≈  is achieved.
Hence, low-frequency disturbances are attenuatedand the mismatch between the plant    and thenominal model     is compensated. Thus, thebehavior of the real plant is to be the same as thegiven nominal model. On the other hand,     is achieved above the cutoff frequencyof   . Hence, high-frequency measurement noise
is attenuated. As a result, the following observations
come to light. Firstly, the most important design
parameter in the DOB design is the low-pass filter
 , and secondly, the main concern is the tradeoff
between making    small and     
small.









We apply to the filter using the Binomial filter
design method. The method can consider toimportant facts. Now, we are to use the robustness
measure Eq(2) as the design method of a  filter
for second-order systems. First, we assume the filter of the following form:   

 





(3)

          

where  is the filter time constant,   
the binomial coefficient,  the denominator orderand  the numerator order. Hence,  ≥   .
Second, since ours system can be described by thesecond order transfer function, we assume that thenominal plant is given as follows:         (4)where,  is a damping ratio,   the undamped
natural frequency and  a constant.

- 

In short, this technique considers the numerator
order, the denominator order and relative order ofthe nominal plant. The disturbance rejection performance

DisturbanceObserverFig.2 Disturbance Observer (DOB)
order
denominator 

condition
performance󰀺robustness 󰀺
disturbance 󰀺
numerator 󰀺rejection rate 󰀺robustness ⇩relative 󰀺sensor noise ⇩time constant 󰀺sensor noise ⇩Table.1 Factors of  Filter©ISAROB 2007

(2)

DOB is made to disappear the effect of
perturbation, but it doesn't mean to remove
perfectly. Therefore, we need to be concerned aboutrobustness. Since Eq(2) implies the degree ofrobustness of DOB system against the perturbation,
we define it as “robustness measure of a DOB
system” denoted by    . Actually, there are threeimportant factors in designing a  filter: the filter
time constant, numerator order and denominator
order (or relative degree) of  filter. Studies in [4]and [5] papers show condition and performance ofeach factors at Table.1.

  
 Plant(1 − Q( s )) Pn ( s) ⎤⎡ (1 − Q( s ))⎥ <γ
−1
−Q( s )⎣ −Q( s ) Pn ( s )⎦σ max  ⎢Fig.3 Structure of the simulink

65

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007of the filter depends on the numerator order andtime constant. And robustness is decided by thedenominator and relative order. Also, the relative
order
decidesmeasurement
noise
reduction
performance.

as the relative degree decreases and as thedenominator order increases with the same relative
degree[4].
Where, the    filter selects  (relative degree is two) And it proved throughsimulation. Fig.7 shows effect of disturbance in
response without disturbance in Fig.5.

3

3.2 Design of Important Parameter

Design of DOB

Fig. 3 shows simulink at MATLAB based Fig.2
DOB. Where, Theta(2) is the Plant output andangle_2(disturance) disturbance signal, and thesystem uses unit step input.

3.1

Initial environment of  Filter

Fig. 4 depicts maximum singular values of (2)according to frequencies. The robustness measurecorresponds to the maximum among maximum
singular values for each    filter. As wesuggested in Table.1, the robustness becomes better

Fig. 4 Robustness comparison according to    firters(b) Response without
disturbanceFig. 5 Disturbance & Response without disturbance (deg)The system model is defined as                 

(5)

To decide optimum parameters of DOB, is referred
Table.1 about factors effect. In order to prove
performance of disturbance rejection, is used thesimulink of MATLAB. It fix   because of
second-order system, find a optimum time constantτ. Fig. 7 shows results of simulation when τchange 0.0005 to 1. Where, a optimum time
constant is 0.0005. And, Fig. 8 is results ofsimulation to decide denominate order. And
optimum denominate order confirm forth-order.
Finally, Fig. 9 shows results about numerator orderchange (τ is 0.0005, denominate order isforth-order). We can estimate optimum nominal
order of second-order.

Fig.7 Response according to τ (   )τ=1, 0.5, 0.1, 0.05, 0.01, 0.005, 0.001, 0.0005(a) Disturbance(b) Detail view(a) Full viewFig. 8 Response according to d (τ=0.0005, n=0)
Rejection performance :        

Fig. 6 Response with disturbance©ISAROB 2007

66

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

 filter can design to simulation. In short, a time
constant is 0.0005, relative order second-order,
denominator order forth-order, and numerator ordersecond-order.

performance over 95%.

3.4

This work was supported by the Brain Korea 21
Project.

Stabilization

Also, Fig. 9 shows performance of disturbancerejection. Where, output angle is changed 0.01 to
0.0005 degrees after   filter design. Namely,
disturbance rejection rate is over 95%. In order toconsider degree of stability, is used to five times ofdisturbance. The effectiveness of the disturbanceobserver in rejecting high shock disturbances isdemonstrated in simulation. In the Fig. 10, thesimulation shows disturbance rejection performanceover 95%.

4 Conclusions
This paper presents improving performance ofhigh shock disturbance rejection. We propose adisturbance observer (DOB) system for an
instrument that is affected by regular shock. To
consider to important facts, apply to the filter using
the Binominal filter design method. Optimum valueof the  filter parameters is foundthroughsimulation using simulink of MATLAB. In order toconsider degree of stability, is used to five times ofdisturbance. Then demonstrates disturbance rejection

Fig. 9 Response according to numerator (τ=0.0005, d=4)
Rejection performance :        

Fig. 10 Five times of disturbanceRejection performance :        

©ISAROB 2007

67

Acknowledgment

References[1]

T
Shimono,S
Katsura,K
Ohnishi,"Improvement of Operationality for Bilateral
Control based on Nominal Mass Design inDisturbance Observer," IEEE on Industrial
Electronics Society, pp. 6, 6-10 Nov. 2005 .
[2] Seong-Ho Song, Yoon-Tae Im, Baek-Sop Kim',
and Seoyong Shin, "Robust Control of Linear
Systems with Nonlinear Uncertainties via
DisturbanceObserverTechniques,"
inProceedings IEEE Conference on Emerging
Technologies and Factory Automation, vol. 1,
pp. 241-244, 16-19 Sept. 2003
[3] Ying Zhang, Ling Cai, Qingde Meng,, Ming,
Dong, "A Novel Active Disturbances Rejection
Fuzzy Servo System Position Controller,"
ISSCAA on Systems and Control in Aerospaceand Astronautics, pp. 5, 1st International
Symposium on 19-21 Jan. 2006
[4] Youngjin Choi, Kwangjin Yang, Wan Kyun
Chung, Hong Rok Kim, Il Hong Suh, "On therobustness and performance of disturbanceobservers for second-order systems," IEEE
Transactions on Robotics and Automation, vol48, pp. 315-320, Feb. 2003.
[5] Bong Keun Kim; Wan Kyun Chung; "Advanced
Disturbance Observer Design for MechanicalPositioning Systems," IEEE Transactions on
Industrial Electronics, vol 50, pp. 1207-1216,
Dec. 2003.
[6] H. S. Lee and M. Tomizuka, "Robust motion
controller design for high accuracy positioning
systems," IEEE Transactions on Industrial
Electronics, vol. 43, pp. 48-55, Feb. 1996.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Learning landing control of indoor blimp robot for autonomous energy recharging
Yoshihiro Minagawa1 , Hidenori Kawamura1 , Masahito Yamamoto1
Toshihiko Takaya2 , and Azuma Ohuchi1
1

Graduate School of Information Science and Technology, Hokkaido University
North 14, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
E-mail : {mina, kawamura, masahito, ohuchi}@complex.eng.hokudai.ac.jp
2
Ricoh Software Inc.
North 7, West 4, Kita-ku, Sapporo, Hokkaido 060-0807, Japan
E-mail : toshihiko.takaya@rsi.ricoh.co.jp
URL : http://harmo.complex.eng.hokudai.ac.jp/

Abstract
We report on learning landing control of autonomous energy recharging for indoor blimp robots.
Indoor blimp robots have potential applications inmonitoring, surveillance, and entertainment. It might
be necessary for long term ﬂight to achieve these applications,. Since blimp robots cannot load a heavy
battery, it is diﬃcult for long time ﬂight. We focusedon autonomous energy recharging to solve this problem, especially moving to the charging station, we callit landing control. We make use of learning control
for landing control because of its satisfactory accuracy. The results of experiments demonstrated thatlearning control is eﬀective for landing control.
key words - indoor blimp robot, autonomous energy
recharging, landing, controller

1ment. For long periods movement, we describe autonomous energy recharging system, especially landing control to charge battery.

2

Indoor Blimp Robot

We use columned blimp robot. Compared to ellipsoidal typed balloon, columned type balloon havethe advantages of uniform air resistance for directions,moving precisely in the desired direction directly without rotation. These features might be necessary toachieve the applications for indoor blimp robots.

introduction

Indoor blimp robots have the features of using
buoyancy to move three dimensional movements, moving less energy, and safety in crashed compared tosmall air-crafts or helicopters. For these features, indoor blimp robots have enormous potential for applications such as entertainment movement, monitoring
activity at high attitudes.
However they have diﬃculties to control becauseof nonlinear thrust, balloon imbalance, and air resistance, so many researches focus on control designfor indoor blimp robots, such as maintaing a ﬁxed
position[1], and circular or triangle motion[3]. These
control design are based on achieving applications ofahead, also it might be necessary for long periods
movement. Because of payload restrict, indoor blimp
robots cannot load batteries for long periods move-©ISAROB 2007

68

Figure 1: Overview of indoor blimp robot: balloon
and control system.
Figure 1 shows a columned blimp robot and control
system overview. Blimp robot consists of a balloon
for enabling to ﬂoat and a driving parts for moving.
Driving parts consist of the controller, thrusters, andsensor.
In design of balloon, we decided on diameter as
0.94[m] and height as 0.8[m] to enable the blimp toThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ﬂoat by buoyancy.
The controller consists of a T-Engine board andRBT M C board. Image processing calculations anddecisions on outputs to control the blimp are run on
the T-Engine board and sent to the RBT M C board ascontrol commands for propellers on thrusters. Basedon control commands, the RBT M C board controls
motors that drive propellers at a sampling time ∆T =0.3[s], which is based on consumption time for image
process and control decision.
The blimp recognizes positional information andenvironmental conditions through camera sensor,ARcamera, which sends image data to the controller via
an internal bus and is ﬁtted on thder T-Engine board..
The image resolution is 160 × 144 pixels, and color
information is composed of 16bit data in RGB color
space.
The blimp robot has six propellers, ch0 and ch2
for x-axial movement, ch1 and ch3 for y-axial movement, and ch4 and ch5 for z-axial movement. Propeller thrust is adjusted by controlling motor rotation
due to hardware constraints. We attempt to adjust
thrust by switching On/Oﬀ signals controlling motor
rotation.

3

Autonomous energy recharging

Autonomous recharging is the key of long periods
movement and activities for mobile robots. In the
research activity of 2D mobile robots, autonomousrecharging are proposed[4],[5]. Many researches focus
on 1)making the charging station including environment , and 2)control design to the station. In the ﬁeldof 3D mobile robots, indoor or outdoor robots, thesefunctions are required.

3.1

The left ﬁgure of Figure 2 shows the environmentand charging station for indoor blimp robots. The
blimp recognizes its position information from landmarks placed on ground and moves based on position
information, see as [3]. The blimp moves to the two
130[cm] height poles, which set 75[cm] apart, for autonomous energy recharging, which movement called
as landing control for indoor blimp robots.
The right ﬁgure of Figure 2 shows landing overview.
Indoor blimp robot must move to the two-poles
from the upper space. Landing control is necessary
to descend straight. As landing control accurately,
1)putting plane coordinates, 2)slowing horizontal velocity, and 3)retrying docking to the station apart from69

Landing overview.

Figure 2: Experimental environment and charging station
the station, are required. The control design of the
blimp robots satisﬁes these requirements.

3.2

Control design

It is diﬃcult to design an analytical controller basedon a dynamic model because a blimp has such nonlinear characteristics as air resistance, and inertia during
movement.
Researchers have experimented with PID [3],fuzzy[6],and learning [2] controllers for blimp robots because
they do not need the complex analysis required for dynamic model. We use a PID controller for movements,
such as circular and triangle, and learning controller
for landing control. PID controller for the blimp robot
might be diﬃcult for landing control with satisfactory
accuracy in the related work [3]. Learning control has
the advantages compared with PID control, such asparameters are a little and it reﬂects on an inertia, airresistance and temperature.
3.2.1

Environment©ISAROB 2007

Experimental enviornment

PID control

In our PID controller, the manipulated variable m(t)is given as the ratio of the rotation time for each propeller in sampling time ∆T . The manipulated variables mx (t), my (t), mz (t) are decided by the relative
velocity from the blimp robot to the target point. The
manipulated variable mθ (t) is calculated by the relative angular. These manipulated variables are deﬁned
as follows.
mx (t) = KP x ex (t) + KIx Σex (t)∆T + KDx

Dex
∆T

Dey
∆T
Dez
mz (t) = KP z ez (t) + KIz Σez (t)∆T + KDz∆T

my (t) = KP y ey (t) + KIy Σey (t)∆T + KDy

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Deθ (t)∆T
where KP is proportional gain, KI is integral
gain and KD is derivative gain, and De(t) =e(t) − e(t − ∆T ). Thrusts M0 (t),...,M5 (t) generated for propellers ch0,...,ch5 are determined by using
mx (t),my (t),mz (t),mθ (t) as follows.
M0 (t) = my (t) + mθ (t), M1 (t) = mx (t) + mθ (t),M2 (t) = my (t) − mθ (t), M3 (t) = mx (t) − mθ (t),M4 (t), M5 (t) = mz (t).
mθ (t) = KP θ eθ (t) + KIθ Σeθ (t)∆T + KDθ

3.2.2

Learning control

Learning control is realized by updating the learning
table, its horizontal axis is the relative distance andvertical axis is the relative velocity. The blimp robot
becomes the preferable condition using learning control after updating the learning table. Figure 3 shows
the overview of the learning control,where 0 is the positive rotation and 1 is the negative rotation. When therelative distance do not reach to the zero of the horizontal axis and the relative velocity gets across thezero of the vertical axis, the learning table is updatedwith inversion of each element. There is the possibility
to update the learning table in movements to adjust
the environment condition.

Figure 3: Overview of learning control

3.2.3

Landing area

4
4.1

70

Experiment
Experimental setup

In the experiment, we conﬁrm the achievement of
the landing control using learning control and the complicated motion for applications. The blimp robot
moves for the square and rotational motion. After thesquare and rotation, the blimp robot tries the landing
motion. The square motion includes basic motions
for straight-line and twisting a corner. These motion
can be applied to various movements for indoor blimp
robot. In the square motion, the blimp robot is controlled to pass apexes on the square, 200[cm] on a sidein XY -plane. The blimp robot is controlled to move
150[cm] and 250[cm] high for Z coordinate alternately
on each apex. Before the rotational motion, the blimp
robot moves to the target point for the landing motion. In the rotational motion, the blimp robot rotatefrom π/2[rad] to −π/2[rad]. The rotational motion
can be applied to the autopilot and monitoring. In
the landing motion, the blimp robot tries to descend to
the target point its 3D coordinate Ps = (0, 150, 120).
Landing to the station is required accuracy , X and Y
coordinate within 10[cm] from the center of the station, and yaw angular within π/16[rad].
The blimp robot change its control from PID control to learning control after rotational motion. The
experimental environment is the space in the buildingof University as shown Figure 2. The controllable parameters are manually set by repeated trial and error
in preliminary experiments.

4.2

We divide the movement area of the blimp robot into
three for landing achieving with satisfactory accuracy.
We consider that it is possible to land by this area division even if the blimp robot misses the desired orbit
to the station. First area is that the blimp robot getnear to the charging station. In this area, we makeuse of PID control. Second area is near the charging
station, but the precision cannot be satisﬁed. In thesecond area, the blimp robot move to the upper spaceof the charging station by learning control to satisfy
the accuracy of the landing. Third area is that the
blimp robot descend to the charging station. In thisarea, the blimp robot is controlled by its velocity with©ISAROB 2007

learning control not to break the charging station andblimp itself. If the blimp robot moves from the third
area to the second area, the blimp robot moves up andretries landing.

ResultFigure 4, 5, and 6 shows the whole motion of
the blimp robot. In the square motion, the blimp
robot has moved approximately along the objective orbit in XY -plane. Z coordinate movement of the blimp
robot has also moved approximately, a little unstablebecause of the diﬃculty of the neutral buoyancy. In
the rotational motion, the blimp robot has rotated approximately to the objective angular. In the landing
motion, the blimp robot has descended to the target
point Ps instantly. Also, X, Y coordinates and yaw
angular are within the required precision. In anotherexperiment, the blimp robot cannot descend to theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

target point Ps instantly because of unsatisfying precision to the station. In this case, the learning control
is more eﬀective compared to instant movement because of updating its table repeatedly. We conclude
that learning control is eﬀective for landing motion
from the experimental result.

robot. In the experiment, we showed the motion combination of the square, rotational and landing motion.
We could achieve the reliable accuracy for the landing
with learning control.
In the future works, we need to conﬁgure the electrical device to charge the battery. Also, we need toachieve a less time consumption for movements to savethe battery. In these functions, the blimp robot act theperformances, entertainment movements, and monitoring for the long time.

Acknowledgements

Exp1

Exp2

Figure 4: X-Y coordinate trajectory of the blimp robot

We thanks Yoshikatsu Henmi, Hiroshi Murakami
and Yoshihiro Uchimi of Hitachi East Japan Solutions,Ltd., and Dr Dun Wu of North Technology
Co., Ltd. We also thank Masashi Yamaguchi of Cyber.Domain and Dr.Keiji Suzuki of Hakodate Future
University.

References[1] S.Zwaan, A.Bernardino, J.Santos-Vitrol, ”Visual
station keeping for ﬂoating robots in unstructured
environments”,” Robotics and Autonomous Systems,Vol. 39, pp. 145-155, 2002.
Exp1

Exp2

Figure 5: Z coordinate transition of the blimp robot[2] K.Motoyama et.al. , ”Development of Autonomous Blimp Robot with Intelligent Control”,Entertainment Computing - Technologies andApplications, pp. 191-198, Kluwer Academic Publishers, 2003.
[3] H.Kawamura et.al., ”Motion Design for Indoor
Blimp Robot with PID Controller”, Journal of
Robotics and Mechatronics, Vol.17, No5, pp. 500508, 2005.
[4] S.Oh, A.Zenlinsky, K.Taylor, ”Autonomous Battery Recharging for Indoor Mobile Robot”, Proceedings of Austrlian Conference on Robotics andAutomation, 2000.

Exp1

Exp2

Figure 6: Yaw angle transition of the blimp robot

5

Conclusion

In this paper, we reported the learning landing control of autonomous energy recharging for indoor blimp©ISAROB 2007

71

[5] M.C.Silverman et.al., ”Staying Alive: A Docking Station for Autonomous Robot Recharging”,
IEEE International Conference on Robotics andAutomation, 2002.
[6] M.Sugisaka et.al., ”Development of the Fuzzy Control for a GPS-located Airship”, Proceedings of theTenth International Symposium on Artiﬁcial Lifeand Robotics, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

User-recognition system for an autonomous robot
in human living environments

Yoshitaka Hane

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
hane@mmcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract
In this report, we describe a user-recognition system for anautonomous robot. The proposed method enables the robot torecognize the person who instructs it without placing any
markers in human living environments. The system is based on
differences information between some camera’s images, matches
the template, and recognizes color based on HSV information.
The threshold can be lowered by combining features of these informations. This system enables recognition under various
environments.
A moving object is initially extracted by using thedifference between frames. Next, to reduce the load, the image isnarrowed to the area on which the difference is concentrated. The
system matches the template and forecasts the user’s position. In
addition, it determines a flesh-colored area by using HSV
information. When two or more people are present in the field ofview, the system determines that the person with thelargest-width silhouette is closest to the robot. After confirmingthat the person is standing still in its presence, the robot waits forthe potential user to identify his or her intention to use the robot;
this is signaled by the user raising his or her right hand. This
process is repeated every 100 milliseconds.

robot. It can be rotated to some sides (90 degrees to top direction,
65 degrees to lower degrees, 90 degrees to right direction, and 90
degrees to left direction) by two DC motors. This camera
contains approximately 300,000 pixels. All devices are controlled
by a personal computer, and electric power is supplied by leadbatteries.
To work, the robot needs to receive a command from theuser. The robot can be easily sent instructions from devices suchas remote controls, personal computers and so on. However, thisstep is inconvenient. We consider that the robot should recognize
human activity via its visual and auditory sensors, and thereby
understand a command so that it may allow human users to act
more naturally in their living environments. In light of this, wehave developed a system based on images taken by the CCD
camera installed in the robot aimed at determining the face of thehuman who has made a command. The information is passed on
to the human instruction recognition system and the humantracing system.

1 Introduction
Due to the insufficient number of workers in Japan’s
low-birthrate society, autonomous self-driving robots will becalled upon to provide various services within human living
environments. Robots are currently used in industry, where they
simply perform a given motion previously made by humans.
However, such robots are less useful for tasks in the home. We
are developing an autonomous personal robot with the ability toperform practical tasks in a human living environment using
information derived from sensors and a knowledge database.
Our robot has a drive mechanism composed of two front
wheels and two back wheels. The two front wheels are attachedto the motor, which operates them independently, while the backwheels are castor wheels. DC servo motors are used for the
robot’s drive mechanism, and position control and speed control
are achieved by means of the control system of the drive
mechanism. One CCD camera is installed on the head of the©ISAROB 2007

72

Fig.1 our developed robot

2 User-position recognition system
2.1 Outline of the system
To communicate with people, the robot must confirm theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
position of people within its field of view. We have thus
developed a system that can achieve position recognition. The
system is informed by the fact that people move slightly evenafter they stop, unlike a static object. It certain moving objects
from the camera image, and selects one prospective area fromthem. It determines whether the image is that of a human beingby comparing the image with certain human characteristics.
When it is judged that the image is that of a human being, thissystem outputs the position information.

2.2 Method for estimating distance error

Fig.2 Difference between flames

By this system, we consider that this system is able tooperate in real time and that it is not affected by changes inbackground

Ⅰ. Image Acquisition
The image obtained by the CCD camera is read into a PC
in the robot.

One blockFig.3 Lengthwise histogram of Fig2

Ⅱ. Processing using the difference between frames
We use the difference between frames to extract certain
objects moving within the camera’s view. It is hard to be affectedby a change of a background. This allows this system to resist
being affected by changes in background. First, our robot
acquires an image via the camera and saves it. Next, it acquires a
succession of images. They are compared with the first image
until differences of more than a total of 2% appear. Whendifferences of RGB color model exceed the threshold, the systemdetermines that a difference appears. When a moving object is
not detected, even if the system performs ten comparisons, it
photographs the first image again. In this way, our robot acquires
a binary image including a human outline (Fig.2).

Fig.4 two human beings in view

Ⅲ. Determining one object for range processing
The purpose of this system is to determine the position ofa human face. The view does not have to be obtained unless ahuman being is present in the image. Therefore this system cantreat an image in less view in other processes. At first, our systemcreates a histogram of the binary image in the lengthwise
direction, the image having been acquired in the previous process,and calculates the average value of the histogram. Some
smaller-than-average values are taken as noise, and they are
removed. The value of the histogram at a near position isconsidered one block. (Fig. 3) The widest of the blocks is judged
the range of the human being. By doing so, the robot recognizes
the nearest human being as a user. (Fig. 4) From this point on,
only the range of this image is determined.

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Ⅳ. Template matching processing
When this system begins its operation, it reads the
template imitating the head of a person. The outline acquired by
the previous process is compared with the template. The size ofthe template changes according to size of the search range.
Generally, this process requires a great deal of calculation time.
Thus, real time operation is achieved by reducing the number ofcomparisons. When the matching rate is higher than thethreshold and reaches its maximum, the position is output as the
position of a human face.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Ⅴ.Confirmation by using color information

2.3 Experiment

An image is difficult to identify using only conventional
processing. The system has to finally confirm that the image isthat of a human being. Thus, skin color is used to ultimately
determine, using the template matching process, the identity andposition of the human image. In this case, color information
processing uses the HSV color model.(Fig.5)

The system’s performance was evaluated under three
conditions.

When our system performs these processes, and it isdetermined that the head of a person is in view, it outputs the
position information. The flow of this system is shown in Figure
6.

The strong lighting condition in case C was a fluorescentlight placed above the robot’s head, coming into view when the
robot turns its head upward. The experiments were conducted ina conventional human living environment.
In case A, this system was seen to operate effectively. In
case B, it was able to identify a person and follow him or her
throughout the field of view, but could not continue following theparticular person and irregularly shifts its focus from object toobject. In case C, the system has a slight difficulty in identifying
the person’s color, so the robot’s ability to follow the person
under this condition deteriorates. (Fig.7)
We consider that these results demonstrate that the system candetect a person and follow him or her in a human living
environment.

A: One person under normal lighting
B: Two person s under normal lighting
C: One person under strong lighting

Fig.5 Check with HSV information

START

Get Pictures(A)
Difference between flams

Image procession

Matche with Template

Confirmation with HSV(B)

Find person?

No

Yes

Output the position

END

(C)

Fig.6 System flow

Fig.7 Experiment©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3. Sign recognition system

Formula (1) is for calculating “moment feature”. The “i”
and “j” are each position on the x-axis and the y-axis.

3.1 Outline of the system
The user-position recognition system outputs the positionof a person within the robot’s field of view. However, the robot
has to determine whether the person requests it. We created a rulein which the human operator, after the robot’s face (camera) turns
in a person’s direction, must raise his or her right arm to the sideof the face. After confirming the signal, the system recognizes thehuman being as the user.

(2)

tan 2 θ +M (2,0) − M (0,2)
tan θ − 1 = 0
M (1,1)

Formula (2) is for calculating the slope of the principal
axis. When this formula is used, the center of gravity has to moveto the origin of the coordinates.

3.2 Method of sign recognition
The robot recognizes a humans’ position by repeatedlyusing the user-position recognition system. When these outputs
show values close to one another’s, it is determined that theperson is a generally fixed position. The robot then initiates thesign recognition system. First, the system establishes arecognition range beside a human face and saves the image. The
system photographs the range in succession again. The image iscompared with an image obtained at the beginning of the process.
In the range, when differences exist within the range and theuser’s skin color is detected, the system identifies the user’s
signal.

Fig.9 Recognition a signal from user

3.3 Method for the first position of the arm recognition
As soon as this robot recognizes the sign from the user,
this system gets the first position of the hand. The difference
image between before and after raising the user’s right hand inprior process is cleared away the small noise by the Closing andOpening methods. Next, this system puts labels on left pixel
blocks, judges the biggest blocks as the palm of hand, and erases
the other small blocks. The slope of the palm is calculated with
the next model. (Fig.8, 9, 10, Formula (1), (2))

Fig.10 Different image of the right hand

4. Conclusions
We have proposed a system, composed of an ocellus
camera and for use in a indoor environment, that extracts ahuman‘s position from a field of view and determines whether
the human requires use of the robot. This system constitutes an
opening of communication between a robot and a human being.
Our next subject of study is the development of a system
for tracking the movement of a human arm using an initial value
as the position of the right arm which the sign recognition system
outputted. The robot will communicate with people effectively
by identifying the direction of the human arm and acting upon it.

Principal axis

Ｙ

Center of gravity

θ

Ｘ

0

References

Fig.8 Principal axis(1)

M ( p, q ) = ∑ i j
p[1] Toru Azuma, Yoshihiro Takenaga, Eiji Hayashi,qDevelopment of a self-driven personal robot、Artificial Life andRobotics(AROB 8th ’03)、pp.1-3、Oita, Japan, 2003

i, j

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of a Robot Following a Human
Using Color Information
Kouichi Tsugimura, Joo Kooi Tan, Seiji Ishikawa
Department of Control Engineering,
Kyushu Institute of Technology, Sensuicho 1-1,
Tobata, Kitakyushu 804-8550, Japan{tugimura, etheltan, ishikawa}@ss10.cntl.kyutech.ac.jp
Abstract
Carrying luggage is one of the daily tasks done in ourlife. It is tight work to carry heavy luggage especially forsenior people. This research pays attention to helping
those senior people carrying luggage by a mobile robot.
The robot developed in this research aims at carrying
luggage and following the possessor. Therefore, theperson whose luggage is carried by the robot does notneed to tell the destination to the robot. The person is
also possible to stop on the way freely. The extraction
method of a person that the user specifies, the following
method of the person, and the control method of a mobile
robot using captured images are described in this paper.
The effectiveness of the developed robot is examined by
the experiment performed on the corridor in the building.
Some results are shown and discussion is given.
Keywords: Mobile robots, human support, color
tracking, robot vision

1. INTRODUCTION
When a person becomes aged, assistance in a daily life
is needed because the body becomes weak. However,vivid life can be achieved as long as there is assistance tohim/her. Recently, senior citizen's ratio has increased inJapan. Therefore, the number of those who needassistance is increasing, whereas the number of those
who can provide assistance is decreasing. Then, thedevelopment of a robot which assists the function of ahuman body becomes more and more important.
Operation of carrying luggage is one of popular tasks
done in our daily life. This research pays attention to
helping senior people by carrying their luggage.
A robot system is proposed in this paper which finds a
person who has luggage, approaches to the person, andcarries the luggage in place of him/her, if he/she hopes to
do so. The robot follows the person to his/her destination.
Therefore, the person does not need need to tell the robot
his/her destination. He/she is also possible to stop on theway freely. The system structure is explained along withthe algorithms and experimental results are shown.

©ISAROB 2007

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Fig. 1 A robot system

2. ALGORITHM
2.1 A luggage carrying robot system
First of all, the size of the luggage carrying robot is
explained. The robot is unsuitable for carrying luggage if
it is small. On the other hand, the user may feel thepressure if it is too big. Therefore, a robot of the heightalmost equal to the chest of an adult is introduced in thisresearch. (Fig. 1)
2.2 Particle filter
The particle filter [1] is used in this research to track aparticular person. The particle filter is an approximatedprobability distribution by a weighted sample setSs( n ) ,(n)s : hypothetical state| n 1...N(1): probabilityn : particle number

Particle filter has many advantages for tracking
objects as they are robust to partial occlusion and change
in shape.
2.3 Setting of the center position of an object
A method is explained for the reduction of theinfluence of occlusion by the change in the distance ofthe object interested.
When the distance between the robot and the object isfarther as shown in Fig. 2(a), the camera view is given
by Fig. 3(a), whereas, when the distance is nearer as inFig. 2(b), the view becomes the one depicted in Fig. 3(b).
Therefore occlusion occurs when the object is nearer.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

To solve this difficulty, emphasis is placed on the upper
part of a human body, which is realized by using a kernel
centered at the center of the upper body (See Fig.4).
In this particular study, Epanechnikov Kernel [2]defined as follows is used as a kernel.
(a)k (r )1 1
c d ( d 2)(1 r ) if r 1
20
otherwise(2)

d : dimension
cd : volume of the unit d-dimensional sphere
2.4 Division of a histogram(b)
Fig. 2 Robot and an object(a)

The robot follows a walking person. The walking
posture is given in Fig. 5(a). It does not become thosepostures given in Fig. 5(b) or (c). Therefore the top andthe bottom information of a person can be used.
The region of a person is divided into an upper partand a lower part as shown in Fig. 6. The upper half of the
body is given a label 0, and the lower half of the body isgiven a label 1. With each part, position x ( x, y )has the HSV values and the label, which makes aquadruple. If H, S and V are digitized into H, S and V
levels, respectively, they make H S V 2 M
bins. These bins are numbered and denoted by h(x) .
The influence of light is reduced by using the HSVcolor model. Therefore, M bins considering the two
positions labeled 0 and 1and the HSV color space is
employed in this research.
The distribution model p ( x c )p ( x c ) ( u ) u 1...Mat the center x c is defined with each part as follows;(b)Fig. 3 Camera viewHHHp( x c ) ( u )fcenter

a

xc

k

xi
a

i 1

H

xc

I

[ h( x i ) u ](3): Kronecker delta function

u : the bin number

H

f

H

I
i 1

Fig.4 Shape of an object

k

1
xc

xi

(4)

a

The parameter I is the number of all the coordinates inthe squared region.
The Bhattacharyya coefficientis used for the
calculation of the agreement between two distributions
p(x) and q(x), which is defined by[ p, q](a)p (u ) q ( u )

(5)

u 1

(b)
(c)
Fig.5 Walking postureq : the target model
The coefficient
small.

©ISAROB 2007

M

77

approaches 1 when the difference isThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007x2.6.1 Camera control
The camera moves right to left to track an object.
Speed of the camera is changed by the proportional
control so that it captures the object at the center of thevisual plane. The strategy is formulated as follows;

0

1

y

d&c

pc ( xt xc )&d c : speed of camera
pc : proportion constant
xt : object position
xc : image center

Fig. 6 Histogram division
2.5 Observation model
The shape of an object is expressed in two squares inthis research. The hypothetical state is expressed by theexpression given in the form ofs x, y, x&, y& , H .
(6)x, y : location of the centerx&, y& : motion
H :length
The transition of the object at time t is expressedby the state.

st

As t 1 w t 1 .

(7)

A : deterministic component
w t 1 : stochastic component

A present state is inferred from the previous state by
matrix A , and the noise is added by vector w .
Parameter a used in the kernel of Eq.(3) is computed byaH2(3H ) 2

(8)

The weight which uses the Bhattacharyya coefficientis converted into the probability by Eq.(9).

1
e
2

(n)

(1

2

(9)

Finally the position of the object is decided by taking
the average of the state as follows;

E (S )N(n) (n)

s

(10)

n 1

s : hypothetical state: probabilityn : particle number
2.6 Mobile robot control
A camera has been installed in the developed mobile
robot. We need two control strategies; one for thecamera and the other for the mobile robot.

©ISAROB 2007

2.6.2 Mobile robot control
The control of the mobile robot consists of the speed
control and the control of the steering wheel. The speed
is controlled so that the width of the object may become
constant in the captured image. The view angle of theobject is calculated from its position in the image andthe angle of the camera.
The speed of the robot is controlled by the following
formula;srp r1 ( H p 2 H ) i r1 ( H ps r : speed of robot
H p : width of target
pr 1 : proportion constantir1 : integration constant

2H )

(12)

On the other hand, the steering wheel is controlled by the following formula;d r pr 2
ir 2.
d r : steering wheel angle(13): angle of object[ p ( s ( n ) ),q ])
2

(11)

78

pr 2 : proportion constantir 2 : integration constant

3. COMPOSITION of THE SYSTEM
A person following robot system developed in thisresearch is composed of a single camera, a video capturedevice, a note PC (Pentium M, 1.6GHz), and a mobile
robot. The entire equipment is built on the mobile robot
using a single frame of approximately 0.85 meter high.
Overview of the system is given in Fig.7.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 7 Composition of the system

4. EXPERIMENTAL RESULTS
Fig. 8 Result on human tracking
4.1 Performance of human tracking
A person wearing a light jacket with dark blue check
pattern and a black pair of trousers is captured in a
double green square window and the region is tracked
as the person moves. The experimental result of the
tracking is shown in Fig. 8.
4.2 Person following
We performed the experiment on person following
indoors using the developed robot system. An image
containing a target person was taken initially and his
clothes were registered manually to the system. Then,
according to the target person walking away on thecorridor, the robot followed him capturing his images.
The performance is shown in Fig. 9. The robot followed
just behind a person keeping the distance constant.

Fig. 9The robot following a specified person

REFERENCES
5. CONCLUSIONSIn this research, a robot system was developed forfollowing a person and carrying luggage. A satisfactory
result was obtained by the experiment. However, there
are some problems that the illumination and thebackground sometimes give negative influence to theperson following because the proposed technique onlyuses color information. Uemura et al. [3] proposed acolor region tracking technique robust to illumination
change. The technique may be employed in the present
system. The interactive nature of the robot system is also
going to be improved to realize various robot activities.

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[1] K. Nummiaro, E. Koller-Meier, L. V. Gool: “Acolor-based particle filter”, Proc. First Int. Work. on
Generative-Model-Based Vision, vol.1, pp.53-60(2002).
[2] D. Comaniciu, V. Ramesh, P. Meer: “Real-timetracking of non-rigid objects using mean shift”, IEE
E Conf. on Comput. Vision and Pattern Recogn., vol.
2, pp.142-149 (2000).
[3] H.Uemura, J.K. Tan, S.Ishikawa:“A color tracker
employing a two-dimensional color histogram under
unstable illumination”, Proc. SICE-ICASE Int. Joint
Conf. 2006, pp.2725-2728 (Oct., 2006).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of a self-care robot-Study of automatic holding of object using Robotic armHisatoshi Henta

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Kawazu, Iizuka City, Fukuoka, Japan, 820-8502
henta@greenall.i.ccr.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Kawazu, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract

Aichi Expo 2005. Kitasap2 was developed through aThis paper describes the development of a self-care
support robot, developed to support elderly people withmobility problems. The Kitasap2 consists of a hostcomputer and the physical body of a robot equipped witha robotic arm. The two are connected by a wireless LAN.
The robotic arm has six degrees of freedom and isequipped with a robotic hand having three fingers. This
paper describes the development of the ability of therobotic arm to hold and transport an object so as to reduce
the user's operational load. It is necessary that such an
operation be based on easily given instructions from theuser. Therefore, we have developed systems in which
selection and the recognition of the object are carried out
using images provided by an omni-directional camera
and a CCD camera.

Technology Development Organization).
This paper describes the development of the ability of
the robotic arm to hold and transport an object so as toreduce the user's operational load.
It is necessary that such an operation be based on easily
given instructions from the user. Therefore, we havedeveloped systems in which selection and the recognitionof the object are carried out using images provided by anomni-directional camera and a CCD camera.

2．System Architecture
The Kitasap2 consists of a host computer and thephysical body of a robot equipped with a robotic arm.
The two are connected through a wireless LAN. The
robotic arm has six degrees of freedom and is equipped

1．Introduction
Currently, the birthrate in Japan continues to decline,
while the proportion of elderly people increases. As the
population of older people increases, so does the numberof elderly with mobility problems. In the near future, wewill see an increased burden placed on attendants of theelderly as a result of the falling birthrate. The quality oflife of the immobile elderly could be improved byassistance from a robot capable of doing housework. The
development of a “self-care support robot” that supports
an elderly person’s independence can help to decrease ahelper's workload.
We have developed the self-care support robot Kitasap2
and exhibited it in the “Prototype Robot Exhibition” at©ISAROB 2007

commission from NEDO (New Energy and Industrial

80

with a robotic hand having three fingers. The robot also
has various sensors, including an omni-directional camera(at the top of the robot), a small CCD camera (inside therobotic hand), a laser range sensor, and some encoders.
All of the robot’s devices are controlled by the computer.
Lithium-ion batteries supply the robot’s electric power.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Omni-dir ectional camera

J3

HDD

J4

J2

PC×2

J1

Robotic arm

Kitasap2

J0

Joystick

Host computer

J3

CCD Camera
Laser r ange sensor
Motor/Encoder

Battery

J1

J5

Hand

J0

Fig. 3 Comparison between old and new robotic arm

3.2 The Actuator

Fig. 1 Overview of the Robot system
User side

J2

Hand

Robot side

J oystick

We adopted the actuator "Dynamixel DX-117," made by

Robotic ar m
DX-117×8

USB

PC (RT-Linux)

ROBOTIS, to provide the power for each joint of therobotic arm. The DX-117 is an actuator composed of a

Display of GUI

USB

microcomputer, motor, gears and an angle sensor.

PC1(Linux)

NTSC
Main pr ogr am
Ether net
CBA
Image pr ocessing
IEEE1394GUI
Wir eless LAN
IE EE1394 boar dRouterCaptur e boar d
Wir eless LAN boar d
USB

RS232C

Laser range sensor

CCD camera

Moreover, because this actuator uses RS485 for theOmni directional
cameracommunication method, the actuator for each joint is

Micro computerMotorconnected as a multidrop link. For this reason, theactuator’s wiring can be simple. Specifically, this isbecause the control calculation for the angle of each joint
can be done on the actuator side by using the DX-117.

Fig. 2 System Structure of the robot

Previously, this calculation had been done on the PC side,
requiring that the load of the calculation be large.

3．Robotic arm
3.3 Kinematics
3.1 Structure of the Robotic ar m
To perform the calculation processing easily, theMany degrees of freedom are required for the robotic

calculation of the inverse kinematics of the robotic arm

arm to hold an object automatically. So far, it has been

was carried out according to the following method.

necessary to move the physical body of the robot in order

The wrist of the robotic arm moves up and down in ato hold an object because its degree of freedom was notvertical direction and extends and retracts horizontally

sufficient.

while the position of the wrist remains horizontal.

Recently, we produced a robot arm that has six degrees

Moreover, the robotic arm rotates around the J0 axis.

of freedom and a total length of 450 millimeters, and we

The calculation involves, first, subtracting the length ofmounted it on Kitasap2. Fig. 1 shows a comparison

the wrist from the point of the fingers (Xe, Ze), to give thebetween the old and new robotic arms.

point of the wrist joint (X6, Z6). Next, we draw a circle

The robotic arm is equipped with a robotic hand having

whose center is located at point2 (X2, Z2), with its radius

three fingers. Each finger has two joints and can grab

being equal to the length of link A. Next, we draw a circle

objects like a human finger. Moreover, a CCD camera

whose center is located at point 6 (X6, Z6) with its radiusthat is used to recognize an object is mounted inside thebeing equal to the length of link B.

robotic hand.

Finally, we decide on the point of the elbow joint (X4,
Z4) and the angle of each joint of the arm.

©ISAROB 2007

81

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

position of the recognized object and image are displayed(X4,Z 4)
Link Bθ4on the image, as shown in Fig. 5(R).
Link A

Center of image

ObjectFinger s
θ2

X(Xe,Ze)Wr istθ6(X6,Z 6) (X2,Z 2)
Center of r ecognized object

Fig. 5The image obtained by the omni-directional

camera (L).and the image obtained by CCD camera
Fig. 4 Kinematics(C),(R).

4．The automatic holding system

4.3 Automatic holding

In this system, we first select the object whose image isWhen the object is selected and recognized, the angles

obtained by the omni-directional camera. Next, the objectof the robotic arm’s joints are adjusted automatically.

is recognized based on the CCD image. This allows theFirst, when the object is put in front of the robot, therobotic arm to be adjusted automatically and to

robotic arm’s position is as shown in Fig. 6①. Then, theautomatically grasp the object.

robotic arm is adjusted to the center position of therecognized object, corresponding to the center of the

4.1 System for selecting the object

image supplied by the CCD camera. (Fig. 6②) Next, thehand is extended horizontally, and the object is grasped

This system uses the image obtained from a perspectiveby the robot. Finally, the fingers close tightly, and the

projection conversion from the omni-directional image

holding operation is completed(Fig.6③).

obtained by the omni-directional camera.
First, the object is displayed like the image shown in Fig.

Omni-dir ectional camera

5(L) when it is put front of the robot. The user selects theMφobject he wants to hold by clicking its image. Then, the90-φclicked point and memorizes the color information about

Object③

the object. Finally, this color information is converted into
HSV data.

Heightsystem gets pixel information (RGB data) from the
①

G

O
②

4.2 System for recognizing the object
This system uses the image obtained by the CCD

Distance

Fig. 6 Automatic holding

camera and the color information about the object. First,
the object is displayed like the image shown in Fig. 5(C).

5．Experiment

Next, all pixel data about the image is converted into
HSV data and compared with the color information about

The experimental environment is shown in Fig. 7(a).

the object. Then, a group of pixels similar to the color of

The object is put in front of the robot, where is displayed

the object is recognized as the object. Next, the center

like the images shown in Fig. 7(b),(c). We directed the©ISAROB 2007

82

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

arm to hold the green object that was put in front of therobot.
First, the robotic arm was adjusted to the center positionof the recognized object, corresponding to the center ofthe image supplied by the CCD camera. The wrist of the

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

robotic arm moved up and down in a vertical direction
while its position remained horizontal. Next, the roboticarm rotated around the J0 axis. Fig. 8 shows the change in
the image when the robotic arm automatically adjusted.

Fig. 9 Experiment of automatic holding of the object(a) Object and r obot

6．Conclusion
We have developed a system for a robotic arm that(b)automatically holds an object in order to reduce a user's(c)Fig. 7 Experimental environment

operational load.
The developed system is able to select and recognize an

Fig. 9 shows the appearance of the experiment while theobject in an image obtained by an omni-directional

object was being held. The robotic arm as it adjusted tocamera and a CCD camera. In addition, the robotic arm

the center position of the recognized object is shown in

can be adjusted automatically to grasp the object.

images (1) to (3). Next, the arm’s appearance when the

Our next subject of study will be to develop anotherwrist was extended and held the object is shown inmethod for the recognition of the object.

images (4) to (6). Finally, the arm’s appearance when thefingers are closed tightly and the robotic arm returns to its

Acknowledgment

original position is shown in images (7) to (9).
The authers would like to ackowledge the advice andassistance of Professor Tadashi Kitamura of Kyushu
Institute of Technology who died in 2006.

References
(1)

(2)

(3)

[1] Kazumasa Yamazawa, Yoshio Onoe, Naokazu Yokoya,
and Haruo Takemura: "Telepresence by Real-Time
View-Dependent Image Generation from Omnidirectional
Images", Systems and Computers in Japan, Vol.31, No.6,
pp.56-64 (2000.6).

(4)

(5)

(6)

Fig. 8 Center correction©ISAROB 2007

83

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Adaptive Control of a Looper-like Robot based on the CPG-Actor-Critic Method
Kenji Makino∗

Yutaka Nakamura†

Tomohiro Shibata∗Shin Ishii∗∗ Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan† Graduate School of Engineering, Osaka University
Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan
Abstract
Adaptability to the environment is crucial for mobile robots, because the circumstance, including the
body of the robot, may change. A robot with a large
number of degrees of freedom possesses potential toadapt to such circumstances, but it is difficult to design a good controller for such a robot. We previously proposed a reinforcement learning (RL) method
called the CPG-actor-critic method, and applied it toautomatic acquisition of vermicular locomotion of alooper-like robot through computer simulations. In
this study, we developed a looper-like robot and apply our RL method to the control of this robot. Experimental results demonstrate fast acquisition of avermicular forward motion, supporting the real applicability of our method.

1

introduction

To realize a mobile robot that acts in a real environment, adaptability to changes in the environment due
to its dynamic nature and various disturbances is necessary. Reinforcement Learning (RL) is a framework
for autonomous acquisition of control rules and has
been successfully applied to various automatic control
problems [1]. Because real robots often have a large
number of degrees of freedom (DOF), RL methods tocontrol them need some devices to avoid the problemof ”curse of dimensionality”.
Motivated by the animal’s control mechanism forrhythmic locomotion, which is induced by neural circuits in the spinal cord of vertebrates called central
pattern generators (CPGs), robot control schemes using a CPG controller have been studied mainly in thefield of robotics [2][3]. Because the parameter of the
CPG controller is designed such that the CPG controller and the robot interact with each other and are©ISAROB 2007

84

eventually entrained into a stable limit-cycle attractor, the robot controlled by a CPG controller is robust
against possible disturbances from the environment.
Although there have been some studies of designinga CPG controller, autonomous learning framework for
a CPG controller is necessary to realize the adaptability to dynamic nature of the environment including
its own body. We formerly proposed an RL framework called the CPG-actor-critic model for designing a
CPG controller [4]. Since control signals are restricted
to be rhythmic in favor of the CPG, this RL method
would be able to avoid ”curse of dimensionality”. In
this method, the parameter of the CPG controller is
updated according to the gradient of the performanceindicator, the average reward per step for example,
with respect to the parameter (policy gradient) andthis gradient can be obtained by interaction with theenvironment. After learning, the CPG controller becomes to generate stable locomotion suited to the environment surrounding the robot.
In the current study, we configure a real looper-likerobot and its CPG controller. Because a looper has
an ability to move by many of simple and rhythmic
telescopic motions, a looper-like robot would have an
adaptability to various environments. In our previous study, we showed through computer simulations
that a good CPG controller can be obtained by the
CPG-actor-critic method [5]. In real environments,
however, there may arise much difficulty; e.g., it is difficult to approximate contact between the robot andground, outputs of sensors generally include unknown
noise and time delays. In this study, we apply the
CPG-actor-critic method to the real looper-like robot
we have developed, for achieving automatic acquisitionof “vermicular” locomotion.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

Robot System

Ball joint

5

Looper-like robot

1
Head
link

The looper-like robot we have developed is depicted
in Fig.1. This robot is composed by three links,
eight actuators and six passive wheels. The first to
fourth actuators are linear actuators which are eachlocated between two links, and used to expand-andcontract the body of the robot. We call these actuators body actuators. The length of these actuators
is about 13cm at minimum and about 18cm at maximum. Black semicircles in Fig.1 denote the ball joint,
so that the body of the robot bends when lengths ofthese actuators are different from one another. The
fifth to eighth actuators are also linear actuators which
are located to the head or tail link. They move vertically to clamp corresponding links to the ground. We
call these actuators leg actuators.
In order to make the robot move forward, it isrequired to repeat expand-and-contract motions, byclamping the head link and the tail link in an appropriate but different timing.

6

Leg actuator

3
Center
link

2

7

Tail
link

4
8

Top view

Body actuator

Leg actuator

Wheel

Side view

CPG controller
Because a looper-like robot has a substantial potential to move by a rhythmic locomotion, we employ
a CPG controller [5] which outputs rhythmic control
signals. As shown in Fig. 2, the CPG controller comprises three neural oscillators each of which consistsof two neurons, and each actuator is controlled by asingle neural oscillator. The (2i − 1)-th and the 2i-th
neuron’s dynamics are defined as1
c ẏ2i−1=1c ẏ2i=Figure 1: Looper-like robot−y2i−1 + tanh(W s y2i−1 + W I y2i + ui ), (1)−y2i + tanh(−W I y2i−1 + W s y2i )where y2i−1 and y2i denote the (2i − 1)-th and the2i-th neuron’s states, respectively. W S , W I and c
are the self-excitatory connection weight, the mutual
inhibitory connection weight and the time constant,respectively. ui is an input to the i-th neural oscillator, in this study, calculated as the weighted sum of
the robot’s state variable x and the output of the CPG
controller y: as ui = θj y0 +· · ·+θk x0 +· · · . Each neuraloscillator outputs sinusoidal control wave whose amplitude and frequency are mainly determined by W S andW I , and c, respectively. The phase relation between
the neural oscillators and the robot are determined by
the input to the CPG neurons u, i.e., the phase canbe tuned by changing the weight parameter θ of the
CPG. In this study, this weight parameter θ is trainedby the CPG-actor-critic method (see below).

©ISAROB 200785

CPG Controller
Control signalus=(x,y)State

Figure 2: Control scheme using a CPG controller

3

Learning method

Here, we describe our RL algorithm for the CPG
controller, which we formerly proposed and called the
CPG-actor-critic model [5]. In this controlling by a
CPG controller, the control signal depends not only
on the state of the target system, but also its own
state, because the CPG controller has its own dynamics. This is problematic because most RL algorithms assume the target policy is stationary (time-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

independent), and furthermore, heavy computation
would be required for training recurrent neural networks like the naive CPG controller. In order to overcome these difficulties, the CPG controller is divided
into two parts, the basic CPG and the actor [4]. The
basic CPG is a part of the CPG controller with fixed
connection parameters, i.e., W I , W S , and c are fixed.
We treat the physical system and the basic CPG asa single dynamical system to control, and we call thissystem a CPG-coupled system.
Since the actor turns out to be a feed-forward neural network having no its own dynamics in the CPGcoupled system, we can easily apply usual RL algorithms. The control signal u for the CPG-coupledsystem is conceptually represented asu ∼ π(u, s),(2)where π denotes the control policy of the actor ands ≡ (x, y) is a state of the CPG-coupled system. For
the sake of simplicity, we assume that Eqs. (1) and(2) are discretized in time by an appropriate method,
and the learning system receives an immediate reward r(s(t), u(t)) at a discrete time step t. The policyπθ (s, u) is defined by a parametric stochastic policy,i.e., the probability of a control signal u at a state s isgiven by p(u|s; θ), where θ is a parameter vector of thepolicy. The objective of RL here is to obtain the policy
parameter that maximizes the expected
reward accuPmulation defined by ρ(θ) ≡ Eθ [ t γ t−1 r(s(t), u(t))],where γ ∈ (0, 1] is a discount factor. The partial differential of ρ(θ) with respect to the policy parameterθi is calculated [6][7] as∂ρ(θ)= hψi (s, u)Qθ (s, u)i,
∂θi

(3)

Experiment

Setup
The aim of the experiment have is to examine if
our RL can be applied to a real looper-like robot, wethen conducted an experiment attempt to obtain acontroller which allows the looper-like robot to movein the forward direction. For the simplicity of the experiment setting, all body actuators were controlled
by a single neural oscillator, and front leg actuators(5-th and 6-th actuators) and hinder leg actuators(7-th and 8-th actuators) were controlled by a single
neural oscillators, respectively. The output signal τ tothese actuators is defined by(
1, y2i−1 > 0
τi =.
0, otherwise
When τi = 1(0), the corresponding actuator expands(contracts). The control signal u to the CPG-coupledsystem is defined by
u1
u2
u3

=
=
=

θ1 X1 + θ2 X2 + ²1
θ3 y1 + θ4 y2 + ²2 ,
θ5 y1 + θ6 y2 + ²3

(5)

where X = {x2 − x3 − l, ẋ2 − ẋ3 }, x2 and x3 denotethe position of the head link and that of the tail link,respectively. ²i (i = 1, 2, 3) is a small random noiseobeying a normal distribution. l denotes the mean
length between the head link and the tail link. The
policy parameter θ determines phase relations among
the robot and the neural oscillators and was adjusted
by RL. W S , W I , and c which are CPG system parameters were fixed at 1.1, 0.7, and 4.0, respectively.
The immediate reward r(s(t)) was given byr(s(t)) = ẋ1 ,∂ln πθ and Qθ (s, u) denotes thewhere ψi (s, u) ≡ ∂θiaction-value function (Q-function). h·i stands for the
expectation with respect to the stationary distribution of the state-action pair (s, u). When the Qfunction is approximated by Pa weighted sum of basefunctions ψ : Qwθ ((s, u)) ≡i wi ψi (s, u), where w
is the weight vector of the approximate Q-function,
the optimal weight in the least square sense, w̃ =2
arg minw h(Qθ (s, u)−Qwθ (s, u)) i, provides the natural
policy gradient with no estimation bias for the gradient, so that the policy parameter can be updated [1]asθi := θi + η w̃i ,(4)where η is the learning rate. the optimal weight w̃
is thus estimated simultaneously based on the leastsquare method [4][1].

©ISAROB 2007486

where x1 denote the position of the center link. Because the robot currently possesses no sensors to measure its position, an USB camera is placed above theexperimental field.
We chose the state representation for RL as follows. Because the phase portrait between two neurons presents a circle whose center is the origin, theangle ω1 = arctan(y2 /y1 ) carries the essential feature of the first neural oscillator. Similarly, ω2 =arctan(y4 /y3 ) and ω3 = arctan(y6 /y5 ) should be useful features of the second and the third neural oscillators, respectively. Furthermore, when the looperlike robot repeats expansion and contraction, ω4 =arctan((ẋ3 − ẋ2 )/(x3 − x2 )) is also an important feature of the robot’s movements. Because the phase difference seemed to be more important than the phase

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

position (pix cel)

average reward
0.06



0.04

0.02



#HGTaNGCTPKPI$GHQTGaNGCTPKPI




0


-0.02
0



20

40

60

80

100


learning episodes

̂Figure 3: Learning curve. The horizontal axis denotes
the number of learning episodes, and the vertical axis
denotes the average reward in one episode. The line
shows the moving average over 30 episodes.
in this experiment, we employed 49 basis functions forthe approximate state-value function: φ16(i−2)+j (s) =exp(−3 cos(ωi − ω1 + π/16j) − 1) for i = 2, 3, 4 and j =1, 2, ..., 16 and φ49 = 1. At the beginning of an episode,the learning parameter was initialized at random: θ ={0.0006, 0.0001, 0.0012, −0.0005, −0.0004, 0.0011}. In
each episode during the training, the robot was controlled by the current actor for 45 sec.

ResultFig.4 shows the learning curve, indicating that appropriate control was achieved after about 10 training
episodes. Fig.5 shows actual movements of the looperlike robot before learning and after learning(after 100
learning episodes). Before learning, the robot accidentally moved backward first, and then started to move
forward slowly. After learning in contrast, the robot
became to move forward much faster. After the learning 100 episodes, the CPG parameters grew as: θ ={0.0007, −0.0064, −0.0027, −0.0011, −0.0102, 0.0017}.

5

Conclusion

In this study, we have configured a CPG-based control architecture for a real looper-like robot, and applied RL to obtain a controller for the robot to movein the forward direction. The experiments showedthat a good CPG controller for this robot can be efficiently obtained by our RL method. Applying our RL
method to many other situations including changes in
the ground condition and changes in its body, for example, is our future work.

©ISAROB 2007

87






VKOG UGEQPF



Figure 4: Control result, showing the location of theposition of the robot’s center.

References[1] J. Peters, S. Vijayakumar and S. Schaal: ”Reinforcement learning for humanoid robotics”, Third
IEEE International Conference on Humanoid
Robotics 2003, Germany (2003).
[2] G. Taga, Y. Yamaguchi and H. Shimizu: ”Selforganized control of bipedal locomotion by neural oscillators in unpredictable environment”, Biological
Cybernetics, 65, pp. 147.159 (1991).
[3] Y. Fukuoka, H. Kimura and A. H. Cohen: ”Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts”, International Journal of Robotics Research, 22, 3.4,
pp. 187.202 (2003).
[4] Y. Nakamura, T. Mori and S. Ishii: International
conference on parallel problem solving from nature(PPSN VIII), pp. 972.981 (2004).
[5] Y. Nakamura, T. Mori, S. Ishii: ”Natural policy gradient reinforcement learning method for alooper-like robot”, International Symposium on
Artificial Life and Robotics (AROB 11th ’06),
GS3-3. (2006)[6] V. R. Konda and J. N. Tsitsiklis: ”Actor-critic
algorithms”, SIAM Journal on Control and Optimization, 42, 4, pp. 1143.1146 (2003).
[7] R. S. Sutton, D. McAllester, S. Singh and Y.
Manour: ”Policy gradient method for reinforcement learning with function approximation”, Advances in Neural Information Processing Systems,
Vol. 12, pp. 1057.1063 (2000).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Explorations in evolutionary humanoid robotics.
Malachy Eaton
Department of Computer Science and Information Systems,
College of Informatics, University of Limerick, Irelandemail: malachy.eaton@ul.ie
Keywords: Evolutionary computation, Humanoid robotics
Abstract:
The field of evolutionary humanoid robotics isa branch of evolutionary robotics specificallydealing with the application of evolutionary
principles to humanoid robot design. Previous
studies [1,2] demonstrated the possible future
potential of this approach by evolving walking
behaviours for simulated humanoid robots
with up to 20 degrees of freedom. In thispaper we extend the work presented in theprevious publications in several ways. We
present preliminary results in the analysis of
the behaviour of specific high-fitness evolved
controllers and of the evolutionary processitself by examining the changes in diversity
over time in the evolutionary process.
We then investigate the effect of minor
alterations in robot morphology in walking
ability. These include an alteration of thesurface area of the robot in contact with theground (foot size) and the effect of theimmobilization of individual joint or joints in
the robot. The latter study may be of potential
future use in prosthetic design.
We explore also the possibility of theevolution of humanoid robots which can copewith different environmental conditions.
These include reduced ground friction (ice)and modified gravitation (moon walking).
Finally we present initial results in theimplementation of our simulated humanoid
robots in hardware using the Bioloid robotic
platform.

Introduction
The humanoid robot is simulated using the
Webots mobile robot simulation package[3,4,5]. This system allows for the creationand modification of a large variety of robot
types and robot worlds and it also allows forthe creation of controllers for these robots. It
also employs an accurate physics simulator
allowing for the potential transfer of evolved©ISAROB 200788

robots from simulation to real embodied robots
with little or no modification.
A number of keyframe values are defined andthen passed to a separate existing utility; thesequence manager. These keyframes are values
that must be reached at a specific point in the
movement. An interpolation function in thesequence manager then fills in the motor
values between individual keyframes. Every
keyframe must be passed through in turn; once
the final frame is reached the cycle repeats. A
genetic algorithm is used to provide the values
for the individual keyframes. The initial
fitness function employed was a simple
function mainly based on the robot remaining
standing together with the distance traveled by
the robot in a forward direction. Different
varieties of walking behaviours weredeveloped by the evolved robots and manyobservers commented of the lifelike nature ofsome of the walks developed.
Nolfi and Floreano provide a good
introduction to the general topic ofevolutionary robotics [6]. For a discussion on
the general topic of the performance evaluationof bio-inspired embodied and simulated
artifacts Eaton et al [7]. See references [8-11]
for other work in this general area.

Experimental setup
In previous experiments it was found that the
proportion of the maximum range ofmovement allowed to the robot for each joint
was an important factor in evolving successful
Initial experiments placed no restriction on therange of movement allowed and walks did not
evolve unless the robot was restricted to astooped posture and a symmetrical gait, eventhen results were not impressive. In theseexperiments we include a value in the genomethat specifies the fraction of the maximum
movement range that is allowed for each joint.
The genome length is 336 bits comprising 4
bits determining the position of the 20 motors
for each of 4 keyframes; 80 strings are used

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Evolution of walking in a robot withfull functionality
Figure 1 shows the maximum and averagefitness values together with the diversity
measure described above, for a 20 degree of
freedom robot with full functionality, and with
16 bits defining the maximum joint range. The
20 degrees of freedom comprise three for eachleg (knee and two ankle joints), three for eachhip, two for the back (twist and bend), two foreach shoulder, and two elbow joints. The
graph shows results as averaged over threeruns and the fitness function is as described
previously [2]. Diversity value is given on theright-hand vertical axis. The diversity value
starts very low initially (corresponding to low
similarity) as we would expect, as all strings
are initially created at random.
Sharp
increases in the diversity measure correspondthen to increases in maximum fitness as highly
fit individuals attempt to make many copies ofthemselves. This is followed by a reduction
thereafter as the genetic operators of mutationand crossover strive to maintain diversity inthe population.
A fitness above about 800 should correspondto a reasonable walk in the forward direction,
1200 or above corresponding to stable walks inthe forward direction. We see that the average©ISAROB 200789

maximum value peaks at around the value
3000, corresponding to a fast forward walk,with the knees kept fairly straight.
Evolution of bipedal locomotion

3500

80

3000

70
60

2500

50
Fitness

Max fitness

2000
40

Average fitness

1500

Diversity measure

30
1000

20

474

431

388

345

302

259

216

87

173

0
130

10

0
1

500

44

per generation. 16 bits define the fraction of
the maximum movement range allowed. The
maximum range allowed for a particulargenome is the value specified in thecorresponding to each motor divided by the
number of bits set in this 16-bit field, plus 1,
divided by 2. The genetic algorithm uses
roulette wheel selection with elitism; the top
string being guaranteed safe passage to thenext generation, together with standard
crossover and mutation. Two-point crossoveris applied with a probability of 0.5 and theprobability of a bit being mutated is 0.04.
These values were arrived at after someexperimentation.
In order to gain some insight into theevolutionary process we use a slightly
modified version of the degree of population
diversity described in by Leung et al [12]. Thismeasure provides an easy to calculate anduseful measure of population diversity: i.e.
how alike the different strings in a population.
We subtract this value from the genome bit
length to produce our inverse degree of
population diversity measure (IDOPD). Thisvalue will vary from 0 (no similarity in thestrings) to a value corresponding to the
genome length (all genomes have the samevalue at every bit location).

Generation

Figure 1. Evolution of walking in a fully
functional robot

Effect of alterations
morphology

in

robot

We now investigate the effect of restraining
motion in part of the robot. We do this byimmobilising the robots right knee joint, andboth ankle joints. This might correspond to asituation of a person walking with a prosthetic
leg. Figure 3 shows the results of thisexperiment again averaged over 3 runs

Figure 2. Walking with right leg restrained

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Walking with one leg restrained

1800

3000

25

2500

20

2000

80
70
60

1400

Fitness

1600

Fitness

1200
15

1000

Max fitness

50

1500

40

Average fitness
800

10

20

600
400

500

5

200

10

298

265

232

199

166

67

133

100

0
1

481

441

401

361

321

281

241

201

161

121

1

81

41

0

34

0

0

Generation

Generation

Figure 3. Average of three runs with rightleg restrained
The robot learns to walk albeit with a reduced
maximum fitness compared to the robot withno constraints. Figure 2 illustrates a typical
walk which develops. The right (constrained)
leg moves sideways and forwards, coming
well off the ground, as the right arm moves
backwards in a steadying motion. The left leg
follows in a shuffling motion, and the cycle
repeats.
This pattern of motion proved
surprisingly effective.
We have also
experimented with the alteration in foot size
successfully producing walking with much
reduced foot size but space precludes adetailed discussion of these results.

Effect of different environmental
conditions
We are currently investigating the effect thatdifferent environmental conditions have on theevolution of walking (skating) behaviour;
specifically walking on a surface with reduced
friction, simulating icy conditions, andwalking under conditions of reduced gravity.
Fig. 4 shows the effect of reducing coulomb
friction to 0.01, simulating the effect of very
icy conditions. This is for a single run so thevariation of diversity with fitness can beclearly seen. A quite effective sideways
skating motion develops around generation
150. Further investigations are continuing to
see what different patterns of skating develop.
We have also started experiments in moon-likegravity producing walking, but far slower thanin earth-like gravity; these results will be
discussed in more detail in a later article.

©ISAROB 2007

Diversity

30

1000

Diversity

Maximum fitness

90

Figure 4. Results for reduced friction run

From simulation to reality
We are now beginning work to implement oursimulated robots in the real world using the
Bioloid robot platform. This platform is
produced by Robotis Inc. Korea and consistsof a CPU (CM-5), a number of senso-motoricactuators (Dynamixel AX12+) and a large
number of universal frame construction pieces.
Using this platform it is possible to construct awide variety of robots, from simple wheeled
robots to complex humanoid robots with manydegrees of freedom. To gain initial experience
with this kit we constructed a “puppy-bot”(Fig. 5) which can walk on four legs, avoid
obstacles and perform several cute tricks.
With this experience we then constructed the
Bioloid humanoid robot (Fig. 6), which has 18
degrees of freedom in total. A modified
version of this humanoid robot was used for
Humanoid Team Humboldt in the RoboCup
competitions in Bremen 2006. [13]The Bioloid system has two pieces of software
provided; the behaviour control programmer,
and the motion editor. The behaviour control
programmer programs the humanoids response
to different environmental stimuli, while themotion editor describes individual motions
based on the keyframe concept described in
our work. We are currently building an
accurate model of the Bioloid humanoid in
Webots, and working on translating theinformation in our sequence control file into aformat understandable by the Bioloid motion
editor. Once this work is completed we hopeto evolve walking, and other behaviours, in
Webots using our accurate model, and thentransfer the evolved behaviour directly to the
Bioloid humanoid robot.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 5. The “puppy-bot”

Figure 6. The Bioloid humanoid robot

Summary and future work
Using an accurate physics simulator ahumanoid robot has evolved bipedal
locomotion under a variety of different
morphological constraints and under different
environmental conditions. We have looked at
the changes in diversity over the evolutionary
process and at the behaviour of evolved robots.
Finally we introduced the Bioloid robotic
platform, which because of its modular andextensible nature is well suited to research inevolutionary robotics, and the 18 DOF Bioloid
humanoid robot. Work is continuing in all of
the areas described above, and particularly inmoving our simulated humanoid robots into
the real world using an enhanced (20 DOF)
version of the Bioloid humanoid robot .

References:[1] Eaton M, Davitt T.J. (2006) “Automatic
evolution of bipedal locomotion in a simulated
humanoid robot with many degrees of
freedom” in Proceedings of the 11th
International Symposium on Artificial Life and©ISAROB 200791

Robotics”, Jan 23-25,2006, Japan, pp. 448451.
[2] Eaton M. (2006) “Evolutionary humanoid
robotics: first steps towards autonomy?” in 50th
Anniversary Summit of Artificial Intelligence:
SummitProceedings,
Monte
Verita,Switzerland, July 9-14 2006.
[3] Michel O. (2004), "Webots: Professional
Mobile Robot Simulation", International
Journal of Advanced Robotic Systems, Vol. 1,
No. 1, pages 39-42.
[4] Webots. http://www.cyberbotics.com.
Commercial Mobile Robot Simulation
Software.
[5] Mojon S (2003) “Realization of a Physic
Simulation for a Biped Robot” Semester
project,
SchoolofComputerandCommunication Sciences EPFL, Switzerland
2003.
[6] Nolfi S, Floreano D.(2000) “Evolutionary
Robotics: The Biology, Intelligence, andTechnology of Self-Organizing Machines”.
MIT press, 2000.
[7] Eaton M, Collins JJ, Sheehan L (2001),“Towards a benchmarking framework forresearch into bio-inspired hardware-software
artefacts”, in Artificial Life and Robotics, Vol.
5 No. 1, 2001 pp. 40-46.
[8] Sellers W, Dennis L, Wang W et al(2004)“Evaluating alternative gait strategies using
evolutionary robotics” J. Anat (2004) Vol 204,
pp343-351.
[9] Collins S, Ruina A, Tedrake R et al(2005)“Efficient bipedal robots based on passivedynamic walkers” Science Vol. 307, Feb.
2005, pp. 1082-1085.
[10] Zykov V, Bongard J, Lipson H (2004)“Evolving Dynamic Gaits on a Physical
Robot” Proceedings of Genetic andEvolutionary Computation Conference, Late
Breaking Paper, GECCO'04, 2004[11] Boeing A, Hanham S, Braunl T(2004)“Evolving autonomous biped control
from simulation to reality” in Proc. 2nd
International Conference on Autonomous
Robots and Agents” December 13-15, 2004,
pp. 440-445.
[12]Leung Y, Gao Y, Xu Zong-Ben (1997)“Degree of population diversity – a perspective
on premature convergence in genetic
algorithms and its Markov chain analysis”,
IEEE Transactions on Neural Networks, Vol.8,
No.5 pp.1165-1176.
[13] Hild M, Jungel M, Spranger M(2006)“Humanoid team Humboldt team description
2006” in Proceedings CD Robocup 2006,
Springer-Verlag, Bremen, Germany, June
2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Vision based control for line following blimp robot
Ryouta Nishioka∗ , Hidenori Kawamura∗ , Masahito Yamamoto∗Toshihiko Takaya∗∗ , Azuma Ohuchi∗∗

Graduate School of Information Science and Technology, Hokkaido University
North 14, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan
E-mail : {nishioka, kawamura, masahito, ohuchi}@complex.eng.hokudai.ac.jp∗∗
Ricoh Software Inc.
North 7, West 4, Kita-ku, Sapporo, Hokkaido 060-0807, Japan
E-mail : toshihiko.takaya@rsi.ricoh.co.jp
URL : http://harmo.complex.eng.hokudai.ac.jp/

Abstract
We report on following the line based on camera image for indoor blimp robot. Indoor blimp robot can be
moved to the altitude and the place with diﬀerences inthe building. There are applications of the guide andthe round in the building by using those features. It is
generable target orbit to put the one that becomes alandmark on the moved place. Therefore, we put theline as a landmark. The line is showing ﬂat information in the target orbit, and information on height canbe obtained according to the width of the line. In thispaper, we report the extraction method of the line,
and how to follow the line.
key words - indoor blimp robot, following line, autonomous robot

1

Introduction

The blimp robot can be moved to the altitude andthe place with diﬀerences in the building, it is an advantage for blimp robot that the place to which therobot of a humanoid type is not moved easily can be
moved. Moreover, it is possible to move safely in thebuilding compared with a small airplane and the helicopter, etc. and use to the applications like the guidein the building, the round and the watch are thought
by using of such a feature.[1] There is a problem ofenlarging the balloon to obtain big buoyancy, so it isdiﬃcult to use a lot of sensors. Moreover, the control is diﬃcult because the inﬂuence of inertia is large,
and it sensitively reacts to turbulence by movement of
air-conditioning and the person on an environmental
inside.
It is needed to make it follow to the generation of
the target orbit and the orbit to achieve such an ap-©ISAROB 2007

92

plication. It is moved to the target point recognizing athree-dimensional position of an environmental insideby using the landmark in a research up to now. However, the target orbits on the plane can be given byclosely arranging the landmark in the moving place inthe application that moves only a speciﬁc indoor place.
Then, it aims to move to the target point to assume
the landmark to be a line, to give the target orbits,
and to make on the orbits follow in research. The line
is an easy landmark that expresses the target orbits,
and the height of the target orbits is recognized fromthe width of the line. Because the calculation cost of
this is lower than the method such as putting the signon the line, it is thought the control way is eﬀectivein the place where the turbulence that rotates by 180
degrees momentarily does not occur.
In this paper, the composition of the balloon robot
used, the extraction method of the line, and how todecide to thrust are described as follows.

2

Blimp robot

Blimp robot has an elliptical body to reduce air resistance. These airships are generated by high-speed
movement. However, it is not necessary important forindoor blimp robots to generate high-speed movement.
So we designed a columned blimp robot.[1][2][3] This
design can make air resistance in the direction of movement equal. And the column type can greatly take thevolume. And, it is preferable the airframe’s without
turning when thinking a narrow indoor space is moved
to be able to move directly. This blimp robot has
three propulsion units.Ch4 is enabled vertical movement, and ch0, ch1, ch2 and ch3 are enabled dimensional horizontal plane movement.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007by 16 bits RGB color space. The image information
obtained with the camera is sent to T-Engine board,and thrust that processes the image there is decided.
The motor control command is sent from T-Engine
board to RBTMC board and the motor is rotated.

3Line extraction

To follow the line, width and the centerline of theline are acquired from the image. The purpose of thewidth of the line is to request the height of the blimp
robot, and it is followed to the centerline of the line totake both sides of the line in the image. To calculatethem, the edge point of the line is searched out. Theedge point is a verge of the color of the line and thecolor of the ﬂoor. The distinction of the color of thecolor of the line or the ﬂoor is judged from a color
near the threshold by the YUV space. It is diﬃcult in

Figure 1: blimp robot and overview

Figure 2: propeller unit

We decided on diameter D[m] and height H[m] todetermine balloon size. Total weight of blimp robot
W[g] is calculated as follow calculation.
W = πρhe H(

D
D 2) + πcD(H + ) + U
2
2(1)
Figure 3: Information on line in camera image

ρhe = 178.5[g/m ] is helium density and, c[g/m ] is
the unit weight of balloon material. Balloon material
is aluminum ﬁlm, whose unit weight c is 30.0[g/m2 ].
U is the weight of the propeller unit consisted of thesix propellers, the camera sensor, and the controller.
Weight U is about 480[g]. And, Bu is the buoyant
force.
3

Bu = πρair H(

2

D 2
)
2

(2)

ρair =1226.0[g/m3 ] at 0 ℃,1atm. Bu must be larger
than W. We set diameter D to 0.94[m], and height H
to 0.8[m].
The propellers are positioned independently axial
direction ﬁxed to the blimp. Because the motors forpropellers are driven by ON/OFF signal, it is diﬃcult
to generate certain amount of thrust. So 10 switching
points set at sampling time ∆T=0.3[s]. Blimp robot
is equipped AR camera as a sensor. The image resolution is 160×144 pixels. And, Color information is sent

©ISAROB 200793

the point of the calculation time to judge whether it
is an edge point of all pixels of the camera image. So,
we search edge points from the pixel of the image ofa round. And, it searches for the pixel of the image
in putting several points and it looks for an internal
point of the seeing line. An internal point is a point
judged that four pixels in surroundings are the colorsof the line. When ,every one point, it is searched forto the edge of the image from the internal point, and
it is judged an internal point, the edge point, and thecolor of ﬂoor, the edge point is judged as an edge pointof the line.
When the line is taken in the camera image, it might
have the edge point in four points. Two straight linesof the edge line are calculated from the edge point infour points. The centerline of the line is calculated byaveraging expressions two straight lines.
The method of calculating the width of the line calculates the distance with another edge line from one

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007edge point.
linewidth =| j − Ai − B |√1 + A2(3)

J and i are shown the pixel position in the camera image of one edge point. A and B are shown the gradientand the cut in the camera image of another edge linefrom the edge point. And, the value of the width ofthe line changes by the position of the calculated point
because the edge lines are not parallel when calculating only in one point. Therefore, the width of the line
is calculated with each edge point in two points on thesame edge line, and the average value is used as widthof the line at that time.
Next, the distance of the blimp and the line is calculated. The standard of the distance is a centerlineof the line. The distance of the center of the camera image and the centerline of the line are calculated
because it is made in the composition of this balloon
robot so that the position of the camera may come tothe center of the airframe. Because the size of the camera is 160 × 144 now, a center pixel of the camera becomes point (72, 80). The point that distances shorten
most in the centerline of the line for this point is calculated. These points are centerlinei and centerlinej .
Because the distance of these points is a unit of the
pixel, the number of pixels for the actual width of aline and the width of the line acquired from the image
is used for an actual distance to change.
dline =√
(centerlinei − 72)2 + (centerlinej − 80)2
line
×
(4)
linewidth

The line is actual width of the line, and linewidth iscalculated width of line. And, the angle to the linewith centerline is calculated. This angle is calculatedby trigonometric function as follows.
1)θ = Cos−1 ( √1 + A2center(5)

Control method

It is described how to decide to thrust in this section. Because it is possible to be thought about thedirection of Z of the propeller independently, we think
about the control by the X-Y plane. It is necessary©ISAROB 2007

θtarget = Cos−1 ( √

94

dline
)
F ORW ARD 2 + dline2(6)

θtarget is the angle of target, and FORWARD is a constant number to decide the angle of the target. Blimp
robot is diﬃcult to decide the analytical controller.[3]Because the parameter can be decided by the trial anderror, the PID control is used for the control of the direction of height. However, it is made to control by
the PD control because it is thought that the inﬂuenceof the paragraph of I eﬀective after time passes doesn’tcome out in the adjustment of the angle.
∫dez (t)(7)mz (t) = KzP ez (t) + KzI ez (t)dt + KzD
dtdeθ (t)(8)mθ (t) = KθP eθ (t) + KθD
dt
KzP and KθP are proportional gain, KzI is integral gain, and KzD and KθD are derivative gain.
e(t) is deﬂection with velocity of target, and de(t) =(e(t) − e(t − ∆T )). mz is thrust in propeller ch4, andmθ is thrust in propeller ch1 and ch3. And, numberof propellers ch0 and ch2 are propeller for generating
constant thrust.

5
5.1

Acenter is gradient of centerline.

4

to control to approach the centerline in X-Y plane.
The blimp robot is made to follow to the line by keeping producing thrust in the direction and adjusting
the angle hand like the robot of the car type[4] andthe mouse type. Then, it keeps putting out constantthrust in two of four propellers of the X-Y plane, andthe angle is adjusted by other two. Constant thrust is
generated with ch0 and ch2 of Figure 2, and the angle
is ch1 and ch3 of Figure 2. To adjust the angle to besuitable for the direction of the centerline of the line,
the angle of the target is set. The angle of the target
makes it can be inﬂuenced by deﬂection with the line
though it grows and set greatly.

Experiment
Experimental setting

In this experiment, the line is drawn at the straightline and blimp robot moves on that. The line puts red
paper with length of 5[m] on the ﬂoor. And, the widthof the line is set by 0.1[m]. If the width of the line is
0.1m, it sees it by 32 pixels when the position of thecamera of the blimp is 0.5[m], and 16 pixel when 1[m].
The interval that searches for the edge point is madefour pixels. And, the constant to decide the angle ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the target to control the angle is set as 100. Moreover,
the target position of height is assumed to be 1[m].
This is because of thinking the blimp robot moves inthe building. The camera has equipped the under theairframe of the robot and the top part of the robot isabout 2.3[m] at 1[m] in the height of camera.

5.2

ResultFig.4, Fig.5 and Fig.6 show with the target value
when blimp robot is made to follow the line. Even if
each graph is seen, the part where the value changes
greatly is seen. This is caused from the large diﬀerence from the previous value when the width of theline is calculated. Fig7 is the one that the number ofextracted edge points was shown. It is showed when
it is a number of edge points less than it though thewidth of the edge calculates correctly if the edge pointis gotten by four points. Moreover, because height andthe distance with the line are calculated based on thevalue of the width of the line, this value is personallyeﬀective in the control. And, power toward directionthat came oﬀ might be larger than power to face theline by the turbulence such as the winds. It is not possible to control by disappearing the line in the camera
image. The angle is an error margin of about 0.1[rad]against the angle of the target.
In this experiment, it mostly went oﬀ from the line
around 3[m] of the line, and it was possible to move by
5m because it advances straight when the inﬂuence ofthe deﬂection of the angle and the wind is little. Everyexperiment, the erratic value was seen in the graph ofheight.

Figure 6: Deﬂection with Figure 7: Number of theangle of target
edge points
was often thrown away form the line by the power ofthe disturbance such as the winds.
In the future work, it is necessary that blimp robot
is moved by guessing actual height whether to improvethe extraction accuracy of the width of the line. In
addition, the line is not made easy to lose sight byinstalling the ﬁsh-eye lens in the camera to get wider
view. We want to move blimp robot in not only thestraight line but also the curve.

References[1] Keiko Motoyama, et.al., ”Design of Action-value
Function in Motion Planning for Autonomous
Blimp Robot”, IEEJ Transactions on Electronics,
Vol. 10, 2004.
[2] Hidenori Kawamura, et.al., ”Motion Design for Indoor Blimp Robot with PID Controller”, Journalof Robotics and Mechatronics, Vol.17, 2005.
[3] Hisao Kadota, et.al., ”Vision-Based Motion Control of Indoor Blimp Robot”, Proceedings of The
4th International Conference on Advanced Mechatronics, 2004.
[4] David Collins, Gordon Wyeth, ”Cerebellar Control
of a Line Following Robot”, In Proceedings Australian Conference on Robotics and Automation,
1999.

Figure 4: Deﬂection with Figure 5: Deﬂection withheight of targetline

6Conclusion

In this paper, we reported the following to the line
for indoor blimp robot. It needs to be improved in theextraction accuracy of the line because graph of height
actual has resulted greatly the vibration. Moreover, it©ISAROB 2007

95

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007                                                                                                    !"       "             #   "       $  %   !"                      ! !           &              !                          !   '()                '*)           '+)             ',-.)            %          %          "     &    "        "            "          %        "                     !      /  !            !"   
!     !   !
  !  '()           !0! %   !                !     "  !" % ©ISAROB 200796

            "                     1 !%                     !  
              !    1   !   %     %        "   "    !    2 %             "      0!          !  !     2
"%     #              3   % 4      "           %   3  %        5 %              !         '6)   
 !!         "  !
    !                !"      !        % !              
1       %    !"     !        4                   %                    
                "                       %   (N − 1)   "   "    M    "    t    st %ct  bt %   "%   M < N                

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007"  1    %         "   %    %    !0     " T
 #   %   N   "  (st , cTt )
    "     


st+1
ct+1

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s
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ct


0N −1
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 A                 N %  !
b = 0M                 !   A      ct  1
 %                 
ct ≡

1
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   (st , cTt ) 




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st+N −1 .

st = C

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st+1 = CAC −1 st(=S t+1 f t+1 = CAC −1 S t f t((f t ≡ S −t st .

(*     st !    3 "  f t ! %       CAC −1 = S %  
 % A = C −1 SC ⎛a1⎜ −a21 + a2⎜⎜
⎜ −a1 a2 + a3⎜
A=⎜
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st+1 = a1 st + ct
ct+1 = (−a21 + a2 )st − a1 ct + ct+1
ct+2 = (−a1 a2 + a3 )st − a2 ct + ct+2




(6

ct+N −1 = (−a1 aN −1 + a3 )st − aN −1 ct + ct+N −1

   ak = 0  k = 1, . . . , N       st     C = I    A = S <   !   +%


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(AN −1 )1©ISAROB 2007

:

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1  %

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.
−a IN −1 :%   ak    k   a  !    (%  !

   A%   Q    !  
 "   ! %   (      ct %    %   3  sTt ≡ stC= b   %   In  0n    n  
     n     " %  "%  T           " "      9 !   !  
ct  A%   Q = A −1  +  !

(  A       
1  "         N     A 3       S≡(Ak )1    #    Ak %  a  A   

A=

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97

1

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               %   Q   !  

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007  ! (;  "               
N %            "   st , st+1 , . . . , st+N −1   %  
%t+N −12 % *(  

f t+1 = S −t−1 CAC −1 S t f t + (L − CAC −1 L + I)Cb.

*;

  

*=

sτ sT
τ

ft =

τ =t



 %       !

#  $   % 


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st+1
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>!   #   
k−1

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Aj b
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   (k − 1)    b %    % b = LCb  
⎛

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  %  ©ISAROB 2007

B ≡ I − S −t−1 CAC −1 S t

*6

I − B = S −t−1 CAC −1 S t=S−t(I − B)S&   

++
+,



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!"    %            # !       ! !  '() 1
   %     0 
        ! >        "     
a11
a21


aT
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,
A22

+8

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98

t

          I − B   B      0 

Â =

*:

+*

    t%      !"   B    (0, 2)
  ! t = 0%  ! S(I −B) = CAC −1   A=  
sst − LCb = C t .
ct 



 C  #  6%   
sst = C t + LCb
ct

+(

*+
*,

+=



I − S −t−1 CAC −1 S t f = (L − CAC −1 L + I)Cb  "

1
(L − CAC −1 L + I)Cb
1−c    CAC −1 = cS  |c| < 1
7  f t     "  f % " %

1    %       "  b 5            7 b   %    "    

! (*       

â11
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
âT
12
.
Â22

  t   +.The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

   !                 %   %st+1 = a11 st + aT
12 ct
T= a11 st + aT
12 a21 st−1 + a12 A22 ct−1 ,ŝt+1 = â11 st + âT
12 ct
T= â11 st + â12 âT
21 st−1 + â12 Â22 ct−1 .

+6
+:
+;
,=

             3  ε(Â) = (ŝt+1 − st+1 )2 /2  +:             Â%       ?    % "    / &  a11  a12      a21 % A22 %       " ct < +6  +;%    !   a11
 a12    


â
Δ 11
â12

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 
s 
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' %     (         "                 %     ©ISAROB 2007

99

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Three dimensional medical image recognition of the brain by feedback GMDH-type
neural network self-selecting optimum neural network architecture
Tadashi Kondo

Junji Ueno

School of Health Sciences,
the University of Tokushima,
Tokushima, 770-8509, Japan

School of Health Sciences,
the University of Tokushima,
Tokushima, 770-8509, Japan

Abstract
The feedback Group Method of Data Handling(GMDH)-type neural network algorithm proposed in thispaper is applied to 3-dimensional medical image
recognition of the blood vessels in the brain. The neuralnetwork architecture fitting the complexity of the medical
images is automatically organized by the feedback
GMDH-type neural network algorithm so as to minimize
the prediction error criterion defined as Akaike’s
Information Criterion (AIC). In this feedback GMDH-type
neural network algorithm, the optimum neural networkarchitecture is automatically selected from three types ofneural network architectures such as the sigmoid function
type neural network, the radial basis function (RBF) typeneural network and the polynomial type neural network.
The recognition results show that the feedback GMDH-type
neural network algorithm is useful for the 3-dimensional
medical image recognition of the blood vessels in the brain
and is very easy to apply the practical complex problem
because the optimum neural network architecture is
automatically organized.
Keywords: GMDH, Neural network, Medical image
recognition

1 Introduction
The Group Method of Data Handling (GMDH)-type
neural networks and their applications have been proposedin our early works [1],[2]. The GMDH-type neural networks
can automatically organize the neural network architectureby using the heuristic self-organization method [3],[4]. In
this study, a feedback GMDH-type neural network
algorithm self-selecting the optimum neural networkarchitecture is proposed. In the feedback GMDH-type
neural network algorithm, the optimum neural networkarchitecture is automatically selected from three types ofneural network architectures such as the sigmoid function
type neural network, the radial basis function (RBF) typeneural network and the polynomial type neural network.
Furthermore, the structural parameters such as the numberof layers, the number of neurons in the hidden layers and theuseful input variables are automatically selected so as tominimize the prediction error criterion defined as Akaike’s
Information Criterion (AIC) [5] or Prediction Sum of
Squares (PSS) [6]. The feedback GMDH-type neural©ISAROB 2007

100

network has a feedback loop and the complexity of theneural network increases gradually using feedback loop
calculations so as to fit the complexity of the nonlinear
system.
The feedback GMDH-type neural network algorithm
proposed in this paper is applied to 3-dimensional medical
image recognition of the blood vessels in the brain. The
neural network architecture fitting the complexity of the
medical images is automatically organized by the feedback
GMDH-type neural network algorithm so as to minimize
the prediction error criterion defined as AIC.

2 Feedback GMDH-type neural network
The architecture of the feedback GMDH-type neuralnetwork proposed in this paper has a feedback loop as
shown in Fig.1. The feedback GMDH-type neural network
algorithm can select the optimum neural networkarchitecture from three types ofneural networkarchitectures such as the sigmoid function type neuralnetwork, the RBF type neural network and the polynomial
type neural network. The feedback GMDH-type neuralnetwork algorithm uses three types of neuron architectures
which are the sigmoid function type neuron, the RBF typeneuron and the polynomial type neuron. In the feedback
GMDH-type neural network, optimum neuron architectures
fitting the characteristics of the nonlinear system areautomatically selected by using AIC.
The feedback GMDH-type neural network is shown as
follows.
Σ f
X1

Σf

X2
X3
.
.
.
XP

Σ f
.
.
.

Σ f

Σ

φ

.
.

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Σ f
Σf

Fig.1 Architecture of the feedback GMDH-type neuralnetwork

2.1 First loop calculation

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

First, all data are set to the training data. In this algorithm,
it is not necessary to separate the original data into thetraining and test data because AIC can be used fororganizing the network architectures. Then the architectureof the input layer is organized.
1) Input layer
uj=xj (j=1,2,…,p)(1)
where xj (j=1,2,…,p) are the input variables of the system,and p is the number of input variables. In the first layer,
input variables are set to the output variables.
2) Hidden layer
All combinations of the r input variables are generated.
For each combination, three types of neuron architectures
which are the sigmoid function type neuron, the RBF typeneuron and the polynomial type neuron, are generated and L
neurons which minimize AIC value are selected for eachtype of neuron architectures.
Furthermore, for each combination, optimum neuron
architectures fitting the characteristics of the nonlinear
system are automatically selected by using AIC.
a) Sigmoid function type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj +w8uiuj2+w9uj3 – w0θ(2)
f : (Nonlinear function)yk =1
1 + e ( − zk )ii) The second type neuron
Σ: (Linear function)zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Nonlinear function)yk =1
1 + e ( − zk )(3)2ii) The second type neuron
Σ: (Linear function)zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Nonlinear function)2y k = e ( − zk )

(4)

(5)

(7)

(8)
(9)

c) Polynomial type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj+w8uiuj2+w9uj3 – w0θ(10)
f : (Linear function)©ISAROB 2007

L

φ * = a0 + ∑ ak y k

(14)

k =1

b) RBF type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj+w8uiuj2+w9uj3 – w0θ(6)
f : (Nonlinear function)y k = e ( − zk )yk= zk(11)ii) The second type neuron
Σ: (Linear function)(12)
zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Linear function)yk= zk(13)
Here, θ1 =1 and wi (i=0,1,2,…) are the weights between
the first and second layer and estimated by applying the
stepwise regression analysis [7] to the training data. Only
useful input variables ui (i=1,2, …) are selected by using
AIC. In the first type neuron, the value of r, which is thenumber of input variables u in each neuron, is set to two. In
the second type neuron, the value of r, which is the numberof input variables u in each neuron, is set to be greater than
two and smaller than p. p is the number of input variables xi(i=1,2,…,p). The output variables yk of the neurons arecalled as the intermediate variables.
L neurons having the smallest AIC values are selected
for three types of neuron architectures which are thesigmoid function type neuron, the RBF type neuron and thepolynomial type neuron. The output variables yk of L
selected neurons for three types of neuron architectures are
set to the input variables of the neurons in the output layer.
3) Output layer
For three types of neural network, the outputs yk of theneurons in the hidden layer are combined by the following
linear function.

101

Here, L is the number of combinations of the input variables
and yk is the intermediate variables. The useful intermediatevariables yk are selected by using the stepwise regression
analysis in which AIC is used as the variable selection
criterion.
Equation (14) is calculated for three types of neuralnetwork architectures which are the sigmoid function typeneural network, the RBF type neural network and thepolynomial type neural network. Then, the neural networkarchitecture which has smallest AIC value is selected as the
GMDH-type neural network architecture from three typesof neural network architectures
Then, the estimated output values φ* which is selected in
the output layer is used as the feedback value and it iscombined with the input variables in the next loop
calculation.

2.2 Second and successive loop calculations
The optimum neural network architecture is selectedfrom three types of neural network architectures in the
output layer. Therefore, in the second and successive loop
calculations, only one type of neuron architecture, which isthe sigmoid function type neuron or the RBF type neuron or
the polynomial type neuron, is used for the calculation.
First, the estimated output value φ* is combined with theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

input variables and all combinations between the estimatedoutput value φ* and the input variables are generated. The
same calculation as the first feedback loop is carried out foreach combination. Here, only one type of neuron
architecture, which is selected in the first loop calculation, isused in the calculation. When AIC value of the linear
function in (14) is increased, the loop calculation is
terminated and the complete neural network architecture isorganized by the L selected neurons in each feedback loop.

3 Application to 3-dimensional medical image
recognition of the blood vessels in the brain
In this study, regions of the blood vessels in the brain isrecognized automatically using the following two
recognition procedures. Multidetector row computed
tomography (MDCT) images of the brain are used in thisstudy. In the first recognition procedure, the feedback
GMDH-type neural network is organized to recognize thebrain regions and then these regions are extracted usingorganized neural network. In the second recognition
procedure, another new feedback GMDH-type neuralnetwork is organized to recognize the blood vessel regions
and then these regions are extracted using organized newneural network. Using these recognition procedures, theblood vessel regions are recognized and extracted.

post-processing of output image of the neural network,small isolated regions outside or inside of the brain regions
are eliminated by the image processing such as the dilatationand the erosion. Then, outlines of regions of the brain wereexpanded outside by N/2 pixels and outline of the brain
were extracted. Fig.3 shows output image after thisprocessing. In order to check matching between original
image and output image of the neural network, the output
image was overlapped on original image after
post-processing. Overlapped image is shown in Fig.4. From
Fig.4, we can see that extracted regions are very accurate.

Fig.2 Original image(1)3.1 Recognition of brain regions
In this study, an original MDCT image shown in Fig. 2 isused for organizing the feedback GMDH-type neuralnetwork. Then, image features are extracted and used as
input variables of neural network. Statistics of image
densities in neighboring regions, N×N pixel regions, areused as image features. The following statistics are used as
input variables. 1) mean, 2) standard deviation, 3) variance,4) median, 5) minimum, 6) maximum, 7) range. Out ofthese statistics, only three parameters namely, mean,
standard deviation and variance are selected as useful input
variables. Output value of neural network is zero or one.
When N×N pixel region is contained in regions of the brain,neural network sets pixel value at the center of N×N pixel
region to one and this pixel is shown as white point. Neuralnetwork was organized when values of N are 5, 10 and 15.
When N equals 5, output image is most accurate.
Calculation of the feedback GMDH-type neural network
was terminated at the fourth layer. Three useful neurons
were selected in each hidden layer. RBF neural networkarchitecture was selected as the feedback GMDH-type
neural network in the first feedback loop calculation.
Feedback GMDH-type neural network output brain image
and post-processing analysis of brain image was carried out,
based on which regions of the brain were extracted. In©ISAROB 2007

102

Fig.3 Output image after
post-processing (1)Fig.4 Overlapped image (1)3.2 Recognition of blood vessel regions in the brain
The blood vessel regions are recognized by the
feedback GMDH-type neural network and extracted. First,
gray scale image of the brain (Fig.5) was subtracted fromthe original image (Fig.2) by using the output image of the
feedback GMDH-type neural network (Fig.3). This grayscale image is used as a new original image to organize new
feedback GMDH-type neural network. The new feedback
GMDH-type neural network was organized and recognized
the blood vessel regions. The organization procedures of the
new feedback GMDH-type neural network are the same as
those of the brain regions. Image features are extracted andused as input variables of neural network. Statistics ofimage densities in neighboring regions, N×N pixel regions,
are used as image features. Only three parameters namely,
mean, standard deviation and variance are selected as useful
input variables. Neural network was organized when valuesof N are 5, 10 and 15. When N equals 5, output image isThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

most accurate. RBF neural network architecture wasselected as the feedback GMDH-type neural networkarchitecture in the first feedback loop calculation. Feedback
GMDH-type neural network output blood vessel image andpost-processing analysis was carried out, based on which
regions of the blood vessels were extracted. Fig.6 shows
output image after the post-processing. Then, gray scale
image of the blood vessel was subtracted from the original
image by using the output image of the feedback
GMDH-type neural network (Fig.6). Figure 7 shows
overlapped image of blood vessel regions. These
subtraction processing were carried out for the all slices of
MDCT. Then, 3-dimensional images of the blood vessels
were generated using gray scale images for all slices of
MDCT by the rendering software. Figure 8 are
3-dimensional blood vessel images.

In this paper, this algorithm was applied to 3-dimensional
medical image recognition of the blood vessels in the brain
and it was shown that feedback GMDH-type neural network
algorithm was a useful method for 3-dimensional medical
image recognition of the blood vessels in the brain because
the neural network architecture is automatically organizedby the feedback GMDH-type neural network algorithm.

(a)(b)
Fig.8 3-dinensional images of the blood vessels

References

Fig.5 Subtraction image of the brain (1)Fig.6 Output image of blood Fig.7 Overlapped image ofVessel regions (1)blood vessel regions (1)4 Conclusion
In this paper, the feedback GMDH-type neural network
algorithm self-selecting optimum neural networkarchitecture was proposed. In this algorithm, optimum
neural network architecture is automatically selected fromthree neural network architectures such as sigmoid function
neural network, RBF neural network and polynomial neuralnetwork. Furthermore, structural parameters such as thenumber of layers, the number of neurons in hidden layers
and useful input variables are automatically selected tominimize prediction error criterion defined as AIC.

©ISAROB 2007

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[1] T.Kondo, “GMDH neural network algorithm using theheuristic self-organization method and its application to thepattern identification problem”, Proc. of the 37th SICE
Annual Conference, pp.1143-1148, 1998.
[2] T.Kondo, “Revised GMDH-type neural networks using
prediction error criterions AIC and PSS”, Proc. of SCIS &ISIS 2004, WP-6-4, pp.1-6, 2004.
[3] S.J.Farlow ed., Self-organizing methods in modeling,
GMDH-type algorithm, Marcel Dekker, Inc., New York,1984.
[4] A.G.Ivakhnenko, “Heuristic self-organization in
problems of engineering cybernetics”, Automatica, Vo.6,
No.2, pp.207-219, 1970.
[5] H.Akaike, “A new look at the statistical model
identification”, IEEE Trans. Automatic Control, Vol.AC-19,
No.6, pp.716-723, 1974.
[6] H.Tamura and T.Kondo, “Heuristics free group methodof data handling algorithm of generating optimum partial
polynomials with application to air pollution prediction”,
Int. J. System Sci., Vol.11, No.9, pp.1095-1111, 1980.
[7] N.R.Draper and H.Smith, Applied Regression Analysis,
John Wiley and Sons, New York, 1981.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Learning how, what, and whether to communicate:emergence of protocommunication in reinforcement learning agents
Takashi Sato
Eiji UchibeKenji Doyastakashi@oist.jp
uchibe@oist.jp
doya@oist.jp
Initial Research Project (IRP), Okinawa Institute of Science and Technology (OIST)
12-22, Suzaki, Uruma, Okinawa, Japan, 904-2234
Abstract
This paper examines whether and how a primitive form of communication emerges between adaptive agents by using their excess degrees of freedom inaction and perception. As a case study, we consider
a game in which two reinforcement learning agents
learn to earn rewards by intruding into the other’s
territory. Our simulation showed that the agents withlights and light sensors could learn turn-taking behaviors by avoiding collisions using visual communication.
Further analysis revealed that there was a variety inwhat message is mapped to what signal, and in somecases there was role diﬀerentiation into a sender anda receiver.
Keywords: Reinforcement Learning, Intrusion Game,
Emergence of Protocommunication, Role Diﬀerentiation

1

Introduction

The prototype of communication, or protocommunication, would have emerged to help individuals toearn rewards and to improve ﬁtness. Then how did
protocommunication emerge based on what capacityof individuals? These questions have been discussed
in various ﬁelds for a long time, but unlike other questions in archeology, these questions are hard to answer
as there is no fossil of communication until written languages emerged recently. Thus, we address this question by ”understanding by construction” using mathematical modeling and computer simulations [1].
The studies on emergence of communication can beclassiﬁed into two broad categories: one adopting evolutionary optimization [2, 3] and the other employing
learning agents [4, 5]. However, a major limitation inmost previous studies is that they assumed the preexistence of the basic frameworks for communication,such as signals and meanings or a speaker and a hearer,and just veriﬁed the evolution or learning of mappings
between signals and meanings by taking the success of©ISAROB 2007

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communication itself as the objective function. Therefore, it is diﬃcult for those studies to answer how communication emerged from a world where concepts likesignals, words, and speaking did not exist.
The purpose of this study is to test if communication can emerge between individuals who have basic behavior learning functions but do not have dedicated mechanisms or absolute needs for communication. Speciﬁcally, we run a case study of an ”intrusion game” in which two agents move on a linear trackand earn rewards by intruding into the other’s territory while avoiding collisions. We consider what action, sensation and memory capacities are necessary
for learning of cooperative behaviors, and when it islearned, what meanings agents assign to their excess
degrees of action and sensation. Further, we investigate the developmental process of cooperative behaviors by communication and the cases of role diﬀerentiation into a speaker and a hearer.

2

Intrusion Game

We consider an “intrusion game (IG),” which simpliﬁes situations like a turf war between foraging animals. Two players can move back and forth on a one
dimensional space with four slots. Players are boundedby walls on the “west” and “east” ends of the track
and cannot jump over or stay together in the same slotwith another agent. Figure 1 depicts six possible setsof positions that the players can takes. We denote thesix position patterns by 0 to 5. The “west” player can
get a reward by entering the east half of the track (i.e.,
position pattern 5) and the “east” player by enteringin the west half (i.e., position patter 2) without a collision. A punishment (negative reward) is given whena player collides with a wall or another player.
A crucial problem in this game is how the players
resolve the conﬂict at the position pattern 4. If theplayers act selﬁshly, i.e., to maximize its own reward,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4

Simulation Results

We performed 10 simulation runs each for the fourtypes of agents with the following setups: positive reward +1 for successful intrusion, negative rewards -1
each for collision with the wall and the other agent,² = 0.01, α = 0.01, and γ = 0.9.
Figure 1: Six possible position patterns of the players,
denoted by 0 to 5.

4.1

Agents’ Behaviors

First, we present examples of typical behavioral
patterns obtained from the analysis of the change ofeach agent’s position pattern (Fig. 2).
both would take an action to move forward, but it willcause a collision with negative rewards to both players.

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by the following equation.
Q(st , at ) := Q(st , at ) +[]α rt+1 + γ max Q(st+1 , a) − Q(st , at ) ,

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4

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Figure 2: Four examples of typical behavioral patterns. (a) Non-cooperative dominance by one agent.
(b) Asymmetric cooperation. (c) Suboptimal cooperation. (d) Optimal cooperation.

awhere α is a learning rate (0 < α < 1), γ is a discountrate (0 < γ < 1), and r(t) is the reward given after action a(t) was taken at state s(t). We used the ²-greedy
policy in which an actions is randomly selected withprobability ² and otherwise an action that maximize
Q-value for a given state is selected.
In order to investigate how the agents’ sensory, action, and memory capabilities aﬀect the learned behaviors, we tested four types of agents. A null or Ntype agent simply has two moving actions (backward
or forward) and can sense the position pattern (0 to 5)of the two agents. In addition, a light-capable, or Ltype agent has actions of turning on or oﬀ its headlightand also a light sensor to see if the other agent’s light ison or oﬀ. A memory-based, or M-type agent keeps thememory of its previous action (backward or forward)
to augment its state space. A light-and-memory, or
LM-type agent has both light signaling and memory
capabilities.

©ISAROB 2007

107

Figure 2(a) shows a non-cooperative dominance by
one agent. In this pattern, only one agent can earn apositive reward every two steps. The other can get noreward, but can receive negative reward if it changes
its behavior. Figure 2(b) presents an asymmetric cooperation which can be seen only in LM-type agents.
In this case, one agent can get two positive rewards
during a six step cycle, the other can obtain while
only once. Figure 2(c) depicts a suboptimal cooperation leading to one reward every six steps. Figure 2(d)shows an optimal cooperation in which both agents
earn a reward every four steps.
We analyze the occurrence frequency of four typical behavioral patterns for the four types of agents.
As can be seen in Fig. 4, the agents without light(N- and M-type) can learn only the non-cooperative
dominance. In contrast, the agents with light (L- andLM-type) can show various cooperation. Further, LMtype agents can achieve the optimal cooperation more
frequently than L-type agents without action history.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Non. Coop.
100%

Asymmetric Coop.

100%

Subopt. Coop.

100%

13%

the east agent. The other diagrams in Fig. 4 indicate(b) asymmetric, (c) suboptimal and (d) optimal cooperation. A common feature in these cooperative casesthat the agents experienced took both position pattern
sequences 4 to 2 and 4 to 5 in the early stage before
becoming able to switch between the two.

Opt. Coop.
33%

28%
75%
19%
59%

50%

13%

4.3

35%
25%

0%

N

M

L

Variety of Signaling

We observed emergence of various types of communication emergence. Figure 5 exempliﬁes four typicaltypes of communication that realizes the optimal cooperation.

LM

Agent types

Figure 3: Occurrence frequency of four typical behavioral patterns for four types of agents.

4.2

Developmental Process

Next we examined how the behaviors changed bylearning before converging to one of four typical patterns. Figure 4 shows the developmental history offour LM-type agents who acquired one of four typicalbehavioral patterns at ﬁnal episode. We recorded the
Q-values of the agents every 1,000 steps and let theagents play the IG (with ² = α = 0) from all possible
initial states1 .
0313
045424100%04
1304042424(a) Non-cooperative dominance
13

45

13

75%

24

0313
0454
04

25%
0%
0

100%

75%

10000

45
13

50%

0454
45

(b) Asymmetric cooperation
24

50%

0454
04

13 13

0%
2000

30000

40000

50000

0

10000

2000

30000

40000

50000

(d) Optimal cooperation
100%

13040424

13

13

75%

0424540454

24245404542425%(c) Suboptimal cooperation
100%

042454
2454

75%

0454

50%

0424
242454

45

50%

0424

2454

0454
13
04
0%

25%

25%

04
0%
0

10000

2000

30000

40000

50000

0

10000

2000

30000

40000

50000

Figure 4: History of occurrence frequency of behavioral patterns. The x-axis and the y-axis of each ﬁgure are the steps and the occurrence frequency of converged behavioral patterns, respectively. Each numerical string represents a sequence of position patterns
in a cyclic behavior.

Figure 4(a) shows an example of the history of apair that converged to non-cooperative dominance by
1 For example, the number of states of the L-type agents is 24,
where initial positions and light states are 6 and 4, respectively.

©ISAROB 2007

108

Figure 5: Typical examples of emerged communication. (a) Symmetric signaling. (b) Asymmetric signaling. (c) One-way communication between a senderand a receiver after a role diﬀerentiation. (d) Cooperation without communication.
Figure 5(a) shows an example of symmetric signaling in which agents can resolve the conﬂict at the position pattern 4 by alternately turning on the lightwhile stepping forward. This means that the agentscan convey their next actions as messages by theirlights. An asymmetric signaling can also be observed,
in Fig. 5(b), in which one agent turns the light on to
step forward, while another agent turns the light on
to step back.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Unlike the symmetric and asymmetric signaling,
communication examples shown in Fig. 5(c) and (d)can be seen only in LM-type agents. Figure 5(c) depicts a one-way communication between a sender (theeast agent) and a receiver (the west agent) after a role
diﬀerentiation. In this case, only the east agent uses
its light source, and the west agent behaves according
to the east agent’s light signal. The east agent, who
cannot rely on the light signal of the west agent, determines its actions based on memory of its own pastaction in order to solve the conﬂict on the position
pattern 4. Figure 5(d) is a case of optimal cooperation without communication. As can be seen, the east
agent always turns on its light when entering the position pattern 4 from 2 or 5. Therefore, the west agent
cannot solve the conﬂict on the position pattern 4 byusing the east agent’s light signal. Both agents behave
based only on the memory of their past actions.

5world where a teacher of communication or even any
dedicated mechanisms for communication do not exist.
Using computer simulations of IG, we have shownthat agents who can turn on/oﬀ their lights as redundant actions became able to spontaneously acquire
meanings of light signals and cooperate with the otheragent. We have also found that agents with working
memories can diﬀerentiate their roles as a sender and a
receiver. Further, our simulation demonstrated that acooperation without communication can emerge frominteraction between the agents having both signaling
and memory capabilities.
Our simulation results suggest that repeated interaction between individuals with a reinforcement learning function can play an important role in establishingprotocommunication, even if individuals do not havededicated mechanism for communication.

References

Discussion

Our simulation study showed that simple communication can emerge from iteration of searching for rolesof redundant actions through a generic reinforcement
learning process and interaction with the others.
Animal communication is deﬁned as a transmissionof signals that senders can proﬁt by reactions of receivers [7]. Our simulation conﬁrms that this typeof communication can be emerged from repeated interactions between reinforcement learning agents withenough physical capacity. Tomasello claims that communicative signals can be created by forming eachother’s behaviors between two individuals through iteration of social interaction [8]. Our simulation results
also support his claim.
Tomasello furthermore advocates that the following
is important for acquisition of habitual use of linguistic
symbol [8]. An individual 1) understands that the others are individuals with some intents, 2) participates ina joint attention situation, 3) comprehends the other’s
intent in such situation, and 4) can use a symbol that
others used toward the individual. Although our reinforcement learning agents did not explicitly have suchfunctions, the simple two-person setup of the gameprobably made it unnecessary to use attentive mechanisms.

[1] T. Hashimoto, Knowledge Science (in Japanese), K.
Sugiyama, et al. (Eds.), Kinokuniya shoten, pp.126–
131, 2002.
[2] A. Cangelosi, D. Parisi, “The emergence of a language
in an evolving population of neural networks,” Connection Science, 10(2), pp.83–97, 1998.
[3] D. Marocco, S. Nolﬁ, “Emergence of communicationin embodied agents: co-adapting communicative andnon-communicative behaviours,” A. Cangelosi, et al.
(Eds.), Modeling language, cognition and action: Proceedings of the 9th Neural Computation and Psychology
Workshop, 2004.
[4] L. Steels, P. Vogt, “Grounding adaptive language
games in robotic agents,” C. Husbands, I. Harvey,(Eds.), Proceedings of the Fourth European Conferenceon Artiﬁcial Life, MIT Press, 1997.
[5] S. Tensho, et al., “Gradual emergence of communication in a multi-agent environment” (in Japanese), IPSJ
SIG-ICS, 139, pp.73–78, 2005.
[6] R. S. Sutton, A. G. Barto, Reinforcement Learning,
MIT Press, 1998.
[7] T. R. Halliday, P. J. B. Slater, (Eds.), Animal Behavior, Blackwell Scientiﬁc Publications, 1983.
[8] M. Tomasello, The Cultural Origins of Human Cognition, Harvard Univ. Press, 1999.

6

Conclusion

We have proposed an intrusion game (IG) for investigating the emergence of protocommunication from a©ISAROB 2007

109

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007       
                   Ý    Ý     ÝÝ     ÝÝÝ            !  "        #    $%  $% &#'     ,  )           +       $         Æ     +       +  ##  8##      ./ +)        1   9             )   1               + )        7         +     +) : +  ./  +   ;     3         +        
1         ) )    *   +      Æ       ! +   +       ) )  ,  +    ©ISAROB 2007

110

O

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O

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   .'/       
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 +    +   7 

An odor composed of
3 kinds of molecules(a) There are 200 to 400 thousands (b) Mice only pay attention on a part ofkinds of odorant molcules.
molcules composing an odor.

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   *       + )          % *3           +    ) ,     * 5      5 * %      ) )     $( $?  @A +  +   .8/ ,           + )       @A    $?   +)  .&/   %    1      The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Odorant Map

A

Higher Brain
Function

Odor box

B

C

Receptor Cells Olfactory Bulb

Piriform Cortex

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

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7     



 

1  +   7      +)  *   +) -%   .'/  B    =B;>   *  6 6        *      
 =  A> 1    *  *  % *   *  )         ! ?   *       1    *5             A        !  ? 1*)    *  * 5     *    )     1  )   *        *   *   C         )   *            *  ?   7  9  +* * %    )  5 )            
 +*        ,   7    *    *   .A< ?<,!/
       )     )  =,!>  ) +) = ?>   =A> 1 ©ISAROB 2007

111

6 <  B;  % +    =     5     '>   *      *             )       *   =.A/ . ?/ .,!/  . ?<,!/>.'/  
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          +*    
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       +     ? ,! , * +   5            +  ?  ,!
6      -%        )                           
  %3       0
+*    *           ) *   )    







    ,     *         ))   +  +    !
*         *  6 ,The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Eb

Eb

Inhibitory
Excitatory

Ci

Accuracy rate %

100

IA

80

50

IA

Mouse A
Mouse B

1

S1

S2

Eb
Ci
IA

S3
S4

6 8< (     7 = +   >6   )     )    =$(>         3  )    ) + + =$?>3  )        73
 D )       7           )                   +   +    .8/ .&/ 1    *  +E    +) ++    1    )   + *      
1   +  '### )  $(        )  $(      )     ./ 1                )            3     )     )     1  )     7          .# '/
1        5 +)    *9             *  7         ./< => F ' G 7  = ' =>  
 >
='>*          3 =>          3    5             
E      ) *    *  6           *  0 5    1          )  5   => F  => 1           +) 9  ='>      $? ) = )>

     
1   ) + +      7 )  % *       + )  ©ISAROB 2007

Confidence
index C(t) 0112

S5Odorant

R
Receptor

B
Olfactory bulb

Z
P
Piriform cortex

6 < !      ) )   % *               7      )      )         1    '###         )+ +   1               ./ 1                6         + )       @A       .&/,                5                      )          "            5     )           1       )  7 )      )   + )        )3       =>   *<=> F



=>    =>  =>    =>*         
*         )   ) 1 
      =>  7         9  ='> 1              @A

       
1  @A  +        
7 =!@A>       7 =@@A> ,       !@A          $? *     @@A      .8/ 1            @A ) *      ) 6   *  +)      + )    7  The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007  @A    $? .&/  *      )   7     0       +*      )
   )           1    =>       @ )  +)   * 9 <  => => =>  => F

Start





*   =>      *      )    )       )             )
 5       9  ='>1      * +         ) $    #              *   *       #  *   ,   *)   ) 
  +         )

     
1    *  +*      )   ; +)         .# '/     +)   ++   .'#/<
 = G '>F  => G Æ => Æ  => F  =>    =>   *       3                  Æ  => 1 
  *  +*       )
   +)   ++   1     +*     )     +  ) ./          *   
:    +    +)   +   6   5      +        5 7  => =# 
 =>  '> C              5 7  +) ' *           5 7  +)'    5     +       )    5 7 C 

©ISAROB 2007

113

End

No
Input a odorant column
Calculate the outputs of each layer
Is the output ofZ layer correct?



*    =>      *      )     ) $         =>        )  +)   * 9 <
=> F
=8>
 =>   =>Yest 㪔 tmaxNo

Yes

Decrease C(t)Yes

C(t) > 0.5

No
Decrease the inhibitory weights
between P and B layer.
Update the other weights by
the Hebbian rule.

I. Olfactory
system(discriminteodors)

II. Higher
brain(judgement)

Increase C(t)No

C(t) > 0.5

Yes
Update all weights by
the Hebbian rule.

III. Change
connective
weights

6 &< 1  H *  *     5 7  =>       + )         )     )   
+)   ++    *   *   =>      + )      +)   
  =#    '> 1        +;  6&  ,              5             +     0         
6      +*      )
*  ;           .# '/
6          * +    
* %   "' $  * 5        )    D )
1  *   ! = F  F  F  F '  F
 '
 F #>*   )         C       *     * * +E  +    *     ) 5   +)        *  6&
1            * %  "'   *  63 *  =>  =+>  *  5   ) *   F '   F '  ) 1         
+)  9 1             *       9  1       + ) *       )    )     5      *  6 6'#   ) 6  6=>   + +          ) 5 *% ) * The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
R

B

P(b)䇭t = 156 < A      "'
Odor
s1 1
s2 1
M2 s 3 1
s4 0
s5 0

R

B

P

Z

5

10

0

15

W P(4,3),B(3,2)

-0.25
-0.50
-0.75

6 < A    + ) *          )
1.0

0.5

0
1

5

Steps t1015

6 '#< A      5 7
s1 1
s2 1
M1 s 3 0
s4 0
s5 0(a)䇭t = 1

s1 1
s2 1
M2 s 3 1
s4 0
s5 0(a)䇭The states of M1 when odor B is inputed(b)䇭t = 156 < A      " F ' 1              *   *     
  !    *   ! *   )    *  *   +)        *  6&3       "'   !    *   1     + +  6=+>  *         )5   ) *   F '!        + )   *      )     )   7    + ) *         *  6           *  + !         "'     ) +)        *      + )     "'  +
 5
-7       *  * ++  *        * %  " *    ; * 0  
 *   + )         )    ) 1         * 
6''3 =>  =+>  *   5  *   F '  F '   ) C   F '       !   *   +    ©ISAROB 2007

1

1.0

0.0
Activation
Level(a)䇭t = 1

s1 1
s2 1
M1 s 3 1
s4 0
s5 0

Steps tZ

Inhibitory weight

s1 1
s2 1
M1 s 3 1
s4 0
s5 0

Confidence index C(t)

Odor

114

s1 1
s2 1
M2 s 3 0s4 0
s5 0(b)䇭The states of M2 when odor B is inputed

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B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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< 7           1 ",1 @ #8' '&./ J "   B B    2"        )         ) )4 @   - + ) K 8 L&' '.'#/ I$ ++ 21  ;  +  4  C ) -* B % '8The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Graphical Analysis of Time-series Data
from Waste Incinerator Using Self-organizing map
Takashi Yamaguchi
Kenneth J. Mackin
Department of Information Systems,
Tokyo University of Information Sciences265-8501 1200-2 Taniate, Wakaba-ku, Chiba, Tokyo University of Information Sciences
Abstract
The emission of dioxins from waste incinerators is oneof the most important environmental problems today. It
is known that optimization of waste incinerator
controllers is a very difficult problem due to the complex
nature of the dynamic environment within theincinerator.
In this paper, we propose applying self-organizing
map (SOM) for visualizing topological information ofwaste incinerator data for time-series data, and aimed at
finding correlation in real waste incinerator data to
predict high dioxin emission.

1.

Introduction

The emission of dioxins from waste incinerators is oneof the most important environmental problems today. It
is known that optimization of waste incinerator
controllers is a very difficult problem due to the complex
nature of the dynamic environment within the incinerator.
This is because the environment in the incinerator is acomplex dynamic environment in which the different
items are dependent on each other, and is not a simple
dependency relationship.
There has been past research in intelligent estimationof dioxins emission from waste incinerators. Fujiyoshi etal [1] has proposed applying fuzzy control to incinerator
control to decrease the dioxins emission. Ichihashi et. al[2] has applied statistical analysis to calculate thecorrelation of various input signals with dioxins
emissions. Fukushima [3] has proposed applying fractal
fuzzy control in order to estimate and control dioxins
emission.
For this research we investigate methods applying
SOM to classify time-series data for complex
environments, and propose applying self-organizing map(SOM) for visualizing topological information of waste
incinerator data for time-series data. Our aim is to findcorrelation in real waste incinerator data to predict highdioxin emission.

©ISAROB 2007116

2. Training
Incinerators

Requirements

for

Waste

For this experiment, we used sensor data from a waste
incinerator plant as the time-series data, and aimed at
visually classifying instances of high dioxin emission.
We used real waste incinerator data provided by Hitachi
Zosen Corporation. Figure 1 shows the schematic
diagram of the fluidized bed waste incinerator.
The data consists of the following 12 sensor values
measuring various conditions of the incinerator. Flapper
angle (0 - 100.00%), oxygen concentration in incinerator
exit (0 - 25.000%), garbage rate (t/H), incinerator
temperature (0 - 1200.0 ℃ ), carbon monoxide
concentration (0 - 500.0ppm), incinerator pressure(-2000.0 - 1000.0ppm), cooling liquid rate
(0-1.0000m3/h), conveyer belt speed (0 - 7.000rpm),
primary air supply(0 - 7.500KNm3/h), secondary air
supply base (0 - 7.500Nm3/h), secondary air supply
modification (0 - 7.500KNm3/h).
It is known that CO (carbon monoxide) concentration
over 100ppm show strong correlation with dioxins

Sensor input
Waste

Flapper Angle
1,000℃d

Secondary Air Fan*１*１
Conveyer belt
OxygenIncinerator

*2

600℃

*2

Cooling liquid pumpPrimary Air Fan
Sludge removal

Figure 1.

Schematic diagram of Fluidized Bed

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
concentration. For this research, we use the COconcentration as the target output, and aim to reduce theaverage CO concentration as well as to reduce thenumber of CO concentration peaks over 100ppm.

3. Learning Time-series
Self-organizing map

Data

using

Self-organizing map (SOM) is a type of artificial
neural networks proposed by Kohonen[5]. It is trainedusing unsupervised learning to produce low dimensional
representation of the training samples while preserving
the topological information of the input space. SOM
applies neighborhood learning to enable the creation ofclose output for similar input. This feature is effective
for classification and visualizing in complex problems.
On the other hand, standard SOM cannot directly
handle time-series data. Several approaches to allow
SOM to learn patterns in time-series data have beenpreviously proposed, such as adding a feedback loop[7]to create a multi-layer SOM, and adding delayed timeunits to SOM[6].
For this research, we used delayed-time units in orderto map time-series data into the input pattern space.
Figure 2 shows the model of time-series data thatconsists of two elements (A, B) is input to SOM using
delayed-time unit. Its approach to learn patterns intime-series data does not require change in the SOM
method, because it is possible to implement theadditional input by preprocessing.

patterns of maps. The first pattern of maps is visualized
from difference between predicted values and the actual
values of each sensor recorded for the same time frame.
For the values of predicted sensor values, the weightvalues of winner node at each time frame were used. In
this map, the purpose is to display the state of the sensor
value in each time frame.
The second pattern of maps is visualized from weightvalues of each element of the time-series data. For
example, using time-series data that consists of threeelements (A, B, C), three maps are constructed for eachelements. These maps can be displayed by using AND
operation (ex. [A AND B,] [A AND B], and [B AND C]).
This function has the aim of displaying correlation fromeach element of data.
Additionally, for this experiment we draw the tracksof the winner node for each time frame over the above
two maps. Figure 3 shows the method for drawing tracksof the winner node. Tracks of winner nodes are drawn ina line such that newer data are emphasized. Drawing
tracks of winner node have the aim of presenting thechange in the temporal pattern in the visually.
Past

Time

Standard SOM
Competitive Layer

Output Example
Input Layer
A (t)

B (t)

A (t-1)B (t-1)A (t-2)Current
B (t-2)

Input A
Input B

Winner Node on
Competitive Layer
Figure 3.

Winner Node

Visualizing Tracks of Winner Node

Delayed-Time Unit

Figure 2.

Model of SOM using Delayed-Time Unit

4. Method for Visualizing Information ofTime-series Data
For this research we use delayed-time units to learn
time-series pattern on SOM. For the output we construct
the map using the weight values of the competitive layer.
For this experiment we construct the following two©ISAROB 2007

117

5.

Experiment Result

For the SOM input data, we use all of the 12 sensor
types, and 20 continuous time-series data (each data at
30 seconds delay for 10 minutes) per each sensor type as
1 time-series sequence. The input data used for learning
was selected from a day high frequency of abnormal
range of CO (>100ppm) from the incinerator sensor database. 15000 time-series sequences were used for the
input data set.
For this experiment we used following SOM. The

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
competitive layer of size the 100x100, and the weights
for the competitive layer was initialized by random. Forneighborhood function we use the Gaussian type. For thedecision of winner node we use Euclidian distance.
Figure 4 and Figure 5 is the maps that constricted byproposed method at the same time frame. Figure 6 shows

the change of the time series of CO concentration
corresponding to tracks of the winner node. Figure 4
shows that the map that consists of difference between
predicted values and the actual values was able tovisually represent low and high dioxin emission
combustion states. Similarly, Figure 5 shows that themap that consists of an element
ement of sensor data was able
to visually classify abnormal range.

6.

Figure 4. Map of difference between predictedvalues and the actual values

Conclusion

Through computer simulation, we showed that the
proposed SOM method was able to visually represent
low and high dioxin emission combustion states. Thechange in state of the waste incinerator was able to be
confirmed by drawing tracks of the winner node visually.
Further, we felt that the tracks of the winner nodecontain important information on the time-series pattern.
For future works, we will consider methods tovisualize time-series patterns, as well as reevaluate
learning method for time-series data using SOM.
Specially, we plan to divide the learning of time-series
data in two stages. We hope that by separately training
the time-series pattern and state at a time frame will
enable visualizing these information more directly.

References

Map of an Elements Weight Values( CO concentration)

CO

Figure 5.

0.8

0
1

101

Figure 6.

©ISAROB 2007

201

301

401

501
601
Time Frame

CO Concentration

118[1] Makoto Fujiyoshi, Ryutaro Fukushima, Mitiharu
Masuya, “Intelligent Control System for Fluidized Bed
Incinerator”, Proceedings of 18th Fuzzy System
Symposium, pp.25-28, 2002 (in Japanese)[2] H. Ichihashi, et. al, “Fuzzy Bi-plot of Correlation
Analysis for Waste Incinerator”, Proceedings of 19th
Fuzzy System Symposium, 2003 (in Japanese)[3] R. Fukushima, “Fractal Fuzzy Intelligent Control
System”, Proceedings of 20th Fuzzy System Symposium,2004 (in Japanese)[4] Kenneth J. Mackin, Ryutaro Fukushima Makoto
Fujiyoshi, "Waste Incinerator Emission Prediction using
Recurrent Neural Networks", Proceedings of the 2nd
International Conference on Soft Computing andIntelligent Systems, 2004 (in Japanese)
[5]
Teuvo
Kohonen,Self-Organizing
Maps,
Springer-Verlag, 1984
[6] Takashi Yamaguchi, Kenneth J. Mackin,"Architecture of Self-Organizing Map for Time Series
Data", Proceedings of 21th Fuzzy System Symposium,2005 (in Japanese)[7] Takashi Yamaguchi, Kenneth J. Mackin,"Architecture of feedback SOM for learning time-series
data ", Proceedings of 20th Annual Conference of theJapanese Society for Artificial Intelligence, 2006 (inJapanese)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Design of a Real Time Evaluation System for
Multiple Neuro-Biological Signals
T. Zhang*, M. Nakamura**, T. Sugi** and H. Shibasaki****
Dept. of Automation, Tsinghua University, Beijing 100084, China**
Dept. of Advanced Systems Control Engineering, Saga University, Saga 840-8502, Japan***
National Institutes of Health, Bethesda, MD 20892-1428, USA
Abstract
This paper introduces the design of a real timeevaluation system for multiple neuro-biological signals.
In this system, multiple types of neuro-biologicalsignals can be automatically processed in a single
system in real time, including EEG, EMG, EOG, ECG,
etc. It not only can display the wave shapes of thesesignals both in online and offline, calculate the featureparameters, but also can evaluate these signals based on
different criteria. Therefore, it is possible to provide
information about multiple types of neuro-biologicalsignals simultaneously, which will provide great
assistance for doctors to interpret neuro-biologicalsignals for judging the diseases and for engineers todefine the control signals for BCI device using different
types of neuro-biological signals. This system will bean open system that can integrate more kinds ofneuro-biological signals. New functions can be easily
added into this system according to different requests.

methods of processing and evaluating neuro-biologicalsignals, developing a prototype of this system for EEG[4], designing some types of BCI devices [5][6], and soon. In this research, we design a real time evaluation
system for multiple neuro-biological signals based on
our previous works. In the paper, we will introduce thestructure, main functions, operation of this system. In
the discussion part of this paper we will introduce thepossible applications of this system and the future
research on this system.

2.

System Structure

In the real time evaluation system for multiple
neuro-biological signals, as illustrated in Fig.1, it
mainly has the following modules: (1) human-friendly
interface module; (2) real time pre-processing module;(3) evaluation module; (4) output module for theinterpretation of neuro-biological signals and the designof controlling signals for BCI device.
Real Time Evaluation System for Neuro-biological System

1.

Introduction

Interface Module

At present, there already have plenty of researcheson the processing and evaluation of neuro-biologicalsignals and the applications of these signals for theinterpretation of these signals and control ofBrain-Computer Interface (BCI) devices. The main
methods include Fourier analysis, brain mapping,feature extraction and pattern recognition [1–3].
However, it is lack of an effective system which canprocess and evaluate multiple types of neuro-biologicalsignals in a single system in real time, so that it ispossible to provide information about several types ofneuro-biological signals simultaneously. Therefore, it
can be adopted to assist doctors to interpret
neuro-biological signals for judging the diseases andassist engineers to define the control signals for BCI
devices. In this research we want to develop a new realtime evaluation system for multiple neuro-biologicalsignals, such as electroencephalogram (EEG),
electromyogram (EMG), electrooculogram (EOG),
electrocardiogram (ECG), and so on.
So far, we have already carried out a lot of works
for developing this system, such as summarizing the©ISAROB 2007

119

original
signals

processed
signals

Pre-processing
Moduleprocessed
signals

Evaluation
Moduleevaluation
results

processed
signals

Output Module

Fig.1 System structure of real time evaluation system
for multiple neuro-biological signals
Each module contains the following parts.
(1)Interface
module:ahuman-friendly
windows-type menu, displaying wave shape region,
displaying processing and evaluation results region, etc.
(2) Pre-processing module: judgement of types ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

neuro-biological signals, segmentation of signals,
elimination of noise in the signals, selection ofqualified signals, etc.
(3) Evaluation module: calculation parts fordifferent types of signals, comparison parts among
segmentations of multiple signals, generating
evaluation reports, etc.
(4) Output module: output control signals throughI/O interface of computer, printing signals as well as
evaluation reports, etc.
The system structure is designed for implementing
the functions introduced in the following section.
Actually, it can be extended according to the requests
on this real time evaluation system for multiple
neuro-biological signals.

offline from the existed signals. Any ways are both
important to obtain the qualified signals..
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3.

The real time evaluation system for multiple
neuro-biological signals mainly has the following threefunctions: (1) real time pre-processing; (2) evaluationof multiple neuro-biological signals; (3) output forinterpretation of signals and controlling BCI devices
using information from multiple neuro-biologicalsignals. In the following parts, these three functions are
explained in detail.

3.1 Pre-processing of multiple neuro-biologicalsignals
The purpose of pre-processing of multiple
neuro-biological signals is to obtain qualified signals.
First of all, it needs to automatically judge which typeof signals will be processed. Since the basic features ofvarious neuro-biological signals are quite different, it
will be easy to judge the type of signals. For example,
for each type of neuro-biological signal, one specificfrequency region is defined as the judgement criterion.
In the system, to determine the type of signals, it is only
needed to check the specific frequency region.
Secondly, there always have a large mount ofneuro-biological signals. According to the request, they
can be divided into each segment. For example, acontinuous signal can be divided into a set of segments
and each segment may have 5 second long. Therefore,
it will be helpful to make the further processing. This
segmentation work can be automatically made by thesystem in online or offline.
Thirdly, in the pre-processing of signals, one of theimportant jobs is to obtain qualified signals. The
so-called qualified signal is based on the requirement.
For example, Fig.2 (a) illustrates the qualified EEG
signals and Fig.2 (b) the qualified EMG signals. The
selection of qualified signals is based on the calculationof feature parameters. In addition, the noisecontaminated in the signals should be eliminated. There
are two ways to eliminate the noise. One is to eliminate
them when recording the signals in real-time. Anotheris to pick up the segments contaminated with noises©ISAROB 2007

100 V

1s

System Functions

120

1s

(a)
(b)
Fig.2 Example of wave shape displaying on the screen,(a) A qualified EEG signal, (b) A qualified EMG signal.
When selecting the qualified signals andeliminating the noise, feature parameters are quite
important. For example, the features of EEG records ineach channel can be expressed by the periodogram
parameters, including:z Amplitude: Az ( x) = 6 S z ( x)z Symmetry:Pz ( x, y) = 6 S z ( x − y ) / 6 S z ( x + y ) × 100
z Asymmetry:G z ( x, y ) = 6 S z ( x + y) / 6 S z ( x − y) × 100
z Extension: E z ( x, y ) = 6 S z ( y ) / 6 S z ( x) × 100
z Duration: D z ( x ) = S z ( x ) / S T ( x ) × 100
z Central frequency:

Fz ( x) = f zc ( x) | Pmax ⋅Pmax =max

f lower ≤ f z ( x )≤ f upperP ( f z ( x))z Normalization of parameter:Φ Qz (i ) = (Q z (i ) − min Q z (i )) /(max Q z (i ) − min Q z (i ))where x, y both represent each electrode of Fp1, F3, C3,
P3, O1, Fp2, F4, C4, P4, O2, F7, T3, T5, F8, T4, T6; zdenotes the respective EEG components: L(0–0.5 Hz),δ(0.5–4 Hz), θ(4–8 Hz), α(8–13 Hz), β(13–25 Hz),T(0.5–25 Hz), H(35–50 Hz); f z (x) is the frequency

within the frequency band of z in channel x; f zc (x ) is
the central frequency within the frequency band of z inchannel x and corresponding to the maximal power
spectrum; f lower is the lower limit of the frequency
band; f upper is the upper limit of the frequency band;and i is the segment number.
The following items are employed in theparameters definition:z S z (x ) is the amount of EEG components
calculated by the summation of periodogram withthe frequency band of z in channel x;

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007z S T (x ) is

the amount of EEG components
calculated by the summation of periodogram withthe frequency band of 0.5–25 Hz in channel t;z S z ( x − y ) is the amount of EEG components
calculated by the summation of periodogram withthe frequency band of z in channels x and y, inwhich the EEG time series of channel y is
subtracted from that of the channel x;
z S z ( x + y ) is the amount of EEG components
calculated by the summation of periodogram withthe frequency band of z in channels x and y, inwhich the EEG time series of channel x adds thatof the channel y.
Based on the above parameters and relative criteria
of judgement, the qualified EEG records can beselected. Due to the limitation of space, we will not
introduce the criteria of judgement.

3.2 Evaluation of multiple neuro-biologicalsignals
One of most important functions of the real timeevaluation system for multiple neuro-biological signals
is to evaluate various types of neuro-biological signals.
Therefore, for this purpose the system should be able tocarry out calculation, comparison and making report.
The evaluation of neuro-biological signals is also
based on the feature parameters of neuro-biologicalsignals. For different types of neuro-biological signals,
the evaluation items and equations are quite different.
For example, based on the following criteria, thetechnical artifacts in EEG can be evaluated.

z Electrode:Aδ ( x) ≥ 25µV ,(x: respective electrdes on oneside)Pδ ( x, y ) / 11 ≤ 30% , (x, y: respective

neighborhood electrode on one side of thescale)Pδ ( x, y ) / 8 ≤ 50% , (x, y: a pair of



∑

∑

homologous electrodes on both sides)z Base-line drift:
 AL ( x) ≥ 60 µV , (x: respective electrodes)
With the same way, various types ofneuro-biological signals can be evaluated for different
items.

3.3 Output of the system
The output is an important function of the real timeevaluation system for multiple neuro-biological signals
because it is related with the application of the system.
In this system, it has two types of output ways. Some
information will be displayed on the screen of thecomputer. Some information can be directly output
through I/O interface of computer so that it can be
adopted to control the BCI devices.
Concerning the output of information by displaying
on the screen, as illustrated by Fig.3, it could be thesignal recordings, control signals, evaluation reports,
etc., for multiple neuro-biological signals.

Fig.3 Output of information by displaying on the screen of computer©ISAROB 2007

121

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Concerning the output of information through I/Ointerface of computer, it can be directly adopted tocontrol BCI devices. For example, a developed meal
assistance robot has been directly controlled by thesignals generated by EOG [5]. Besides, considering therequirement from users, based on the above system,
multiple neuro-biological signals can be processed andadopted simultaneously to control BCI devices.

course of getting the neuro-biological signals. In
addition, with this system we can create aneuro-biological signals database for each specificsubject. This system not only can be regarded as a tool
to process the multiple neuro-biological signals, butalso can be regarded as a platform for interpretation ofneuro-biological signals and controlling BCI devices.

6.
4.

Since the real time evaluation system for multiple
neuro-biological signals has a human-friendly interface,
the operation of the system is easy by use of its menu.
Based on its different functions, its operation has threestages.
Stage 1: Pre-processing:For per-processing, the operation procedure is as
below:
Step 1: Input data;
Step 2: Select pre-processing mode: online oroffline;Step 3: Start pre-processing program;
Step 4: Display the pre-processing results.
Stage 2: Evaluation
For this stage, the operation procedure is as below:
Step 1: Input the processed data;
Step 2: Select the evaluation items;
Step 3: Start the evaluation program;
Step 4: Display the evaluation reports.
Stage 3: Output
For this stage, the operation procedure is as below:
Step 1: Input the processed data;
Step 2: Select the output mode: displaying or
outputting control signals;
Step 3: Start the output program;
Actually, in the system there also have many otheritems, which can be executed by use of menu of thesystem. In the near future, we will also extend thefunctions of the system.

5.

Conclusions

System Operation

Discussion

The proposed real time evaluation system for
multiple neuro-biological signals has many attractive
applications. Basically, it can be adopted for doctors tointerpret the neuro-biological signals and therefore
diagnose the diseases. Particularly, it can provide
enough information from multiple neuro-biologicalsignals. In addition, the output of the system can bedirectly used to design the control signals to control the
BCI devices. Besides, the proposed system can be used
for other situation. For example, when using the online
processing of neuro-biological signals, engineers can
assist doctors to obtain qualified signals during the©ISAROB 2007

122

This paper introduces the design of a real timeevaluation system for multiple neuro-biological signals.
This system not only can pre-process various types ofneuro-biological signals online or offline, but also canevaluate the signals based on the user’s requirement.
The output of the system can be possibly adopted toassist doctors to interpret neuro-biological signals for
judging the diseases and define the control signals for
BCI devices. In the near future, we will continue todevelop this system and extend its functions.

References[1] E. R. John, L. Prichep, J. Fridman and P. Easton,“Neurometrics: computerassisted differential diagnosisof brain dysfunctions”, Science, vol. 239, pp.162–169,
1988.
[2] J. Hasan, K. Hirvonen and A. Varri, “Validation ofcomputer analyzed polygraphic patterns duringdrowsiness and sleep onset”, Electroenceph
ClinNeurophysiol, vol 87:, pp.117–127, 1993.
[3] M. Nakamura, H. Shibasaki, K. Imajoh, S. Nishida,
R. Neshige and A. Ikeda, “Automatic EEGinterpretation: a new computer-assisted system for the
automatic integrative interpretation of awake
background EEG.”, Electroenceph Clin Neurophysiol,
vol. 82, pp. 423–431, 1992.
[4] M. Nakamura, Q. Chen, T. Sugi, A. Ikeda and H.
Shibasaki, “Technical quality evaluation of EEG
recordingbasedonelectroencephalographers’
knowledge”, Medical Engineering and Physics, Vol. 27,
No. 1, pp.93-100, 2005.
[5] T. Zhang and M. Nakamura, “Neuralnetwork-based hybrid human-in-the-loop control formeal assistance orthosis”, IEEE Trans. on Neural
Systems and Rehabilitation, Vol.14, No.1, 2006.
[6] M. Nakamura and T. Zhang, “Human assistancerobot following the human intention extracted fromneuro-biological signals”, Proceedings of the 16th
International Conference on Systems Engineering(ICSE 2003), pp.506-511, Coventry University, UK,
Sept. 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Feedback GMDH-type neural network algorithm self-selecting
optimum neural network architecture
Tadashi Kondo

Junji Ueno

School of Health Sciences,
the University of Tokushima,
Tokushima, 770-8509, Japan

School of Health Sciences,
the University of Tokushima,
Tokushima, 770-8509, Japan

Abstract
In this study, a feedback Group Method of Data Handling(GMDH)-type neural network algorithm self-selecting the
optimum neural network architecture is proposed. In thisalgorithm, the optimum neural network architecture is
automatically selected from three types of neural networkarchitectures such as the sigmoid function type neuralnetwork, the radial basis function (RBF) type neuralnetwork and the polynomial type neural network.
Furthermore, the structural parameters such as the numberof layers, the number of neurons in the hidden layers and theuseful input variables are automatically selected so as tominimize the prediction error criterion defined as Akaike’s
Information Criterion (AIC). The feedback GMDH-type
neural network has a feedback loop and the complexity of
the neural network increases gradually using feedback loop
calculations so as to fit the complexity of the nonlinear
system. This algorithm is applied to the identification
problem of the complex nonlinear system.
Keywords: GMDH, Neural network, Identification

1 Introduction
The Group Method of Data Handling (GMDH)-type
neural networks and their applications have been proposedin our early works [1],[2]. The GMDH-type neural networks
can automatically organize the neural network architectureby using the heuristic self-organization method [3],[4]. The
GMDH-type neural networks can also determine such
structural parameters as the number of layers, the number ofneurons in the hidden layers and the useful input variables.
In the GMDH-type neural networks, the neural networkarchitecture is organized so as to minimize the prediction
error criterion defined as Akaike’s Information Criterion(AIC) [5] or Prediction Sum of Squares (PSS) [6].
In this study, the feedback GMDH-type neural network
algorithm self-selecting the optimum neural networkarchitecture is proposed. In this algorithm, the optimum
neural network architecture is automatically selected fromthree types of neural network architectures such as the
sigmoid function type neural network, the radial basis
function (RBF) type neural network and the polynomial
type neural network. The feedback GMDH-type neuralnetwork has a feedback loop and the complexity of theneural network increases gradually using feedback loop

©ISAROB 2007

123

calculations so as to fit the complexity of the nonlinear
system.
The feedback GMDH-type neural network algorithm
proposed in this paper is applied to the identification
problem of the complex nonlinear system. The optimum
neural network architecture fitting the complexity of thenonlinear system is selected from three types of the neuralnetwork architectures. The identification results of the
feedback GMDH-type neural network are compared withthose of the GMDH algorithm and the conventional
multi-layered neural network trained using the backpropagation algorithm. It is shown that the feedback
GMDH-type neural network is a very useful identification
method of the complex nonlinear system because theoptimum neural network architecture is automaticallyorganized so as to minimize AIC.

2 Feedback GMDH-type neural network
The architecture of the feedback GMDH-type neuralnetwork proposed in this paper has a feedback loop as
shown in Fig.1. The feedback GMDH-type neural network
algorithm can select the optimum neural networkarchitecture from three types ofneural networkarchitectures such as the sigmoid function type neuralnetwork, the RBF type neural network and the polynomial
type neural network. The feedback GMDH-type neuralnetwork algorithm uses three types of neuron architectures
which are the sigmoid function type neuron, the RBF typeneuron and the polynomial type neuron.
Σ f
X1

Σf

X2
X3
.
.
.
XP

Σ f
.
.
.

Σ f

Σ

φ

.
.

Σf
Σ f
Σf

Fig.1 Architecture of the feedback GMDH-type neuralnetwork

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.1 First loop calculation
First, all data are set to the training data. In this algorithm,
it is not necessary to separate the original data into thetraining and test data because AIC can be used fororganizing the network architectures. Then the architectureof the input layer is organized.
1) Input layer
uj=xj (j=1,2,…,p)(1)
where xj (j=1,2,…,p) are the input variables of the system,and p is the number of input variables. In the first layer,
input variables are set to the output variables.
2) Hidden layer
All combinations of the r input variables are generated.
For each combination, three types of neuron architectures
which are the sigmoid function type neuron, the RBF typeneuron and the polynomial type neuron, are generated and L
neurons which minimize AIC value are selected for eachtype of neuron architectures.
Furthermore, for each combination, optimum neuron
architectures fitting the characteristics of the nonlinear
system are automatically selected by using AIC.
a) Sigmoid function type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj +w8uiuj2+w9uj3 – w0θ(2)
f : (Nonlinear function)yk =1
1 + e ( − zk )ii) The second type neuron
Σ: (Linear function)zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Nonlinear function)yk =1
1 + e ( − zk )(3)2ii) The second type neuron
Σ: (Linear function)zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Nonlinear function)2y k = e ( − zk )

(4)

(5)

(7)

(8)
(9)

c) Polynomial type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj+w8uiuj2©ISAROB 2007

L

φ * = a0 + ∑ ak y k

(14)

k =1

b) RBF type neuron:i) The first type neuron
Σ: (Nonlinear function)zk= w1ui+w2uj+w3uiuj+w4ui2+w5uj2+w6ui3+w7ui2uj+w8uiuj2+w9uj3 – w0θ(6)
f : (Nonlinear function)y k = e ( − zk )+w9uj3 – w0θ(10)
f : (Linear function)yk= zk(11)ii) The second type neuron
Σ: (Linear function)(12)
zk= w1u1+w2u2+w3u3+…+wrur – w0θ ( r<p )f : (Linear function)yk= zk(13)
Here, θ1 =1 and wi (i=0,1,2,…) are the weights between
the first and second layer and estimated by applying the
stepwise regression analysis [7] to the training data. Only
useful input variables ui (i=1,2, …) are selected by using
AIC. In the first type neuron, the value of r, which is thenumber of input variables u in each neuron, is set to two. In
the second type neuron, the value of r, which is the numberof input variables u in each neuron, is set to be greater than
two and smaller than p. p is the number of input variables xi(i=1,2,…,p). The output variables yk of the neurons arecalled as the intermediate variables.
L neurons having the smallest AIC values are selected
for three types of neuron architectures which are thesigmoid function type neuron, the RBF type neuron and thepolynomial type neuron. The output variables yk of L
selected neurons for three types of neuron architectures are
set to the input variables of the neurons in the output layer.
3) Output layer
For three types of neural network, the outputs yk of theneurons in the hidden layer are combined by the following
linear function.

124

Here, L is the number of combinations of the input variables
and yk is the intermediate variables. The useful intermediatevariables yk are selected by using the stepwise regression
analysis in which AIC is used as the variable selection
criterion.
Equation (14) is calculated for three types of neuralnetwork architectures which are the sigmoid function typeneural network, the RBF type neural network and thepolynomial type neural network. Then, the neural networkarchitecture which has smallest AIC value is selected as the
GMDH-type neural network architecture from three typesof neural network architectures
Then, the estimated output values φ* which is selected in
the output layer is used as the feedback value and it iscombined with the input variables in the next loop
calculation.

2.2 Second and successive loop calculations
The optimum neural network architecture is selectedfrom three types of neural network architectures in the
output layer. Therefore, in the second and successive loop
calculations, only one type of neuron architecture, which isthe sigmoid function type neuron or the RBF type neuron orThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the polynomial type neuron, is used for the calculation.
First, the estimated output value φ* is combined with the
input variables and all combinations between the estimatedoutput value φ* and the input variables are generated. The
same calculation as the first feedback loop is carried out foreach combination. Here, only one type of neuron
architecture, which is selected in the first loop calculation, isused in the calculation. When AIC value of the linear
function in (14) is increased, the loop calculation is
terminated and the complete neural network architecture isorganized by the L selected neurons in each feedback loop.

3 An application
identification

tononlinearsystemTo verify the performance of the feedback GMDH-type
neural network, it is applied to the nonlinear system
identification problem.

20∑| φ − φ |J 1 = i =1 20∑| φ |(19)
where φi (i =1,2,⋅⋅⋅,20) were the actual values and φi* (i=1,2,⋅⋅⋅,20) were the estimated values by the feedback
GMDH-type neural network. The values of J 1 for four
output variables are shown in Table1.
(6) The prediction accuracy
The prediction accuracy was evaluated by using thefollowing equation:40∑| φ − φ |J 2 = i =21 40(1) Input variables
Four input variables were used but the useless input
variables x4 was automatically eliminated.
(2) Number of selected neurons
Four neurons were selected in the hidden layer.
(3) Selection of the neural network architecture
Polynomial type neural network architecture was selected as
the GMDH-type neural network architecture in the first
feedback loop calculation.
(4) Structure of the neural network
The calculation of the GMDH-type neural network wasterminated at the seventh feedback loop calculation.
(5) Estimation accuracy
The estimation accuracy was evaluated by using thefollowing equation:©ISAROB 2007

125

*
i

i

∑| φ |

i = 21

3.2 Identification results obtained by using the
GMDH-type neural network

i

i =1

3.1 Nonlinear system identification problem
The nonlinear system is assumed to be described by thefollowing equations:
φ1 =(1.0+1.1x1+1.2x2+1.3x3)4 + ε1(15)
φ2 =(1.0+1.4x1+1.5x2+1.6x3)4 + ε2(16)
φ3 =(1.0+1.7x1+1.8x2+1.9x3)4 + ε3(17)
φ4 =(1.0+2.0x1+2.1x2+2.2x3)4 + ε4
(18)Here, φ1 ∼ φ4 show output variables and x1 ∼ x3 show input
variables. ε1∼ ε4 show noises. Furthermore, x4 is added as the
input variable of the neural network in order to check thatthe feedback GMDH-type neural network can eliminate theuseless input variables. The neural network is organized byusing twenty training data. The prediction is carried out by
using twenty testing data so as to check the generalization
ability.

*
i

i

i

(20)
where φi (i =21,22,⋅⋅⋅,40) were the actual values and φi* (i=21,22,⋅⋅⋅,40) were the predicted values by the feedback
GMDH-type neural network. The values of J2 for four
output variables are shown in Table1.
Table 1 Prediction and estimation accuracy
Models
GMDH-NN
GMDH
NN

J
J1
J2
J1
J2
J1
J2

Φ1
0.013
0.025
0.056
0.055
0.119
0.114

Φ2
0.022
0.028
0.058
0.058
0.133
0.133

Φ3
0.023
0.029
0.038
0.044
0.108
0.102

Φ4
0.024
0.029
0.039
0.044
0.11
0.109

(7) Variation of AIC and estimated values
The variation of AIC in Eq.(14) of the output variables φ1 isshown in Fig.2. It decreased gradually by the feedback loop
calculations and converged at the seventh feedback loop
calculation. The estimated values of φ1 by the feedback
GMDH-type neural network is shown in Fig.3. We can seethat the estimated values are very accurate.
3.3 Comparison of the feedback GMDH-type neuralnetwork and other models
The identification results were compared with those of
the GMDH algorithm and the conventional multilayered
neural network trained using the back propagation
algorithm.
(1) The GMDH algorithm
The identification results were referred from [8]. Four input
variables were used but the useless input variable x4 wasautomatically eliminated. Four intermediate variables wereselected. The calculation was terminated at the fourth layer.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The values of J1and J2 are shown in Table1.
(2) The conventional multilayered neural network
The neural network had three layered structures. Four input
variables were used in the input layer and eight neurons
were used in the hidden layer. The neurons in the output
layer were described by the linear function. The values of J1
and J2 are shown in Table1.

-50
-100

5 Conclusion

AIC -150

In this study, the feedback GMDH-type neural network
self-selecting optimum neural network architecture wasproposed. This algorithm can automatically organize amultilayered neural network architecture fitting thecomplexity of the nonlinear system by using the heuristic
self-organization method. It is very easy to apply thisalgorithm to the identification problem of the practicalcomplex system because the optimum neural networkarchitecture fitting the complexity of the nonlinear system is
automatically organized. This algorithm was applied to thenonlinear system identification problem and it was shownthat this algorithm was a very useful method.

-200
-250
1

2

3

4

5

6

7

Loop Number
Fig.2 AIC variation°architecture. So the accuracy of the neural network was not
good.
The feedback GMDH-type neural network can organize
the conventional neural network architecture (sigmoid
function type architecture) and the GMDH architecture(polynomial type architecture). This algorithm contains thecharacteristics of the conventional neural network and the
GMDH algorithm and it is a very flexible method for theidentification problem of the complex nonlinear system.

° GMDH-type NN •• Actual values

40000

30000

References

φ1
20000

10000

0
0

5

10

15

20

25

30

35

40

Data number
Fig.3 Estimated values of φ1 by feedback GMDH-type
neural network

4 Discussion
From the identification results, we can say the following:(1) Both estimation and prediction errors of the feedback
GMDH-type neural network were smallest in the threeidentified models. We can see that the feedback
GMDH-type neural network was a very accurate
identification method for the nonlinear system.
(2) In the feedback GMDH-type neural network, AIC value
at the first loop calculation was not small but it was
gradually decreased by the feedback loop calculations. So
we can see that the feedback loop calculation plays a very
important role in the feedback GMDH-type neural network.
(3) In the conventional neural network, the effects of highorder terms of the input variables are not considered.
Furthermore, it does not have the ability of self-selecting
useful input variables and the optimum neural network©ISAROB 2007

126

[1] T.Kondo, “GMDH neural network algorithm using theheuristic self-organization method and its application to thepattern identification problem”, Proc. of the 37th SICE
Annual Conference, pp.1143-1148, 1998.
[2] T.Kondo, “Revised GMDH-type neural networks using
prediction error criterions AIC and PSS”, Proc. of SCIS &ISIS 2004, WP-6-4, pp.1-6, 2004.
[3] S.J.Farlow ed, Self-organizing methods in modeling,
GMDH-type algorithm, Marcel Dekker, Inc., New York,1984.
[4] A.G.Ivakhnenko, “Heuristic self-organization in
problems of engineering cybernetics”, Automatica, Vo.6,
No.2, pp.207-219, 1970.
[5] H.Akaike, “A new look at the statistical model
identification”, IEEE Trans. Automatic Control, Vol.AC-19,
No.6, pp.716-723, 1974.
[6] H.Tamura and T.Kondo, “Heuristics free group methodof data handling algorithm of generating optimum partial
polynomials with application to air pollution prediction”,
Int. J. System Sci., Vol.11, No.9, pp.1095-1111, 1980.
[7] N.R.Draper and H.Smith, Applied Regression Analysis,
John Wiley and Sons, New York, 1981.
[8] T.Kondo, “GMDH type Neural Network Algorithm
Identifying a Network Structure with the Heuristic
Self-Organization Method”, Trans. ISCIE, Vol.11, No.4,
pp.198-207, 1998.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Remarks on adaptive type neural network direct controller
with separate learning rule of each layer
Takayuki Yamada
Department of Computer and Information Sciences
Faculty of Engineering
Ibaraki University
4-12-1 Nakanarusawa, Hitachi, Ibaraki, 316-8511, Japan

Abstract
This paper proposes a new learning rule ofmulti-layer neural network controllers in order to eliminate
an inference of neural network weights of each layer andto discuss stability condition of nonlinear three layer
neural network controllers. This learning rule is that theneural network weights between one layer and next layer
are only changed at same time and other neural network
weights are not changed, and after some sampling numbers,
the neural network weights between other layer and nextlayer are changed. The proposed learning rule is applied
to an adaptive type neural network direct controller and asimulation result shows it performed well.
1. Introduction
Many studies have been undertaken in order toapply both the flexibility and the learning capability ofneural networks to control systems.[1]-[3] However,there are few attempts to clear the stability conditions ofneural network control systems. Among these attempts,
we proved local stability condition of a three layer neuralnetwork direct controller whose neurons have a linear
input output relation.[1][2] We also confirmed that theinterference between neural network weight learning ofeach layer causes more difficulty to analyze the stability
condition in comparison with the stability analysis ofconventional adaptive controllers in this study. This
interference is caused by an usual neural network learning
rule so as to change whole neural network weights at sametime. In other words, the usual neural network learning
rule has good efficiency of the neural network weight
convergence, but it causes difficult stability analysis of theneural network controllers. One of simple methods to
avoid this interference is to use a two layer neural network
as a controller.
However, the nonlinear mapping
capability of such neural network is limited because it wasproved that a nonlinear three layer neural network was able
to approximate any continuous nonlinear functions. That

©ISAROB 2007

127

is, this method means that such neural network controllers
have less nonlinear mapping capability in comparison with
the nonlinear three layer neural network.
Another
method to avoid above problem is to use a linear threelayer neural network. However, the linear three layer
neural network can not realize sufficient approximation for
nonlinear functions. This also means that such neuralnetwork controllers have less nonlinear mapping
capability.
Thus, we propose a new neural network learning
rule for three layer nonlinear neural network controllers inthis paper. It is called separate learning rule of each layer
in this paper. This learning rule is that the neural network
weights between one layer and next layer are only changed
at same time and other neural network weights are notchanged, and after some sampling numbers, the neuralnetwork weights between other layer and next layer arechanged. That is, we can eliminate the interference
between layers and realize easier discussion about thenonlinear neural network controller stability although theseparate learning rule has slow convergence in comparison
with the usual neural network learning rule. We canrealize the same nonlinear mapping capability because theneural network has the nonlinear three layer structure.
The proposed learning rule is applied for an adaptive typeneural network direct controller and the discussion about
its stability condition is presented. A simulation result of
the adaptive type neural network direct controller with theseparate learning rule shows that the proposed learning
rule can be realized.
2. Interference between neural network weights
This section explains the structure of the adaptive
type neural network direct controller briefly.[1][2] Theinterference between neural network weight learning ofeach layer is focused and it is discussed why the stability
analysis of the nonlinear neural network controllers withthe three layer structure is difficult in comparison with that

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007of controllers with linear two layer structure such as
conventional adaptive controllers.
Based on this
discussion, necessity of the separate learning rule of eachlayer is confirmed.
The following object plant is selected in this paper.

Y(k) = f(Y(k-d)•••Y(k-d–n),U(k–d)•••U(k–d–m))(1)

Where Y(k) is the plant output, U(k) is the plant input, k is
the sampling number, d is the dead time, n&m are theplant orders and f is the nonlinear function which
expresses a nonlinear characteristic of the plant. The
output error ε(k) is defined by the following equation.

As shown in eq.(5), the learning of the weight matrix W
uses the weight vector ω and the learning of the weightvector ω uses the weight matrix W. This fact causes theinterference between the neural network weight learning ofeach layer and the difficulty of the stability analysis incomparison with that of the linear two layer structure
controllers such as the conventional adaptive controller.
The separate learning rule is proposed as one solution ofabove problem in the next section.

Yd
Neural network

U
Plant

Y

Fig.1 Scheme of direct controller.

!(k) = Yd(k) – Y(k)(2)

Where Yd is the desired value. Figure 1 shows thescheme of the direct controller. As shown here, the plant
input is only composed of the neural network output in the
direct controller. From eqs.(1) and (2), the neuralnetwork input vector I is selected as follows;

Learning forweight vector !

Learning forweight matrix W

Learning forweight vector !

Learning
sectionLearning
sectionLearning
sectionLearning forweight matrix W
Learning
sectionSampling number

IT (k) = [Yd (k+d ),Y(k)•••Y(k–n),U(k–1)•••U(k–m)](3)

To simplify, the following discussion selects that theneural network has three layers, its output neuron is one
and both the number of neurons in the input and hidden
layers are the same as the order of the input vector I.
When the adaptive type learning is selected, the usualneural network learning rule is expressed as the following
equation through the use of the δ rule.

"J(k)"W( k–d )#J(k)!(k+1) = !(k–d ) – "'# !(k–d )W(k+1) = W(k–d ) – !'J(k) =

1 2
! (k)
2
(4)

Where W is the weight matrix composed of the neuralnetwork weights between the input and hidden layers , ω is
the weight vector composed of the neural network weights
between the hidden and output layers, J is the cost functionand η’ is the parameter determining convergence speed.
When the neural network is linear, we derive the following
learning rule from eq.(4).

W(k+1) = W(k–d ) – !"(k)#(k–d )IT(k–d )!(k+1) = !(k–d) – "#(k)W(k–d)I(k–d)"Y(k)! = !'()"U(k–d )©ISAROB 2007

(5)

128

Fig.2 Scheme of separate learning of each layer.
3. Separate learning rule of each layerThis section proposes the separate learning rule ofeach layer as a solution to overcome the interference
between the neural network weights. Figure 2 shows its
scheme. This learning rule is that the neural network
weights between one layer and next layer are only changed
and other neural network weights are not changed withinsome sampling numbers which is called learning section.
For this example, the weight vector ω is selected in thefirst learning section. That is, the learning rule for theweight ω of eq.(4) is only used. In the second learning
section, the neural network weights between other layer
and the next layer are changed. For this example, theweight matrix W is selected and the learning rule for theweight matrix W of eq.(4) is only used. As mention
above, the neural network weights of each layer are
independently changed. That is , we can eliminate theinterference of the neural network weights.
Thenonlinear mapping capability is not reduced because ourneural network has the three layer structure.
The stability condition of the weight vector ω isbriefly discussed in the neighborhood of the converged
vector. The following equation is obtained from theseparate learning rule and eq.(4).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
!0 – !(k+1) = !0 – !(k–d ) – "'#Y(k)Sg(WcI(k–d))#U(k–d)(6)

Where ω0 is the converged vector within one learning
section and Sg is the sigmoid function. When the weightvector ω is changed, the weight matrix W is the constantmatrix Wc. Form eq.(6), the following stability conditionof the weight vector ω is obtained when the Taylor
expansion of the output error with regard to the weightvector ω is used. (Details are mentioned in the Appendix)#Y(k)0 ! "( #U( k–d ) )$0 ! 2"Y(k)! = !'()"U(k–d )(7)

Where λ0 is the maximum eigen value of the following
matrix P.

P = Sg(WcI (k–d)){ Sg(WcI (k–d))} T(8)

The stability condition of the weight matrix W can beobtained in the similar method if we can use someassumptions. (Details are mentioned in the Appendix)

0.8

Plant output(10)

Where Xg is the parameter determining the sigmoid
function shape. Xg=0.5 is selected in this simulation.
Figure 3 shows the simulation result where η=1.5
and the learning section is 2 cycle of the desired value.
The solid line is the plant output Y and the broken line isthe desired value Yd. As shown here, the output error
suddenly increases after 2 cycle of the desire value, but
after that, the plant output converges with the desired value
as learning progresses.
This result shows that the
proposed learning rule performs well.

This paper proposed the new learning rule of themulti-layer neural network controller in order to eliminate
the inference of the neural network weights of each layer .
We can discuss the stability condition of the nonlinear
three layer neural network controller through the use ofthis learning rule. It was applied to the adaptive typeneural network direct controller and the simulation result
showed that it performed well.

0.4

1000
Sampling number

2000

Fig.3 Simulation result.
4. Simulation
This section shows a simulation result of theadaptive type direct controller with the proposed separate
learning rule of each layer. The simulated plant is follows:
A simulated plant is selected as follows;

Y(k) = - a1 Y(k-1) - a2 Y(k-2)+U(k-1) +bU(k-2) -a3 Y(k-3)+ Cn o nY2 (k-1)©ISAROB 2007

X {1-exp(-4x/Xg )}f(x)= g2{1+exp(-4x/Xg )}4. Conclusion

Plant output
Desired value

00

Where a1, a2 & b are the plant parameters, a3 is the parasiteterm and Cnon is the nonlinear term. For this simulation,a1=-1.3, a2=0.3, b=0.7, a3=-0.03 and Cnon=0.2 are selected.
The rectangular wave is also selected as the desired value
Yd. We select the following sigmoid function f(x) as the
input output relation of the hidden layer neuron.

(9)

129

Acknowledgment
The author wishes to express his thanks to Mr.Masaki
Kobayashi, graduated student, Ibaraki University, for his
programming and simulation.
References[1]T.Yamada and T.Yabuta, “Some Remarks on
Characteristics of Direct Neuro-Controller with Regard to
Adaptive Control”, Transactions of the Society of
Instrument and Control Engineers, Vol.27, No.7,
pp.784-791,1991 (in Japanese)[2]T.Yamada and T.Yabuta, “Adaptive Neural Network
Controllers for Dynamics Systems”, Journal of Robotics
and Mechatronics Vol.2, No.4, pp.29-41, 1989
[3]K.S.Narendra and K.Parthictsarathy, “Identification andControl of Dynamics System Using Neural Networks”,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
IEEE Transaction on Neural Networks, Vol.1, No.1,
pp.4-27, 1990

the weight vector ω(k) is constant vector whose symbol is
ωc. When the chain rule is use, the following equation is
obtained from eqs.(2) and (4).

Appendix
First, this appendix discusses the stability
condition of the weight vector ω.
The following
parameter error vector ζ is defined.

!(k) = "0 – "(k)(a-1)

It is assumed that ε(k)=0, when ω(k)=ω0. From eq.(6)and the first order Taylar expansion of the output error ε(k)with regard to the weight vector ω(k-d), Following
equations are obtained.
!T(k+1) = !T(k–d )[E – "'g2 (k)Sg(WcI (k–d)){ Sg(WcI (k–d))} T ]!Y(k)g(k) =!(k) = g(k) Sg(IT(k–d )WcT)!U(k–d)
(a-2)

!J(k)
!Y(k) !U(k–d)= –"(k)!W(k–d)!U(k–d) !W(k–d)

From the neural network structure, the following equation
is obtained.
n!U(k)= "c iSg'{
Wij(k)I j(k))}I j(k)!Wij(k)j= 1#!"= "(k+1) – "(k–d)= !T(k–d )Q!(k–d )Q = – 2!'g2(k)P + !' 2g4 (k)P2

n

(a-4)

When λi is defined by the eigen value of β, λi is notnegative and the rank of β is 1. That is, the positive λi is
1 and this is the maximum eigen value λ0. Fromeqs.(3)(4), when the following equation is satisfied, Δϕ is
not positive and the neural network controller is stablewith regard to the weight vector ω learning.

j

(a-7)

From eqs.(4) and (a-5)-(a-7), the learning rule of theweight matrix W is expressed as the following equation.
(a-8)! = !'("Y(k))"U(k–d )Here, when the input vector I is continuous, we can derive
the following equation from eq.(8).

W(k+1)I(k+1) ! W(k+d)I(k–d)= W(k–d )I(k–d) + !'"(k)g(k)#(k–d) $cIT(k–d )I(k–d)

(7)

130

(a-9)

The following equation is the definition of the parameter
error vector ζ for the weight matrix W.

!(k)= W0 I(k) – W(k)I(k)(a-10)

Where W0 is the converged weight matrix W within one
learning section. From eqs.(4)(a-9)(a-10) and the firstorder Taylar expansion of output error ε(k) with regard toW(k-d)I(k-d), we can obtain the following equation.

!T(k+1)= !T(k–d){ E – "'g2 (k)#(k–d )!"c "cT#(k–d)IT(k–d)I(k–d)}

Next, the stability condition of the weight matrix
W is discussed. When the weight matrix W is changed,©ISAROB 2007

ij

(a-3)

Q = V – 1(!' 2g4 (k)"2 – 2 !' g2 (k)")V#Y(k)W (k) I (k)) }!
j= 1

W(k+1) = W(k–d ) + !'"(k)g(k)#(k–d )$cIT(k–d )Since P defined by eq.(8) is the real symmetric matrix
whose eigen values are not negative, there is a real
orthogonal matrix V so as to P=V-1βV where β is adiagonal matrix whose diagonal elements are the eigen
values of P. From eq.(a-3), the following equation isderived.

0 ! "( #U( k–d ) )$0 ! 2(a-6)

Where Sg’ is the derivative of the sigmoid function Sg
with regard to its input and n is the number of the input
and hidden layers. We can define the diagonal matrix Γ
whose iith diagonal element is follows;

Sg'{
Where E is the identity matrix. When ϕ(k)=ζT(k)ζ(k) is
selected as a candidate of the Lyapunov function, we canobtain the following equation.

(a-5)(a-11)

From eq.(a-11), we can obtain the stability condition of theweight matrix W in the same way.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Three-Dimensional Parallel Turing Machines
Takao Ito1 , Makoto Sakamoto1 , Yuusuke Nagamizu1 , Kouichi Iihoshi1 ,Naoko Tomozoe1 , Hiroshi Furutani1 , Michio Kono1 , Satoshi Ikeda1 ,Tatsuhiro Tamaki2 , and Katsushi Inoue3
1

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN
Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN
3
Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN
2

Abstract
Informally, a parallel Turing machine (PTM) proposed by Wiedermann is a set of identical usual sequential Turing machines (STM’s) cooperating on two
common tapes − storage tape and input tape. Moreover, STM’s which represent the individual processorsof the parallel computer can multiply themselves in
the course of computation. On the other hand, during the past about twenty-five years, automata on athree-dimensional tape have been proposed as computational models of three-dimensional pattern processing
and several properties of such automata have been obtained. In this paper, we propose a three-dimensional
parallel Turing machine (3-PTM), and investigate its
some properties. Especially, we deal with a hardwarebounded 3-PTM, whose inputs are restricted to cubic
ones. We believe that this machine is useful in measuring the parallel computational complexity of threedimensional images.
Key Words: computational complexity, hardwarebounded computation, nondeterminism, parallel Turingmachine, three-dimensional automaton

1

Introduction

A parallel Turing machine (PTM) is a set of identicalsequential Turing machines (STM’s) cooperating on two
common tapes − storage tape and input tape [5]. Moreover, STM’s which represent the individual processorsof the parallel computer can multiply themselves in thecourse of computation. In [5] it is shown, for example,
that every PTM can be simulated by an STM in polynomial time, and that the PTM cannot be simulated byany sequential Turing machine in linear space.
In [1], two-dimensional version of PTM was investigated. On the other hand, due to the advances in many
application areas such as computer vision, robotics,©ISAROB 2007131

and so forth, it has become increasingly apperant thatthe study of three-dimensional pattern processing has
been of crucial importance. Thus, we think that the
research of three-dimensional automata as a computational model of three-dimensional pattern processing has also been meaningful. During the past about
twenty-five years, automata on a three-dimensional tapehave been proposed and several properties of such automata have been obtained. In this paper, we propose
a three-dimensional parallel Turing machine (3-PTM),
and investigate its some properties. Especially, we deal
with a hardware-bounded 3-PTM, a variant of the 3PTM, whose inputs are restricted to cubic ones. The
hardware-bounded 3-PTM is a 3-PTM, the number ofwhose processors is bounded by a constant or variable
depending on the size of inputs. The investigation of
hardware-bounded 3-PTM’s is more useful than that of
3-PTM’s from the practical point of view.

2

Preliminaries

Definition 2.1. Let Σ be a finite set of symbols, a
three-dimensional tape over Σ is a three-dimensional
rectangular array of elements of Σ. The set of all threedimensional tapes over Σ is denoted by Σ(3) . Given atape x ∈ Σ(3) , for each integer j (1 ≤ j ≤ 3), we letlj (x) be the length of x along the jth axis. The set of
all x ∈ Σ(3) with l1 (x) = n1 ,l2 (x) = n2 and l3 (x)=n3 isdenoted by Σ(n1 ,n2 ,n3 ) . When 1 ≤ ij ≤ lj (x) for eachj (1 ≤ j ≤ 3), let x(i1 , i2 , i3 ) denote the symbol in x
with coordinates (i1 , i2 , i3 ). Furthermore, we definex[(i1 , i2 , i3 ),(i01 , i02 , i03 )],only when 1 ≤ ij ≤ i0j ≤ lj (x) for each integer j(1 ≤ j ≤ 3), as the three-dimensional input tape ysatisfying the following conditions:(1) for each j (1 ≤ j ≤ 3), lj (y) = i0j − ij + 1;

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

(2) for each r1 , r2 , r3 (1 ≤ r1 ≤ l1 (y), 1 ≤ r2
≤ l2 (y), 1 ≤ r3 ≤ l3 (y)), y (r1 ,r2 ,r3 ) = x (r1 +i1 -1,
r2 +i2 -1, r3 +i3 -1). (We call x[(i1 , i2 , i3 ),(i01 , i02 , i03 )] the[(i1 , i2 , i3 ),(i01 ,i02 , i03 )]-segment of x.)
For each x ∈ Σ(n1 ,n2 ,n3 ) and for each 1 ≤ i1 ≤ n1 , 1≤ i2 ≤ n2 , 1 ≤ i3 ≤ n3 , x[(i,1,1),(i1 ,n2 ,n3 )], x[(1,i2 ,1),(n1 ,i2 ,n3 )], x[(1,1,i3 ),(n1 ,n2 ,i3 )], x[(i,1,i3 ),(i1 ,n2 ,i3 )],and x[(1,i2 ,i3 ),(n1 ,i2 ,i3 )] are called the i1 th (2-3) planeof x, the i2 th (1-3) plane of x, the i3 th (1-2) planeof x, the i1 th row on the i3 th (1-2) plane of x, andthe i2 th column on the i3 th (1-2) plane of x, and are
denoted by x(2-3)i1 , x(1-3)i2 , x(1-2)i3 , x[i1 , ∗, i3 ], andx[∗, i2 , i3 ], respectively.
Definition 2.2. Three-dimensional parallel Turing machine (denoted by 4-PTM) is a 10-tuple M = (Q, E,
U , qs , q0 , Σ, Γ, F , δn , δf ), where(1) Q = E ∪ U ∪ {q0 } is a finite set of states;(2) E is a finite set of nondeterministic states;(3) U is a finite set of fork states;(4) qs is the quiescent state;(5) q0 ∈ Q - {qs } is the initial state;(6) Σ is a finite input alphabet (# ∈/ Σ is the boundary symbol );(7) Γ is a finite storage tape alphabet containing thespecial blank symbol B;(8) F ⊆ Q − {qs } is the set of accepting states;(9) δn :E × (Σ ∪ {# }) × Γ →2(Q−{qs })×(Γ−{B})×Din ×Ds (where Din= {east,west, south, north, up, down, no more} and Ds ={left, right, no more}) is a next nondeterministic movefunction; and(10) δf : U × (Σ∪{# }) × Γ → U1≤k≤∞ ((Q−{qs })×(Γ − {B}) × Din × Ds ) is a next fork more function
with the restriction that for each q ∈ U , each a ∈ Σ ∪{# }, and each A ∈ Γ, if δ(q, a, A) = ((p1 , c1 , d11 , d21 ),(p2 , c2 , d12 , d22 ), . . ., (pk , ck , d1k , d2k )), then c1 = c2 =. . . = ck .
As shown in Figure.1, M has a read-only threedimensional rectangular input tape with boundary symbols “#’s”, and one semi-infinite storage tape (extended
to the right), initially filled with the blank symbols. Furthermore, M has inifinite processors, P1 , P2 , . . ., eachof which has its input head and storage-tape head. M
starts in the situation that (1) the processors P1 is in theinitial state q0 with its input head on the upper northwestmost corner of the input tape and with its storagetape head on the leftmost cell of the storage tape, and(2) each of other processors is in the quiescent state qs
with its input head on the upper northwestmost corner©ISAROB 2007

132

Figure 1: Three-dimensional parallel Turing machine.

of the input tape and with its storage-tape head on theleftmost cell of the stroage tape.
Five-way three-dimensional parallel Turing machine(denoted by FV3-PTM) is a 3-PTM, input heads ofwhose processors cannot move up. In this paper, weare concerned with three-dimensional parallel Turingmachines whose input tapes are restricted to cubic
ones. Let L : N → N and H : N → N be functions. A 3-PTM (FV3-PTM) M is called L(n) spacebounded if for any n ≥ 1 and for any input tape x
with l1 (x) = l2 (x) = l3 (x) = n, M on x uses at mostL(n) cells of the storage tape, and M is H(n) hardwarebounded if for any n ≥ 1 and for any input tape x withl1 (x) = l2 (x) = l3 (x) = n, M on x activates at mostH(n) processors. We use the follwing notations:· D3-PTM(L(n), H(n)): the class of sets of cubic tapes accepted by L(n) space-bounded andH(n) hardware-bounded deterministic 3-P T M ’s ·N3-PTM(L(n), H(n)):
the class of sets of cubic tapes accepted by L(n) space-bounded andH(n) hardware-bounded nondeterministic 3-P T M ’s ·DFV3-PTM(L(n), H(n)): the class of sets of cubic tapes accepted by L(n) space-bounded and H(n)hardware-bounded deterministic FV3-P T M ’s · NFV3PTM(L(n), H(n)): the class of sets of cubic tapes accepted by L(n) space-bounded and H(n) hardwarebounded nondeterministic FV3-P T M ’s

3Main Results

This section mainly investigates accepting powers of
FV3-PTM’s, based on hardware complexity.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A function L : N → N is fully space constructible bya k head one-dimensional deterministic Turing machine
if there is a k head one-dimensional deterministic Turingmachine [4] M such that for any n ≥ 1 and any input
word x of length n, M on x marks off exactly L(n) cellsof the storage tape and halts. (In this case, we say that
M constructs the function L.)
Theorem 3.1. Let H : N → N be a function which
satisfies the following (1), (2), and (3), where k ≥ 1 isan integer :(1) H is fully space constructible by a k head onedimensional deterministic Truing machine;(2) ∃n0 ∈ N, ∀n ≥ n0 [H(n) ≥ k];¡¢ n(3) H(n)≤ 2 (n ≤ 2).
2
Furthermore, let H’ : N → N and L : N → N befunctions which satisfy the following (4) and (5):¡ 0 ¢ ¡¢
(4) ∃n0 ∈ N, ∀n ≥ n0 [ H 2(n) ≤ H(n)
];
2
¡
¢
0
2 H(n)
log n,(5) max { H (n)¡¢ 2
0
2 H(n)H (n)2 ¡ log¢L(n),L(n)H 0 (n) H(n)} = o(n).
2Then,
DF V 3-P T M (H(n),H(n)) − N F V 3-P T M (L(n),H 0 (n)) 6= φ.
Proof: Let T (H) be the following set depending on thefunction H in the theorem:¡¢(3)
T (H) = { x ∈ {0, 1} | ∃n ≥ 2 H(n)[l1 (x)=l2 (x)¡H(n)¢ 2=l3 (x)=l4 (x)=n & ∀i (1 ≤ i ≤ 2 ) [the ith plane of¡¢x is identical with the (2 H(n)+1-i)th plane of x]] }.
2
To prove the theorem, we show that T (H) ∈ DF V 3P T M (H(n), H(n)) − N F V 3-P T M (L(n), H(n)).
T (H) is accepted by an H(n) space-bounded and H(n)hardware-bounded DF V 3-P T M M which acts as follows. Suppose that an input tape x with l1 (x) =l2 (x) = l3 (x) = n is presented to M . Let M1 be a
k head one-dimensional deterministic Turing machine
which constructs the function H. By simulating the action of M1 on the first plane of x, the first k processors
P1 , P2 , . . . , Pk of M mark off exactly H(n) cells of thestorage tape.
After this, each processor Pi (2 ≤ i ≤ k) positions
its storage-tape head on the ith cell (from the left) of
the storage tape, and processor P activates processors
Pk+1 , Pk+2 , . . . , PH(n) in such a way that for each j(k + 1 ≤ j ≤ H(n)), the storage-tape head of Pj ispositioned on the jth cell (from the left) of the storage tape. Then P1 positions thehead at the¡ input¢
northwsetmost corner of the (2 H(n)+2
− H(n))th2
plane of x, which for each i (2 ≤ i ≤ H(n)), Pi po-©ISAROB 2007

133

sitions the input head on the northwestmost corner ofthe (H(n) − i + 1)th plane of x. And P1 systemati¡¢
cally traverses the (2 H(n)+ 2 − H(n))th plane, . . .,2
¡H(n)¢
the 2 2 th plane (from the first column to the lastcolumn in each plane, and from the first row to the lastrow in each column), and compares these planes withthe (H(n)−1)th plane, . . . , the first plane, respectively,by using the information from P2 , P3 , . . . , PH(n) .
These input heads are then positioned at the northwestmost end of the H(n)th plane of x. The same procedure is used inductively to verify that H(n)th plane¡¢through the (2 H(n)+ 1 − H(n))th plane has a desired2
form.
Next, we show that T (H) ∈/ N F V 3-P T M (L(n),H’(n)). Suppose to the contrary that there is an N F V 3P T M (L(n),H’(n)) M ’ accepting T (H). Let s and t be
the numbers of states of the finite control of each processor and storage¡¢ tape symbols of M ’, respectively. For
large n ≥ 2 H(n), let
2
V (n) = { x ∈ {0, 1}(3) | l1 (x) = l2 (x) = l3 (x) = n &¡¢∀i (1 ≤ i ≤ H(n)) [the ith plane of x is identical with
¡H(n)¢ 2¡¢
the (2 2 + 1 − i)th plane of x] & [(1, 1, 2 H(n)+ 1),2(n, n, n)] ∈ {0}(3) }.
Below, we consider the computation of M’ on input
tapes in V (n). Clearly, each tape x in V (n) is in T (H),and so x is accepted by M’.
A configuration of M’ is an infinite-tuple (α,((i1 , j1 , k1 ), q1 , h1 ), ((i2 , j2 , k2 ), q2 , h2 ), ..., ((im , jm , km ),qm , hm ), . . . ) where α is the non-blank contents of thestorage tape of M’, and for each m ≥ 1, (im , jm , km ), qm
and hm are the input head position, the state of the finite control and the position of storage-tape head of themth processor of M’, respectively. The type of a configuration C =(α, ((i1 , j1 , k1 ), q1 , h1 ), ((i2 , j2 , k2 ), q2 , h2 ),..., ((im , jm , km ), qm , hm ), . . . ), denoted by Type(C),is an infinite-tuple ([i1 ], ..., [im ], . . .), where for eachm ≥ 1,¡¢
i
if im ≤ H(n)2
¡H(n)¢[im ] = { m2 2otherwise.
Let c1 (x), c2 (x), ..., clx (x) be the sequence of configurations of M’ during an (arbitrary selected) accepting
computation of M’ on a tape x in V (n). Here lx is thelength of this computation. Let d1 (x), d2 (x), . . . , dl0 x (x)be the subsequence obtained by selecting c1 (x) andall subsequent ci (x)’s such that T ype(ci (x)) 6=
T ype(ci+1 (x)). We call d1 (x), d2 (x), . . . , dl0 x (x) the pattern of x. Let p(n) be the number of possible pattern of¡¢− 1) + 1 ≡M 0 on x in V (n). Since L0x ≤ H 0 (n)(2 H(n)2
Q(x) (note that M ’ uses at most H’(n) processors when
it reads tapes in V (n)), we get the following inequality:0L(n)p(x) ≤ ((s(n + 1)(n + 1)(n + 1)L(n))H (m)t
)Q(n) .

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007Now we classify the tapes in V (n) according to theirpatterns. There must exist a pattern dˆ1 , dˆ2 , . . . , d̂l whichH(n)corresponds to a set S(n) of at least 2n×n×( 2 ) / p(n)¡H 0 (n)¢ ¡H(n)¢tapes in V (n). Since≤(from condition
22(4) in the theorem), the same observation as in the proofof Theorem 3 in [2] reveals that for any computation of
M ’ on an x ∈ V (n), there exists an index i such that¡¢the ith plane of x and the (2 H(n)+ 1 − i)th plane of2
x are never being read simultaneously.
Let i0 be such a value for the pattern dˆ1 , dˆ2 , . . . , d̂l .
we now define a binary relation E on tapes in S(n) as
follows: For each u and v in S(n), let¡¢/ {i0 , i0 , 2 H(n)+ 1 − i0 ) [ith planes of u
u Ev ⇔ ∀i (∈2
and v are identical].
Obviously the relation E is an equivalence relation,
H(n)2and there are q(n) = 2n (( 2 )−1) E-equivalence classesof tapes in S(n). From condition (5) in the theorem, we
can easily show that |S(n)| > q(n) for large n. Therefore, there exist two different tapes in S(n) which belong
to the same equivalence class. Let x and y be such two
different tapes in S(n). And let z be the tape obtained¡¢
from x by replacing the (2 H(n)+ 1 − i0 )th plane with2
¡H(n)¢
the (2 2 + 1 − i0 )th plane of y. By an argument
similar to that in the proof of theorem 1 in [6], it canbe shown that there is an accepting computation of M ’
on z. Consequently, z must be accepted by M ’. This
contradicts the fact z is not in T (H).
¤
We consider the following functions:0(n = 0)· log (1) n = {dlogne(n ≥ 1),

It is shown in [3] that the function log (k) n (k ≥ 1) are
fully space-constructible by three head one-dimensional
deterministic Turing machines. From this fact and Theorem 3.1, we have:−

N F V 3-P T M

Corollary 3.2. Fpr each X ∈ {D, N } and each k ≥ 3,
XF V 3-P T M (log (k) n, log (k+1) n) ⊆ XF V 3-P T M(log (k) n, log (k) n).
Letting H(n) = k + 1 (where k is a positive integer),H’(n) = k, and L(n) = o(n) in Theorem 3.1, we have :DF V 3-P T M (k + 1, k + 1) − N F V 3-P T M (0(n), k)6= φ.

©ISAROB 2007

DF V 3-P T M (k + 1, k + 1) = DF V 3-P T M (1, k + 1),
we have the following corollary.
Corollary 3.3. For any integer k ≥ 1,
DF V 3-P T M (1, k+1) − N F V 3-P T M (o(n), k) 6= φ.

4

Conclusion

This paper investigated fundamental propertiesof three-dimensional parallel Turing machines withbounded number of processors. We conclude the paper by giving several open problems.
(1) What is a relationship between the accepting
powers of F V 3-P T M ’s and 3-P T M ’s?
(2)What is a relationship between the accepting powers of deterministic and nondeterministic F V 3-P T M ’s?
(3) What is a hierarchy of the accepting powers of
F V 3-P T M ’s, based on the hardware complexity depending on the side-length of input tapes?

References[1] K. Okinaka, K. Inoue, A. Ito,and Y. Wang, “A noteon hardware-bounded parallel Turing machines”,
Proc. of the 2nd International Conference on Information, Beijing, China, pp. 90-100, 2002.
[2] A. L. Rosenberg, “On multihead finite automata”,
IBM J. Res. and Dev., Vol. 10, pp. 388-394, 1996.

and for each r ≥ 1,
· log (r+1) n = log (1) (log (r) n).

Corollary 3.1. For each k ≥ 3,
DF V 3-P T M (log (k) n, log (k) n)(log (k) n, log (k+1) n) 6= φ.

From this and from the obvious fact that134[3] M. Sakamoto, K. Inoue, and I. Takanami, “Threedimensionally fully space constructible functions”,
IECE Transactions on Information and Systems,
E77-D(6), pp. 723-725, 1994.
[4] S. Sakurayama, K. Inoue, I. Takanami, H.
Taniguchi, and H. Matsuno, “A note on multihead on-line Turing machines”, Technical Report of
the Yamaguchi University, Vol.3, No.2, pp.183-192,
1983.
[5] J. Wiedermann, “Parallel Turing machines”, Technical Report RUU-CS-84-11, Dep. of Computer Science, University of Utrecht, the Netherlands, 1984.
[6] A. C. Yao and R. L. Rivest, “k+1 heads are better
than k”, J. ACM, Vol.25, No.2, pp.337-340, 1978.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Remarks on Recognizability of Topological Components
by Three-Dimensional Automata
Yuusuke Nagamizu1 , Makoto Sakamoto1 , Takao Ito1 , Kouichi Iihoshi1 , Naoko Tomozoe1 ,Hiroshi Furutani1 , Michio Kono1 , Satoshi Ikeda1 , Tatsuhiro Tamaki2 , and Katsushi Inoue3
1

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN

2
3

Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN

Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract
It is conjectured that the three-dimensional pattern processing has its our difficulties not arising in two-dimensional case.
One of these difficulties occurs in recognizing topological properties of three-dimensional patterns because the threedimensional neighborhood is more complicated than
two-dimensional case. Generally speaking, a property
or relationship is topological only if it is preserved
when an arbitrary ’ rubber-sheet ’ distortion is applied
to the pictures . For example, adjacency and connectedness are topological ; area, elongatedness, convexity,
straightness, etc. are not. In recent years, there havebeen many interesting papers on digital topologicalproperties. For example, an interlocking component
was defined as a new topological property in threedimensional digital pictures, and it was proved that noone marker automaton can recognize interlocking components in a three-dimensional digital picture. In thispaper, we deal with recognizability of topological components by three-dimensional Turing machines, andinvestigate some properties.

of three-dimensional digital pictures : Let S1 and S2
be two subsets of the same three-dimensional digital
picture. S1 and S2 are said to be interlocked when
they satisfy the following conditions:(1) S1 and S2 are toruses,(2) S1 goes through a hole of S2 ,(3) S2 goes through a hole of S1 .
The interlocking of S1 and S2 is illustrated in Fig.1.
This relation may be considered as a chainlike connectivity.

KeyW ords : digital geometry,interlocking component, one marker automaton, three-dimensional automaton, Turing machine, topological component

1

Introduction

Digital geometry has played an important role incomputer image analysis and recognition[3]. In particular, there is a well-developed theory of topological properties such as connectedness and holes for
two-dimensional arrays[4]. On the other hand, threedimensional information processing has also becomeof increasing interest with the rapid growth of computed tomography, robotics, and so on. Thus it has
become desirable to study the geometrical properties such as interlocking components and cavities forthree-dimensional arrays[2,5]. In[2], interlocking components was proposed as a new topological property©ISAROB 2007

135

Fig. 1: Interlocking components.
It is proved that no one marker automaton can recognize interlocking components in a three-dimensional
digital picture in [2]. In this paper, we investigate
recognizability of topological properties such as interlocking components by three-dimensional Turing machines.

2

Preliminaries

Definition 2.1. Let Σ be a finite set of symbols. A
three-dimensional tape over Σ is a three-dimensional
rectangular array of elements of Σ. The set of all threedimensional tapes over Σ is denoted by Σ(3) . Given

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
a tape x ∈ Σ(3) , for each j(1≤j≤3), we let lj (x) be
the length of x along the j th axis. When 1≤ij ≤lj (x)for each j(1≤ j≤3), let x(i1 , i2 , i3 ) denote the symbolin x with coordinates (i1 , i2 , i3 ), as shown in Fig. 2.
Furthermore, we definex[(i1 , i2 , i3 ), (i01 , i02 , i03 )],when 1≤ij ≤i0j ≤lj (x) for each integer j(1≤j≤3), as thethree-dimensional tape y satisfying the following :(i) for each j(1≤j≤3), lj (y)=i0j − ij + 1;(ii) for each r1 ,r2 ,r3 (1≤r1 ≤l1 (y), 1≤r2 ≤l2 (y),1≤r3 ≤l3 (y), y(r1 , r2 , r3 )=x(r1 + i1 − 1, r2 + i2 − 1,
r3 + i3 − 1).

move},
if((q 0 ,u0 ),(a0 ,v 0 ),d)∈δ
((q,u),(a,v))
then
a=a0 ,
and
0 0(u,v,u ,v )∈{(+,−,+,−),(+,−,−,+),(−,+,−,+),(−,+,+,−),(−,−,−,−)}.
We call a pair (q,u) in Q×{+,−} an extended state,
representing the situation that M holds or does nothold the marker in the finite control according to thesign u = + or u = −, respectively. A pair (a,v) inΣ × {+,−} represents an input tape cell on which themarker exists or does not exsit according to the sign
v = + or v = −, respectively.
Therefore, the restrictions on δ above imply the following conditions. (A) When holding the marker, M
can put it down or keep on holding. (B) When notholding the marker, and (i) if the marker exists on thecurrent cell, M can pick it up or leave it there, or (ii)
if the marker does not exist on the current cell, M
cannot create a new marker any more.
Definition 2.3. Let Σ be the input alphabet of 3N M1 M . An extended input tape x̃ of M is any threedimensional tape over Σ×{+,−} such that(i) for each j(1≤j≤3), lj (x̃)=lj (x),(ii) for each i1 (1≤i1 ≤l1 (x̃)), i2 (1≤i2 ≤l2 (x̃)), andi3 (1≤ i3 ≤l3 (x̃)), x̃(i1 , i2 , i3 ) = x(i1 , i2 , i3 , u) forsome u ∈ {+, −}.
Definition 2.4. A configuration of 3-N M1 M =(Q, q0 , F, Σ, δ) is an element ofFig. 2: Three-dimensional input tape.

((Σ ∪ {]}) × {+, −})(3) × (Q × {+, −}) × N 3 ,Definition 2.2. A three-dimensional nondeterministic one-marker automaton 3-N M1 is defined by thesix-tuple
M = (Q, q0 , F, Σ, {+, −}, δ),where(1) Q is a finite set of states;(2) q0 ∈Q is the initial state;

where N denotes the set of all nonnegative integers. The first component of a configuration c =(x̃,(q, u),(i1 , i2 , i3 )) represents the extended input tapeof M . The second component (q, u) of c represents theextended state. The third component (i1 , i2 , i3 ) of c
represents the input head position. If q is the state
associated with configuration c, then c is said to be
an accepting conf iguration if q is an accepting state.
The initial conf iguration of M on input x is
IM (x) = (x− , (q0 , +), (1, 1, 1)),

(3) F ⊆Q is the set of accepting states;(4) Σ is a finite input alphabet (]∈Σ
/ is the boundary
symbol);(5) {+,−} is the pair of signs of presence and absenceof the marker; and(6) δ: (Q×{+,−}) × ((Σ∪{]}) × {+,−})→
2(Q×{+,−}) ×((Σ∪{]}) × {+,−}) × {east,west,south,north,up,down,no move}) is the next-movef unction, satisfying the following: For any
q,q 0 ∈Q, any a,a0 ∈ Σ, any u,u0 ,v,v 0 ∈ {+,−},
and any d ∈ {east,west,south,north,up,down,no©ISAROB 2007

136

where x− is the special extended input tape of
M such that x− (i1 , i2 , i3 )=(x(i1 , i2 , i3 ), −) for each
i1 , i2 , i3 (1≤i1 ≤l1 (x̃)),1≤i2 ≤l2 (x̃,1≤i3 ≤l3 (x̃)). If M
moves determinately, we call M a three-dimensional
deterministic one-marker automaton 3-DM1 .
Definition 2.5.
A f ive-way three-dimensional
T uring machine is defined by the six-tuple
M = (Q, q0 , F, Σ, Γ, δ),
whereThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
(1) Q is a finite set of states;(2) q0 ∈Q is the initial state;(3) F ⊆Q is the set of accepting states;(4) Σ is a finite input alphabet (]∈Σ
/ is the boundary
symbol);(5) Γ is a finite storage-tape alphabet (B∈Γ is theblank symbol); and(6) δ⊆(Q×(Σ∪{]})×Γ)×(Q×(Γ−{B})×{east,west,
south,north,down,nomove}×{right,left,nomove}).
If M moves determinately (nondeterminately), wecall M a f ive-way three-dimensional deterministic(nondeterministic) T uring machine FV3-DTM(FV3-NTM).
Let L: N →R be a function. A five-way threedimensional Turing machine M is said to be L(m)space bounded if for all m≥1 and for each x withl1 (x)=l2 (x)=l3 (x)=m, if x is accepted by M , thenthere is an accepting computation path of M on x
in which M uses no more than L(m) cells of thestorage tape. We denote an L(m) space-bounded
FV3-DTM (FV3-NTM) by FV3-DTM(L(m)) (FV3NTM(L(m))).
Definition 2.6. Let T (M ) be the set of threedimensional tapes accepted by a machine M , andlet £[3-DM1 ]={T |T (M ) for some 3-DM1 M }. £[3N M1 ], etc. are defined in the same way as £[3-DM1 ].
We can easily derive the following theorem by using
ordinary technique[6].
Theorem 2.1. F or any f unction L(m) ≥ log m2 ,£[F V 3-N T M (L(m))]⊆Uc>0 £[F V 3-DT M (2c(L(m)) )].

3

Simulation of three-dimensional
one-marker automata by threedimensional Turing machines

In this section, we first investigate the sufficient spaces (i.e., upper bounds) for five-way threedimensional Turing machines to simulate threedimensional one-marker automata[6].
Theorem 3.1. £[3-DM1 ]⊆£[FV3-NTM(m2 log m2 )].
Proof : Suppose that a 3-DM1 M = (Q, q0 , F, Σ, δ)
is given. We partition the extended states Q × {+, −}into disjoint subsets Q+ = Q×{+} and Q− = Q×{−}which correspond to the extended states when M is

©ISAROB 2007

137

holding and not holding the marker in the finite control, respectively. We assume that M has a uniqueaccepting state qa , i.e., |F | = 1. In order to make ourproof clear, we also assume that M begins to movewith its input head on the southmost and eastmost
bottom boundary symbols ]’s of input tape , i.e., position (l + 1, m + 1, n + 1) and, when M accepts an
input, it enters the accepting state at the same position (l + 1, m + 1, n + 1) with the marker held in thefinite control.
Suppose that an input tape x with l1 (x) = l, l2 (x) =m, and l3 (x) = n is given to M . For M and x, define
three types of functions fh↑− ,fh↑+ and fh↓− .
fh↑− (q − , i, j) = (q 0− , i0 , j 0 ): Suppose that we makeM start from the configuration (x− ,q − ,(i,j,h−1)), i.e.,
no marker existing either on the input x or in the finite
control of M . After that, if M reaches the hth planeof x in some time, the configuration corresponding to
the first arrival is (x− ,q 0− ,(i0 ,j 0 ,h)),fh↑+ (q + , i, j) = (q 0+ , i0 , j 0 ): Suppose that we makeM start from the configuration (x− ,q + ,(i,j,h − 1)),
i.e., holding the marker in the finite control of M .
After that, if M reaches the hth plane of x with its
marker held in the finite control in some time (so,when M puts down the marker on the way, it must
return to this position again and pick up the marker),the configuration corresponding to the first arrival is(x− ,q 0+ ,(i0 ,j 0 ,h)),fh↓− (q − , i, j) = (q 0− , i0 , j 0 ): Suppose that we makeM start from the configuration (x− ,q − ,(i,j,h+1)), i.e.,
no marker existing either on the input tape or in thefinite control of M . After that, if M reaches the hthplane of x in some time, the configuration corresponding to the first arrival is (x− ,q − ,(i0 ,j 0 ,h)),l: M never reaches the hth plane of x.
Then, we can show that there exists an FV3NTM(m2 log(m2 )) M 0 such that T (M 0 )=T (M ).
Roughly speaking, while scanning from the top plane
down to the bottom plane of the input , M 0 guesses↑−↑+↓−fh↓− , constructs fh+1
and fh+1, checks fh−1
, and finally at the bottom plane of the input, M 0 decides by↑−↑+using ft+1
and ft+1
whether or not M accepts x. In order to record these mappings for each h, O(m2 ) blocksof O(log m2 ) size suffice, so in total, O(m2 log m2 ) cellsof the working tape suffice. It will be obvious that
T (M )=T (M 0 ).
¤From Theorems 2.1 and 3.1, we get the following.
Corollary 3.1. £[3-DM1 ]2
2⊆£[F V 3-DT M (2O(m log m ) )].
We next show that m4 space is sufficient for FV3NTM’s to simulate 3-N M1 ’s. The basic idea of theproof are the same as those of Theorem 3.1.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Theorem 3.2. £[3-N M1 ]⊆£[F V 3-N T M (m4 )].
From Theorems 2.1 and 3.1, we get the following.
Corollary 3.2. £[3-N M1 ]4⊆£[F V 3-DT M (2O(m ) )].
Next, we show that the algorithms described in theprevious section are optimal in some sense.
Definition 3.1. Let x be in Σ(3) (Σ is a finite setof symbols) and l1 (x)=l2 (x) = m. For each r (1≤r≤Q[l3 (x)/m2 ]) (where Q[l3 (x)/m2 ] denotes the quotientwhen l3 (x) is divided by m2 ),x[(1, 1, (r − 1)m2 + 1), (m, m, rm2 )]is called the rth (m, m)-block of x. We say that thetape x has exactly c(m, m)-blocks if l3 (x) = cm2 ,where c is a positive integer.
Definition 3.2. Let (m1 , m1 ),(m2 , m2 ),. . . be a sequence of points (i.e., pairs of three natural numbers),
and let {(mi , mi )} denote this sequence. We call a sequence {(m1 , m1 )} the regular sequence of points if(mi , mi ) 6=(mj , mj ) for i 6=j.
Lemma 3.1.
Let {x ∈ {0, 1}(3) | ∃ m≥1 [l1 (x)=l2 (x)=l3 (x)=m & l2 (x)=m & (each plane ofx contains exactly one ‘1’) & ∃ d≥2[(x has exactly d(m, m)-blocks, i.e., l3 (x) = dm2 ) & (the last (m, m)blocks is equal to some other (m, m)-block)]]}. T hen,
(1) T1 ∈ £[3-DM1 ], but(2) T1 ∈£[F/
V 3-DT M (2L(m) )] (so, T1 ∈£[F/
V 3N T M (L(m))]) f or any f unction L(m) suchthatlimi→∞ [L(mi )/(m2i log(m2i ))] = 0
for some regular sequence of points {(mi , mi )}. Then,by using the well-known technique [6], we can get thedesired result.
¤From Lemma 3.1., we can conclude as follows.
Theorem 3.3. T o simulate 3 − DM1 ’s, (1) F V 3N T M ’s require Ω(m2 log(m2 )) space and (2) F V 32
2
DT M ’s require 2Ω(m log(m )) space.
Next, we can get the following lemma by using thesame technique as in the proof of Lemma 3.1.
Lemma 3.2. Let T2 ={x ∈ {0, 1}(3) | ∃ m ≥ 1 [ l1 (x)=l2 (x)=l3 (x)=m & & ∃ d ≥ 2 [(x has exactly d (m, m)blocks, i, e., l4 (x)=dm2 ) & (the last (m, m)-block is
dif f erent f rom any other (m, m)-block)]]}. T hen,
(1) T2 ∈ £[3-N M1 ], but(2) T2 ∈/ £[F V 3-DT M (2L(m) )] (so, T2 ∈£[F/
V 3N T M (L(m))]) f or any f unction L such thatlimi→∞ [L(mi )/(m4i )] = 0 f or some regularsequence of points {(mi , mi )}.
From Lemma 3.2., we can conclude as follows.

limi→∞ [L(mi )/(m2i log(mi2 ))] = 0
f or some regular sequence of points {(mi , mi )}.

Theorem 3.4. T o simulate 3-N M1 ’s,

Proof : (1) We construct a 3-DM1 M accepting T1 as
follows. Given an input x with l1 (x)=l2 (x)=l3 (x)=m,
M first checks, by sweeping plane by plane, that eachplane of x contains exactly one ‘1,’ and M then checks,
by making a zigzag of 45◦ -direction from top plane tobottom plane, that x has exactly d (m, m)-blocks forsome integer d ≥ 2. After that, M tests by utilizing its
own marker whether the last (m, m)-block is identicalto some other (m, m)-block: In order to check whether
the pth plane of the hth (m)-block is identical to thepth plane of the last (m)-block (1≤p≤m2 , 1≤h≤d), M
first puts the marker on the position (i,j,m2 (h−1)+p).
After that, M vertically moves down until encounters
the bottom boundary, after which it moves up (m2 −p)plane by making a zigzag of 45◦ -direction. At thistime, M arrives at the pth plane of the last (m, m)block. M then finds the ‘1’ position on the plane and©ISAROB 2007

moves up vertically from this position. In this course,
each time M meets a ‘1’ position, it checks whether or
not there is a marker on the plane (containing the ‘1’position). In this way, M enters an accepting state justwhen it finds out some (m, m)-block, each of whoseplanes is identical to the corresponding plane of thelast (l, m)-block. It will be obvious that T (M )=T1 .
(2) Suppose to the contrary that there exists an
FV3-DTM(2L(m) ) M accepting T1 , where L(m) is afunction such that

138

(1) F V 3-N T M ’s require Ω(m4 ) space, and
4(2) F V 3-DT M ’s require 2Ω(m ) space.

4

Recognizability of interlocking components in three-dimensional images

In this paper, we show that interlocking components are not recognized by any space-bounded threedimensional Turing machines.
First of all, we consider a three-dimensional input
tape T3 that is 7 units in thickness. So, for some m,
T3 ={ (i1 ,i2 ,i3 ) | 1≤i1 ,i2 ≤ m+2,1≤i3 ≤7 }.
Fig.3(a)represents T3 . Now we define two different 5× 5 × 5 patterns as shown in Fig.3(b)(c). Then we
consider an arbitrary n-by-n matrix of those 5 × 5 ×
5 patterns (see Fig.3).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5

Conclusion

In this paper, we investigated recognizabilityof topological components by three-dimensional automata, and showed that interlocking components
are not recognized by any space-bounded threedimensional deterministic or nondeterministic Turingmachines. By the way, what is the situation for a twoor three marker automata, or for alternation (see [1])?
This question seems very intersting. We will deal withthe problem in further papers.

References
Fig. 3: Three-dimensional input tape including interlocking components T3 [2].

[1] A.K. Chandra , D.C. Kozen, and L.J. Stockmeyer,
Alternation, J. of ACM , 28(1):114-133(1981).
[2] A.Nakamura and K.Aizawa, Detection of ic interlocking components in three-dimensional digital
pictures, Imformation Sciences 40:143-153(1986).

Then, we can get the following lemma from Lemma
2.1 in [2].
Lemma 4.1. 3-DM , cannnot recognize interlocking
components of an arbitrary given digital picture.
From Theorem 3.3 and Lemma 4.1, we can get thefollowing.
Theorem 4.1.
Interlocking components are
not accepted by any F V 3-DT M (L(m))(F V 3N T M (L(m)))
f oranyf unction
L(m)
such
that
limm→∞ [L(m)/2m2 log m2 ]=0(limm→∞ [L(m)/m2 log m2 ] = 0).
Next, we can get the following lemma by using atechnique similar to that in the proof of Lemma 2.1 in[2].
Lemma 4.2. 3-ND1 cannnot recognize interlocking
components of an arbitrary given digital picture.
From Theorem 3.4 and Lemma 4.2, we can get thefollowing.
Theorem 4.2.
Interlocking components are
not accepted by any F V 3-DT M (L(m)) (F V 3N T M (L(m))) f or any f unction L(m) such that4limm→∞ [L(m)/2m ] = 0 (limm→∞ [L(m)/m4 ] = 0).

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[3] A.Rosenfeld and K.C. Kak, Digital Picture Processing, Academic Press, New York(1976).
[4] A.Rosenfeld, Picture Languages, Academic Press,
New York(1979).
[5] A.Rosenfeld, Three-dimensionl digital topology,
Information and Control 50:119-127(1981).
[6] M.Sakamoto, Three-dimensional alternating Turing machines, P h. D. T hesis, Y amaguchi
U niversity (1999).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Leaf-Size Hierarchy of Four-Dimensional Alternating Turing Machines
Kouichi Iihoshi1 , Makoto Sakamoto1 , Takao Ito1 , Naoko Tomozoe1 , Yuusuke Nagamizu1 ,Hiroshi Furutani1 , Michio Kono1 , Satoshi Ikeda1 , Tatsuhiro Tamaki2 , and Katsushi Inoue3 ,1

Dept. of Computer Science and Systems Engineering, Miyazaki University, Miyazaki 889-2192, JAPAN

2

Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN

3

Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract
The recent adurances in computer animation, motion image processing and so on prompted us to analyze computational complexity of multi-dimensional
information processing to explicate the properties offour-dimensional automata, i.e., three-dimensional automata with the time axis. From this point of view, we
first introduced four-dimensional alternating Turingmachines 4-AT M ’s, and investigated leaf-size bounded
computation for 4-AT M ’s in [4,6]. In this paper,
we continue the investigations about 4-AT M ’s, andmaily investigate leaf-size bounded computation of 4AT M ’s. Basically, the ‘leaf-size’ is the minimum number of leaves of some accepting computation trees ofalternating Turing machines. Leaf-size, in a sense, reflects the minimum number of processors that run inparallel in accepting a given input.
KeyWords : alternation, configuration, four-dimensional input tape, leaf-size, space bound, Turing machine.

1

Introduction and Preliminaries

In 1967, the problem of computational complexity was also arisen in the two-dimensional information processing. Blum et al. first proposed twodimensional automata, and investigated their patternrecognition abilities [1]. Since then, many researchers
in this field have been investigating a lot of properties about automata on two- or three-dimensional
tapes. In 1976, Chandra et al. introduced the concept of ‘alternation’as a theoretical model of parallel computation [2]. After that, Inoue et al. introduced two-dimensional alternating Turing machines asa generalization of two-dimensional nondeterministic
Turing machines and as a mechanism to model parallel computation [5]. Moreover, Sakamoto et al. presented three-dimensional alternating Turing machines
in [7,9].

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140

On the other hand, recently, due to the advances
in many application areas such as computer animation, motion image processing, and so forth, it has
become increasingly apparent that the study of fourdimensional pattern processing, i.e., three-dimensional
automata with the time axis should be of crucial importance. Thus, we think that it is very useful for analyzing computation of four-dimensional pattern processing to explicate the properties of four-dimensional
automata. From this viewpoint, we introduced somefour-dimensional automata[6, 10].
In this paper, we continue the investigations aboutfour-dimensional alternating Turing machines [4, 6],
and mainly investigate leaf-size hierarchy of fourdimensional alternating Turing machines which eachsidelength of each input tape is equivalent. Leaf-sizebounded computation was introduced as a simple, natural new complexity measure for alternating Turingmachines[5]. Basically, the ‘leaf-size’ (or ‘blanching’)is the minimum number of leaves of some accepting
computation trees of processors that run in parallel in
accepting a given input.
Let Σ be a finite set of symbols. A four-dimensional
input tape over Σ is a four-dimensional rectangulararray of elements of Σ. The set of all the fourdimensional input tapes over Σ is denoted by Σ(4) .
Given an input tape x ∈ Σ(4) , for each j(1 ≤ j ≤ 4),
we let lj (x) be the length of x along the jth axis. The
set of all x ∈ Σ(4) with l1 (x) = m1 , l2 (x) = m2 , l3 (x)= m3 , and l4 (x) = m4 is denoted by Σ(m1 ,m2 ,m3 ,m4 ) .
If 1 ≤ ij ≤ lj (x) for each j(1 ≤ j ≤ 4), let x(i1 , i2 , i3 ,i4 ) denote the symbol in x with coordinates (i1 , i2 , i3 ,i4 ). Furthermore, we define x [(i1 , i2 , i3 , i4 ), (i01 , i02 ,
i03 , i04 )], when 1 ≤ ij ≤ i0j ≤ lj (x) for each integer j(1 ≤j ≤ 4), as the four-dimensional input tape y satisfying
the following:(i) for each j(1 ≤ j ≤ 4), lj (y) = i0j − ij + 1;(ii) for each r1 , r2 , r3 , r4 (1 ≤ r1 ≤ l1 (y), 1 ≤ r2 ≤l2 (y), 1 ≤ r3 ≤ l3 (y), 1 ≤ r4 ≤ l4 (y)), y(r1 , r2 ,r3 , r4 ) = x(r1 + i1 − 1, r2 + i2 − 1, r3 + i3 − 1,
r4 + i4 − 1).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
As usual, a four-dimensional input tape x over Σis surrounded by the boundary symbols #’s (# ∈/ Σ).
Furthermore, four-dimensional tape is the sequence of
three-dimensional rectangular arrays along the timeaxis. By Cubex (i) (i ≥ 1), we denote the ith threedimensional rectanglar array along the time axis in x
∈ Σ(4) which each sidelength is equivalent.
Let Σ1 , Σ2 be finite set of symbols. The projection(4)(4)
is a mapping τ̃ : Σ1 → Σ2 which is obtained byextending a mapping τ : Σ1 → Σ2 as follows : τ̃ (x)=x0
if and only if (i)li (x) = li (x0 ) for each i(1 ≤ i ≤ 4), and(ii)τ (x(i1 , i2 , i3 , i4 ))=x0 (i1 , i2 , i3 , i4 ) for each (i1 , i2 ,
i3 , i4 )(1 ≤ i1 ≤ l1 (x), 1 ≤ i2 ≤ l2 (x), 1 ≤ i3 ≤ l3 (x),(4)
1 ≤ i4 ≤ l4 (x)). If T ⊆ Σ1 , we let τ̃ (T ) = {τ̃ (x) | x∈ T }.
We now recall the definition of a four-dimensional
alternating Turing machine (4-AT M ), which canbe considered as an alternating version of a fourdimensional Turing machine (4-T M ) [6].
4-AT M M is defined by the 7-tuple
M = (Q, q0 , U ,F , Σ, Γ, δ), where(1) Q is a finite set of states;(2) q0 ∈ Q is the initial state;(3) U ⊆ Q is the set of universal states;(4) F ⊆ Q is the set of accepting states;(5) Σ is a finite input alphabet (# ∈/ Σ is the boundary
symbol);(6) Γ is a finite storage-tape alphabet (B ∈ Γ is theblank symbol), and(7) δ ⊆ (Q × (Σ ∪ {#}) × Γ) × (Q × (Γ − {B}) ×{east, west, south, north, up, down, future, past,no move} × {right, left, no move}) is the nextmove relation.
A state q in Q − U is said to be existential. As
shown in Fig. 1, the machine M has a read-only fourdimensional input tape with boundary symbols #’sand one semi-infinite storage tape, initially blank. Of
course, M has a finite control, an input head, and astorage-tape head. A position is assigned to each cell of
the read-only input tape and to each cell of the storagetape, as shown in Fig. 1. The step of M is similar tothat of a two- or three-dimensional Turing machine[3–5, 7], except that the input head of M can move ineight directions. We say that M accepts the tape x if
it eventually enters an accepting state. Note that themachine cannot write the blank symbol. If the input
head falls off the input tape, or if the storage head falls©ISAROB 2007141䊶䊶䊶 storage tape䊶䊶䊶䊶䊶䊶 storage-tape headthe 2nd axis

M 䊶䊶䊶 finite control

west

the 3rd axis䊶䊶䊶䊶䊶䊶 input headeast

the 1st axis
upfour-dimensional
input tape

m
m

north

south

m

䊶 䊶䊶䊶䊶䊶 䊶䊶䊶䊶䊶䊶

down

past
the 4th axis

futuremFig. 1: Four-dimensional alternating Turing machine.

off the storage tape (by moving left), then the machine
M can make no further move.
Let L(m): N → R be a function with one variablem, where N is the set of all positive integers and R
is the set of all nonnegative real numbers. With each
4-AT M M we associate a space complexity function
SP ACE that takes configurations to natural numbers.
That is, for each configuration c = (x, (i1 , i2 , i3 , i4 ),(q, α, j)), let SP ACE(c) = |α|. M is said to be L(m)space-bounded if for each m ≥ 1 and for each x withl1 (x) = l2 (x) = l3 (x) = l4 (x) = m, if x is acceptedby M , then there is an accepting computation tree ofM on input x such that for each node v of the tree,
SP ACE(L(v)) ≤ dL(m)e1 . We denote an L(m) spacebounded 4-AT M by 4-AT M (L(m)).
A 4-AT M (0) is called a four-dimensional alternating finite automaton, which can be considered as an alternating version of a four-dimensional finite automaton (4-F A), and is denoted by 4-AF A.
In order to distinguish among determinism, nondeterminism, alternation with only universal states,
and alternation, we denote a deterministic 4-T M[nondeterministic 4-T M , 4-AT M with only universal states, deterministic 4-T M (L(m)), nondeterministic 4-T M (L(m)), 4-AT M (L(m)) with only universal states, deterministic 4-F A, nondeterministic 4F A, 4-AF A with only universal states] by 4-DT M [4N T M , 4-U T M , 4-DT M (L(m)), 4-N T M (L(m)), 4U T M (L(m)), 4-DF A, 4-N F A].
Let M be an automaton on a three-dimensional
tape. We denote by T (M ) the set of all threedimensional tapes accepted by M . As usual, for eachX ∈ {D, N , U , A}, we denote, for example, by £[3XT M ] the class of sets of all the four-dimensional
tapes accepted by 4-XT M ’s. That is, £[4-XT M ] ={T | T = T (M ) for some 4-XT M M }. £[4-XT M
1 dre means the smallest integer greater than or equal to r.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Proof: Let M be a four-dimensional deterministic
Turing machine which constructs the function L. Let
Tk [L,M ] be the following set, which depends on k, L
and M :P
Tk [L, M ] ={x ∈ ( × {0, 1})(4) | ∃m ≥ 2 [l1 (x) =l2 (x) = l3 (x) = l4 (x) = m&∃r(r≤L(m)[(when the tape h˜1 (x) is presented to M ,its read-write head marks off r cells of
the storage tape and then halts) & ∃i
(1 ≤ i ≤ m − 1) [h˜2 (x[(1, 1, m, 1), rk+1 ,rk+1 , m, 1)]) = [h˜2 (x[(1, 1, i, 1), (rk+1 ,rk+1 , i, 1)])]]]},
P
whereis the input alphabet of M , and h˜1 (h˜2 ) is
the projectionPwhich is obtainedbyP
P extending the
mapping h1 :× {0,1} →(h:
×{0,1}→{0,1})
2
Psuch that for any c = (a,b) ∈× {0,1}, h1 (c) =a(h2 (c) = b).
We first show that Tk [L,M ] ∈ £[4-AT M (L(m),L(m)k )]. Suppose that an input x with l1 (x) = l2 (x)= l3 (x) = l4 (x) = m (m ≥ 2) is presented to M1 . M1
directly simulates the action of M on h˜1 . If M does
not halt, then M1 also does not halt, and will not accept x. If M1 finds out that M halts (in this case, notethat M1 , has marked off at most cells of the storage
tape because M constructs the function L), then M1
existentially chooses some i(1 ≤ i ≤ m − 1) and moves
its input tape head on x(1,1,i,1). After that, M1 universally tries to check that, for each 1≤j≤rk , where r
is the length of the non-blank part of the storage tapejust after M1 has found out that M halts,h˜2 (x[((j − 1)r + 1, (j − 1)r + 1, i, 1), (jr, jr, i, 1)])=h˜2 (x[((j − 1)r + 1, (j − 1)r + 1, m, 1), (jr, jr, m, 1)]).
That is, on x((j − 1)r + 1, (j − 1)r + 1, i, 1) (1≤j≤rk ),M1 enters a universal state to choose one of two further
actions. One action is to pick up and store the segment h˜2 (x[((j−1)r+1, (j−1)r+1, i, 1),(jr, jr, i, 1)]) on
some track of the storage tape, to compare the segment
stored above with the segment h˜2 (x[((j − 1)r + 1, (j −1)r + 1, m, 1),(jr, jr, m, 1)]), and to enter an accepting
state only if both segments are identical. The otheraction is to continue moving to x(jr + 1, jr + 1, i, 1)(in order to pick up the next segment h˜2 (x[jr + 1, jr +1, i, 1), ((j+1)r, (j+1)r, i, 1)])) and compare it with thecorresponding segment h˜2 (x[(jr + 1, jr + 1, m, 1), ((j +1)r, (j + 1)r, m, 1)]).
Note that the number of pairs of segments whichshould be compared with each other in the future canbe easily seen by using r cells of the storage tape. It
will be obvious that the input x is in Tk [L,M ] if andonly if there is an accepting computation three of M1
on x with at most L(m)k leaves. Thus Tk [L,M ] ∈£[4-AT M (L(m), L(m)k )].
On the other hand, we can next show that Tk [L,M ]∈/ £[4-AT M (L(m), L0 (m)k )] by using the well-known
counting argument [3,7]. This completes the proof of
the theorem.
¤

2 brc means the greatest integer smaller than or equal to r.

Corollary2.1. Let k ≥ 1 be a positive integer. Let L(L(m))], and £[4-XF A] also have analogous meanings.
Let L(m): N → R be a function. For each tree t, let
LEAF (t) denote the leaf-size of t (i.e., the number ofleaves of t). We say that a 4-AT M M is Z(m) leaf-sizebounded if for all x with l1 (x)=l2 (x)=l3 (x)=l4 (x)=mand for each computation tree t of M on x, LEAF (t)≤ dZ(m)e.
By 4-AT M (L(m),Z(m)), we denote a Z(m) leafsize bounded 4-AT M (L(m)).
Especially, a 4AT M (0,Z(m)) is denoted by 4-AF A(Z(m)). Define £[4-AT M (L(m), Z(m))] = {T | T = T (m) forsome 4-AT M (L(m), Z(m)) M }. We use 4-AF A(k)(4-U F A(k), 4-DF A) to denote a 4-AT M (0,k) (4U T M (0,k), 4-DT M (0)).

2

Unbounded Leaf-Size Hierarchy

A function L(m) : N → R is called fourdimensionally space constructible if there is a strongly
4-AT M (L(m)) M such that for each m ≥ 1, there
exists some input tape x with l1 (x) = l2 (x) = l3 (x)= l4 (x) = m on which M halts after its storage headhas marked off exactly bL(m)c2 cells of the storagetape.(In this case, we say that M constructs the function L.)
We first show a hierarchy of complexity classes
based on leaf-size bounded computations.
The main theorem is
Theorem 2.1. Let k ≥ 1 be a positive integer. Let L: N → N and L0 : N → N be any functions such that
(1) L is a four-dimensional space-constructible function such that L(m)k+1 ≤ m (m ≥ 1),(2) lim L(m)L0 (m)k /log m = 0, andm→∞(3) lim L0 (m)/L(m) = 0.
m→∞

Then there is a set in £[4-AT M (L(m),L(m)k )], but
not in £[4-AT M (L(m),L0 (m)k )].

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
: N → N and L0 : N → N be any functions satisfying
the condition that L0 (m)≤L(m)(m≥1) and satisfying
conditions (1), (2) and (3) described in Theorem 2.1.
Then,£[4-AT M (L(m),L0 (m)k )](£[4-AT M (L(m),L(m)k )].
For any r in N, log(r) m be the function defined asfollows :(0(m = 0)log (1) m =logm (m ≥ 1),nlog (r+1) m = log (1) (log (r) m).
As shown in Theorem2.1 of [8], the function log(r) m(r≥ 1) is three-dimensionally space-constructible, andthus four-dimensionally space-constructible. It is easy
to see that for each r ≥ 1, log(r+1) m ≤ log(r) m(m ≥ 1)and lim log(r+1) m/log(r) m = 0. Further, for each rm→∞≥ 2 and each k ≥ 1, lim log(r) m(log(r+1) m)k /log mm→∞= 0. From these facts, we have the following.
Corollary2.2. For any r ≥ 2 and any k ≥ 1,£[4-AT M (log(r) m,(log(r+1) m)k ) (£[4-AT M (log(r) m,(log(r) m)k ).

3

Constant Leaf-Size Hierarchy

We next investigate a constant leaf-size hierarchy: Are k + 1 leaves better then k? We first showthat in the case of an alternating Turing machine with
only universal states, no hierarchy exists for any spacebound.
Theorem 3.1. For any k∈N and any function L(m),£[4-U T M (L(m), k)] = £[4-DT M (L(m))].
Proof: Given a k leaf-size bouded 4-U T M M
and an input tape x, a 4-DT M M 0 performs a
depth-first-search on the computation tree of M on x
without any extra cells of the working tape : Normal
tree-search method needs one stack for backtracking,Instead, M 0 adopts only the forward tracking from theroot to each leaf and uses finite internal memories in
the finite control. Note that since M has constantleaves, the branching structure of universal configurations of M on x is also constant. After each traversalof a path and finding out its leaf is labeled with anaccepting configuration M 0 adds the newly obtained
information about the tree structure into a memory
cell of the finite control. Then M begins to walk fromthe root to the next leaf, whose route can be specified©ISAROB 2007

143

by referring the memories of the finite control. When
the whole travel have been done and if M is surely kleaf-size bouded, M 0 enters an accepting state. Notethat M 0 accepts exactly T (M ) and that M 0 is L spacebouded if and only if M is L space-bouded.
¤
Corollary3.1. For any k ∈ N,£[4-U F A (k)] = £[4-DF A].
In contrast to universal machines, we can show that
there exists an infinite hierarchy of o(logm) spacebouded four-dimentional alternating Turing machines
based on leaf-size by using the block of input tape andthe counting argument[3,7].
Theorem3.2. For each k ∈ N,if L(m)=o(logm), then£[4-AT M (L(m), k)] ( £[4-AT M (L(m),k + 1)].
Corollary3.2. For each k ∈ N,£[4-AF A (k)] ( £[4-AF A (k + 1)].

References[1] M. Blum et al., IEEE Symposium on Switchingand Automata Theory :155-160(1967).
[2] A. K. Chandra et al., J. ACM 28(1):114-133(1981).
[3] J. E. Hopcroft et al., Introduction to Automata
Theory, Languages, and Computation, AddisonWesley, Reading, Mass. (1979).
[4] K.Iihoshi et al., Proc. 11th AROB(2006).
[5] K. Inoue et al., Theoret. Comput. Sci. 27:61-83(1983).
[6] H. Okabe et al., Proc. 7th Words Multiconf. on
SCI :241-246 (2003).
[7] M. Sakamoto et al., Inform. Sci. 72:225-249(1993).
[8] M.Sakamoto, et al., Trans. IEICE Jpn. E77D(6):723-725(1994).
[9] M. Sakamoto, Ph.D. Thesis (1999).
[10] M. Sakamoto et al., WSEAS Trans. on Computers, Issue 5, Vol. 3 :1651-1656 (2004).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Relationship between the Accepting Powers of
Alternating Finite Automata and Nondeterministic On-Line Tessellation
Acceptors on Four-Dimensional Input Tapes
Naoko Tomozoe1 , Makoto Sakamoto1 , Kouichi Iihoshi1 , Yuusuke Nagamizu1 ,Takao Ito1 , Hiroshi Furutani1 , Michio Kono1 ,Satoshi Ikeda1 , Tatsuhiro Tamaki2 , and Katsushi Inoue3
1

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN

2

Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN

3

Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract
Recently, due to the advances in dynamic image processing, computer animation, and so forth, it has become increasingly apparent that the study of fourdimensional pattern processing should be very important. Thus, we think that the research of fourdimensional automata as the computational model offour-dimensional information processing has been significant. During the past about five years, automata
on a four-dimensional tape have been proposed andseveral properties of such automata have been obtained. One model is the four-dimensional alternating
finite automaton (4-AFA) which is an alternating version of a four-dimensional finite automaton, and another is the four-dimensional nondeterministic on-line
tessellation acceptor (4-NOTA) which is a natural extension of the three-dimensional nondeterministic online tessellation acceptor to four dimensions. In thispaper, we mainly investigate a relationship between
the accepting powers of 4-AFA’s and 4-NOTA’s.
Key Words : alternation, finite automaton, fourdimensional input tape, nondeterminism, on-line tessellation acceptor

1

Introduction and Preliminaries

The question of whether processing fourdimensional digital patterns is much difficult than
two- or three-dimensional ones is of great interest from

©ISAROB 2007144

the theoretical and practical standpoints. In recent
years, due to the advances in many application areas
such as dynamic image processing, computer animation, and so on, the study of four-dimensional patternprocessing has been of crucial importance. Thus, wethink that the research of four-dimensional automata
as the computational model of four-dimensional
pattern processing has been meaningful. This paper
mainly deals with four -dimensional alternating
finite automaton (4-AFA) and four -dimensional
nondeterministic on-line tessellation acceptor (4NOTA), and investigate some results concerning a
relationship between the accepting powers of 4-AFA’sand 4-NOTA’s [1,4].
PLet
be a finite set of symbols.
A four P
dimensional tape overis a four-dimensional rectP
angular array of elements of . The set of all fourPP
dimensional tape overis denoted by (4) . GivenP
a tape x∈ (4) , for each j(1≤j≤4), we let lj (x) be thelength of x along the jth axis. When 1≤ij ≤lj (x) forj(1≤j≤4), let x(i1 , i2 , i3 , i4 ) denote the symbol in x
with coordinates (i1 , i2 , i3 , i4 ). Furthermore, we definex[(i1 , i2 , i3 , i4 ), (i’1 , i’2 , i’3 , i’4 )], when 1≤ij ≤i’j ≤lj (x)for each integer j(1≤j≤4), as the four-dimensional tapey satisfying the following:(i) for each j(1≤j≤4), lj (y)=i’j -ij +1;(ii) for each r1 , r2 , r3 , r4 (1≤r1 ≤l1 (y), 1≤r2 ≤l2 (y),1≤r3 ≤l3 (y), 1≤r4 ≤l4 (y)), y(r1 , r2 , r3 , r4 )=x(r1 +i1 -1,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
r2 +i2 -1, r3 +i3 -1, r4 +i4 -1). We let the input tapes,
through this paper, be restricted to ones which eachsidelength is eguivalent in order to increase the theoretical interest,as shown in Fig.1.

2

Results

We first investigate a relationship between the accepting powers of 4-AFA’s and 4-NOTA’s.
Lemma 2.1. £[4-AFA]*£[4-NOTA].

䊶䊶䊶 storage tape䊶䊶䊶䊶䊶䊶 storage-tape headthe 2nd axis

M 䊶䊶䊶 finite control
the 1st axiswest

the 3rd axis䊶䊶䊶䊶䊶䊶 input head

east

up

four-dimensional
input tape

m
m

north

south

m

䊶 䊶䊶䊶䊶䊶 䊶䊶䊶䊶䊶䊶

down

past
the 4th axis

futuremFig. 1: Four-dimensional input tape.
A 4-AFA M is a four-dimensional finite automaton whose state set is partitioned into universal andexistential states. The machine M has a read-only
four-dimensional tape. A step of M consists of reading one symbol from the input tape, moving the input head in specified direction d∈{east, west, south,
north, up, down, future, past, no more}, and enteringa new state, in accordance with the next-move relation. A seven-way four -dimensional alternating finite
automaton (SV4-AFA) is a 4-AFA whose input headcan move in only seven directions — east, west, south,
north, up, down, or future.
A 4-NOTA M is an infinite mesh-connected fourdimensional array of cells. Each cell of the fourdimensional array consists of a nondeterministic finitestate machine. The nondeterministic finite-state machines are all identical. M decides whether a fourdimensional tape is accepted or not by on-line fashions. For more details of the definitions of 4-AFA and
4-NOTA, see [4] and [1], respectively.
Let T(M) be the set of four-dimensional
tapes accepted by a machine M, and let £[4AFA]={T|T=T(M) for some 4-AFA}. £[SV4-AFA]and £[4-NOTA] are defined in the same way as£[4-AFA]. Further, for a set T(M) of four-dimensional
tapes accepted by a machine M, the complementationof T(M) is denoted by T̄(M).

©ISAROB 2007

145

Proof : We consider the four-dimensional tapeembedding of directed bipartite graphs with equalPnumber of vertices on both sides [2,3]. Let ={vi ,e, w, s, n, u, d, f, p, +, x, 0} be a finite set of symbols used for the embedding. We use the following
embedding rule. The symbol vi represents the i thvertex for each i (1≤i ≤2n), symbol + means an intersection of two edges (i.e., where they join or split),
symbol x is for a cross-over of two-edges, symbol 0
represents a blank space, and symbol e, w, s, n, u,d, f, and p are the symbols needed to form eastward,
westward, southward, northward, upward, downward,
future, and past edges, respectively. Let P be a four¯dimensional tape embedding of a directed bipartite
graph with k=2m(m+1)-m vertices on both sides. The
size of P will be (4m+3)×(4m+3)×(4m+3)×(4m+3)¯(including the boundary symbols). The (2m+2)thplane of some two cubes of P defines 2k vertices¯
of a bipartite graph, where the westmost k vertices
on the (2m+2)th plane of one cube form one groupand eastmost k vertices on the (2m+2)th plane ofthe other cube form the other group. The 2k vi ’s
are placed such that there are blanks separating thefirst m vertices of one cube from the second m vertices of the other cube on each odd-numbered row,and there is a blank between consecutive vi ’s in boththe eastmost and weatmost groups. An example ofsuch embedding is given in Fig.2. Consider languageP
L1 ={P|P∈ (4) and P is a four-dimensional tape em¯bedding of some acyclic directed bipartite graph withequal number of vertices on both sides}. We can showthat L1 can be accepted by a 4-AFA but not by any
4-NOTA.
□
Lemma 2.2. For
every 4-AFA A, L̄(A) is
accepted by a 4-NOTA.
Proof : Let A be a 4-AFA. Define the complement 4-AFA of A to be Ā. That is, Ā is obtained byswapping the universal and existential states, and theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
accepting and nonaccepting states of A. Note that ingeneral, L̄(A)6=L(Ā), since A may reject an input byentering an infinite loop. We construct a 3-NOTA M
to accept L̄(A). Let x be an input pattern. Given x,
M tries to guess and verify the existence of a (possibly
infinite) computation tree of Ā on x whose leaves are
all labeled with accepting configurations. Let π denotethe computation tree of Ā on x that M will guess. Let
R(i1 , i2 , i3 , i4 ) denote the set of all states of Ā whenits input head is at the (i1 , i2 , i3 , i4 ) cell, 1≤i1 ≤l1 (x),1≤i2 ≤l2 (x), 1≤i3 ≤l3 (x), 1≤i4 ≤l4 (x), in the guessedcomputation tree π. For each q∈R(i1 , i2 , i3 , i4 ), call(x,( i1 , i2 , i3 , i4 ), q) a configuration (of Ā) represented
by q. Generally, the (i1 , i2 , i3 , i4 ) cell of M operates
as follows. It receives the sets R(i1 -1, i2 , i3 , i4 ) andR(i1 , i2 , i3 , i4 ) from the (i1 -1, i2 , i3 , i4 ) cell, the sets
R(i1 , i2 -1, i3 , i4 ) and R(i1 , i2 , i3 , i4 ) from the (i1 , i2 -1,
i3 , i4 ) cell, the sets R(i1 , i2 , i3 -1, i4 ) and R(i1 , i2 , i3 ,i4 ) from the (i1 , i2 , i3 -1, i4 ) cell, and the sets R(i1 , i2 ,
i3 , i4 -1) and R(i1 , i2 , i3 , i4 ) from the (i1 , i2 , i3 , i4 -1)
cell, and verifies that R(i1 , i2 , i3 , i4 ) is consistent with
the neighboring sets R(i1 -1, i2 , i3 , i4 ), R(i1 , i2 -1, i3 ,i4 ), R(i1 , i2 , i3 -1, i4 ), R(i1 , i2 , i3 , i4 -1), R(i1 +1, i2 , i3 ,i4 ), R(i1 , i2 +1, i3 , i4 ), R(i1 , i2 , i3 +1, i4 ), R(i1 , i2 , i3 ,i4 +1). That is, the following conditions must hold :(a) none of the members of R(i1 , i2 , i3 , i4 ) represents aterminating nonaccepting configuration; (b) if q∈R(i1 ,
i2 , i3 , i4 ) and q is universal, then all immediate successors of the configuration (x, ( i1 , i2 , i3 , i4 ), q) arerepresented by the states contained in R(i1 -1, i2 , i3 ,SSS
i4 ) R(i1 , i2 -1, i3 , i4 ) R(i1 , i2 , i3 -1, i4 ) R(i1 , i2 , i3 ,SSS
i4 -1) R(i1 +1, i2 , i3 , i4 ) R(i1 , i2 +1, i3 , i4 ) R(i1 , i2 ,SS
i3 +1, i4 ) R(i1 , i2 , i3 , i4 +1) R(i1 , i2 , i3 , i4 ); and (c) ifq∈R(i1 , i2 , i3 , i4 ) and q is existential, then at least oneof the immediate successors of the configuration (x, (i1 , i2 , i3 , i4 ), q) is represented by the states containedS
S
in R(i1 -1, i2 , i3 , i4 ) R(i1 , i2 -1, i3 , i4 ) R(i1 , i2 , i3 -1,
SSSi4 ) R(i1 , i2 , i3 , i4 -1) R(i1 +1, i2 , i3 , i4 ) R(i1 , i2 +1,SSSi3 , i4 ) R(i1 , i2 , i3 +1, i4 ) R(i1 , i2 , i3 , i4 +1) R(i1 , i2 ,
i3 , i4 ). Also, the (i1 , i2 , i3 , i4 ) cell passes the sets R(i1 ,
i2 , i3 , i4 ) and R(i1 +1, i2 , i3 , i4 ) to the (i1 +1, i2 , i3 ,i4 ) cell, the sets R(i1 , i2 , i3 , i4 ) and R(i1 , i2 +1, i3 , i4 )to the (i1 , i2 +1, i3 , i4 ) cell, and so on. It addition,©ISAROB 2007146

the(1,1,1,1) cell makes sure that R(1,1,1,1) contains
q0 .
The 4-NOTA M constructed above verifies that forevery configuration in the guessed tree π, either it isa terminating accepting configuration or it is nonterminating and all of its immediate successor configurations exist. It is easy to see that, if x is rejected by A,
then there exists a (possibly infinite) computation treeof Ā on x whose leaves are all labeled with accepting
configurations, and vice versa. Hence, M accepts
L̄(A).
□
Lemma 2.3. £[4-NOTA]*£[3-AFA].
Proof : Suppose that £[4-NOTA]⊆£[4-AFA].
Let T1 be the same language that we considered in the
proof of Lemma 2.1. From Lemma 2.2 and hypothesis, L̄1 is accepted by a 4-AFA. By Lemma 2.2, L1 isaccepted by a 4-NOTA. But L1 is not accepted by any
4-NOTA, as shown in the proof of Lemma 2.1. This isa contradiction. Hence, £[4-NOTA]*£[3-AFA]. □From Lemmas 2.1 and 2.3, we have the following
result.
Theorem 2.1. £[4-AFA] and £[4-NOTA] are incomparable.
Next, we investigate a relationship between the accepting powers of SV4-AFA’s and 4-NOTA’s.
Lemma 2.4. £[SV4-AFA]
complementation.

isclosedunder

Proof : Let A be an SV4-AFA and Ā be the complement of A as in the proof of Lemma 2.2. By using
the same idea as in the proof of Theorem 4.4 in [3],we can construct an SV4-AFA A’ from Ā such that
L(A’)=L̄(A).
□Theorem 2.2. £[FV4-AFA](£[4-NOTA].
Proof : From Lemma 2.4, £[SV4-AFA] is closed
under complementation. The inclusion follows from
Lemma 2.2. That it is proper follows since £[SV4AFA]⊆£[4-AFA] and £[4-AFA] is incomparable with£[4-NOTA].
□

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
[4] H. Okabe, S. Taniguchi, T. Makino, S. Nogami, Y.
Nakama, M. Sakamoto, and K. Inoue, Computation for motion image processing by 4-dimensional
alternating Turing machines, Proceedings of the
7th World Multiconference on Systemics, Cybernetics and Informatics, Orland, USA, 2003.

Fig. 2: An example of embedding (m=1).

3

Conclusion

This paper mainly investigated a relationship between the accepting powers of alternating finite automata and nondeterministic on-line tessellation acceptors on four- dimensional input tapes. It is interesting to investigate closure properties about their fourdimensional automata. We will treat this problem infurther papers.

References[1] K. Inoue and A. Nakamura, An n-dimensional
on-line tessellation acceptor (in Japanese),
The Transaction of the IECE
J59-D(4):229236, 1976.
[2] K. Inoue, I. Sakuramoto, M. Sakamoto, and I.
Takanami, Two topics concerning two-dimensional
automata operating in parallel, International J .
of Pattern Recognition and Artificial Intelligence6(2&3):211-225,1992.
[3] T. Jiang, O. H. Ibarra, and H. Wang, Some
results concerning 2-D on-line tessellation acceptors and 2-D alternating finite automata,
Theoretical Computer Science 125 : 243-257,
1994.

©ISAROB 2007

147

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Guaranteed cost control of discrete time systemwith performance index including cross term†

Nobuya TAKAHASHI, ‡ Kenji HIRANUMA, † Michio KONO, † Osamo SATO
† Faculty of Engineering, University of Miyazaki‡ Department of Electronic and Mechanical Engineering,Tokyo University of Mercantile Marine

1

Introduction

Riccati equation as SDARE.
Let us consider the discrete time linear control sys-

Guaranteed cost control problem is the design

tem with uncertainty in state matrix,

method of the robust control system [1]. This methodx(k + 1) = A(ξ)x(k) + Bu(k)guarantees the robust stability of disturbed control
system by using existing of the upper bound ofquadratic performance index.

(1)

A(ξ) is deﬁned as

Kono extended thisA(ξ) = A0 +method to the case with cross term in performanceindex and show the condition for robust stability [2].

p


ξi Ai

(2)

i=1

In this paper, we extend this problem to the discrete

where A0 is nominal structure of the state matrix, Aitime system. As the result of formulation, stochastic

is structure of the uncertainty, and ξi is size of thediscrete Riccati equation is obtained. We show anduncertainty. Performance index is deﬁned asprove the condition of the closed loop system to bestable. Finally, through showing the numerical exam-J=ple, we validate out method.
=

∞



x(k)T Qx(k) + u(k)T Ru(k) + 2x(k)T Su(k)k=0∞l(x, u)

(3)

k=0

2

Derivation of discrete time stochastic Riccati equation
In this section, we derive the stochastic discrete Ric-

cati equation with performance index including crossterm of input vector and state vector. Wonham pro-where Q ≥ 0, R > 0 and S ≥ 0 are weighting matrices
for state vector, input vector and cross term of stateand input vector, respectively. These matrices takeappropriate dimension size. Lyapunov function V (·)
isV (x(k)) = x(k)T P (k)x(k)posed this equation which is obtained as the result of
stochastic control problem [3]. In this literature, thestochastic discrete Riccati equation is formed as the

From the principle of optimality, we have
H(V, x, u, ξ)discrete Riccati equation with additional structured
term. In this section, we extend this problem to theperformance index with cross term with state vector
and input vector. We abbreviate stochastic discrete©ISAROB 2007

148=

l(x, u) + V (x(k + 1), k + 1) − V (x(k), k)=

xT C T Cx + uT Ru + 2xT Su − xT P (k)x+(A(ξ)x + Bu)T P (k + 1)(A(ξ)x + Bu)(4)

T
The Twelfth
on Artificial Life and Robotics= xT CInternational
Cx + uT Ru +Symposium
2xT Su+ 1)∆A +12th∆AT ’07),
P (k + 1)A0
+AT0 P (k2007(AROB
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007−∆AT P (k + 1)BΩ(P (k + 1))+xT A(ξ)T P (k + 1)A(ξ)x + uT B T P (k + 1)Bu
+2xT A(ξ)T P (k + 1)Bu − xT P (k)x ≤ 0−Ω(P (k + 1))T B T P (k + 1)∆A x ≤ 0
(5)

(7)

In the LQR problem of discrete time system, by using

From the positive semi-deﬁnitively of H(·), we obtainsolution P (k) of the SDARE, the optimal input vector

following inequalityu(k) is deﬁned as follows,

T (x, k, P ) =+C T C − P (k) ≤ 0u(k) = −(B T P (k+1)B+R)−1(AT0 P (k+1)B+S)T x(k)(6)
Then, let (B T P B + R)−1 (AT0 P B + S)T = Ω(P ) and=T0 (P (k + 1))

T

AT0 P (k + 1)A0 − (AT0 P (k + 1)B + S)(B T=

x C Cx + x Ω(P (k + 1)) RΩ(P (k + 1))xT

T

·P (k + 1)B + R)−1 (AT0 P (k + 1)B + S)T (9)−xT SΩ(P (k + 1))x − xT Ω(P (k + 1))T S T x+xT A(ξ)T P (k + 1)A(ξ)x

T1 (P (k + 1))+x Ω(P (k + 1)) B P (k + 1)BΩ(P (k + 1))x
T

T

(8)

Now, T0 (·) and T1 (·) aresubstitute into equation (5), we haveT

T0 (P (k + 1)) + T1 (P (k + 1))

T= ∆AT P (k + 1)∆A + ∆AT P (k + 1)A0

−xT A(ξ)T P (k + 1)BΩ(P (k + 1))x

+AT0 P (k + 1)∆A − ∆AT P (k + 1)BΩ(P (k + 1))−xT Ω(P (k + 1))T B T P (k + 1)A(ξ)x − xT P (k)x−Ω(P (k + 1))T B T P (k + 1)∆A

≤0

(10)

where, let U1 is the upper bound matrix of T1 (·), form

Now, let us substitute structured uncertainty A(ξ) =inequality condition (8), we have following diﬀerence

A0 + ∆A, thenequation.
T0 (P (k + 1)) + C T C + U1 − P (k) = 0= xT C T Cx + xT Ω(P (k + 1))T RΩ(P (k + 1))x

(11)

−xT SΩ(P (k + 1))x − xT Ω(P (k + 1))T S T x

Suppose that there exists stationary solution of equa-+xT AT0 P (k + 1)A0 x + xT ∆AT P (k + 1)∆Ax

tion (11)P = T0 (P ) + C T C + U1

+xT AT0 P (k + 1)∆Ax + xT ∆AT P (k + 1)A0 x(12)

Therefore, we obtain the SDARE with performance+xT Ω(P (k + 1))T B T P (k + 1)BΩ(P (k + 1))x

index including cross term as follows−xT AT0 P (k + 1)BΩ(P (k + 1))xP

−xT Ω(P (k + 1))T B T P (k + 1)A0 x=

AT0 P A0 − (AT0 P B + S)(B T P B + R)−1·(AT0 P B + S)T + C T C + U1

−xT ∆AT P (k + 1)BΩ(P (k + 1))x−xT Ω(P (k + 1))T B T P (k + 1)∆Ax − xT P (k)x= xT C T C + AT0 P (k + 1)A0 + Ω(P (k + 1))T B T(13)In equation (13), if we omit the upper bound matrix,
it coincide with the SDARE of nominal system.

·P (k + 1)BΩ(P (k + 1)) − AT0 P (k + 1)B

3·Ω(P (k + 1)) − Ω(P (k + 1))T B T P (k + 1)A0

Robust stability+Ω(P (k + 1))T RΩ(P (k + 1)) − SΩ(P (k + 1))

In this section, under the assumption that there−Ω(P (k + 1)) S − P (k) + ∆A P (k + 1)∆A

exists a solution P of SDARE, we prove stability ofT©ISAROB 2007

T

T

149

Thethe
TwelfthInternational
Symposium
on by
Artificial
Robotics
2007(AROB
12th ’07),
closed-loopsystem which
is designed
using out Life andSubstituteA(ξ)= A0 + ∆A
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
proposed method= AT0 P A0 + AT0 P ∆A + ∆AT P A0 + ∆AT P ∆A
Theorem 1 In the system (1), optimal input u∗−AT0 P BΩ(P ) − ∆AT P BΩ(P )which minimized the performance index (3) is obtained−Ω(P )T B T P A0 − Ω(P )T B T P ∆Au∗ (k) ==+Ω(P )T B T P BΩ(P ) − P−(B T P B + R)−1 (AT0 P B + S)x(k)−Ω(P )x(k)= AT0 P A0 − AT0 P BΩ(P ) − Ω(P )T B T P A0

(14)+Ω(P )T B T P BΩ(P ) − P + T1 (P )Then the closed-loop system Ac (·) isx(k + 1) = Ac (A(ξ), B, P )x(t)(19)

Where, we substitute P of equation (13) into (19), we
(15)

have
= AT0 P A0 − AT0 P BΩ(P ) − Ω(P )T B T P A0

where+Ω(P )T B T P BΩ(P ) − P + T1 (P )Ac (A(ξ), B, Ω(P )) = A(ξ) − BΩ(P )= AT0 P A0 − AT0 P BΩ(P ) − Ω(P )T B T P A0+Ω(P )T B T P BΩ(P ) + T1 (P )Now, we suppose following assumption−{AT0 P A0 − Ω(P )T (B T P B + R)Ω(P )Assumption 1 Let+C T C + U1 }ATc P BΩ(P ) + Ω(P )T B T P Ac−Ω(P )T RΩ(P ) + C T C = DT D,= −AT0 P BΩ(P ) + Ω(P )T B T P BΩ(P )−Ω(P )T B T P A0 + Ω(P )T B T P BΩ(P )(16)+Ω(P )T RΩ(P ) − C T C − (U1 − T1 (P ))then DT D is positive semi-definite.

= −(A0 − BΩ(P ))T P BΩ(P ) − Ω(P )T B T P
Next, we derive theorem for asymptotical stability of·(A0 − BΩ(P )) + Ω(P )T RΩ(P ) − C T C

closed-loop system.

−(U1 − T1 (P ))= − ATc P BΩ(P ) + Ω(P )T B T P Ac − Ω(P )T RΩ(P )+C T C − (U1 − T1 (P )) ≤ 0(20)

Theorem 2 The closed-loop system (15) with optimal feedback control input (14) is asymptotic stability, if there exists positive semi-definite solution P of

From equation (20), we obtain the stabilizable condi-

SDARE (13) and assumption 1 is satisfied.

tion(proof of Theorem 2)

In discrete time system,from stability condition in the sense of Lyapunov, we

ATc P BΩ(P ) + Ω(P )T B T P Ac

have−Ω(P )T RΩ(P ) + C T C ≥ 0
ATc P Ac − P ≤ 0

(17)

(21)
Q. E. D.

Substitute Ac into inequality (17), left-hand side of4

the inequality becomes

Eigenvalue upper bound=(A(ξ) − BΩ(P ))T P (A(ξ) − BΩ(P )) − P

From equation (10), uncertainty is described as=

A(ξ)T P A(ξ) − A(ξ)T P BΩ(P ) − Ω(P )T B T P A(ξ)

T1 (P ) =+Ω(P )T B T P BΩ(P ) − P©ISAROB 2007

(18)

150

∆T AP ∆A + ∆AT P (A0 − BΩ(P ))+(A0 − BΩ(P ))T P ∆A

(22)

Thewhere
TwelfthArtificial
Robotics
2007(AROB
12th ’07),
P isInternational
the stationary Symposium
solution of on
SDARE.
From Life andPolesof the perturbed
closed-loopsystem is obtained
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
equation (2), substitute uncertainty of system matrixasp∆A = i=1 ξi Ai and we obtain( 0.81650 , − 0.81650 )=pξi ζj Dij +

i=1

p

 T
Ai P (A0 − BΩ(P ))i=1T+ (A0 − BΩ(P )) P Ai

This result shows that the perturbed system remains
in stable, then we had conﬁrm robust stability for dis(23)whereturbance of our proposed method.

6

Dij = ATi P Aj + ATj P Ai

Conclution

Because ATi P (A0 − BΩ(P )) + (A0 − BΩ(P ))T P Ai andIn this paper, we consider the guaranteed cost con-

Dij are symmetric matrices, then there exist orthogo-

trol problem for the performance index including crossnal matrices Yi and Zij which satisfy

term of discrete time system. We show the structure
YiT ATi P (A0 − BΩ(P ))+(A0 − BΩ(P ))T P Ai Yi = Λi
TZij
Dij Zij = Γij

of uncertainty and discuss about stationary condition.
We apply eigenvalue upper bound matrix to this prob(24)(25)

Yi , Dij , Λi and Γij , the upper bound matrix of T (P )is expressed asUE =YiT Λi Yi +i=1

we conﬁrm the robust stability of the closed-loop system for the system matrix disturbance. Future study

where Λi and Γij are diagonal matrices. By usingp

lem and show the numerical example. From this result,is to consider about linear upper bound. It is pointed
out that there exists relationship between linear upper bound matrix and LMI solution of the structured

p
p 


1
Z T Γij Zij
2 i=1 j=1 ij(26)

where UE is called eigenvalue upper bound matrix. In

uncertain system.

References

the next section, we show the numerical example.
[1] S. S. Chang and T. K. C. Peng, “Adaptive guaranteed cost control of systems with uncertain pa-

5

Numerical example

rameters”, IEEE Transactions on AC, vol. 17, no.
4, pp. 474–483, 1972.

We consider following system parameter,




0 1
0 0
, A1 =
,
A0 =
1.5 0
0.5 0
 


0
1 0
B=
, Q=
,
1
0 1
 
1
S=
, R=1
0

[2] M. Kono, N. Takahashi, O. Sato and A. Sato,“Generalization of guaranteed cost control - Extension to the case with cross terms”, Proceedingsof the SICE Annual Conference 2002 in Osaka,
pp. 542-545, August 5-7, 2002. Osaka, Japan.
[3] W. M. Wonham, “On a matrix Riccati equation

Using these parametr, we solve the SDARE with eigenvalue upper bound and obtain stationary solution P .
Poles of the nominal closed-loop system is obtained as( 0.40825 ,©ISAROB 2007

− 0.40825 )151

of stochastic control”, SIAM Journal of Control,
vol. 6, no. 4, pp. 681–697, 1968.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Isomorphic Structure of Graphs with the Maximum Number of Trees
Hideshi Ido*, Yosuke Ohama*1, and Sigeru Omatu*** Electrical Engineering and Computer Science, *1 Advanced Engineering Course,
Niihama National College of Technology,
7-1 Yagumo, Niihama, Ehime 792-8580, Japan
ido@ele.niihama-nct.ac.jp** Computer and Systems Sciences, Graduate School of Engineering,Osaka Prefecture University, Sakai, Osaka 599-8531, Japanomatu@sig.cs.osakafu-u.ac.jp

Abstract
Two graphs GA and GB with the same number of nodes
n and the same number of branches b are said to be
isomorphic if these graphs have the same adjacency
relations between nodes. We have got many graphs withthe same maximum number of trees in the process ofcontinuous branch additions. These graphs have same
numbers of nodes n, branches b and trees t. We certified
those graphs are isomorphic by a computer algorithm.
This algorithm is based on permutations of nodes with thesame degree in a sequence of node degrees. Examples ofisomorphic graphs are presented in the process of adding
branches one by one. If two graphs with same numbers of
nodes n and branches b are isomorphic, these graphs havesame numbers of trees. The further problem will be toprove the reverse condition.
Keywords: Isomorphic graph, Maximum number oftrees, Continuous branch addition
1

Introduction

The number of trees of a graph would be an index toestimate the reliability of probabilistic communication
networks with either high or low link probabilities whenthose probabilities are independent and equal [1].
Some analytical methods have been reported to
calculate the number of trees of graphs obtained from thecomplete graph by deleting several types of subgraphs[2],[3]. Other ones are to maximize the number of trees
when some branchs or subgraphs are deleted from thecomplete graph [4],[5]. Another one is to get theexpressions of trees of a graph which is obtained byconnecting graphs of special structures [6].
These analytical methods are based on obtaining theexpressions derived from a node determinant of the target
graphs with some special structures. But simple
expressions haven’t been known for graphs without any
structural conditions so far.
On the other hand, using numerical method, weconstructed graphs with the maximum number of trees byadding a new branch continuously to the original graph[
7].
In this paper, we have noticed that two or more graphswith the same maximum number of trees appeared many

©ISAROB 2007

156

times in the process of branch additions. All of thosegraphs are made sure to be isomorphic for some examples
which are started branch additions from a tree of series
branches with nodes n=7 and a star-shaped tree of n=8.
In the following sections, we will show two algorithms
for graph construction and isomorphism. Results of
estimation by connectivity are also presented to examine
strength of these graphs.
2

Preliminary results [7]First, we introduce some graph theoretical terms. A
graph denoted by G = (V , E ) is composed with a node
set V and a branch set E. Each branch denoted by (i, j )connects a pair of nodes i and j . A graph is said to be
simple if it has no parallel branches, no self-loops and no
directed branches. In this paper, we treat only simple
graphs as shown in Fig.1 (a). A tree is a subgraph whichconnects all nodes of a graph G and includes no closed
circuits, as shown in Fig.1 (b).
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Fig.1. (a) Simple graph, (b) Tree
The complete graph of n nodes has one branch (i, j )between every node pair i , j , and includes

1 n( n − 1)2( ) can be constructed frombranches. The matrix A = aij

a graph G with n nodes, where for 1 ≤ i, j ≤ n − 1 , eachdiagonal element aii is the number of branches incident tonode i , and each non-diagonal element aij is –1 if there
is a branch (i, j ) between two nodes i and j , otherwise
0. Then the number of trees T of the graph G isrepresented by the determinant | A | . Since the value of
T is always an integer for any graph, the determinant T
must be calculated by using double precision floating

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

point number for matrix A . Usual Gaussian elimination isavailable for this purpose.
Algorithm: Max-trees
Step1. Find the set P of position (i, j ) where there isn’t
an branch (i, j )in the given original graph

G (V , E ) . Now, set G0 ← G (V , E ) and k ← 0.
*

Step2. Calculate the number of trees T of each graphobtained by adding a branch (i, j ) to the graph Gk ,for each position (i, j ) ⊂ P , respectively.
Step3. Find the branch (i, j ) that gives the maximum
number of trees among T * ’s in Step2. Replace
Gk +1 ← Gk + branch (i, j ) , P ← P - position (i, j ) ,and k ← k+1.
Step4. If the set P is empty, then terminate. Otherwise
go to Step2.
3

Isomorphic structure of graphs
3.1

Isomorphic graphs

When the algorithm starts from a series-branch tree
graph of Fig.2(a), we obtain the sequence of branches
represented by numbers near dotted lines as shown in
Figs.3.

In the process to obtain this branch sequence, there aremany isomorphic graphs with the same maximum numberof trees as shown in Table 1.
Table 1. Isomorphic graphs with the same
maximum number of trees of Fig.3.
Added
branchTotal Max. number Isomorphic
branches bof treesgraphs*

0
6
1
1
1
7
7
1
2
8
19
7
3
9
51
2
4
10
117
1
5
11
231
4
6
12
408
2
7
13
720
1
8
14
1200
1
9
15
1840
4
10
16
2800
1
11
17
4200
3
12
18
6125
2
13
19
8575
3
14
20
12005
2
15
21
16807
1
* Number of isomorphic graphs including itself
For example, on added branch 5 of the Table 1,there are
4 graphs with 11 branches and 231 trees as shown in Fig.4.
In this figure, each of 4 branches represented by dottedlines A,B,C, and D is the candidate to be added next.

(a) Series-branch tree.

(b) Star-shaped tree.

1
Fig.2. Initial graphs.

7

2
C

1

8

4

A

7
3

3

6

5

2

B

6

(a) 1st ~ 4th.

5(b) 5th ~ 8th.

4

Fig.4. Four candidate branches
11

15

10

14
12

According to the criterion to select one branch thatgives the maximum number of trees, any one among 4
branches can be selected. But its selection may cause
different results in the later process of branch additions.
Now, we denote graphs including branch A, including
branch B, including branch C, and including branch D as
GA, GB, GC, and GD, respectively in Fig.4.
GA and GC are isomorphic because of symmetricstructure. Also GB and GD are isomorphic. To investigate

139(c) 9th ~ 12th.

(d) 13th ~ 15th.

Fig.3. Branch sequence for
Series-branch tree of n=7.

©ISAROB 2007

157

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

isomorphism between GA and GB, we have constructed
the next algorithm.
3.2

Algorithm for Isomorphic Structure

A node degree d(v) of a node v of a graph G is anumber of branches incident to node v in G. A degree
sequence S of a graph G is a sequence of node degrees of
all nodes of a graph G. We assume that S is arranged in adecreasing order and separated into subsequences S1,
S2,･･･,Sm, where Si includes the same degrees. In general,
two graphs GA and GB must have the same degree
sequences SA and SB to be isomorphic. Then we assume
that degree sequences SA and SB corresponding to graphs
GA and GB are composed with the same subsequences SA=SB=(S1, S2, ･ ･ ･ ,Sm). The number of nodes for
subsequence Sk is denoted by |Sk|.
Outline of the algorithm to determine isomorphism
between GA and GB is as follows.
Algorithm Isomorphic
Step1. Set k=1(Subsequence k is noticed).
Step2. Generate next permutation of |Sk| nodes
corresponding to Sk of SB.
If permutation exhausted, GA is not isomorphic to
GB (terminate the algorithm).
Step3. Examine adjacency relations of graphs GA andpermuted GB within the range of S1 ~ Sk.
If this examination has succeeded and k<m, thenset k ← k+1 and go to Step2.
If this examination has succeeded and k=m, thenGA is isomorphic to GB (terminate the algorithm).
Step4. Otherwise, set k ← k-1 and return to Step2.
For programming of this algorithm, the generation of
permutation in Step2 is skipped for subsequence Sk with
|Sk|=1.
Using this algorithm, all graphs with the maximum
number of trees in Table 1 have been found out to be
isomorphic. Of course, cases of symmetric structures areomitted. For another initial graph of star-shaped tree
shown in Fig.2(b) with nodes n=8, the number of
isomorphic graphs are shown in Table 2.
Isomorphic graphs have appeared many times in theprocess of continuous branch additions. The rate of anumber of cases that give two or more isomorphic graphs
is 9/14=64% in Table 1, and 15/20=75% in Table 2.
3.2

Estimation of Graphs with the Maximum
Number of trees by Connectivity

The number of trees of a graph G has the relation withthe reliability of communication networks with the samebranch probability p.
As another criterion to estimate communication networks,
we introduce the connectivity of corresponding graph.

©ISAROB 2007

158

Table 2. Isomorphic graphs with the same maximum
number of trees started from Fig.2(b).
AddedTotal Max. number Isomorphic
branch branches bof treesgraphs*
0
7
1
1
1
8
3
21
2
9
9
10
3
10
27
3
4
11
72
6
5
12
168
4
6
13
377
4
7
14
841
1
8
15
1537
7
9
16
2800
2
10
17
4928
1
11
18
8056
4
12
19
12440
2
13
20
19200
1
14
21
28800
1
15
22
40800
4
16
23
57600
1
17
24
80640
3
18
25
1105922
19
261474563
20
2719660822128262144
1* Number of isomorphic graphs including itself
A local connectivity k(i,j) between distinct two nodes iand j of a graph G=(V,E) is the minimum number ofnodes removed to separate G. Then the connectivity of
Table 3. Connectivity k(G) of the graph G
appeared in Table 1.

Branches b
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

Ideal k
1
2
2
2
2
3
3
3
4
4
4
4
5
5
5
6

Real k(G)
1
2
2
2
2
3
3
3
3*
4
4
4
5
5
5
6

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5. Conclusion

Table 4. Connectivity k(G) of graphs started

from series branch tree of nodes n=8.
Branches b

Ideal kReal k(G)

7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7

1
2
2
2
2
3
3
3
3
4
4
4
4
4*
4*
5
5
6
6
6
6
7

We demonstrated that graphs with maximum number oftrees constructed by simple algorithm Max-trees haveisomorphic structures in. many cases as more than 60%.
Graphs with the maximum number of trees are needed tobe the models of reliable networks.
Connectivity k(G) of a graph G is adapted as an anothercriterion to estimate the strength of networks against
damages of nodes and branches. Except for few cases
graphs with maximum number of trees give good
performance.
We used computer algorithm to examine isomorphismfor graphs with the same number of nodes n, branches b,
and trees. If the same numbers of trees means isomorphicof graphs, it is very easy to examine isomorphism. This is
the future problem.
Referencesgraph G denoted by k(G) is the minimum value of local
connectivity k(i,j). Connectivity k(G) represents thestrength of the network against damages of nodes andbranches.
Table 3 shows a comparison between ideal connectivity
k and real k(G) for the graphs in Fig. 2. Ideal connectivity
k is given by [2b/n], where [ ] denotes Gaussian
notation[1]. Table 4 shows for graphs started from series
branch trees with n=8.
From Tables 3 and 4, graphs with the maximum
number of trees constructed by algorithm Max-trees give
good results in most cases except for cases marked with *.
Graphs shown in Tables 3 and 4 started from series
branch trees tend to balance node degrees into twoconsecutive integers.

©ISAROB 2007

159

[1] Leggett J.D.,Bedrosian S.D., “Synthesis of Reliable
Networks”,IEEE Trans. Circuit Theory, Vol.CT-16, No.8,
pp.384-385, 1969.
[2] Kasai T.,Kusaka H.,Yoneda S. and Taki I., “Total Numberof Trees in Four Kinds of Incomplete Graphs Derived froma Complete Graph (in Japanese) ”,Jour. Inst. Elect. Comm.
Engrs. Japan.Vol.49, No.1,pp.69-77, 1966.
[3] Ishikawa H., Ido H.,and Sasaki T., “Number of Trees in a
Certain Incomplete Graph (in Japanese)”, Trans. IEICE,
Vol.57-A,No.5, pp.423-424, 1974.
[4] Shier D.R., “Maximizing the Number of Spanning Trees
in a Graph With n Nodes and m Branchs”,Jour. Res. of Nat.
Bur. of Stds.-B. Math. Sci.,Vol.78B, No.4, pp.193-196,
1974.
[5] Ido H., Ishikawa H., and Sasaki T., “Number of Trees in aSpecified Incomplete Graph and its Min-Max Problem (inJapanese)”, Trans. IEICE, Vol.58-A,No.1, pp.81-82,
1975.
[6]Hatakeyama M., Sato K., “Construction Metod for aConnection Graph of Cycles from a General Graph (inJapanese)”, Proc. IEICE Gen Conf. A-1-6, 2003.
[7] Ido H., Ohama Y., and Takahashi T.., “Graph Construction
with the Maximum Number of Trees by Continuous Edge
Addition”, Artificial Life and Robotics, Vol.10, No.2,
pp.185-188, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Relations between Network Reliability and Number of Trees of Graph
Hideshi Ido* and Sigeru Omatu***Electrical Engineering and Computer Science,Niihama National College of Technology,
7-1 Yagumo, Niihama, Ehime 792-8580, Japan
ido@ele.niihama-nct.ac.jp** Computer and Systems Sciences, Graduate School of Engineering,Osaka Prefecture University, Sakai, Osaka 599-8531, Japanomatu@sig.cs.osakafu-u.ac.jp

Abstract

represented by expression (1) below.

Network reliability is the probability that all stations of
the network are connected by only unfailing links under
independent link failures. The communication network
can be represented by an undirected graph G, where
communication stations and links are corresponding tonodes and branches of its graph, respectively. Therefore,
network reliability can be defined as the probability that
there exists at least one tree in the corresponding graph.
We assume that each branch connects its two end nodes
with the same independent probability p and disconnectswith probability q=1- p. In case of p≒1 or p≒0, it is
known that the most reliable network has the maximum
number of trees within graphs of n nodes and b branches.
In this paper, we present a network reliability Pr(G)and a number of trees t(G) of graph G by using thegeneration algorithm of exclusive events which include
trees. Then given two graphs GA and GB with same
numbers of nodes n, branches b, and different number oftrees t(GA)<t(GB), we demonstrate network reliabilities
Pr(GA)<Pr(GB) with respect to probabilities p(0<p<1) incase of smaller graphs with n=4, b=4 and n=5, b=7.
Keywords: Network reliability, Number of trees,
Exclusive events

Pr(G)= Pr(T1∪T2∪･･･,∪Tk)

It is not an efficient method to expand expression (1) to
calculate Pr(G) for a large graph G with relatively many
trees k= t(G). To avoid these tedious operations, theprocedure was proposed that generates exclusive events
including trees and cuts of graph at the same time.[1] This
procedure can also calculate probability Pr(G) within anarbitrary error. Now, assuming each branch connects its
two end nodes with independent, equal probability p anddisconnects with probability q=1-p, it is known that themost reliable network has the maximum number of trees
in case of p≒1 or p≒0.[2]We first present a procedure to estimate the number oftrees t(G) and the network reliability Pr(G) of graph G byusing the generation algorithm of exclusive events
including trees of graph G.
Next, given two graphs GA and GB with same numbersof nodes n, branches b, and different number of treest(GA)<t(GB), we demonstrate network reliabilities
Pr(GA)<Pr(GB) with respect to probabilities p(0<p<1) incase of smaller graphs with n=4, b=4 and n=5, b=7.
2

1

(1)

Preliminary

Introduction

If we focus on relations of connection between stations
by links, the communication network can be represented
by an undirected graph G, where communication stations
and links are corresponding to nodes and branches of its
graph, respectively. A graph denoted by G = (V , E ) is
composed with a node set V and an edge set E. For
simplicity, graph G is assumed a simple graph which is anundirected graph with no self-loops and no multiple
branches.
Network reliability is defined as the probability that all
stations of the network are connected by only unfailinglinks under independent link failures. Then the network
reliability Pr(G) of a graph G is a probability that there
exists at least one tree composed with only normal
branches.[1],[2] Namely, if we can obtain all trees ofgraph G as T1, T2, ･ ･ ･ ,Tk, then Pr(G) would be©ISAROB 2007

160

First, we introduce some graph theoretical notations. A
graph denoted by G is composed with a node set and abranch set. In graph G, we denote an event of existing
branch e by e, and an event of not existing branch e by e .
Union and product of events are denoted by + and ･,
respectively. Then two events τ i and τ j ( i ≠ j ) that
include at least one tree of G are said to be exclusive
events if τ i ･ τ j =φ(empty event). Union event T X [G] of
exclusive tree events including all trees of G can begenerated by next expression (2) with respect to a
multiple branch { e1 , e 2 ,･･･, e k } of k(k≧1) branches.[1]TX [G]=( e1 + e2 +･･･+ ek )･ T X [G( e1 + e2 +･･･+ ek )](2)⊕ ( e1 ･ e 2 ･･･ e k )･ T X [G( e1 ･ e 2 ･ ･･･ ･ e k )]The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where, G( e1 + e2 +･･･+ ek ) and G( e1 ･ e 2 ･ ･･･ ･ e k ) aregraphs derived from G by short circuiting and open
circuiting all branches of multiple branch { e1 , e2,・・・, ek },

a
c

G1

respectively. ⊕ denotes direct sum of exclusive events.
As the result of repeating (2), we have obtained next
expression (3).

ab

d

d

τ i ＝ ∏ (eij1 + eij 2 + ･･･ + eijk )

(3)

b+c

j =1

c

s

∏ (e ・e ･ ･･･ ･e ) ； i ＝1,2,･･･, t X
il1

l =1

il 2

ilm

d

d

where, n denotes the number of nodes, s is the number ofmultiple branches short circuited in j-th order and open
circuited in l -th order respectively, and t X is thenumber of exclusive tree event. Since simultaneous
occurrence probability Pr( τ i ･ τ j ･･･ τ k )=0dtX

Ｒ(G)＝Pr( TX [G])＝ Pr (U τ ) ＝ ∑ P r (τ )i

Fig.2 Generation of exclusive
tree events.

tX

i

i =1

(4)
n −1

k

j =1

h =1

Pr( τ i )＝ ∏ (1 − ∏ (1 − p ijh ) ) ･

s

m

l =1

h =1

2

4

1

d

3

3

(a) G1(b) G2

τ 1 = ab(c + d )τ 2 = ab dc
τ 3 = a bdc
As a result of these exclusive events, the number oftrees is t(G2)=4. For equal branch probability p (q=1-p),
network reliability was obtained as (6).

Ｒ(G2)= p 2 (1 − q 2 ) + 2 p 3 q

Fig.1. Graphs n=4, b=5.
Figure 2 represents the process of generatingexclusive tree events τ i using (2) with respect to (multiple)
branch incident to a node in the order of 1,2,･･･ of graph G1
in Fig.1(a), where each branch is discriminated by symbols
a , b , c and d . As shown in Fig.2, graph G1 has t X =2

exclusive tree events τ 1 and τ 2 , where τ 1 and τ 2 include
two and one trees, respectively. Then the total number of treest(G1)＝3. Now if all of four branches of G1 operate normallyby the same probability p(q=1-p), the network reliability
Ｒ(G1) of corresponding graph G1 can be calculated into

©ISAROB 2007(5)

For graph G2 in Fig.1(b), next three exclusive tree
events were derived in similar way.

c

b

i

＝ p 2 (1 − q 2 ) + p 3 q

2

a

Uτ )
i =1

il h

Figure 1 shows two graphs G1 and G2 with same
numbers of nodes n＝4 and branches b＝4.

4

Ｒ(G1)＝Pr( TX [G1])＝Pr (∏ ( ∏ (1 − p ) )Number of trees and network reliability1τ 2 = a bcd

expression (5) using (3) and (4).

tX

i =1

d

τ 1 = a (b + c)d( i ≠ j ≠ ･･･ ≠ k ) with respect to these τ i ’s, network
reliability Pr(G) result in (4).

3

a

c

n −1

・

d

c

b

TX [G]＝ τ 1 ⊕ τ 2 ⊕ ･･･ ⊕ τ t X

b

161

(6)

Figure 3 shows two graphs G3 and G4 with n=5, b=7.

2

2

a
1

e

c
b
3

a

f

d
(a) G3

4
g

5

1

f

c d

4
g

b
3

e
(b) G4

Fig.3 . Graphs with n=5, b=7.

5

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

In the similar way as graph G1, there are 8 exclusive
tree events that each includes 1~8 trees of G3. There are
10 exclusive tree events that each includes 1~6 trees of.
G4 Then the number of trees is t(G3)=21 and t(G4)=24.
For equal branch probability p (q=1-p), network
reliability was obtained as (7) and (8).

presented using exclusive tree events for smaller numberof nodes and branches. The number of trees would play animportant role for network reliability. Since the number oftrees becomes huge for large graph, it is tedious to obtain
analytical expressions of reliability. Then numericalcomputation, for example the number of trees become
useful.[3]Ｒ(G3)= 2 p 4 q 3 + 4 p 3 (1 − q 2 )q 2 + p(1 − q 2 ) 3+ p 3 (1 − q 3 )q

(7)

Ｒ(G4)＝ 3 p 4 q 3 + 4 p 3 (1 − q 2 )q 2 + p 2 (1 − q 2 )(1 − q 3 )+ p (1 − q ) q + p (1 − q )q
2

2 2

3

3

(8)

Comparing (5),(6) with (7),(8), it is observed that
increase of nodes and branches makes reliability
expressions rapidly complicated.
In case of p≒1 or p≒0, the necessary condition thatthe network is most reliable becomes the graph to havethe maximum number of trees. If the network with the
maximum number of trees is unique, the abovenecessary condition becomes also sufficient condition.[2]We compare the network reliabilityＲ(G) with respect
to branch probability p=0.1〜0.9 for graphs of same
numbers of nodes n and branches b used in Figs. 1 and 3.
The result is shown in table 1.
Table 1. Network reliability and the number of trees.
nodes n,branchesｂ

n=4, b=4

n=5, b=7

graph

G1

G2

G3

G4

trees t(G)

3

4

21

24

Network relia.
R(G)branch prob. p

R(G1)

R(G2)

R(G3)

R(G4)

0.9

0.87480.96390.903450.984150.8

0.71680.87040.792990.917500.7

0.54880.73990.65283

0.78753

0.6

0.38880.59040.48833

0.606530.5

0.25

0.43750.320310.406250.4

0.14080.29440.174900.225280.3

0.06480.17190.07234

0.094280.2

0.02080.07840.01834

0.024130.1

0.00280.01990.001450.00192

Table 1 shows that graphs G2 and G4 which have larger
number of trees give higher reliability compared withgraphs G1 and G3.
4. Conclusion
Analytical expressions for network reliability are©ISAROB 2007

162

References[1] Ido H., “A Note on Calculation of System Reliability(in Japanese)”, Trans. IEICE, Vol.61-A,No.1, pp.72-74,
1978.
[2] Leggett J.D.,Bedrosian S.D., “Synthesis of Reliable
Networks”,IEEE Trans. Circuit Theory, Vol.CT-16, No.8,
pp.384-385, 1969.
[3] Ido H., Ohama Y., and Takahashi T.., “Graph Construction
with the Maximum Number of Trees by Continuous Edge
Addition”, Artificial Life and Robotics, Vol.10, No.2,
pp.185-188, 2006.

Appendix
Exclusive tree events for graph G3

τ 1 = a(b + c)(d + e)( f + g )τ 2 = a(b + c)d e fg
τ 3 = ab c ed ( f + g )τ 4 = ab c e fdg
τ 5 = a bcf (d + e + g )τ 6 = a bcf (e + d ) g
τ 7 = a bc fd (e + g )τ 8 = a bc fedg

Exclusive tree events for graph G4

τ 1 = a (b + d ) f (c + e + g )τ 2 = a(b + d ) f (e + c) g
τ 3 = ab d f (c + g )e
τ 4 = ab d fceg
τ 5 = a bdf (c + e + g )τ 6 = a bdf (c + e) g
τ 7 = a bd f (c + e) g

τ 8 = a b cde( f + g )τ 9 = a b cde fg
τ 10 = a b cd fge

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Acoustic Signals Separation of Mechanical Devices Using the ICA
with Band-Pass Filters
Sigeru Omatu
Computer Science and Intelligent Systems, Graduate School of Engineering
Osaka Prefecture University, Sakai, 599-8531,Osaka, Japan
Tel: +81-722-254-9278, Fax: +81-722-257-1788, E-mail: omatu@cs.osakafu-u.ac.jp

Abstract
In this paper, a method of separating the acoustic signals of
motors and gears of mechanical devices by using theindependent component analysis (ICA) with band-passfilters is proposed. The frequency distribution of a recorded
acoustic signal of the operating mechanical device can bedivided into three fields, the low-frequency field, whichcorresponds to the frequency characteristics of the gear,
the medium-frequency field, which is mixed with thefrequency characteristics of the gear and the motor, and thehigh-frequency field, which corresponds to the frequency
characteristics of the motor. Since only the mediumfrequency components are the mixture of acoustic signals ofgears and motors, the ICA with band-pass filters is
expected to separate the acoustic signals of motors andgears more accurately than the conventional ICA. The
simulation and experimental results show that the proposed
method can separate the acoustic signals of motors andgears of mechanical devices successfully.

Keywords:
ICA, Signal processing, Neural Networks

Simulation Results

By applying the fast fourier transform (FFT) to a recorded
acoustic signal of the operating mechanical device, we
observe that its frequency distribution can be divided into
three fields, the low-frequency field, which corresponds tothe frequency characteristics of the gear, the mediumfrequency field, which is mixed with the frequency
characteristics of the gear and the motor, and the highfrequency field, which corresponds to the frequency
characteristics of the motor. Since the frequencies of a163

Amplitude

In the quality evaluation of mechanical devices, it isimportant to separate the acoustic signals of motors andgears in order to identify the causes of failures. The ICA
method, which is developed to solve the cocktail-party
problem, can separate two independent acoustic signals
from their mixtures by using the information measure ofstatistically independent properties [1]-[3]. However, many
applications in practice denote that the ICA does not
perform well in separation by using the observed acoustic
signals directly [4]-[5]. In order to separate the independent
acoustic signals correctly, additional data processing isnecessary before applying the ICA.

Suppose there are two independent signals s1 and s 2 , theirfrequency characteristics are illustrated in Figures 1 and 2,
respectively where f 1 , f 2 and f 4 are constant and f 3 isvariable.

f1

f2

Frequency

Figure 1 - Frequency characteristic of s1
Amplitude

Introduction©ISAROB 2007

motor may be harmonics of the fundamental frequencies ofa gear, which causes the independence assumption of thesources to fail and affects the separation accuracy.
Therefore, the mixed acoustic signals with less frequency
components are expected to be separated more accurately.
In this paper, the ICA with band-pass filters is used toseparate the acoustic signals of gears and motors. We firstrecord the acoustic signals of the operating mechanicaldevices. By applying the band-pass filters, the respective
components of low- frequency, medium-frequency andhigh-frequency can be obtained. Then the mediumfrequency components are given to the ICA. After
separation, the acoustic signals of gears and motors arerecovered by adding the low-frequency and high-frequency
components to the separated results, respectively. In thispaper, the mixtures of two independent signals are also
designed to simulate the separation process of acoustic
signals of a gear and a motor. Both the simulation results
and the experimental results show that the better separation
results can be obtained by using the mixed mediumfrequency field than using the whole frequency field.

f3

f4Frequency

Figure 2 - Frequency characteristic of s 2 .
If we use two microphones to record the acoustic signals,
we have two observed signals x1 (t ) = a11 s1 + a12 s 2 ,x 2 (t ) = a 21 s1 + a 22 s 2 . We use the ICA to separate the two
independent signals s1 and s 2 from the observed signals.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table I shows the separation results where “Y” denotes thatthe independent signals s1 and s 2 can be separated
correctly and “N” denotes that they cannot be separated
correctly. From Table I, it can be seen that sometimes wefail in separating the acoustic signals s1 and s 2 by using
the observed signals x1 and x 2 directly.
Table I - Separation results of observed signals (unit: Hz)
f3
30
40
50
60
70
80
90
100
110
Y/N

Y

Y

Y

N

Y

Y

Y

N

f3

120

130

150

160

170

180

190

200

210

Y/N

N

Y

Y

Y

Y

N

Y

Y

Y

f 1 = 20 , f 2 = 140 , f 4 = 240

f3

220

230

Y/N

Y

Y

Y

Amplude [dB]

Gear Box

50 mm

30 mm
Microphone R

Microphone L
100 mmFigure 5 - The acoustic signals recording system
An example of acoustic signals recorded by microphones L
and R are shown in Figures 6 and 7, respectively where thesampling rate is 8,000. Their frequency characteristics areshown in Figures 8 and 9.
60
40

0

50

100
150
200
Frequency [Hz]

250

300

Figure 3 - Frequency characteristic of separated signal s1 'with band-pass filters
8
7
6
5
4
3
2
1
0

Motor

Amplitude [rms]

8
7
6
5
4
3
2
1
0

According to the above simulation results, we separate theacoustic signals of motors and gears of mechanical devices
by using the ICA with band-pass filters. The acoustic
signals recording system is shown in Figure.5. Two
microphones, which are held in different locations, are used
to record the acoustic signals of operating mechanicaldevices. By applying the band-pass filters, we obtain therespective components of low-frequency, mediumfrequency and high-frequency. Since only the mediumfrequency components are the mixture of acoustic signalsof gears and motors, we input the medium-frequency
components to the ICA. Then the acoustic signals of gears
and motors can be recovered by adding the low-frequency
and high-frequency components to the separation results of
the ICA, respectively.

20
0

-20
-40
-60

0

5000

10000
Time [ms]

15000

20000

Figure 6 - Acoustic signal recorded by the left microphone

0

50

100
150
200
Frequency [Hz]

250

300

Figure 4 - Frequency characteristic of separated signal
s 2 ' with band-pass filters.
Similarly, other unsuccessful separation experiments ofTable I are redone by using the ICA with band-pass filters.
The simulation results show that all the signals are
separated successfully. And the separation experiments of
mixed acoustic signals with multi-frequencies also showthat the ICA with band-pass filters performs better than theconventional ICA in acoustic signals separation.

©ISAROB 2007

164

80
60
40
20
0
-20
-40
-60
-80

A m plitude [rm s]

Amplude [dB]

However, after filtering the frequency components f 1 andf 4 with a band-pass filter, the frequency components f 2
and f 3 can be separated successfully by using the ICA.
Thus, the original acoustic signals s1 and s 2 can beobtained by adding the frequency components f1 and f 4
to the separation results of the ICA, respectively. As an
example, Figures 3 and 4 show the frequency
characteristics of separated signals s1 ' and s 2 ' by using the
ICA with band-pass filters, respectively wheref 3 = 100 Hz . From these figures, it can be seen that thetwo acoustic signals of s1 and s 2 are separated correctly.

Experimental Results

0

5000

10000
Time [ms]

15000

20000

Figure 7 Acoustic signal recorded by the rightmicrophone
Since the rotational speed of the motor is 3600 rpm and therotor has 12 poles, the fundamental frequency of the motor
is about 360 Hz. Similarly, since the gear ratio is 30:1, thefundamental frequency of the gear is about 12 Hz. Thus, it
can be considered that the medium-frequency is the range

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 200710of 300 to 2,000 Hz and the relevant band-pass filters aredesigned.
1
Amplitude [dB]

1
Amplitude [dB]

10

0.1
0.01

0.01
0.001
0.00010.001

1e-0050.0001
1e-005

0.1

1

10

100
1000
Frequency [Hz]1000
Frequency [Hz]Figure 12 - Spectrum of the separated acoustic signal byusing the ICA with a band-pass filter (motor)

10000

Figure 8 - Spectrum of acoustic signal of left microphone
10

Amplitude [dB]

1

Amplitude [dB]

1000
100
10
1
0.1
0.01
0.001
0.0001
1e-0051e-005

1

10

100
1000
Frequency [Hz]

10000

10

0.1
0.01
0.001
0.0001
1e-0051000
Frequency [Hz]Figure 10 - Spectrum of Figure 8 with a band-pass filter
10
1
0.1
0.01
0.001

Figure 13 - Spectrum of the separated acoustic signal byusing the ICA with a band filter-pass (gear)

From the above figures, it can be seen that the ICA withband-pass filters performs better than the conventional ICA
in acoustic signals separation. The spectrum of Figure 14(b) is similar with the one of Figure 15 (b), especially thepeaks of amplitudes appeared in both figures, which arelocated in the multiple of fundamental frequency of the
motor, denote that the separation results of acoustic signalsof the motor and the gear are not good.

0.0001

100
10

1000
Frequency [Hz]Figure 11 - Spectrum of Figure 9 with a band-pass filter
In Figures 10 and 11, the medium-frequency fields ofacoustic signals of left and right microphones with the
band-pass filter are given respectively. The filtered signals
are used as the input of the ICA. The spectra of theseparated acoustic signals are shown in Figures 12 and 13.
Since a peak of amplitude nearby 1,000 Hz, which is about
3 times of the fundamental frequency of the motor, can beobserved in Figure 12, it is regarded that Figures 12 and 13
show the medium-frequency fields of acoustic signals of
the motor and the gear, respectively.

©ISAROB 2007

165

Amplitude [dB]

1e-0051000
Frequency [Hz]To verify the effectiveness of our proposed method, we also
give the separation results by applying the recorded
acoustic signals of mechanical devices to the ICA directly.
The frequency characteristics of the separated acoustic
signal are shown in Figures 14 (a) and 15 (a), and the
medium-frequency characteristics are shown in Figures 14(b) and 15 (b). Comparing with Figures 8 and 9, it can beconcluded that Figures 14 and 15 show the frequency
characteristics of the motor and the gear, respectively.

1
Amplitude [dB]0.001
0.0001Figure 9 - Spectrum of acoustic signal of right microphone

Amplitude [dB]

0.1
0.01

1
0.1
0.01
0.001
0.0001
1e-005

1

10

100
1000
Frequency [Hz]10000

Figure14 (a) - Frequency characteristics of the separated
acoustic signal by using the ICA (motor)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
1

components can achieve better separation performance byusing the ICA. Therefore, for those independent signals
which are mixed only in medium-frequency field, the ICA
with band-pass filters can separate the independent original
signals more accurately than the conventional ICA. Using
the proposed method, we have solved the acoustic signals
separation problem of gears and motors of mechanicaldevices successfully.

Amplitude [dB]

0.1
0.01
0.001
0.0001
1e-0051000

Figure 14 (b) - Medium-frequency characteristics of theseparated acoustic signal by using the ICA (motor).

100
Amplitude [dB]1000
Frequency [Hz]

100

1

1

10

100
Frequency [Hz]1000

Figure 16 - Spectrum of recovered acoustic signal of the
gear

0.001
1

10

100
1000
Frequency [Hz]

10000

10

Figure15 (a) - Frequency characteristics of the separated
acoustic signal by using the ICA (gear)
100
10
Amplitude [dB]

0.01
0.001
1e-0050.01

0.00011
0.1

0.00010.1

1
Amplitude [dB]

Amplitude [dB]

10

10

0.1
0.01

1

0.001

0.1

0.00010.01

100Frequency [Hz]

1000

1000
Frequency [Hz]Figure 15 (b) - Medium-frequency characteristics of theseparated acoustic signal by using the ICA (gear)
The acoustic signals of the gear and the motor are
recovered by adding the low-frequency and high- frequency
components to the separation results of Figures 12 and 13,
respectively. The spectra of recovered acoustic signals of
the gear and the motor are shown in Figures 16 and 17
where the amplitudes of medium-frequency are adjusted
according to the amplitudes of low-frequency and highfrequency, respectively. Comparing with the above figures,
it can be concluded that the separation results arereasonable. The separated acoustic signals of the gear andthe motor are also checked by a technician, the sounds ofthe motor and the gear denote that the acoustic signals of
the gear and the motor are separated successfully by using
the ICA with band-pass filters.

Conclusions
In this paper, a method of separating the acoustic signals ofgears and motors of mechanical devices by using the ICA
with band-pass filter is proposed. The simulation results
denote that the mixed acoustic signals with less frequency©ISAROB 2007

10

Figure 17 - Spectrum of recovered acoustic signal of the
motor

0.001
0.0001

1

166

References[1] Hyvarinen A, Karhumen J, and Oja E (2001),
Independent Component Analysis, Wiley Publisher, NewYork[2] Bingham E, and Hyvarinen A(2002), A Fast FixedPoint Algorithm for Independent Component Analysis
of Complex Valued Signals, International Journal of
Neural Systems, Vol. 10, No. 1, pp. 1-8[3] Hochreiter S, and Schmidhuber J(1999), LOCOCODE
Performs Nonlinear ICA without Knowing the Numberof Sources, Proceedings of the First International
Workshop on ICA and Signal Separation, Aussois,
France, pp. 149-154[4] Lee TW(1998), Independent Component Analysis:
Theory and Applications, Kluwer Academic Publishers,
Boston[5] Cichocki A, and Amari S (2002), Adaptive Blind Signal

and Image Processing: Learning Alogarithms andApplications, Wiley Publisher, New YorkThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Reliability of Bank Note Classifier by Neural Networks
Sigeru Omatu
Computer Science and Intelligent Systems, Graduate School of Engineering
Osaka Prefecture University, Sakai, 599-8531,Osaka, Japan
Tel: +81-722-254-9278, Fax: +81-722-257-1788, E-mail: omatu@cs.osakafu-u.ac.jp

Abstract
This paper addresses the reliability of neuro-classifiers forbank note recognition. A local principal component
analysis (PCA) method is applied to remove non-linear
dependencies among variables and extract the mainprincipal features of data. At first the data space ispartitioned into regions by using a self-organizing map(SOM) model and then the PCA is performed in eachregion. A learning vector quantization (LVQ) network is
employed as the main classifier of the system. By defining
a new algorithm for rating the reliability and using a set oftest data, we estimate the reliability of the system. The
experimental results taken from 1,200 samples of US dollar
bills show that the reliability is increased up to 100% when
the number of regions as well as the number of codebook
vectors in the LVQ classifier are taken properly.

Keywords:
Bank Note Classifier, Neural Networks, Reliability

Introduction
The recognition of bank note recently has been concerned
effectively by using of neural networks, and it is shown thatneuro-classifiers are robust for recognition of defective,
taint, and worn out bank note. Takeda et al. [1] have used arandom mask for preprocessing the data and then a multilayer neural network as the classifier for recognition ofbank note. Teranishi et al. [2] have applied a method basedon acoustic cepstrum patterns for extracting the features ofbill and then a competitive neural network as the classifier.
Tanaka [3] has employed a probabilistic principal
component analysis (PCA) for extracting the main
characteristics of bill data.
Due to high risk of misclassification in such systems, thereliability of recognition becomes of a high importance.
Basically the classifier must be fully robust for frayed or
dirty bills of different models, and also has insensitivity toshift rotation and different directions of inserting bill. In
fact, even if the classification rate is 100% over the data
space, still it is necessary to make sure about the reliabilityof classification over all variety of real data.
We have already proposed in our pervious study [4] amethod which uses the PCA algorithm for extracting thefeatures of bill image data and utilizes a learning vector
quantization (LVQ) network as the main classifier, where
the reliability is evaluated thorough a new definedalgorithm using Gaussian mixture densities for distributionof data. We have found out that in case of large variability©ISAROB 2007

167

in input data or any non-linear correlation between the data,some additional discrimination process is needed to keep
the reliability high enough.
As the main limitation of PCA is its global linearity, that is,
it only defines a linear projection of data and does notmodel non-linear relationship among variables, somedevelopments of non-linear principal component analysis(NLPCA) have been presented to address this limitation [5].
However, both PCA and NLPCA algorithms try to model
the entire data by the same global features. As analternative, the complexity of the data can be modeled byusing a mixture of the local linear PCA. The local PCA
algorithm clusters the input data into regions and performs
PCA on the data that falls within each region.
In this paper, we apply a local PCA method where a SOM
model is used as for clustering the data into homogenous
regions. Our approach is similar to Kerschen [6] in the senseof local PCA application but differs in the clustering method
as he has used vector quantization (VQ) for clustering phase.
The current system is intended for classifying different kindsof bank note, however, we examined only US dollar bills.
The experimental results show a growth in reliability by
0.2% after using feature extracted by the local PCA modelcomparing to the method based on the conventional PCA,
and a growth by 2.3% comparing to classification without
the PCA.

Bill Data Preprocessimg
The original image of bill money comes as a 10x170 arrayof data taken through three main advance sensors and two
auxiliary ones. Each sensor uses two different waves lengths
for generating two channels of data. At first by using a linear
function we generate a new channel of data based on two
channels of each sensor. Thus totally 15 channels are
obtained among them we select 6 main channels which
represent the main characteristic of data. A simple
compression algorithm is used to reduce the size of datafrom 170 pixels in each channel to 30. Then a linear
transformation is applied for normalization as follow:
x −x
zi = i
G +C
Sx
where xi is the pixel value in each channel, x is the meanvalue of pixels, Sx is standard deviation, and G=512 and
C=128 are the coefficients of gain and offset, respectively
whose values are taken experimentally. Thus, a matrix of
6x30 size is provided for using in feature extraction step.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Data Compression
CA is one of the most popular methods for preprocessing,compression and feature extraction of data and it isdiscussed in most documents on multivariate analysis. The
most common derivation of the PCA is in terms of astandardized linear projection which maximizes thevariance in the projected space. As explained in the
introduction, the PCA only removes linear correlation
among the data and is only sensitive to second order
statistics, i.e., it is assumed that the distribution of data is
Gaussian. In case of non-linear relations among thevariables, we need to consider higher order statistics toeliminate the dependencies which are not removed by the
PCA. Here, we apply a local PCA model where the data isclustered into regions by using a SOM model at first andthen the PCA is performed on the data of each region. The
procedure is explained in the following.
A Pre-Clusteringyi = f (xi) = ΦΤ (xi - µ)which is a reduced r-dimensional representation of data
vector xi.
But here considering the q local regions which have been
already provided by SOM, we apply a sort of functions fi (.)
with i=1,…,q instead of a single encoding function. As we
consider the local regions small enough according to
number of different classes, we expect an adequate
representation of data within each region by using this local
PCA algorithm. The procedure is taken place as follow:
For each cluster of data corresponding to each region of the
SOM with the mean vector value of µj, the covariancematrix is estimated as:1∑j =( x − µ j )(x − µ j ) T
N j x∈S∑jwhere Nj is the number of vectors lied in the cluster Sj.
Then by determining the eigenvectors (p j1, …, p jr) of eachmatrix Σj, the function fj and thereby the transformed vector
yi can be obtained for each region as follow:

SOM is shown to have desirable properties compared toclassical clustering methods. It provides a natural measurefor the distance of a vector from a cluster which is adaptive
from the local statistics of the data [7]. The SOM forms a
map corresponding to the data distribution so that regionsof the map can be interpreted as clusters in the data space.
The main key point is defining a set of codebook vectors mi, i=1,2,..,m which represent units of the map. Then as for agiven input x, it is mapped to a unit associated to mc suchthat:|| x – mc || = min {|| x - mi ||}As explained in the following, in this paper the data
dimension n is 180 and the r dimension is taken as 30. The
number of regions q is taken as 30 according to SOM
partitions as described in the following A.
Accordingly through application of the PCA over allpartitions a new 30 dimensional data set is produced whichcontains the main features of 180 dimensional data.

mi codebook vectors are updated through a training process
iteratively as:mi(t+1) = mi(t) + hci(t) [x(t) – mi(t)]where t indicates the iteration and hci is a neighborhood
function taken as:
hci =α (t). exp(-dci2/ 2 r2(t) ).
Here, α and r are learning rate and neighborhood radius,
respectively both decrease monotonically as a linear
function of time. dci is the distance between mc and mi . We
consider a 6x5 map size for clustering the preprocessed data
and mapping the data of 24 classes onto 30 partitions. The
initial radius for neighborhood is taken 10 and initial
learning rate is taken 0.2.

Kohonen’s LVQ is a supervised learning algorithm
associated with the competitive network [7] which basicallyconsists of an input layer and an output layer, and an arrayof weight vectors wi where wij denotes a connection weight
between the jth node in the input layer and ith node in the
output layer (Figure 1). Given a training data set X, eachlabeled with a class identifier, and a set M of codebooks
vectors, the LVQ network adaptively modifies thesecodebooks so that they represent the class probability
distribution in the training data set. This modification ofcodebooks consists of applying a “punishment” when acodebook is near a sample of a different class and “reward”
when it is near a sample of its own class.

i

B PCA ModelingIf xi is supposed to be an n-dimensional vector of a data set
with i=1,…,N, then the goal of the PCA is to find r
dimensional axes pi onto which the retained variance under
projection is maximal. These axes are given by the
eigenvectors associated with r largest eigenvalues of thecovariance matrix of data as:Σ Φ=Λ Φwhere Φ = [p1,,p2,…,pr] is the eigenvectors matrix, Λ is the
eigenvalue matrix as diag{λ1,λ2,....,λρ} with λ1>λ2 >....>λρ,and Σ is the covariance matrix which is defined as:Σ = Ε [(x−µ)(x−µ)Τ]where µ=E[x] is the mean vector of data.
Then the transformed data vector yi is determined as:©ISAROB 2007

168

yi= fj(xi) = [pj1, …, pjr]T (xi -µj)

xi ∈S jClassification

Since the LVQ network is beneficial in classification ofdata with large number of inputs and explanation of themisclassification, it is applied as the main classifier ofpresent system. As we consider 6 kinds of US bills
including 1, 5, 10, 20, 50, and 100 dollars and for each bill
there exists 4 direction of inserting (Figure 2), totally 24 (i.e.
6x4) output categories are considered for the classifier. The
system is trained by taking trial number of codebook
vectors for each class looking for the best classification rate
and maximum reliability. A total number of 120 codebook
vectors (averagely 5 vectors per class) is experimentallyfound to be the best. The number of iterations for eachtraining epoch is taken 10,000 while a linear function asα (t ) = α (0) (1.0 − t / T ) is supposed for learning where T

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007is the number of iterations.. Therefore, the LVQ classifier
has a number of 30 neurons (the number of extracted
features) in the input layer and a number of 120 neurons in
the output.
class1

where L is the number of codebooks within each class, αi
is a normalizing coefficient, (Σαι=1, αι> 0), and θ is the
cross point of each pdf with the interval boundary of nearest
density from another class (Figure 3).

classcclass2

Weight Vectors wi

x1

xj

xN

Figure 1 - A Schema of LVQ Network Structure

Figure 3 - The Overlap between Codebook Densities of
Two Near Classes
B

Thus, the total reliability rate of the system finally can be
determined by averaging these class reliability values.

C

Experimental Results and Discussion

Figure 2 - Four Different Directions for Inserting a Bill

Reliability Evaluation
We propose a simple but effective algorithm for evaluating
the classification reliability. After the LVQ classifier istrained and the codebook vectors are determined, the test
data set is used to estimate the parameters of probability
density function (pdf) supposing a Gaussian distribution
around each codebook vector as:(pi (ξ ) = σ i−1 (2π ) −d / 2 exp − (ξ − µi ) 2 / 2σ i 2)where ξ is the distance between data vector and codebook
vector (i.e. || xi - mi ||) , µi and σi are the mean and variancein pdf of codebook vector i respectively , and d is thedimensionality of the feature vectors (here, 30). Assuming
the Gaussian probability density function, the interval [ µi –4.5σi , µi + 4.5σi ] can be considered as an area that covers
almost 100% of probabilities (100- 5.122 E-5). For a givenclass of codebook vectors if the densities have no overlap
within this interval with densities of other classes, thereliability for this class is supposed to be 100%, but in casethat this interval is overlapped with other classes, thereliability can be calculated as:
L

RM =

i

i

©ISAROB 2007

θ∑ α ∫µi −4.5σ ipi (ξ ) dξ169

A set of 2,400 sample data from 6 kinds of US dollar bills
including 1, 5, 10, 20, 50, and 100 dollar and four directions
for each bill (i.e. 100 samples for each direction), is used forlearning the LVQ classifier. Also a number of 1,200 samples
containing both normal data and slightly shifted data (toright and left) is taken for evaluating the system (that is 50
samples for each direction). The bills we used were ofvarious levels of fatigue and made in different years.
However, globally they can be considered as normal bills
not frayed ones. The order of inputting data is quite random
either in learning and testing phase. As for learning the LVQ
classifier we have tried different number of codebook
vectors from 3 to 10 per class looking for the best results.
On the other hand, concerning to local PCA application, we
have tried different number of regions from 24 to 48 as theoutput of SOM to study its influence on the classification
and reliability. The result of classification rate and reliability
is shown in Table 1. The reliability is evaluated through thealgorithm we have defined in the following. As it can beseen in Table 1 by increasing the number of codebook
vectors in classifier as well as the number of regions in local
PCA process, the reliability can be increased significantly.
We have found that by taking a number of 120 codebook
vectors in the LVQ classifier and 30 regions for the local
PCA the reliability of system can be extended up to 100%.
Figure 4 indicates the relation between number of regions
and reliability of system, clearly. As can be observed
increasing the number of regions makes a significant
increment in reliability value firstly but after some extend,
the reliability is not influenced anymore by larger number ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

regions and if the number of regions increases so much, itmakes an inverse affect on the reliability.
Table 1 –The Classification Results and Reliability Test
Data (The Number of PCA Components in all Cases Is 30)

No. ofCodebooks60

80

No. ofRegions

Recog.
Rate
(%)

Reli.
Rate
(%)

1 (Standard PCA)
12
24
30
48
1
12
24
30
48

100
100
100
100
100
100
100
100
100
100

82.1
90.1
90.4
94.6
94.7
96.6
97.5
98.3
98.4
98.4

1

100

99.8

12

100

99.98

24

100

99.99

30

100

100

120

200

48

100

100

1
12
24
30
48

100
100
100
100
100

99.99
99.99
100
100
100

100
99.9
99.8
99.7
99.6
99.5
99.4
99.3
99.2
10

20

30

40

50

60

70

80

Number ofRegions

Figure 4 - The Relation between the System Reliability
and Number of Regions in Local PCA
As we have used a test data set which contains shifting data,i.e., the data of bills with slightly shift to right or left at theinserting time, the high classification rate of system showsthat the system is robust enough on shifting and sliding.

©ISAROB 2007

In this paper we have presented a local PCA approach forfeature extraction of data in classification of bank note. The
aim is to model the complexity of data and correlation
between variables by using a simple linear model. The
method first exploits a SOM model to cluster the data space
into disjoints regions. Then a standard PCA model isapplied in each region. The experimental results taken from1,200 US dollar bills show that by taking a proper numberof regions and also an optimized number of codebook
vectors for LVQ classifier, the reliability of system can beincreased up to 100%. Comparing to the conventional PCA
method which was our pervious approach, the present
method shows a significant growth in reliability rate.
However, we have applied only US dollars for training andtesting the system, it can be easily generalized for otherkinds of bank note and considered as a multi-currencyclassifier with wide variety of data.

References

Reliability (%)

0Conclusions

170[1] Takeda F and Omatu S (1993), Bank Note
Recognition System Using Neural Network with
Random Masks, Proceeding of the World Congress on
Neural Networks, Portland, USA, Vol. 1, pp. 241-244[2] Teranishi M, Omatu S and Kosaka T (2000), Neuroclassifier of Currency Fatigue Level Based on
Acoustic Cepstrum Patterns, Journal of Advanced
Computational Intelligence, Vol. 4, No. 1, pp. 18-23[3] Tanaka M (1999), Modeling of Mixtures of Principal
Component Analysis Model with Genetic Algorithm,
Proc. 31st Int’l Symposium on Stochastic Systems
Theory and Its Applications, Yokohama, Japan, pp.
157-162[4] Ahmadi A and Omatu S (2003), A High Reliability
Method for Classification of Paper Currency Based on
Neural Networks, Proceeding of The EighthInternational Symposium on Artificial Life andRobotics (AROB 8th '03), Oita, Japan, Vol. 2, pp. 601604[5] Kramer
MA(1991),
Non-linear
Principal
Components analysis Using Auto-associative Neural
Networks,” A.I.Ch.E Journal, Vol. 37, pp. 233-243[6] Kerschen G. and Golinval JC (2002), Non-linear
Generalization of Principal Components Analysis:
From A Global to Local Approach, Journal of Soundand Vibration , Vol. 254, No. 5, pp. 867-876[7] Haykin S (1999), Neural Networsk, New Jersey:
Prentice Hall

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A New Concept of Flexible Organization forDistributed Robotized Systems
Peter Sapaty

Anatoly Morozov

Institute of Mathematical Machines and Systems, National Academy of Sciences
Glushkova Ave 42, 03187 Kiev Ukraine
Tel: +380-44-5265023, Fax: +380-44-5266457
sapaty@immsp.kiev.ua

Robert Finkelstein
Robotic Technology Inc., 11424 Palatine Drive, Potomac Maryland 20854
Tel: 301-983-4194 Voice, Fax: 301-983-3921
RobertFinkelstein@compuserve.com

Masanori Sugisaka
Department of Electrical and Electronic Engineering, Oita University
700 Oaza Dannoharu 870-1192 Japan,
Tel: 097-554-7831, Fax: 097-554-7841
msugi@cc.oita-u.ac.jp

Dale Lambert
Fusion for Situation Awareness Initiative, Defence Science and Technology Organisation
PO Box 1500 Edinburgh South Australia 5111
Tel: (+61) 8 8259 7175, Fax: (+61) 8 8259 5589
Dale.Lambert@dsto.defence.gov.au

Abstract
The presented concept for management of distributed
dynamic systems is based on installing in all systemcomponents of a universal intelligent module interpreting
special high-level language, in which any centralized or
distributed control can be expressed. The mission scenario
in the language, starting from any interpreter, is collectively
executed by their network. The interpreters perform
appropriate operations in nodes, while passing other parts ofthe scenario, together with intermediate data, to otherinterpreters in a coordinated manner. This process covers
the system at runtime and sets up distributed command andcontrol infrastructures providing the overall integrity andgoal orientation. The approach allows us to manage
robotized systems in unpredictable and hostile environments,
with possible failures of components. The language
description and programming examples in it are provided.

parts, through which any external orders to these systems
are received and both internal and outside impacts
realized. This infrastructure should also support theoverall system stability and integrity and guide its
internal behavior in accordance with the existing rules
and local and global goals pursued.
But in complex environments, the systemorganization may be indiscriminately damaged at any
moment of time, with skills vanished in nodes, relations
broken, and infrastructures destroyed. To put the systemback into life, we may need to restore its parts, as well as
the whole, at runtime [1].
Moreover, quite different philosophies, supported by
new technologies, to the organization of dynamic
emergent systems may be needed. First of all, the power
and universality of the traditional command and control(or C2) may be questioned, as this already takes place inthe area of crisis and disaster response [2,3].

1 Introduction

The C2 approach is based on the idea that the right
way to manage disasters is through centralized control
and hierarchies. But actual community crisis response
networks, as indicated by reaction on the recent wellknown disasters, looked nothing like the military-like C2hierarchies [2]. They consisted of loosely-coupledcollections of individuals, groups, and organizations that
continually changed, having permeable boundaries.

In managing large systems, whether civil or military, we
usually think of them as of something already existing, withproper expertise in parts (nodes) and relations between them.
We also assume that there exists a sort of command andcontrol infrastructure, usually hierarchical, covering all its

Rather than being organized according to theprinciples of command and control, disaster response
activities were undertaken through a complex and varied
set of organizational arrangements characterized by ahigh degree of emergence and improvisation. New

Keywords: crisis management, cooperative engagement
capability, ubiquitous command and control, distributed
systems, high level language, mission scenario,
interpretation network, robotized systems.

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networks formed that blended the activities of existingorganization with those of emergent groups, which dealt
with local problems as they emerged, using the existing
resources
in
novel
ways.
The
decentralized
multiorganizational responses appeared to be a major
strength, rather than weakness, and a source of resilience.
Centralization and hierarchy only slowed down andhampered response efforts.
In this paper, we propose to automate the process ofruntime composition of dynamic distributed systems from
dissimilar, possibly casual, elements and creation of any
infrastructures covering them -- by shifting the overallsystem organization to a higher, semantic level withorientation on both manned and unmanned systems
dedicated to operate in emergency situations.
First, we briefly describe the two well-known
organizational approaches to distributed dynamic systems,
namely, Cooperative Engagement Capability (CEC) andUbiquitous Command and Control (UC2) [6-8]. We thenreveal a technology, based on a higher-level control
language, which can effectively support both CEC and UC2,
as well as any other, especially crisis management, systems.
Related programming examples will be presented too.
The previous applications of the technology presented
include intelligent network management, distributed
interactive simulation, distributed knowledge bases, group
behavior, and support of robotized infrastructures [4, 5].

2 Basic Tends in Organization of Distributed
Systems
We outline here the CEC and UC2 approaches,originally with military orientation, but with obvious
significance to any systems with limited local resources but
oriented on global problems. The first one provides global
awareness to the distributed system in any its local node,and the second one allows us restructure the system’scommand and control at runtime, without loss of global
functionality and goal orientation. CEC and UC2 actuallycomplement each other, rather than compete, with UC2,
however, being potentially a broader approach, covering the
CEC capabilities as well.

2.1 Cooperative Engagement Capability
The Cooperative Engagement Capability [6] brings a
new power to distributed systems, not by adding new sensor
and impact components, but by distributing data from themin a significantly different manner. CEC fuses high quality
tracking data from participating sensors and distributes it toall other participants in a filtered and combined state, using
identical algorithms to create a single, common picture.
CEC has a robust communications system with considerable
improvement to bandwidth and countermeasures, as well as
the advantages offered by the global positioning system.
CEC’s main components and functions are as follows.
The Data Distribution System provides line-of-sight(LOS) communication with other units. The Cooperative
Engagement Processor provides processing of data received
from other units and incorporates it with own platform data

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to form a single composite data, forming the same
picture on each unit. The Data Distribution Function
provides real time data transfer in LOS and over-thehorizon. The Command/Display Support Function
performs doctrine management and distribution, alsogroup operations in defending against threats. The
Sensor Cooperation Function provides increased
detection and track performance by using composite
track data from active sensors. The Engage Decision
Function provides the capability for decisions to be madeby an automated process based on doctrine entered by
the Net Control Unit. The Engagement Execution
Function supports the control process for impact ofdesignated targets and responds to command directions
and decisions.

2.2 Ubiquitous Command and Control
A Ubiquitous Command and Control system [7, 8] isa system of assets, all of which possess a similar C2
capability. UC2 systems represent devolution of decision
making power from C2 centers to platforms which aredesigned to provide alternative functionality. Under thisphilosophy, command and control becomes an additional
function performed in any manned or unmanned units,
and ubiquitous C2 systems are so named because they
advocate a C2 capability on every platform. Automationis the primary mechanism for acquiring a similar C2
capability in any unit, and some decision making can be
fully automated. Other aspects will perform better withhuman interaction, with the choice between the twobeing mediated empirically.
The automated and human decision making are fully
integrated, and this includes the option of allowing themachine to override or substitute the human. UC2
systems primarily endorse a distributed and decentralized
management structure. It also introduces a command
fusion problem, as each decision maker may fuse
requests for its resources from multiple sources. In theinformation age, C2 centers may often become the prime
targets for surgical impacts. In defending against thelatter, one approach is to build duplicate C2 centers.
Supporting this, UC2 also enables C2 functionality toreconfigure as necessary, offering greater sustainability,
as well as quality of system performance, with graceful,rather then instantaneous, degradation.

3 Flexible Distributed Management Model
The distributed computation and control model andtechnology, previously known as WAVE [4] andWAVE-WP (or World Processing) [5], is based on ahigher-level language describing parallel distributed
solutions in computer networks as a single seamlessspatial process rather than traditional collection andinteraction of parts (agents). Communicating copies ofthe language interpreter (as universal control modules U
in Fig. 1) should be installed in important system
resources (like internet hosts, mobile robots, or mobile
phones), which may be emergent. Parallel spatial
scenarios written in the language can start from any

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
interpreter, covering the network at runtime, cooperating orcompeting with each other in the distributed space.

Self-evolving scenario

U
U
Emergent
resources

Rules serve as various supervisory, regulatory,
coordinating, integrating, navigating, and data processing
functions, operations or constraints over moves, which,for example, may be:U• elementary arithmetic, string or logic operations ondata returned by moves;

Universal
control• hops in physical, virtual or combined spaces
parameterized by moves;• hierarchical fusion and return of (remote) data
provided by moves;Figure 1. The universal control network.
The spreading scenarios can create dynamic knowledge
infrastructures arbitrarily distributed between computers(robots). Subsequently or simultaneously navigated by sameor other scenarios, these infrastructures can effectively
support distributed databases, command and control, global
situation awareness, parallel inference, and autonomous
decisions. It is possible to operate in this seamless virtualworld fully ignoring its physical distribution, whereas
virtual networks can migrate (partially or as a whole) inphysical networks while being processed. The distributed
virtual world can optimize and guide movement andoperations in the physical world, say, by robotic groups(armies).
The system mission in the language sets what the systemshould do in a distributed space rather than how to do this,which resources to use, or how C2 should be organized. The
main burden on actual composition of the system, its
internal organization, and runtime recovery is shifted toefficient distributed implementation of the scenario
language. This allows us to continuously support thedevelopment of missions and global goal orientation
regardless of the state of system resources, keeping thesystem operable if at least a single node remains functional.
Any top down, bottom up and combined solutions areavailable within this spatial programming paradigm. The
approach often provides hundreds of times application code
reduction and simplification, allowing us to concentrate on
efficient global solutions rather than implementation details.

4 Distributed Scenario Language
Let us symbolically call it within the current application
context as Distributed Scenario Language (or DSL),
whereas the earlier versions carried names WAVE [4],WAVE-WP [5], and WPL [9]. The DSL syntax in the mostgeneral form can be represented as follows:wave→ rule ({ move , })move → constant | variable | wave
variable → nodal | frontal | environmental
The program, or wave, is represented as one or moreconstructs called moves, which are separated by a comma
and embraced altogether by another construct called rule (in©ISAROB 2007

the functional style, using parentheses). The name“wave” reflects the general space navigation ideology of
the approach, where the program code can cooperatively
cover the distributed system in parallel wavelike steps.
And similarly, “move” highlights the potential mobilityof all language constructs.

173• parallel and distributed control over the developmentof moves as programs, covering control flow of usual
languages too;• special contexts of navigation in space, for example,
causing creation of different infrastructures by the
evolving and spreading moves.
• sense of values expressed by moves, for their proper
use by other rules.
Moves can represent values directly, as a constant orvariable, or can recursively be arbitrary waves
themselves. Variables can be classified as nodal, or
stationary, associated with space positions and shared bydifferent waves; frontal, moving in space with theprogram control; and environmental, accessing thenavigated environment in the points reached. If control
splits by parallel moves, each move receives independent
copies of all frontal variables. Constants may reflect
information or physical matter, the latter to be processed
if proper physical equipment is available.
Wave is applied in a certain position of the
distributed world, providing data processing and space
navigation and transformation, eventually terminating inthe same or in other positions (which may be multiple
and remote). It provides final result that unites local
results in the positions (or nodes) reached, and also
produces a resultant control state. These two can be
subsequently used for further data processing anddecision making on higher program levels.
If moves are set to advance in space one after theother (defined by a proper rule), each new move isapplied in parallel in all the nodes reached by theprevious move, with the rest of the program also moving
to the new locations (virtually or if needed, physically).
Different or same moves (by other rules) can also apply
independently from the same node, reaching new nodes
asynchronously and in parallel.
The syntax shown above can represent any programin DSL, but if convenient, other notations can be used,
like the infix one. For example, the program
advance (move1, move2, move3)
ordering three moves develop sequentially, each move

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B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
from the positions in space reached by the previous move,can be represented as:
move1. move2. move3
with the period indicating advancement in space. Similarly,
parallel (move1, move2, move3)
can be written as:
move1; move2; move3
with the semicolon setting independent and paralleldevelopment. As another example,
assign (R, multiply (sum (a, b, c), d))can be substituted by:
R = (a + b + c) * d
For improving readability, spaces can be inserted inarbitrary places of the programs; they (as well as carriage
returns) will be automatically removed (except when residein strings in quotes) during the program interpretation. Also,
it is often useful to show programs using indentations, withplacing related opening and closing parentheses exactly one
over the other.
More details about different constructs and their
meanings can be obtained from the previous versions of thelanguage [4, 5].

5 Parallel Language Interpretation
The peculiarities of the syntax and semantics of DSL
allow us to provide its effective, fully distributed andparallel, interpretation without central resources. During thisprocess, the spatial unwrapping and replication of therecursive program formula takes place, rather than its
traditional reduction.
A DSL program covers and matches the physical or
virtual world in parallel, establishing full control over the
distributed space. Each operation is performed in thereached nodes on local data there (environmental, and in
nodal variables), on what has been brought to these nodes infrontal variables with the program control, and on theobtained and returned results (possibly, remote) by
subordinate waves.
The intermediate and final results of the work of DSL
programs may be scattered throughout the whole navigatedspace; they may be grouped and returned into a certain point(or points) if this is needed. Different evolving parallel anddistributed waves can cooperate or compete in the common,
open, distributed space.
The DSL interpreter consists of a number of specialized
modules working in parallel (like parser, data processor,
control processor, and communication processor). These
are handling and sharing specific data structures supporting
persistent virtual worlds and temporary hierarchical control
mechanisms, like wave queue, incoming and outgoing
queues, local part of the distributed knowledge network,
track forest, nodal, environmental and frontal variables, etc.
[4, 5].
The interpreter may have its own physical body (say, in©ISAROB 2007174

the form of mobile or humanoid robot), or can bemounted on humans (e.g. in mobile phones). The wholenetwork of the interpreters can be mobile and open,changing the number of nodes and communication
structure between them, as robots or humans can move atruntime. The DSL operations may trigger a combinationof data processing and physical movement in space, withexchange of information and physical matter between theinterpreters both electronically and in a direct contact.

6 Dynamic Creation
InfrastructuresofDistributed

We will consider here simplified programs in DSL,
which are creating different infrastructure topologies
over the distributed, and possibly scattered, system nodes.
The main feature of all these programs is that they can do
the job in a fully distributed manner, by covering andflooding the dynamic and open system in parallel andcooperative mode, without any central resources.
These programs, as well as those in subsequent
sections, have a quite different semantics than usual ones,
as each their construct may work in other locations inspace (and in other computers), not in the same as theprevious ones. Despite this, all the programs constantly
preserve full integrity and controllability as the whole,similar to traditional single-machine programs operatingin the same memory. This possibility is effectively
achieved by a powerful implicit distributed track system
and also internal “command and control” infrastructures
underlying the distributed DSL interpretation, see forprevious versions at [4, 5].

6.1 Star
Starting in any node, the following program forms
oriented links with name star to all other nodes thatcan be reached directly or indirectly (i.e. via other nodes)
from the current node (as in Fig. 2).
Create links (+ ‘star’, other nodes)
6

2

star
4
4

5

star

star
7

3

Figure 2. A star infrastructure.

6.2 Full Graph
Starting in any node too, the program below firsthops to all directly or indirectly reachable nodes,
including itself, and then from all the nodes reachedforms a non-oriented link named full to all other nodes,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

reached directly or indirectly too (see Fig. 3).
Hop (all nodes).
Create links (‘full’, other nodes (inferior))

62hierarchyhierarchy

4
4

To avoid duplicate links, the formation of a link between
any two nodes takes place only in one way, allowing thesuperior node (or inferior, as another solution) to create thelink (by comparing certain node’s values, e.g. addresses).

5

hierarchy

7
3

6

2

full

Figure 4. A hierarchical infrastructure.
4

4

full

5

We can also offer a combined solution where, first,
subordinate nodes are tried to be chosen within the range
given, and second, if this is not possible, among any
nodes reached (directly or indirectly). We may also set
up the maximum number of subordinate nodes allowed
as the precondition for the both options, with theresultant program being as follows:

full

full

7

3

Figure 3. The full graph infrastructure.

6.3 Hierarchy
Starting from any node, the following program, in a
repeated advancement in the distributed space, createsoriented links named hierarchy from the current nodes
to new nodes reached. A new iteration (together with thewhole program code) starts in parallel from all nodes
reached by the previous iteration.
Repeat (Create links (+ ‘hierarchy’, first come,range (20)))

Repeat (Create links (+ ‘hierarchy’, first come,Or (range (20), other nodes),quantity (2)))
Another solution, easily programmable too, may be
where the range is floating, gradually increasing unlessthe needed number of subordinate nodes is found.

6.4 Line

To have the resultant network structured as a tree (with
its root in the node the program started), the program allows
entering nodes only once, on the first arrival into them. It
also tries to establish the next level of hierarchy withincertain vicinity (range) from the current nodes, to makethis hierarchy optimized territorially.
This program, however, may not cover the whole system
if the distance between nodes may exceed the range given.
In this case, we may decide either to increase the range or
select the descendent nodes of the hierarchy among anyother nodes that can be reached, at any distance, also
allowing us to have only a certain number of subordinate
nodes for each node, as follows (see Fig. 4):

Any other distributed topologies can be created in asimilar way. For example, a line connecting all nodes canbe just produced by the previous program by allowing
only a single new node at each step (let it uses now nonoriented link line), as follows (see Fig. 5):
Repeat (Create links (‘line’, first come,Or (range (20), other nodes),quantity (1)
)
)
6

2

line

4
4

Repeat (Create links (+ ‘hierarchy’, first come,other nodes, quantity (2)
)
)

5

line
7
3

Figure 5. A line infrastructure.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

6.5 Ring
With minimal extension, the previous program cancreate a ring, by connecting the last node of the line with its
first node (also changing link names to ring and making
them oriented), as follows, where the starting node addressis always accessible by the special variable START (see Fig.
6):
Repeat (Create links (+ ‘ring’, first come,Or (range (20), other nodes),Quantity (1))).
Create link (+ ‘ring’, START)
2

ring

control scenarios given from the beginning in frontal
variables Command and Control (these scenarios may
be of human, robotic, or mixed orientation). C2 also
simultaneously passes them and itself for a recursive
activation and execution in all directly subordinate nodesof the hierarchy, which will be acting in the same way,
and so on. At each level of hierarchy (incremented
downwards), the original command and control scenarios
are detailed for their execution with taking into accountpeculiarities of this level, as shown in Fig.7.
Level i-1

DSL
code

Data

Command i

Level i

Execution

6

Control i

…

4
4

5

Level i+1

ring

Figure 7. The C2 interpretation in nodes.

ring
7
3

Figure 6. A ring infrastructure.
All these programs can work in a fully distributed way,without any central control, as already mentioned. All theinfrastructures above can be built (and can exist)
simultaneously between the same nodes, and any changes tothem can be easily done at runtime, without interrupting
local or global processes which may take place over them.

7 Hierarchical Command and Control
Using the infrastructures built above, we can organize
any centralized or distributed system management andcontrol in DSL. Let us show how traditional command andcontrol can work via the hierarchical infrastructure, startingfrom its root node and using oriented links hierarchy.
The following program applies recursive command andcontrol procedure (enclosed in braces) in each reached nodeof the hierarchy, after being delivered there in frontal
variable C2, as its content.
Frontal (C2, Command, Control, Level).
Assign (C2,
{Increment (Level).
Detail and apply (Command, Level);
Detail and apply (Control, Level);(Hop (+ ‘hierarchy’). Apply (C2))}).
Assign (Command, command scenario).
Assign (Control, control scenario).
Apply (C2)This procedure C2, in its turn, applies in parallel (whichis indicated by semicolons, see Section 4) the command and©ISAROB 2007

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The scenarios delivered to, and activated in, nodes
can be written in DSL in such a way that could becapable of accessing and sharing data and operations in
any nodes of the whole hierarchy, not only in the current
one, therefore the organization in Fig. 7 can potentiallybe much more advanced and flexible than the strictly
hierarchical command and control. The program abovereflects only a possible organizational skeleton, or
framework, that can be set up in DSL.
This program works on the already existinghierarchical infrastructure, where it starts from the root
and then covers with activity the whole infrastructure at
runtime, beginning to work during, rather than after,
being deployed. We can modify this program to becapable of creating the very infrastructure it operates on,
during its coverage, not before. We can also form atemporary hierarchy without explicit links (based oninternal, invisible interpretation infrastructures) and forthe current mission only, with its automatic self-removal
after the mission is completed. This latter case can be
represented by the following modified program.
Frontal (C2, Command, Control, Level).
Assign (C2,
{Increment (Level).
Detail and apply (Command, level);
Detail and apply (Control, Level);(Hop (first come, range (20)).
Apply (C2))}).
Assign (Command, command scenario).
Assign (Control, control scenario).
Apply (C2)This program absorbs mechanisms of the hierarchycreation programs discussed earlier, with their spatial

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

repetition effectively expressed now by spatial recursion.

8 Collection and Distribution of Targets
8.1 Using Hierarchical Infrastructure
We can write a spatial program in DSL which selfspreads from the root node to all other nodes of thehierarchical infrastructure, picks up data on all objects(targets) seen in all nodes passed and reached, and merges
and fuses all this data while echoing upwards the hierarchy.
The data fusion may include removal of duplicate records,
as the same targets can be seen from different nodes
simultaneously.
Having collected in the root node all targets detected bya distributed system, we can replicate and spread theirrefined list back to all nodes of the hierarchy by the parallel
self-descending process again, allowing each node to selectindividually targets of inertest from their list and impact
those it can. All these operations can be performed on thehierarchical infrastructure by the following simple program,which globally loops in the root node, while repeating thedetection-collection-fusion, followed by distributionselection-impact indefinitely (as shown in Fig. 8).
Loop (
Assign (frontal (Seen),
Fuse (
Repeat (Free (detect (targets));
Hop (+ ‘hierarchy’)))).
Repeat (Free (select and impact (Seen));Hop (+ ‘hierarchy’)))Topcontrol

3

5

Local
loop
GlobalvisionDistribution

Local
sensor
data

Local
loop
Globalvision

6

4

Local
loop
GlobalvisionLocal
sensordata

7

Impactingtargets

Figure 9. Fully distributed global vision.
4

7

Impactingtargets

Fig.ure 8. Hierarchical collection and distribution of targets.

8.2 Using Any Infrastructure
Many other efficient solutions of collection anddistribution of targets detected by a distributed system canbe proposed in DSL. One of them, the most universal andsimple, is a fully distributed one, with using any available
infrastructure (including the ones we have built before like

©ISAROB 2007

Local exchange& fusion

3

Globalloop
5Nodal (Seen).
Hop (all nodes).
Loop (
Wait (60).
Fuse and assign (Seen, detect (targets)).
Select and impact (Seen);
Fuse and assign ((Hop (all links). Seen), Seen
)
)

4

6

4

Each node, fusing what it directly sees with what itgets from other nodes, regularly exchanges all
information it accumulates with direct infrastructureneighbors only, updating the records in them if thetargets brought are fresher or new. With all nodes doing
this in an infinite local loop, the information about all
targets seen by all nodes of the distributed system will beeventually reaching all its nodes (of course, if the system
is connected in principle), thus guaranteeing global
awareness in each node. The following simple program
implements this distributed algorithm, additionallysetting certain time delay between the iterations in eachnode (see also Fig. 9).

2

Collection & fusion
2

star, tree, line, ring or their combinations). The idea is in
the following.

177

This fully distributed algorithm can work well also
without any infrastructures built in advance, by merely
regularly accessing from all nodes any other nodes intheir vicinity (say, within available or given radio or
laser range, and not necessarily same neighbors eachtime, as nodes can move), by substituting the line withthe hop by the following one:
(Hop (range (100)). Seen), Seen
These were only the simplest programming examplesof dynamic, on the fly, creation of distributed
infrastructures and using them for emergent commandand control and global vision and targeting. Morecomplex ones can be effectively written too, without any
limitations on centralization or, vice versa, distribution ofactivities (and any their combinations) in distributed
systems, with program code optimally staying in nodes
or moving between them, cooperatively covering thewhole system or its pars with activities needed. And all

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
this (re)organization can be implemented at runtime, on theactive and operating system, which is especially importantin crisis situations, where improvisation and flexibility ofdealing with emergent resources are the qualities of primaryvalues.

9 Conclusions
A new approach to organization of distributed dynamic
systems has been presented which, as was shown, caneffectively support and implement basic trends oforganization of distributed systems with limited local
resources but capable of solving complex global problems.
Among these trends are the well known Cooperative
Engagement Capability and Ubiquitous Command andControl. Using our approach, the CEC and UC2 systems
will also be able to function under indiscriminate damagesof their components and infrastructures, while preserving
functionality and goal orientation.
The approach presented is based on a special language
describing mission scenarios on a semantic level while
delegating usual programming routines to automaticinterpretation. Communicating interpreters of the language
can be installed in internet hosts, mobile robots and cellular
phones, integrating any manned and unmanned resources
under a unified control. Being both high level and fully
formal, the approach may open a real way to a massive useof mobile robotics in advanced crisis relief missions, where
job division and subordination between humans and robots
may be emergent. Its advantages for robotized crisis
management applications may include the following:• It drastically simplifies the overall control of multirobot systems, making it comparable to the control of asingle robot, regardless of the number of robotic
platforms used, which can vary at runtime.
• Does not need any central resources as its global control
can start from any available manned or unmanned unit
and cover the system at runtime.
• Allows robotized systems to be designed andimplemented from the topmost, linguistic, level withconsiderable reduction of the time and funds needed incomparison with usual bottom up integration.
The previous, or WAVE, version of the technology has
been written in C under Unix, and is being used in different
countries, especially for intelligent network management,with recent results in Ireland [10, 11] and Canada [12]. The
WAVE system is available on the Internet, and can be
downloaded, say, from [13]. The current version of thelanguage is in patenting and reimplementation process; it
can be installed on any platform within a short time. The
universal wave chip, as hardware language interpreter for
distributed crisis management systems, is being planned too.

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Vol.4, No.4, ISSN 1543-5865, July/August 2006, USA,©ISAROB 2007

178

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ISBN 0-471-65575-9 (www.amazon.com).
[6] The Cooperative Engagement Capability, John
Hopkins APL Technical Digest, Vol. 16, No. 4,
1995.
http://www.jhuapl.edu/techdigest/td1604/APLteam.
pdf.
[7] Lambert, D.A. (1999a), “Ubiquitous Command andControl”, Proceedings of the 1999 Information,
Decision and Control Conference, Adelaide,
Australia,pp.
35–40,
IEEE.
http://www.eleceng.adelaide.edu.au/ieee/idc99/papers/lambert_dale_2.pdf.
[8] Lambert, D.A, and Scholz, J.B. (2005), “A Dialectic
for Network Centric Warfare”, Proceedings of theInternational Command and Control Research andTechnology
Symposium(ICCRTS).
http://www.dodccrp.org/events/2005/10th/CD/papers/016.pdf.
[9] P. Sapaty, “Crisis Management with Distributed
Processing Technology”, International Transactions
on Systems Science and Applications, vol. 1, no. 1,
2006, UK, pp. 81-92, ISSN 1751-1461(http://www.xiaglow-institute.org.uk/itssa/itssa.htm).
[10] S. Dragos and M. Collier, “Macro-routing: a newhierarchical routing protocol”, Proc. 47th annualIEEE Global Telecommunications Conference,
Texas, USA.
[11] Sanda-Maria Dragos, “Scalable QoS Routing in
MPLS Networks Using Mobile Code”, PhD
Dissertation, School of Electronic Engineering,
Dublin City University, July 2006, 235p.
[12] Gonzalez-Valenzuela, S. and Leung, V.C.M, "QoSRouting for MPLS Networks Employing Mobile
Agents". IEEE Network Magazine, Special Issue,
May-June 2002.
[13] Wave system public domain in Canada:
http://www.ece.ubc.ca/~sergiog/wavefiles/.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Skill-Based Manipulation and Error Recovery
in Maintenance Tasks
Akira Nakamura and Kosei Kitagaki
Intelligent Systems Research InstituteNational Institute of Advanced Industrial Science and Technology (AIST)
Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568 Japana-nakamura@aist.go.jp, k.kitagaki@aist.go.jp

Abstract
Dexterous manipulation is an important function forworking robots. Manipulator tasks such as assembly anddisassembly can generally be divided into several motion
primitives. We call these "skills" and explain how mostmanipulator tasks can be composed of skill sequences. We
are currently planning to construct a maintenance robot forhousehold electrical appliances. Skill techniques are
effective when a robot needs to achieve high-accuracy
results. However, since failure also occurs easily, it isimportant to implement error recovery technology.
Various errors are classified and a new type of error
recovery processing is presented in this paper.
Key words: manipulation skill, planning, modeling,visual sensing, error recovery

1. Introduction
To be useful in several fields, manipulation robots
need to achieve various tasks using special techniques. We
analyzed human motions in such tasks as assembly anddisassembly and found that movements consisted ofseveral significant motion primitives. We call these"skills" and have demonstrated that most tasks of amanipulator can be composed of sequences of skills[1]–[6]. We demonstrated that robots can perform various
human tasks by using this concept of skills. Skill level
control is positioned in the hierarchy of manipulator
control between task level control and servo level control.
Programmers can describe a task program easily as asequence of these skills without taking into account servo
level control. Skills in which the contact states vary duringassembly and disassembly tasks are particularly
significant. We considered three important fundamental
skills for these tasks: "move-to-touch," "rotate-to-level"and "rotate-to-insert."
We have researched maintenance robots working invarious plants, such as a nuclear power plant, andconsidered the skill technique used by such robots. As atarget for future research, we will consider manipulation©ISAROB 2007179

robots used for the maintenance of such items as
household electrical appliances, furniture and stationery.
At present, we are working to produce a prototype of amaintenance robot for system components and personal
computers (Fig. 1). Recent recycling and environmental
problems will increase the need for robots that perform
repair and inspection. The robots open and close theequipment enclosures and replace parts (Fig. 2). It is thus
necessary for these robots to be able to perform tasks
requiring high accuracy, such as loosening a screw using ascrewdriver.
Manipulation tasks with skills are performed by thesequences of visual sensing, geometric modeling,planning and execution. In actual manipulation, however,errors often occur for various reasons and the processes areinterrupted. Failure causes can be divided into several
kinds of errors such as execution errors, planning errors,
modeling errors and sensing errors. Various approaches
for error recovery have been studied [7]–[10]. However,few realistic methods for error recovery have been
proposed for various errors that might occur when
performing maintenance tasks. We have grouped theerrors into several classes according to potential causes. If
an error occurs, the parameters of planning, modeling or
sensing are corrected by specifying the class and then thetask process is performed again. We propose a method oferror recovery that uses the concept of error classification.
This method allows the flow of system processes,
Vision system(Hand-eye type)

Start

Manipulator
1. Open the case
2. Investigate the cause of the trouble
3. Identify failed parts

SystemComponents

4. Replace the parts
5. Close the case
End

Fig. 1 Maintenance robot forsystem components

Fig. 2 Maintenance procedure

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Start

Task Level
P

Sensing(T)Modeling(T)

z
OA

P

Planning(T)yzSkill Level

OA y

Q

(a) Move-to-touchf skill

Qi=1(b) Move-to-touchc skill
i=iFig. 3 Move-to-touch skills

Sub-task(i) (i = 1, 2, ・・・ , m)Sensing(Pi)P

Modeling(Pi)

P
OA

z

Re-planning(Pi)

PreSkilli1(= Skilli0)

y

Q

Q

(i)

(ii)

Fig. 4 Rotate-to-level skill

P

P

Skillij

Skilli1

Skillin

Execution(Si0)

Execution(Si1)

Execution(Sij)

Execution(Sin)

Sensing(Si0)Sensing(Si1)

Sensing(Sij)

Sensing(Sin)Modeling(Si0)Modeling(Si1)Modeling(Sij)Modeling(Sin)Confirmation(Si0)

Confirmation(Si1)

Confirmation(Sij)

Confirmation(Sin)

P

z
OA y

i=m

Q

(i)

Q

Q

(ii)

(iii)

End

Fig. 5 Rotate-to-insert skill

Fig. 6 Process flow

including error recovery, to be derived easily andsystematically. In this paper, we explain the concept ofmanipulation skills and skill-based processes formaintenance, and we propose systematic classification oferrors and recovery from such errors.
The next section explains manipulation skills and thecomposition of skill sequences. The processes of visual
sensing, geometric modeling, planning and execution arethen explained for task and skill levels in section 3. A
maintenance robot must first of all open the case of ahousehold appliance by loosening screws. Additional
skills used in the loosening task are explained in section 4.
The classification of errors and error recovery to improve
the success of task achievement are shown in sections 5
and 6, respectively.

2. Manipulation Skills
This section explains our concept of skills. See
References [1]–[3] for more details.
In assembly and disassembly tasks, the skills in whichcontact states vary are particularly significant. In
References [4], [6], we considered three skills,"move-to-touch," "rotate-to-level" and "rotate-to-insert,"
all of which play an important part in such tasks.
(1) Move-to-touch Skill: The move-to-touch skill isdefined as the transition of a grasped object P in a constantdirection that continues until contact with another object Q
occurs (Fig. 3).
(2) Rotate-to-level Skill: This skill is defined as therotation around either a contact point or a contact edge to©ISAROB 2007180align the face of the grasped object P with the face ofanother object Q (Fig. 4).
(3) Rotate-to-insert Skill: This skill is the motion ofrotating the object P obliquely into the hole in anotherobject Q to insert it accurately (Fig. 5).
A specific task is composed of sequences of skill
primitives such as these move-to-touch, rotate-to-level androtate-to-insert skills. The skill sequences can be decided
by several methods. We have already presented a method
using variations of the number of contact points in skill
primitives [4].

3. Process of Sensing, Modeling,
Planning and Execution
The procedures for sensing, modeling, planning andexecution are shown in Fig. 6. In this scheme, the planningof the task level is first performed, and then the executionsof the skill level are performed according to the sequences
derived from the task planning. See References [5] formore details. In the following descriptions, we assume thatan error does not occur in each component.
(Step 1) Task Level
At the task level, the skill sequence composing a giventask is decided. First, visual sensing of the working
environment of the robot is performed using a visionsystem and modeling is done. Next, planning follows, andskill command sequences and the initial position andorientation of an object to be grasped and manipulated arederived.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

(Step 2) Skill Level

cone.

At the skill level, each Sub-task(i) (i=1,…,m) is
performed successively and each skill Skill ij (j=1,…,n) in
Sub-task(i) (i=1,…,m) is executed successively. Before
this sequence in each Sub-task(i) (i=1,…,m) is performed,
the transition of the grasped object P to the initial state iscompleted. We represent the transition as PreSkill i1 ( =Skill i0 ) (i=1,…,m).

This is an error caused by a difference in the real object
and geometric model in the computer software being used,such as CAD modeling software. This might be caused by,for example, a difference in the size, a polygonal
approximation and an expression of rounding andchamfering of an edge.
(4) Sensing error

4. Task of Loosening a Screw
We considered three fundamental skills in section 2.
However, we will also consider several skills used in thetask of loosing a screw. Let us consider loosening a
Phillips screw using a Phillips screwdriver.
(4) Rotate-to-bite Skill: This skill is a rotation aroundthe axis of the screwdriver to fit the tip of the screwdriver
into the flutes of the screw head (Fig. 7). This skill is
performed with pushing force.
(5) Rotate-to-loosen Skill: This skill is defined as an
initial rotation to loosen the fixed screw (Fig. 8). This isperformed by matching the axes of rotation of a part and atool.
(6) Rotate-to-extract Skill: This skill is defined as therotation of the screw to pull the screw out. The skill
continues after the rotate-to-loosen skill.
We will consider the task of loosening a screw using ascrewdriver [5]. We assume that the task of loosening ascrew using a screwdriver is composed of the following
skills (Fig. 9); Skill1: Move-to-touch skill, Skill2:
Rotate-to-bite skill, Skill3: Rotate-to-(loosen ／ extract)skill (without interruption).

5. Classification of Errors
In the processing flow described in section 3, thepossibility of errors was ignored since it was assumed that
all the components were operating under ideal conditions.
However, errors could actually occur for various reasons.
Whether an error has occurred or not is judged at
Confirmation(S ij ) in each skill of the procedure flow. In
the task of opening the outside case of an electricalappliance for maintenance, errors are classified as follows.
(1) Execution error
This is an error caused in the manipulator mechanism
such as a backlash of a manipulator gear. There are many
possible failures for a manipulator with bad accuracy, andthis includes failures occurring stochastically.
(2) Planning error
This is an error caused by inaccurate parameter values
in planning. For instance, when a control uncertainty coneis used for the fine motion planning of the manipulator, anerror occurs due to an incorrect angle at the top of the©ISAROB 2007

(3) Modeling error

181

This is an error occurring during visual sensing. For
example, possible causes might be occlusion, imperfectcalibration or undesirable lighting conditions.

6. Error Recovery
A process flow that takes error recovery into account isshown in Fig. 10. At the step of Confirmation(S ij ) in eachskill primitive, whether the result is correct or a failure isjudged by an automatic process or by an operator. Error
recovery is performed using the following error
classification.
(1) Class 1: The sub-task(i) is executed again without
correcting the parameter when it is judged to be an
execution error.
(2) Class 2: The sub-task(i) is executed again withchange of planning parameter when it is judged to be aplanning error.
(3) Class 3: The sub-task(i) is executed again withchange of modeling parameter when it is judged to be amodeling error.
(4) Class 4: The sub-task(i) is executed again withchange of sensing parameter when it is judged to be asensing error.
(5) Class 5: The process being executed is interrupted
when it is judged to be required by several changes, andthe process returns to the start of the task.

7. Conclusions
It is necessary to increase the reliability ofmaintenance robots that work on household appliances
since most of the tasks have to be performed inhigh-precision environments. Therefore, error recovery isimportant. We have demonstrated a new processing flow
for recovery from errors of various causes in skill-based
manipulation tasks. We have expressed the procedure forerror recovery systematically by taking into account theclassification of errors.
In the future, we will further study optimum
adjustment methods for error recovery parameters and afully automatic confirmation method of skill achievement.
We will attempt to apply our method to real maintenancerobots.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Start

Task Level

Screwdriver

Sensing(T)Modeling(T)Screw

Planning(T)Tip ofscrewdriver

Skill Level

Screwhead
i=1

Fig. 7 Rotate-to-bite skill

i=i i=mi=1

i=iSub-task(i) (i = 1, 2, ・・・ , m)Sensing(Pi)Modeling(Pi)Fig. 8 Rotate-to-loosen
skill

Re-planning(Pi)right

Stop the process and executeall necessary changes.

Change ofsensing parameters

j=0

Confirmation(Sij)Class 4Modeling(Sij)

Skill2

Class 1

Sensing(Sij)

Change ofmodeling parameters(j = 0, 1, ・・・ , n)Execution(Sij)

Skill1

Class 3Skillij

Change ofplanning parameters

j=jClass 2

j=0

PreSkill1

Class 5

error
j=j
j=n

Classification of error

Recovery for error of Skillij (j = 0, ・・・ , n)Skill3j=ni=mFig. 9 Skill sequence of loosening
with a Phillips screwdriverEnd

Fig. 10 Process flow with error recovery

Acknowledgment
A part of this study was financially supported by the
Budget for Nuclear Research of the Ministry of Education,
Culture, Sports, Science and technology, based on thescreening and counseling by the Atomic Energy
Commission.

References[1] Hasegawa T, Suehiro T, Ogasawara T, Matsui T, Kitagaki K,
Takase K (1990), An integrated tele-robotics system with ageometric environment model and manipulation skills. In:
Gotoh T, Kato I, Paul RP (eds) Proceedings of the IEEE
International Workshop on Intelligent Robots and Systems(IROS '90), Tsuchiura, Japan, July 3-6, 1990, pp.335-341[2] Suehiro T, Takase K (1990), Skill based manipulation system(in Japanese). J Robotics Soc Jpn 8:551-562[3] Hasegawa T, Suehiro T, Takase K (1992), A model-based
manipulation system with skill-based execution. IEEE Trans
Robotics Autom 8(5):535-544[4] Nakamura A, Ogasawara T, Suehiro T, Tsukune H (1996),
Skill-based backprojection for fine motion planning. In:
Asada M, Arai T, Kak A, Sandini G (eds) Proceedings of the©ISAROB 2007

182

IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS ’96), Osaka, Japan, Nov 4-8, 1996,
pp.526-533[5] Nakamura A, Ogasawara T, Kitagaki K, Suehiro T (2001),
Using robust and simplified geometric models in skill-based
manipulation. In: Tarn T J, Burdick J, Papanikolopoulos N(eds) Proceedings of the IEEE/RSJ International Conferenceon Intelligent Robots and Systems (IROS 2001), Hawaii,
USA, Oct 29-Nov 3, 2001, pp.138-145[6] Nakamura A, Ogasawara T, Suehiro T, Tsukune H (1998),
Fine motion strategy in three-dimensional space using
skill-based backprojection. Artificial Life and Robotics
2(3):134-137[7] Donald BR (1989), Error detection and recovery in robotics.
Springer-Verlag, Berlin Heidelberg New York, pp.1-256[8] Evans EZ, Lee CSG (1994), Automatic generation of error
recovery knowledge through learned reactivity. In: Gruver
WA, Stephanou HE (eds) Proceedings of the IEEE
International Conference on Robotics and Automation(IRCA '94), California, USA, May 8-13, 1994, pp.2915-2920[9] Xue G, Fukuda T, Asama H (1995), Error recovery in theassembly of a self-organizing manipulator by using active
visual and force sensing. Autonomous Robots 1(2):179-186[10]Moon CW, Lee BH (2001), A model-based error
recovery scheme for a multi-robot system. Robotica 19(4):371-380

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Information Display System using Active Projector in Intelligent Space-Integration of distributed devices based on RT-Middleware
Yoshihisa TOSHIMA

Noriaki ANDO

Hideki HASHIMOTO

Dept. of Electrical EngineeringIntelligent Systems Research Institute
Institute of Industrial Science
The University of Tokyo
Advanced Industrial Science and Technology
The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo
Tsukuba Central 2, 1-1-1 Umezono,
4-6-1 Komaba, Meguro-ku, Tokyo
153-8505 JAPANTsukuba, Ibaraki 305-8568 JAPAN
153-8505 JAPANtoshima@hlab.iis.u-tokyo.ac.jp
n-ando@aist.go.jp
hashimoto@iis.u-tokyo.ac.jp

Abstract
This paper presents information display system using
active projector in Intelligent Space. Intelligent Spacehas distributed sensors for observing and actuators for
acting in the space, in order to provide various services
to human. Proposed Information display support inIntelligent Space has realized providing interactive
information based on the human movement using active
projector, which is installed in this space.
To make the environment space or several
applications easily to be applicable with our active
projector system, RT-Middleware is used (AIST, Japan)
due to its features in flexibility and expansion of thesystem in the future.
First, Information display system using active
projector in Intelligent Space and its implementation
configuration are introduced. Next, the solutions ofissues on active projection are described. Finally, thesystem design of Intelligent Space based on
RT-Middleware is discussed.

active projector provides interactive informative
services based on human movement.
Active projector is located on pan-tilt stand, which isable to project toward any position. Also it is network
based and micro-controller embedded [2].
By utilizing the interactive information, many
applications can be realized, for example, walking pathguidance, the sign or mark illustration in the public
space or private space in daily life.

Fig. 1 Information Display System in Intelligent
Space.

Key Words: Intelligent Space, RT Middleware, Active P
rojector, Information display

1

2
Information display system based on
RT-Middleware

Introduction

This paper presents information display system using
active projector in Intelligent Space. Intelligent Space isan environmental system realized by cooperation of
Robot technology (RT) elements such as robots, sensors
or actuators inside a space (room, corridor or street) andhas distributed sensors for observing and actuators for
acting in the space, in order to provide various services
to human [1]. The purpose of proposed information
display system is to provide informative support andhas realized providing interactive information based on
the human movement using active projector, which isinstalled in this space.
The concept of information display system inIntelligent Space is shown in Fig. 1. On observation ofIntelligent Space, the space can obtain human
movement as user information, and the space can obtainobject information (embedded information in the space)such as the position of bookshelf, the direction of
emergency exit or their contents as spatial orenvironmental information. By using those information,©ISAROB 2007

183

2.1

RT-Middleware based system

In the case of the environmental system such asIntelligent Space, the system tends to be large-scale andcomplex and it is one of the essential problems.
Intelligent Space desires more flexible and more
expandable system to correspond the change of theenvironment or scenario because we assume various
spaces as Intelligent Space. So the system requires easy
integration and cooperation with RT devices and simple
addition, modification or deletion of devices andfunctions. RT-Middleware supports new framework to
make composite components for RT-Component [3]. In
the other words, RT-Component becomes robot
technology module which has common input/output
channels. Therefore, by making and preparing
RT-Components for sensors, actuators or applications,and connecting those RT-Components, we can integrate
flexible system.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

RT-Component-Lite[4] is a Component for small
devices as micro controller and active projector, wedeveloped, is embedded RT-Component-Lite for
expansion of application into daily life.

file
Image

PC (ZPS server)

ReceiverActive projector
PIC board (RTC-Lite)
Transmitter (Tag)PC (OpenRTM-aist)

Fig. 2 Configuration of information display system.
2.3 System architecture using RT-Components
The system architecture based on RT-Middleware isshown in Fig. 3. The system is built up with the
combination of RT-Component and consists of threecomponent parts. Sensing Components obtain user
information. ZPSOut2 Component receives two tags
position data from ZPS server. HumanState Component
calculates position and direction of the user by two tags
position and sends user information to Application
Components.
Next, Application Components decides reference foractive projector and calculates image filename andcharacteristics of an image (projection target, image
size, image direction) according to the application. By
preparing Application Components and switching theconnection of Components, various applications could
be realized.
Finally, Device Components control the projected
image generation and the pan-tilt drive according to therequest. Image Projection Component outputs projected
image according to image source, image size anddirection. MovePConverter Component converts
pan-tilt angle from reference target position by thiscoordinate transformation equation. RTUnitCtrl
Component is connected with RT Component-Litethrough the control panel so that we could control active

184

name Image generation

Projection data

Sensing Components

RTUnitCtrl

Pan-tilt drive・・・The configuration of information display system isshown in Fig. 2. In this system, ultrasound 3D location
system ZPS (Furukawa Co.) is used for humanpositioning. ZPS consists of ultrasound receivers andtransmitters. A lot of receivers are installed on ceilingand two transmitters (tags) are held on user’s neck andhand. User information is given as position anddirection of user by calculation vector of its two tags.
This user information is sent to active projector
through RT-Middleware installed in PC. OpenRTM-aist(AIST, Japan) is used as RT-Middleware. Finally,
active projector realizes projection according toapplications.

©ISAROB 2007

ZPSOut2 HumanState
Ultrasound3D location
system

2.2 Configuration of hardware

ZPS (Ultrasound 3D Location System)

projector.

MovePConveter

Application Components

Device Components

Fig. 3 System architecture based on RT-Middleware.

3

Solutions of Issues by using Active Projector

3.1

Occlusion Avoidance

When projection light is obstructed by human orobjects, projection occlusion prevents providing correct
information. Therefore to avoid occlusion problem, thesystem modifies projected position. In this system,
occlusion avoidance method is built up as one
RT-Component and is connected between Application
Components and Device Components. The developed
Component receives projection data and user
information and sends modified projection data by
occlusion avoidance method (see Fig. 3).
Proposed occlusion avoidance method is divided into
two steps: detection and avoidance.
1) Occlusion detection
Occlusion occurs when human enter into the area
where human himself obstruct the projection. Therefore,
by creating occlusion area and human model andjudging the overlap between those each other, occlusion
can be detected. In Fig. 4, geometrical image of active
projection is shown. The below figure is overhead viewof upper figure and the figure is transformed into 2D.
Occlusion area is projection light and the shape is corn.
But when the projection is projected over human’s body,occlusion does not occur. In the other word, occlusion
occurs in the area under the human’s height. Hence,
Human model can be assumed as a circle and by
estimation the margin, occlusion area can be assumed asa sector on 2D. Finally, we judge the overlap between
those 2D models.
φxy

M z − Hhαz = HhθzppProjection point

x

y

R1

pm
ph (rh ,θ h )R2pp

θ 2 − θ1

Occlusion area

Human modelRhrθ − plane

Fig. 4 Transformation of geometrical humanmodel and occlusion area from 3D to 2D.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2) Occlusion avoidance
To modify (move) projected position toward easily
viewable position, avoidance method results from thedirection of projected position in the view of humanposition. In the situation that projection target is on theleft side of human model, modified position moves tothe left which is closer than to the right direction to
avoid occlusion. On the contrary, when projection target
is on the right side of human model, it moves to theright for the same reason. If limited rotation angle isreached, the radius direction to projection position is
kept away to avoid occlusion.
Over more, not only human but also other objects
such as chair and table could cause occlusion problem,
our proposed method can treat those objects as thehuman model and perform the occlusion avoidancealgorithm.
The result is shown in Fig. 5. The information display
system performs projection toward the front of user. As
Fig. 5(a) shows, in projection without occlusion
avoidance, image is obstructed by user. On the other
hand, in projection with occlusion avoidance correct
image projection is realized. Fig. 5(b) is the results ofseveral situation in experimental space. When occlusion
occurs (two cases), projection position is modified. We
can verify successfully activation of occlusion
avoidance.

xm [m]

Ultrasoundtransmitter (tag)ym [m]

Occlusion avoidance

2-2α (d ) = W / t (d )Without occlusion avoidance⎛ pQ ⎞⎛p ⎞⎜⎜ ⎟⎟ ≅ H QR ⎜⎜ R ⎟⎟
⎝ 1 ⎠
⎝ 1 ⎠

-2

Humanʼs position and direction(-3.56, 0.72)-4

Experimental SpaceWith occlusion avoidance(b) Several situation in experimental space.

Fig. 5 Occlusion avoidance.
3.2 Compensation of projection image

r1

r4

yt

q4

zt

Op

r2

q2

nq

q 1, q 4

l 1,

q3

r3
r 1, r 4
Op
xt

Activeprojector

l4

Plane Q:
Projected surface

r 2, r 3

q2, q3

R

185

zt

e
an

©ISAROB 2007

Corresponding point

q1

Pl

In the case of active projection, the shape of
projected image is not same as that of the source image
because projection is not always orthogonal to theprojected surface and the distance to projected surface
from projector is not constant. The change of projection
target causes image distortion and varies the image size.
Therefore, projected image requires compensation of
distortion, size and rotation to provide uniform image touser. In the system based on RT-Middleware, thiscompensation is performed in Image Projection
Component (see Fig. 3). Compensation method is
described respectively as the follows.
1) Image size
Projector light radiates out so that projected ima

Proposed method uses normal vector of plane Q for
finding the corresponding points. Plane Q shows
projected surface so that it is possible to compensate
projection toward any surface by setting normal vector
according to the space.
Comparison of projection image with and withoutcompensation is shown in Fig. 7. The system performs
the projection of double-rectangle. As shown in thisfigure, the projection without compensation causes
inexpectant projection image depending on projection

l3

Without occlusion avoidanceWith occlusion avoidance(a) Comparison

(2)

l2 ,

Ultrasoundtransmitter (tag)Projection Image(1)2) Rotation
Projection image (source image) is rotated according
to the request from Application Component. For
example, it enables the arrow toward any position or
projection in response to human direction. So we (user)
don’t want to see the sign or the mark upside down.
3) Distortion
Distortion is caused by the angle-relation between
projector and projected surface. Geometrical definitionis shown in Fig. 6. As this figure shown, active
projector projects toward Op. where plane Q is
projected surface and plane R is orthogonal toprojection direction through Op. r1 to r4 are assumed ascorner of source image or non distortion image. And q1
to q4 are corresponding points with r1 to r4. A relation
between a point pR on plane Q and a point pR on plane R
is shown in Eq. 2 as perspective conversion. This
conversion matrix HQR is a 3×3 matrix and the degree of
freedom is 8. Therefore, if more than four sets ofcorresponding points of pR and pR are given, we canfind HQR and represent image distortion. The
corresponding points can be found by the intersection ofplane Q with the line through ri from projection origin(lens). Finally, inverse matrix of HQR representscompensation of distortion image and we can get
pre-compensated output image.

d

Projection Imagege size depends on distance to projected surface.
By calculation the relation of those factors, it is a
ble to pre-compensate projected image size to request size W. resize ratio α is given by Eq. 1. Wh
ere t(d) is a image size on projection direction d.

Fig. 6 Geometrical definition ofimage distortion

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Sensing Components

Device Components

Application Componentsefile nam
Image

Position HumanState
UltrasoundsensorImage
generation

Projection datam
Co

Directiondanmtarget. On the other hand, the projection withcompensation provides correct information (consideredsize, direction and distortion of projection image)compared with the one without compensation.

CCD camera

Pan-tilt drive

Active projector

(p
iti
os
)
on

・
・
・

Laser

iSpace database

Robot Interface

・・・

・
・
・

Pressure

Mobile robot

Fig. 8 System design based on RT-Middleware.

Without compensationof projection image

With compensationof projection image

Fig. 7 Compensation of projection image with andwithout compensation.

4 Integration of distributed devices based on
RT-Middleware
RT-Middleware is a kind of software platform basedon distributed object middleware. As described in
section 2, it is suitable for environmental system such asIntelligent Space. Intelligent Space is considered tohave RT elements in a space to utilize RT-Middleware
for the integration of its system. And Intelligent Spaceis expected to become useful and easy to integrate forsystem development. By using RT-Middleware as
platform, Intelligent Space is intensive stage of RT(Robot Technology).
Proposed system based on RT-Middleware isconsidered as integration with various distributed
devices as Intelligent Space system. The system designof information display system (Intelligent Space) basedon RT-Middleware is shown in Fig. 8. Environmental
system including Intelligent Space will be integrated
with various sensors or actuators and has difficulty to
implement as whole system. As shown in Fig. 9, thesystem based on RT-Middleware enables the system tobe flexible, which is not depends on the hardware
device. For example, various sensors can be treated as
position or posture sensors according to their features.
Over more, by converting various actuators or devices
to the components, it is possible to send the command
to control and interact with those actuators. As an
example, we developed a RT-Component for mobile
robot. This mobile robot is implemented in Hashimotolab.(The Univ. of Tokyo, Japan) [5]. Because only theinterface part is created as the component, the robot’s
required subsystems such as feedback control, mapper
are not needed to be modified. Therefore, Application
Components sends target position as command to robot.
By demonstration using active projector and mobile
robot (however the sensor that used to recognize theuser’s position is not integrated into the system yet),when an active projector provides a message” Calling
robot …” toward a user, the robot can come to a user asshown on Fig. 9.

©ISAROB 2007

186

Projected information“Calling Robot…”

Mobile robot is coming

Fig. 9 Demonstration using active projector andmobile robot.

5

Conclusion

This paper presents information display system using
active projector in Intelligent Space. Active projector
provides interactive informative services for user. To
realize this purpose, as two solutions of issues on active
projection occlusion avoidance and compensation ofprojection image are described. We plan to develop
smooth working according to human movement anddemonstrate the effectiveness on informative services.
Proposed system is based on RT-Middleware. With
the feature of RT-Middleware, we will integrate
information display system with other devices such as
vision sensors or robot, and as a first step, thecooperation of active projector and mobile robot had
been performed as described in this paper. In the future,
we plan to design and implement the Intelligent Space
system by using RT-Middleware.

References[1] J. H. Lee and H. Hashimoto, “Intelligent Space –concept and contents”, Advanced Robotics, Vol.16,
No.3, pp.265-280, 2002.
[2] Y. Toshima, Q. Wang, N. Ando, T. Suzuki and H.
Hashimoto, “Network based Display System forIntelligent Space”, ROBOMEC2006, 2P1-E19, 2006 (inJapanese).
[3] N Ando, T Suehiro, K Kitagaki, T Kotoku and W.-K.
Yoon, “RT-Middleware: Distributed Component
Middleware for RT (Robot Technology)”, IROS2005,
pp.3555-3560, 2005.
[4] T. Suzuki, N. Ando, M. Inagaki, K. Ohara, K. Ohba
and K. TanieY, “A Development if RT Component-Litefor Multiple Embedded Devices”, ROBOMEC2006,
1P1-C35, 2006 (in Japanese).
[5] D. Brscic, T. Sasaki and H. Hashimoto,“Implementation of Mobile Robot Control in Intelligent
Space”, SICE-ICCAS2006, A1011, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Elementary Developmental Process of Intentional Agency:
Artificial Construction of Gaze Alternationin Communicative Eye Gaze by Infants
Takeshi Konno

Takashi Hashimoto

School of Knowledge Science,
Japan Advanced Institute of Science and Technology
1–1, Asahidai, Nomi, Ishikawa, 923–1292 Japant-konno@jaist.ac.jp

School of Knowledge Science,
Japan Advanced Institute of Science and Technology
1–1, Asahidai, Nomi, Ishikawa, 923–1292 Japan
hash@jaist.ac.jp

Abstract
It has been pointed out that gaze alternation by infants,
which is a basis for social communication, is related to theprocess of the development of intentional agency. Intentional agency is defined as an act with a desired goal anda means. It has been pointed out that infants understandothers’ intentions based on intentional agency. In our recent work, we constructed a computational model which
acquired gaze alternation behavior with elementary intentional agency. In this paper, through the analysis of the
behavior and the internal states of the constructed model,
we confirm that two mechanisms, discrimination and accumulation, play an important role in developing elementary intentional agency. The former discriminates between
a caregiver and objects that are producing stimuli. The
latter associates the caregiver with the objects by accumulating relationships between sensory states. We also
argue that a nested structure, in which gaze alternation
composed of a goal and a means is utilized as a means toachieve another goal, is an important feature in the development of intentional agency.
Index Terms — Gaze Alternation, Communicative
Eye Gaze, Understanding Others’ Intention, Intentional Agency, Constructive Approach.

1

Introduction

We study the gaze alternation behavior in communicative eye gaze by infants through the constructionof a computational model. Gaze alternation is behavior by infants alternately gazing at a caregiver andparticular objects. Tomasello [1] points out that gazealternation, which is a basis of social communication,is related to the developmental process of intentional
agency. Intentional agency is defined as an act composed of a desired goal and a means. It has been
pointed out that the infants understand the intentionsof others based on intentional agency. From this viewpoint, gaze alternation is not merely behavior returning an infant’s gaze point to a caregiver after looking atparticular objects, but intentional behavior of gazing
at a caregiver based on an infant’s desire. This intentional behavior can be developed into more a commu-©ISAROB 2007

187

nicative use of eye gaze – i.e., social referencing andthe utilization of gaze alternation.
In our recent work, we constructed a computationalmodel [2] in which acquiring the visual orientation ofgazing at a caregiver or at objects in the center of thevisual field became a means by reinforcement learningand in which acquiring the gaze alternation between
a caregiver and objects became a goal through memorizing sensory states. However we have not yet clearlyshown the mechanisms by which intentional agency is
acquired. In this paper, in order to study the elementary developmental process of intentional agency, weinvestigate the mechanisms in the constructed model.
we analyze the behavior and internal states of thismodel, by which goals are separated from actions in
intentional agency.

2

Model

The agent model of an infant (the infant agent,
hereafter) has the functions of visual orientation andgaze alternation. In this section, after explaining theagent’s visual field, we describe the functions.

2.1

Visual Field of Infant Agent

The infant agent’s visual field is a square area thatis 0.2m on each side on a flat surface. The sensory
state of the infant agent is defined by three kinds of information from the visual field and its motion (Fig.1):(a) the feature of a visual stimulus, which is one of
13 stimuli composed of the caregiver’s eye directions(a total of 10) and the type of objects (3 shapes), (b)the position of the visual stimulus consisting of thedirection and the gaze, and (c) the proprioception of
the muscle states that are related to the orientation of
the gaze point. We suppose that the muscle states areintegrated to represent the direction of the gaze point.

2.2

Visual Orientation

The visual orientation is the ability to gaze at the
caregiver and the objects in the center of the visual
field and consists of three modules: the selector, theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

PositionObject Shapes
Resolution: 3[types]

Proprioception
FeatureDirection

Gaze Point
Direction

ObjectTable 1: Storing condition in the frequency distribution.

VisualField

φC(s*t-1 )Resolution : 36[deg]φResolution : 12[deg]cond.

Neutral Positionof Muscle States

Agent’s Gaze Gaze
Point & Area or Not

Resolution : 36[deg]

Gaze
Point

C(st )

Figure 1: State information about the feature, position,

Additional part
Visual Field

SensoryStates:s

Sensor

Selector

Associated States

Z -1
s*t-1
+
+

st

Associator

Proprioception

Action:as*t

Motionlearner

Evaluator

Effector

RewardOBJ

and proprioception in the visual field. In the position, astate of gaze is determined by whether or not the stimulus
comes within the gaze area. The gaze area is created to besmall circle from the gaze point.

OBJ

0

1

0

1

0

1

0

A

A

B

B

B

B

1

A

A

B

B

B

B

0

A

A

A

A

B

A

1

A

A

A

C

B

A

0

A

A

B

A

A

A

1

A

A

B

A

A

C

F (st , s∗t−1 , s),p(s|st , s∗t−1 ) = PNc∗0s0 =1 F (st , st−1 , s )(3)where Nc is the total number of the sensory states.

3

Experiments and Results

3.1

Experimental Setups

At first, we display the caregiver and the objects
in the agent’s visual field in its visual orientation system. By reinforcement learning, the agent develops a
probability distribution of the action-value function,
shown in Fig.3. The agent acquires the ability of visual orientation because the probability distribution of
the moving gaze point develops to match the directionof the caregiver and the objects.
Second, in a training phase, we display the caregiverand the objects alternately in the agent’s visual field

Gaze Alternation

The gaze alternation system is equipped with anassociator in addition to the visual orientation system(Fig.2). The associator stores relationships between
three sensory states: the present sensory states, st ;previous associated states, s∗t−1 ; and future sensory
states, st+1 in a frequency distribution. The frequency
distribution is updated according to the sensory states.
F (st , s∗t−1 , S) = F (st , s∗t−1 , S) + 1 ,where S is conditioned by


if
 st
∗
S = st−1 if
st+1 if

(1)

0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05

cond.A ,cond.B ,cond.C .

0.00
-180

(2)

-90
-18 0 1890
180
Differences of Direction Between the Object
Positions and the Moving Gaze Point[deg]Figure 3: The average probability of the moving gaze

1 The moving direction of the gaze point has 30 divisions.
2 For more details, see paper [2].

©ISAROB 2007

0.45
Average Probability p t

2.3

CGV

G(s*t-1 )The condition, cond.A, B, C, is defined by Table.1,
where C(s) judges the feature, the caregiver (CGV ),the object (OBJ) or non-existence (φ), and G(s) determines whether the sensory state is a gaze (1) or not(0). The output of the associator, s∗t , is determinedaccording to a probability:

Figure 2: System block diagram of gaze alternation.
evaluator, and the motion learner, shown in Fig.2
without the additional part (which is surrounded witha dashed line). The selector picks up the most distantstimulus from the gaze point in the visual field, an object or a caregiver; then it outputs information about
the feature and the position of the selected stimulus.
A sensory state, s, is constructed by merging threekinds of sensory information: the feature, position,and proprioception. The sensory state is related toan action to move the gaze point1 by reinforcement
learning algorithm which is known as a standard temporal difference learning with tabular SARSA [3]. In
this algorithm, the action is determined according toa probability with an action-value function. The motion learner reinforces the action which allows the gazepoint to approach a target2 .

CGVCaregiverG(st )

FeatureCaregiver’s
Eye Direction

188

points in the positions of the caregiver and the objects.

400

500

600

700

800

Horizontal Axis of Agent’s Visual Field [mm]Previous
Associated State s*t-1
Present
stSensory State
Associated State s*t

CGV

CGV#

OBJ#

OBJ

OBJ

CGV
CGV

OBJ

CGV#

OBJ#
CGV

OBJ

Figure 4: For the process of gaze alternation between acaregiver and objects which are placed outside the visual
field, the corresponding internal states of the associator are
indicated. The mark ‘#’ means the state does not coincidewith the present sensory state.

Probability

1.0

0.5
0.0

O

O

0.4

Probability

300

1.0

0.5
0.0

360
Ca 360rebj 270gi 270ec
vet P 180r P 180
os
360
o
270
s* itio 90
s* sit 90
]
180
io
t
t
eg
n[
90
n[
on[d
ti
0
ec
ir
de 0 0
de 1
ye D
E
’s
g]
er
iv
g]
Careg
s*t-1

Probability

Caregiver (CGV)

Associate objects with caregiverO

END

Feature Direction

500

Associate caregiver with objects

0.2
0.0

360
bj 270ec
t P 180oss* itio 90
tn[de 0 0
g]

1.0
0.5
0.0

360
bj 270ec
t P 180oss* itio 90
tn[de 0 0
g]

360
270
180
g]
90
ition[deiver Pos
eg
ar
C
s*t-1

3
2
s
Shape
Object
s*t-1

0.4
0.2
0.0

Ca 360re 270give
r P 180os* sit 90
io
t
n[
0
de 0
g]

Probability

Feature600

Probability

Object (OBJ)
START

Probability

700

Proprioception

Vertical Axis
of Agent’s Visual Field [mm]

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

360
270
180
90
n[deg]
PositioObject
s* 1
t-

1.0
0.5
0.0

Ca 360re 270give
r P 180360o270
s* sit 90
io
180
t
n[
0
deg]
90
n[
io
0
pt
ce
de
Proprio s* 1
g]
t-

360
270
180
deg]
90
ception[
Proprio s* 1t-Figure 5: Association probability between the previous
in the gaze alternation system (Fig.2). In this training phase, the objects are placed in the direction the
caregiver looks. After adequate training, we display
the objects outside the visual field. As a result, theagent can acquire a gaze alternation behavior outside
the visual field.

3.2

The Process of Gaze Alternation

The trajectory of the agent’s gaze point with thecorresponding internal states of the associator in thegaze alternation behavior is exemplified by Fig.4. In
the behavior toward the caregiver from the circular
object which is placed on the left side of Fig.4, when
the present sensory state, st , is an object (OBJ) or
non-existence (φ), the associated state, s∗t , is the previous sensory state, s∗t−1 which indicates the caregiver(CGV ). If the present sensory state is the caregiver –specifically, if the caregiver appears in the visual field– the associated state coincides with the present sensory state, and the agent adjusts the direction to moveits gaze point. When the agent gazes at the caregiver,the associated state becomes the future sensory state,st+1 , and designates a new object (OBJ), which does
not appear in the visual field. The new object is associated with the caregiver, based on the frequency
distribution [Eq.(3)].
In behavior to look at a new object, which is the triangular object placed on the right side of Fig.4, while
the present sensory state is the caregiver (CGV ) or
non-existence (φ), the associated state is the same as
the previous associated state, indicating the object(OBJ) to be seen. At the point where the object
comes into the visual field – that is, when the present
sensory state turns into the object – the associatedstate indicates the object. The agent can modify the©ISAROB 2007189

associated states, s∗t−1 , and the associated state, s∗t .

trajectory of the moving gaze point even if the previous direction has been considerably different fromthat of the object to be seen. Through the transitionof internal states of the associator, the agent realizes
the gaze alternation behavior outside the visual field.
When the present sensory state and the previous
associated state are different features, the associator
keeps the previous associated state because the agent
accumulates the sensory states in the frequency distribution according to cond.B in Eq.(2), which is basedon discrimination about the category (that is, the caregiver, the object, or non-existence) using function C(s)in Table.1. If the present sensory state and the previous associated state become the same feature, the associator changes to the present sensory state because
the frequency distribution has accumulated according
to cond.A in Eq.(2). When the agent gazes at the
caregiver or the objects, the associator associates the
caregiver with the objects using the frequency distribution that is accumulating the sensory states according to cond.C in Eq.(2).

3.3

Association Probability Distribution

In the association of the caregiver (CGV ) with theobject (OBJ), the agent uses the probability distribution shown in the left column of Fig.5. In the top
left of Fig.5, the association probability distribution
between the caregiver’s eye directions and object positions is shown. Using this distribution, the agent can
detect the unique object position from the caregiver’s
eye direction since one-to-one correspondence between
these two pieces of information is formed in this distribution. The distributions of the other sensory in-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Agent’s Associated Object
ObjectCaregiver
Visual Field

Visual Field

Visual Field(a) Other objects (b) Multiple objects (c) Object movein visual fieldin oriented direction anywhere

Figure 6: Showing the intentional agency.
formation, the caregiver position and proprioception,
do not form such a one-to-one structure and, thus, areunable to be used to detect the object position.
On the other hand, when the agent associates theobject with the caregiver, the agent can determine thedirection of the caregiver using the state of the proprioception since only the frequency distribution between
the proprioception and the caregiver position possesses
the one-to-one correspondence structure, as shown in
the right column of Fig.5. In sum, we find that the
infant agent makes use of the caregiver’s eye direction
to gaze at the objects and uses the proprioception to
return its gaze to the caregiver.

4
4.1

Discussion
Development of Intentional Agency

We confirmed that the constructed agent model canacquire gaze alternation behavior outside the visual
field. In the behavior of gaze alternation depicted in
Fig.4, three functional properties in the internal statesof the associator are identified: 1. The agent can keep
the associated object when different objects appear inits visual field. Therefore the agent can properly gazeat the object that is indicated by the caregiver shownin Fig.6 (a). 2. For the same reason, if two or moreobjects are placed in the caregiver’s eye direction, as
in Fig.6 (b), the agent can gaze at the associated object. 3. Once the associated object appears in thevisual field, the agent can adapt the moving directionof its gaze point to the direction of the associated object. Therefore, even though the object may move
anywhere, the agent can follow the object, as shownin Fig.6 (c).
In these functional properties, the associated statesplay the role of the goal at which the agent wants
to gaze, and the agent through its visual orientation
ability selects the action of gazing at the caregiver andthe objects as a means of achieving the goal. The threefunctional properties of gaze alternation suggest thatthe agent acquires elementary intentional agency.

ten others hand over the object to the infants. Accordingly, gaze alternation is thought of as a meansof achieving the desired goal of getting the object. In
this behavior, we can discover a nested structure inwhich the behavior composed of a goal and a means isutilized as a means to achieve another goal. This behavior consists of a goal to get the object and a meansof alternatively gazing at the caregiver and at the object. Furthermore, the means consists of the goal ofgazing something which the infants want to see andthe means of moving their gaze point.
The constructed model of gaze alternation has been
composed of the goals of gazing at the caregiver or theobjects and the means of achieving those goals. When
we develop the agent model to perform utilizing gazealternation based on the present model, the nested
structure must be observed.

5

Conclusion

In this paper, in order to study the elementary developmental process of intentional agency, we investigate how the infant agent of our constructed model
acquires intentional agency that involves separating
goals from actions. Through the analysis of the gazealternation behavior and the internal states, we confirm that two mechanisms – discrimination and accumulation – play an important role in acquiring intentional agency. The former discriminates within the
category – that is, between the caregiver, the object,and non-existence – in the visual stimuli and determines whether or not the agent gazes at the stimulus.
The latter accumulates the relationships between thecontinual sensory states in a frequency distribution.
Through these two mechanisms, the agent acquires thegoals – that is, objects or a caregiver – at which theagent wants to gaze and that are to be realized by themeans of moving its gaze point with its visual orientation. Through discussion of the developmental processof intentional agency, we suggest that a nested structure, in which the gaze alternation composed of a goal
and a means is utilized as a means to achieve anothergoal, is an important feature in the developmental process of intentional agency.

References[1] Tomasello,M., “Joint attention as social cognition,” In
Moore,C. & Dunham,P.J.(Eds.), Joint Attention: Its
Origins and Role in Development, Lawrence Erlbaum,
pp.103-130,(1995).

The Nested Structure of Intentional
Agency[2] Konno,T. and Hashimoto,T., “Developmental Construction of Intentional Agency in Communicative EyeGaze,” Proceedings of the International Conference on
Development and Learning, ICDL ’06, Indiana Univ.,
Bloomington, USA, May 31 - June 3,(2006).

It is known that infants tell their desire to others
to get an object by utilizing gaze alternation, and of-[3] Sutton,R.S. and Barto,A.G., Reinforcement Learning,
A Bradford Book, MIT Press, cambridge, MA,(1998).

4.2©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Research of Data Mining for Quantitative Association Rules and Algorithm
for Numerical Attribute

Du Junping
School of Computer Science
Beijing University of Posts andTelecommunications, Beijing, 100876

Wang Rui jie
School of Information Engineering
University of Science andTechnology, Beijing, 100083

Abstract
In this paper we proposed the concept andtypes of the quantitative association rules. We
proposed the data mining for the quantitative
association rules in detail. We also researched
the concept for discretization of numericalattribute, and the strategy of discretization result.
We established the better algorithm fordiscretization of numerical attribute.

1

Introduction

Association rules reflect dependency andassociation of one event and others. Dataassociation in database is representation ofthings’ relation in real world. As a sort ofstructurized data organization’s format, databasemakes use of possibility of adhered data model
to describe association of data (e.g. primary key
and foreign key). At the same time, association
among data is very complicate, which not onlymeans association adhered in data model
mentioned above, but also means a majority ofhidden association. The aim of Data mining for
Association rules is finding hidden association
information. Association can be divided into
simple association,time series association, causal-------------------Supported by Beijing Natural Science Foundation ofChina (4042012), Key Project of Beijing Educational
Committee (KZ200510011009) and Project of National
Science Foundation of China (60442003).

association, quantitative association and etc.
These associations are not always forecasted,
but gained by analyzing data association in
database. As a result, it provides businessdecision making with new value.
Data mining for association rules is the mostcommon method for finding association
knowledge, of which Apriori and its
amelioration. For finding significant association
rules, two threshold vales are needed to be given:
minimum support and minimum confidence.
Mined association rules must satisfy minimum
support stated by users, and it denotes minimum
association degree that a group of associateditems need to satisfy. Mined association rules
need to satisfy minimum confidence stated byusers, and it reflects minimum reliability of one
association rule. In this sense, the aim of data
mining system is mining association rules which
satisfy minimum support and minimum
confidence from source database. Research andapplication for association rules are the mostactive and embedded branches of data mining.
Many theories and algorithm of data miming for
association rules are raised.
Discretization of Numerical Attribute is the
key issue of data miming for association rules,
and actually it partitions attribute field into
intervals. Partition methods play a significant
role in quality of data mining for the quantitative
association rules. Min-confidence: if numbers of
1©ISAROB 2007

191

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

interval are exiguous, the number of groupwhich support interval will increase, and the
support degree of strong item set of inclusioninterval will go up. At the same time, if supportof strong item set’s subset remains the same, therule confidence of right end inclusion subset will
decline. If confidence threshold vale cannot bereached, information will be lost.
Min-support: if numbers of interval
partitioned are overabundant, support of interval
will decline, and entire strong item set will not
be created effectively.

2 Quantitative Association Rules
A. Concept of Quantitative Association Rules
Quantitative association rules are association
rules including classify attribute (Boolean
attribute can be deemed to special classify
attribute) and quantitative attribute. Generally,
quantitative
association
rules
are
multidimensional association rules issues, inwhich the key issue is discretization of
Numerical Attribute to satisfy a certain data
mining criterion. Quantitative association rules
also need to satisfy

X ∩Y = ∅ and constraint

condition of support and confidence. In ageneral way, Quantitative association rules
issues are different from Boolean association
rules in knowledge representation, Boolean
association rules seeks for frequent item sets,
moreover, quantitative association rules seeks
for frequent predication set. Some characters of
Quantitative association rules are listed as
below.
Subset of frequent item set to a certainty isfrequent. This character is consistent with
Boolean association rules.
Suppose X^ is generalization of X, X is
specialization of X^, if X^ is frequent, then X isfrequent, and support (X^) >support (X),
It is impossible that two items in frequent
item set have same attribute. These characters
are easily understood from analogy in previous

Boolean association rules (its demonstration is
ignored); whereas the time that k+1 item set are
created by frequent k item set reduces greatly.
When k+1 item set are created by frequent,algorithm only needs to calculate different
attributes, and it is impossible that the disjoint
intervals with same attribute are frequent.
B. Categories of quantitative association rules
Numerous intervals are created after
Discretization of Numerical Attribute, and thenthese intervals are mapped to Boolean attributedirectly and mined by Boolean association rules.
It is a common method for data mining for thequantitative association rules, and it ischaracterized by complicated format of rules. In
fact, users are usually interested in rules of acertain format, i.e. Reference[3] of data mining
for the quantitative association rules is mostly
based upon a certain rules model. So we canclassify the quantitative association rules mining
to 3 types according to the rules formation.
(1) Rules of mining similar to “numerical value
attribute ∩ classification attribute => numericalvalue attribute ∩ classification attribute”, e.g.
sex=female∩age∈[20,30]=> wage∈[$5,$10].
This sort of rules is complicate, and they arenormal quantitative association rules.
(2) Rules of mining similar to “numericalvalue attribute ∩ classification => classification
attribute”,
e.g.
oppucation=bussiniessman∩age∈[35,50]=>goaboard=Yes. This sort of rules’ left end generally
denotes one subset of database. Any attributes indatabase, and any value combination can beused as left end of rules, the right end denotes
one predefined category, and it is quite similar toclassification rules. The typical classification
methods are decision tree、genetic algorithms、
neural network、Bayesian classification and etc.
The efficiency of resolving classification
problems by association rules is very high,especially to large data set, classicalclassification algorithm is difficult to gain
mining result.
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(3) Rules of mining similar to “classification
attribute=> numerical value attribute ∩classification attribute”, e.g. sex=females=>salary∈[$1000,$2000]. It is opposite to thesecond sort of rules, yet to some problems, wewill not expatiate anymore here.
C. Common steps of quantitative association
rules
Mining quantitative association rules by
Boolean association rules algorithm and its
theory, such as support、 confidence、frequent
item set and other concepts are most effective
approach of resolving quantitative association
problems.
One typical method is ameliorating Aprioralgorithm to adapt to data mining forquantitative association rules, and mainly
divided into five steps that shown as Fig.1.

Fig.1 Common steps of quantitative association
rules(1) Pointed to each numerical value attribute,
Reference [4] of proper discretization algorithm
is selected and the number of intervals isdetermined. The difficulty of this step isselecting proper discretization algorithm. At first,
discretization algorithm has not unified criterion,
we should select one or several sort of algorithm
according to distribution characters of data. For
instance, this text will discuss that equi-depth
partitioning method is not ideal for highly
skewed data discretization, but it works well on
partition of equally distributed data. In the next
place, numbers of intervals partitioned, i.e.
granularity of partition, too many or too little

will lead to information lost.
(2) The value is mapped to continuous
integers to classification attribute. To thosenumerical value attributes which need notpartitioning intervals, such as some numericalvalue attributes with little value, and the value
can be mapped to continuous integers when they
are sorted by size. If numerical value attributes
are discretized to intervals, intervals will bemapped to continuous integers according todiscretized order of intervals. The operation tothese continuous integral value is equal tooperation to data set will be mined, and thesecontinuous integers are transparent to algorithm.
All classification attributes and continuous
attributes are mapped to Boolean attribute, andall of such attributes compose item sets.
(3) Creation of frequent item sets. This step isthe same to the steps of frequent item set createdby Aprior algorithm. Aprior amelioration
algorithm is also applicable here. Based on
Character 3, new item sets will not be createdfrom adjacent sections, in this way they can bemerged by definite merging criterion.
(4) Application for frequent set creation
association rules. If both ABCD and AB are
frequent sets, the rule AB=>CD is true or notneeds to be determined by result of calculatingconf=supp(ABCD)/supp(AB)
exceeds
minimum confidence or not.
(5) The valuable association rules are
determined as output. For helping users to mine
valuable rules and decline redundancy of rules,
the association rules based on interest measurehas been established in Boolean data mining for
association rules, and extensively applied in data
mining for the quantitative association rules.

3
Concept
anddiscretization

strategyfor

Algorithm for discretization of numericalattribute will face the same problems duringresolving classification problems, however,problems of data mining for association rules isdifferent from classification. From the machine3©ISAROB 2007

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learning point of view, data mining for
association rules is unsupervised learning
category, but data mining for classification
model is supervised category. In other words,
discretization algorithm that suits classification
does not necessarily suit data mining for
association rules; hence it is necessary toresearch new discretization methods combinedwith characters of data mining for association
rules. Combined with characters of data mining
for association rules, methods for numericalvalue discretization mainly are equi-width
partition, equi-depth partition and distance-based
partition.
A. Equi-depth Partition Method
During numerical value discretization,equi-depth partition is the most common
discretization method, commonly adequate fordata set with low association among attributes.
For the data set with tight association,
application of equi-depth partition is difficult tomine ideal result. Equi-depth partition method
tends to partition adjacent value with
commonness and high support into different
intervals. When data distribution reach peakvalue near to a certain point, the mechanicalmethod like equi-depth partition cannot reflectcharacters of data itself, hence it does not work
well on highly skewed data discretization. For
example, during tourist data mining, all kinds of
discretization methods suit for processing scenic
spot attribute and other data with low association,such as tourists’ age、numbers of scenic spots.
R is one relation, A is one attribute of it, T is
one tuple, C is one condition of R, T(A) denotes
A attribute value for tuple T.
Bi=[Xi,Yi] of which i=1，2，... ，m, Xi≤Yi≤
Yi+1；
If A is one numerical value attribute, and its
interval is [Xl,Ym]. Interval of A is partitioned
into a series of disjoint fields, viz.
Bi=[Xi,Yi] of which i=1，2，... ，m,Xi≤Yi≤
Yi+1；
Bi is entitled “one bucket of A”, and numberof tuples in T∈R and T(A)∈Bi is entitled “sizeof bucket”, noted as u. if size of tow buckets are
same, and they are entitled “equi-depth bung”.
Algorithm shows as below:
Input: numerical attribute A, number ofbucket n
Output: discretized interval(1) Numerical attribute in order;(2) Scan database, Stat the database item
number N;(3) Get the depth of bucket h=N/h;(4) Scan A in order one by one. According to
Definition 2, get number i and i+h in sequence,
the make the output of discretized interval[li,vi+h].
B. Equi-width Partition Method
The equi-width partition method is the simplest
discretization method which is used to
distributing equably data. Because this algorithm
need only one time to scan the database, it has ahigh efficiency. The equi-width partition method
simply partition from mathematics’ point. It
doesn’t consider the characteristic of the data
distributing. As this method is more directly andadapt to prophase dispose of data attribute, it
always unites the clustering method and getgood discretization result. For example, in the
tourism data mining, it used to discretize
tourists’ age and income, etc.
Algorithm shows as below:
Input: numerical attribute A, number ofbucket n
Output: discretized interval(1) Scan database, Get the max(A) andmin(A);(2) Try the width w of interval, w=(max(A)-min(A) )/n;(3) Make the output （li=min(A)+(i -1)* w,
vi=min(A)+i*w）of
discretizedinterval[l1,v1 ],( l 2,v2 ),…,[ l n,vn].
C. Classification Partition Method
Both of equi-width partition and equi-depth
partition methods are considering neither the4©ISAROB 2007

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data distribution, nor the experts’ proposal to theattribute discretization. They only partition thedata attribute according to the geometry andmathematics. The classification partition method
can take attribute discretization according todata distribution feature based on the field
experts’ proposal. To solve the quantitative
association rules problem, we can use theclassification partition method to partition each
attribute item to proper category which theequi-depth partition method can not solve. Thiscan show data distribution status. The aim ofclassification is to partition similar data to same
category.
For example, in the tourism information data
mining technology, field expert hold up theclassification model. The process of quantitive
data discretization is partitioning one group data
to different class according to the classification
model. The result of classification should makethe data distance in same types much shorter andin different types much longer.
The process of data classification partition is:Firstly field expert take the characteristic value
A[1…k] of the partitioned k intervals, then scan
every data in sequence, classify data to thecategory which is nearest to data number, updatecharacteristic value of this category.
Algorithm shows as below:
Input: Numerical attribute A, Interval
characteristic value A[1…k]Output: Discretized k intervals
do(1) Read one data, Calculate the distance
between it and every characteristic value;(2) Partition data to category which is nearest
to data number;(3) update characteristic value to averagevalue of its interval;
until all data partitions finished.

5

Conclusion

In this paper we proposed the algorithm fordiscretization of numerical attribute inquantitative association rules mining. Throughthe analysis of the above 3 discretization
algorithms, we defined divergent density as
evaluation criterion of discretization effect.
Definition: If discrete category[a,b],c is thenumber belong to the discrete category,thenρ=c/|b-a|×100% is the density of thisdiscrete category. We call the density averagevalue of all discrete category which isn

ϖ = (∑ ρi )
i =1

1
n

as discrete density. If thisdiscrete density is larger, the effect of partition isbetter.
By this method, we calculate the discretedensities of above 3 discretization algorithms:
The discrete density of equi-width partition is10%. The discrete density of equi-depth partitionis 40.84%. The discrete density of classification
partition is 100%. To this data group, the effectof classification partition is the best. But because
the cost of classification partition algorithm ismuch higher, we should take different
discretization methods according to data feature.
References[1] R .A. grawal, T. Im ielinski, and A .
SwamiMining association rules between setsof ite ms in large data bases，SIGMOD'93，
Washington，1993：20 7－216.
[2] Jong Soo Park, Ming-Syan Chen, and Philip
S.Yu，An efective hash based algorithm formining association rules，Kdd-94.
[3] Hilderman Robert, Hamilton Howard.
Knowledge Discovery and Interestingness
Measures: A Survey. Technical Report CS 99- 04, University of Regina, Saskatchewan,
Canada, 1999.
[4] Zhang Chaohui, Chen Yucang, Zhang Qian,
An Algorithm for Mining Quantitative
Association Rules, Journal of Software,
Beijing, China, 1998(11)：801－805.
[5] Liu Tongming, etc. The Data Mining
Technology and Applications, National
Defence Industry Press, Beijing, China, 2001

5

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B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

TV Program Recommender Using User's Preference
Jungmin Kim, Seongpyo Cheon, Byung-Wook Jung, Jinyoung Kim, and Sungshin Kim
School of Electrical and Computer Engineering, Pusan National University, Busan, Korea
Pusan National University 30 Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: kjm16@naver.com, buzz74@pusan.ac.kr, wooroogi@pusan.ac.kr king_ioi@pusan.ac.kr, andsskim@pusan.ac.kr
viewing TV information, and it is utilized not only in the
Abstract − Due to the expansion of TV channels in the
personalizedrecommendation
method,which
digital era, the number of TV programs selected byrecommends in the order of title, genre, and channel
users has had an explosive increase. This expansion
information that the user mainly watched on the
makes hard to find preferred TV programs. Because ofcorresponding weekday and time, but also in the general
this, it is necessary to automatically customized
recommendation method, which uses is used generalrecommendation system for TV programs. In thisinformation such as gender, age, occupation, etc. TV
paper, we propose a novel recommendation system toprogram recommendation algorithms could satisfy theovercome this problem. The proposed recommendation
user’s preference well through the personalized
system has two main algorithms that consist ofrecommendation algorithm, but when there is not enoughpersonalized recommendation algorithm and a generalviewing information for the corresponding user, therecommendation
algorithm.
The
personalized
possibility of failure is also increased. Therefore, whenrecommendation algorithm is constructed fromthe viewing information of a user is not sufficient, we
individual user-information (e.g. channel, title, time,
recommended TV programs using the generaletc) and the other is constructed from personal
recommendation method.
information (e.g. age, gender, occupation, etc).
The rest of this paper is organized as follows: Section
However if we don’t have sufficient user-information2
reviews
related work in the field. An overview of theregarding the preferences of TV programs then thesoftwaresystemarchitecture is given in section 3.
recommendation has a high failure rate. To overcome
Section4
presents
the implementing and simulation
this difficulty, we propose the use of a general
result. Finally, section 5 summarizes the paper.
recommendation algorithm in this paper. In this paper,
we address the problems and solutions by describing
2. Related Workthe proposed algorithms and experiments.
The
recommendation
methoduses
generalrecommendation and information abstraction method
Keywords: TV recommendation, preference, anytime,using Data-mining. The general Recommendation method
personalized, generalis divided into the Content-based Recommendation (CBR)
method and the Collaborative Recommendation (CR)method [3-6].
1. Introduction
The content-based Recommendation requires the user
TV is the requisite mass media on this society,to ask questions to the retrieval system and the systembecause it allows us to experience many things that weshows results to the questions as in Fig. 1 [4, 5].
never experienced before second hand, and it is anentertainment program to relieve our tension when we
get tired. Because the were not as many channels inUserprevious broadcasts as the TV programs Included in
cable TV and sky wave TV, we could learn our favorite
channel number. However, TV offers a variety TV
Recommendion
Meta
User
programs and custom broadcasting services to users
Systeminformation
Profileas the expansion of TV channels in the digital era isresulting in a soaring number of TV programs
available to users. In this environment, the broadcast
Fig. 1 Content-based Recommendation.
service that answers the demands of users focuses onuser and Research on TV program recommendation
The collaborative Recommendation collects people
has been vigorously conducted for 10 years [1, 2].
whose inclinations are similar to the group andBased on three months of data from May 1, 2006
recommends programs with assumption that other people
to July 31, 2006 received from AGB Nielsen Media
also are interested in TV programs that people enjoy
Research, we utilized the users’ preference analysis forwithin the related group. Such a general recommendation
the recommendation of TV programs. We used the first
method has problems with the quality ofdimensional analysis method among various statistical
recommendation. The quality drops greatly when weanalysis tools. Data was analyzed using individual
have little user's information data and when there are few©ISAROB 2007

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B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

people whose inclinations are similar. We propose arecommendation engine that has a content-based
recommendation method and a collaborative
recommendation method that mutually supplement
each other in order to solve this problem [5, 6].
Data-mining and information abstraction methods
bring rules and information using this data; thisabstraction process is shown in Fig. 2.
5 Interpretation

Based on three months data from May 1, 2006 to July
31, 2006 received from AGB Nixon Media
Research, we utilized users’ preference analysis forthe recommendation of TV programs. And Table 1
shows a summary of the related data.
The user's Information consists of nine items: gender,
age, dwelling form, dwelling area, income, scholarship,
etc. This is shown in Fig. 5.
• Internal/external information• On/off-line
data

Knowledge

4 Data Mining

Data Cleaning

Data Transform

Transformed
Data

Feature Extraction

Classification

Neural networks, PCA,
genetic algorithms, decision
trees, fuzzy set

PreprocessedData

1 Selection• Bootstrap, Mote Carlo

Patterns

3 Transformation

2 Preprocessing

Data Generation• Fourier/Wavelet TransformMoving Average, Filter

Target Data

Prediction• Decision trees, neuralnetworks, Bayesian
networks, SVM• Regression, Bayesian
networks, neural networks,
DPNN

Rule Extraction

Clustering• Nearest neighbor, Kohonen
map, decision tree, fuzzy
set• Fuzzy C-Means, K-Means,hierarchical clustering,statistical methods

Data
Inference Rule

Data Model

Fig. 2 Data-mining architecture.
Figure 2 shows a typical process in which
information is abstracted from data. There are verymuch technological methods to Data-mining, and there
are Data-mining loadmaps that arrange datasystematically and bring rules or information
effectively from data in Data-mining. A Data-mining
loadmap is shown in Fig. 3.
In this paper, we could increase the reliability of therecommendation and optimize the recommendation
system by analyzing data from various subjects anddomains using a Data-mining loadmap.
3. System Architecture
The proposed software architecture is based on CBR(Content -based Recommendation) and CR (Collaborative
Recommendation). The system architecture consists of aUser Interface, which receives user information frominteractive TV and a TV Program Search Engine, which
receives TV program information on the air, from theRecommendation Engine, and from the Database. The
system architecture is shown in Fig. 4. Each
Recommendation Engine and Database is described in
Section 3.a, and Section 3.b.
A. Data Analysis
Table 1 Summary of
the AGB Nielsen Media Research’ data
SectionRemarkThe number ofparticipant
The number of
TV program
Information of
TV viewing
Genre©ISAROB 2007

• Estimation, Inference,association etc.

Prediction, estimation,
classification etc.

550 family - 3,953 persons(man : 2003, woman : 1950)
98,350 (it gather all channel from May, 2006
to July, 2006)
It gather channels and start/end times ofindividual TV viewing
94 (overlapping mark is able to do)

197

Integrated System• Fault analysis, optimization,
set-point
management etc.

Datamining &Knowledge discovery

Data gathering
Variable Analysis

Decision support
systemStatistical methods

Data preprocessing

Fig. 3 Data-Mining loadmap.
User
User Interface
Interface
Database
User
User Info.
Info.
Administrator
Administrator

User
User Info.
Info.

Recommendation
EngineView
View History
History Info.
Info.

TV
TV Program
Program
SearchSearch Engine
EngineTV
TV Program
Program Info.
Info.

Fig. 4 System architecture.
Genre data from sky-wave TV is separated into threearchitectures according to each TV program schedule.
However, the whole work to standardize the code isinsufficient. In the case of cable and satellite
broadcasting, doesn't offer to separate informationaccording to each program schedule. In order to solve
this problem, we integrate a combination of 94 items
according to genre to 13 representative genres and used
these on sky-wave and cable TV.
We made a database to analyze the AGB Nielsen
Media Research's data. Correlation to each entity isshown in Fig. 6.
We used the first dimensional analysis method among
various statistic analysis tools for data analysis. Because
the user' information has 9 items, in the case of to extend
dimension gradually, the number of case is overfull.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 7 1D analysis result of viewing TV.
B. Recommendation Engine

Fig. 5 Code arrangement information connected to
information from viewing TV.
TV_US_CODE

TV_USERSPKUSERID

PK
PK

GENDERAGE
DWELLING_AREA
DWELLING_FORM
DWELLING_OWN
INCOMESCHOOL_CAREEROCCUPATIONMARRIAGE

TV_VIEW
PK
PK
PK

VIEW_DATEUSERID
VIEW_STARTTIMEVIEW_CHANNEL
VIEW_ENDTIME

CODE_ID
KIND_FLAG

Fig. 8 General Recommendation method.

SHORT_NAME
FULL_NAMEUSE_FLAG

TV_PG
PK
PK
PK

PG_DATEPG_CHANNELPG_STARTTIMEPG_ENDTIMEPG_TITLE
GENRE1
GENRE2
GENRE3Fig. 6 Entity relation of the AGB Nielsen Media
Research' data from viewing TV.

Fig. 9 Personalized Recommendation method.

In order to solve this problem, after one dimension
analysis, we separated user information items into
groups where binary classification is possible andother groups where binary classification is impossible.
The results of the one dimension analysis are shown in
Fig. 7. Gender, dwelling form, and matrimony can beclassified using binary classification according to theresults of the one dimension analysis. However, Thebinary classification method can classify when
integrate code according to dwelling form by “Is it
apartment?” or “is not it apartment?”. And dwellingarea is excepted because there are so many '20 - 49'
overwhelmingly.
Age, income, scholarship and occupation reflect
personalized inclinations of the user's profile. This
paper chooses item in order to reflect individual
inclination using binary classification.

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The recommendation engine consists of CR(Collaborative Recommendation), and CBR (Contentbased Recommendation), and supplementation eachlacking part. CR (Collaborative Recommendation) andCBR (Content-based Recommendation) are called thegeneral recommendation method and the personalizedrecommendation method here. The recommendationengine solves the problem of insufficient user' viewing
information or can not believe the result. Actually, when
we used the personalized recommendation method, we
could get a satisfying recommendation result. However,the probability of failure was high when we had little
data of user's TV viewing information. In this case, we
could recommend TV programs using the generalrecommendation method, but because the generalrecommendation method also has a high probability of
failure when user's inclination is unique, weThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

supplemented the methods with each other
The TV program recommendation method must
recommend a program to the user based on TV
program time and TV program schedules. In this paper,
the general recommendation classifies binaryclassification items and other items, and utilizes them.
We divided the group through a combination of binaryclassification items. And then we created a new
subgroup using items that reflect individual inclinationaccording to the group in which it was divided. The
general recommendation algorithm is shown in Fig. 8.
The personalized recommendation method of TV
programs was analyzed using individual viewing TV
information and recommended using the order of TV
program title, genre, and channel information that theuser mainly watched on the corresponding weekday
and time. The personalized recommendation algorithm
is shown in Fig.9.
4. Implementation and result
We used MATLAB R2006A GUI in order to embody
whole program on Windows 2003 server. We use onlyuse data of sky-wave based on three months data fromMay 1, 2006 to July 31, 2006 received from AGB Nixon
Media Research. And we made a database to interlock
DBMS (DataBase Management System) and ODBC(Open DataBase Connectivity) using data of sky-wave.
The general TV Program recommendation application
described the histogram of a group to similar to user inorder to analyze the user's TV viewing informationaccording to the day of the week when the user
chooses the date and time that they want a
recommendation. In the case of the personalizedrecommendation application, because the personalizedrecommendation application analyzes the user's TV
viewing information more than the user's general
information and recommends using the order of thetitle, genre, and channel information, we experimented
after adding the user's TV viewing information screen.
The screen of the general recommendation applicationand the personalized recommendation application are
shown in Fig. 10.
We could get satiable results that compare theresults of the General Recommendation and the resultsof the Personalized Recommendation in order tosearch the recommendation results.
5. Conclusion
We made a database applying the fundamental
pretreatment process to resource data. In addition, wearranged each data code in this paper and grasped
attribute of data using statistical analysis techniques.
The general recommendation method and personalizedrecommendation method mutuality complement the
proposed method, and we had satisfactory results fromthe experiment.
We are going to improve the integration domain of
the recommendation algorithm through continuous
research.

©ISAROB 2007

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a. General Recommendation Applicationb. Personalized Recommendation Application
Fig. 10 Application (Korean language).
ACKNOWLEDGEMENTS
This work was supported by the Second-stage of theBrain Korea 21 project in 2006.
References[1] T. Isobe, M. Fujiwara and H.kaneta, “Development
and features of a TV navigation system,” IEEE
Trans. on Consumer Electronics, vol. 50, no. 1. pp.
393-399, Nov. 2003.
[2] J. Xu, L. Zhang, H. Lu and Y. Li, ‘The development
and prospect of personalized TV program,”
Proceedings of the IEEE 4th International
Symposium on Multimedia Sofhuare Engineering(MSE OZ), Califomia, USA, 2002.
[3] Metadata, Specification Series of the TV-Anytime
forum, Document SP003v1.1, August17. 2001.
[4] Peter W. Foltz and Susan T. Dumais, Personalized
Information Delivery: An Analysis of Information
Filtering Methods, CACM 35(12) pp.51-60, 1992.
[5] Marko Balabnovic and Yova Shoham, Fab: ContentBased, Collaborative Recommendation, CACM
40(3) pp.66-72, 1997.
[6] Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P.,
and Riedl, J. GroupLens: An open architecture forcollaborative filtering of netnews. Proceedings ofthe 1994 Computer Supported Collaborative Work
Conference. 1994.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Multi-labeled Classiﬁcation based on Error-correcting Output Coding
Tetsuya Yamashita

Takashi Takenouchi

Shin Ishii

Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama, Ikoma, Nara 630-0192, Japan{tetsuya-y, ttakashi, ishii}@is.naist.jp
Abstract
We propose a new framework to deal withmulti-labeled classiﬁcation problems based on errorcorrecting output coding (ECOC). In multi-labeledclassiﬁcation problems, it is required to assign multiple classes to a single input. In this study, we show
naive multi-label approach can be improved based on
probabilistic modeling of misclassiﬁcation process inthe ECOC method. We examine performance of the
proposed method with synthetic datasets and showthat the proposed method accurately predicts multiple labels of a new input, relatively to the existing
method.

1

Introduction

In a multi-labeled classiﬁcation problem, a single
input is assigned to multiple classes or categories.
A typical example of multi-labeled problems is the
text categorization problem on the World Wide Web,
where each text may belong to some of multiple categories. For such a problem, there are two conventional approaches. One is the binary classiﬁcation approach in which each text is classiﬁed into multiple
classes by integrating individual results from binaryclassiﬁers. The other approach simultaneously deals
with multiple classes by considering multinomial models; Ueda and Saito [5] proposed Parametric Mixture
Model (PMM) in which the multinomial distribution
is extended to represent the dependence of multiple
classes. In this study, we take the former approach.
In the context of multiclass classiﬁcation problems,
the error-correcting output coding (ECOC) method
was formerly proposed by Dietterich and Bakiri [2].
This method decomposes the original multi-class classiﬁcation problem into multiple binary classiﬁcation
problems whose each output is {+1, -1}. In the framework of ECOC, a k class problem is decomposed intol binary classiﬁcation problems and each class label isrepresented by a code word which is a row vector of©ISAROB 2007

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a code matrix W ∈ {+1, −1}k×l . An example code
matrix in a four-class problem would be


1
 −1

 −1

W =
 −1
 1

 1
1

−1
1
−1
−1
1
−1
−1

−1
−1
1
−1
−1
1
−1


−1
−1 

−1 

1 
.
−1 

−1 
1

Then, each binary classiﬁer is trained using binary labels associated with the corresponding column vectorof W . We can predict the class label of a new input using the outputs of binary classiﬁers, where thesimplest method is the Hamming decoding. For outputs of binary classiﬁers, the closest code word in W
with respect to the Hamming distance is used as thepredicted class label of the input. Allwein et al. [1]proposed a more ﬂexible framework that allows W toinclude 0 which signiﬁes “do not care” classes in thecorresponding binary classiﬁer. Moreover, the Hamming distance was extended to general loss functions.
In this study, we apply the framework of ECOC to
multi-labeled problems by formulating a probabilistic
model that represents the relationship between a code
word and a set of outputs from classiﬁers. We in particular develop the method based on the informationtransmission theory. The model regards a misclassiﬁcation of each binary classiﬁer as a bit inversion in
the code word due to a noisy channel. An addition ofparity bits which leads to redundant representation ofmultiple labels and adaptive identiﬁcation of the noisy
channel based on the probabilistic model enables anaccurate prediction of multiple labels.
In section 2, basic setting and a probabilistic modelof the noisy channel are formulated. In section 3, we
examine the performance of the proposed method, incomparison to the simple multi-labeled classiﬁcation
method, using synthetic datasets.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

MethodIn this section, we describe the probabilistic modelof the bit inversion, which represents the misclassiﬁcation error, in the multi-label classiﬁcation, and its
decoding method.

2.1

Notation

Let x be an input vector and y ∈ {1, −1}b be avector of labels associated with b categories. Typically,x is an element of the Bag-of -W ords [3]. We assume
that a dataset {xi , y i }N
i=1 is given. Let yj (1 ≤ j ≤ b)
be a component of y, deﬁned by{
1 x belongs to class jyj =(1)−1 otherwise.
We consider parity bits z ∈ {1, −1}c associated withy, whose j-th component is denoted as zj . Note thatthe parity z = z(y) : {1, −1}b → {1, −1}c is designed
in a priori manner.
Using the dataset and the corresponding set of parity bits, {z 1 , ..., z N }, we can train (b + c) binary classiﬁers. We denote outputs of binary classiﬁers associated with the code word y as ỹ, and those augmented
by the parity bits z as z̃, both of which are assumed
to be disturbed by a noisy transmission channel.

2.2

Probabilistic
methodmodel

anddecoding

This decoding method is equivalent to the Hamming
decoding with parity bits and reduces to the naive
multi-labeled decoding method when the parity bits z
are omitted.

2.3

Estimation of conﬁdence

In model (2), β was set at a positive constant, implying all classiﬁers are assumed to have the same reliability (conﬁdence). This assumption is not natural,
however, because performances of classiﬁers are diﬀerent from each other according to the diﬃculty in corresponding classiﬁcation problems or the underlying
geometrical structure of dataset. Takenouchi and Ishii[4] introduced the conﬁdence of classiﬁers into multiclass classiﬁcation problems. Following this existing
study, we introduce the conﬁdence for each classiﬁer,
and then the probabilistic model is extended asp(ỹ, z̃|y; β, γ) =p(ỹ|y; β)p(z̃|y; γ)
b
(∑
)
= exp
(βj y˜j yj − ϕ(βj ))
j=1
c
(∑

×exp

k=1

where β = (β1 , ..., βb ) and γ = (γ1 , ..., γc ) are parameter vectors representing the conﬁdence of each dimension of the noisy channel. Parameters β, γ can be
estimated as to maximize the log-likelihood:

The probabilistic model of (ỹ, z̃) given y is deﬁned

L(β, γ) =asp(ỹ, z̃|y) =p(ỹ|y)p(z̃|y)= exp(β ỹ t y − bϕ(β))exp(β z̃ t z − cϕ(β))= exp(β(ỹ t y + z̃ t z) − (b + c)ϕ(β))), (2)where t denotes a transpose, ϕ(β) = ln(eβ + e−β ) isa normailzation constant, and β is a positive constantthat represents the noise level of the noisy channel.
We assume that the label prior p(y) is the uniform
distribution, then p(y|ỹ, z̃) is calculated as follows.
p(y|ỹ, z̃)p(ỹ, z̃|y)P (y)= ∑y p(ỹ, z̃|y)P (y)∝ (ỹ t y + z̃ t z).

©ISAROB 2007

N∑log p(ỹ i , z̃ i |y i ; β, γ).

(6)

i=1

Stationary conditions of equation (6) become
∂L
∂βj

=

N
∑

(ỹji yji −

i=1

=
∂L
∂γk

=

0,

(7)

N
∑

(z̃ki zki −i=1=

exp(βj ) − exp(−βj ))exp(βj ) + exp(−βj )exp(γk ) − exp(−γk ))exp(γk ) + exp(−γk )

0.

(8)

Those equations can be analytically solved as(3)This is equivalent to the Hamming distance between(ỹ t , z̃ t ) and (y, z), then an estimate ŷ of the originallabel y can be decoded asŷ = argmaxy p(y|ỹ, z̃).

)(γk z˜k zk − ϕ(γk )) (, 5)

(4)

201

1 1 − Cyj
1 1 − Czkβˆj = ln, γˆk = ln
,
2
Cyj
2
Czk

(9)

1 ∑ 1 − (y˜j yji )is the error rate of
N i=1
2
the classiﬁer associated with the code yj and Cz =N

where Cyj =i

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20071 ∑ 1 − (z˜k i zki )is that with the parity bits zk . AfN i=12
ter identifying the characteristics of the noisy channel,
the label ŷ can be decoded as a maximum a posteriori(MAP) estimate:Nŷ = argmaxy p(y|ỹ, z̃; β̂, γ̂).

5

4

(10)

3

Note that the posterior probability in equation (10) is
rewritten asp(y|ỹ, z̃; β̂, γ̂)

=
∝

b
(∑

βj y˜j yj +

j=1

c
∑

2

x2

p(ỹ, z̃|y; β̂, γ̂)p(y)∑y p(ỹ, z̃|y; β̂, γ̂)p(y)

1

1

1

)
γk z˜k zk (. 11)

1

3

3

3

3

3
3
32

1
1

1
22
32

3 2
1 3
3 333 12 1 1 112 2
2
2
131 11
3
1
1 2
2
2
1
1 1
2 1 22 2
12
2
1
22
3 1
2
12
2
2

3
2

1

0

1

x1

2

3

4

5

Figure 1: An example dataset. Each numerical character indicates the class to which an input vector is
assigned. A symbol 5 indicates an input assigned totwo categories and + means that the corresponding
input does not belong to any category.

Diﬃculty of decoding process

0.9
0.8
0.7

The accuracy rate

In the previous subsection, we applied the MAP
decoding (10) in which the number of candidates tobe searched becomes exponential, 2b . If the number
b of categories is large, this decoding process becomes
hard. To avoid this problem, one practically good way
is to restrict
the multiplicity being small such as∑b{y| i=1 yi2+1 5 3}.
This restriction seems plausible, in practical for example, in the text categorization problem, because
major part of texts are likely assigned each to a few
classes. Using this assumption, the number of code
candidates to be searched is reduced to a C3 , whichmakes the MAP decoding feasible.

0.6
0.5
0.4
PA of (4)
PA of (11)
CA of (4)
CA of (11)

0.3
0.2

3

2

1

2

k=1

3

3

3
3
133

0

Namely, this decoding is a weighted version of theHamming decoding, but the weight is estimated by
the maximum likelihood estimation from multi-labeledclassiﬁcation results of the training data. This approach is natural, because our decoding is based on
the weighted Hamming distance and its weight is determined based on the classiﬁcation performance ofeach binary classiﬁer for the training dataset.

2.4

3 3

3

Experiment result

0.1
0In this section, we examine the performance of the
proposed method by comparing with the naive classiﬁcation based method, using synthetic datasets. The
parity bits were randomly generated and the MAP decoding was employed in the proposed method. For
binary classiﬁers, we employed the simplest linear discriminant analysis. A typical example of dataset consisting of 90 input data with 3 categories, is shown in
Figure 1.

©ISAROB 2007

202

0

2

4

6

8

10

12

14

16

18

20

The number of parity

Figure 2: PA and CA of the two decoding methods,
the Hamming decoding with parity bits(equation(4))
and the weighted Hamming decoding with parity
bits(equation(11)), against the length of parity bits.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

For performance evaluation, we used two kinds ofquantities: complete accuracy rate (CA) and partial
accuracy rate (PA). CA is the rate of events in which
the vector (three-dimensional, in this case) of decodedlabel completely coincides with that of the original label, and PA is the rate with which the element of thedecoded label is consistent with that of the originallabel.
Figure 2 shows averaged CA and PA of the proposed method for 10 trials. The error bar is the standard deviation in the 10 trials. In this ﬁgure, we compared the Hamming decoding with parity bits (equation(4)) and the weighted Hamming decoding withparity bits(equation(11)). For comparison, we also applied a naive multi-labeled classiﬁcation method, that
is, a set of individual binary classiﬁers assign the label
for the corresponding category. CA and PA of thenaive method were 0.1111 and 0.5056, respectively.
From the comparison, we can see (1) the proposed
methods outperformed the naive method; and, (2)when the parity length was c ≥ 6, the performanceby our methods was consistently good.

4

Conclusion

We proposed a novel method for multi-labeled classiﬁcation problems based on the framework of ECOC.
This regard the classiﬁcation errors of classiﬁes as
the noise of the channel. Using the parity associatedwith the label, we can improve the classiﬁcation accuracy in comparison to the naive classiﬁcation based
method. As a future work, we plan to apply the proposed method for real datasets. For this application,
the decoding method discussed in subsection 2.4 will
be improved. However, the design of parity bits on in
our future work.

References[1] E. L. Allwein, R. E. Schapire, Y. Singer. “Reducing
Multiclass: A unifying Approach for Margin Classiﬁers,” Jounal of Machine Learning Research, 1,
113-141, 2001.
[2] T. G. Dietterich, G. Bakiri. “Solving multiclass learning problems via error-correcting output
codes, Journal of Artiﬁcal Intelligence Research,
2, 263-286, 1995.
[3] S. T. Dumais, J. Platt, D. Heckerman, M. Sahami.
“Inductive learning algoritms and representations
for text categorization, ACM-CIKM’98, 1998.

©ISAROB 2007

203

[4] T. Takenouchi, S. Ishii. “Multiclass classiﬁcation
by an extention of ECOC decoding, 2006 Workshop on Information-Based Induction Sciences,
2006 (in Japanese).
[5] N. Ueda, K. Saito. “Parametric Mixture Models
for Multi-Labeled Text,” Advances in Neural Information Processing Systems 15, 721-728, 2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A control method of an electric vehicle1

Masanori Sugisaka1, Hiroaki Tanaka1 and Masayoshi Hara1
Department of Electrical and Electronic Engineering, Oita University, Oita, Japan(+81 -975-54-7831, E-mail: msugi@cc.oita-u.ac.jp, mhara@cc.oita-u.ac.jp)

Abstract: The research of the electric vehicle in our laboratory is developed in two fields. One is the control of the
speed (operation of the accelerator and the brake), the other is the controls of the steering wheel. We controlled the speed by using turning on and turning off control at the early stage. We could get a moderate resultin this experiment. And we controlled the speed by the another control method that also had generality be
ing possible to correspond to road situation. As a result, we could do the appropriate control of accelerator.
Needing the control of the accelerator and the brake is combined with the control of steering wheel is running at a constant speed. This becomes a necessary, indispensable technology in doing the driving support and actually running on the road.
Keywords: operation of the accelerator and the brake, control method

1. INTRODUCTIONI f the automatic driving technology based on“Active Safety” develops, the number of accidents by ahuman factor decreases. The control ofthe steering wheel by the image data processing using CCD camera was possible recent work. But contro
l of an accelerator and a brake is not automatic.
So in this system, combining the operation of the accel
erator and the brake is a problem. Needing the controlof the accelerator and the brake is combined with thecontrol of steering wheel is running at a constant
speed. Therefore, we research the speed control as a basic research to combine the control of the accelerator andthe brake with the steering control by the visual syste
m. We controlled the speed by using turning on andturning off control at the early stage of the research.
We could get a moderate result in this experiment. But
we controlled the speed by the PID control that also had
generality being possible to correspond to more road si
tuation.

Table 1A configuration of an electric vehicle
Length2,395(mm)Wide1,010(mm)Height1,490(mm)Weight470(kg)Max speed45(km/h)Motor
DC brassieres motor
Max power
5.2(kw)

3. CHARACTERISTIC OF ACCELERATAOR A
ND BRAKE

2. ELCTRIC VHICLE
We shows this vehicle in Fig.1 and configurationsof this vehicle in Table 1.

The accelerator of the electric vehicle does a usu
al accelerator operation by rotating the motor. In another word, the accelerator wire is pulled by depres
sing the accelerator when the driver person, and it
leads to the drive of the DC motor.
Another wire is pulled by rotating, and the DC
motor is driven the stepping motor the act of pulling the accelerator wire. Moreover, the brake ties to t
he stepping motor the brake pedal with the wire. T
hey greatly depend on the parameter, the number ofmovement pulses, and the passing speed used to c
ontrol the stepping motor, and control the amount of depressing of the accelerator brake and the depres
sed speed by changing these parameters.

Fig. 1 An electric vehicle©ISAROB 2007

Fig.2 Motor of accelerator

204

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

u(k) : number of moving pulsee(k) : differencey(k) : speed value
One of the experiment result is presents Fig. 4. I
t is understood that the shifting value at the speed
that is the problem in the current result is consider
ably improved from the experiment result. Moreover,it becomes a repetition of a detailed error marginabout other shifting value parts, and the more error
has disappeared. It became the result of understan
ding number of the accelerator increased in shape corresponding to this about number of the accelerato
r in the inclination of going up. However, there isan insufficient part as a control of the speed.

4. Control method

We get speed characteristic of this vehicle fromexperiments and below equation.
Speed characteristic with load
1.46
(R(k ) − R(k − 1))(m / s )751.2
1.46 ⋅ 3.6(R(k ) − R(k − 1))(km / h )=751.2

y (k ) =

70.00

12.00

60.00

11
10.00

50.00

9
8.00

40.00

6.00

30.00

4.00

20.00

2.00

10.00

0.00

0.00
0

10

20

30

40

50

60

70

80

90

Times k(sec)(1)
here: sampling time 1(sec)Fig.4 Speed chara. using PID control

The difference of the encoder value is taken in t
his computational method, and the speed is calculat
ed by the change.
We do experiments use this equation. And contro
l method is using simple PID control. At first, we
do a constant moving speed experiments. It is for t
he characteristic of this vehicle moving speed. It ru
ns in the straight line by using a general road as t
he experiment method. It is set that the measureme
nt time is assumed to be 60(sec), and the stop ope
ration is done after it runs of 40(sec). The accelera
tor and the brake operation between those are due to the program, and the experimenter (driver) does t
he steering wheel operation.
The expression from which the number of move
ment pulses is:ku (k ) = 33.657e(k ) + 1 ⋅ ∑ e( j ) + 1 ⋅ (e(k ) − e(k − 1)) (2)j = k −1e(k ) = r (k ) − y (k )(3)©ISAROB 2007

14.00

205

5. CONCLUSION
In this research, first of all, the base of the speed control was done. Next, the speed by PID was c
ontrolled. Various parameters in the PID control were decided from the experiment. However, the characteristic was led by using the speed characteristic of not a real load but no-load-condition that the spe
ed settled to some degree. The adjustment of eachparameter has properly adjusted a necessary part repeating the experiment. One of the results was sho
wn. However, it is thought that the problem by thedelay of the processing speed occurs in both the steering control, the accelerator, and the brake contro
l by increasing the speed.
It is necessary to examine the combination with thesteering control as a problem in the future with the improvement at the parameter and the sampling time.

Accelalator ∑u(k)/26.7(%)

Here, the moving speed is a parameter in the amount of depressing of the accelerator and the brakein the number of output pulses corresponding to e
ach depressed speed. It is based on the specification of the stepping motor of the amount of the rotati
on in the number of pulses in this and the specification of the rotational speed at the pulse frequency.

Speed y(k)(km/h)

Fig.3 Motor of brakeThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Inﬂuence for Human Boredom in Interaction by Mutual Prediction
M. Shimamoto, T. Shiose, H. Kawakami and O. Katai
Dept. of Systems Science
Graduate School of Informatics, Kyoto Univ.
Kyoto, Japan, 606-8501
Abstract

2

In the research ﬁelds of Human-Robot interaction(HRI) and Human-Agent interaction (HAI), humanboredom of robots or agents is a signiﬁcant problem
that needs solving. Clarifying the human characteristic of boredom may be conducive to designing agentsand on HRI framework. In this study, we focus on mutual prediction, which is a distinct property of animalanimal interaction, including humans, and we aim toclarify how mutual prediction aﬀects human-agent interaction. In this paper, we explain our experimental
setup for investigating inﬂuence of mutual prediction
on human boredom in human-agent interaction and of
subjective congnition of opponents. In these experiments, we set two axes to represent experimental conditions, one is subjective congnition of an opponent,
which means subjects believe the opponent is humanor a computer, the other is the presence or absence ofthe opponent’s prediction.

1

Introduction

Since the emergence of robots designed to communicate with humans, research in HRI (Human-Robot
Interaction) and HAI (Human-Agent Interaction) has
gathered momentum [1, 2]. In this research ﬁeld,
human boredom of robots or agents is one of themain problems preventing sustained human-robot orhuman-agent interaction.
In this study, we aim to clarify one aspect of the human property of boredom with human-agent interaction. We focus especially on mutual prediction, which
is a distinct property of interaction between intentional entities such as humans and animals that havethe abilities of prediction, adaptation and so on. We
aim to clarify how mutual prediction aﬀects humanboredom with human-agent (computer) interaction.

©ISAROB 2007

206

2.1

Research Background
Psychological Saturation and Boredom in Interaction

In the ﬁeld of psychology, boredom is called “psychological saturation”, and it has been researched fromthe early twentyth century. Psychological saturation isa psychological state in which someone can’t stay witha certain uniform action any more and stops it, evenwhen he/she is told to continue preforming that uniform action. Psychological saturation was manifested
experimentally by Karsten [3].
However, when we consider HRI or HAI, there is another aspect of human boredom that can’t be treatedas psychological saturation. Psychology saturation isa psychological state speciﬁc to a certain uniform action, whereas in HRI or HAI, if a robot has a abilities of prediction and adaptation, there is a nested
structure of prediction and adaptation between humans and robots (agents) due to interaction between
humans and robots (agents). That means humans naturally predict and adapt to objects, but objects also
predict and adapt to humans. The phenomenon above
is called mutual prediction or mutual adaptation [2, 4].
In this study, we examine how mutual predictionaﬀects human boredom, which can’t be treated as psychological saturation.

2.2

Mutual Prediction

We behave based on our prediction of others’ behavior in daily life. Naturally others also behave basedon their prediction of our behavior. In the case ofsuch interaction that establishes mutual prediction ofeach other’s behavior, there is inﬁnite regress causedby nested structures. Consequently, there is no optimal solution in such interaction.
Luhmann focused on mutual prediction in interaction, and considered how laws and regulations are constituted by mutual prediction [5]. He separated prediction into two types: “congnitive prediction” and “pre-The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

scriptive prediction”. Congnitive prediction is that humans reform and accommodate their predition when
actual results are diﬀerent from the predited one. On
the other hands, prescriptive prediction is that humans
do not reform their prediction even when actual results
are diﬀerent. Once prediction is formed, prescriptive
prediction can be applied to a certain uniform action
or physical objects because these do not change drastically. On the contrary, we need to apply congnitive prediction to objects that possess the abilities ofprediction and adaption such as humans, animals, or
agents because there is a nested structure of prediction
and adaptation.
Iizuka and Ikegami proposed a computationalmodel of mutual prediction in interaction [4]. They
used a simulated mobile robot with a recurrent neuralnetwork, and two agents performed turn-taking behavior. And they reproted that there are two phasesof turn-taking behavior: one is a stable phase because
each agent can predict the other and there are certain trajectory patterns, the other is unstable phasebecause prediction of each agent collapses and there
are chaotic trajectories. The above phenomenon occur
because of mutual prediction, which composes nested
structure.
As described above, there is a diﬀerent aspect in
HRI or HAI from interaction between humans andphysical objects. We suppose that the ambivalentdirectional properties described above aﬀect humanboredom in HRI or HAI.

2.3

The Diﬀerece from Subjective Congnition on Opponetns

The study by Reeves and Nass is pioneering research in the ﬁeld of human-computer interaction[6].
They reported that people unconsciously treat computers and television as the same as humans.
On the contrary, Gallagher et al. reported that theactivated region of the human brain is diﬀerent whensubjects believe an opponent is human from when they
believe an opponent is a computer [7]. In their rockscissors-paper game experiments, subjects were taughtthat an opponent is either human or a computer, eventhough the opponent is always a computer whose behavior was programmed randomly in both conditions.
Although winning percentages of subjects are the same
in both conditons, the activted region of the humanbrain actually diﬀered according to the conditions.
Takahashi et al. also reported that subjects’ behavior diﬀers due to subjective congnition of an opponent[8]. When subjects were taught that an opponent washuman, their behavior became more explorative than

©ISAROB 2007

207

that of the condition in which they were taught thatthe opponent was a computer. Their experimental results indicate that when people believe an opponent is
a computer, people behave more exploitatively whichmeans they use “prescriptive prediction” rather than“congnitive prediction”.
As the above reports outline, human behavior differs between when people believe an opponent is human and when it is a computer. This diﬀerence mayarise from people’s belief or perceived notion that acomputer must be routine and that it must be ruledby constant rules and designs.
Based on the above perspective, we aim to clarify
the following two points in this study.
• First, we aim to clarify the diferences among subjects’ boredom from subjective congnition to an
opponent.
• Does mutual prediction aﬀect human boredom ina little more complex situations than the rockscissors-paper game?
In the next section we explain the experimental
setup for a method to these two points.

3

Experimental Setting

In this study, we conduct experiments using a simple card game. In this game, a subject and an opponent put down a card from the three each is holding(numbered 1 to 3) at the same moment, and the one
who puts down the higher card wins. Players can not
put down a card before he/she put down once in one
set, and the player who wins two of three games wins aset. For example, if a subject put down cards 3→1→2
in series and an opponent put down cards 2→3→1, thesubject wins two games and, therefore, wins that set(Fig:1). Subjects repeat this card game a few dozen
times.
This card game is essentially no diﬀerent from therock-scissors-paper game, but it may be a little moretactical rather than the rock-scissors-paper game because the card to be put down is restricted by thesequenc in which cards are put down. There are 36
possible patterns by which a player and an opponent
can put down cards sequentially in a set. The probabilities of the subject’s winning and losing are both
1/6, and the remaining 2/3 are for a draw. If the subject wins a set, he/she gets +100 points, if the result
is a draw, 0 points, and if he/she loses, -100 points.
Subjects play this card game using a display and a

1

2

2

3
3

2

1

1

subject

Condition 2

computeraffectation

1

2

1

3

2
3

Condition 1

opponent

1

subject

3

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

experiment
helper2

1

subject

Figure 2: experimental conditions

Win

lose
congnition of an opponent aﬀects subjects’ boredom.
That is to say, we investigate whether subjects believe
the trial is boring because they think the opponent is
a computer.

Figure 1: transition of a set
keyboard, and they cannot see the opponent’s display
and keybord directly.

3.2

3.1

In this study, we focus on mutual prediction in interaction, and aim to clarify how mutual predictionaﬀects human boredom. In experiment 2, we build amechanism that can predict subjects’ behavior fromtheir behavioral history. We then investigate how themechanism aﬀects subjects’ boredom and subjective
congnition of an opponent. We set following two conditions.

Experimetn 1

In this experiment, we examine whether the diﬀerence in subjects’ boredom arises from subjective congnition of an opponent. We set following two conditions:• subjects believe the opponent is human;• How cards are put down decided at random• subjects believe the opponent is a computer;To make subjects believe the opponent is human,
we allocate an experimental assistant, and have theexperimental assistant pretend to play the card game.
We set up the assistant’s display and keyboard so as
to be invisible to the subjects (Fig:2).
The order of putting down the cards in series is decided randomly by a program in both conditons. It isnatural that subjects become bored with the game itself, therefore, order of the experimental conditions becomes equal to about two conditions so as to counteract the eﬀect of the sequence. After ﬁnishing each condition, we ask the subjects to answer questionnaires
about the trial.
To analyze patterns of subjects’ behavior and thequestionnaire results, we examine whether subjective©ISAROB 2007

Experimetn 2

208• How cards are put down is decided by the themechanism that may predict sabjects’ behavior
In both conditions, we do not tell subjects whether
the opponent is human or a computer. As in experiment 1, the order of experimental conditions become
equal to about two conditions so as to counteract theeﬀect of sequences, and we ask subjects to answer
questionnaires about the trial.
We set the predictive mechanism that the probability of how to put down cards changes based on thesequence in which subjects put down their cards. We
describe the process of subject putting down cards
as as1 , as2 , as3 in a trial, and that of a computer as
ac1 , ac2 , ac3 . Furthermore we deﬁne a situation ass = {as1 , as2 , as3 , ac1 , ac2 , ac3 }. The ﬁrst card to be

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

put down of a computer in a trial is decided as follows.
Count(d|s)
P1 (d|s) = ∑Count(a|s)(1)

aAfter a trial, we add +1 to Count(a = A|s = S),
where A is the ﬁrst instance of subjects putting donwcard in that trial, and S is the situation of the previous
trial.
The second card to be put down of a computer issimply decided by the ﬁrst instance of both the subject
and the computer. just as with the ﬁrst instance, we
add +1 to the count after a trial, and the probabilities
change.
Count(d|as1 , ac1 )P2 (d|as1 , ac1 ) = ∑Count(a|as1 , ac1 )(2)aIn this experiment, subjects must judge whether opponent is either human or a computer, and we investigate how establishing a certain degree of mutual prediction aﬀects subjective congnition of an opponent,
and whether if subjective congnition has a relationship with human boredom in interaction.

4

Sammary

In this study, we aimed to clarify one aspect of thehuman property of boredom. Especially, we focusedon mutual prediction, that is a distinct property ofhumans and animals. We discussed how HRI andHAI are diﬀerent from interaction between humans
and physical objects from the viewpoint of mutual prediction and mutual adaptation, and we explained experimental setttings for investigating a the inﬂuenceof mutual prediction on human boredom. The experimental results will be reported on the day of our presentation.
We plan to measure not only responses in subjects’questionnaires but also their biological information in
the near future, and we would like to deal with humanboredom quantitatively to some extent. We intend touse electrocardiograms and skin conductance, which
are used to measure stress and excitation, to achieve
this.

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209

References[1] C. Breazeal, “Social Interactions in HRI: The
Robot View,” in IEEE SMC Transactions, PartC., 2004.
[2] S. Yamada and T. Yamaguchi, “Mutual Adaptation to Mind Mapping in Human-Agent Interaction,” IEEE International Workshop on RobotHuman Interaction (ROMAN-2002), pp. 105-110,
2002.
[3] A. Karsten, “Psychology Satuartion,” Psychological Research, Vol. 10, pp. 612-628, 1928.
[4] H. Iizuka and T. Ikegami, “Adaptability and Diversity in Simulated Turn-taking Behaviour”, Journalof Artiﬁcial Life, Vol.10, No.4, pp.361-378, 2004.
[5] N. Luhmann, “Rechtssoziologie”, Rowohlt
Taschenbuch Verlag, 1972. (translated
Japanese)

into[6] B. Reeves and C. Nass, “The Media Equation:
How People Treat Computers , Television and New
Media Like Real People and Places,” University of
Chicago Press, 1996.
[7] H. Gallagher, A. Jack, A.Roepstroﬀ and C. Frith,“Imaging the Intentional Stance in a Competitive
Game,” NeuroImage, Vol. 16, No. 3a, pp. 814-821,
2002.
[8] H. Takahashi and T. Omori, “Intentional Stance
Regulates Behavioral Balance of Exploration and
Exploitation in a Competitive Game,” “2nd Annual Computational Congnitive Neuroscience Conference, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A property of associative memory models with replacing units
Akira Date
Faculty of Engineering
University of Miyazaki
Miyazaki 889-2192

Koji Kurata
Faculty of Engineering
University of the Ryukyus
Okinawa 903-0213

date@cs.miyazaki-u.ac.jp

kurata@mibai.tec.u-ryukyu.ac.jp

Abstract
This paper describes a property of associative memory
networks in which a number of units are replaced when the
networks learn. In our network, every time the network
learns a new item or pattern, a number of neurons die andthe same number of neurons are born. It is shown that the
memory capacity of the network depends on the number of
replaced units, and that the memory capacity is maximized
when the number of replaced units is optimal. The optimal
number of replaced units is small and seems to be independent of the network size. Although our model was not
motivated by higher nervous function, the results suggestthat small number of newly born neurons might be optimal
in some sense for the distributed memory system.
Keywords: associative memory, collective memories,
catastrophic interference, new neuron

the same properties; avoidance of catastrophic forgetting,keeping recent memories. In our network, every time the
network learns, neurons die and are born. The number of
replaced units was varied in the experiment to maximize
the memory capacity. We found that the optimal numberof replaced units was small and independent of the network
size. Although our model was not motivated by higher nervous function, the results might suggest that small numberof newly born neurons is enough and optimal for a temporal memory system such as the hippocampus.

2

Associative Memory
INPUT

OUTPUT

1 Introductionxn

synapseThe purpose of this paper is to describe a property of
associative memory networks in which a number of units
are replaced when the networks learn a new item. The neural network models of associative memory have been studied extensively since 1960’s [1, 2, 3, 4, 5]. Most familiar learning for association among memories is the Hebb
rule or a correlation-based learning in which each connection weight between two neurons are modified according to
the correlation between the activities of the neurons. The
learning is local, and easy to implement in the circuits.
There exists a memory capacity for the network. The addition of new memories beyond the capacity overloads thesystem and makes all memory states irretrievable (catastrophic forgetting) unless there is a provision for forgetting
old memories.
If the dynamics of weight connections of the network
have decay [3, 4] or saturation[1], the catastrophic forgetting does not occur and the network can keep recent memories. Here we describes the alternative network which share©ISAROB 2007

210

x2
x1

UNITS

Figure 1: A network of associative memory

2.1

Network dynamics

In our network of associative memory (Fig.1, [1]), each
unit, j, has two states, and is described by a variable

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007300

n=2000R=0
Number of Correct Recalls

250

200

R=1
150

R=2

100

R=3
R=4
R=5

50

R=10
0

R=0, 1

100

200

300

Number of Learned Memories

t

400

500

Figure 2: Number of memories correctly recalled as a function of number of learned ones.

x j = ±1. The instantaneous state of the system of n units
can be thought of as an n-dimensional vector having components xi . The units are inter-connected by a network ofsynapses, with a synaptic strength wi j from unit j to unit i.
The instantaneous input to unit i isnui = ∑ wi j x j

(1)

j=1

where x j is the present state (±1) of unit j. The state of thesystem changes in time; Each unit i readjust its state, setting xi = ±1 according to whether ui , the input to i at thatmoment is greater or less than zero. The units act asynchronously in a random order. This algorithm defines thetime evolution of the state of the system. For any symmetric connection matrix {wi j }; wi j = w ji , there are stablestates of the network of units; Starting from any arbitrary
initial state, the system reaches a stable state and ceases to
evolve [1].

2.2 Content addressable memory
The Hebb rule has served as the starting point for the
study of information storage in simplified models. Suppose
we wish to store the set of states xµ , µ = 1, 2, ¢ ¢ ¢ m. To
learn a new memory x1 , increment wi j by
∆wi j = xi1 x1j .

©ISAROB 2007

(2)

211

This learning process is local; the increment for connection
wi j does not depend on the global structure of the state or
past memories, but only on xi1 and x1j . It is fast and does
not need to learn each memory repeatedly.
This network now functions as an associative memory.
If started from an initial state which resembles somewhat
state x1 and which resembles other xµ (µ 6= 1) very little,
the state will evolve to the state x1 . The state x1 is evocable memory, and the system correctly reconstructs an entire
memory from any initial partial information, as long as thepartial information was sufficient to identify a single memory. Detailed properties of the collective operation of thisnetwork have been studied extensively [1, 2, 3, 4, 5].

2.3

Catastrophic forgetting or interference

Computer modeling of memory storage according toequation (2) was carried out for n = 2000. 550 random
memory states (x1 , ¢ ¢ ¢ , x550 ) were chosen in each of whichhalf of units were in the active firing state on the average. The network learned one by one by updating wi j incrementally, i.e., new memories were continually added to
wi j . There is a memory capacity for this network [1, 2, 5];
About 280(= 0.14n) states can be simultaneously memorized without error in recall. The addition of new memories
beyond the capacity overloads the system and makes all
memory states irretrievable (catastrophic forgetting) unless

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1

n=2000t=550

Direction Cosine

0.8

R=4

0.6

R=0

0.4

0.2

0

-0.2

R=10
0

100

200

300

400

500

memory µ
Figure 3: Closeness between each learned state xµ and the recalled state for each memory, xµ , µ = 1, ¢ ¢ ¢ , 550.
there is a provision for forgetting old memories.
The network learned 550 memories one by one in whicha memory xt was learned at time t. We tested at each timet, whether network can recall each learned memory without
error. Each learned state xµ , µ = 1, ¢ ¢ ¢ ,t was given as astarting state to the network. The network quickly reacheda stable state x. We compared xµ to x thus obtained. The
proximity of the two states was measured by retrieval overlap
1 n µ
a = ∑ xi xi .
n i=1
We counted the number of successfully recalled memories
in which the proximity was larger than 0.9. A curve of
R = 0 in Fig. 1 illustrates how many states there wereremembered without error in recall at time t; The network was able to remember all of memorized patterns fort < 280, where it was able to learn, But at t = 280, forgetting started, and any memories could not be rememberedcorrectly after t = 380., i.e., the network could not recall
even the most recent memory.

3

Results

For simplicity, we assume the following throughout thepresent experiment: Every time the network learns a newmemory, a number of neurons, say R neurons, die and the©ISAROB 2007

212

same number of neurons are born; we assumed that the
total number of neurons does not change in time. It corresponds to reset connection weights wi j = w ji = 0, j =1, ¢ ¢ ¢ , n for replaced neurons i. Units were replaced fromthe oldest one, i.e., they were replaced always in the sameorder. Simulation were carried out on a computer forn = 2000 with varying R, number of replacing units. Results are shown in Fig.1. Larger was the number of replacing units R, earlier the start of forgetting. For R = 1,
forgetting started at around t = 180 and for R = 3 it started
at around t = 60. Our interest was the properties of the
network whose structure had already been stable (t > 500).
The network for R = 0, 1 could not recall any memory att > 420. For large R ‚ 2, the network could recall a number of memories; 44 memories were recalled successfully
for R = 4.
To see which 44 memories out of 550 were kept successfully, we tested the network at t = 550; each learned memory xµ was given as a initial state, and the closeness of stable state of the network and memories was measured. Results are shown in Fig.2. As we expected, only the newest
memories were recalled correctly. Results of the networks
for R = 4 and R = 10 show that the number of memory
successfully retained (a network capacity) was dependent
on R, the number of replacing units. It turned out that there
was a optimal replacing number near R = 3 for n = 2000.
Computer simulation was carried out for n = 5000 with

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

varying R, we found that the optimal replacing number was
the same as before, near R = 3. The number of successrecalls depends on R, and the network for R ≈ 3 was the
optimal to maximize the number, which seems to be independent to the network size.

4 Discussion
To our surprise, the optimal R ≈ 3 seemed to be independent to the size of the network. The proof of this conjecture might be possible under the following assumption:
The present network is equivalent to the network in whichweight connections have decay [3, 4] or saturation[1]. The
networks share the same properties: avoidance of catastrophic forgetting, keeping recent memories.
The CA3 area of hippocampus is involved in associative
memory recall [6, 7], and often modeled by a fully connected network in which each memory is represented by
the activity of distributed and sparsely coded pattern. The
models proposed so far for explanations of neurogenesis in
hippocampus were layered networks which was designed
based on known anatomical knowledge [8, 9, 10, 11, 12] inwhich hypothetical functions were assigned such as making sparse representation, enlarging a dimension of input
signal, assigning distinct codes to similar inputs. In oursimple system, replacing only a small number of units prevent from the catastrophic forgetting, maximizing a memory capacity. Memories in the hippocampus seems to moveinto the cortex within 6 weeks. The role of the hippocampus memory systems seems to retain recent limited numberof memories. Our example also might answer from a mathematical viewpoint the question of why most newly bornneurons die before they mature, and substantial number of
replaced neurons observed is so small. It seems that theproperties revealed in the present paper does not change bya slight modification of the model. (e.g., sparsely coded
memory, the order of turnover)Acknowledgments
This works was partly supported by Grant-in-Aid(16700232) from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT).

References[1] J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Academy of Science USA, vol. 79, pp. 2554–2558, 1982.

©ISAROB 2007

213

[2] D. J. Amit, H. Gutfreund, and H. Sompolinsky, “Storing infinite numbers of pattern in a spin-glass modelof neural networks,” Physical Review Letters, vol. 55,
pp. 1530–1533, 1985.
[3] J. P. Nadal, G. Toulouse, J. P. Changeux, and S. Dehaene, “Network of formal neurons and memory
palimpsests,” Europhysics Letters, vol. 1, pp. 535–
542, 1986.
[4] M. Mézard, J. P. Nadal, and G. Toulouse, “Solvablemodels of working memories,” Journal of Physique,vol. 47, pp. 1457–1462, 1986.
[5] S. Amari and K. Maginu, “Statistical neurodynamics of associative memory,” Neural Networks, vol. 1,
pp. 63–73, 1988.
[6] B. L. McNaughton and R. G. M. Morris, “Hippocampal synaptic enhancement and information
storage within a distributed system,” Trends in Neurosciences, vol. 10, pp. 408–415, 1987.
[7] K. Nakazawa, M. C. Quirk, R. A. Chitwood,
M. Watanabe, M. F. Yeckel, L. D. Sun, A. Kato, C. A.
Carr, D. Johnston, M. A. Wilson, and S. Tonegawa,“Requirement for hippocampal CA3 NMDA receptors in associative memory recall,” Science, vol. 297,
pp. 211–218, 2002.
[8] D. Marr, “Simple memory: a theory for archicortex,” Philosophical transactions of the Royal Societyof London B, vol. 262, pp. 23–81, 1971.
[9] D. Willshaw, “An assessment of marr’s theory of the
hippocampus as a temporary memory store,” Philosophical transactions of the Royal Society of London
B, vol. 329, pp. 205–215, 1990.
[10] S. Becker, “A computational principle for hippocampal learning and neurogenesis,” Hippocampus,
vol. 15, pp. 722–738, 2005.
[11] M. O. M. Guillermo A. Cecchi, “Computationalmodels of adult neurogenesis,” Physica A, vol. 356,
pp. 43–47, 2005.
[12] L. Wiskott, M. Rasch, and G. Kempermann, “A functional hypothesis for adult hippocampal neurogenesis: Avoidance of catastrophic interference in thedentate gyrus,” Hippocampus, vol. 16, pp. 329–343,
2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Force Control of 6-DOF Pneumatic Joystick
Takashi MORIKI*, Yoshito TANAKA**, Yutaka TANAKA***, Isao YOKOMICHI**,Yasunobu HITAKA**, Junko ISHII**, Chikamune Wada**

Department of Graduate School of Life Science and Systems Engineering,
Kyushu Institute of Technology, Japan**
Department of Control and Information Systems Engineering,
Kitakyushu National College of Technology, Japan***
Department of Mechanical Engineering, Hosei University, Japan
E-mail：stanaka@kct.ac.jp

Abstract2

Force control of 6-DOF joystick

In this study we develop a new type joystick with force control. A developed joystick consists of asix-degree-of-freedom (6DOF) Stewart platform type(1)of joystick using pneumatic actuated parallel link mechanism. The thrust force of the cylinder is controlled by the
PWM of the solenoid valve that uses PIC. When thethrust force of the cylinder is controlled, the control
characteristic of the cylinder is improved by the effect of
the accumulator so that it might make amends for the
influence on the compressibility of air exerted on thecharacteristic of the thrust force of the cylinder. It is confirmed that the thrust force of the cylinder can be applied
to the pneumatic joystick, and the z-axial force control ispossible in the experiment.

2.1

Parallel mechanism

1

Introduction

The necessity of the rehabilitation work support thetechnology expected for the social life independence ofsenior citizen nowadays. The function to support themulti degree of freedom movement of the rehabilitationequipment is necessary corresponding to the patient withvarious symptoms (2),(3). Then, we paid attention to a parallel mechanism with high rigidity that has a flexible
motor function and performs high-speed and highly accurate six degree of freedom movement. We developed
the force control system of 6-DOF Pneumatic Joystick
and implemented it to the prototype model(3),(4). Forcecontrol system makes up a compact composition thatconsists of the small air pressure cylinder and the solenoid valve, and achieves the control of the thrust force ofthe cylinder by the PWM control of the solenoid valve.
This system was applied to the test pneumatic joystick,and the result of controlling force will be reported.

©ISAROB 2007

214

Parallel and serial mechanisms are illustrated in
Fig.1(5). As shown in Fig.1 (a), parallel mechanism typically consists of a moving platform that is connected to a
fixed base by several limbs(2). Typically, the number oflimbs is equal to the number of degrees of freedom suchthat every limb is controlled by one actuator and all theactuators can be mounted at or near the fixed base. Twoplates (upper and base plates) are connected through six
articulated links in which a linear actuator can change thelink length. The links are jointed by universal joints. The
length of links controls the position and orientation of theupper plate with respect to the base plate. The individual
errors in the active links are not cumulative. The external
forces applied to the upper plate are distributed among allparallel links and actuators. The parallel mechanism has
usually the basic advantages of high positioning accuracy,high rigidity, high operational speed and very high load
capacity compared to serial mechanisms as shown inUpper plate

Link

Link

Base plate(a) Parallel mechanism
Fig.1(b) Serial mechanism

Parallel and serial mechanisms

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 1 Working range

Fig.1 (b). The disadvantage of the parallel mechanism isto have a small volume of its working range relative tothe dimensions of the links(4).

2.2Translation(mm)

System configuration

Fig.2 shows the force control system configuration of
the pneumatic joystick that consists of a parallel mechanism. An arbitrary signal is given from PC, and theon-off values of solenoid valve are controlled by the
PWM control with PIC. Then, the thrust force of six cylinders that compose the joystick is changed. The force
control of the joystick is achieved by controlling thethrust force of each cylinder.

Force
display
Solenoid
valve

CylinderPC
PWM analysis

DA

PIC

Supplypressure

PWM control

Fig.2

2.3

−30.7〜41.9

ｙ−32.6〜32.6

ｚ−16.6〜16.3

φ−9.1〜9.1

θ−8.9〜9.2

ψ−41.8〜41.8

PWM control that uses PIC

PIC is a kind of the microcomputer that the microchip
technology company developed. It controls connectedpart of the computer and peripherals, and 35 instruction
words are used.
PIC16F877 is used to provide the PWM output function and the AD output function.
The PWM control is a method to change the turning ontime to the pulse wave at a constant cycle, and themodulation rate τ is the ratio of the on time in a constant cycle.

6-DOF pneumatic joystick

Subject

Rotation(degree)ｘ

System configuration

Fig.3 (a) shows design model by three-dimensional
CAD. Fig.3 (b) shows the pneumatic joystick test structure. The joystick consists of platform, pneumatic cylinder, solenoid valve, and fixed base.

2.4

Control of thrust force of cylinder

The PWM control of the solenoid valve is used to control the thrust force of the pneumatic cylinder. Fig.4 (a)and (b) show respectively turn on and off of three-way
solenoid valve (4).
QS ，
pS

p
Q1 ，

p

ｚ ψ
θ
ｙ

Q2

Platformφｘ(a) ON(b) OFF

Fig.4

Three-way solenoid valve

CylinderSolenoid
valve
Fixed base(a) CAD modeling
Fig.3(b) Test structure

Pneumatic joystick system

Working range is calculated from this joystick by thereverse-movement study analysis in consideration of thegeometrical restraint condition by the amount of expansion and contraction of the cylinder. Table 1 shows theworking range of six degree of freedom.

©ISAROB 2007

pS
QS ，

215

Moreover, the driving circuit of pneumatic cylinder isshown in Fig.5. The τ decided with PC is converted in
DA, the solenoid valve is controlled by PWM control
based on τ by PIC, and the thrust force of the cylinder iscontrolled(4).
The relation between the average control pressure at
this time and the modulation rate τ of PWM becomes
expression (1). Here, Ps is supply pressure.
Thrust force F of the cylinder is given from expression(1) by expression (2).
p = pSτ2τ 2 + (1 − τ ) 2･･･(1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

F = ηAp S

τ2･･･(2)

τ 2 + (1 − τ ) 2

Here, A is an area of the pushing side of the cylinder,η is a load factor.

DA

PC

PIC
(PWM control)
SSR

Force sensor

0.15MPa and the amount of the cylinder expansion andcontraction are adjusted to be 2mm.
The experiment value has been improved by greatly
taking the capacity of the accumulator.
From the result, when the amount of the cylinder expansion and contraction is small, that is, when the volume between the solenoid valve and the cylinder is small,the compressibility of air influences the pressure characteristic. And it is understood that the force characteristic
is depend on the capacity of the accumulator.

F
Cylinder expansion and contraction

Fig.4 Driving circuit of pneumatic cylinderpSAp
The relation between the average control pressure at
CylinderSolenoid
valve

Filter

Supplypressure

Force (N)Regulator◇
2 (㎜)□ 15 (㎜)△ 27 (㎜)― ModelFig.5 Driving circuit of pneumatic cylinder
Duty τ

3

Experimental result

3.1

Characteristics of pneumatic cylinder(a) Without accumulator

Solenoid valve

Fixed wall

Force sensor

Fig.6©ISAROB 2007

CylinderExperiments with a single cylinder

216

Accumulator volume

Force (N)We experimented with a single cylinder. The load cell
is set up between a fixed wall and the cylinder to measure the thrust force of the cylinder as shown in Fig.6. The
supply pressure is adjusted to 0.15MPa. In three conditions ( 2, 15, and 27mm ) with different amount of thecylinder expansion and contraction (min 0, max 30mm),
the thrust force of the cylinder for the change in themodulation rateτis measured. The measurements andpredictions by expression (2) are shown in Fig. 7 (a).
When the amount of the cylinder expansion and contraction is small, difference of the characteristic of the thrustforce by prediction and experiments exists.
Then, the different capacity of the accumulator of three
conditions (314, 942, and 1570mm3) is set between thesolenoid valve and the cylinder, and the results of thethrust force of the cylinder in each case are shown in
Fig.7 (b). Here, the supply pressure is adjusted and◇
0 (㎜ 3)□314 (㎜ 3)△942 (㎜ 3)○ 1570 (㎜ 3)
―

Model

Duty τ
(b) With accumulator
Fig.7 Cylinder force

3.2

Characteristics of pneumatic joystick

The thrust force of the pneumatic cylinder is controlled,
and it supplies the force control of the pneumatic joystickshown in Fig.1. The force sensor (0〜100N) consists ofthe strain gauge, and the force in z-direction is measured.
The same modulation rate is given for six cylinders, andthe force of z-axis is measured. Fig.8 shows results ofexperiment, when the posture is adjusted to be neutral
position (cylinder expansion and contraction 15mm) andthe supply pressure is 0.15Mpa.
The calculated value is summation of each thrust forceof 6 cylinders in vertical direction obtained from expression (2).
The prediction agrees well with the measurements, andthe z-axis force control is possible. A force of approxi-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

mate 53N is generated when the supply pressure is
0.15Mpa, and it has been understood that enough forces
experienced by man is generated.

Force in z direction (N)―Measurements
Model

Duty τ

Fig.8

4

Force control of joystick

Summary

The pneumatic joystick has been developed, and thesix-degree of freedom force control was achieved.
The conclusion is shown below.
(1) The thrust force of the cylinder is generated by the
PWM control of the solenoid valve. And the control
characteristic of the thrust force of the cylinder was confirmed by the change in the amount of the cylinder expansion and contraction(2) The compressibility of air can be compensated withaccumulator, and the control characteristic of the thrustforce of the cylinder has been improved.
(3) The pneumatic 6-DOF parallel mechanism type joystick was developed. The z-axis force control was evaluated by the experiment, and the force control by the
PWM control is possible.

References[1] Stewart, D., A Platform with 6 degrees of freedom,
Proc. Inst. Mech. Eng., Vol.180, Part I, No.15,
pp.371-386, 1965-66.
[2] Lung-Wen Tsai, Robot Analysis, John Wiley &Sons,Inc.1999, pp116[3] Takaiwa, M., Noritsugu, T., Development of Human-Robot Haptic Interface Using Pneumatic Parallel
Manipulator, Proc. 4th JHPS Int. Symp. on Fluid
Power, pp.181-186, 1999.
[4］Tanaka, Y., Nakajima, T., Sawada, T., Desktop Typeof Force Display Using Pneumatic Parallel Mechanism, Proceedings of the Fourth International Symposium on Fluid Power Transmission and Control(ISFP’2003, Paper for Plenary Session), April 2003,
pp.267-271.

©ISAROB 2007217[5］http://www.ms.t.kanazawa-u.ac.jp/design/
contents/research/parallelmechnizm/hpm.html

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007 

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 *  3                 

Platform         φ
CylinderoAi   '(      '          )*   )+', %         !              !         ) )   !     )! )    !  "   ©ISAROB 2007218

   4( 5        4               

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007     #"  ;    . 
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        #"     .   "     .
>   &!   !   . ©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007 ! !    7 ' 4 0  .  '3      " 
           !   ! ! " A  .  .  <  '3

Yaw Motion

cylinder length[mm]40" #  

cyl_1
cyl_3
cyl_5

20

0

cyl_2
cyl_4
cyl_6

-20

-30

-20

-10

0

10

20

30

yaw angle [deg]
cyl_2
cyl_4
cyl_6

12

cylinder force[N]%           !         2                  : ) ')    '       %    2                !        "    " !               3             $           !
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8

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cyl_1
cyl_3
cyl_5

¼

cylinder length[mm]

cyl_2
cyl_5
cyl_6

20
cyl_3

0
cyl_1

-20

0

x[mm]

-10

0

10

20

30

yaw angle[deg]   3( B    %     &  
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cyl_4

40

-40

-20

 #

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-80

0
-30

40

80

20
cyl_4

cyl_1

cylinder force[N]cyl_5$ 

cyl_6

10
cyl_3

+', *   1          %   ?  5  '=9   % / '3
 0<'0= '>3
+4, C - - *  C
% 
1 B ! 1   1*? D        1       '93
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0
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-10

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   2( B ©ISAROB 2007

0

x[mm]

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    

221

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Braille Block Recognition for an Autonomous Wheelchair
Masayoshi Tabuse

Yuki Takehara

Dept. of Environmental Information
Kyoto Prefectural University
Kyoto, 606-8522, JapanNTT Data Sanyo System Corp.
Osaka, 570-8686, Japan

Abstract
By using a Braille block on a sidewalk, a seniorcitizen and a handicapped person may run safely getting
on an autonomous wheelchair. A Braille block is a sign to
guide a visually handicapped person and its standard
color is yellow, but the colors other than a standard color
are often used. In addition, misrecognition of Brailleblock by a color is often happen under various light
conditions. Therefore, we examine the recognition
method of Braille blocks using two dimension discretecosine transform (DCT). We find that the average
recognition rates of guide blocks and of warning blocks
are 77% and 68%, respectively.
Keywords: Braille Block Recognition, DCT, Autonomous
Wheelchair

1

Introduction

According to a report on disability children andpersons [1], there are about 300,000 visually handicapped
persons in Japan. Visually handicapped persons of thefirst class, who cannot walk using their sight, are about
100,000. 80 percent or more of them lose their sight
midway through their life. About 30,000 persons canwalk alone with a white cane and the other persons
cannot walk without a help or doesn't walk at all.
Aging society is proceeding and persons who lose
their sight increase, so walking support systems are more
important in the future. One of walking support systems
is an autonomous mobile robot [2,3]. It must have afunction that it can run in various environments.
Therefore, there are various researches of an autonomous
mobile robot, which runs out of doors recognizing typicalobjects in a complex environmental condition.
In this research, we propose a recognition method ofa Braille block set up on a sidewalk for a visuallyhandicapped person by discrete cosine transform.

2

Fig.1 A warning block

3

A Braille block is a sign to guide visuallyhandicapped persons and set up on a sidewalk. A Brailleblock in our work is shown in Fig. 1, 2. There are twokinds of Braille blocks. One is a warning block where
small round convex parts line up like the lattice andanother is a guide block where rectangular convex partsline up in parallel. A standard color of a Braille block isyellow so that a weak-sighted person can recognize iteasily by the contrast with surroundings. However, blocksof another color are often used in consideration of thecorrespondence with a surrounding spectacle. A Brailleblock is used in the world and its size and shape arevarious types. So in this research we consider a Braille222

Fig.2 A guide block

Detection and Recognition of a Braille
Block

Since a warning block consists of small roundconvex parts lining up like the lattice and a guide block
consists of rectangular convex parts lining up in parallel,
we consider a Braille block a constant periodic pattern.
Therefore, we propose a method of using two
dimensional discrete cosine transform that resolves lightand shade information in the image to the frequency
element to detect and recognize a Braille block.
3.1 Two Dimensional Discrete Cosine Transformation
Discrete cosine transform (DCT) is orthogonaltransformation that uses the cosine function for thetransformation base, and the image data is transformed
into the frequency element. Two dimensional DCT andinverse DCT of an M×N pixels image f(x,y) are given by
DCT :M −1 N −1⎛ (2 x + 1)uπ ⎞ ⎛ (2 y + 1)vπ ⎞F (u, v) = α (u )α (v) ∑ ∑ f ( x, y ) cos⎜⎟
⎟ cos⎜
2N
⎠
⎠ ⎝
⎝ 2M
x =0 y =0

IDCT :
M −1 N −1⎛ (2 x + 1)uπ ⎞ ⎛ (2 y + 1)vπ ⎞f ( x, y ) = ∑ ∑ α (u )α (v) F (u , v) cos⎜⎟
⎟ cos⎜
2N
⎠
⎠ ⎝
⎝ 2M
u =0 v =0

where
⎧
⎪

1

⎪
α (u ) = ⎨ M

Braille Block©ISAROB 2007

block designed in Japan.

⎪ 2
⎩⎪ M

⎧
⎪⎪
, α (v) = ⎨⎪u = 1,2,K, M − 1
⎪⎩

u=0

1
N
2
N

v=0
v = 1,2,K, N − 1

and f(x,y) is an image and F(u,v) is a frequency element(DCT coefficient).
In this research 640×480 pixels image is divided into
the block of 12 pieces, called the unit block, that consistsof 160×160 pixels. We perform two dimensional DCT toeach unit block and obtain a frequency element thatindicates the feature of a Braille block.
3.2 Feature Mask
As a preprocessing of Braille block recognition, afeature mask of the warning block and the guide block is
constructed by DCT coefficient. First we prepare three
images of warning blocks and guide blocks, respectively,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

as training images and perform DCT to them to determine
the frequency elements that show the feature of a Brailblock. In each image there are 12 unit blocks, so that wehave 36 unit blocks for the warning block and the guideblock, respectively.
Next we select DCT coefficients, which are highrank 10% of absolute values of DCT coefficients except
for the direct current element included in all the unit
blocks. Then we calculate the average and the standard
deviation of their DCT coefficients and determine
average - standard deviation as a threshold, which judges
whether a unit block is a Braille block.
In addition, there is a characteristic distribution oflarge absolute value of DCT coefficients of the warning
block and the guide block respectively. Figure 3 and 4 are
the distributions of large absolute value of DCT
coefficients of the high rank 3% for a Braille block.
Therefore, the remarkable frequency domains are 40×40
except the low frequency area of 10×10, which show theperiodicity of the light and shade image of the warning
block and the guide block.
Moreover the distribution of DCT coefficients of thehigh rank 10% is also different depending on the
inclination angle of a Braille block. For instance,
distributions for various inclination angle are shown in
Fig. 5 in the guide block. The feature mask of the warning
block and the guide block is constructed according to theinclination angle of each block. The feature mask used inour experiment is shown in Table 1 and 2.

(a)angle = 0°〜 90°(b)angle = 90°〜 180°
Fig.5 Changes of distributions of DCT coefficients forangles of guide blocks
3.3 Perspective Transformation
The video camera that takes a picture of Brailleblocks is set up in the electric wheelchair at constantheight and the angle. Therefore, we convert images ofBraille blocks into images taken at a vertical angle using
perspective transformation shown in Fig. 6. When theheight of the video camera from the ground is represented
by H, and the angle with a horizontal plane is represented
by θ, the perspective transformation equations are givenby⎧Hx cos 2 θ⎪⎪ x' = H − y sin θ cosθ⎨
Hy cos 3 θ⎪ y' =⎪⎩
H − y sin θ cosθ(a) angle=0°(b) angle=30°

Fig.3 Distributions of DCT coefficients for warning
blocksFig.6 Perspective transformation(a) angle=0°(b) angle=30°

4Fig.4 Distributions of DCT coefficients for guide blocks©ISAROB 2007

223

Detection of a Braille Block

4.1 Detection of a Braille Block in Each Unit Block
The image taken with the video camera istransformed by perspective transformation, DCT

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

coefficients corresponding to the feature mask are takenout at each unit block, and they are compared with thethreshold. If the ratio of absolute value of DCT
coefficients larger than the threshold is 50% or more, weclassify the unit block as a Braille block, and if the ratio isless than 50%, we classify the unit block as another image.
In Fig. 7, we shows a Braille block classification chart, inwhich the unit block classified as a Braille block is
described by [O] and the unit block is described by [X] if
it is not a Braille block.

Velocity of electric wheelchair : 1.0km/h
Experimental location: Kyoto Prefectural University

Fig.9 Electric wheelchair

Fig.7 Perspective transformation image and Braille blockclassification chart
4.2 Braille Block Area
Next we determine a Braille block area in theclassification chart. If there are unit block classified as a
Braille block ([O]) in surroundings of a unit blockclassified as other ([X]), the unit block is classified as a
Braille block ([X] --> [O]) as shown in Fig. 8.

Fig.8 Extraction of Braille block area

5

Recognition of a Braille Block

If the unit block is denoted by [X] both in thewarning block classification chart and in the guide blockclassification chart, its unit block is recognized as a partwhich is not a Braille block. If the unit block is denotedby [O] only in the guide block classification chart, its unit
block is recognized as a guide block. If the unit block isdenoted by [O] both in the warning block classification
chart and in the guide block classification chart, its unit
block is recognized as a warning or guide block according
to classification of neighborhood unit blocks.

6

6.2 Experiment
We run the electric wheelchair along Braille blocks
in Kyoto Prefectural University. Images taken with thevideo camera are converted into vertical angle images
using perspective transformation. Then we examine therecognition rate of Braille blocks.
6.3 Results
We investigate recognition of 10 warning block
images and 20 guide block images captured with thevideo camera. For 10 warning block images, theexperiment results are shown in Fig. 10, where [W], [G]
and [ ] denote a warning block, a guide block and other,respectively. For 20 guide block images, the experimentresults are shown in Fig. 11.
The recognition rates of 10 warning block images
and 20 guide block images shown in Table 1 and 2,
respectively. We find that the average recognition rate of
the warning block and the guide block are 68% and 77%,
respectively, and the average recognition rate in the partthat was not the Braille block is 64%.

(a) First image

Experiment and Results

6.1 Experimental Conditions
Experimental conditions are as follows.
Video camera : SONY DCR-TRV20
Height of camera : 58cm
Downward angle of camera : 40°
Electric wheelchair : SUZUKI MC2000 (Fig. 9)©ISAROB 2007(b) 5th image

224

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

7

Conclusion

In this paper, we propose a recognition method of a
Braille block using specific frequency elements of thewarning block and the guide block obtained by discretecosine transform. In the experimental results recognition
rate of the warning block and the guide block were 68%
and 77% on the average, respectively. These results are
not sufficient because we use only one condition thatjudge whether each unit block is a Braille block.
In the future, we consider a method of recognizing a
Braille block by processing time series images to raise therecognition rate. Moreover, we consider a method thatcan distinguish a Braille block from other objects, whichhave a similar periodic pattern.

(c)10th image
Fig.10 Recognition of warning blocks(a)First image

Table 1 Recognition rates of warning blocks# of unit#of unit blockrecognition
blocks in a recognized arate
warning block warning block
1
10
6
0.60
2
12
9
0.75
3
12
5
0.42
4
12
11
0.92
5
12
8
0.67
6
12
8
0.67
7
11
5
0.45
8
10
9
0.90
9
10
6
0.60
10
10
8
0.80
ave
0.68

image

(b)5th image# of unit
blocks in
other
2
0
0
0
0
0
1
2
2
2

# of unit blockrecognition
recognized
rate
other
2
1.00
0
－
0
－
0
－
0
－
0
－
0
0.00
1
0.50
2
1.00
2
1.00
0.78

Table 2 Recognition rates of warning blocks# of unit # of unit blockrecognition
image blocks in arecognized arate
guide block
guide block
1
10
8
0.80
2
10
6
0.60
3
10
6
0.60
4
11
9
0.82
5
10
7
0.70
6
9
7
0.78
7
10
8
0.80
8
10
10
1.00
9
10
10
1.00
10
9
7
0.78
11
10
8
0.80
12
10
7
0.70
13
9
6
0.67
14
9
4
0.44
15
9
6
0.67
16
9
6
0.67
17
10
8
0.80
18
10
9
0.90
19
9
9
1.00
20
9
7
0.78
ave
0.77

(c)10th image(d)15th image# of unit blocks
recognition
recognized
rate
other
1
0.50
0
0.00
2
1.00
1
1.00
2
1.00
3
1.00
2
1.00
2
1.00
2
1.00
0
0.00
2
1.00
0
0.00
3
1.00
2
0.67
1
0.33
1
0.33
1
0.50
1
0.50
3
1.00
0
0.00
0.62

References[1] “Report on disability children and persons”,
The Ministry of Health, Labour and Welfare statistics
database system, 2001.
[2] T.Yoshida, A.Ohya, S.Yuta, “Braille Block Detection
for Autonomous Mobile Robot Navigation”,
IROS2000, pp.633-638, 2000.
[3] Y.Shi, S.Kotani, H.Mori, “A Route Understanding
Support System of the Robotic Travel Aid”, IEICE
Transactions, J86-D-I, pp.269-279, 2003.

(e)20th image
Fig.11 Recognition of guide blocks©ISAROB 2007

# of unit
blocks in
other
2
2
2
1
2
3
2
2
2
3
2
2
3
3
3
3
2
2
3
3

225

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Throwing Motion of Manipulator with Passive Revolute Joint
Asaji SATO
Dept. of Mechanical Engineering,
Miyakonojo National college of Technology,
Miyakonojo, 885-8567, Japan

Osamu SATO, Nobuya TAKAHASHI, Michio KONO
Dep. of Computer Science and Systems Engineering,
Faculty of Engineering, University of Miyazaki,
Miyazaki, 889-2192, Japan

Abstract

2

In this paper, equations of motion of a manipulator,
whose mechanism has a passive revolute joint, are derived
in consideration of characteristics of driving source.
Considering the final condition about displacement andvelocity, trajectories of velocity for saving energy arecalculated by iterative dynamic programming. And, thedynamic characteristics of manipulator controlled based on
the trajectory for saving energy are analyzed theoretically .

Keywords: Manipulator, Trajectory, Dynamic Programming,
DC Motor, Minimum Energy, Throwing Motion

1 Introduction
For the purpose of enlarging the work space, it isnecessary for studying the optimal control of the manipulator
in throwing motion. In a previous report [1], a casting
manipulator is introduced, and the merit of this type is its
large work space compared with its simple mechanism. But,
the consideration of energy consumption of driving source is
not enough. Also, the throwing motion of 2-DOF robot
was studied to reduce the target error[2]. But, theconsideration about trajectory of saving energy is notenough.
In previous report by the authors[3] , trajectories forsaving energy of manipulator, whose mechanism has two
active joints, were easily calculated by iterative dynamic
programming.
In rescue and agricultural field, it is considered that
hand of tray type with passive joint is available for throwing
the object which is various shape. In this paper, equations of
motion of a manipulator, whose mechanism has a passive
revolute joint, are derived in consideration of characteristicsof the DC servomotors, and a performance criterion forsaving energy is defined in consideration of energy
consumption of driving source. When the manipulator isoperated in a vertical plane, the system is highly non-linear
due to gravity and an analytical solution can not be found.
Then, a numerical approach is necessary. Considering thefinal condition about displacement and velocity, trajectories ofvelocity for saving energy are calculated by iterative dynamic
programming. Initial searching region, which is surroundedby two sine-wave translated in parallel, is shifted tominimize the energy consumption of the motor. The
dynamic characteristics of manipulator controlled based on
above mentioned trajectory are analyzed theoretically.

Modeling of manipulator with passive joint

The dynamic equations of the manipulator with twodegrees of freedom as shown in Figure 1 which is able tomove in a vertical plane are as follows.

 a11 a12  θ&&1   a13 a  =   21 a22  θ&&2   a23 
whereτ 1 , τ 2 ; torque which acts on link 1 and 2 ,θ1 , θ 2 ; angular displacement of link ,a11 = I G1 + m1lg12 + m2l12 + m2l1lg 2 cos θ 2
a12 = m2l1lg 2 cos θ 2
a13 = τ 1 − τ 2 + m2l1lg 2 (θ&1 + θ&2 ) 2 sin θ 2− ( m1lg1 + m2l1 ) g cos θ1
a21 = I G 2 + m2lg 2 2 + m2lg 2l1 cos θ 2
a22 = I G 2 + m2lg 2 2
a23 = τ 2 − m2lg 2l1θ&12 sin θ 2 − m2 glg 2 cos(θ1 + θ 2 )Joint 2 is passive revolute joint, and τ 2 = 0 .

(a)(b)
Fig. 1 Mechanism of manipulator
Table 1
Parameter

226

Parameters of the manipulator
Value

ParameterValuel1 , l 2(m)

0.1

m1 , m2 (kg)0.1

l g1 , lg 2

(m)

0.05

A1

( Nm )

0.04

2

8.33×10−5

T1( sec )

0.52

I G1 , I G 2 (kgm )©ISAROB 2007

(1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
b1 = kv + ( Ra / kt ) Dm , b 2 = ( Ra / kt ) Im , b3 = Ra / kt ,

τ1 (Nm)

0.05
0.1

0.2

0.3

0.4

0.5
0.6
t (sec)

-0.05
0
～
～
-1

0.1

0.2

0.3

0.4

0.5
0.6
t (sec)θ1 (rad)0

ia : electric current of the armature ,Ra : resistance of armature ,I m : moment of inertia of armature,
Dm : coefficient of viscous damping.
And, the consumed energy is E = ∫ (e ⋅ i a )dt .

-2

v=

1

θ2 (rad)(7)

The velocity of object for throwing is expressed as

-1.5

0

i a = (e − k vθ&) / R a . (6)Then, the electric current is

0.1

0.2

0.3

cos φ

g
2 x f − l1 cos θ1 f tan φ − 2 ( y f − l1 sin θ1 f )

wheret (sec)
0.5
0.6

0.4

x f − l1 cos θ1 f

( x y ) ; position of arrival(θ ,θ ) ; angle of link 1 and 2 at release time .
f,

1f

-1

f

2f

Figure 3 shows a flow chart for iterative dynamic
programming method. In frame (A), the trajectory forsaving energy is searched by dynamic programming [4]. In
frame (B), the searching region is shifted to minimize theconsumed energy, and width of the region is changed smaller.
Figure 4 shows the trajectory for searching, and the initial
trajectory for searching is expressed as
t = 0.02 , 0.18 , t = 0.3 , 0.54 , t = 0.6 (s)Θ1 ( t ) =Fig. 2 Response of manipulator with a passive joint
We shall take the parameters of the system as shown in
Table 1. The simulations of the system are done as follows.
The period of pendulum movement of link 2 isapproximated as
T1 = 2π

I G 2 + m2lg 2 2
m2 glg 2.

 2π t , T1 

τ1 = A1 sin τ2 = 0 .

This proposed trajectory is used as a center line of initial
searching region of the iterative dynamic programming, andthe region is shifted along the axis of coordinate tominimize the consumed energy of the motor.

(2)Then, output torques of the motor 1 and 2 are π 
t f  θ1 f − θ1i v  
v 2−  1 − cos  t   .
t + θ1i +  t 
2 l1 t f
2  tf2l1   f 

Start
Given input dataθ i j ,θ& i j , θ f j ,θ& f j , T , N , ∆θ(3)

A response of the manipulator from initial position (θ1i =−π / 2 , θ 2i = 0) is shown in Figure 2. Amplitude ofpendulum movement caused by inertia force increases as the
response time increases.

Calculate initial searching region
n ←1

(A)

3 Throwing motion of manipulator
n>30

The applied voltage of the servomotor ise 1 = b1θ&1 + b 2θ&&1 + b 3τ 1 + b 3τ f 1 sign(θ&1 )(4)n ← n+1

Search the trajectories
for saving energy by D.P.

Yes

No

(B)
Shif the searching region
to minimize the energyand( ∆θ ) n +1 ← C ⋅ ( ∆θ ) n
C = 0.5 ～ 0.7
n>30 → ∆ θ <0.02 rad

End

where
τ 1 = ( I G1 + m 1l g1 2 + m L l 1 2 )θ&&1 + ( m 1l g1 + m L l 1 ) g cos θ 1©ISAROB 2007

(5)

227

Fig. 3 Flow chart for simulation

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 2 Parameters of the manipulator for throwing

Parameter

Value

Parameter

Value

l1

(m)

0.080

kt1 , kt 2 (Nm/A)

0.046

l2

(m)

0.035

kv1 , kv 2 (Vs/rad)

0.046

lg1

(m)

0.044

Ra1 , Ra 2 ( Ω )

3.5

lg2

(m)

0.031

Dm2, Dm2 (Nms/rad)

7.9×10−5

I G1(kgm2)

1.07×10−5

τ f 1 ,τ f 1 (Nm)0.0013

IG 2

2

−5

0.36×10

m1

(kg)

0.020

I m1 , I m 2 (kgm ) 8.5×10−6

m2

(kg)

0.004

mL

(kg)

0.005

(kgm )
2v = (l1 ⋅ θ&1) 2 + {lO ⋅ (θ&1 + θ&2 )}2 + 2l1 ⋅ lO ⋅ θ&1(θ&1 + θ&2 ) cos(θ 2 )1Angular displacement θ (rad)We shall take the parameters of the system as shown in
Table 2.
A response of the manipulator from initial position( θ 1 i = −π / 2, θ 2 i = 0 ) to the position of release( θ 1 f = π / 4, θ 2 f = −π / 10 ) is shown in Fig.5, under thecondition that distance from origin to the point of arrival is
x=−0.6[m], release angle is φ = 3π / 4 , and the working time
is T=0.6. In Figure 5, it is shown that the locus of every
sampling time (0.04 [s]) is like a pendulum movement, andcircles are locus of object under the condition that link 1 and
2 are stopped at the time of release (0.6 [s]). They show the
motion of object caused by kinetic energy at the time ofrelease.
Figure 6 shows the response of angular displacement andangular velocity in throwing motion of Fig. 5. The angularvelocity of links are θ&1 = 28 [rad/s] , θ&2 = −18 [rad/s]
at the time of release (0.6 [s]). And, velocity of object,≅ 2.5 [m/s]Initial
Midle
Final

0

( l O =0.027 [m] ; length between joint and center of object )is enough for throwing motion. The kinetic energy of object
is 0.016[J], and on the other hand consumed energy of the
motor is 0.79[J]. From these analysis, it is considered thatthe manipulator with a passive joint is available for throwing
the object.

-1
-2
-3
0

0.1

0.2

,

0.3

0.4

0.5
0.6
t (sec)(s)(b)
Fig. 5 Throwing motion of manipulator©ISAROB 2007

0.1

0.3

0.4

t (sec)
0.5
0.6

0.2
・
θ

0.3

0.4

0.5

0.2

0.3

0.2

θ1

40
20

0
-20

θ・2
0.1

0.6

t (sec)

1

0.8
0.6
0.4
0.2

E (J)

(a)

0
-2

θ2

-4

θ・ (rad/s)

0.1 (m)2

θ (rad)φ

Consumed energy Angular velocity Angular displacement

Fig. 4 Trajectory for searching

0

0.1

0.4

0.5
0.6
t (sec)Fig. 6 Response of the manipulator with a passive joint

228

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4 Experimental results

References

In this section, the results of fundamental experimentare shown to examine the effectiveness of modeling for thesimulations.
Figure 7 shows an experimental apparatus, which isused in previous report by the authors [3]. A tray for holding
the object is connected to link 2 by a passive revolute joint. It
is able to use Equation (1) for calculating the motion of link
2 and the tray, under the condition that motion of link 1 ( θ 1 )is the same as link 2 ( θ 2 ). And, the parameters of system areshown in Table 3. The motors 1 and 2 (rated 24 V, 60 W) areon the frame. Figure 8 shows a mechanism of manipulator.
Figure 9 shows the locus of every sampling time (0.05[s]) , under the condition that initial position is ( θ 1 i = −π / 4 ,θ 2 i = −π / 2 ), the finale position is ( θ 1 f = π / 4 ,θ 2 f = 0 ), and the working time is T=0.4 [s].
Figure 10 shows the experimental response under thecondition that sampling time of the control is 0.002 [s], thefeedback gain for angular displacement is 50 [V/rad], andthe feedback gain for angular velocity is 0.5 [Vs/rad]. In
Figure 10, the response of angular displacement θ 2 andθ 3 are measured by rotary encoder, and angular velocity θ& 3 iscalculated by angular displacement. Theoretical results(broken line) agree with experimental results (solid line).
From these results, it is confirmed that modeling for thesimulations is effective.

1.

5 Conclusions

H.Arisumi, K.Yokoi, T.Kotoku et al , “Casting
Manipulation ( Experiments of Swing and Gripper
Throwing Control ) ,” International Journal of JSME,
Vol.45, No. 1C, pp.267-274, 2002.
2. N.Kato, and T.Nakamura , “ Throwing Control for 2-DOF
Robot (in Japanese) ,” JSME, Vol. 63,
No. 614C,
pp.3571-3576, 1997.
3. A.Sato, O.Sato, N.Takahashi et al ，“ Trajectory for Saving
Energy of Direct-Drive Manipulator in Throwing Motion,”
Proc. of 11th Conference on Artificial Life and Robotics,
pp.783-786, 2006.
4. O.Sato, H.Shimojima, and H.Yoinara , “ Minimum-energy
Control of a Manipulator with Two-degree of Freedom,”
Bulletin of JSME, Vol.29, No.248, pp.573-579, 1985.

Table 3 Parameters of experimental apparatus

Parameter

Value
0.080

Parameter
IG1 (kgm2)

l1
l2

(m)

1.73×10−5

(m)

0.115

IG2 (kgm2)

8.42×10−5

lg1

(m)

0.044

IG3 (kgm2)

9.43×10−6

lg2

(m)

0.078

m1

(kg)

0.0202

lg3

(m)

0.030

m2

(kg)

0.0468m3(kg)

0.0218−6

D3 (Nms/rad) 7.35×10

Value

θ (rad)10Experimental results
Theoretical results
0.1

0.2

θ1
0.3

θ2

-1

0.4

t (sec)Fig. 7 Experimental apparatusm3-2

θ3 (rad)

0

0.1

0.2

0.3

θ2

t (sec)0.4

l1

l g1

20
0.1

0.2

0.3

θ1

θ3

I G3

I G2

Final
G1

θ2

x

Fig. 8 Mechanism ofmanipulatort (sec)0.4-20

InitialFig. 9 Locus of manipulator

Fig. 10 Experimental results©ISAROB 2007

m1 I

l g3
m2

l g2

-1

0

l2

y

1

θ・3 (rad)Angular velocity

Angular displacement

Angular displacement

The results obtained in this paper are summarized asfollows.
(1) It is considered that manipulator with passive revolute joint
is available for throwing motion.
(2) From experimental results, it is considered thatmodeling for simulation is effective.

229

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Study on Real Time Intelligent Control of a Three-Fingers Hand System
Xuan-Thu Le, Se-Bong Oh, Woo-song Lee,Won-Il Kim, and Sung-Hyun Han
Division of Mechanical and Automation Eng., Kyungnam Univ., Masan, 631-701, Korea
E-mail: shhan@kyungnam.ac.kr Tel: +82-55-249-2624 Fax: +82-55-249-2617

Abstract:
We present an experimental performance for thevalidation of a robotic hand gripper for space
applications. In particular this researches focus on thecompatibility of the gripper with the Fara robot arm,
developed by Esp. Co., Ltd. This hand gripper is thewide working space compared with its physicaldimensions and the capability to deal with free-flying
objects in no-gravity conditions. This capability isachieved by using force/torque sensor and by properly
controlling and coordinating the gripper and thecarrying arm. After a brief illustration on the mainfeatures of the gripper, the experimental activity ispresented and the results achieved are discussed.

1. INTRODUCTION
The development of robotic hand gripper to
execute automatic operations in space is foreseen togrow and cover a relevant part of the activities. With
this respect, as already demonstrated in the industrial
environment, a bottleneck is constituted by the end
effector, that often is a very simple device with poor
sensoriality and limited operational capabilities. Besides
the numerous prototypes of articulated robotic hands,
developed in more than 30 years of research, mainly inacademic environment, see e.g. [1],[2] among manyothers, limited effort has been devoted to seek andevaluate alternative solutions, maybe simpler from themechanical point of view than a multi-fingered hand,
but with sufficient dexterity to perform in any case nontrivial operations on a wide range of objects.
Therefore, referring specifically to the case of
space applications, a scenario could be considered inwhich operations have to be performed in a no-gravity
environment, where objects cannot be constrained andare therefore free to float in space. At the moment, thisgripper is installed on a six degree of freedom arm, seeFig. 1. In order to emulate the capabilities of the FARA
arm and to develop suitable coordinating strategies
taking into account the kinematics capabilities of thewhole arm/gripper system, [4]-[8].

2. HAND GRIPPER SYSTEM DESIGN AND
ANALYSIS
The gripper has been designed considering its
installation on the FARA arm proposed. This system©ISAROB 2007

230

Fig. 1: The hand gripper installed on FARA
manipulator
aims to substitute the astronauts in periodical operations
with a semi autonomous robotic device. The
end-effector for the robot manipulator needs therefore
compactness, simplicity and reduced weight as well ascapability of operation even on irregular floating
objects.
Besides the three D.O.F gripper, main objective of
this research, the overall robotic system consists of thefollowing main components: a 6 D.O.F arm with an“open control”, a standard force/torque sensor at thewrist and a vision system. These components areschematically shown in Fig. 2.
The gripper has three one D.O.F fingers whose

Fig. 2: The overall system.

distal phalange can move on a linear trajectory. These
fingers are disposed radically, in a symmetricconfiguration as shown in Fig. 3. This kinematics
configuration has several interesting features, as
described in details in [4]-[6], including the capabilityof firmly grasping objects with irregular shapes andThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

with a rather wide range of dimensions.
In this manner, it is possible to control the motionof each finger, its distance from the object and theforces applied on it during the grasp.

proximity sensor. This information can be used both tostart the grasp of the object (if all the fingers are at thesame distance from it) or to maintain constant the
distance between the finger and the object.
The force control is based on the same PI structureof the position and proximity controllers, and at the
moment can be classified as a simple compliance
control obtained by specifying the complianceparameter K, see Fig. 4.

Fig. 3: The gripper in different configurations.
Fig. 4: Position/force control scheme.

3. INTERLLIGENT CONTROL OFROBOTIC HAND
The real time control of the gripper is based, at the
moment, on standard HW/SW components. The control
is performed with a DSP (TMS320C31) board
connected to the motor drives and to an input board forthe sensors. This board has been purposely designed
because of the relatively high number of signals to beacquired in real-time. Currently, the DSP is hosted on aPC. From the software point of view, besides a real-timekernel on the DSP board, an interface between the DSP
and the PC has been developed, allowing to use bothreal-time software and high-level environments for userinterface.
At the moment, the servo control level has been
implemented considering a simple logic switching
between three classes of controllers: a position control(based on the position sensor), a proximity control(based on the proximity sensor) and the force control,
based on the force/torque sensor.
The set points and the controlled variables of theservo loops are considered according to two mainmodalities: position control or proximity control. In thefirst case, the absolute position of the fingertip iscontrolled by planning the desired motion with afourth-order polynomial function and assigning thedesired motion time. The controlled variable is the
position x (the radial distance from the center ofsymmetry of the gripper) of the fingertip obtained bymeans of the forward kinematics1 from the joint
position measured by the Hall effect sensor.
In the second case, the controlled variable is the
distance of the finger with respect to the approachedobject. This modality is activated when the finger issufficiently close to the object. The controlled variable
is now the distance from the object, as measured by the©ISAROB 2007

231

Obviously, a proper switching logic between theabove three control modalities must be adopted in the
different phases of the execution of the tasks in order toensure a smooth behavior of the gripper.
The prototype of the gripper has been installed on a
6 D.O.F anthropomorphic robot, a FARA with an
open-control architecture, a PC connected to thestandard robot controller C and equipped with aforce/torque sensor on the wrist. The open control
architecture allows in particular synchronizing the tasksof both the gripper and the arm for micro-motion duringtask execution.
The real time OS chosen for this application isLinux[10] running in our case on a Pentium IV PC. This
PC may carry out the robot position control, based on
the feedback provided by the position sensors, the wrist
force/torque sensor and by the vision system. At thesame time, the operating system allows thecommunication between the robot control task
(executed as real-time procedure in the Linux
environment) and the corresponding routines on the
DSP board for the gripper control. It is possible tocontrol the robot under Linux in two main modalities. In
the first, the servo loops for each actuator are performed,
the standard robot controller. In this modality, a newposition set point is generated by the PC every 10 msec.
In the second case, the PC performs directly the controlof each actuator, with a sampling period of 1 msec.

4. EXPERIMENT AND RESULTS.
Examples of these experimental results areshown in Fig. 6-Fig. 8. Set-up is shown in Fig. 5.
A number of laboratory experiments have been

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

performed both on single finger modules andsensorial/actuation subsystems in order to test theFig. 5: Experiments set-up

Fig. 8: Measurements of the position (a) andproximity (b) sensor.

Fig. 6: Tracking a moving objects by exploration
with the proximity sensors and computation of thenormal directions.

Fig. 9: Motion of the finger (a) during an approachand a gasp and force applied on the object (b).
Fig. 7: Grasp of a floating object

efficiency of each finger structure and of the control
system. The validation has also included verification of
the procedures for the object approach, based on the useof both the distance and the position sensor information,and the use of the force/torque sensors.
Concerning the approach and contact phases, it
must be observed that the possibility of independently
moving the fingers has noticeably increased thecapability of grasping moving objects. As a matter offact, the object may be tracked with a coordinatedmovement of both the arm and the fingers. Once themotion is tracked, the grasp may be firmly applied
without loosing contact.
Finally, an experiment involving force control isshown in Fig. 9.

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232

5. CONCLUSION
We presented a new technique to control of athree-fingered with 12 degrees of freedom robotic
gripper for real application.
It presents a very large workspace with respect toits body size, and is capable of operation both on small
and on large objects; its sensory equipment seems to be
sufficiently rich and more than adequate for the
expected tasks. Future improvements will concern therefinement of the current version of the gripper and theconclusion of the verification phase, in particular withrespect to the force control and to the possibility ofapplying simple manipulation procedures on the grasped
objects.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

REFERENCES[1] A. Rusconi, R. Mugnuolo, F. Bracciaferri, A.
Olivieri, F. Didot, “EUROPA (External Use of
Robotics for Payloads Automation)", ASTRA
2000, ESTEC, Noordwijk, NL, 5-7 dic.2000.
[2] C. Melchiorri, "Slip Detection and Control
Using Tactile and Force Sensors”, IEEE Trans.
on Mechatronics, special Issue on Advanced
Sensors for Robotics, Vol. 5, No. 3, Sept. 2000,
pp. 235-243.
[3] L. Biagiotti, C. Melchiorri, G. Vassura,“Experimental activity on grasping objects infree floating conditions”, ASTRA’'2000, ESTEC,
Noordwijk, NL, 5-7 dic. 2000.
[4] C. Melchiorri, G. Vassura, “"Design of aThree-Finger Gripper for Intra-Vehicular
Robotic Manipulation”", 1st IFAC Workshop on
Space Robotics, SPRO98, Montreal, CA, Oct.
19-22, 1998.
[5] http://www.aero.polimi.it/projects/rtai/[6] G. Vassura, C. Melchiorri, “"Three Finger
Graspingfor
Intra
Vehicular
Space
Experiments”", WAC’'2000, Maui, Hawaii, June
11-16 2000.
[7] C. Melchiorri, G. Vassura,"Implementation ofWhole Hand Manipulation Capability in the
U.B. Hand System Design”", J. of Advanced
Robotics, Special Issues on “Enveloping Grasp
and Whole-Hand Manipulation”, pp. 547-560,
Vol. 9, No. 5, 1995.

©ISAROB 2007

233

[8] T. Oomichi et al. Development of working
multifinger hand manipulator Proc. IEEE Int.
Workshop on Intelligent Robots and Systems,
873–-880,1990.
[9] M. Ishikawa et al. A CMOS vision chip with
SIMD processing element array for 1ms image
processing. Dig. Tech. Papers of IEEEInt.
Solid-State Circuits Conf., pages 206– 207,1999.
[10] R.A. Grupen et al. A survey of general-purpose
manipulation. Int. J. Robot. Res., 8(1): 38–-62,
1989.
[11] L. Biagiottil, C. Melchiorril, and, G. Vassura,
Position/Force Control of a Arm/Gripper
System for Space Manipulation. Proc. of the2003 IEEE/RSJ Intl. Conference on Intelligent
Robots and Systems, October 2003.
[12] J. Butterfass et al. DLR-hand II: Next
generation of a dexterous robot hand. Proc.
IEEE Int. Conf Robot. and Automat., pages
109– 114, 2001.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Study on Design of Multifingered Robot Hand with 15 D.O.F and Applications
Xuan-Thu Le, Se-Bong Oh, Haeng Bong Shin, Yoon Kyu Kim, and Sung-Hyun Han
Division of Mechanical and Automation Eng., Kyungnam Univ., Masan, 631-701, Korea.
E-mail: shhan@kyungnam.ac.kr Tel: +82-55-249-2624 Fax: +82-55-249-2617

Abstract
The focus of this paper is to design a flexible threefingered hand system with 15 D.O.F for dynamic
manipulation with an intelligent controller, and to build
a useful database for dynamic manipulation based on
the experimental results. The weight of the hand module
is only 0.6 kg, but flexible motion and powerfulgrasping are possible. To achieve such a dynamic
motion in a robotic hand, we have developed a flexible
fingered hand with a control system incorporating
image recognition system in which we deal with the
problems of not only accuracy and range of motion but
also the flexibility of hand.

1. INTRODUCTION
Recently several hands have been developed in which
actuators are placed directly in each finger link. Several
human-like robotic hands have been developed with thisapproach [1], [2]. These robotic hands have manydegrees of freedoms to achieve the dexterous grasp of ahuman hand, but power and speed are not enough toachieve dynamic manipulation. General Hand islightweight and powerful, but the number of degrees of
freedom is not enough to achieve precise motion.
In order to develop a dexterous and skillful artificial
hand, such as a human hand, various types ofmultifingered hands have been researched. Such hands
were designed with attention to range of motion andaccuracy, and less attention to speed of motion [3]-[5].
Examples of this dynamic change are “"pushing”",“"hitting”", “"throwing”", “"catching”", etc. A humantakes advantage of such dynamic motion for mani-pulation. This is one of the reasons that a humanmanipulation is more dexterous and flexible than arobotic hand.
There has been considerable theoretical work on amultifingered hand addressed to: grasp stability, force
analysis, and dynamic control [6]. But in most of thisresearch static or quasi-static movement was assumed,
and there has been little work on dynamic manipulation.
Various types of robotic hands have been developed[7], [8]. In most hands a wire driven mechanism is usedwith control through high-power actuators that areplaced outside of the main body of the hand [8]-[12].
But the mechanism is complicated and the total system
is big and heavy.

©ISAROB 2007234

On the other hand, in dynamic manipulation,transitions in the contact condition often occur when thefingers let a target go, or the fingers touch a target. It isdifficult to observe a target only using force and tactile
sense; vision plays an important role in this case. To
observe a moving object in realtime, a flexible visionsystem in which the sampling rate is more than 1.2 kHz
is effective. Several types of vision chips have beendeveloped [13], [14], and this technique was applied toa sensory motor fusion system, and to produce highspeed grasping.
In this paper we describe a newly developed
intelligent flexible hand system and its application to acatching task. The hand has 10 joints and 3 fingers. A
newly developed small harmonic drive gear and a highpower mini actuator are fitted in each finger link, and astrain gauge sensor is in each joint.

2. FLEXIBLE HAND SYSTEM DESIGN
The mechanism of a flexible hand gripper requires
the mass of the hand should be as low as possible. It ishighly desirable that the hand weigh less than 1kg.
Furthermore the low mass of the finger mechanism isdesirable not only to achieve flexible motion but also
for stable control.
Our philosophy about dynamic manipulation is
maximization of the power and minimization of themechanism. In particular three factors are important: (1)light weight, (2) high speed and high acceleration, (3)accuracy.
Fig. 1 shows the mechanical design of the hand, andFig. 2 shows a scene of the Gripper control. It has threefingers, and we call the fingers “"left finger’', “"index
finger”" and “"right finger”" looking from the left side.
We call the joints of each finger, inter-phalange joint,
metacarpal-phalange joint, trapezoid-metacarpal joint,corresponding to human anatomy.
To achieve the light mechanism, we reduced thenumber of joints and fingers as much as possible. We
used three fingers, which is the minimum number toachieve a stable grasp.
The index finger has 3 degrees of freedom, and theother fingers have 3 D.O.F, so that the hand has 15
D.O.F total. In general a hand needs 9 D.O.F to move atarget to any position and orientation. But in our hand 1

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

D.O.F of the index finger is omitted, and the wrist jointof the manipulator takes its place.

the amount of current is controlled by software in
realtime.

Fig. 2: Scene of the hand gripper control
The finger has strain gauges at the inter-phalange
and metacarpal-phalange joints for force control. In
addition a 6-axis force/torque sensor and a tactile sensor
will be mounted on each fingertip.
The flexible motion imposes a heavy load on thefinger mechanism. For this reason a simple mechanism
should be used for reduction gear, transmission, etc. In
most traditional hand systems a wire-driven mechanism
is used. But this is not suitable for a lightweightmechanism, because it is large and complicated.
In our hand a newly developed small harmonic drive
gear and a high-power mini actuator are fitted in eachfinger link. A harmonic drive gear has desirable
properties for control such as no backlash and a highreduction rate.
As the transmission mechanism between actuatorand joint, we adopt a bevel gear. This is because theaxis of the actuator should be orthogonal to the axis of
the joint, and a bevel gear is simple and strong enoughto achieve flexible rotation. Normally a bevel gear has alarge amount of backlash. To reduce backlash the bevel
gear was processed precisely, and derris coated on thesurface.
We added dual vision to the hand system, as shownin Fig. 3. Vision is with a massive parallel vision systemcalled column-parallel high-speed vision system [14]. It
has 128 × 128 photo detectors with an all pixel parallel(a) Front view(b) Top viewFig. 1: Kinematics design
The fingers are arranged so as to grasp both circularand prismatic objects. Both left and right thumbs play
the role of the “"thumb”" of a human hand. Each thumbhas only 3 D.O.F, which is less than the 5 D.O.F of ahuman thumb, but various types of grasping can beachieved because the joint can move in a wide range.
In order to achieve “"lightning”" high acceleration,
we have developed a new actuator that allows a large
current flow for a short time.
The design is based on the new concept thatmaximum output should be improved rather than rate
output. As a result this actuator can generate maximum
power only for a short period of time, but the power
output is high, to prevent the actuator from overheating,©ISAROB 2007

235

Fig. 3: Visual feedback control system

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

processing array based on vision chip architecture andan exclusive summation circuit for calculating moment
values. Because the visual processing is executed inparallel in the processing array, flexible visual
processing (moment detection, segmentation, etc.) is
achieved within 1ms.
Early image processing is performed in order toachieve segmentation of the image, extraction of thetarget area, and computation of the image moments.
From these data, the position of the target is computed.
Each vision sensor is mounted on an active vision,
as shown in Fig. 4. Because the joint angle sensor has
high resolution, high resolution of 3D position sensing

3. PERFORMANCE TESTThe main advantage of a multi fingered hand is thatit can grasp various objects by changing its shape.
Several classifications of grasping have been proposed.
In this proposal various grasps are classified into threelarge categories: a power grasp that passively resists
arbitrary external forces exerted on the object, a precise
grasp to manipulate the object, and an intermediate
grasping which some fingers are used for a power grasp
and the other fingers are used for a precise grasp.
We achieved these typical grasp types in ourdeveloped hand. Fig. 5 shows the results. It is not(a) Sine wave(a)Fig. 5: Grasping examinations(b) Step
Fig. 4: Result of Performanceis achieved by fusing image information with joint angle
information. The information acquired by the visionsystem is sent to the hand actuator at the rate of 1.2 kHz,
which is the same rate as the joint angle sensor and thejoint torque sensor.

©ISAROB 2007236

always necessary that all types of grasping be achieved,
but it is most useful to achieve dexterous manipulation.
Catching is one of the most important tasks for
dynamic manipulation. In this section catching is shownusing our flexible hand with a visual feedback
controller.
To simplify the problem, suppose that the target andthe hand are on a 2 dimensional plane, the target is asphere, and two fingers catch the target. From various
experimental trials, we have decided on the catchingstrategy shown in Fig. 6.
The fingertips impact the target, and the target is
moved to a stable grasp position. Finally two fingers
catch the target, and the impact from both sides’ stops
the falling motion of the target.
To simplify the problem we assume that the interphalange joint is always controlled at 90deg. In Fig.7,
22q0 ∈ Q is the target position, qi ∈ Q is the i-th tip
22
position, ei1 ∈ Q and ei2 ∈ Q are the unit vectors fixed
on the finger link, Q ∈ Q is a target radius, and θ i ∈ QThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

optimal grasp points, and it is regarded as the process tomake a “virtual wall” along the trajectories of both
fingertips. If the speed of fingertip is fast enough totrack the target, the target falls along the virtual wall.
For this reason, the locking and holding phases are alsoachieved.
Because ei1, ei2, and qi are a function of the joint
angle θi, Eqn. (1) is rewritten as(a) Holdingf i (θ i , q0 ) = 0 ,(b) Rebounding(i = 1, 2),(2)where fi represents a nonlinear function. The desired
trajectory of metacarpal-phalange joint qdi ∈Q is given
as the solution of Eqn.2. It is written as

θ d = gi (q0 ) ,i(i = 1, 2),(3)where gi is the implicit function of fi.
As a result, the control method is written asτ i = K p [g i (q0 ) − θ i ] − K d θ&i ,(c) Locking(d) Approaching

Fig. 6. Catching strategy

represents the angle of the metacarpal-phalange joint of
the i-th finger.
With two fingertips the optimal grasp points areboth sides of the sphere: q0 ± Qex, where ex is the xaxis unit vector. In order to bring the tip close to thepoint in the approaching phase, we define the following
virtual constraints:[(-1)i Q0ex + q0 - qi] eTil = 0,(i = 1,2).
(1)This means that the fingertips are always directed to the

237

(4)

where τi is the command torque of the metacar-pophalangeal joint, and Kp and Kd are appropriate
scalars. Because the finger mechanism is light and theoutput of the actuator is high power, the inertia of thelink almost can be ignored, and the PD feedback control
achieves good performance.
In the rebounding phase, it is desirable that the
movement of the fingertips caused by the rebound issmall enough. To solve this problem, the gain Kp isincreased temporary in the moment both fingertips
touch the target.
We used a rubber ball with radius of 5cm as a target,
and we dropped it from about 1.2m in height. The speedof the falling ball is about 5.9m/s just before it hits theground.
The catching task for the ball is:- Approaching (0÷40ms)- Locking (40÷50ms)- Rebounding (50÷60ms)- Holding (60ms÷).
Fig. 8 shows the changes in the target position qo,
and Fig. 9 shows the changes in the distance d1 and d2.
The success rate was more than 95% and tolerance of
position error of the target was about ±1.5cm from thecenter of the palm.

Fig. 8. Time response: target position

Fig. 7. Catching algorithm©ISAROB 2007

(i= 1, 2).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 9. Time response: distanceSeveral types of failure modes were observed. The
direction of a bounced ball depends on the coefficient offriction and restitution. It is difficult to know theaccurate values of these parameters, but the errors intheir measurement may be ignored if the speed of thefingertip is fast enough.

4. CONCLUSION
We have described a lightweight flexible hand with
15 D.O.F, and the associated visual feedback control.
We are now developing a flexible manipulation system,
which consists of a dual flexible multi-fingered
hand-arm system, and a dual active vision system. In the
future this new hand-arm system will be used for tasks.
The need for a robotic hand that works in the realworld is growing. And such a system should be able toadapt to changes in environment. We think that theconcept of a flexible hand system with realtime control
implementation will become an important issue inrobotic research.

REFERENCES[1] H. Kawasaki et al. Dexterous anthropomorphic
robot hand with distributed tactile sensor: Gifu
hand II. IEEE/ASME Trans. Mechatronics, 7(3):296–303, 2002.
[2] S.C. Jacobsen et al. Design of the Utah/MIT
dexterous hand. Proc. IEEE Int. Conf. Robotics
and Automation, pages 1520–-1528, 1986.
[3] A.A. Rizzi and D.E. Koditschek. Further
progress in robot juggling: The spatial
two-juggle. Proc. IEEE Int. Conf. Robot. andAutomat., 3:919–-924, 1993.
[4] W.T. Townsend. The BarrettHand grasper –programmably flexible part handling andassembly. Industrial Robot, 27(3): 181–188,
2000.
[5] M. R. Cutkosky. On grasp choice, grasp models,
and the design of hand for manufacturing tasks.
Proc. IEEE Int. Conf. Robot. and Automat.,5(3): 269–279, 1989.
[6] M.T. Mason and K.M. Lynch. Dynamic
manipulation. Proc. Int. Conf. Intelligent Robots
and Systems, pages 152–-159, 1993.

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[7] A. Namik and M. Ishikawa. Robotic catching
using a direct mapping from visual information
to motor command. Proc. IEEE Int. Conf. Robot.
and Automat., 2003.
[8] J.K. Salisbury and J.J. Craig. Articulated hands(force control and kinematics issues). Int. J.
Robotics Research, 1(1): 4–-20, 1982.
[9] A. Namiki et al. 1ms sensory-motor fusion
system. IEEE/ASME Trans. Mechatron., 5(3):244–-252, 2000.
[10] T. Oomichi et al. Development of working
multifinger hand manipulator Proc. IEEE Int.
Workshop on Intelligent Robots and Systems,
873–-880,1990.
[11] M. Ishikawa et al. A CMOS vision chip with
SIMD processing element array for 1ms image
processing. Dig. Tech. Papers of IEEEInt.
Solid-State Circuits Conf., pages 206– 207,1999.
[12] Akio Namiki, Yoshiro Imai, and Makoto
Kaneko, Development of a High-speed
Multifingered Robot Hand System and Its
Application to catching, Proc. of the 2003
IEEE/RSJ Intl. Conference on Intelligent Robots
and Systems, Las Vegas, Nevada, October 2003.
[13] J. Butterfass et al. DLR-hand II: Next
generation of a dexterous robot hand. Proc.
IEEE Int. Conf Robot. and Automat., pages
109– 114, 2001.
[14] K.B. Shimoga. Robot grasp synthesis
algorithms: A survey. Int. J. Robot. Res., 15(3):230–-266, 1996.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Accuracy Improvement of a 5-axis Hybrid Machine Tool
Jun-Woo Kim*, Hye-Yoon Kim, and Han-Sung Kim
School of Mechanical Engineering and Automation,
Kyungnam University,
449 Wolyoung Dong, Masan, Kyungnam, 631-701, Korea (hkim@kyungnam.ac.kr)

Abstract
In this paper, a novel 5-axis hybrid-kinematic machine
tool is introduced and the research results on accuracy
improvement of the prototype machine tool are presented.
The 5-axis hybrid machine tool is made up of a 3-DOF
parallel manipulator and a 2-DOF serial one connected inseries. The machine tool maintains high ratio of stiffness tomass due to the parallel structure and high orientation
capability due to the serial-type wrist. In order to acquire
high accuracy, the methodology of measuring the output
shafts by additional sensors instead of using encoder outputs
at the motor shafts is proposed. In the kinematic view point,
the hybrid manipulator reduces to a serial one, if the passive
joints in the U-P serial chain at the center of the parallel
manipulator are directly measured by additional sensors.
Using the method of successive screw displacements, thekinematic error model is derived. Since a ball-bar is lessexpensive than a full position measurement device andsufficiently accurate for calibration, the kinematic
calibration method of using a ball-bar is presented. The
effectiveness of the calibration method has been verified
through the simulations. Finally, the calibration experimentshows that the position accuracy of the prototype machine
tool has been improved from 153 to 86 [µm] .

1

accomplished. In order to guarantee high accuracy, themethodology of measuring the output shafts by additional
sensors instead of using encoder outputs at the motor shafts
is proposed. For the parallel manipulator, we additionallyinstall two angular encoders and one linear encoder at the UP chain and two angular encoders at the output shafts of theserial manipulator as shown in Fig. 1. Therefore, we cancalculate the position and orientation of the tool tip by theoutputs of the five sensors connected in series. Hence, we
can consider the hybrid manipulator as a serial manipulator,i.e., the U-P-R-R chain. In modeling kinematic errors, weused the method of successive screw displacements [1]instead of the Denavit-Hartenberg notations because theformer is more general and straightforward [2].
Since a ball-bar is less expensive than a full position
measurement device and sufficiently accurate for calibration,
the kinematic calibration on the proposed machine tool will
be performed by using a ball-bar. For a length measurement,i.e., using a ball-bar, the kinematic calibration is reduced toa nonlinear least square method of minimizing the
calculated and measured lengths of a ball-bar. The
effectiveness of the kinematic calibration method with aball-bar is verified through the simulations. Throughcalibration experiment, the accuracy has been improved
from 153 to 86 [µm] .

Introduction

U - joint

A Conventional 5-axis machine tool provides highaccuracy and large workspace. However, due to the serialkinematic structure, it generally requires very massive
structure to maintain high stiffness. In order to increase theratio of stiffness to mass, the 3-DOF parallel-kinematic
manipulator is employed as an arm and for highorientational capability, the 2-DOF serial-kinematic
manipulator is used as a wrist. Hence, the proposed machine
tool has a 5-DOF hybrid- kinematic structure as shown in
Fig. 1. This machine tool will be applied for both machining
and assembling tasks of automobiles’ cylinder blocks.
Specifically, the parallel manipulator has three U-P-S legs at
the side and one U-P leg at the center, where the prismatic
joints in the U-P-S chains are controlled by linear actuatorsand the prismatic joint in the U-P chain is passive.
In design and control of a machine tool, to obtain highaccuracy is one of the most important tasks to be©ISAROB 2007

239

S - joint

P - joint
P - joint

R - joint
U - joint

R - joint
Tool Tip

Fig. 1 Structure of the hybrid-kinematic machine tool.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

Position Analysis
Table 1 Screw axis locations and reference position.

The forward kinematics of the machine tool can beobtained by considering the central leg, i.e., U-P-R-R serial
chain in the middle if the U-P passive joints are measured.
At a reference position, the kinematic parameters may belisted in Table 1, where si and s0 i denote the direction andlocation of the ith joint axis with respect to the fixed
coordinate system A(x, y, z) when the machine tool is at thereference position.
Substituting the coordinates of the joint axes into theequation of the general spatial displacement, known as
Rodrigues’s formula, the screw transformation matrices:
0
0⎤
⎡1 0
⎡ cθ 2 0⎢0 cθ − sθ 0⎥⎢
1
1
1⎥, A =⎢ 0
A1 = ⎢
2⎢0 sθ1 cθ1 0⎥⎢ − sθ 2 0
⎥
⎢
⎢
0
1⎦
0
⎣0 0
⎣ 0
⎡1 0 0 0 ⎤⎡cθ 4 − sθ 4 0⎢0 1 0 0 ⎥⎢⎥ , A = ⎢ sθ 4 cθ 4 0
A3 = ⎢
4⎢0 0 1 t 3 ⎥
⎢ 0
0
1
⎥
⎢
⎢
0
0
0
1
0
0
0
⎦
⎣
⎣
− ( L0 + L1 ) sθ 5 ⎤⎡ cθ 5 0 sθ 5
⎥
⎢ 0
1 0
0
⎥,
A5 = ⎢⎢− sθ 5 0 cθ 5 ( L0 + L1 )(1 − cθ 5 )⎥
⎥
⎢
0 0
1
⎦
⎣ 0

sθ 2
0
cθ 2
0

0⎤
0⎥⎥
,
0⎥
⎥
1⎦

(1,0,0)2(0,1,0)(0,0,0)3(0,0,1)(0,0,0)4(0,0,1)(0,0,0)5(0,1,0)(0,0, L0 + L1 )q⎤
⎡I
= A1 (θ1 ) A2 (θ 2 ) A3 (t 3 ) A4 (θ 4 ) A5 (θ 5 ) ⎢ 3×3
1 ⎥⎦
⎣01×3

q0 ⎤
1 ⎥⎦

(2)

(1)

vectors, uq and v q , are to be determined after the joint

$2

O

y

ra

A3
x

z

A1

(3)

We observe that the position of the point M depends only
on the first three joint variables, θ1 , θ 2 , and t3 . From thegeometry, the direction vector and the travel distance of theprismatic joint is determined by

d2
d3
L0
$3

d1

angles are found. The point M can be obtained by
m = q − L2 w q$1

s3 = m / m and t 3 = m − ( L0 + L1 )(4)

Furthermore, the other two angles can be easily obtained in
the following.

B2P

rb v pwp

B3up

s3 = R1 R2 R3 w q 0 where w q 0 = [0,0,1]

T

(5)

L1

$4
M

$5
vq
wq

Q
uq

This equation reduces to
L2⎡ s3 x ⎤ ⎡ sθ 2 ⎤⎢ s ⎥ = ⎢− sθ cθ ⎥12⎥
⎢ 3y ⎥ ⎢⎢⎣ s3 z ⎥⎦ ⎢⎣ cθ1cθ 2 ⎥⎦Fig. 2 Kinematic parameters of the machine tool.

©ISAROB 20071

where q0 = [0,0, L0 + L1 + L2 ]T denotes the too tip Q whenthe machine tool is at the reference position. At thereference position, the orientation of the moving frame
coincides with that of the fixed frame.
For the inverse kinematics of the U-P-R-R serial chain,
only 5 of the 12 parameters associated with the end-effector
position vector and rotation matrix can be specified at will.
This is because the manipulator has only 5 degrees of
freedom. In this work, the position vector ( q ) and theapproach vector ( w q ) are specified and the other two unit

A2

B1

s0i
(0,0,0)where cθ i = cos θ i , sθ i = sin θ i and t3 denotes the linear
displacement of the prismatic joint in the middle of themachine tool.
The target position of the tool tip Q can be given by
⎡ Rq
⎢0
⎣ 1×3

0⎤
0⎥⎥
,
0⎥
⎥
1⎦

si

Joint

240

(6)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Then,
θ 2 = sin −1 ( s3 x ) , and θ1 = Atan2[− s3 y / cθ 2 , s3 z / cθ 2 ](7)

From Eq. (2), we obtainw q = R1 R2 R3 R4 R5 w q 0 .

(8)

Since θ1 , θ 2 , and t3 are already known, the above equation
can be reduced to3w q = R4 R5 w q 0(9)direction exist. The total kinematic errors associated withdirections and locations of joint axes are 18 and there are 5
joint offsets (δθ1 , δθ 2 , δ t3 , δθ 4 , δθ 5 ) . Therefore, 23 kinematic
errors should be estimated by the following kinematic
calibration.
Once the kinematic errors in the serial chain is determined,
the relation between the U-P chain and three S-P-U chainsneeds to be investigated. The kinematic errors in the S-P-U
chains are summarized as follow:
Joint location errors: δ ai and δ bi for i = 1,2,3
Length offset errors: δ d i for i = 1,2,3

where 3 w q = R3T R2T R1T w q . So, we can obtain the following

The kinematic errors will be determined by using Eq. (12).

relation.

Table 2 Kinematic parameters for an actual model.

⎡ 3 wqx ⎤ ⎡cθ 4 sθ 5 ⎤⎢3 ⎥ ⎢⎥⎢ wqy ⎥ = ⎢ sθ 4 sθ 5 ⎥⎢ 3 wqz ⎥ ⎢⎣ cθ 5 ⎥⎦
⎦
⎣

Joint

si

1

( 1 − u12y − u12z , u1 y , u1z )(0, δ y1 , δ z1 )2

(u 2 x , 1 − u 22x − u 22z , u 2 z )(δ x2 ,0, δ z 2 )Then,θ 5 = cos −1 ( 3 wqx ) , and θ 4 = Atan2[ 3 wqy / sθ 5 , 3wqx / sθ 5 ] (11)3(u3 x , u3 y , 1 − u32x − u32y )(0,0,0)4(u 4 x , u 4 y , 1 − u 42x − u 42 y )(δ x4 , δ y 4 ,0)

It is noted that two solution sets are possible in the working
range, i.e., θ 5 = θ 5* or -θ 5* .

5(u5 x , 1 − u52x − u52z , u5 z )(δ x5 ,0, L0 + L1 + δ z5 )(10)

The inverse kinematics of the 3-DOF parallel manipulator
is simply determined by
d i = R p bi + p − a ifor i = 1,2,3

(12)

where
⎡ Rp
⎢
⎣01×3

p⎤
⎡ I 3×3⎥ = A1 (θ1 ) A2 (θ 2 ) A3 (t3 ) ⎢
1⎦
⎣01×3

p0 ⎤
T
⎥ , p0 = [0,0, L0 ] ,1⎦⎡ 1/ 2 ⎤⎡− 1⎤⎡ 1/ 2 ⎤⎥⎥⎢⎢a1 = ra ⎢ 3 / 2⎥ , a 2 = ra ⎢ 0 ⎥ , a3 = ra ⎢⎢− 3 / 2⎥⎥ , and⎢⎣ 0 ⎥⎦⎢⎣ 0 ⎥⎦⎢⎣ 0 ⎥⎦⎡ 1/ 2 ⎤⎡− 1⎤⎡ 1/ 2 ⎤⎥⎥⎢⎢b1 = rb ⎢ 3 / 2⎥ , b2 = rb ⎢ 0 ⎥ , b3 = rb ⎢⎢− 3 / 2⎥⎥ .
⎢⎣ 0 ⎥⎦⎢⎣ 0 ⎥⎦⎢⎣ 0 ⎥⎦

34

s0i

Kinematic Calibration

In this work, the kinematic calibration method will be
performed by using a length measurement, i.e., ball-bar [37]. In the following derivation, the superscripts, “c” and “m”
are used to denote the calculated and measured values,
respectively, and the subscript, “j” is employed to indicate
the measurement number. As illustrated in Fig. 3, thecalibration methods using length measurement is to find theset of kinematic parameters satisfying the following
equation:q c (θ mj + δ θ , β ) − c = l mj
q c (θ mj + δ θ , β )(13)correction by calibration

q

m
j

l mj

Error Modelcenter of ball - barcWhen there some kinematic errors exist in the U-P-R-R
serial chain, the kinematic parameters for an actual model
can be summarized in Table 2. For a revolute joint, four
kinematic errors (two for the direction of joint axis and twofor location error) can be considered, however, for a
prismatic joint, only two kinematic errors for the joint©ISAROB 2007

241

circle of ball - barreference frameFig. 3 Outline of the calibration methods using a ball-bar.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where q(⋅) denotes the forward kinematics function of the
U-P-R-R serial chain, θ mj and δ θ denote the measured
joint angle and the joint offset vectors, β is the kinematic
error vector in Table 2 to be updated, and c = c + δ c andl mj denote the position vector of the fixed center of a ballbar and the measured length of a ball-bar, respectively.
The purpose of this work is to develop and to verify theeffectiveness of a kinematic calibration method suitable forthe prototype of a 5-axis hybrid machine tool being
developed as shown in Fig. 4. The main kinematicparameters of the machine tool are listed in Table 3.
Prior to a real calibration experiment in near future, we
assume a virtual prototype with realistic kinematic errors.
The error bound is determined from the information onmachining and assembling tolerances and its direction is
generated by the random function in Matlab. We also
assume that the prototype machine will be calibrated by aball-bar, specifically, the QC10 ball-bar of Renishaw withthe accuracy, ±0.5[µm] . For the collection of measurement
data, one center of the ball-bar is fixed at
c = [0, 0,1500]T [mm] expressed in the reference frame. The
n = 32 measurement points are selected on the hemispheresurfaces with 150 mm radius. The n sets of encoder values
from the three linear actuators and the U-P-R-R serial chain
and the corresponding n ball-bar lengths are measured at thesame time, which are actually calculated from the virtual
prototype for numerical simulations. Using Eq. (13) and“lsqnonlin” function of Matlab, the kinematic parameters
and the position error of the ball center are updated so as tominimize the error between the calculated and measured
lengths of the ball-bar.

Parameters
Radius of the fixed plate ( ra )600

Radius of the moving plate ( rb )250

Reference height ( L0 )840

Distance between A and C ( L1 )291

Tool length ( L2 )340

Initial length of an actuator ( d i 0 )910

Linear actuator stroke ( ∆d i )800

We assume that the maximum error bounds for angle andlength are ±25 [arcsec] and ±25 [µm] , respectively. The
assumed kinematic errors for a virtual prototype are written
in parentheses of Table 4. The estimated kinematicparameters by the suggested calibration method are also in
Table 4. It is noted that estimated values are close to theassumed ones and at least the signs are identical. It is also
noted that estimated values does not necessarily following
the assumed ones.
In order to show the effectiveness of the suggested
calibration method, the well-known circular test on the XY
plane with the radius of 150 mm is presented in Fig. 5. From
the kinematic calibration and error compensation, themaximum absolute kinematic error has been reduced from214.5 [µm] to 48.2 [µm] .
Table 4 Assumed and estimated kinematic errors.
i

si

u1 y
1

u1z
u2 x
2

u2 z
u3 x
3

u3 y
u4 x
4

u4 y
u5 x
5

u5 z

δ cx
Fig. 4. Prototype of a 5-axis hybrid machine tool.

Unit [mm]Estimated(Assumed)
14.6132

s0i

δ y1
δ z1
δ x2

(Assumed)δθ 1

3.6879(5.7716)-12.6657
37.9109(21.7735)(5.3421)-1.0796-22.1156

Estimated(-4.7147)(-13.4431)
13.2356δθ i(Assumed)(-16.1867)(22.5065)-4.7791

Estimated

δ z2

δθ 2

9.8016(14.5969)

12.9004(20.8452)(-0.7009)
15.6955(19.5649)
13.4825

δ t3

7.7064(15.6583)

δθ 4-1.3791(13.1048)-6.1924δ x4-22.605

δ y4
δ x5
δ z5

δθ 5

1.5168(11.9104)-7.3568(-7.3566)(-2.7648)-46.8734(-15.5173)-29.9936(-22.1054)(16.0704)-3.5677(21.0906)

6.9893(19.6825)(-24.0748)
1.3792

12.5541(-4.4865)(-2.1766)

δ cy

11.9864(-15.3284)

δ cz-7.9520(9.1112)[Unit: [arc sec] for angle and [µm] for length]
Table 3 Parameters for a prototype machine tool.

©ISAROB 2007

242

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Circular Test Before and After Calibration
1000
DESIRED
BEFORE
AFTER
800

600

Desired
After
Before

400

200
]
m
u[
si
x
aY

0

-200

(a) before calibration(b) after calibration
Fig. 7. Circular tests with radius 150 mm.

-400

-600

5

Conclusion

-800

-1000
-1000

-800

-600

-400

-200

0
X-axis [um]

200

400

600

800

1000

Fig. 5. Circular test before and after calibration.
Using the proposed algorithm and verified calibration
procedure through simulation, calibration experiment has
been performed on the prototype machine tool as shown in
Fig. 6. In the experiment, The Renishaw QC10 ball-bar with
±0.5 [µm] is used. The n=32 points on the hemispheresurface with radius 150 mm are collected for updating
kinematic parameters. We first calibrated some major errors,for example, the offset lengths of linear actuators. Then, thewell-known circular test was performed (refer to Fig. 7(a)).
The circular test result after calibration experiment is shownin Fig. 7(b). From the two tests, it is verified that position
accuracy of the prototype machine tool has been improved
from 153 to 86 [µm] .

In this paper, a new hybrid-kinematic structure for bothmachining and assembling is proposed. By attaching
additional sensors to passive joints in the central leg, thehybrid structure can be considered as a pure serial
manipulator. Using the successive screw displacements, thekinematics analysis is performed and a kinematic error
model is developed for the 5-DOF serial chain. The 5-DOF
serial chain is calibrated by using a length measurement, i.e.,ball-bar. Once all the kinematic parameters in the serial
chain are determined, the kinematic parameters associatedwith the parallel-kinematic manipulator will be estimated. Inorder to verify the effectiveness of this suggested method,
the simulation results of the kinematic calibration for a
virtual prototype have been presented. We further performed
the calibration experiment on the prototype machine tool.
From the kinematic calibration and error compensation, themaximum kinematic error has been reduced from 153 to86 [µm] .

REFERENCES
[1]
[2]
[3]
[4]

[5]

[6]
[7]

Fig. 6. Calibration experiment using a ball-bar.

©ISAROB 2007

243

L. W. Tsai, Robot Analysis: the mechanics of serial andparallel manipulators, Wiley, 1999.
B. W. Mooring, et al, Fundamentals of Manipulator
Calibration, Wiley, 1991.
H. S. Kim, “Kinematic Calibration of a Cartesian Parallel
Manipulator,” International Journal of Control, Automation,and Systems, Vol. 3, No. 3, pp. 1-8, 2005.
A. J. Patel and K. F. Ehmann, “Calibration of a Hexapod
Machine Tool using a Redundant Leg,” International
Journal of Machine Tools & Manufacture, Vol. 40, pp. 489512, 2000.
Y. Takeda, G. Shen, and H. Funabashi, 2004, “A DBB-based
Kinematic Calibration Method for In-Parallel ActuatedMechanisms Using Fourier Series,” Journal of Mechanical
Design, Vol. 126, No. 5, pp. 856-865
B. Siciliano, 1999, “The Tricept Robot: Inverse Kinematics,
Manipulability Analysis and Closed-loop Direct Kinematics
Algorithm,” Robotica, Vol. 17, pp. 437-445.
L. Beyer and J. Wulfsberg, 2004, “Practical Robot
Calibration with ROSY,” Robotica, Vol. 22, pp. 505-512.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Design of Automatic Grease LubricatorType of the Cylindrical Cam Piston Pump
Wooseok Choi, Jinseok Yang, Hyun Soo Koo, Duck Hyun Wang, Se-Han Lee
Mechanical Engineering and Automation
Kyungnam University 449 Wolyoungdong, Masan City, Gyungnam, 631-701, South Korea
wooseok@gmail.com
leesehan@kyungnam.ac.kr

Abstract

environmental temperature. When the chemical reaction

A new piston mechanism is proposed for an automatic
grease lubricator, which is able to supply equipment withgrease automatically for smooth motion. The mechanism
adopts a cylindrical cam system, which is able to convert
rotational motion to translational motion, and iscomposed without any check valve for preventing backflow even though existing lubricator has two checkvalves. Since the mechanism has a feature of all in one,which combines piston and valves. The automatic greaselubricator includes a micro controller system, whichmakes feeding pressure, period, and amount of grease inaccuracy.

starts, it is hard to respond to unexpected situations [1].
The pressurizing piston type requires very
complicated structure with check valves while it shows
very good feeding performances.
In this research, we proposed a simple new mechanism
for easy control. It adopts cylindrical cam piston pumpand is almost similar to the pressurizing piston typelubricator. The mechanism proposed here is simple form
and requires no check valves that make the mechanism
complicated.

Key Words: Grease lubricator, Cylindrical cam, Piston
pump, Microcontroller, Check Valve.

1. Introduction
As the industrial technologies are developed,
automation is widely spreading for product efficiency
and unmanned production system. Automatic greaselubricator is equipment that provides adequate amount offresh grease constantly to the shaft and the bearings ofmachines. It minimizes the friction heat and reduces thefriction loss of machines. For proper operating ofmachinery, accurate feeding period and amount of greaseis most important thing. To obtain these performances, it
is required of high accuracy for feeding greasemechanism.
There are many kinds of grease lubricators such as agas generating, a spring pre-pressurized, a pressurizing
piston type for creating pressure. In case of gas
generating type, chemical reaction is used for creatingpressure. The gas type is subject to be affected by©ISAROB 2007

244

2. Design of cylindrical cam-driving
mechanism of piston pump system
The piston mechanism proposed here is creatingpressure for feeding grease by using special feature
formed around the piston without check valve, whichconventional lubricator uses for preventing back flow ofgrease. In order to activate the special feature formed
around the piston, there needs to rotate the piston.
In order to realize rotational and reciprocal motion of
the piston simultaneously, a cylindrical cam mechanism
is adopted. The piston mechanism proposed here isshown in Fig. 1. It is required the bulky images ofcylindrical cam for development purpose.
Fig. 1 shows an essential feature of the mechanism
proposed here. As the driving motor is rotating, thepiston is moving reciprocally and rotating
simultaneously by guide pin and cylindrical cam formed
around the piston. [2]The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

reciprocally, while a conventional lubricator is closing
and opening the holes with check valves. It is noticeable
that there is position of closing the two holes while there
is no position of opening the two holes.
This feature is very important. Though any
unpredictable stop motion of the piston occurs, i.e. thepiston stops at any position, there is no chance to open
inlet and outlet hole simultaneously. It means that itnever happens to flow grease by itself at any situations.

Grease Pack
Motor
Supe
r
Gear

Cylinder
BlockCoupling

Fig. 1: Cylindrical cam mechanism prototype
The motion of the cylindrical cam is that as it rotates
the piston is moving reciprocally and rotating withinteraction between cylindrical cam slot and guide pin.
One revolution of the motor is able to produce one cycleof reciprocal motion. The reciprocal motion of the piston
is responsible for creating pressure and the rotating
motion of that is responsible for closing-opening ofvalves. Fig. 2 and Fig. 3 show graphically view of theprocedure of multi motions of the piston, respectively.

(a) Rotation angle of 0˚(b) Rotation angle of 90˚

2.1 Motion of cylindrical cam and piston
Fig. 2(a) and Fig. 3(a) show initial stage of the piston
motion. In this case, the piston moves close the inlet holeand outlet hole for flowing grease simultaneously. Whenthe piston is only rotating with cam rotation the piston
itself is about to open the inlet hole while the outlet holeis still closed. In succession the piston is rotating andretreating, the volume of the cylinder is increasing(becoming low pressure) and the grease is sucked. Fig.
2(b) and Fig. 3(b) show the piston reaching at 90 degrees
position.
Fig. 2(c) and Fig. 3(c) show the piston reaching at 180
degrees position which is stroke of 5.5mm. At that time,
the cylinder is full of grease, the inlet and outlet hole areclosed simultaneously. As the piston is rotating
successively, the cylinder volume is about to be
decreased becoming pressurized and grease is outing.
Fig. 2(d) and Fig. 3(d) show piston is outing grease andopening outlet hole while the inlet is still closed. The
mechanism proposed here is feeding grease withrepeating the procedure described above.
As the piston proposed here is rotating, the piston
itself is closing and opening inlet and outlet hole

©ISAROB 2007

245(c) Rotation angle of 180˚(d) Rotation angle of 270˚
Fig. 2: View of Procedure of multi motions of the piston(a) Rotation angle of 0˚(b) Rotation angle of 90˚(c) Rotation angle of 180˚(d) Rotation angle of 270˚
Fig. 3: Front view of the procedure of multi motions of
the piston

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.2 Design value of the grease

condition should be met.

A required amount of grease for one stroke is about
0.125cc (0.125cc/rev.) Diameter and stroke of the piston
are governed by feeding pressure and motor output
torque. The government equation is following. As thepiston is moving reciprocally, the created pressure isvariable according to the cam feature. For practical sense,
average pressure is reasonable. In order to calculate theaverage pressure, an energy conservation equation isneeded. The input energy by rotational motion is equal tothe output energy by reciprocal motion as following
dF = πτ(1)where d denotes piston stroke, F does piston
thrust, τ denotes motor torque.
In order to calculate an important variable, pressure p ,the both sides of (1) are divided by sectional area A .
dF πτ
F π
=
→ p= = τ
A
A
A V

(2)

where V = Ad denotes the volume of the cylinder.
Equation (2) says that feeding pressure is dependent
on motor output torque and displacement which isvolume per revolution. Details of the piston diameter andstroke are determined by manufacturing conditions.
There exists a minimum dimension to form cam andvalve feature around piston.

Spline Curve

5.5
0

π

2π

Fig. 4: Displacement of cam
Fig. 4 shows a cylindrical cam trajectory candidatethat stands for piston stroke versus motor rotation. The
cylindrical cam trajectory is generally formed by spline
curve. It is noticeable that the cylindrical cam trajectoryis adjusted not to occur only rotational motion without
reciprocal motion at stage of opening or closing theholes, since the grease fluid is incompressible. At thefirst and end stage of the piston stroke, the following©ISAROB 2007

246

∆d
∆θ

=0

(3)

where ∆d denotes infinitesimal stroke of the piston,∆θ does infinitesimal angle of the piston rotation. A
candidate of the piston dimensions is listed in Table 1.
Table 1. Dimensions for Piston
RequireFlux(cc)

PistonDiameter(mm)

RequireDisplacement
(mm)

0.125

5.40

5.4580

Important dimensions of the piston, diameter and stroke
are determined based on manufacturing. The reason isthat difficulty of manufacturing is not free fromproductivity. A diameter and stroke of the piston should
be more than 5mm and 5.5mm, respectively. Dimensions
listed in Table 1 are nearly met the above conditions.
A high performance automatic grease lubricator
should be able not only to out proper pressure but also to
supply destinations regularly with proper amount ofgrease. These conditions for an automatic greaselubricator are able to be met by implementing microcontroller and its peripherals.
In this research, there is introduced an 8-bit microcontroller, AT89C2051 [3](manufactured by Atmel) andperipherals, LB1630[4] for driving motor and SG2BC[5]for detecting motor ration state.
In order to program software for the micro controller,
we use C-language, which is very familiar for user to use.
In order to control the motor precisely, the main part ofthe software consists of an internal timer and interrupts.
Fig. 5 shows a flow chart of the software.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

torque (2770g·cm) produced by the reduction motor, thecorresponding pressure is obtained as following by using
eq. (2)Start
Mode input
INT setting

p=

π

τ=

π × 2.77 × 9.8 × 10 −2

2.29 × 10 −5 × 5.5 × 10 −3
V= 6.2 × 106 ( N / m 2 ) = 62.0( kg f / cm 2 )Time to Run?

(4)

Motor On
1 Turn?
Motor Stop

Fig. 5: Flow chart of the software
In order to verify the mechanism proposed here, amicro controller board is constructed. Fig 6 shows thetest board

The maximum nominal torque is performed at anominal current of 300mA. On the other hand, themanometer as shown in Fig. 8 actually shows pressure ofabout 5Mpa (=50kgf/cm2). Fig. 9 shows producedpressure with respect to applied motor input current. The
discrepancy (about 22.5%) is supposed to be caused bymechanical friction force between the prototype
cylindrical cam and guide pin.

Fig. 6: Micro controller for the mechanism test

3. Experimental result
A lubrication mechanism test was conducted, Fig. 7
shows the photo of the experimental set-up (left) andessential parts of the piston system (right). [6][7]Fig. 8: Pressure value of 50kgf/cm2 measured bymanometer

Fig. 7: Experimental set-up and its exploded parts
First, for identifying only transmission load (reduction
gear train, etc.), no-load current of the driving motor wasmeasured and it was 95mA. At the maximum nominal©ISAROB 2007

247

Fig. 9: Diagram of comparative theoretical andexperimental Data

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

We can be detecting the defect on the firstexperimental test, which is frictional wear in the piston.
Fig 10 shows wear of piston feature.

Fig. 10: Frictional wears of piston
This is important defect while the driven cylindrical
cam piston mechanism. We settle the defect after
changing the material of piston.

4. Conclusion
An automatic grease lubricator using cylindrical cam
mechanism of piston pump was developed. The
mechanism proposed here is simple as it eliminates acheck valve, which is able to prevent back flow.
Eliminating a check valve is helpful for cheaper product.
Additionally, Cylindrical Cam Mechanism performs highforce at instantaneous stop range where Coulomb
friction force may be maximized.
As closing and opening the valve is synchronized to
the rotation of the piston automatically, a microcontroller needs to control only the driving motor andhas no burden to control the flow valves.
The discrepancy between theoretical and actualpressure is supposed to be caused by mechanical friction
force between the prototype cylindrical cam and guidepin. And also find the defect about the same material
problem which is occurred frictional wear between thepiston and cylinder.

Acknowledgment
This research was supported by the New University
for Regional Innovation program of the Ministry of
Education & Human Resources Development (NURI
MECHANO21)

References©ISAROB 2007

248

[1] P. I. Cherednichenko, Lubricator Design as A factor
in the Yarn Quality, pp. 68-69[2] Robert L. Norton, Design of Machinery, New York,
2004, Chap. 8, pp. 378-384.
[3] AT89C2051 datasheet from http://www.atmel.com[4] LB1630 datasheet from http://www.sanyo.co.jp[5] SG2BC datasheet from http://kodenshi.co.jp[6] Teale. J. L., Lebeek, A. O., “An Evaluation of the
Average Flow Model for Surface Roughness Effects
in Lubrication Technology”, 1980, Vol. 102. July, pp.
360-367.
[7] D. H. Wang, K. Y. Lee and S. H. Lee, “TheDevelopment of Automatic Grease Lubricator Driven
by Gear Mechanism with Controlled Operation
Time”, J. of KSPE, 2006, Vol. 23, No. 2, pp. 199-206.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Non-contact 3-D surface profiler using optical fiber
Yong Kwan Kwon** ,Seo Heon Kim** , Eun Chang Heo**, and Byoung Chang Kim*
*School of Mechanical and Automation Engineering, Kyungnam University (bckim@kyungnam.ac.kr)** School of Mechanical and Automation Engineering, Graduate school, Kyungnam University
449 Wolyoung Dong, Masan, Kyungnam, 631-701, Korea

Abstract
We present a non-contact 3-D surface profiler speciallydevised for the measurement of silicon wafer or CSP(Chip
Scale Package) of which surface profile is difficult to bemeasured with conventional interferometers. The profiler
comprises multiple light sources made of single-mode
optical fiber. It turns out to be well suited for the warpage
inspection of microelectronics components.

1. Introduction
Recently new profile measurement techniques are neededin the industry because of various product developments.
Especially microelectronics components such as chippackage and silicon wafer are difficult to be measured withconventional non-contact profiler. Which is based on thefollowing two points of view. Their profile should be
fabricated with within a few micrometer accuracy, but theirsurfaces have a characteristic of light scattering.
Until now, the existing typical metrology to overcome these
problems has two methods. At first, Kwon and Wyant havemeasured rough surfaces by using CO2 laser of 10.6μm
wavelength.[1] The second, Petriccione and Ume have justapplied typical Shadow Moire method to measure roughsurface.[2] Because of using extended wavelength; this two
metrology could not obtain very high accuracy.
Therefore, we present a non-contact 3-D surface profiler
using optical fiber system that has been specially devised forthe inspection of microelectronics components such as
unpolished backsides of silicon wafers and plastic molds ofintegrated-circuit chip packages.

2. Principle of the proposed method
Figure 1 shows the overall optical configuration for themeasurement method proposed in this investigation. The
system is constituted with six light sources and a CCD
camera of two-dimensional photo-detectors array. The lightsources are deployed along the circumference of a circle,
while the camera is located about at the center of the circle
facing the object surface during measurement.

©ISAROB 2007

249

Figure 1. Geometrical arrangement of the 3-D profiler
configured for the profile measurement of microelectronics
surfaces. S1 to S6 are the light sources, each of which
projects a unique fringe pattern generated by theinterference of two spherical wavefronts diffracted from apair of single-mode optical fibers. The light sources areactivated one by one in sequence, while resulting fringe
patterns are observed using a CCD camera of twodimensional photo-detector array.
Each light source is an independent one made of a pair of
single-mode optical fibers that are connected to a He-Ne
laser via a 2X1 coupler as shown in Figure 2. The fibers arepolished in their ends and housed in a single ceramic ferruleside by side with a predetermined lateral offset. Each single
mode fiber works as a light source emitting an almost
perfect spherical wave front. Two spherical wave fronts
emanated from the two fibers constituting a light source
interfere with each other and subsequently generate a uniquefringe pattern over the target surface to be tested. A He-Ne
source provides coherent light to the two fibers through a
2X1 optical fiber coupler, while one of the fibers iselongated using a piezoelectric PZT tube to produce phaseshifting.
Two spherical wave fronts emanate by diffraction at theends of the fibers, which then interfere with each other andgenerate a unique fringe pattern on the object surfaces to be
profiled. The PZT tube extends the length of a fiber toinduce phase shifting in the resulting fringe pattern.
The camera captures fringe patterns while switching on
and off all the diffraction sources one by one in sequence

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

to obtain multiple fringe images projected from different
geometrical angles as shown in Figure 3.

Figure.2 Optical configuration of a two-point diffraction
source made of a pair of single-mode optical fibers coupled
to a single coherent source

There are three fringe variables; a(x,y,z) is the background
intensity, b(x,y,z) is the amplitude of intensity variation,and Φ(x,y,z) is the absolute phase. Among them, theabsolute phase Φ(x,y,z) relates to the xyz-coordinates ofthe surface profile to be measured with a most
uncomplicated relationship, i.e., by the difference of the
distances to the two diffraction sources plus the differenceof the initial phases. Adopting well-established phaseshifting technique allows for the principal value ofΦ(x,y,z) to be accurately determined within the range of -π
to π[3], for which one of the fibers in each diffraction
source is elongated using a piezoelectric PZT extender.
Therefore, at least three measurements of Φ(x,y,z) are
required to determine the coordinates (x,y,z), each ofwhich should be performed with a different diffraction
source.

3. Experimental results

Figure.3 Fringe images captured by CCD camera which areprojected on the non-polished backside of silicon wafer
from three different geometrical angles.
For analysis, two conjugate spherical wave fronts from asingle diffraction source is designated as u1 and u′1, whose
complex amplitudes are expressed in the xyz-coordinate
system as
A1(1)
u ( x , y, z ) =exp[ − j( kr ( x , y , z ) + φ )]1

1

r1 ( x , y , z )A series of tests has been performed against actual roughsurfaces whose profiles cannot be measured using
conventional Fizeau or Twyman-Green interferometers dueto their too high surface height irregularities.[4]Figure 4 shows a measurement result, which was obtained
from the backside profile of a silicon wafer in the middle ofintegrated-circuit fabrication process.

1

where r1(x,y,z) = (x−x1)2 + (y−y1)2 +(z−z1)2 andu 1/ ( x , y , z ) =A2
exp[ − j( kr1/ ( x , y , z ) + φ1/ )] (2)r1/ ( x , y , z )where r1/ (x, y, z) = (x − x1/ )2 + (y − y1/ )2 + (z − z1/ )2 .
Note that φ1 and φ′1 are the initial phases of u1 and u′1 which
are measured at their origins located at (x1,y1,z1) and(x′1,y′1,z′1), respectively. Then, the intensity of the fringe
pattern generated on the object surface by the interference
between u1 and u′1 is worked out as2

I (x, y, z) = u1 (x, y, z) + u1' (x, y, z) = a(x, y, z) +b(x, y, z) cos[Φ(x, y, z) ]
2

2

A A
A
A
where a(x, y, z) = 12 + 22 , b(x, y, z) = 2 1 /2 ,
r1 r1
r1
r1/

(3)

Φ(x, y, z) = k(r1 − r1' ) + Δφ, andΔφ =φ1 −φ1/ .

©ISAROB 2007

250

Figure.4 A measurement result of the non-polishedbackside profile of a silicon wafer for warpage inspection;
an exemplary three-dimensional profile measured andreconstructed by the proposed 3-D surface profiler.
Another measurement example is presented in Figure 5,
which has been performed for the warpage inspection ofchip scale package (CSPs) that are tape-mounted on ball
grid arrays (BGAs). The measured result indicates that the
global surface is distorted by high temperature environment
during curing process.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure.5 Inspection of IC package warpage; an exemplary
three-dimensional profile measured and reconstructed by the
proposed 3-D surface profiler.

4. Conclusions
A new 3-D surface profiler has been proposed to measure
the warpage of electronics with light scattering surfaces.
This method uses multiple sets of light sources made of two
single-mode optical fibers emitting spherical wave fronts togenerated unique fringe patterns on the target surface. The
experimental results turn out to be well suited for the
warpage inspection of microelectronics components.

References[1] O. Kwon, J. Wyant, C. Hayslett, “Rough surface
interferometry at 10.6μm”, Appl. Opt. vol.19, pp.1862,
1980.
[2] G.J. Petriccione, I.C. Ume, “Warpage studies of HDI
test vehicles during various thermal profiling”, IEEE
Transactions on advanced packaging, 22(3), 624(1999)[3] I.B. Kong, S.W. Kim, “General algorithm of phaseshifting interferometry by iterative least-square fitting,”
Opt. Eng. Vol.34, pp.183, (1995)[4] B.C.Kim, S.W. Kim, “Absolute interferometer for
three-dimensional profile measurement of roughsurfaces,” Opt. Lett, vol.28, pp.1, (2003)©ISAROB 2007

251

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fault Tolerant Control of Magnetic Actuators
Uhn Joo Na

Jin Seok Won

Dept. of Mechanical Design, Graduate School,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

School of Mechanical and Automation Eng.,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

Dong Dae Lee

Cheol Woong Ju

Dept. of Mechanical Design, Graduate School,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

Abstract
This paper develops the theory for a fault-tolerant,
permanent magnet biased, homopolar magnetic bearing.
If some of the coils or power amplifiers suddenly fail,the remaining coil currents change via a novel
distribution matrix such that the same magnetic forces
are maintained before and after failure. Lagrange
multiplier optimization with equality constraints isutilized to calculate the optimal distribution matrix thatmaximizes the load capacity of the failed bearing. The
overall load capacity of the bearing actuator is reduced
as coils fail.

1 Introduction
A magnetic bearing system is a mechatronics
device consisting of a magnetic force actuator (an active
magnetic bearing, or AMB), motion sensors, power
amplifiers, and a feedback controller (DSP), thatsuspends the spinning rotor magnetically without
physical contact, and suppresses vibrations. Magnetic
bearings find greater use in high speed, highperformance applications since they have many
advantages over conventional fluid film or rolling
element bearings, such as lower friction losses,
lubrication free, temperature extremes, no wear, quiet,high speed operations, actively adjustable stiffness anddamping, and dynamic force isolation. Though magnetic
bearings find more applications in industry, reliability
requirements limit magnetic bearings from being used inhighly critical applications. Failure of components suchas coils or power amplifiers in magnetic bearings may
result in a failure of the entire system.
Fault tolerant control provides continued operationof magnetic bearing actuators even if its power
amplifiers or coils suddenly fail. Much research has
been devoted to fault-tolerant heteropolar magnetic
bearings. Maslen and Meeker [1] introduced a faulttolerant 8-pole heteropolar magnetic bearing actuatorwithindependently
controlledcurrents
and©ISAROB 2007

252

School of Mechanical and Automation Eng.,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea
experimentally verified it in [2]. Flux coupling in aheteropolar magnetic bearing allows the remaining coils
to produce force resultants identical to the unfailed
bearing, if the remaining coil currents are properly
redistributed. Na and Palazzolo [3, 4] also investigatedthe optimized realization of fault-tolerant magnetic
bearing actuators and experimentally showed it on a
flexible rotor such that rotor displacements after failure
can be maintained close to the displacements before
failure for up to all combinations of 4 coils failed andcertain combinations of 5 coils failed out of 8 coils. Na
and Palazzolo [5] introduced a fault-tolerant control
scheme utilizing the grouping of currents to reduce therequired number of controller outputs and to remove
decoupling chokes.
The present work describes the theory andfollowing numerical analysis for the novel fault-tolerant
homopolar magnetic bearing. Energy efficient
homopolar magnetic bearings with fault tolerantcapability may find great use in some applications suchas flywheel energy storage systems and momentum
wheels.

2 Bearing Model
The schematic drawing of an 8-active pole,permanent magnet biased homopolar magnetic bearing isshown in Fig. 1.
θ3

θ2

θ4

θ1
y

x

θ5
θ8

θ6

θ7

Figure 1.
Schematic of an 8-Active Pole,
Permanent Magnet Biased Magnetic Bearing

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Assuming that eddy current effects and material
path reluctances are neglected, Maxwell’s equations are
reduced to the equivalent magnetic circuit for thehomopolar magnetic bearing. The feedback control
voltages vcx and vcy , determined with any type ofcontrol law and measured rotor motions, are distributed~

to each pole via T in normal operation, and create
effective stiffness and damping of the bearing to suspend
the rotor around the bearing center position. With theand even maintain the same decoupled magnetic forces
as those of an unfailed magnetic bearing. Maslen andMeeker [1] introduced a linearization method which
effectively decouple the control forces for a failed
bearing by choosing a proper distribution matrix.
Though not identified in [1], the direct voltage stiffness
kv is used to yield the same linearized control forces as
those of the unfailed bearing. The necessary conditions
to yield the same decoupled magnetic control forces are;~uniform current distribution with T as well as thesymmetric bearing geometries, magnetic forces are( x , y ) decoupled and vary linearly with respect tocontrol currents and rotor displacements around thebearing center position. If symmetry is lost due to a coilfailure, magnetic forces are no longer decoupled andlinear with respect to control currents and rotor
displacements, and even it may be difficult to maintainstable control. Reassigning the remaining currents with aredifined current distribution scheme may remedy thisby providing the same decoupled magnetic forces as
those before failure. Magnetic forces developed in theactive pole plane are described as;(1)
f x = vT M x v
f y = vT M y v(2)⎡ 0 1 / 2 0⎤
⎡ 0 0 1 / 2⎤,
M x = kv ⎢⎢1 / 2 0 0⎥⎥ M y = kv ⎢ 0 0 0 ⎥⎢⎥⎢⎣ 0⎢⎣1 / 2 0 0 ⎥⎦0 0⎥⎦

If the distribution matrix Tˆ is determined such that Eq.
(20) should be met, the magnetic forces at bearing center
position in Eqs. (12) and (13) lead to;f x = kv vcx , f y = kv vcy

h1 (Tˆ ) = Tˆx Qx 0Tˆx = 0
T
h (Tˆ ) = Tˆ Q Tˆ = 0
T2

k pxy ⎤ ⎡ x ⎤ ⎡k vxx+
k pyy ⎥⎦ ⎢⎣ y ⎥⎦ ⎢⎣k vyx

k vxy ⎤ ⎡vcx ⎤k vyy ⎥⎦ ⎢⎣vcy ⎥⎦∂ω ϕ =0Qϕb = − Gb

5Qbϕ = − Gb

y0 y

x

y0 y

h10 (Tˆ ) = H T Qby 0Tˆy = kv / 2

4

ω =0

ω

∂D
Gc
∂ϕ

Employing an optimal current distribution matrix T
may decouple the linearized forces of the failed bearing,©ISAROB 2007yh8 (Tˆ ) = H T Qby 0Tˆx = 0
T
h (Tˆ ) = Tˆ Q Tˆ = 0
9

The parameters ϕ and ω represent either x or y . The
position stiffnesses of the homopolar bearing remainunchanged with a coil failure since the position
stiffnesses are only influenced by the bias flux driven
with permanent magnets. The voltage stiffnesses aredefined as;(5)k= 2 H T Q ϕ =0 Tˆ
where

v

(4)

H

bϕ

bx 0 y

T

7

∂D
Gb
∂ϕ

vϕω

(8),

h6 (Tˆ ) = Tˆx Qy 0Tˆx = 0
T
h (Tˆ ) = Tˆ Q Tˆ = 0

(3)

ω =0

where

x0 y

T
h4 (Tˆ ) = Tˆx Qx 0Tˆy = 0
h (Tˆ ) = H T Q Tˆ = k / 2

The position stiffnesses are defined as;∂Qϕbyh3 (Tˆ ) = H T Qbx 0Tˆy = 0

The magnetic forces in Eqs. (1) and (2) can belinearized about the bearing center position and the zero
control voltages by using Taylor series expansion. The
linearized magnetic forces are;k pϕω = − H T(7)Equations (6) can be written in 18 scalar forms, and thenboils down to 10 algebraic equations if redundant terms
are eliminated. The equality constraints to yield the same
control forces before and after failure are;3 Bias Linearization⎡ Fx ⎤⎡k pxx⎢ F ⎥ = − ⎢k
⎣ y⎦
⎣ pyx

(6)

253

Optimal Distribution

Some examples of distribution matrices arecalculated for the 8-pole homopolar magnetic bearing
with the nominal air gap g 0 (0.508 mm), pole face areaa 0 (602 mm2), number of coil turns n (50 turns). It is
assumed that permanent magnets are selected to produce
bias flux density of 0.6 Tesla in the air gaps of the activepole plane. The design of the permanent magnets for a
homopolar magnetic bearing is beyond the scope of thispaper. The direct voltage stiffness k v is then calculated
as 106.651 N/volt. A distribution matrix for an 8-pole
homopolar bearing with the 7th-8th coils failed operation
is calculated as;

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
⎡ 1.9337 − 0.5171⎤⎢ − 0.7557 2.1604 ⎥⎢⎥⎢ − 0.2844 0.3313 ⎥⎢⎥− 0.3109 0.2836 ⎥T78 = ⎢⎢− 2.1707 0.7506 ⎥⎢⎥⎢ 0.5313 − 1.9159⎥
⎢
0
0 ⎥
⎢
⎥
0
0 ⎥⎦
⎢⎣

(9)

A distribution matrix with the 6th-7th-8th coils failed
operation is calculated as;
0.5941 ⎤⎡ 1.5852⎢− 0.9194 1.4034 ⎥⎢⎥⎢ − 0.5185 1.2468 ⎥⎢⎥− 0.3469 1.6607 ⎥T678 = ⎢⎢− 2.2336 − 0.0018⎥
⎢
⎥
0 ⎥
⎢ 0
⎢ 0
0 ⎥
⎢
⎥
0 ⎦⎥
⎣⎢ 0

(10)
Figure 2. Current Plot for a Series of Failures

5 Conclusion

A distribution matrix with the 5th-6th-7th-8th coils failed
operation is calculated as;⎡ 1.8351⎢ 0.2480⎢⎢− 0.2480⎢− 1.8351
T5678 = ⎢
⎢
0
⎢
0
⎢
⎢
0
⎢
0
⎣⎢

0.8207⎤1.6421⎥⎥1.6422 ⎥⎥0.8206⎥
0 ⎥
⎥
0 ⎥
0 ⎥
⎥
0 ⎦⎥

(11)

Similarly, the distribution matrices can be calculated fora failed homopolar bearing up to all combinations of 5coils failed out of 8 coils. In the previous fault tolerant
scheme with heteropolar magnetic bearings [3, 4],
distribution matrix solutions do not exist for a certain
combination of 5 failed coils (for example, no solution
exists for 5 adjacent coils failed heteropolar bearings).
The following system dynamics simulation
illustrates the transient response of a rotor supported bymagnetic bearings during a coil failure event. An
unbalance force of meΩ with m (2.0 grams), e (0.01m) and Ω (spinning speed) are applied at the two
2~

bearing locations. The distribution matrix of T isswitched to T5678 and T45678 when 4 adjacent coils failed
at 0.02 seconds and then 5 adjacent coils failed at 0.04
seconds. The rotor speed is held constant at 20,000
RPM. Figures 2 shows transient response of the current
inputs to the outboard bearing from the normal unfailed
operation through the 5-6-7-8th coils and 4-5-6-7-8th coilsof the outboard bearing failed at 0.02 seconds and 0.04
seconds, respectively. This indicates that very much thesame rotordynamic responses are maintained throughout
the series of failure events, while currents and fluxes in
the homopolar magnetic bearing change significantly.

©ISAROB 2007

254

A fault tolerant control scheme is developed for anenergy efficient homopolar magnetic bearing. The
homopolar bearing actuator using the fault tolerant
control algorithm can preserve the same linearizedmagnetic forces by redistributing the remaining currents
even if some components such as coils or power
amplifiers suddenly fail. The distribution matrix T ofcontrol voltages is determined by using the Lagrange
Multiplier optimization with equality constraints for afailed bearing in a manner that the load capacity should
be maximized. Simulations show that very much thesame vibrations (orbits or displacements) are maintained
throughout failure events while currents and fluxes
change significantly with different distribution scheme.
Only control currents as well as control fluxes are
redistributed for the failed bearing while bias flux driven
by permanent magnets remains constant. Less strict
constraints of only 10 equations are required for the fault
tolerant homopolar bearing to produce the samemagnetic forces, while 12 constraint equations are
required for the fault tolerant heteropolar bearing in [3].
This released conditions may give some benefits to therealization of fault tolerant homopolar bearings. The
solution space of distribution matrices is extended for thehomopolar bearing. The distribution matrices can becalculated for a failed homopolar bearing up to all
combinations of 5 coils failed out of 8 coils. In theprevious fault tolerant scheme with heteropolar magnetic
bearings, no solutions exist for certain combinations of 5
failed coils. The load capacities of the failed homopolar
magnetic bearings are greatly increased compared tothose of heteropolar magnetic bearings.
Fault tolerance of the magnetic bearing actuator isachieved at the expense of additional hardware
requirements and reduction of overall bearing load
capacity. Therefore, the fault tolerant magnetic bearing
should be designed enough to support loads even in caseof a severe failure (5 coils failed out of 8 coils).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Otherwise, disturbances from unbalance, runouts, andsideloads should be maintained at low level to prevent
saturation.
.

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

References[1] E. H. Maslen and D. C. Meeker, Fault Tolerance ofMagnetic Bearings by Generalized Bias Current
Linearization. IEEE Transactions on Magnetics 31(1995) 2304 – 2314.
[2] E. H. Maslen, C. K. Sortore, G. T. Gillies, R. D.
Williams, S. J. Fedigan, and R. J. Aimone, A Fault
Tolerant Magnetic Bearings. ASME Journal ofEngineering for Gas Turbines and Power 121 (1999)
504-508.
[3] U. J. Na and A. B. Palazzolo, Optimized Realizationof Fault-Tolerant Heteropolar Magnetic Bearings for
Active Vibration Control. ASME Journal of Vibration
and Acoustics 122 (2000) 209-221.
[4] U. J., Na, A. B., Palazzolo, and A., Provenza, Test
and Theory Correlation Study for a Flexible Rotor on
Fault-Tolerant Magnetic Bearings. ASME Journal of
Vibration and Acoustics 124 (2002) 359-366.
[5] U.J. Na and A. B. Palazzolo, The Fault-Tolerant
Control of Magnetic Bearings With Reduced Controller
Outputs. ASME Journal of Dynamic Systems,
Measurement, and Control 123 (2001) 219-224.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Automated wildlife monitoring using self-configuring sensor networks deployed innatural habitats
Vlad M. Trifa1,3∗ Lewis Girod2

Travis Collier3

1

2

ATR CNS Humanoid Robotics
Comput. Neuroscience Lab†
Kyoto, 619-0288 Japan

Comp. Science and AI Lab.

To understand the complex interactions among animals within an ecosystem, biologists need to be able
to track their location and social interactions. There
are a variety of factors that make this difficult. We
propose using adaptive, embedded networked sensing
technologies to develop an efficient means for wildlife
monitoring. This paper surveys our research; wedemonstrate how a self-organizing system can efficiently conduct real-time acoustic source detection andlocalization using distributed embedded devices.

Introduction

It is now well-recognized that artificial life systems
can make useful contributions to a wide variety ofproblems in biology [8]. Typically, these contributions
have come from the study of complex adaptive systems, simpler versions of natural life. Such abstractions permit isolation and control of features of interest [3]. In this paper we describe a novel applicationof adaptive systems for biology: looking at natural
systems with the purpose of describing their structure
and behavior.
The presence of human observers in the field is bothtime consuming and disruptive to the habitat underobservation. An automated system would be desired.
However, deployment of unattended recording stations
is also fraught with difficulty. Among the current limitations are limited recording capacity and energy, andlimited ability to able to adapt to rapidly changing
environments.
∗ Email: vlad.trifa@ieee.org† The work presented here was mainly carried out at UCLA.

©ISAROB 2007

Charles E. Taylor33Department of Ecology and
Evolutionary Biology
Massachusetts Inst. of Tech.
University of California
Cambridge, MA, 02139 USA Los Angeles, CA, 90095 USA

Abstract

1

Daniel T. Blumstein3

256

We illustrate that sensor network technology can beused as an efficient and powerful data collection system that can be easily used by biologists with little
programming experience. Beyond simply recording of
raw data, these tools have the potential to perform autonomous wildlife monitoring by being programmed todetect and react in a proper way to pre-specified conditions, with almost no human intervention. In addition,
they form a network that enables remote managementof the system, system health assessment, re-tasking,real-time triggering of additional sensing modalities,
and visualization of real-time data from the field.
The distributed structure of these systems allows
us to deploy them to cover wide territories and to
capture data from different modalities in response toevents in real time, e.g., capturing image data only
when animals are active. Distributed signal processing algorithms are also a promising approach to data
reduction. Spatial filtering techniques based on beamforming using a distributed collection of small arrays,
can often identify a target species in situations where
many species and individuals are present. Also, wewill illustrate how self-organizing and adaptive methods can be used to develop robust and efficient methods to detect and localize acoustic sources.

2

Tools and methods

This section describes the embedded platform weused for our experiments, and then we will briefly describe different contexts where adaptive methods havebeen employed.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.1

CENS nodes

For our experiments we have developed a network of
Acoustic Embedded Networked Sensing Boxes (Acoustic ENSBox) as a prototype for wildlife monitoring
system [5, 6, 7]. Each system is a small embedded
computer running Linux, self-contained in a waterproof case, with an external four microphone array and802.11b wireless communication (Figure 1). In com-correlation, and time-of-flight measurements of radio
and acoustic signals, however many of these alternatives are not a good fit for these applications. For
example, GPS reception is often poor in locations ofinterest such as forests and canyons, and even withgood reception GPS requires differential corrections
to meet the stringent precision requirements for theseapplications.
The Acoustic ENSBox self-localization is based on
measuring time-of-flight and direction of arrival of
acoustic signals. This solution has many advantages,
including high precision and high resilience to noise.
Initially, each node emits a chirp in turn, while allother nodes estimate the range R based on time offlight and bearing θ using local time differences ofarrival. Then, a centralized algorithm combines the(R, θ) estimates for every node, and a non-linear leastsquares algorithm is used to compute the relative map(X, Y, Z, Θ) of the network. Finally, the relative map
is fit to surveyed locations to obtain a map in absolute coordinates. This capability eliminates the needto survey all the array locations, a process that often takes hours to complete and that is generally extremely challenging to accomplish accurately.

2.3
Figure 1: Left: Acoustic ENSBOX, the embedded device we used for our experiments. Right: close-up of
the microphone array.
parison to other wireless sensor systems such as the
Crossbow Mote, the Acoustic ENSBox has the computational, storage, and network resources to process
audio data in real time and to implement distributed
algorithms, including high precision 3D location andorientation self-calibration.
This combination of high processing power andcommunication with a small form factor makes the
ENSBox platform particularly well-suited to explore
novel solutions for animal vocalization analysis andaccurate acoustic source localization through collaboration of multiple arrays.

2.2

Self-configuration

Collections of arrays enable localization by combining bearing estimates and time-difference of arrivals.
However, none of this potential can be realized without the ability to rapidly deploy the sensor arrays
and to determine their precise location and orientation. There are many positioning techniques, including those based on GPS, magnetic compass, sensor©ISAROB 2007

257

Adaptive detection

We have developed a detection module that can detect animal vocalizations from background noise, bycontinuous adaptation to the current noise level. A
generic statistically optimum approach to solve thisproblem is based on the constant false alarm rate
(CFAR) method that identifies high energy segments
in continuous streams of audio data.
The algorithm first estimates the statistical distribution of theamount of energy in specific frequency bands contained in the ambient noise on n consecutive samples(we assume that noise follows a normal distribution
N (µ, σ 2 ) in frequency space). Afterwards, the energy
present in the same bands is monitored, and a threshold function detects when the energy changes significantly from a statistical point of view, that is whenthe energy of the current segment exceeds the threshold defined µ + β · σ, where β is a threshold parameter(usually β = 3). However, noise in real environments
usually varies significantly over time, in which case it isnecessary to update the noise distribution as it varies.
For this purpose, exponentially weighted moving average (EWMA) can be used to update iteratively themean µ and variance σ of the noise power as follows:µt+1 = αµnew + (1 − α)µt
σt+1 = ασnew + (1 − α)σt

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

likelihood (AML) method for the localization of wideband acoustic sources. The ML estimation method isknown to be an optimum estimation procedure. The
term approximate refers to the condition that the data
length is finite and consequent edge effects yield aslight sub-optimality from the ML method. The AML
algorithm has been used to perform localization of single and multiple acoustic source(s), even when they
overlap in time and frequency, in the near/far-fields
as well as in open-field and in reverberant scenarios.
For each possible angle of arrival, the signals recordedby each microphone are recombined using a model ofthe array and the coherence of the resulting signal is
obtained for each angle.

Figure 2: Adaptive detection algorithm. Top: Amplitude of the signal of a field recording. Bottom:
Evolution of the energy in the signal is represented
as the thin line. The thick line represents the detection threshold. When a song is detected, one can seethat the threshold is not changed to avoid influencing
the statistical estimation of background noise.
Where α ∈ [0, 1] is the changing rate. A low value forα should be used, as we want to avoid including spurious short sounds as part of the background noise modeland updating the noise distribution based on insignficant events. Figure 2 illustrates the detection processof seven bird songs recorded in the rain-forest at Monte
Azules Biosphere Natural Reservation in Mexico.
Using a modified and streamlined version of this algorithm, described in [4], we have also detected yellow
bellied marmot (Marmota flaviventril ) alarm calls in
real-time on a network of ENSBoxes in the field. The
marmot detector computes a 32-point FFT over eachwindow of samples and computes the magnitude of thecomplex sum of the frequency bins corresponding to
the bands used by marmot calls (3-6 KHz). This energy value is then passed into a CFAR detector, with ahysteresis detection to ensure that the complete call isacquired. We found that we could improve efficiency
without losing detections by applying the FFT only to
1 out of every 4 32-point windows.

2.4

Collaborative localization

Kung Yao and students have developed a localization algorithm that can track multiple sources in realtime [1]. They developed an approximate maximum-©ISAROB 2007

258

Figure 3: Results of the collaborative localization algorithm, presented as a 2D pseudo-likelihood map.
Black lobes represent the likelihood for source AOA.
Individual estimates of the angle of arrival (AOA) for
each node are combined using their location as estimated by the self-calibration process.
In our implementation, every node that detects a
vocalization will also compute a likelihood describing
the likely bearing to the source. These likelihoods arecollected at a central point and combined together intoa 2D pseudo-likelihood map, according to the positions
and orientations computed in the self-calibration step.
This map is formed by projecting each likelihood metric outwards from each node to form the joint approximate likelihood of a source at every point in the 2D
space. Beyond source localization, this information
can also enable further signal enhancement throughbeam-forming, in which signals captured from different sensors are combined together to amplify the target signal and attenuate noise.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

Results and discussion

Kirschel and Yoosook Lee for their contribution to thispaper.

The automated self-localization system illustrates
many of the same requirements as our target applications, by being a distributed sensing application itself. This feature of the Acoustic ENSBox solves the
problem of fastidious deployment by automatically determining array orientations to within 1 degree, andarray positions to within 9cm in a 40x70m wooded
area. This process can be run periodically, so thatcalibration of the system is maintained even when thelocation of the sensors is changed.
We originally implemented an offline version of thedetection algorithm to automatically isolate tropicalbird songs from hours of recordings on a standard
desktop computer. Using the streamlined implementation, we have been able to reduce half an hour of raw
recording to only 13 seconds of audio, capturing all of
the marmot calls as well as a few false positives fromother sources. The CFAR method is known to be statistically optimum in the sense that, for a fixed CFAR,
the probability of event detection is maximized, withthe assumption that the nominal background noise isa quasi-stationary stochastic process.
The collaborative localization algorithm has beenused to localize marmot alarm calls in a field test at
the Rocky Mountain Biological Laboratory (RMBL),
in Colorado. The results of one of the localization tests
are shown in Figure 3. The RMBL tests demonstrated
that these algorithms could reliably locate marmots
by their calls to within 1.5 meters, when comparedwith ground truth based on human observations, giventhat collaboration reduces the ambiguity of the local
estimations computed by each node.
This paper described how adaptive methods can beused to develop a robust and self-organized monitoring
systems. We have been able to detect animal vocalizations in very noisy environments using an adaptive
threshold mechanism. This approach can be usefulin several contexts where detection of acoustic activity is required; for example, human-robot interaction
with humanoid robots [2]. Also, we explained how
several acoustic sources can be localized using an efficient direction of arrival estimation method, and how
collaboration between sensors can improve the results.

Acknowledgements
This research was supported by the National Science Foundation under Award Number 0410438 and0120778. The authors would like to thank Alex

©ISAROB 2007

259

References[1] C.E. Chen, A. Ali, H. Wang, S. Asgari, H. Park,
R.E. Hudson, K Yao, and Charles E. Taylor. Design and testing of robust acoustic arrays for localization and beamforming. In IEEE IPSN, 2006.
[2] Gordon Cheng, Akihiko Nagakubo, and Yasuo Kuniyoshi. Continuous humanoid interaction: An
integrated perspective - gaining adaptivity, redundancy, flexibility - in one. Robotics and Autonomous Systems, 37:161–183, 2001.
[3] Travis C. Collier and Charles E. Taylor. Selforganization in sensor networks. Journal of Parallel and Distributed Computing, 64(7):866–873,
2004.
[4] L. Girod, K. Jamieson, Y. Mei, R. Newton,
S. Rost, A. Thiagarajan, H. Balakrishnan, and
S. Madden. Wavescope: A signal-oriented data
stream management system. In Third Biennial
Conference on Innovative Data Systems Research(CIDR07). (To appear), January 2007.
[5] Lewis Girod. A Self-Calibrating System of Distributed Acoustic Arrays. PhD thesis, Universityof California Los Angeles, 2005.
[6] Lewis D. Girod, Martin Lukac, Vlad Trifa, andDeborah Estrin. The design and implementation of
a self-calibrating distributed acoustic sensing platform. In The 4th ACM Conference on Embedded
Networked Sensor Systems (SENSYS 06), 2006.
[7] Vlad Trifa. A framework for bird songs detection,
recognition and localization using acoustic sensor
networks. Master’s thesis, École PolytechniqueFédérale de Lausanne, 2006.
[8] H. Wang, C.E. Chen, A. Ali, S. Asgari, R.E. Hudson, K. Yao, D. Estrin, and C.E. Taylor. Acoustic
sensor networks for acoustic woodpecker localization. In SPIE conference on Advanced Signal Processing Algorithms, Architectures and Implementations, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Analysis of Circuit Breakers Using Artiﬁcial Stock Market
Shigeto Kobayashi

Takashi Hashimoto

School of Knowledge Science,
Japan Advanced Institute of Science and Technology
1–1, Asahidai, Nomi, Ishikawa, 923–1292 Japans-kobaya@jaist.ac.jp

School of Knowledge Science,
Japan Advanced Institute of Science and Technology
1–1, Asahidai, Nomi, Ishikawa, 923–1292 Japan
hash@jaist.ac.jp

Abstract
In various stock markets, there is a system called“circuit breakers” that interrupts dealing of stocks fora certain period when stock price changes greatly. In
this paper, we consider the inﬂuence of the circuit
breakers on a stock market using an agent-based artiﬁcial market simulator called “U-Mart”, by controlling
the period of interruption and the criterion to invoke
the circuit breakers. From experiments, we found thefollowing: circuit breakers play an important role inthe control of price ﬂuctuations and the stabilizationof the settlement system, while the circuit breakers reduce the stock trading volume. We also suggest thatthe period of interruption is important parameter in
an institutional design of the circuit breakers, since thestock trading volume and the volatility are sensitive tothe period.

1

Introduction

Financial markets are vulnerable to “bubbles” andexcess volatility. In such a case, sharp stock priceﬂuctuations may signiﬁcantly aﬀect, not only ﬁnancial markets, but entire socioeconomy. Most systems
in a ﬁnancial market contribute to the stability of amarket by eliminating uncertainty from the market
ﬂuctuations and from trading. In addition, rapid priceﬂuctuations may cause the fall of liquidity in the market, and may disturb price discovery. Circuit breaker
is one of the market systems founded in order to reduce the stock price volatility risk. In this paper, wedeﬁne the circuit breakers as a temporary stoppage of
trading.
Introduction of circuit breakers was advocated in
the report of the Brady Committee[1] that investigated “Black Monday”. According to the Brady Report, the main purpose of introducing this system is
to directly reduce excessive stock price volatility andto mitigate market disruptions. There have been somearguments about the pros and cons of the system since©ISAROB 2007260

its introduction after Black Monday. However, an extremely large-scale change of a stock price that invokes
the circuit breakers is so rare that empirical researchon the system has not been possible, and the eﬃciencyof the system has not been conﬁrmed.
It is very diﬃcult to design market systems that
contribute to the stability of the market, as a largescale economic experiment cannot be performed in areal stock market. Therefore, by using a simulation
technique, we examined whether the circuit breakers
reduce volatility and contribute to the stability of amarket.

2

Circuit breakers implemented in “UMart”

We incorporated the circuit breakers into the artiﬁcial market simulator “U-Mart”1 . In the U-Mart
system, stock index futures are traded, and prices
are calculated independently of the actual prices.
The U-Mart system adopts the pricing method called“itayose” in which orders are accumulated for a certain
period. A price is decided so as to achieve the maximum contracted volume for the accumulated orders.
We introduced interruptions of trading stock into the
U-Mart system by halting itayose for certain periods.
For the conditions of interruption, we referred to thesystem of the Osaka Securities Exchange (OSE) dealing with Nikkei 225 futures. We halt the trading ofstock, when the stock price advances (or declines) in
excess of a certain range from the standard price, andat the same time, diﬀers in excess of a certain range
from the fair value. The conditions are deﬁned by|Pc − Ps | > Rp ,(1)

|Pc − Pt | > Rm

(2)

where, Pc is the current stock price, Ps is the standard price that is the last day’s closing price, Pt is the
1 For further details, the reader should refer to [2].

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

theoretical price2 , Rp is the price range determinedbased on Ps , and Rm is the mispricing range determined based on Ps . When the market prices simultaneously satisfy the two conditions of Eqs.(1) and (2),trading is halted.

• Trading days (period): 15• Sessions (itayose) per day4 : 8• Short-term interest rate: 3-month CD rate
• Dividend yield: Expected yield of the Nikkei Index

3

Outline of simulation

We considered how the operation of the circuit
breakers aﬀects a market using our modiﬁed U-Mart
system. As a main parameter, we controlled the period
of interruption (the times of itayose to halt), Ts = 0(no circuit breakers), 1, 2, 3 and 4. We ﬁxed the pricerange at Rp = 700 yen.
The conﬁguration of the machine agents is indicatedin Table 1. The standard agent set adopts the simple
technical analysis. The contest agent set is a participant in the U-Mart machine agent contest. A random
agent has the strategy of placing an order at randomaround a latest spot price or a futures price3 . The
ratio of two types of random agents, “SpotRandomAgent (SRA)” and “FuturesRandomAgent (FRA)”, isset to 1:1. A total of 200 machine agents is prepared.
Table 1: The conﬁguration of the machine agents
Agent Set
standard agent set
contest agent set
random agent set

Strategy
8
8
2

Ratio
3
2
variable2 theoretical price = latest stock price + theoretical basis

theoretical basis = last day’s closing spot price * {(short-term
interest rate - dividend yield) * T / 365)}
T: number of days (period)
3 The agents buy or sell randomly. The SRA sets the limited
price on order around the latest spot price. The FRA sets thelimited price on order around the latest futures price.

261

Simulation with changing periods ofinterruption

We analyzed the liquidity in the market, which reﬂects how many orders can be executed without causing stock price change in a market[4]. A high liquidity
denotes that a lot of orders are executable in a matterof minutes. In order to realize smooth price discovery after interruption in dealing the stock, it is necessary to ensure the liquidity in a market. Even if thesteep fall in stock prices is avoided and the ﬂuctuationin stock prices is reduced by interruption of dealing,
the liquidity declines, and this may discourage investing activity if stock trading volume decreases greatly.
Thus, we mainly observed the volume of trading andthe historical volatility. All experimental results are
the averages of 100 trials, and “steps” of the horizontal axis in the ﬁgures shows times of itayose.

4.1

In this simulation, in order to observe the diﬀerences in market behaviors among diﬀerent conﬁgurations of agents, we conducted experiments by changing
the ratio of random agents to other agents. Ueki etal.[3] have pointed out that random agents may play
an important role such as market maker who gives liquidity to the market. Hence, we set the ratios (Ar ) of
the random agents to the total of number of agents at
Ar = 0%, 5%, 10%, 20%, 40%, and 60%, respectively.
We adopted the daily Nikkei Index that showed adownward trend (2000/4/17–2000/10/10) as the spot
price series given to the U-Mart system. The othersettings are as follows.

©ISAROB 2007

4

Total trading volume

Total trading volume is the total of the stocks
traded in 15 days. The changes of the total trading
volume are shown in Fig. 1. It turns out that the total
trading volume falls when the period of interruption
increases, irrespective of the ratio of random agents.
This is considered to be the result of simply having
lost the opportunity to trade due to the circuit breakers. In changes of the total trading volume according to the ratio of random agents, there is a tendency
for the total trading volume to increase as the ratio
becomes larger. We surmise that this is because thesupply and demand balance is maintained when the
SRAs continue to place orders at random around thespot prices, even if the supply and demand is skewed
by a large price ﬂuctuation.

4.2

Historical volatility

Proponents of circuit breakers often claim that halting dealings reduces ﬂuctuation of the stock price. Let
4 Halting itayose for 4 times means that the market is closed
for half a day in this simulation.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 1: The changes of the total trading volume
related to the periods of interruption for various ratiosof random agentsus examine whether this is actually the case. In general, volatility is used to measure the ﬂuctuation of
the price in a market. The following formulas deﬁnehistorical volatility (vx ) in this paper:vuuvx = tNX

1(xi − mx )2 ,N − 1 i=1

(3)

xi = log Si − log Si′ (i = 1, · · · , N ) ,

(4)

mx =

N
X

1
xt ,
N i=1

(5)

where Si is the present stock price, Si′ is the latest
stock price, and N is the sample size in a trading period. Equation(4), xi , calculates the logarithmic rateof change of stock price and Eq.(5), mx , calculates asample mean of xi .
The changes of historical volatility related to theperiods of interruption are shown in Fig. 2. The historical volatility decreases as the period of interruption increases, especially for a small ratio of random
agents. Due to the interruption of dealing, the stock
price recovers to a proper price without falling to thebottom. But this eﬀect is weakened when the ratioof random agents is higher. In such a case, the priceﬂuctuation is extremely low, since the random agents
give suﬃcient liquidity for a market, as we describedin the preceding section. Even when the ratio of random agents is small and the ﬂuctuation is relatively
large, unless the period of interruption is long enough,historical volatility does not decrease. Thus, in order to suppress a short-term ﬂuctuation, the length ofinterruption of trading should be ﬂexibly decided.

©ISAROB 2007

262

Figure 2: The changes of historical volatility related
to the periods of interruption for various ratios of random agents: Only numbered lines show the signiﬁcant probability according to the Kruskal-Wallis test
among the historical volatility of the periods of interruption, Ts = 1, 2, and 3.

4.3

Bankruptcy

The circuit breakers give decision-making time toindividual investors, so that those who cannot always
monitor a market can catch up with change of a market, and they are protected against a drastic loss[4].
The increase of bankrupt may destroy a settlement
system of a futures market based on netting. We consider many agents going bankrupt by dealings as afactor to destabilize a settlement system, and investigated how the circuit breakers aﬀect bankruptcy.
As shown in Fig. 3, we found that the number ofbankrupt agents decreases greatly after introduction of
the circuit breakers, regardless of the ratio of random
agents. We conﬁrmed that the diﬀerence in the number of bankrupt agents is signiﬁcant using the MannWhitney U test, under the criterion of P < 0.05. The
circuit breakers may prevent agents with heavy losses
from going bankrupt.

5

Simulation with price ﬂuctuations inthe spot price series

In this section, we investigated the inﬂuence of thecircuit breakers on a market under larger price ﬂuctuation. In this experiment, the spot price series of the
Nikkei Index is increased range of ﬂuctuation. The
amounts of ﬂuctuation are 1%, 3%, and 5%.
In this section, random agents were set to 20%. In
this experiment, the tendency for the total trading volume to decrease, as in the former simulations, was ob-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 3: The changes of the number of bankrupt
agents related to the periods of interruption for various
ratios of random agents: Only numbered lines show
the signiﬁcant probability according to the MannWhitney U test between the number of the bankrupt
agents of the period of interruption, Ts = 0 and 3.

served whenever the period of interruption increased.
Concerning total trading volume, it turns out that thecircuit breakers have only the eﬀect of decreasing volume, even if price ﬂuctuation is large.
The changes of the historical volatility of the futures price are shown in Fig. 4. In the case where theexpansion rate is 5%, though the interruption period
is one period (Ts = 1), there is a big eﬀect to control
volatility. The signiﬁcant diﬀerence of the historicalvolatility between no circuit breakers (Ts = 0) and circuit breakers with one period (Ts = 1) was proved byMann-Whitney U test. Since ﬂuctuation is artiﬁciallyadded to the spot price, the price ﬂuctuation in 1 timeof itayose becomes larger than in the former simulations. Therefore, it is thought that the circuit breakers
are critically eﬀective against price ﬂuctuation control.
However, when the range of price ﬂuctuation is small,the range of decline of volatility is small. In Ts = 4,
reduction in volatility has taken place sharply for all
rates of change.
From these results, in order to reduce excessive
volatility, it is better to invoke a circuit breaker fora short period, in the case that a big price ﬂuctuation
happens intermittently. If more ﬂuctuation control isrequired, the period of interruption should be lengthened, but we must be prepared to decrease the total
trading volume.

6

Summary and conclusion

From experimental results, we found the following:
The circuit breakers work to suppress the ﬂuctuations©ISAROB 2007

263

Figure 4: The changes of the historical volatility of the
futures price with ﬂuctuation of the spot price seriesof stock prices, but decrease the total trading volume.
The longer trading is interrupted, the more eﬀectively
the volatility of the stock market is decreased. Whenthe degree of ﬂuctuation is large, the decrease is conspicuous. Since the operation of the circuit breakers
lowers the number of bankrupt agents sharply, it issuggested that introducing the circuit breakers prevents the bankruptcy of the agents Therefore, it isthought that the circuit breakers contribute also tostabilization of the settlement system of a market.
It is concluded that the circuit breakers play an important role for the control of price ﬂuctuations andthe stabilization of the settlement system, while thecircuit breakers reduce the stock trading volume. We
also suggest that the period of interruption is an important parameter in institutional design of the circuit breakers, since the stock trading volume and thevolatility are sensitive to the period.

References[1] Brady Commission Presidential Task Force, Reportof the Presidential Task Force on Market Mechanisms, 1988.
[2] T. Terano et al, “U-Mart: an artiﬁcial market tobridge the studies on economics and multi-agent
systems,” Proceedings of Fourth Paciﬁc Rim International Workshop on Multi-agents, pp. 371-385,
2001.
[3] J. Ueki et al, “Simulation Study Using U-Mart(in Japanese),”Papers of the Annual Conference of
The JAFEE, Vol. 6, pp. 323-329, 2002.
[4] K. Omura et al, Microstructure in the Stock Market(in Japanese), Nihon Keizai Shimbun, Inc, 1998.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Interactive musical editing system to support human errors andoffer personal preferences for an automatic piano
Method of searching for similar phrases with DP matching and inferring
performance expression

Yuta Hikisaka

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
hiki@mmcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract

the editor must spend an enormous amount of timeworking with the arrangement in order to simulate theexpressions of an actual performance.
Therefore, in this research, we have developed aninteractive musical editing system to edit music more
efficiently [1].
We have analyzed MIDI data regarding theperformances of highly skilled pianists in order toobserve the stylistic tendencies of their performances. As
a result, it was found that phrases having similar patterns
in the same composition were performed in similar styles.
Moreover, it was found that the pattern of notes in thescore sometimes influences the music’s expression.
In this research we developed a system thatsearches for similar phrases throughout a musical score
and infers the style of the performance. We propose amethod using DP matching as a way to search for similarphrases. This system converts notes into character strings.
As well, it runs DP matching using character strings andcalculates the degree of disagreement between thesestrings. We use these calculations as an index todetermine whether the strings resemble each other.
Moreover, we designed a method for inferring theperformance expression of similar phrases found by DPmatching.
In this paper, we describe the results of searchingfor similar phrases using DP matching and inferring
performance expression.

We have developed a system that allows a piano toperform automatically. In order to play music in themanner of a live pianist, we must add expression to the
piano’s performance. In the case of piano music, there
are often 1000 or more notes in the score of even a shortpiece of music, and the editor must spend a huge amountof time to accurately simulate the emotionally expressive
performance of a highly skilled pianist. Therefore, wehave developed an interactive musical editing system toedit music more efficiently utilizing a database.
We have analyzed MIDI data regarding theperformances of highly skilled pianists in order toobserve the stylistic tendencies of their performances. As
a result, it was found that phrases having similar patterns
in the same composition were performed in similarstyles.
Therefore, we developed a system that searches for
similar phrases throughout the musical score and infers
the style of their performance. Here, we propose amethod using DP (Dynamic Programming) matching as
a way to search for similar phrases and a method for
inferring performance expression.
Key words: automatic piano, knowledge database,computer music, DP matching

1. Introduction
We have developed a performance system for an
automatic piano. In this system, 90 actuators are installedin the 88 keys and 2 pedals of a grand piano. Those
actuators operate key strokes and pedaling to be executedon the piano. (See Figure 1)
Reproducing music with the piano is similar insome ways to reproducing music on the computer.
Essentially, variations in tempo, dynamics, and so on areneeded to arrange the respective tones in the desired way.
However, in the case of piano music, there are 1000 or
more notes in a score of even a short piece of music, and©ISAROB 2007

264

Figure 1: View of the automatic piano

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Musical Editing Support System
2.1 System Architecture
The structure of the system is shown in Figure 2.
The user edits performance information via the user’s
interface on the computer display (We discuss thisperformance information in the next paragraph). Also,
the user can access a database of musical grammar, theuser’s preferences, and so on. As a result, editorial work
is reduced and efficient editing becomes possible.

Figure 3: Automatic translation with database

2.3.1 Musical Rules Database
This database has the architecture of musicalgrammar necessary to interpret symbols in musicalnotation. This database consists of five tables containing“Dynamics marks”, “Articulation marks”, “Symbol of
Changing Dynamics or Changing Tempo (Symbol that
affects the speed of a note or the increase or decrease of
the volume)”, “Time signature”, and “Tempo marks”.
This database enables a general performance
expression to be applied to music automatically.
Figure 2: Structure of the editing system

2.3.2 Score Database

2.2 Format of Performance Information
The automatic piano that we have developed uses
a music data structure that is similar to MIDI. We defined
performance information, dividing it into two categories,
the notes and the pedals.
The note information consists of six parameters
involved in producing a tone: “Key (note)”, “Velo(velocity)”, “Gate”, “Step”, “Bar”, and “Time”. “Velo” isdynamics, given by the value of 1～127. “Gate” is theduration of the note in milliseconds. “Step” is the intervalof time until the next note, and it also exhibits tempo.
“Bar” is the vertical line placed on the staff to divide themusic into measures.
The pedal information consists of four parameters:“Key (indicating the kind of pedal, “Damper” or“Shifting”)”, “Velo (the pedaling quantity)”, “Time (theduration of applying the pedal)”, and “Bar”.

2.3 Automatic Translation with Database
Our system can automatically apply a roughperformance expression using a Musical Rules Databaseand Score Database. (See Figure 3)

This database has symbols including notes, time
signature, rests and so on in standard musical notation.
Symbols were pulled together in the order of bars, andbar symbols are arranged in a time series.
This database consists of three tables, the“Element table” (showing the position of the notes andthe composition of the chords), the “Symbol table”(showing the position of the music symbol) and the“Similarity Table” (showing the position of the repetitionof the phrase).

①
②
③
④
⑤
⑥

Note or Rest (0: Note, 1: Rest)
Note Value (000: whole note, 001: half note …etc)
Tie (If the note has a tie then this number is 1.)
Ornament(If the note has an ornament then this number is 1.)
The number of dots
(Exceptionally, if it is “11” then the note is tuplet.）
Additional Information
The number of tuplets. (Triplets: 0011)
The type of an ornament. (Trill: 0101）
etc…

Figure 4: Note Value©ISAROB 2007

265

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
The element table contains the field “Note Value”.
The data in this field indicates the type of note, e.g., aquarter note, a triplet, and so on. “Note Value” isexpressed by three hexadecimal numbers. The “NoteValue” numbers are shown in Figure 4.

because a letter should not be the same as the firstcolumn with the second column since it expresses onenote with two columns. An example of a Note Patternconversion is shown in Figure 6.

3. Searching for Similar Phrases
As a result of the analysis, it was found thatphrases of the same pattern existing in the same tune are
performed in a similar expression[2].
This time, we used DP matching to search forsimilar phrases.

Figure 6: Example of Note Pattern

3.1 DP matching

3.2.2

DP matching[3] is a technique used well in the fieldof speech recognition, bioinformatics and so on. It has afeature that can calculate similarity between two words
that are different in a number of characters from eachother.
In Figure 5, the route of minimum cost in eachpoint is taken and the route with the lowest cost is
assumed to be an optimal path finally. The cost at thattime is defined as the distance between patterns. In thissystem, this distance is handled as a threshold to judge
whether the phrases are similar to each other.

The flow of the similar phrase search is shown in
Figure 7.
Essentially two rounds of search processing are
performed. The first processing round narrows down the
point that may be the point of resemblance in all searchranges. A pattern of the same number as the searchphrase is pulled out and the distance between two
patterns is calculated using DP matching. If they are incomplete accord (distance = 0) then the phrase is stored
in the Similar Table. If the distance is lower than thethreshold then the phrase is stored in the Temporary
Table. In the second round of processing, DP matching is
performed again using the phrases in Temporary Table
while increasing the number of characters. In otherwords, the system looks for the most similar phrases inthe surrounding phrase. The threshold of this system has
been decided by trial and error.

The Method of Searching

Figure 5: Method of calculating the distance3.2 Searching with DP matching
In this passage, we describe a method of searchingwith DP matching. We had to convert a musical score
into character strings (Note Pattern) before searching forsimilar phrases. This process is explained below.

3.2.1

Note Pattern

Our system converts a score into a Note Patternusing Note Value (See Passage 2.3) to perform DP
matching. Of the three columns of Note Value, we used
the two columns from the left. The system replaces thesecond column with letters of the alphabet (from G)©ISAROB 2007

266

Figure 7: The flow of the similar phrase search(Search range and expand range are set beforehand.)The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Searching Result

Actual performance

Inferred expression

An actual search was performed using the searchmethod explained in paragraph 3.2. Part of the result of
the search using this system is shown in Figure 8. The
horizontal axis of each graph is time (in milliseconds),
and the vertical axis is velocity (1 to 127). This graph
was made based on performance data by Gerhard Oppitz.
The arrangement of each note also looks like thesearched phrase. Especially, it is understood that theperformance expression was also very similar in thegraph of (a) whose distance is 0. The graph of (b) does
not completely correspond. But based on the change in
Velo, it seems similar.

Figure 8: The result of the searching

4. Inferring Phrase Expression System
The similar phrases searched for by the method of
the description in Chapter 3 are inferred using theexpression of the performance of the search phrase.

Velo

3.2.3

70
60
50
40
30
20
10
0
0

1943

2223

3054

3334

4501Time(ms)Figure 9: Inferred performance expression

5. Conclusion
We designed a method of searching for similarphrases and inferring the performance expression using
DP matching and constructed these as one system.
In the similar phrase search, the system was able tosearch for similar phrases using DP matching in a shorttime, and it was even possible to search for phrases
whose resemblance might not be immediately apparent.
In the inference of the performance expression, aperformance expression was able to be inferred using thebest associating by the DP matching from the search.
The performance expression of some similarphrases that exist in the tune can be inferred at the sametime using this system. It is regarded that the work
efficiency increases by this automatic editing in contrast
to editing the piece by hand from the beginning, without
sacrificing quality.
In this study, a general performance expression
was used for musical symbols such as Crescendo. But, itis thought that it will be possible to infer the user’s
intentions for such symbols as well in the future.

4.1 Method of inferring phrase expression

Reference

The correspondence of the note patterns isrevealed when the distance is minimized in DP matching.
Next, the ratio of Velo to the previous sound is calculated
in each sound of the searching phrase. Velo of theassociated phrase is inferred using a ratio. Thus, the Veloof a similar phrase is inferred. The value of Time uses thevalue decided by Automatic translation (See Passage2.3).

[1] Takamatsu Y, Hayashi E, et al, “Study on the
Performance Information Edit Support System for an
Automatic Piano (in Japanese)”, Technical Report ofInformation Processing Society of Japan, MUS-52,
pp.111-118, 2003.

4.2 Inference Result
The result of the inference using this inference
system is shown in Figure 9. The phrase of the seventh
bar of Beethoven’s Sonata No. 8 was inferred from thefirst bar, which Gerhard Oppitz performed.
Thus, the inferred phrase was similar to theexpression of the performance of the search phrase.

©ISAROB 2007

267

[2] Hikisaka Y, Hayashi E, et al, “Interactive musicalediting system for supporting human error andoffering personal preferences for an automatic piano-A system of inferring phrase expression-”, Proc. of
the 11th International Symposium on Artificial Lifeand Robotics, pp 296-299, 2006.
[3] Nakagawa S, “Speech recognition based on
probabilistic model (in Japanese)”, IEICE, pp18-28,
1988.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Self-reproduction on 1-bit communication cellular automata
Ryoh Takebayashi, Koutarou Tachibana and Hiroshi Umeo
Univ. of Osaka Electro-Communication, Graduate School of Engineering,
Faculty of Information Science and Technology, Department of Informatics,
Neyagawa-shi, Hatsu-cho, 18-8, 572-8530, Osaka, Japan

Abstract
Many researchers have constructed a self-reproduction model on cellular automata so far. C. G. Langton constructed a simple model on the 2-D cellular automata, and it is a dynamic-loop [4]. We consider embedding the Langton’s model in 1-bit communication
cellular automata. The 1-bit communication cellular
automata are restricted communication-capacity to 1bit. The Langton’s model has 8 internal states andgenerally it is impossible to represent 8 by 1-bit. Thus
it occurs time-loss in communication of information.
In this paper, we will embed Langton’s model in 1-bit
communication cellular automata without time-loss.
However the embedding model has a large numberof internal states, so we construct the second model.
In the second model, by excluding some properties ofLangton’s model, we reduced internal states.

1

Introduction

Studies for life phenomena have been made formany years. Especially, structures of many lives havebeen made clear by progress of technology from middle of 20th century. However, evolution process of life
phenomena and process that cells constitute a large
scale structure according to genes are not clear.
Some of researches, which are actively done in artiﬁcial life, are about self-reproduction of life. Especially,a lot of self-reproduction models on 2-D cellular automata have been suggested by many researchers [2-4,
6, 7, 9, 10]. A self-reproduction model constructed by
C. G. Langton on 2-D cellular automata is very simple.
It has a dynamic-loop structure. In this paper, we will
embed the simple model in communication-restricted
cellular automata.

2

Self-reproduction loop

At ﬁrst, self-reproduction model was suggested byJohn von Neumann. Neumann considered a machinethat has information for self-reproduction, and replicates itself, and then he get an idea. It is that a universal machine is able to do such behavior. Moreover,©ISAROB 2007268

for simpliﬁcation of problem, Neumann used the 2D cellular automata and he constructed an universal
machine that has 29 internal states on 2-D cellular
automata [6].
The 2-D cellular automata are a parallel processing
model where ﬁnite automata are placed as plane. We
call each automaton cell. Each cell has a set of internal
states Q and a transition function δ such that Q4 → Q.
In the 2-D cellular automata, inputs of transition function are states of neighbors (north, west, east, south
and itself). Thus, transition function δ on 2-D cellular
automata is deﬁned as the following.
δ(qn , qw , q, qe , qs ) = q (qn , qw , q, qe , qs , q  ∈ Q)

(1)

Codd designated that Neumann’s model can behavein 8 internal states [3], and Serisawa designated that
Neumann’s model can behave in 3 states (Neumannneighbors) or 2 states (Moore neighbors) [9, 10].
On the other hand, Langton considered a more simple model, because cells in nature don’t seem to beuniversal machine. So Langton constructed such selfreproduction model on 2-D cellular automata. Langton’s model uses 8-states per cell [4]. We call Langton’s self-reproduction model on 2-D cellular automata
Langton-Loop by the shape. Figure 1 shows transitionof Langton-Loop, where t means time. Langton-loop
exists in cellular space ﬁlled by state ”0” cells. The
state “0” is called quiescent state.
Langton-Loop is surrounded by cell wall states. We
call a part which is surrounded cell wall inner-loop.
Langton-Loop reproduces a pattern by state transitions in inner-loop. Here, we consider separating
Langton-Loop into loop-part and arm-part, to assist
understanding transition of Langton-Loop (Fig. 2).
By the signals from loop-part, Langton-Loop extends
arm-part and builds left hand corners 3 times at thetime which is decided beforehand.
A signal to arm-part consists of ﬁve state ”7” andtwo state ”4”. Loop-part send out these states to theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
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0/1 0/1
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i+1

m

t = 128

Figure 4: 2-D 1-bit communication cellular automata

Figure 1: Langton-Loop

Loop

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Figure 2: Loop-partand arm-part

Q and transition function δ, as well as 2-D cellular
automata. Transition function δ in 2-D 1-bit communication cellular automata is deﬁned to the following,by the property.

2

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2

2

2

2

2

2

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2

2

2

2
2

2

δ(bn , bw , q, be , bs ) = (bn , bw , q  , be , bs )Figure 3: Direction of
signals

end of arm-part (Fig. 3). When state ”7” reaches theend of arm-part, arm-part can extend length of one
cell.
By state ”4” reaches the end of arm-part two times,
arm-part turns. The ﬁrst state ”4” changes a directionof arm-part’s extension to left hand by using temporary state ”3”. Then, when second state ”4” reaches acell which assumes state ”3”, the state ”3” cell changes
state to state ”1”. So arm-part can change directionof extension.
By iteration of this action, loop-part is constructed.
For that purpose, loop-part must continue sending thesignal which consists state ”7” and state ”4” to armpart. This action is done by sending state ”7” andstate ”4” to arm-part, and feed back into loop-part atonce (Fig. 3).

3.1

n

j+1

i-1

i

2

3

j

1

t = 28t = 83j-1

1

Self-reproduction loop on 1-bit communication cellular automata
2-D 1-bit communication cellular automata

The 1-bit communication cellular automata weresuggested by Mazoyer [5] and Umeo [8]. The 2-D 1-bit
communication cellular automata are 2-D cellular automata that are restricted communication-capacity to
1-bit. Figure 4 shows structure of 2-D 1-bit communication cellular automata. Each cell of 2-D 1-bit communication cellular automata has a set of inner-states©ISAROB 2007

269

(2)

Where q, q  ∈ Q and bn , bw , be , bs , bn , bw , be , bs ∈ {0, 1}.
In 1-bit communication cellular automata, an information from a north, south, east, west cell is always 0 or1.

3.2

Self-reproduction model Mα

In this paper, we suggest two self-reproduction
models. We call ﬁrst model Mα and second modelMβ . At ﬁrst, we will explain about Mα
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2D2

2D

2D

2D

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2D

4

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2R1

1_7L5

7L6

4L1

0_4L1 1_4L1

4U2

2L2

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4

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2R1

1_7L5

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0_4L1 1_4L1

5

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2L

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2U

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0_4U2

2L

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2R

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2L

2U

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2U

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2L

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2R

7D5

2L

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2R

1_7D4

2L

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2R

1LU1

2L

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7

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2R

1_7D4

2L

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8

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2R

Q7D_4

2L

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2R

1LU2

2L

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8

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2R

Q7D_4

2L

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9

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2R

7D4

2L

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2R

1LU3

2L

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9

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2R

7D4

2L

10

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2R

1_7D3

2D

2D

2D

2D

2D

2R

1LU4

2L

2D

2D

2D

2D2

Q

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Q

Q

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10

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2R

1_7D3

11

Q

Q

2R2

Q7D_3

7R3

1_7R2 Q7R_2

7R2

1_7R1 Q7R_1

7R1

1LR4

1LR3

1LR2

1LR1 1_4R2

2L2

Q

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11

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2R2

12

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2U1

2U

2U

2U

2U

2U

2U

2U2

2U

2U

2U

2U

2U2

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Q7L_6 1_7L6

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2D1

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4U2

2L2

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2U

0_4U2

2L

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2R

1_4U2

2L

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2R

1LU1

2L

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2R

1LU2

2L

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2R

1LU3

2L

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2D

2D

2D

2D

2D

2R

1LU4

2L

2D

2D

2D

2D

2D

2D

2D

2D2

Q

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Q7D_3

7R3

1_7R2 Q7R_2

7R2

1_7R1 Q7R_1

7R1

1LR4

1LR3

1LR2

1LR1 1_4R2 0_4R2

4R2

1_4R1 0_4R1

1_7MR 2WL

Q

Q

2U1

2U

2U

2U

2U

2U

2U

2U2

2U

2U

2U

2U

2U

2U

2U

2U

2U2

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6

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Q7L_6 1_7L6

t=0
1

2

3

1

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4

2D

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2D

2D

2D

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2D2

2WD4 2WD5

4

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2R1

Q7L_5 1_7L5

7L6

4L1

0_4L1 1_4U1

2L2

Q

Q

Q

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2WR2

7L2

Q7L_4 1_7L3

5

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Q

2R

7D5

2L

2U

2U

2U

2U

2U

4U2

2L

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2U2

6

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2R

1_7D4

2L

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2R

0_4U2

2L

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7

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2R

Q7D_4

2L

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1_4U2

2L

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7D4

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1LU1

2L

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2R

1_7D3

2L

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2R

1LU2

2L

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2R

Q7D_3

2D

2D

2D

2D

2D

2R

1LU3

2L

2D

2D

2D

2D

2D

2D

11

Q

Q

2R2

7D3

1_7R2 Q7R_2

7R2

1_7R1 Q7R_1

7R1

1LR4

1LR3

1LR2

1LR1 1_4R2 0_4R2

4R2

1_4R1 0_4R1

12

Q

Q

Q

2U1

2U

2U

2U

2U

2U

2U

2U2

2U

2U

2U

2U

2U

2U

2U

2U

2U

2U

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Q7L_6 1_7L6

9

10

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2WD7 2WD3
4R1

2WU7 2WU3

t = 28

2D1

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10

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t = 83

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2D

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2D

2D

2D

7U4

2L2

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4

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2R1

1_7L1

7L2

2WU4 2WU5 Q7U_4

2L

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Q7D_1

2L

2U

2U

2U

2U

1_7U4

2L

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2L

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7U5

2L

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1LD4

2L

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Q7U_5

2L

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8

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2R

1LD3

2L

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1_7U5

2L

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9

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2R

1LD2

2L

2D

2D

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7U6

2L

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2D

1_7R6 Q7R_6

2L1

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2U2

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2D

7U4

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0_4L1 1_4L1

4L2

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1LL2

2U

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2L

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2L

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2WR2 1_7D3 2WL2

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2D

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2D

2R

7U6

2L

QD

2D

Q7D_5

2D

1_4D2 0_4R2

4R2

1_4R1 0_4R1

1_7R6 Q7R_6

5L

Q

2R2

7D5

1_7R4 Q7R_4

Q

2U1

2U

2U

2U

2U

2U

2U

2U2

2U

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2U

2U

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2U

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7L3

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Q7L_3 1_7L3

4R1

15

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7U1

2L

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7U2

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2D

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2L

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6R

1_7R3 Q7R_3

7R3

1_7R2

2L1

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t = 128

Figure 5: Self-reproduction model Mα
We constitute Mα by embedding Langton’s model
to 1-bit communication cellular automata. Mα has
263 internal states. Figure 5 shows model Mα . 1-bit
signal is represented by small triangles in the ﬁgure. If
a small triangle exitst, it means sending 1 signal anda small triangle don’t exist it means sending 0 signal.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Mα also has arm-part and loop-part and performs as
Langton-Loop. All cells except reproducing pattern
are ﬁlled by quiescent state.
When an algorithm is executed on 1-bit communication cellular automata, there are problems proper to1-bit communication cellular automata. One of problem is number of transition rules an state. Since a cellon 1-bit communication cellular automata changes thestate by its state and 1-bit signals from neighbors, thecell may refers a same transition rule, even if neighbor’s state is diﬀerent. We solve the problem as following.
Mα extends arm-part by cycling signals in looppart, as well as Langton-Loop. For above problems,
each cell has to recognize its own part, a junction ofloop-part and arm-part, the corner of arm-part, thetermination of arm-part, or others.
At ﬁrst, we explain about a termination of armpart. The cell at termination of arm-part recognizes
its part by receiving 1-bit signals by forward and left,right cells against the direction of signal. The cell at
termination of arm-part extends arm-part by “7-0”signal (Fig. 6) and makes corner by “4-0” signal.
Q

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2D2

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Q7R_1

7R1

2WL2

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1_7R1 1_7R2 1_7R1

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7R2

1_7R2 1_7MR

2L2

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t = t0

2U

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t = t0 + 1

tion, each cell recognizes the direction of signals froman internal state.
The cell at the corner of loop-part recognizes that
it is the corner of loop-part by receiving 1-bit signal
from the cell on the forward and right toward the direction of signals. So the cell can change the directionof signals to the left toward (Fig. 9).

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2R2

7U2

2L2

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4R2

2WL5

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1LR1 1_4R2 0_4R2

3L

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1LR2

1LR1 1_4R2

2L1

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t = t0t = t0 + 1

0

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2D

2D

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1LL2

1LL3

1LU4

2L2

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1LL3

1LL4

7U1

2L2

Q

1LL4

7L1

Q7U_1

2L2

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2U

2U

7U1

2L

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2U

2U

Q7U_1

2L

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2U

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2R

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2L

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2L

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2R

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2L

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t = t0 + 1

t = t0 + 2

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7U1

2L

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Q7U_1

2L

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2R

1_7U1

2L

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2L

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2D

2R

1_7U1

2L

2D

2D

2R

7U2

2L

2D

Q7R_2

7R2

1_7R1 Q7R_1

7R1

1_7R2 Q7R_2

1_7R2 Q7R_2

7R2

1_7R1

2U

2U

2U2

2U

2U

2U

2U

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2U

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2U

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7R2

1_7R1 Q7R_1

7R3

t = t0 + 1

t = t0 + 2

Self-reproduction loop Mβ

The second model Mβ has 77 internal states. Figure
11 shows action of Mβ . Mβ also has loop-part andarm-part, cells surrounding pattern assume quiescentstate. The algorithm of reproduction is also same; bysending signal from loop-part, arm-part is extended
and make a corner. However, as Fig. 11 shows, there
are some diﬀerent points.

0

Figure 8: No information of the direction
However, a bad eﬀect occurs by above problem. Although at both two cases in Fig. 8, the cell whichassumes state “1” recognizes that its position is termination of arm-part, the cell doesn’t recognize thedirection of extension. Because, the cell doesn’t haveinformation of the direction. So, we add informationof the direction to each state. For example, state “2” istransformed into “2U”, “2D”, “2R”, “2L”(each statemeans north, south, east, west). By this transforma-©ISAROB 2007

Q

Q

Figure 10: A junction of loop-part and arm-partt = t0 + 2

0

0

Q

Q

A cell at junction of loop-part and arm-part recognizes that itself by receiving 1-bit signal from the cellon the right toward the direction of signals. So, thecell can send signals to loop-part and arm-part (Fig.
10). Mα can self-reproduce by these actions without
time-loss.

3.3

0

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Q

t = t0Figure 7: Makeing corner by “4-0” signal

0

Q
2D1

Figure 9: Recognition of cornert = t0 + 2

Q

2WD7 2WD3Q
2Dt = t0Figure 6: Extension by “7-0” signal
2D

Q
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t = 69Figure 11: Self-reproduction model Mβ
Mβ is diﬀerent from Langton-Loop in three points.
The ﬁrst point is that Mβ doesn’t have cell wall state
which is state “2” in Langton-Loop. The second pointis that Mβ reproduces to four direction (north, south,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
west, east) at the almost same time. The third point isthat signals which cycles in loop-part for reproduction
are a sequence of 1-bit signal.
The initial pattern of Mβ is square and the lengthof a side is more than two cells and arbitrary. Reproduced loops have same size as well as initial pattern.
Initial pattern must have 1-bit signals which can bepropagated unti-clockwise and have length of a side.

4Loop
UL 1L 1L 1L 1UR
1D

1U

1D

1U

1D

1U

Arm

DL 1R 1R 1R 1DR 1R 1R

Figure 12: Loop-part andarm-part(Mβ )Figure 13: Direction of
signalIn this model, arm-part extends length of one cellby a 1-bit signal (Fig. 14). While 1-bit signal continuously send to the end of arm-part, arm-part extends.
But, when 1-bit signal interrupts, the end of arm-partassumes a state which can build a left hand corner.
As a result, when another signals arrive to the end ofarm-part, arm-part extends to left hand (Fig. 15).
0

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t = t0t = t0 + 1

0

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t = t0 + 2

0

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0

t = t0t = t0 + 1[2] J. Byl: Self-reproduction in small cellular automata, Physica, 34D, pp. 295-299, 1989.
[3] E. F. Codd: Cellular automata, Academic Press,
1968.

[7] J. A. Reggia, S. L. Armentrout, H. H. Chou, Y.
Peng: Simple systems that exhibit self-directed
replication, Science, vol. 259, pp. 1282-1287,
1993.

t = t0 + 2

Figure 15: Changing direction of the line(Mβ )On the other hand, each corner cell of loop-partsends two signals, when each corner cell gets input
signal. These two signals which are duplicated at junction of loop-part and arm-part are respectively propagated to loop-part and arm-part as well as LangtonLoop (Fig. 13). Thus, corner cells of loop-part havefunction that controls signals sent arm-part. Continuous 1-bit signals which are sent to arm-part are sent
5 times, to build new loop-part. Signals from once tofourth times have a function which extends arm-partand builds new loop-part. Signal of the ﬁfth times©ISAROB 2007

[1] T. Arita: Artiﬁcial life, Science Press, 2000.

[6] John von Neumann: Theory of self-reproducing
automata, University of Illinois Press, 1966.

0

1R

References[5] J. Mazoyer: A minimal time solution to the ﬁring squad synchronization probrem with only one
bit of information exchanged, Ecole Normale Superieured Lyon, Technical report, No. 89-03, pp.
51, April, 1989.

Figure 14: Extension of arm-part(Mβ )0Conclusion

In this paper, we have constructed two models
which behaves on 1-bit communication cellular automata as well as Langton-Loop. The model has 263
internal states. And by excludting some propertyof Langton-Loop, we get a model that has 77 internal states. These models designate that the relationof communication capacity and internal states has a
trade-oﬀ, we want to analyze strictly as well.

[4] C. G. Langton: Self-reproduction in cellular automata, Physica, 10D, pp. 135-144, 1984.

0

1R

0

has a function that gives ability of self-reproduction
to new loop-part. Therefore, signals of the ﬁfth times
will have been propagated in new loop-part. Thus,
when signals of the ﬁfth times have inputted to newloop-part, self-reproduction that includes an ability ofself-reproduction is ﬁnished. A corner cell that havesent signals 5 times assumes a state that does not send
signal to arm-part.

271[8] H. Umeo: “A design of cellular algorithms for 1bit intercell communications and related cellularalgorithms, Proc. of MCU’98, vol. 1, pp. 210-227,
1998.
[9] T. Serizawa: Universal cellular automata withminimal states and neighbors(1), Technical reportof IEICE，AL 77-26, pp．1-10, 1977.
[10] T. Serizawa: Universal cellular automata withminimal states and neighbors(2), Technical reportof IEICE，AL 77-26, pp．11-18, 1977.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

On the Abundance of Energy Sources and Evolution of Collective Swarm in AutoConstructive Artificial Life
A.R. Adzni
Center for Artificial Intelligent,
School of Engineering andInformation Technology,
Universiti Malaysia Sabah,
Locked Bag 2073, 88999 Kota
Kinabalu, Sabah,
Malaysia.
adzni@yahoo.com

Jason Teo
Center for Artificial Intelligent,
School of Engineering andInformation Technology,
Universiti Malaysia Sabah,
Locked Bag 2073,
88999 Kota Kinabalu, Sabah,
Malaysia.
jtwteo@ums.edu.myto explore especially in context of self-reproducing
system.

Abstract
We demonstrated a collective swarm of flying
agents simulated in Breve auto-constructive artificial life,
where their behavior is governed by its own simple set ofprograms expressed in Push programming language.
Agents are programmed to seek the food sources to regrow their energy as a main goal to survive for the next
generation, thus collective agents may be capable ofreproducing their own children. In this paper, the mainconcepts we are trying to get across concern on how
reproductive competence may be affected by the quantityof food energy moves through an environment. We
performed 50 runs test per parameter setting and will
eliminate at 6000 generations each run. From the result,
we found that the more increasingly the quantity of foodenergy are injected would encourage the more increasein the percentage of reproductive competence achieved
within 50 runs. The reproductive competence means thepoint in which generation agents are able to reproduce
their own children for the next generation without any
new agent been injected.
Keywords–Swarm
Behavior,
CollectiveIntelligence, Artificial Life, Auto-construction,
Genetic Programming, Parametric Analyses

Introduction
Artificial life refers to creating and simulating the living
system to model life form, which can be implemented incomputer. Artificial life also is defined as “life made byhuman”. The main purpose of Alife is to create synthetic
life in an artificial component and compare it with thenatural life, as we know it [1]. Alife has a great potential©ISAROB 2007

Azali Saudi
Center for Artificial Intelligent,
School of Engineering andInformation Technology,
Universiti Malaysia Sabah,
Locked Bag 2073,
88999 Kota Kinabalu, Sabah,
Malaysia.
azali@ums.edu.my

272

Nowadays, a number of self-reproducing
systems have been explored. In 1940, Cellular Automata(CA) environment was invented by John Von Neuman
who created a set of rules describing how in one specificstate of the system a particular configuration of cells is tobe transformed or converted to another state of thesystem. One of the most well known CA rules, namelythe ``game of life'', was conceived by Conway in the late1960s.
From this drawback, many researchers come out
with their own system to model life form throughcomputer simulation. An example of open-ended
selection was Tierra, which was a simulation of evolving
self-replicating programs with a mortality queue toensure they had to try and copy or maintain themselves[2]. Similar to Avida and cosmos, where Avida wasinspired by Tierra system that; relies on externalmutating mechanism [3]. These systems must be seeded
initially with the simple human coded self-replicating
program. This is in contrast with Breve auto-constructive
evolution, which relies on the Push program to support
self-reproductive “PushPop” program in which thehigher performance individuals are able to reproduce
their own children [4].
By applying Breve application provides us a
powerful framework [5], we presented a set ofcollective agents in 3D simulation. Over the years, one of
the main difficulties that a user faces is on how to decide
an appropriate set of parameter values, as user has tospecify a number of parameter when applying a Genetic
Evolution Algorithm.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Thus, evolved dynamic parameter values offood energy, which is defined as feeder in Brevesimulation. The aim of this work is to investigate how
the energy sources in environment would have an affectthe reproductive competence in auto-constructive
artificial life.

Methodology Approach(a)

Breve environments
The swarm intelligence of flying agents waspresented in 3D simulation using the piece of software
called Breve environment. This system provides aframework for artificial life that can create realistic
object in 3D simulation and includes a simple
programmable language such as “Push” language for use
in evolutionary computation [5].
The behavior of each agent in simulation
environment was governed by one complete code
program expressed in Push programming language. Push
program has a simple syntax and can process multiple
data types that allow Push to support the automatic
evolution such as “auto constructive evolution. Thus, thegenetic operator and other evolutionary system evolve
themselves.

(b)

Figure 1.0: (a) The direction of feeder moving to axis-x
and axis-z.
(b) The direction of agents moving to thenearest food sources.
Parameters that were altered
Dynamic parameter refers to the number of
feeders injected into Breve simulation. As shown in table
1.0, five different numbers are evolved before and after
the point of default value. By applying this parametervalue, we would like to investigate how this different
number of food would have an effect on the reproductive
competence in auto-constructive artificial life.

Experiment setup

Parameter Values
10
12
14
16
18
20
22
24
26
28
30

Characteristic of feeder

Table 1.0: Parameter values are alteredAuto-constructive evolution is a framework forevolutionary computation system [6], an evolving
population of programs expressed in the Push
programming language. This system is responsible for
producing a new program through the mutation process
for the next generation and evolves within theindividuals as the system run.

The environment in this simulation will be
supplied with the needs of organisms such as energy
sources are defined as feeder. Feeder is simulated inwhite sphere and contains energy that needed by agents
as food to increase their energy and also for longevity.
As shown in Figure1.0 (a), the movements of feeders
were in limited areas and horizontally either axis-x oraxis-z the movement on the axis-y (dot line arrow) andwere not included to avoid the difficulties of seeking forfood.
The agent with polygon shape in simulation asshown in Figure 1.0 (b), was programmed to fly andclose the food energy with the aim to re-grow energy.

©ISAROB 2007

273

RangesDefault Value

Before running the algorithm, for eachparameter values we performed 50 runs with 50 different
seeds and will be eliminated at 6000 generations.
Reproductive competence
Each agent represented a complete Push
program that will be evaluated. Agents were
programmed to seek for food in order to increase theirenergy. Unsuccessful agent will quickly die thus thenumber of agents will decrease.
If the number of agents falls below user-defined
threshold of 10 individual, the system will automaticallyinject new agents into the system in order to maintain theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
minimum population size. If this phenomenon occurs to
the next generation, then the swarm in that particularpoint of generation will not be considered to be
reproductively competent. Alternatively, if thisphenomenon does not occur, agents may be capable toreproducing their own children, in which case thecollective swarm will be considered to be reproductive
competent.

According to this table, the smallest value ofmean shows the reproductive competence achieved
earlier where agents are able to reproduce to make theirown population for the future generation without any
new agent randomly being injected. This can be seen atparameter 12 with average of 2 rather than 303.00 atparameter 10.
Parameter

Mean

10
12
14
16
18
20
22
24
26
28
30

303.002.00
300.00181.38165.54180.91130.14151.14
189.12190.40186.17

Standard
Deviation
0.00
0.00
164.01140.10166.76178.24154.31
149.14194.86
198.42
195.38

Measuring reproductive competence
We measured the point in times where
reproductive competence is achieved within 5500
generations completely without any new agent injected
into simulation as shown in table 1.2:
Components
Dynamic Parametersetting
Generations

1
2
3
4
5
.
.
.
456
.
2356
.
3564
6000

Feeder10(x 50 runs)
Agents generated by:
SystemAgents
1
0
.
.
.
0
1
1
0
0
0
0
0
0

0
0
1
.
.
.
1
0
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0
0
1
1
0

Table 1.3: Average of reproductive competence within
5500 generations

Average(456 + …….. + y)/ N
Percentage(N/50)*100
N = The number of runs which the reproductive
Competence achieved.

From our analysis, this may due to the cause of
the distribution of food energy located near the groupthat could have been caught by agents. As we can see thebehavior of agents in Figure 1.2(a), even if a group ofagents compete with each other but the same species(same color) among them will share energy to theirfriends. Thus, unsuccessful agents with less energy will
get additional energy from their group’s friend thatwould give it a chance to find food energy nearby Figure
1.2(b). However, if a group of agents were different
species that is different color as shown in Figure 1.2(c),because of their rest energy that could not catch a nearby
feeder.

Table 1.2: The way to measure the reproductive
competence

Result and discussion(a)Table.1.3, displays a set of reported data on themean and standard deviation of reproductive competenceachieved within 5500 generations in 50 runs of test. As
statistics, average refers to the measurement of thecentral tendency of the data set of reproductive
competence and in relation to the mean we alsomeasured the standard deviation of how spread out are
the data values aways from the mean.

©ISAROB 2007

274

(b)

(c)
Figure 1.2: Behavior of collective agents:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
to the quantity of food more than agents that cause lesscompetence among agents. Similarly even a 2% decreaseat parameter 22 instead of at parameter 20 after 44%
runs, we also found that the mean of reproductive
competence shows the better result of 130.14 rather thanat parameters 26 and 28.

Figure 1.3, showing the percentage of reproductive
competence achieved within 50 runs. As we can see, thepercentage of reproductive competence achieved at axisy increased slowly against dynamic parameter values of
feeder but suddenly showed a large difference atparameter 20 of 20% from parameter 18. This may due
70
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46

40
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26

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10
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6

0
10

12

14

16

18

20

22

24

26

28

30

D yna m ic P a ra m e t e r S e t t ings

Figure 1.3: Percentage of reproductive competence achieved within 50 runs

Conclusion
The elements that appear in this work are focused on themain aspect on how reproductive competence may beaffected by the quantity of food energy moves through anenvironment. The most specific approach is on the
population of energy sources analysis, which estimatesthe energy budget of a particular population. The
analysis is done by determining the amount of energy inthe form of organic matter that is consumed, assimilated
and excreted by individual organisms. The second
approach is the study on the capability of each individualorganism to survive, how fit are these individuals incompeting with each other. In summary, the dynamics ofenergy in environment, each of which has its own
inherent strengths and weaknesses. There are manyother factors that would also affect the growth of
population in environment. This would be interesting to
explore for the future work.

Acknowledgments
Many thanks to PTPTP (Pelan Tindakan Pembangunan
Teknologi Perindustrian) for Postgraduate scholarship,
University Malaysia Sabah for this research. I would liketo thank to Mr. David Low for his help in grammatical,
Mr. Saidin and Riz Faizal for helping in Lab facilities.

References[1] C.Adami, Introduction to life. Springer-Verlag. New
York 1998.

©ISAROB 2007

275

[2] T.S. Ray, An Approach to the Synthesis of Life, in
C. G.Langton Taylor, J. D Farmer & Rasmussen, eds,
Artificial Life II Vol. X of Series: Sante Fe InstituteStudies in the Sciences of Complexity, Addison-Wesley
Publishing Company Inc.,Redwood City, California,
1992 pp. 371-408.
[3] T.J Taylor and J.Hallam, Studying Evolution with
Self-Replicating Computer Program. Fourth European
Conference on Artificial Life, University of Edinburgh.
In P. Husband and I. Harvey, editor.MIT Press/ Bradford
Books, 1997, pp. 550-559.
[4] L. Spector and A Robinson, Genetic Programmingand Autoconstructive Evolution with the Push
Programming Language. In Genetic Programming and
Evolvable Machines. Cognitive science, Hamsphire
College, Amshert, MA 01002. Vol.3, NO.1, 2001. pp.740[5] J.Klein, BREVE: A 3D Environment for thesimulation of Decentralized Systems and Artificial Life,
Proc. Eighth Intl. Conf. on Artificial Life. The MIT
Press, Cambridge, MA (2002), 2003, pp. 329-334.
[6] L. Spector. Autoconstructive evolution Push, PushGP
and PushPop. Proceeding of the Genetic andEvolutionary Computation Conferenc, GECCO-2001,
san Francisco, CA:Morgan Kaufmann Publishers,
pp.137-146.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Interactive musical editing system to support human errors andoffer personal preferences for an automatic piano- Information system regarding a musical sign Yuichi Chigoi

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
chigoi@mmcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract
We have developed an automatic piano that canaccurately control the motion of both the keys and thepedal based on performance data that a user has inputted
for a particular piece of music. However, this systemcannot sight read a new piece of music, as in thesimulation of a human being’s expressive performance.
Therefore, we developed a program that can memorize
and use knowledge databases and user preferences
concerning the interpretation of a piece of music.
We analyzed performance data of performances ofhighly skilled pianists in order to observe performancetendencies, and found that phrases of similar patterns
existing in the same composition were performed withsimilar expression by the same pianist. Moreover, it was
found that the pattern of notes in the score sometimes
influence how the expression emerges.
Therefore, we developed a system for inferringphrase expression from the patterns of the notes. We
evaluated the system by comparing performance
information inferred from the databases with the pianist’s
actual performance.
Key words: automatic piano, knowledge database,music interface, user’s preference, computer music,
inference systemarrangement in order to simulate the expressions of an
actual performance.
Even for a skilled computer user, it becomesprohibitively burdensome to reproduce a score and add
expression simultaneously using the automatic piano anda computer music system. The reason is because themusical data cannot be rewritten all at once. By contrast,
highly skilled pianists can sight-read an unfamiliar pieceof music, even if the performance is not completely inaccord with a specific musical interpretation. The
computer system cannot sight-read a new piece of music,
and cannot simulate a human pianist’s expressive
performance.
Therefore, in this research, we have developed aninteractive musical editing system to edit music more
efficiently[1]~[4].
We devised a method for inferring a performancefrom information on the score and a particular user’s
editing characteristics for similar phrases. We evaluatedthe system by comparing performance information
inferred from individualized databases with the pianist’s
actual performance.
In this paper, we describe the method of inference
based on analytical results, and its result in an actual
simulated performance.

1. Introduction
We developed a performance system for an
automatic piano. In this system, 90 actuators are installedin the 88 keys and 2 pedals of a grand piano. Those
actuators operate key strokes and pedaling to be executedon the piano. (See Figure 1)
Reproducing music with the piano is similar in someways to reproducing music on the computer. Essentially,variations in tempo, dynamics, and so on are needed toarrange the respective tones in the desired way. However,in the case of piano music, there are 1000 or more notes
in a score of even a short piece of music, and the editor
must spent enormous amounts of time working with the©ISAROB 2007

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Figure1. View of the automatic piano

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Musical Editing Support System

Table 2.

2.1 System Architecture

Parameter
Key
Velo Time Bar
Unit
m sec
Reference Damper or Shift 0～127

The structure of the system is shown in Figure 2. The
user edits music via the user’s interface on the computer
display. The user can also access a database that hasmusical grammar, the user’s preferences, and so on. As aresult, editorial work is reduced and efficient editing
becomes possible.

Figure 2.1 Structure of the editing system

2.2 Format of Performance Information
The parameters of performance information are
shown in Tables 1 and 2.
The automatic piano that we have developed uses a
music data structure that is similar to MIDI. We defined
performance information, dividing it into two categories,
the notes and the pedals.
The note information is comprised of the six
parameters involved in producing a tone: “Key (note)”,“Velo (velocity)”, “Gate”, “Step”, “Bar”, and “Time”.
“Velo” is the] dynamics, given by the value of 1～127.
“Gate” is the duration of the note in milliseconds. “Step”
is the interval of time until the next note, and it also
exhibits tempo. “Bar” is the vertical line placed on thestaff to divide the music into measures.
The pedal information is comprised of fourparameters: “Key (indicating the kind of pedal,“Damper” or “Shifting”)”, “Velo (the pedaling quantity)”,“Time (the duration for which the pedal is applied)”, and“Bar”.
Table 1.

2.3 Editing Support Process with Database
The procedure for editing by the system is shown in
Figure 3.
Temporary music data (TMD) is the data of a pieceof music without expression. Because expression has notbeen added, the necessary editing of the TMD isextensive.
Therefore, if the user chooses, the TMD is
automatically translated by the system into original
music data (OMD), similar in structure to TMD; after
that, the user can start to edit it. The automatic translation
program uses a Score Database, Musical Rules Database,and Preference Database, the details of which aredescribed later in the paper. The user adds editing to the
OMD and makes slight adjustments. When editing, thesystem watches over the data the user enters and music
knowledge is provided. Concurrently, phrases in themusic are discovered. When a phrase with the same
pattern as one already edited occurs in the music, it isautomatically translated. After editing, the system
extracts the expressions and preferences that are peculiar
to the user from the OMD. These expressions are stored
in the Preference Database, which is then used when
editing other music, resulting in improved editorial
efficiency.

Figure2.2. Structure of the editing system

2.4 Details of the Databases

The parameters of note information

Parameter
Key
Velo Gate Step Time BarUnit
m sec m sec m sec Reference 21～108 1～127©ISAROB 2007

The parameters of pedal information

2.4.1 Score Database
This database has symbols including notes, time
signature rests, and other standard musical notations.
Symbols were pulled together in order of bars, and bar
symbols are arranged in time series.
This database is composed of three tables, the277

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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“Element table” (showing the position of the note andcomposition of the chord), the “Symbol table” (showing
the position of the music symbol), and the “Same table”(showing the position of the repetition of the phrase).

Therefore, this phrase is played six times total. We
analyzed these data. The general Gate ratio is 0.7 to 0.8.

2.4.2 Musical Rules Database
This database contains the architecture of musicalgrammar necessary to interpret symbols in musicalnotation. This database is composed of five tables
containing “Dynamics marks”, “Articulation marks”,“Symbol of Changing Dynamics or Changing Tempo(symbol that affects the speed of a note or the increase or
decrease of the volume)”, “Time signature”, and “Tempo
marks”.
Analyzing a music symbol according to its usage
allows efficient information processing by the system.

Figure 3.1 The score of bars 11 to 14 of Beethoven’s sonata
Pathetique
Table 3.1 Analytical Result[s]2.4.3 Preference Database
This database contains the expressions of the user’s
characteristic performance. The expressions show the
relationship between tempo and dynamics.
The “Edit” selection in the user’s interface gives theuser access to the parameters for expression. A user canedit his or her parameters, and the respective databases
will automatically change at least one of theirparameters.

3. Construction of the Preference Database
We analyzed a tendency to editing (a characteristic of aperformance) based on a performance of a pianist andbuilt a Preference Database. Among a lot of existingmusic symbols, we focused on the musical notation
indicating Staccato.
We used the Pathetique sonata by Beethoven as theobject music for our analysis.
We used “Gate (length of a sound)”,” Step (an intervalof a sound)” and “Gate ratio (the ratio of gate for step)”
for the performance information that we analyzed.

About a Sforzando

We analyzed a sound-added Sforzando sign and sounds
that were not added.
Figure 3.1 shows the score of bars 11 to 14 of
Beethoven’s sonata Pathetique. This phrase appears inthe score of bars 19 to 22 and 121 to 124.There are no
Sforzando signs in the score of bars 121 to 124. In
addition, there is repeat sign at the top of the score.

©ISAROB 2007

Gate ratio

11-14 (1st)0.130

11-14 (2nd)0.125

19-22 (1st)0.144

19-22 (2nd)0.148

121-124 (1st)0.881

121-124 (2nd)0.905

Table 3.1 shows the analytical result. It also shows that
the Gate ratio tends to become small by addition of the
Sforzando sign. In addition, the Gate ratio thereby
becomes approximately constant.

3.1.2

About a Staccato

Figure 3.2 shows the score of bars 35 to 36 of
Beethoven’s sonata Pathetique. This phrase appears inthe score of bars 39 to 40 and 43 to 44. In addition, there
is a repeat sign at the top of the score. Therefore, thisphrase is played six times total. We analyzed these data.

Figure 3.2 The score of bars 35 to 36 of Beethoven’s sonata
Pathetique
Table 3.2 Analysis of Result

3.1 Analytical Results

3.1.1

Bar number

278

Bar number

Gate ratio

35-36 (1st)0.356

35-36 (2nd)0.277

39-40 (1st)0.350

39-40 (2nd)0.388

43-44 (1st)0.419

43-44 (2nd )0.394

Table 3.2 shows the analytical result. It shows that theGate ratio tends to become small by addition of the
Staccato sign. In addition, the Gate ratio thereby

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
becomes approximately constant
Furthermore, we analyzed the case of a half-note. The
result showed that a half-tone has a smaller Gate ratio
than a quarter-note.
We also analyzed the case of a Staccato that did notcontinue. As a result, the Gate ratio tends to become big.

(a)

(b)

3.1.3

Summary of An Analytical Result

We understood that the Gate ratio changed by thepresence of musical signs from these analysis results.
When the Sforzando sign is added, the Gate ratio tends tobecome small. The case of a Staccato sign being added isspecial. When a staccato continues in a phrase, the Gateratio tends to become approximately constant. However,when the Staccato sign does not continue, the Gate ratio
tends to become big

3.2

Making of GateratioData

We understood that the Gate ratio changed by thepresence of musical signs from these analysis results.
Therefore, we used GateratioData in the Preference
Database to automatically convert user’s editing
characteristics. GateratioData consists of the Gate ratio,role (right or left hand), a kind of note, and a kind ofmusical sign.

4. Automatic conversion by musical sign
We converted the note with a Staccato sign (Figure4.1)
automatically by using Gateratio Data in the Preference
Database. Figure 4.2-(a) shows TMD. Figure 4.2-(b)shows OMD that was converted to TMD automaticallyby using the Musical Database and Score Database.
Therefore, Figure 4.2-(c) shows the OMD that wasconverted to Figure 4.2-(b) automatically by using
Gateratio Data. The process from (a) to (b) is converted
musical process. The process from (b) to (c) is converted
in consideration of the user’s editing character.
Therefore, we are able to offer playing-information thattook in user’s editing character to user ,and we efficiencyof work rose.

(c)Figure 4.2

5.

Automatic Changing Result

Summary

On an interactive musical editing system to support
human errors and offer personal preferences for an
automatic piano, we focused on [he user’s editing
character by the addition of a musical sign, and worked
to store the Gate ratio in the Preference Database.
We were able to develop an automatic conversion
processing function that based on Step. This function canoffer playing information that took in user’s editing
character to user.
In the future, we plan to analyze other musical signs
besides Staccato and Sforzando, improve the Preference
Database, and develop a system that is able to convert
plural musical signs.

References[1] Hayashi, E. et al, “Behavior of piano-action in a grand piano.I”,
Journal of acoustical Society of America, Vol.105, pp.3534-3544,
1999.
[2] Hayashi, E et al, “Interactive musical editing system for supporting
human errors and offering personal preferences for an automatic
piano”, Proc. of the 7th International Symposium on Artificial Lifeand Robotics, Vol.2, pp 513-516, 2002.
[3] Takamatsu, Y., Hayashi, E., et al, “Study on the Performance
Information Edit Support System for an Automatic Piano”,
Technical Report of Information Processing Society of Japan,
MUS-52, pp.111-118, 2003. (in Japanese)[4] Takamatsu, Y., Hayashi, E., et al, “Interactive musical editing
system for supporting human error and offering personal

Figure 4.1

The score ofbars 3of Beethoven’s sonata

preferences for an automatic piano -Preference database for

Pathetiquecrescendo and decrescendo-”, Proc. of the 10th International
Symposium on Artificial Life and Robotics, pp 309-312, 2005.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Cognitive Modeling of Artificial Fish Learning and Memory
Dongmei Ai 1
Xiaojuan Ban2
Shujun Zhang3
Wenli Wang4
1) School of Applied Science, University of Science and Technology Beijing, China
aidongmei@sina.com
2)School of Information Engineering, University of Science and Technology Beijing, China
3)Remote Sensing Institute, Ocean University of China, China
4) Shandong Agriculture Mechanism Administration, China
The study in the animal cognition involves a wide range,
Abstract
and the research mainly starts from psychology, ethology
Artificial fish is a kind of artificial animal living inthree-dimensional virtual environment, and a computer
animation created by using the method of artificial life.
In nature, behaviors of fish are not only driven by its
instinct. They also try to adapt to new environment by
their learning and memory, and then to turn what they
have learned and their memory into the experienceknowledge. The more abundant the experienceknowledge of fish is, the stronger ability the individual
has in adapting to the environment. To enrich the natural
life characteristic of the artificial fish, the relation
cognitive modeling of learning and memory of artificial
fish is established in this paper, based on the ecologicaltheory and their learning and memory mechanism of fishin nature. This is to control the behavior of the artificial
fish, so that they will play a more independent andintelligent role.

1、Introduction
By taking advantage of animal forms, habits andbehaviors, X. Tu [1] has successfully created artificial
fish, called “Xiaoyuan Fish”, which realized the common
basic characteristics of artificial animals throughcomputer animation: bio-mechanics, movement, senseand behavior. Every artificial fish is a self-animating
autonomous agent. “Xiaoyuan’s Fish” starts a new way
for computer animation---artificial life method;
Grzeszczuk[2] and Terzopoulos[3] developed alearning technology, which can automatically synthesize
the vivid movement based on physical animal modeling.
This technology is especially suitable for the movement
synthesizing of the animals with highly soft body, suchas fish, dolphin and snake. It can automatically learn
effective methods in movement control and abstract
proper parameter format out of them. They also
development dolphin modeling in visual environment,
which can perform kinds of “ocean acrobatics”;
John Funge[4] put forward the idea of controllingthe behaviors of animals in the game through cognitivemodeling and expressing the uncertainty in cognition by
adopting method based on interval mathematics andsituation calculus, which has realized the animation
character and behavior with cognitive ability;
Learning and memory are two cognitive processes
At present, the study in the cognitive method oflearning and memory of artificial fish is still not
systematic and more effective methods are called for.

©ISAROB 2007

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and biology with different emphasis and methods.
Manning, an ethologist, thinks that the behavior of ananimal “includes all those processes by which the animal
senses the external world and the internal state of its
body and responds to changes which it perceives.” Andcognition just studies how to respond. Shettleworth[5]put forward some important opinions about animal
cognition and studied the cognitive process of animalcommunication, predator learning, attention modeling,
space cognition, social learning, etc.
Learning and memory are two cognitive processes
closely related to each other, important means to realize
cognition and basic intelligent characteristics of humanand animals. Learning is the neural process when humanand animal acquire environmental knowledge whilememory is the process of storing and reading theacquired knowledge, which is the necessary condition forlearning. The importance of learning and memory lies inthat they can guarantee that human and animal can adapt
to the changing environment and seek existence andproduction. How could the brain learn and memorize, i.e.,
how to acquire and store information, has become one of
the most heated scientific topics at present. Biologists
have pointed out that ocean creatures such as dolphin andfish have very strong learning and memory ability. They
seem to be simple and common, but in fact they are
extraordinarily acute and flexible in some aspects.
Therefore, the study of cognitive modeling of artificial
fish based on learning and memory and the realization of
the advanced cognitive functions of animation charactersof artificial fish is a charming and challenging topic.

2. System structure of artificial fish
In the real-time dynamic complex virtual ocean
environment, every artificial fish is an autonomous
Agent with independent perception, decision andbehavior. The ocean world composed of multiple
artificial fish is a multi-Agent system, in which there
exist the interaction among artificial fish and theinteraction between artificial fish and environments.
Firstly, we provide the cognition-based architecture for
artificial fish Agent.
It adopts the features of hybrid Agent to construct
the whole structure model of cognition-based artificial
fish. (as shown in Figure 1). Artificial fish includes threesub-systems: perception, cognition and behavior. These

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

sub-systems are connected by information flows andcontrol flows. Among them, the content in the dashed
box is the perception system. The sensors in theperception system can pass the perceived external
environment information to the cognition system forprocessing, and then the behavior decision will control
the behavior system to let the actuator generate detailed
actions, which take effects on the external environment.

Sometimes, when artificial fish perceives some
information, they will be driven by instinct directly,
without the stimulation of cognition processing response
action, e.g., collision avoidance. During making decision,
intuition actions have the highest priority, and should be
handled as interrupts in the program.

Instinct

Cognition

Perception System…

Syncretize

SensorExternal
Environment

Information
Processing

Behavior
Decision

Behavior System

Knowledge

Actuator

Perception
Informatio

Behavior

Figure 1：Collectivity Structure Modeling of Artificial Fish

The structures and functionalities of threesub-systems of artificial fish are as follows:
1. Perception system: information acquisition
system, including a set of sensors and information
aggregation module. The artificial fish has various kindsof sensors, e.g., visual sensor, audial sensor, olfactory
sensor. At the same time, different sensors can get
different environment information. We need tosynthesize such information to facilitate the later
information processing and behavior decision.
Perception is the prerequisite of cognition and behavior
planning.
2. Cognition system: information processing system,
which takes the output of perception system as the input.
For perceived information, the agent utilizes stored
experiences and continuous learning to perform
induction, deduction and decision to transform theinformation into the knowledge of its own. The agent
then makes the behavior decision, which is transferred tothe behavior system for execution.
3. Behavior system: execution system for behavior
planning, which directly takes effects on the environment.
It includes a set of behavior programs and a set of
actuators. The behavior programs are the high-level
actions, and the actuators execute the detailed actions.
Every behavior program can be divided into a series of
low-level actions, which are executed by actuators.

3. Relation between learning and memory ofartificial fish
Animals acquire skills and knowledge from otheranimals, and fishes are no exception. There is now strong
experimental evidence that many species of fish exhibitsocial learning and traditional behaviors[6].
Social learning refers to any incidence in which
individuals acquire new behavior or information about©ISAROB 2007281

their environment via observation of, or interaction with,other animals or their products[7].
Social learning is sometimes assumed to be morecommon in, of a more sophisticated form in, or even
restricted to, 'intelligent' or 'large-brained' taxa. However,research over the last 50 years has demonstrated thatsocial learning is common amongst fish, birds andmammals, and should now be regarded as a regularfeature of vertebrate life [7][8].
In the study of memorizing ability of animals,
many ichthyologists have, through experiments, shownthat many species of fish have the ability to seek food byusing visual space information, i.e., they have a certain
capacity of working memory and reference memory.
Thus they can adopt food- clue association and guidetheir behaviors in seeking food with the help of
memorized information. Couzin[9], etc. put forward
self-organizing modeling of three-dimensional grouplineup to study the spatial dynamic characteristics ofanimal groups, such as fish and bird, to show that theminor change in individual interaction may result intransference of group behavior mode and that similar
animal groups have group memory. Ransom Winder[10]etc. studied the issue of group memory. The experimentof improving self-organizing behavior by giving
distributed limited memory to each individual in groups
and achieving the goal in the environmental withobstacles showed that those with memorizing ability canachieve goals more rapidly than those without
memorizing ability.
According the characteristics of fish in learning andmemory, we design the relation modeling of artificial
fish learning and memory. In its environment, artificial
fish processes the perception information: after it hasreceived information through audial sensor, visual sensor,
tactual sensor, olfactory sensor, etc., it will compare theinformation with the past experience knowledge. If it isThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

similar to the past experience, it can solve problems byusing existing experience knowledge; if it is new
information, it will take advantage of social learning
process, receive useful solutions, change it into

knowledge and store it in the memory, which will
become the experience knowledge in the brain of fish.

Cognitive System

Experience knowledge

Perception
information

Behavior
DecisionNew
information
Learning

Analogyknown
information

Knowledge

MemoryApplied Experience knowledge

Figure 2：Relation modeling of artificial fish learning and memory

4. Learning cognitive modeling of artificial
fish
Documented cases of social learning in fish are now
commonplace. there are evidence show that social
learning plays a role in fish : antipredator behavior;migration and orientation; foraging; mate choice. In
these processes, young and inexperienced individuals

often learn from others’ behavior and acquire proper
responses or abstract these responses. Then they canmake correct responses and the response speed is
becoming faster and faster. According to the fishsituation, we put forward the cognitive modeling ofartificial fish learning by using areinforcement
learning algorithm based on BP neural network. as
shown in Figure 3 .

External environment (reinforcement system)
Evaluation
X

BP neural
network

Behavior of
artificial fishYpEvaluation
selection

Delayed bufferX
Training set

Figure 3：Learning modeling of artificial fish

BP Neural Network[11]: system input (status) X,
output Y.;
Evaluation: Based on the output Y, the environment
generates the evaluation signal p;
Delayed buffer: Due to the delay of evaluation duringreinforcement learning, so delay np steps to synchronize
Y ,X and evaluation.
When training cases reach certain number, training set
is putted in network learning . the rule of producing
training set is “ better than average”.
System put evaluation to neural network for adjusting
weight by delay.
The input and output of traditional BP neural network
are produced by increment. The knowledge that studied
ago is forgotten. But reinforcement learning based onneural network may make function mapping to input
according to ancient experiment knowledge byimproving traditional BP.
Artificial fish acquires useful experience knowledge
through the rewarding and punishing mechanism of©ISAROB 2007

282

environment and cultivates good behavior control ability
through behavior system. There are two possible resultsof artificial fish behavior: 1.satisfactory effect; 2.
dissatisfactory effect. If it is satisfactory, the process ofsolving problems is the process of reinforcement learning.
If it is dissatisfactory, it will change and update theexisting experience knowledge by adjusting the solution
according to the environment.

5. Conclusion
This paper takes artificial fish as the research topic.
According to the ecology principles of fish in the natureand the features of fish learning and memory, we build
the architecture of artificial fish and propose the learningand cognitive model of artificial fish. As the social
learning of fish is a gradual learning process, which is
the result of long-term interactions with the environment
and is also the learning in the environment, we propose
reinforcement learning based on BP network to enable

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

artificial fish to perform learning during the interaction
with environments. These models enrich the original
cognitive model of artificial fish, and let it with morefeatures of natural beings.

References[1].X.Tu, D.Terzopoulos, Artificial Fishes: Physics,
Locomotion,
Perception
andBehavior,
Proceedings of SIGGRAPH 94, 1994, pp.24-29[2].Radek
Grzeszczuk,
DemetriTerzopoulos,
Automated
LearningofMuscle-ActuatedLocomotion Through Control Abstraction, (1995),
63-70[3].Demetri Terzopoulos, Tamer Rabie, Radek
Grzeszczuk, Perception and Learning in Artificial
Animals, Artificial Life V: Proc. Fifth Inter. Conf.
on the Synthesis and Simulation of Living
Systems, Nara, Japan, May, (1996)[4].J.Funge. AI for Games and Animation: A Cognitivemodeling approach. U.S.A : A K Peters, Ltd.
Massachusettts, 1999.
[5] Shettleworth, S.J. Animal cognition and animal
behaviour. Animal Behaviour 61, 2001,
pp.277–286.
[6].Culum Brown and Kevin N Laland, Social learningin fishes: a review. fish and fisheries. 4(3) , 2003,
pp.280-295[7]. Heyes, C.M. and Galef, B.G. Social learning inanimals: the Roots of Culture. Academic Press,
London, 1996.
[8] Lefebvre,L.and Palameta, B. Mechanisms, ecology
and population diffusion of socially-learned,
food-finding behavior in feral pigeons. In: Social
Learning (eds Zentall T.R. and B.G. Galef Jr)
Erlbaum, Hillsdale, NJ, (1988),. 141–164.
[9]Iain D.Couzin, Jens Krause, Richard James, etc,
Collective Memory and Spatial Sorting in Animal
Groups, Journal of Theoretical Biology, (2002),
218:1-11[10] Ransom Winder, James A.Reggia, Using
Distributed Partial Memories to Improve
Self-Organizing Collective Movements, IEEE
Transactions
on
Systems,Man
andCybernetics-part
B:
Cybernetics,
34（4）,(2004),1697-1706[11]. Xin lu,Yang gao, Research on a reinforcement
learning algorithm based on neural network.
Journal ofcomputer research and development.
39(8), 2002,pp 981-985.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Design of robotic behavior that imitates animal consciousness
Naoki Goto

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
naoki@imcs.mse.kyutech.ac.jp

Abstr act
A six degrees of freedom serial hand is designed tomove toward an object in an unseen environment. A
consciousness-based architecture (CBA) we developed,
i.e., a hierarchical human development model showing
the relationship between consciousness and behavior, isused to imitate a human groping action. As it moves
toward the target, the robot collects information whichallows the robot to avoid obstacles. CBA organizes suchinformation in order to determine a path for retracing its
motion to the origin without contacting the obstacles it
previously avoided. Experimental results show that CBA
successfully enables the hand to reach its goal while
avoiding obstacles.
Key words: CBA, Learning, Reflection, Hierarchal
Structure.

1 Introduction
Recently, robots other than industrial robots (such ashome robots, personal robots, medical robots, andamusement robots) have seen active development.
Further development of these robots requires
improvement of both their intellectual capabilities andmanual skills as well as further increases in user
compatibility. Up to now, these areas constituted
problematic issues in regard to the robot’s use by robots
other than industrial robot. User compatibility in thiscase entails ease of use, no fatiguing control, robot“friendliness” (i.e., sympathetic use), and human-like
capricious behavior Endowing the robot with“consciousness” of the kind identified in humans andanimals is a part of these requirements.
In our laboratory we have studied an animal’s
adjustment to its environment in an attempt to emulate its
behavior. We constructed a hierarchic structure model towhich consciousness and behavior were hierarchicallyrelated. This model is based on the mechanistic©ISAROB 2007

284

Eiji Hayashi

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp
expression model of animal consciousness and behavior
advocated by the Vietnamese philosopher Tran Duc
Thao[1].
In regard to this, we have developed a softwarearchitecture we call Consciousness-based Architecture(CBA). CBA introduces an evaluation function forbehavior selection, and controls the robot’s behavior.
In the present study, we developed a robotic arm thathas six degrees of freedom, with the aim of providing therobot the ability to autonomously adjust to a target
position. The robotic arm that we use has a handconsisting of three fingers in which a small monocular
CCD camera is installed. The landmark object is detectedin the image acquired by the CCD camera, enabling it toperform holding and carrying tasks. An experiment was
conducted in a work space in which were arranged two
cylindrical obstacles. The robotic arm attempted toeventually reach the landmark object while evading theseobstacles.
When a person attempts to grasp an object in a box
whose internal structure is unknown, he gropes with his
hand in the box. In the present study, this groping action
is performed by the robotic arm by means of CBA.
Holding and carrying tasks, such as approaching a target
position, avoiding obstacles, and detouring aroundobstacles, are performed autonomously.
In this paper, a robot’s autonomous behavior in relation
to a target position is achieved by using CBA. It isshown experimentally that the robotic arm can trace an
optimal return route by studying the route to the target
position. These results verify the utility of CBA.

2 System structure
Figure 2-1 shows an overview of robotic arm used bythis experimental test. Figure 2-2 shows a schematic
view of the robotic arm’s degree of freedom. Figure 2-3
shows a diagram of the experimental system‘s
configuration. The robotic arm manufactured by Kihara
Iron Works is 450 millimeters long and has 6 degree ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

information, allowing the robotic arm to return to thedefault position (state S) without contacting anotherobject.
Start(S)

PC1(Linux)
Main pr ogram
CBA
Arm program

Capture boar d

USB

NTSC

Robot Ar m
CCD camer a

Fig. 2-3 System structure of the robotic arm

Obstacle

Obstacle

Obstacle

Obstacle
TargetFig. 3-1 Schematic diagram of experimental environment

3.2

Fig. 2-1 Overview of robotic arm Fig. 2-2 Arrangement chart ofdegree of freedom

Goal(G)

Consciousness architecture (CBA)

Figure 3-1 shows a diagram of the hierarchicalstructure model called CBA (Consciousness-based
Architecture) that relates consciousness to behavior
hierarchically. In this model, the consciousness field andthe behavior field are built separately. In a dynamic
environment, the robotic arm determines the mostappropriate consciousness level in relation to thisenvironment and selects an action corresponding to its
awareness of its environment and then performs theaction. This model is able to advance to an upper level ofconsciousness and act accordingly when an earlier action
was discouraged by some factor in the external
environment.
Additionally, upper-level consciousness can choose toperform lower-level actions. The mechanism of thismodel is that it selects most comfortable behavior in the
low-level behaviors at pleasure, so the robot aims forgoals.
Consciousness Field

Behavior Modules

3 Autonomous behavior

Level 5

Conscious 5(think)

3.1

Level 4

Conscious 4

Level 3

Conscious 3

Level 2

Conscious 2

Level 1

Conscious 1

Eat
ReflexLevel 0Conscious 0

Sleep
Rest

Figure 3-1 shows the experimental environment
consisting of a robotic arm, the target object, and two
circular cylinders as obstacles. In this figure,consciousness architecture was applied in regard to thespace between the robotic arm in the default position(state S) and the target position (state G).
In the first trial, the robotic arm approaches its
destination from state S (Approach). If the robotic arm
comes in contact with an obstacle, it approaches again(Avoid･Detour) to state G by rotating each joint
accordingly. After that, the consciousness architecture
considers the return route based on conventional©ISAROB 2007

285

Sensors

The lear ning control system of the robotic
ar m

DetourSear chAppr oach
Getaway
Avoid
Move

Actuator

freedom. The robotic hand part of robotic arm has 3
fingers and 1 degree of freedom. Additionally, there is asmall CCD camera (MTV-54K0N[)] in the robotic hand.
We applied a Dynamixel DX-117 manufactured by
ROBOTIS CO., LTD. as an actuator in each joint ofrobotic arm. The DX-117 contains a motor, decelerator,
and an angular sensor in a single unit. This actuator canperform position control by providing the robotic armwith a target angle while limiting its torque and speed,
and so on. The actuator uses an RS485 transmission
method. Hand wiring can be simplified by connecting
each actuator used as a joint of the robotic arm in theform of a daisy chain.

Fig. 3-2 Consciousness-based Architecture (CBA)

3.3

Evaluation function

The following are evaluation functions to select theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

behavior in CBA in order to imitate human groping
action.
Ci (t)Ii (t)

2
j 1

ij

2

j 1

ij

(t )

2
j 1

(t)

ij

2

j 1

ij

(t )

(i

0K p)

(t)

(3-3-1)(3-3-2)In eq.(3-3-1), Ci (t ) is an evaluation function for
determining the robot’s consciousness level, ij (t ) is
degree of perception of an external object, and ij(t) isthe expected value necessary for the robot to perceive an
external object. The variables are normalized within ±1.
If the robot is in a positive state and “feels good,” they
are positive values, and if robot is in a negative state or“feels uncomfortable,” they are negative values.
First, Ci (t ) of each level was calculated and the levelthat the robot is most aware of is determined as therobot’s actual level of consciousness. Next, each I i (t )of each behavior in the level for determining the robot’s
behavior is calculated, and the largest I i (t ) value or abehavior that makes robot most comfortable is selected
as the robot’s behavior, as expressed in eq.(3-3-2). Thus
the robot’s autonomous behavior is realized.

le vel 1
0. 4

level 2

T1

T2

T3

T4

1

2

3

4

level 3T5

level 4

T6

level 5

T7

0.3 5
0. 3

Autonomous behavior

0.2 5

A geometric model of the robotic arm was constructed
and experiments were performed regarding autonomous
detour and shuttle movements. Figure 3-4-1 shows theexperimental environment. Two cylindrically shaped
obstacles are placed between the default position and thetarget position. In the experiment, the robotic arm is not
given information regarding the target position and the
obstacle‘s positions.
When robotic arm arrives at the target position, thedefault position becomes a renewed target position. It is
intended that the robotic arm follow the optimal returnroute without contacting the obstacles during its return
movement.

Fig. 3-4-1 Experimental environment

Figure 3-4-2 shows an experimental result. Figure
3-4-2 shows the movement of the robot’s consciousness
level during the arm’s approach and return. First, theconsciousness level becomes level-2 and the robotic arm©ISAROB 2007

0.4 5

C value

3.4

selects the behavior of “move” and goes straight to thetarget object if the CCD camera in the robot hand locates
the target object. At T1, the robotic arm senses obstacles
when it comes into contact with them. The consciousness
level moves to level-4 and the robotic arm selects the
behavior of “detour” and discourages its previous
movement. The robotic arm then performs a “detour”
action, and when the robotic arm considers that it has
finished detouring the obstacle, it memorizes its position
as a sub-goal and again selects the “move” action oflevel-2 (T2). When the robotic arm reaches the target
position by repeating “move”, “approach”, and “detour”(T3, T4), the robotic arm stops (T5). In the approach
route, the robotic arm contacted the obstacles twice.
However, it could return to its default position without
contacting obstacles by running through the sub-goals(T7).
As the robotic arm reaches its default position, its senseof safety increases little by little. It selects the behaviorof “rest” of level-0, and ceases its action.

286

0. 2
0.1 5
0. 1
0.0 5
0
0

5

6

7

8

9

10

-0.05

Time [sec]Fig. 3-4-2 Temporal data of C value at autonomous behavior

4 Model cor relating between mind andbody
4.1 Development of cor r elative model between
mind and body

We developed a correlative model between the mind
and body. This model integrates “cognition,”“consciousness,” “behavior,” and “circulatory
physiology.” CBA is used as a function of “cognition,”“consciousness,” and “behavior.” A model of circulatory
physiology that integrates a model in which the human
circulatory system is integrated called “HUMAN”, and amodel of the heart beat, “Beat by Beat”, is used asfunction of “circulatory physiology.” This model cancalculate stressors and the degree of stress and cansimulate stress response, tiredness, and changes inhormone level during exercise[2] (Fig.4-1).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

CBA

Cir culator y physiology Model

Selection of a consciousness
level and behavior

Circulation
physiology variable

Computation and processing of
the variable according to stresscomputation

Inhibition of behavior
Continuation of exercise

Stressor Module

Stress Computation Module

Computation of stressor

Computation of the stress intensity

StressorFig. 4-1 Bilaterally linking the two models of CBA andcirculatory physiology

4.2 Method of simulation
An artificial animal was given an exercise stress test by
exercising it at an intensity of 88% for 15 minutes.
Changes in neurotransmitters and blood lactate andhormone levels were compared to real data.

4.3 Exper imental r esults
Figure 4-3-1 shows the simulation results and thosefrom a[3]human experiment regarding noradrenaline in the
blood . Figure 4-3-2 shows the results regarding blood
lactate. The graph shows that noradrenaline in the bloodand blood lactate influence the artificial animal’s
condition regarding tiredness and flush.
20

20

18

18

3030
2525

Noradrenaline of human experiment

16

Simulation result

14

16
14
12

12

2020

10

10

1515
1010
55
00
000

8

8

6

6

4

4

2

2

Noradrenaline[nmol ]: (green)

Noradrenaline[nmol ]: (pink)

3535

0
120
0

15

15

30

30

45

45

60

75

60
75
Time[min]

90

105

90

120

105

Fig. 4-3-1 Simulation result of noradrenaline by exercise stress
1212

Blood lactate of human experim ent

Bloodlacta te[m m ol/l]

1010

Sim ulation result

88
66
44
22
00

000

15
15

30
30

45
45

60
75
60
75
T im e[m in ]

90
90

105
105

120
120

Fig. 4-3-2 Simulation result of blood lactate by exercise stress5

Conclusion

In this paper, consciousness architecture (CBA) wasapplied as a control algorithm for the autonomous
behavior of a robotic arm. Experiments were conductedin which a robotic arm reached for a target position in an
unknown environment by imitating human groping©ISAROB 2007287

action. The robotic arm established sub-goals in
determining an approach route and used these sub-goals
in returning to its default position without contacting
obstacles.
We integrated models of CBA and circulatory
physiology and imposed exercise stress on the model”.
We estimated an artificial animal’s stress and found thatthe simulation result were similar to those obtained in ahuman experiment. In a future study, we will load theintegrated models of CBA and circulatory physiology
into the robotic arm and evaluate the robotic arm’s
behavior.

6

Acknowledgements

The authors would like to acknowledge the advice andassistance of the late Prof. Tadashi Kitamura of Kyushu
Institute of Technology and pray for the repose of hissoul.

References[1] Tran Duc Thao, “Phenomenology and Dialectical
Materialism”, Godo-shuppan CO., LTD. 1989.
[2] Kitamura T. et al, “Development of Correlative Model between
Mind and Body(2nd report)” ROBOMEC04 in Nagoya
CD-ROM. 2A1-H-49.
[3] Marliss, Errol B., and Stuart H. Kreisman, et al,“Gender differences in glucoregulatory responses tointense exercise.” J. Appl. Physiol. 88: 457-466, 2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Design and Implementation of a User Friendly JAVA based GUI
Software for Manoeuvring Mobile Robot
T.C. Manjunath
Research Scholar (doing Ph.D), IEEE / SPIE / IOP Student Member, ISOI / SSI / ISTE Life Member,Interdisciplinary Programme in Systems and Control Engineering, 101 B, ACRE Building,Indian Institute of Technology Bombay, Powai, Mumbai-400076, Maharashtra, India.
Email : tcmanju@sc.iitb.ac.in tcmanjunath@rediffmail.com ; Web : http://www.sc.iitb.ac.in/~tcmanju
Phone : +91 22 25764884 ; FAX : +91 22 25720057

Abstract

1. Introduction

The main objective of this work is to develop a user
friendly GUI developed in JAVA language formanoeuvring a mobile robot named ‘OCTAGON’ withalgorithms for obstacle avoidance in its path of motion. A
user-friendly module is designed and successfully
implemented for the operation of the entire system to dosome specific operations. OCTAGON employs asophisticated application - controlling interface created in
JAVA, as it is a fantastic programming language for any
application software development.

Imagine a day in your life when you wake up in
the morning and find a machine walking up to you
and saying “GOOD MORNING SIR ! Have a cup oftea”. How would you respond to such a situation ?
With so much progress made in the filed of science,engineering and technology, this dream is absolutely
realizable in the automation age. Keeping in pace withthe current technology, we have designed andfabricated a mobile robot named OCTAGON as shownin Fig. 1, which is basically a moveable mobile trolley on
which the articulated robotic manipulator arm is mounted
to perform Pick aNd Place (PNP) operations [1].
These operations are defined by the program typed inby the user using a user friendly language specificallydeveloped for this robot in JAVA. In this paper, weconcentrate only on the software design for controlling andmaneuvering the octagon. The design and fabrication waspresented in the previous conference of AROB-2006. This
work was taken up as a research project work by the
post-graduate students of VJTI under the guidance of theauthor from IIT. The paper is organized as follows. First, a
introduction to the designed robot is given. This is followedby the software design, control algorithms and theconclusions [2].

The software module application facilitates the userinteraction with OCTAGON and has many in built features
such as the security and authentication. When the java codeis executed, the default GUI screen appears as shown in theFig. 2 and all the motors status is displayed. For activatinga particular motion, we can navigate to its page by selected
the left hand menu. The software is designed for maximum
robot control & working efficiency. It is so designed suchthat the user can have complete control over each movablepart of the robot.
Also, the user can easily maneuver the robot & make it
traverse a path towards the object to be picked. He or she
can then manipulate the different limbs of the robotic arm
so that the gripper comes closer to the object & eventually
picks it up. Throughout this process the software interface
guides the user through the usage of various parts of therobot & provides him with responses from the robot. Given
the time on hand, the ease of programming and power of
JAVA, it was an ideal choice for use in designing ourapplication interface to the octagon.
The software is designed for maximum robot control &working efficiency. It is so designed that the user can havecomplete control over each movable part of the robot. Also
the user can easily maneuver the robot & make it traverse ina path towards the object to be picked. He/she can thenmanipulate the different limbs of the robotic arm so that thegripper comes closer to the object & eventually picks it up.
Throughout this process, the software interface guides theuser through the usage of various parts of the robot &provides him with responses from the robot.

©ISAROB 2007

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Fig. 1 The designed OCTAGONThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Software Design
OCTAGON system employs a sophisticatedapplication controlling interface created in JAVA as it is afantastic programming language for any application
software development. The developed software module forcontrolling the robot is shown in Fig. 5. The softwaremodule application facilitates the user interaction with
OCTAGON and has many in built features [8]. The
software is designed for maximum robot control & working
efficiency. It is so designed that the user can have complete
control over each movable part of the robot. Also the user
can easily maneuver the robot & make it traverse a path
towards the object to be picked [3]. He can then manipulate
the different limbs of the robotic arm so that the gripper
comes closer to the object & eventually picks it up.
Throughout this process, the software interface guides theuser through the usage of various parts of the robot &provides him with responses from it.
The features of the developed software for the robot are
as follows :Start up screen for the software application.
Features a complete HELP file, which can be invokedfrom the menu provided.
Has a menu, which enables the user to select different
options.
Simple yet powerful interface.
Cut - copy - paste instruction function.
Facility to save current program.
Facility to save current program with a different name.
Insert function.
Minimization of typing.
Editing of program is possible by use of up / down
buttons.
Manual control of each motor
Fine positioning of the base, rack, elbow and roll.
Facility to rotate the motors clockwise and counterclockwise
Automatic RESET function to bring the robot to thereset/home position on startup.
Dynamic reset function to allow the user to bring therobot to the reset position at any instant.
Auto Teach facility.
Software for octagon manoeuvring : OCTAGON has
the following additional features, viz., REAL TIMEMANUAL MODE CONTROL, REAL TIME RUN MODE
CONTROL, PROGRAMMING INTERFACE, FACILITY
TO LOOP OPERATIONS, ACCURATE POSITIONING,
PARALLEL PORT INTERFACE & CONTROL,INTELLIGENT PATH DECISION MAKING. The screensused in the application are a default screen, which combines
all the features of the PROGRAMMING MODE,MANUAL MODE, RUN MODE and the GRAPHICALMODE [4].

©ISAROB 2007

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Fig. 2 General Graphical User Interface ( GUI ) for the robot
control via JAVA environment

Fig. 3 GUI showing the arm base motor in operation

Fig. 4 GUI showing the roll motor in operation

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

When the java code is executed, the default GUI screen
appears as shown in the Fig. 2 is displayed showing all the
motors status. For activating a particular motion, we cannavigate to its page by selected the left hand menu. The
flow chart for the java code is shown below in Fig. 5.

Fig. 5 Flow chart module of software module

3. Description about the GUI
The graphical user interface has check-boxes which are
discussed in details as follows.
MOTOR SELECTION LIST BOX :The screen features a listbox, where the user selects thedesired motor. The user selects the motor by doubleclicking on the motor name in the list box.
MOTOR SELECTED TEXT BOX :The selected motor is then highlighted and displayed in the
motor selected textbox.
DIRECTION OPTION BUTTONS :The clockwise option button is enabled by default. The
user can change the direction of rotation prior to pressing
the start button. The user is required to click on the option
button for selection of the direction.

©ISAROB 2007

290

START BUTTON :The START button, when clicked, enables rotation of thedesired motor. On clicking the start button, all theremaining components on the screen except the stop button
are disabled as a precautionary measure [5].
STOP BUTTON :The STOP button, when clicked, disables rotation of thedesired motor by sending byte ‘0’ to the port. On clicking
the stop button, all the components disabled by the start
button are re-enabled.
STATUS SIGNALS and MESSAGES :The status signals and messages are an indication, to theuser, of the motor selected, the direction of rotation and thebyte sent to the port.
RESET BUTTON :The screen features a reset button that is coded to respondto the click event. On clicking the button the software
brings the robot to the HOME position or reset position.
The reset is an important function, which is used inconjunction with the auto teach facility provided in the
manual mode screen since it is mandatory that any teachingprocess be preceded by a reset, so that all calculations madeby the robot software are with respect to the reset positionof the robot [6].
Mobile(8 motions)
ForwardBackward
Forward Left
Forward Right
Backward Left
Backward Right
Spin Clockwise
Spin Anti-clockwise

Arm(10 motions)
Arm base clockwise
Arm base anticlockwise
Arm shoulder up
Arm shoulder down
Arm elbow up
Arm elbow down
Arm roll clockwise
Arm roll counter-clockwise
Arm gripper open
Arm gripper close

Table 1 : Table to show 18 motions of OCTAGONAUTO TEACH FACILITY :The manual mode provides the user with an auto teachfacility, in which the program calculates the time delays andthe associated angles, corresponding to the sequence of
manually activated motors in an operation.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4. JAVA Programming Platform
JAVA is a simple, object-oriented, network-savvy,
interpreted, robust, secure, architecture neutral, portable,
high-performance, multithreaded, dynamic language, which
provides you with a complete set of tools to simplify rapid
application development. We wanted to use a system that
could be programmed easily without a lot of esoterictraining and which leveraged today’s standard practice. We
could have used C, or object-oriented programming
language C++. But, Java omits many rarely used, poorly
understood, confusing features of C++ that in our
experience bring more grief than benefit. These omitted
features primarily consist of operator overloading multiple
inheritance, and extensive automatic coercions [7].

early checking for possible problems, later dynamic(runtime) checking, and eliminating situations that are error
prone. One of the advantages of a strongly typed language(like C++) is that it allows extensive compile-time checking
so bugs can be found early. Unfortunately, C++ inherits anumber of loopholes in compile-time checking from C,
which is relatively lax (particularly method/procedure
declarations). In Java, we require declarations and do notsupport C-style implicit declarations. The linker
understands the type system and repeats many of the typechecks done by the compiler to guard against version
mismatch problems. The Java Native Interface (JNI) is thenative programming interface for Java that is part of the
JDK. By writing programs using the JNI, you ensure thatyour code is completely portable across all platforms.

5. Conclusions

Fig. 6 Java Structure
Octagon uses JNI Technology to access parallel port
using advanced JAVA. JAVA is Object-Oriented which abuzzword in industry is. Object-oriented design is very
powerful because it facilitates the clean definition ofinterfaces and makes it possible to provide reusable“software ics”. Simply stated, object-oriented design is atechnique that focuses design on the data (=objects) and on
the interfaces to it. To make an analogy with carpentry, an“object-oriented” carpenter would be mostly concernedwith the chair he was building, and secondarily with thetools used to make it; a “non-object-oriented” carpenter
would think primarily of his tools. Object-oriented design isalso the mechanism for defining how modules “plug andplay”. Java has an extensive library of routines for coping
easily with TCP/IP protocols like HTTP and FTP. This
makes creating network connections much easier than in C
or C++. Java applications can open and access objects
across the net via URLs with the same ease that
programmers are used to when accessing a local filesystem.
Java is intended for writing programs that must be
reliable in a variety of ways. Java puts a lot of emphasis on©ISAROB 2007291

A user friendly graphical user interface in JAVA
programming language was developed and the octagon was
controlled in various modes such as the manual mode, teachmode, programming mode, automatic mode. A number ofpick and place operations were successfully performed by
the developed robot by using the above mentioned 4 typesof modes.

References[1]. Robert, J.S. (1992), Fundamentals of Robotics :Analysis and Control, PHI, New Delhi.
[2]. Klafter, Thomas and Negin (1990), Robotic
Engineering PHI, New Delhi.
[3]. Fu , Gonzalez and Lee (1995), Robotics : Control ,Sensing , Vision and Intelligence, McGraw Hill,
Singapore.
[4]. Ranky, P. G., C. Y. Ho (1998), Robot Modeling,
Control & Applications”, IFS Publishers, Springer,UK.
[5]. T.C.Manjunath (2005), Fundamentals of Robotics,
Nandu Publishers, 4th Revised Edition, Mumbai.
[6]. T.C.Manjunath (2005), Fast Track To Robotics, Nandu
Publishers, 2nd Edition, Mumbai.
[7]. Ranky, P. G., C. Y. Ho (1999), Robot Modeling,
Control & Applications, IFS Publishers, Springer, UK.
[8]. Groover, Weiss, Nagel and Odrey (2000), Industrial
Robotics, McGraw Hill, Singapore.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Intelligent Motor Control Using Advanced Bacterial Foraging Combined With Immune Algorithm
Dong Hwa Kim*, Jae Hoon Cho*, Yong Dal Kim***Dept. of Instrumentation and Control Eng., Hanbat National University,**Dept. of Electrical Eng., Hanbat National University
16-1 San Duckmyong-Dong Yuseong-Gu, Daejon City, Korea, 305-719.
E-mail: kimdh@hanbat.ac.kr, Hompage: aialab.net
Tel: +82-42-821-1170, Fax: +82-821-1164

Abstract
This paper suggests advanced bacterial foraging strategy
using membership function of fuzzy logic and clonal
selection of immune system. Bacteria foraging based
optimal solution is defined by the positions of each member in the population of the S bacteria at the jth chemotactic step, kth reproduction step, and lth eliminationdispersal event. Therefore, chemotactic step is importantto have an optimal solution in system. Up to now, a foraging strategy uses fixed chemotactic step. This paper
introduces clonal selection of immune algorithm andfuzzy logic into bacterial foraging to enhance runningspeed and patch of optimal condition (e.g., group of objective with conditions). This approach provides us withnovel hybrid model based on foraging behavior andclonal selection for a higher running time and optimal
solution.

1. introduction
In the last decade, evolutionary computation based approaches have received increased attention from the engineers dealing with problems which could not be solved
using conventional problem solving techniques. Natural
selection are more likely to apply reproductive success tohave an optimal solution. Since a foraging animal takes
actions to maximize the energy obtained per unit timespent foraging, in the face of constraints presented by its
own physiology such as, sensing and cognitive capabilities and environment. Evolution can provide optimization
within these constraints and essentially apply to engineering field by what is sometimes referring to as an optimal
foraging policy. That is, optimization models can provide
for social foraging where groups of parameters communicate to cooperatively forage in engineering. This paper
provides a brief literature overview of the area of bacterial foraging as it forms the biological foundation for thispaper. Then, this paper also focuses on dealing with anenhanced optimal solution using a hybrid approach consisting of BF (Bacterial Foraging), and CL (Clonal Selection) and fuzzy logic. Finally, we focus on evidence forthe proposed hybrid system for indirect vector control of
induction motor.

2. Hybrid Optimization Based on Bacteria Foraging and Clonal Selection©ISAROB 2007

296

Equation represents the positions of each member in the
population of the N bacteria at the jth chemotactic step,
kth reproduction step, and lth elimination-dispersal event.
Let P(i, j, k, l) denote the cost at the location of the ith
bacterium φ i ( j , k , l ) ∈ R n , and
φ i = ( j + 1, k , l ) = φ i ( j , k , l ) + C ((i )ϕ ( j ) ,(1)

so that C(i)>0 is the size of the step taken in the random
direction specified by the tumble. If at φ i ( j + 1, k , l ) the
cost J(i, j+1, k, l) is better (lower) than at φ i ( j , k , l ) , thenanother chemotactic step of size C(i) in this same direction will be taken and repeated up to a maximum numberof steps Ns . Ns is the length of the lifetime of the bacteria
measured by the number of chemotactic steps. Functions
Pci (φ ) , i=1, 2, . . . , S, to model the cell-to-cell signaling
via an attractant and a repellant is represented by
N ⎡
n
N
⎞⎤
⎛
Pc (φ ) = ∑ Pcci = ∑ ⎢ − Lattract exp ⎜⎜ − δ attract ∑ (φ j − φ ij ) 2 ⎟⎟ ⎥i =1 ⎢
j =1
i =1
⎠ ⎥⎦
⎝
⎣
N ⎡
n
⎛
⎞⎤
+ ∑ ⎢ − K repellant exp ⎜⎜ − δ repellant ∑ (φ j − φ ij ) 2 ⎟⎟ ⎥ ,i =1 ⎣
j =1
⎢
⎝
⎠ ⎦⎥

(2)
where φ = [φ1,...,φ p ]Tis a point on the optimization

domain, Lattract is the depth of the attractant released by
the cell and δ attract is a measure of the width of the attractant signal. K repellant = Lattract is the height of therepellant effect magnitude, and δ attract is a measure of
the width of the repellant.

3. Intelligent Vector Control Using Advanced
Bacteria Foraging Based on Fuzzy Logic and
Immune Algorithm

3.1 Vector Control System of Induction Motor
As the vector controlled induction machine is assumed
to be current fed from an ideal current controlled PWM
inverter, operation with constant, rated flux command
would be discussed. As the indirect vector controller isthe scheme composed of the appropriate decoupling circuit for each of the three orientation possibilities such as

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

stator, air-gap, and rotor flux oriented control, it incorporates only PI speed controller. Decoupling circuits neglectiron loss, magnetic saturation and resistance variations

and have the well-known form, representation of theinduction machine, in terms of space vectors.

Application for Motor Vector PI Control
Current

Speed controller

controller
Current
controller

BF-CL

ω ref
PI
-

R->S: Rotationary to Stationary Transform
S->R: Rotationary to Stationary Transform
2->3: 2 Φ to 3 Φ Transform
3->2: 3 Φ to 2 Φ Transform

PI

+

+

R -> S

2 -> 3PWM

PI+

i drefαi qrefi dref+∫

R <- SThree- PhaseInverter

2 <- 3

np

IM

iq

id

Block diagram of indirect vector PI controller using BFBF-CL.
[Kim Thesis 2006, TIT]
TIT]

29/58

Fig. 1. Block diagram of indirect vector PI controller using advanced BF (bacterial foraging) based on fuzzy logic and clonal selection.

That is, the indirect vector control system neglects thecore loss. The electrical torque in an induction machine
can be expressed as:
Te =3 P Lm e e
λdr iqs ,2 2 Lrwhere,

rotor

(3)

flux

instantaneouse=−
λer = λedr + jλeqr = λedr , λeqr = 0 , iqs

speed

ωe

,

Lr e
iqr . The slipLmequations for an induction motor in an arbitrary synchronously
rotating reference frame are given by:

ωe − ωr = ωsl = −Rr iqre

λ

e
dr

=

L ⎞ 1⎤R
Rr Lm e ⎡⎛iqs = ⎢⎜⎜1 + p r ⎟⎟ e ⎥ r iqse .
eRr ⎠ ids ⎦ Lr
λdr Lr
⎣⎝
(4)

e
e
e
where, iqs
is torque current, ids
is flux current. When idse
and iqs
is decided by ωsl , rotor flux position θ e is givenby:θ e = ∫ ωe dτ = ∫ (ω r + wsl )dτ
t

t

0

0

(5)

In indirect vector control, stator current and slip angle ωsl through θe is controlled, then λqr = 0 become [2].
e©ISAROB 2007

297

3.2 BF-CL based Optimization Tuning of PI Controller for Induction Motor

This paper describes the method in the form of an algorithm to search optimal value of parameters [11-13].
[step 1] Initialize parameters n, N, NC, NS, Nre, Ned, Ped,
C(i)( i=1,2,…,N), φ i , Where, n: Dimension of the search
space, N: The number of bacteria in the population, NC :chemotactic steps, Nre : The number of reproduction steps,
Ned : the number of elimination-dispersal events, Ped :elimination-dispersal with probability, C(i): the size ofthe step taken in the random direction specified by the
tumble.
[step 2] Elimination-dispersal loop: l=l+1[step 3] Reproduction loop: k=k+1[step 4] Chemotaxis loop: j=j+1.
[step 5] Compute objection function and store best individuals in memory cell of clonal selection loop.
[substep a] Differentiate clone from memory cell[substep b] Compute objective function for clonal bacteria done cross over and store in memory cell by bestvalue order.
[step 6] Decide search direction of bacteria foraging action after objective function in memory cell and objectivefunction of [step 5].
[step 7] If j < N C , go to step 5. In this case, if chemotaxis
loop and objective function are satisfied by user, stop
calculation, otherwise go to [step 5] computing chemotaxis loop until the life of the bacteria is over.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 1. Parameter ranges for Learning of bacteria foraging.

Parameters

Value

N: The No. of BF group100

Nc : The No. of chemotaxis loop300

Nre : The No. of reproduction
steps
Ned : The number of eliminationdispersal events
Ped : elimination-dispersal withprobability

keeps the number of bacteria in the population constant.
To do this, if you eliminate a bacterium, simply disperse
one to a random location on the optimization domain.
In bacterial foraging strategy, because the number of
elimination-dispersal events and, for each eliminationdispersal event, each bacterium in the population is subjected to elimination-dispersal (death, then random
placement of a new bacterium at a random location on the
optimization domain) with probability, bacterium swim
straight alternatively running at 10–20 [microsec] andtumbling. When the flagella rotate clockwise and counterclockwise, they operate as propellers and hence an E.
Coli may run or tumble and search avoid unfavorable
environments. The objective is simply to capture thegross characteristics of chemotactic hillclimbing andswarming for optimal solution.
Fig. 2 represents computation procedure of optimal solution based on BF-CL learning and Table 1 illustrates
parameter ranges for learning of bacteria foraging. Fig. 3
shows speed tracking of indirect vector PI controller(time: 0~0.13 sec) and Fig. 4 means flux amplitude tracking of indirect vector PI controller. Fig. 5 is search process for optimal parameters of PI controller using BF-CL
suggested in this paper. Table 2 represents comparison of
PI parameters by each method (BF, GA, Proposed algorithm).

2
5
0.5

T: The No. of clones

5

Tm: Probability of crossover of
0.25
clone

Am* : Gain margin of plant

2.5

φm* : Phase margin of plant

45

Fig. 3. Speed tracking of indirect vector
PI controller (time : 0~0.13 sec).

Fig. 4. Flux amplitude tracking of
indirect vector PI controller.

[step 8] If k < N re , go to [step 4]. In this case, we have
not reached the number of specified reproduction steps,
so we start the next generation in the chemotactic loop.
[step 9] If l < N ed , go to [step 3]. In this case elimination-dispersal: For i = 1,2..., N , with probability Ped ,eliminate and disperse each bacterium, and this results in©ISAROB 2007

298

Fig. 2. Computation procedure of optimal solution
based on BF-CL learning.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 2. PI parameters for each method.

Speed control

Current control

MethodFig. 5. Search process for optimal parameters BF-CL.

4. Conclusion

Ti

Td

Td

Bacterial Foraging

0.98

0.55

4.54

82.72

Genetic Algorithm

0.93

0.76

6.56

114.21

Proposed Algorithm

0.99

0.64

5.32

85.62

Moreover, other species of bacteria or biological based
computing approach could be studied but it remains to be
seen how practically useful the optimization algorithms
are for engineering optimization problems, because they
depend on the theoretical properties of the algorithm,
theoretical and empirical comparisons to other methods,
and extensive evaluation on many benchmark problems
and real-world problems.

References

Recent many approaches of evolutionary of intelligencealgorithms for the evaluation of improved learning algorithm and control engineering have been studying. The
general problem of evolutionary algorithm based engineering system design has been tackled in various ways
because of learning time and local or suboptimal solution.
GA has also been used to optimize nonlinear systemstrategies but it might be local optimized. This paper
suggests the advanced hybrid system consisting of BF(Bacterial foraging Algorithm) and CL (Clonal Selection)
for PID controller tuning of induction motor control system. Ref [13] depicts characteristic to variation of step
size when generations from 1 to 50 and from 270 to 300,
respectively. From Ref [13], the bigger step size, theconvergence is faster. Ref [13] are also showing relationship between objective function and the number of generations in different chemotactic steps. When the chemotactic step is smaller, the objective function has a fasterconvergence with a small generator. In Ref [13] is showing characteristics between objective function and generators for different life times Ns of bacteria in the hybrid
system, GA-BF. In BF system, chemotactic step, total
number of chemotactic reaction of bacteria, step size,
basic unit for movement of bacteria Ns, the number ofcritical reaction S, the number of bacteria G, generations
Mu, mutation Cr, and crossover is very important forlearning condition.
Therefore, this paper extends variable step size against
environmental condition using fuzzy logic and clonal
selection to illustrate characteristics. This approach proposed in this has the potential to be useful in practicaloptimization problems (e.g., engineering design, online
distributed optimization in distributed computing andcooperative control) as models of social foraging are also
distributed nongradient optimization methods. It can also
may be used a wide variety of fruitful research directions
and ways to improve the models (e.g., modeling more
dynamics of cell motion).

©ISAROB 2007

Kp

299

[1] Chung-Liang Lin, Huai-Wen Su, “Intelligent control theory
in guidance and control system design: an Overview,” Proc.
Natul. Sci., Counc. ROC(A), vol. 24, no. 1, pp. 15-30,
2000.
[2] P. J. Fleming and R. C. Purshouse, “Evolutionary algorithms
in control system engineering: A survey,” Control Eng.
Practice, vol. 10, pp. 1223–1241, 2002.
[3] G. J. Gray, D. J. Murray-Smith, Y. Li, K. C. Sharman, and T.
Weinbrenner, “Nonlinear model structure identification using genetic programming,” Contr. Eng. Practice, no. 6, pp.
1341–1352, 1998.
[4] K. Kristinnson and G. A. Dumont, “System identificationand control using genetic algorithms,” IEEE Trans. System,
Man, Cybern., vol. 22, pp. 1033–1046, Sept.-Oct. 1992.
[5] R. A. Krohling and J. P. Rey, “Design of optimal disturbance rejection PID controllers using genetic algorithms,”
IEEE Trans. Evol. Comput., vol. 5, pp. 78–82, Feb. 2001.
[6] Zwe-Lee Gaing, "A Particle Swarm Optimization Approachfor Optimum Design of PID Controller in AVR System",IEEE Trans. Energy Con. Vol. 19, pp. 384-391, No. 2,
June, 2004.
[7] STEPHENS, D. W., and KREBS, J. R., “Foraging Theory,
Princeton University Press, Princeton, New Jersey, 1986.
[8] ALCOCK, J., Animal Behavior, “An Evolutionary Approach, Sinauer Associates,” Sunderland, Massachusetts,
1998.

[9] Dong Hwa Kim, “Robust PID controller tuning using
multiobjective optimization based on clonal selection of immune algorithm,” Proc. Int. Conf. Knowledge-based intelligent information and engineeringsystems. Springer-Verlag. pp. 50-56, 2004.
[10] Dong Hwa Kim, “Genetic Algorithm Combined with Particle Swarm Optimization/Bacterial Foraging and Its Application to PID Controller Tuning,” TIT (Tokyo Institute ofTechnology Thesis, April, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Unmanned Navigation of Container Transporter
Using U-SAT(Ultrasonic Satellite System)
Kil Soo Lee, Su Yong Kim,
Taek Young Shin

Jung Min Lee

Man Hyung Lee

Automation System R&D Team
School of MechanicalEngineering, Pusan
Department of Mechanical and SAMSUNG ELECTRONICS CO.,LTD,
National University,
Intelligent Systems Engineering 416, Maetan-3Dong, Yeongtodng-Gu,
Suwon-City, Gyeonggi-Do, 443-742 Busan, 609-735, KOREA
Pusan National University
KOREA
Busan, 609-735, KOREA

Abstract
The positioning of vehicle is an important part ofcontrol problem. Dead Reckoning is widely used for
positioning of vehicle. However this method has
problems because it accumulates estimation errors.
In this paper, we propose a new method to increase
the accuracy of estimated position using the
U-SAT(Ultrasonic Satellite system). It is shown that
we will be able to estimate the position of vehicle
precisely, in which errors are not accumulated. And
unmanned navigation experiment of the containertransporter, which is one of the part of thedevelopment for the harbor automation project, wasperformed in the case that container transporter
moves to the desired trajectory using U-SAT.
Unmanned navigation of container transporter byusing U-SAT is verified by experiments.

1

Introduction

The process of finding vehicle in environment isa major concern in vehicle navigation. To measure
the position of a vehicle, a variety of studies aregoing on and vehicle positioning has been done in
two basic methods. They are not only absolute andrelative positioning but also a combination of them.
The dead-reckoning method has been widely used
as one of the methods of relative positioning.
Dead-reckoning
methoduses
theencodedinformation which gains the wheels
to determine
the position of the vehicle. But because of wheelslippage,mechanicaltoleranceandsurfaceroughness, this method has its unbounded
accumulation of errors. So the real position ishardly maintained as it moves longer distance [1].
On the other hand, absolute positioning isaccomplished by using a CCD camera, infrared
light, global positioning system (GPS), andultrasonic sensor. Vision system by CCD camera
requires complicated signal processing to analyze
images. In addition, it is expensive and highly
depends on camera calibration and image sensitivity[2]. Positioning by using infrared light is easy andinexpensive. However, this method leads to©ISAROB 2007

300

problems such as low performance and limitationsin terms of its application in an outdoor
environment. The GPS can give accurate information
to identify locations. By a differential GPS, theaccuracy of positioning is also improved. However,this type of sensor usually provides measurements at
1-10 Hz. This sampling rate may not be sufficientif the dynamics of a positioning object changes
relatively fast. Besides, it does not operate well in
the place where no GPS satellite signal is available
or where there are less than four visible satellites[3]. In addition, pseudo-satellites for indoor oroutdoor places have been researched. Ultrasonicranging systems, which are similar to the basic
concept of GPS, have advantages such as low costs
and a high update rate a disadvantage is its low
accuracy which is caused by air turbulence,
humidity,
temperaturedependence,transmitter
misalignment and transmitterbandwidth [4]. In
contrast to the conventional GPS, it is possible tobuild up cell structures in widely ramified buildings.
In the measurement of the distance using direct
ultrasonic waves, the method with high precision is
proposed [5]. Absolute positioning system using
ultrasonic sensors based on this method isrepresented as U-SAT (Ultrasonic Satellites). So inorder to evaluate the performance of U-SAT, it iscompared with RTK-DGPS that is more accurate
than any other absolute positioning system. The
possibility of using U-SAT as pseudolites in theplace where GPS is not available is discussed. And
unmanned navigation experiment of the containertransporter, which is one of the part of thedevelopment for the harbor automation project, wasperformed in the case that container transporter
moves to the desired trajectory using U-SAT.
Unmanned navigation of container transporter byusing U-SAT is verified by experiments.

2 Concept of U-SAT
The measurement of the distance using theultrasonic waves is calculated with sound velocity
and the delivering time. TOF (Time of Flight) isdefined as the time difference between transmitterand receiver. It is shown in Fig.1. The distance isThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig.1 Definition of TOF
determined in Equation (2) and sound velocity isrepresented as a function of temperature.
TOF = T 2 − T 1(1)

d = c × TOF + d 0(2)

c = 331.5 + 0.60714 T(3)

Where, d is distance and c is sound velocity in the
air temperature of T.
This shows the detection accuracy of about 2mmby the ultrasonic frequency detection method [5].
And the distance between separated transmitter andreceiver is calculated by direct ultrasonic waves.
U-SAT is based on this method. Ultrasonictransmitters function as ultrasonic satellites andlocate on the fixed places whose coordinates are
known. So ultrasonic receivers receive ultrasonicwaves transferred from ultrasonic satellites and the
distance between ultrasonic receivers and ultrasonicsatellites is calculated. The basic idea of U-SAT issimilar to that of GPS. Although ultrasonic receivers
exist in the ultrasonic satellites, the position ofultrasonic receiver is calculated respectively.
Therefore multiple mobile robots can be used.
In Fig.2, U-SAT consists of four ultrasonicsatellites. In order to calculate the distance using
ultrasonic waves, the time when ultrasonic satellite
radiates ultrasonic waves must be measured. U-SAT
does not inform the transmission time. So
transmission time is known by using RF signal.
U-SAT calculates the distance by measuring thetime when the ultrasonic waves is received. It is
supposed that there is no time delay during
receiving RF signal. In Fig.2, U-SAT transmitter (1)transfers synchronized RF signals to other U-SAT
transmitters and U-SAT receiver at the period of
83ms. They receive synchronized RF signals andfind when ultrasonic waves are radiated. In
accordance with this period, ultrasonic satellites
radiate ultrasonic waves by turns and U-SAT
receiver calculates the distance by using Equation(2). Synchronized RF signals are transferred withthe period of 83ms in order to avoid theinterference of ultrasonic waves and the influence ofthe reflection of them and this period can be
flexibly regulated according to the environment.
Fig.3 shows the timing diagram for ultrasonicreceiver to be synchronized by RF signal. After
U-SAT receiver receives ultrasonic waves whichradiated ultrasonic satellites respectively, eachdistance d1, d2, d3, and d4 between ultrasonicradiated ultrasonic satellites respectively, eachdistance d1, d2, d3, and d4 between ultrasonic©ISAROB 2007

301

Fig.2 System description

Fig.3 Timing diagram

Fig.4 RTK-DGPS
waves and its satellites are calculated. The
coordinate of the ultrasonic receiver can be obtainedby L.M.S (Least Mean Square method). The
sampling time of GPS is 1Hz while that of U-SAT
is 3Hz shown in Fig.3. Since U-SAT is more
flexible and faster than GPS, it can frequently
acquire the position information.

3

RTK-DGPSGPS is a space-based positioning, navigation, andtiming system developed by U.S. Department of
Defense (DoD). GPS receiver receives GPS signals
from more than four GPS satellites and calculates
its position real time. But it doesn’t function wellin the place where GPS satellite signal doesn’treach or visible satellites are less than four.
In order to increase the accuracy of the position
error, DGPS is most frequently studied around theworld. Usually, code differential positioning cansatisfy positioning accuracy of meter level, while
carrier phase differential positioning can achieve
positioning accuracy of centimeter level for theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
real-time positioning in three dimensions. RTK
realizes real-time positioning by the carrier-phasedifferencing algorithm in moving. As shown in
Fig.4, its principle is that, the reference stationtransmits the collected raw carrier-phase andpseudorange measurements to the rover station, thus
the rover station can calculate the coordinate bydifferencing algorithm. The accuracy of RTK-DGPS
used in this experiment is 1.6 cm.

4

Fig.5 Experiment configuration

System Configuration

A system configuration is shown in Fig.5 inorder to analyze the performance of U-SAT using
DGPS. Four U-SAT transmitters are located on thefour points of the rectangular in 1m height whose
length and width are 5m and 3m respectively. And
they are the ultrasonic receiver, GPS receiver, andthe turntable which is used to evaluate the position
information of U-SAT and GPS. The turntablerotates constant speed in proportion to the voltage
and the length of the arms installed at the turntable
is 1m. GPS and the ultra-sonic receiver areconnected with the turntable by the arms on bothsides. The performance of U-SAT using DGPS is
analyzed. So the accuracy of U-SAT has been
estimated comparing with GPS.

Fig.6 Experimental results of the static position(GPS: +, U-SAT: o)

5 Experimental Results
In this experiment, the position information of
GPS and U-SAT is respectively measured using theconsisted experimental system. Since two positions
information are represented in different coordinate
system, centers of the circle created by eachposition
informationareaccordedusing
transformation.
First the GPS and U-SAT receiver is fixed andthe static position is measured during a few
minutes. Shown in Fig.6, the errors of U-SAT are
wider range than those of GPS. However the
position information of GPS and U-SAT does not
obtain the accumulative errors. Second in case theturntable rotates the constant speed the position
information of U-SAT is compared with that ofGPS. The experiment has been implemented atconstant speed of 0.1 m/s, 0.2 m/s, 0.3 m/s, and
0.4 m/s. The circles of the position information
using GPS and U-SAT are U-SAT does not obtain
the accumulative errors. Second in case the turntablerotates the constant speed the position informationof U-SAT is compared with that of GPS. The
experiment has been implemented at constant speedof 0.1 m/s, 0.2 m/s, 0.3 m/s, and 0.4 m/s. The
circles of the position information using GPS andU-SAT are shown in Fig.7.
In Fig.7, The errors of GPS are not influenced
by the speed but those of U-SAT are increased as
the speed is increased. Since the four U-SAT
transmitters radiate ultrasonic waves by turns, thereceiver can determine the position after U-SAT
receiver finishes receiving the signals from all four

©ISAROB 2007

302

Fig.7 Experimental results of the dynamic position(GPS: +, U-SAT: line)transmitters. Therefore the errors of U-SAT are
influenced by the speed. It causes many problems at
the high speed. To solve this problem, the samplingtime of the system should be change or more
U-SAT transmitters have to be used for fasterreception. At the above experiment, the performanceof GPS is better than that of U-SAT. However,
U-SAT shows a good performance concerning priceand has an improvement possibility through more
researches. In case that several U-SAT receivers
exist, the position of U-SAT receiver can becalculated respectively. Therefore U-SAT is very
suitable for pseudolite system in the place where
GPS is not available. Although U-SAT is affectedby the condition of the outdoor environment, it ismore stable at the indoor environment.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
been evaluated using RTK-DGPS that is moreaccurate than any other absolute positioning system.
Though the performance of RTK-DGPS is better
than that of U-SAT, the result of U-SAT is also
acceptable. In case of the vehicle which moves
slowly or stops, the position information guarantees
very stable performance. In Addition, U-SAT can beused as a proper system in the place where GPS is
not available, such as indoor area. Finally
Experiments were performed in the case that thecontainer transporter moves to the target point using
U-SAT. The container transporter can moveaccurately along the reference path using U-SAT.
As a result of experiment, unmanned navigation of
the container transporter using U-SAT shows good
performance and is suitable. There exist many
problems mentioned above to solve such influencesof speed and environmental conditions. Many
researches are being implemented to improve theaccuracy of U-SAT.

U-SAT
ReceiverController
Board

Fig.8 Configuration of the container transporter

Acknowledgment
This work was supported by the Brain Korea 21
Project.

References
[1]

Fig.9

Unmannedtransporter

navigation

of

the

container

6 Unmanned navigation of the containertransporter
Fig.8 depicts the configuration of the containertransporter. Two ultrasonic receivers are mounted on
the container transporter so that they can obtain theposition information and orientation. The localization
information is sent to PC via bluetooth. And PC
executes its trajectory tracking control. So thecontainer transporter moves along the reference pathin a counterclockwise direction. As seen in Fig.9,
the reference path is situated on the twenty target
points on a track [6].
Experiment was conducted in order to verify thatthe container transporter can move accurately along
the reference path using U-SAT. lateral controller isdesigned as PID controller. Fig.9 shows theexperimental result. A maximum translational
velocity of 0.3m/s was used for this experiment,
resulting in a maximum error of 5cm for this run.
Experimental result shows that the containertransporter is navigated precisely along the reference
path. And the accumulative error can be eliminatedfor this method using U-SAT. The experimental
result shows good performance and is acceptable.

7 Conclusion
In this paper, the performance of U-SAT has©ISAROB 2007

303

Jae Hwan Kim and Poong Hyun Seong,"Experiments on orientation recovery andsteering of autonomous mobile robot using
encodedmagnetic
compass
disc,"IEEE
Transactionson,Instrumentation
andMeasurement, Vol. 45, Feb, 1996, pp. 271~274.
[2] Kai-Tai Song and Wen-Hui Tang, "Environment
perception for a mobile robot using double
ultrasonic sensors and a CCD camera," IEEE
Transactions on, Industrial Electronics, Vol. 43,
June, 1996, 5293219, pp. 372~379.
[3] Alan G. Evans, Everett R. Swift, James P.
Cunningham, Robert W. Hill, Geoffrey Blewitt,
Thomas P. Yunck, Stephen M. Lichten, Ron
Hatch, Stephen Malys, and John Bossler, "TheGlobal Positioning System Geodesy Odyssey,"
Navigation : Journal of The Institute ofNavigation, Vol. 49, No. 1, Spring, 2002, pp.
7~33.
[4] S. Y. Yi and J. H. Jin, "Self-localization of amobile robot using global ultrasonic sensor
system," Journal of Control, Automation, andSystems Engineering, Vol. 9, No.2, Feb, 2003,
pp. 145~151.
[5] D. H. Lee, H. T. An, K. R. Baek, and M. H.
Lee, "A study on resolution Enhancement in
the detection of ultrasonic signal," The KSAE
AnnualFall
Conference,
BranchofBusan·Ulsan·Gyeongnam, 2003, pp. 95~102.
[6] Y. H. Lee, S. I. Kim, M. W. Suh, H. S. Son
and S. H. Kim, "Linearized Dynamic Analysis
of a Four-Wheel Steering Vehicle," The Korean
Society of Automotive Engineers, Vol. 2, No. 5,
1994, pp. 101-109.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Spreader Pose Control Using Dual-electric Compasses
SoonShin Han
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
Ranger112@pusan.ac.kr

HeeSeok Jeong
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
heeseok@pusan.ac.kr

Abstract
A spreader pose control system using dual-electriccompasses has been implemented by measuring the skew
angle of the spreader with dual-electric compasses. In theconventional spreader pose measurement, CCD cameras,laser sensors or tilt sensors are mostly used. However
those sensors are not only sensitive to the weather anddisturbances but also expensive to build the system. To
overcome the shortcomings, an inexpensive and efficientsystem to control the spreader pose has been
implemented using the dual-magnetic compasses. Since
the spreader iron-structures are noise sources to themagnetic compass, it is not considered to use themagnetic compass to measure the orientation of the
spreader. An algorithm to eliminate the interferences ofmetal structures to the dual compasses has beendeveloped in this paper. The 10:1 reduction model of a
spreader control system is implemented and the control
performance is demonstrated to show the effectiveness ofthe dual-magnetic compasses proposed in this research.
Keyword: Magnetic compass, DualElectronic compass, Spreader, Skew angle

compass,

JangMyung Lee
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
jmlee@pusan.ac.kr

pose measurement reduces the monitoring operator’scare about the unmanned crane in the automated port[1].
For the spreader pose measurement, CCD cameras,lasers, and tilt sensors mainly have been utilized in themost operating automated ports[2-4]. However, there aresome limitations in using the conventional sensors forthe measurement of spreader pose. The CCD cameras
require the image processing procedures which take a lotof time, and the images are very sensitive to weather
conditions. The laser sensors are very expensive andhave the dead angle in measuring skew angles.
In this paper, the spreader orientation is measured andcontrolled by using the dual electric compasses which
are robust against the iron structures. In general, thecompass is very sensitive to the iron structures, and it
cannot be used near at the iron structure. Using the dual
electric compasses, the sensitiveness to the ironstructures has been eliminated and also the disturbancesfrom the neighboring objects have been filtered out[5,6]to achieve the skew angle control. Section 2 introduces
the conventional usage of the electric compass. In
section 3, the principle of the dual-electric compasses
proposed in this paper have been described. Section 4analyzes the experimental results, and section 5
concludes this paper and indicates future works on this
topic.

1. Introduction
Many of hub-ports are trying to develop a fully
automated loading/unloading system to escape from theheavy human work loads. Main technologies for the
successful management of the automated port are theautomated loading/unloading using cranes and theautomated navigation of carrying vehicles.
Especially the loading/unloading time is a crucial
factor for the ship staying at a port. The two types ofcontainers, 20 ft and 40 ft, are handled by the specialized
equipment. Main equipments are a container crane and ayard crane. The container crane speed is one of the mostimportant in determining the container processing
capability of a port. The yard crane is carrying thecontainers to stock and to load on the carrying vehicle.
Both of these two cranes have the same structures of ahorizontal-motion trolley and a vertical-motion spreader,and carry the containers to a desired location.
To load or unload the containers precisely, the posecontrol of the spreader is essential with the precise
measurement of the skew and sway angles. The precise©ISAROB 2007304

2. Single Compass Compensation
There are three basic compensations required for thecompass: zero-offset, output sensitivity variation, andnon-orthogonal error. The error compensation techniques
are explained for the general single compass.

2.1 Zero-offset
The output offset of the compass comes from thedevice itself and from the amplifier. The offset depends
on the sensor tolerance and temperature, and the offset
value is obtained as(1)
V offset = (Vx, y max − Vx, y min ) / 2
where

V offsetis zero when there is neither

disturbance nor device-offset.
Using the offset value, the output is corrected as
Vx , y corrected = Vx , y output − Voffset .

(2)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.2 Output sensitivity variation
The two sensors for x- and y-axis may have different
sensitivity to magnetism and amplification factors. The
errors can be compensated by using scale factors. That is,when the scale factor for x-axes is representedas, SV x = 1 , then the scale factor for y-axes, SV y can be
represented as

SVy =V y max − V y min
V x max − V x min.

(3)

Using the scale factor for y-axes, SV y , the outputs
along x- and y-axis are matched to represent the azimuth
angle correctly.

2.3 Non-orthogonal error
In the manufacturing process of the sensor, the nonorthogonal error of up to 2° exists. The error can be
represented as(4a)V y = V max ⋅ sin(α + β )Vx = Vmax ⋅ cos α .

Fig. 1. Relation between earth magnetism and theinterference.

3.2 Output amplitude compensation
The outputs of the dual compasses are recorded on themetal-structure to be used while the structure is rotatedby 360° , the results are represented as two circles in Fig.
2. To remove out the interference effects, the twocircles are properly shifted to the center and to match theradii of the circles to the same.

(4b)

From Eq’s. (4a) and (4b), α andβ can beobtained. Therefore the corrected y-axis output can beobtained as

V y corrected =

Vy
cos β

− Vx ⋅ tan β .

(5)3. Dual Compass Compensation
The compass is very sensitive to the interferencesfrom magnetic materials. To resolve this shortcoming, inthis approach, dual compasses arranged in 180° phasedifference have been utilized as shown in Fig. 1.

3.1 Output phase error
The interference from the metal-structures to the two
sensors in 180° phase difference can be modeled andcompared with the earth magnetism in Fig. 1.
The spread magnetism represents the magnetism of
the iron structures which are magnetized by the earthmagnetism. The interference results in the addition to
one sensor and the subtraction to the other 180° phasedifference sensor with the earth magnetism. If there
exists only the pure earth magnetism, the addition of thetwo x- and y-axis outputs of the two compasses is zero.
However by the effects of the interference, thesummation is not zero in the metal-structures normally.
This compensation algorithm for this interference has
been developed in this paper and will be described indetail in the following subsection.

©ISAROB 2007

305

Fig. 2. Change of magnetized circles with metalstructures.
Through this compensation process, the interference
from the metal-structure is pre-specified and the results
can be utilized for the real-time interferencecompensation.
The normalization process for the two compass
outputs starts with the measured offset value as follows:

where

V1r = V1max − V1offset(6a)

V2 r = V2 max − V2 offset(6b)

V1max and V2 max are the maximum values

obtained from Fig. 2,

V2 offset

V1offset

V12x ,offset + V12y ,offset andV22x ,offset + V22y ,offset . Now the scale factor forthe sensor 2 can be defined, assuming V1 sf = 1 asfollows:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

V2 sf =

V1r
.
V2 r

(7)

Therefore the normalized x-directional output can beobtained asVx1 corrected = Vx1 − Vx1, offset(8a)

Vx 2 corrected = (Vx 2 − Vx 2, offset ) ⋅ V2 sf . (8b)Note that the y-directional output can be also obtainedby replacing x as y in equations (8a) and (8b).
Through the process, the magnetized circle can be
moved to the origin of the measuring coordinates.

3.3 Disturbance compensation
The interference from the neighboring structures
cannot be filtered out on the real time with only a single
compass. In this approach, using the dual compasses, theinterference is eliminated.
To implement a stable skew angle measuring systemusing the dual compasses, here the rejection of dynamic
disturbances using the dual-compasses has been
proposed. By the Eq. (8), the two magnetized circles arenormalized, and the two circles may have exactly same
outputs when there is no disturbance. When there exists
disturbing magnetism, the rotation of θ does not resultin the same variations in both compasses. Instead, thecompasses have the azimuth angles of ρ and σ which
do not have 180° phase difference but have ε error.
The x and y directional error components can beobtained from the outputs of the dual compasses,
respectively, as

errorx =errory =Vx 1 + Vx 22
V y1 + V y 22control accuracy with the dual compasses is high enoughto be used for the spreader pose control within the flipper
error tolerance.

4.1 Experimental environment
Since there were some difficulties in feeding thecontrol cables on the center of the model spreader, thecompasses are installed at each end of the spreader
symmetrically. By the experiment 1, the compasses
become ready to be used by compensating all the offsets.
In the experiment 2, the structural inference is added byputting the two compasses on the metal-frame.
For the calibration process, a motor is installed at thebottom of the spread and its angle is controlled according
to the encoder value.

4.2 Error compensation
By the first experiment, the magnetized circles before
and after compensation have been drawn in Fig. 3(a) and
Fig. 3(b), respectively.
The magnetic field intensity has been increased at acompass while for the other has been decreased. Also theinterference is not constant so that the magnetized circles
become ellipsoids as expected.

(9a)
(9b)

where Vx1 ,Vx 2 ,V y1 and V y 2 are x and y directional
outputs of sensors 1 and 2, respectively. The magnetizedcircle can be described by the radius and phase as
follows:

R = Vx 2 + V y 2

θ = tan −1

Vy
Vx

.

(a). Before interference compensation.

(10a)
(10b)

Since the radius is not constant by the disturbance, theshape of the magnetized circles becomes ellipsoid withthis disturbance.

4. Experiments and Results
In this section, the constitution of the experimental
equipment has been illustrated, which measures andcontrols the skew angle of the 10:1 reduction model
spreader. The dual compasses are utilized for the angle
measurements and the control results show that the©ISAROB 2007306(b). After interference compensation
Fig. 3. Dual compass output.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4.3 Skew angle measurement
The spreaders of the crane operating in the harbors canbe rotated about ±15° during the loading and unloading
operations. And the allowable angle error for both of 20
ft and 40 ft containers are 1° for the normal
loading/unloading operations. Therefore for the fully
automated crane, the skew angle error should be kept
within 1° by the control system.
Using the single compass, the skew angle of the
spreader which is rotated ±15° has been measured andrecorded in Fig. 4.

dynamic disturbances. The skew angle control is themost difficult problem in the control of the spreader forthe loading/unloading operations since there is not any
suitable sensor for the measurement of the skew angle.
With the development of the dual-electric compasses, theautomated crane can be robust against the weather
conditions and be a precise and economical system. In
the compensation processes, there were magnetic
hysteresis which can be modeled and eliminated to
improve the accuracy as a future research.

Acknowledgment
This work was supported by the Regional ResearchCenters Program(Research Center for Logistics
Information Technology), granted by the Korean
Ministry of Education & Human Resources
Development..

References

Fig. 4. Experiments of measuring of skew angle.
Notice that for the error measurement, the highprecision encoder data have been used as the reference
values. As it is illustrated in Fig. 4, the skew angle
measurement by the single compass has the maximum
4° error which is out of the allowable angle error for
the flipper. There is no effective scheme to reduce thiserror so far.
When the dual compasses have been used for the skew
angle measurements, there was nearly recognizable error
in Fig. 5. With the dual compasses, the maximum
measurement error for the skew angle is less than 0.5° .
Therefore it is precise enough to be utilized to control thepose of the spreader as a skew angle sensor.

Fig. 5. Experiment of skew angle.

5. Conclusions
The spreader pose control system has been
implemented by using the dual-electric compasses which
are robust against both the structural interference and©ISAROB 2007

307

[1] Kanji Obata, Koji Uchida, Takashi Chikura,
Hirofumi Yoshikawa and Tadaaki Monzen, "AutomatedTransfer Crane," Mitsubishi Heavy Industries Technical
Review, Vol. 40, no. 2, pp. 1 ~ 5, Jan. 2003.
[2] John R. Holmquist, "Laser Guided Loading System,"IEEE Trans. Industry Applications, Vol. 38, no. 3, pp.
752~757, May, 2002.
[3] Kyung-Moon Lee, Yang-Hwan Kim, Jae-Mu Yun
and Jang-Myung Lee, "Magnetic-interference-free dualelectric compass," Sensors and Actuators, A120, pp.
441~450, Jan. 2005.
[4] J. Ko, W. Kang, Y. H. Kim and Jang-Myung Lee ,"Robust Electric Compass to Dynamic Magnetic Field
Interference," Journal of Control, Automation, andSystems Engineering, Vol.11, no. 1, pp. 27~33, Jan. 2005.
[5] Lenz, J.E, "A review of magnetic sensors,"
Proceedings of the IEEE, Vol. 78, Issue 6, pp. 973~989,
Jun. 1990.
[6] Racz. R, Schott. C and Huber. S, "Electronic
compass sensor," Sensors, Proceedings of IEEE, Vol. 3,
pp. 1446 ~ 1449, Oct. 2004.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

P2P Sensor Data Mining System for School Temperature Measurement System
Ayahiko Niimi
Masaaki Wada

Hiroshi Shimada
Rika Goto
Kei ItoOsamu Konishi
Department of Media Architecture
Future University-Hakodate
116-2 Kamedanakano-cho, Hakodate-shi, Hokkaido 041-8655, JAPAN
Abstract

In this paper, we propose the technique of the sensor data mining by the P2P (Peer-to-Peer) network.
The mechanism that it is possible to share on the P2P
network is considered by receiving information fromthe sensor by the P2P application. Searching request
for sensor unit and mining the sensor data does on
the P2P application. We consider that the proposed
technique is applied to the school environment measurement system. In its system, sensor units are arranged on campus, user can measure room’s temperature and humidity. The temperature sensor and thehumidity sensor are implemented in a microcomputer
board that can connect to the Internet, and we deﬁne
the microcomputer board as a sensor unit. We construct the P2P sensor network on which a PC accesses
the sensor unit and P2P application on its PC uploads
on the P2P network. The P2P network becomes possible disclosing sensor information after more advanced
processing is given by thinking as P2P application not
the sensor unit but on the sensor unit and PC.

Keyword:Sensor Data Mining System, P2P, JXTA, School
Temperature Measurement System

1

Introduction

Recently, we can obtain various data easily by ahigh performance of computer and the Internet. Datamining that extracted signiﬁcant knowledge from alarge amount of data become popular. The techniquefor applying data mining to text information such as
Web page is developed recently though the data stored
in databases was targeted in normal data mining. In
the Internet, various time series data can be obtained.
For instance, the image data of the weather satellite
and the data of various sensors can be obtained. The©ISAROB 2007308

feature of these data is continuous data in the time series. The techniques which applied data mining fromdatabases are used for time series data, but some techniques were improved for time series data.
The digital measurements of the temperature andhumidity, etc. become possible, and connecting thesystem that acquired the measured data on the network becomes possible. However, there are many measurement systems which are rich systems that are used
sensors on PC or which are cheap microcomputer systems that need to construct a special network for thesensor network. We propose the sensor network system using the microcomputer board that can connect
to the Internet. This proposed system can acquire information from the sensor of the microcomputer grouparranged on the network, and can view collected information on Web browser.
In KES2006 [1], it was shown to be able to constructeasily the microcomputer’s sensor network which was
combined microcomputer modules (Micro Cube) andthe database server and the Web application server.
The system that measured the room temperature inschool campus was constructed, it has run for fourmonths, and the eﬀectiveness is veriﬁed.
In this paper, we propose the technique of the sensor data mining by the P2P (Peer-to-Peer) network.
The mechanism that it is possible to share on the P2P
network is considered by receiving information fromthe sensor by the P2P application. Searching request
for sensor unit and mining the sensor data does on
the P2P application. The advantage of P2P systemis scalability of the number of sensors. It becomes tobe able to correspond to the change in the number of
sensors easily by constructing the sensor network on
the P2P network.
Chapter 2 describes the sensor module using Micro Cube. Chapter 3 describes the composition of
the sensor network as server-client system. We describe the installation of Micro Cube, the server andthe client and the technique of collecting and viewing

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

data. Chapter 4 describes the P2P sensor data mining system. In chapter 5, we describe construction of
our proposed system, and discuss about the problem
when constructing sensor network. Section 6 describes
conclusion and enhancing in a future.

2

Sensor Module
In this section, we describe proposed sensor module.

2.1

Outline of Micro Cube

The Micro Cube is a board computer and is composed of several stackable boards [2, 3]. Fig. 1 is aphoto showing one of the combinations of stacked Micro Cube. It has a CPU board with a RENESAS H8
CPU and a TCP/IP Protocol stack. Stackable boards
can vary as follows: Ethernet LAN board, compact
ﬂash board, PCMCIA board, serial board (RS232C
and RS422) and so on. Since the diﬀerent combinations of stackable boards make a seamless connection
with the sensors, users can structure an ad hoc sensor
network very easily. To get sensor information throughthe Internet, HTTP is also employed so that user canget data via a standard Web browser.

resistor (temperature sensor), and CdS sensor (optical sensor) were attached on this board as an input.
Moreover, four digits seven-segments LED and four
two-color LED were attached as an output. Because
an accurate temperature measurement using the thermally sensitive resistor is diﬃcult, a digital sensor isadded in this board for our experiment. Humidity
can be also measured in this digital sensor. Only thetemperature data is acquired this experiment thoughsome sensors are attached on the board. The exchange
and the addition of the sensor can be easily done byexchanging the sensor boards.
To conﬁrm the measurement data easily, the measured temperature was displayed in seven-segments
LED. Moreover, data can be got by HTTP though
the network. When only one sensor module runs, theuser can display a present temperature when the user
accesses it using Web browser.

3

Network Conﬁguration

The sensor network was constructed by using the
microcomputer that explained in Chapter 2. Fig. 2
shows the composition of the constructed sensor network system. First of all, we constructed proposed
sensor network system as server-client system.

Web
Browser

DB/WebServerCampus Network

Figure 1: Photo of a Stacked Micro Cube
micro
Cube

Router

2.2

Instrumentation of the present system

The Micro Cube used in the system to get the information of room condition is composed of the H8/3069
CPU board, LAN board, and special sensor board.
The special sensor board is utilized the board used ofthe programming practice class in Future UniversityHakodate. (The sensor board is shown in Fig. 1) Future University-Hakodate has the programming practice class with the microcomputer and assembler language as “Media Architecture Practice II”. The special board for Micro Cube was designed for its practice class. The push switch, the thermally sensitive©ISAROB 2007

309

micro
Cube

micro
Cube

Figure 2: Network Conﬁguration
The used software is shown below (see table 1). The
data store part is implemented by Perl, and the data
display part is implemented by JSP.
The following steps shows the steps of the collectionof data and the display stored data.
1. Data storage

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 1: Software used in the web database server
SystemOS
HTTP
Database
Software codes

SoftwareRed Hat Linux release 9
Apache 2.0.40
PostgreSQL 7.3.2
Tomcat 5.0.28 and Perl 5.8.0(a) The Perl script accesses to URL of Micro
Cube.
(b) Micro Cube returns the measurement result
by HTML format.
(c) HTML is parsed, and necessary data is preserved in the database.
2. Data browse(a) URL of the server is opened from a Web
browser.
(b) JSP accesses the database.
(c) Necessary
database.

data

is

acquired

from

the

(d) The result is processed to the graph and displayed it on a browser.
The micro cube arranged in school is connectedwith campus network (LAN). The data of each sensor module is acquired with the server set up on thecampus network at regular intervals, and stores in the
database. In this experiment, data is acquired fromthe sensor module every ten minutes. The acquired
data is processed with the Web application server set
up on the same server, and can be displayed from Web
browser of PC on the campus network.
At ﬁrst, Micro Cube connected to campus networkby arranging it in the router because the router had
not been exceeded in LAN of the micro cube. Afterwards, connecting Micro Cube to the campus network
even if we modiﬁed the program, and the router is
not set up became possible so that the router was exceeded.

4

P2P Data Mining System

In this paper, we propose the technique of the sensor data mining by the P2P (Peer-to-Peer) network.
The mechanism that it is possible to share on the P2P
network is considered by receiving information fromthe sensor by the P2P application. Searching request©ISAROB 2007

310

for sensor unit and mining the sensor data does on
the P2P application. The advantage of P2P systemis scalability of the number of sensors. It becomes tobe able to correspond to the change in the number of
sensors easily by constructing the sensor network on
the P2P network.
We consider that the proposed technique is applied
to the school environment measurement system. In
its system, sensor units are arranged on campus, user
can measure room’s temperature and humidity. The
temperature sensor and the humidity sensor are implemented in a microcomputer board that can connect tothe Internet, and we deﬁne the microcomputer board
as a sensor unit. We construct the P2P sensor network on which a PC accesses the sensor unit and P2P
application on its PC uploads on the P2P network.
The P2P network becomes possible disclosing sensor
information after more advanced processing is givenby thinking as P2P application not the sensor unit
but on the sensor unit and PC. Moreover, even if onesensor unit-one peer is not allocated, it is also possible
to arrange peer that integrates some sensor units on
the network. We construct P2P sensor data mining
system used Micro Cube as sensor units and JXTA as
P2P platform.
The sensor data constructs the system that can ﬂexibly correspond also to the diﬀerent kind sensor data
by using the XML form. The advantage of the proposal approach is that it is easy to correspond to thechange in the number of data sources. The change ofthe system is not to hard even when the compositionof the sensor changes. Moreover, the data acquisition
system from the sensor in the remote place can beeasily constructed by constructing the system on theInternet.

5

Experimental Results

The system that explained in Chapter 3 was actually constructed. The system is constructed in December, 2005, and it is running at November, 2006.
Because a lot of modules were able to be reused, thetime that had constructed to development was about
one week.
Some data display examples are shown as follows.
(See Fig. 3) The displayed data can be switched toall or a part of room. The displayed range can beswitched to a day, a week, a month, or all. Fig. 3
shows all data in one chart, and Fig.
At ﬁrst, Micro Cube connected to campus networkby arranging it in the router because the router had

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

one year, and the eﬀectiveness was veriﬁed. We also
proposed P2P sensor data mining system. Because
data began to collect, temperature data is scheduled
to be analyzed in the future. In the analysis of data, itis thought that it is possible to refer to a technique ofthe multiagent base [4, 5] and an analytical techniqueof the analysis of the ﬁxed point observation data [6].
We descussed P2P sensor data mining system withmicrocomputers and JXTA.
The system that expands acquired more diﬀerent
type of sensor information will be constructed, andthen, obtained data is scheduled to be analyzed in the
future. In addition, we want to attach the IR I/O
module on the microcomputer board, and to do theresearch for the ubiquitous computing of the indoor
environment controlling.
Figure 3: All Data in One Chart
not been exceeded in LAN of the micro cube. However, data might not be able to be acquired normallywhen some router’s passing. Then, connecting Micro
Cube to the campus network even if we modiﬁed theprogram, and the router is not set up became possible
so that the router was exceeded.
It took time to divide the problem when data was
not able to be acquired normally because Micro Cube
connected to the campus network. Then, the problem
was considered in cooperation with SE that resided
in school. For the exceeding router problem, we constructed some environment, such as the dummy serverwith the PC-UNIX server, passing the router setting,bypassing the router setting, and it undertook the resolution of a problem while capturing the packet thatﬂowed in the network.
Because data began to collect, temperature data isscheduled to be analyzed in the future.

6

Conclusion

We proposed the technique of the sensor data mining by the P2P for the sensor network system using
the microcomputer board that can connect to the Internet. This proposed system can acquire informationfrom the sensor of the microcomputer group arrangedon the network, and can view collected information on
Web browser. It was shown to be able to constructeasily the microcomputer’s sensor network which is
combined microcomputer modules (Micro Cube) andthe database server and the Web application server.
The system that measured the room temperature inschool campus was constructed, it has run for about©ISAROB 2007

311

References[1] Ayahiko Niimi, Masaaki Wada, Kei Ito, Osamu
Konishi (2006), Construction of School Temperature Measurement System with Sensor Network.
KES’2006, Proceedings, PartIII, Lecture Notes in
Artiﬁcial Intelligence 4253, Springer: pp.813–819.
[2] Wada. M., Hatanaka, K., Kimura, N., Amagai,
K. (2004), Applying an Information Technology
in Fishing Industry I. Acquisition of a Three Dimensional Topography. Journal of Japan Instituteof Navigation, vol. 112, pp.189–198 (in Japanese)[3] Hatanaka, K., Wada, M., Kotaki, M. (2005), Instrumentation for the Measurement of Shallow
Seabed Topography by a Fishing Echo Sounder.
Oceans 2005 MTS/IEEE Conference Proceedings, Ocean Instrumentation I, pp.1–6[4] Niimi, A., Konishi, O. (2003), Data Mining for Distributed Databases with Multiagents.
KES’2003, Proceedings, Part II, Lecture Notes
in Artiﬁcial Intelligence 2774, Springer, pp.1412–
1418[5] Niimi, A., Konishi, O. (2004), Extension of Multiagent Data Mining for Distributed Databases.
KES’2004, Proceedings, Part III, Lecture Notes
in Artiﬁcial Intelligence 3215, Springer, pp.780–
787[6] Toda, M., Osanai, A., Niimi, A., Akita, J.,
Kimura, K., Konishi, O. (2005), Information Access Method to Meteorological Data for Educational Application. ED-MEDIA2005, Proceedings, pp.4404–4408

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A probabilistic modeling of MOSAIC learning
Satoshi Osaga, Jun-ichiro Hirayama, Takashi Takenouchi and Shin Ishii
Graduate School of Information Science
Nara Institute of Science and Technology
Takayama 8916-5, Ikoma, Nara, 630-0192, Japan{satosh-o, junich-h, ttakashi, ishii}@is.naist.jp
Abstract
Humans can generate accurate and appropriate motor commands in various and even uncertain environments. MOSAIC (MOdular Sellection And Identiﬁcation for Control) was originally proposed to describesuch human ability, but it includes some complex andheuristic procedures. In this article, we present analternative and probabilistic model of MOSAIC (pMOSAIC) as a mixture of normal distributions, andan online EM-based learning method for its predictors and controllers. A theoretical consideration showsthat the learning rule of p-MOSAIC corresponds tothat of MOSAIC except for some points mostly related to the learning of controllers. The results of experiments using synthetic datasets demonstrate some
practical advantages of p-MOSAIC. One is that thelearning rule of p-MOSAIC stabilizes the estimationof “responsibility.” Another is that p-MOSAIC realizes accurate control and robust parameter learningin comparison to the original MOSAIC, especially innoisy environments, due to the direct incorporation of
the noise into the model.

1

Introduction

Humans have the remarkable ability to generate accurate and appropriate motor commands in various
and even uncertain environments. Studies of humanmotor controls have shown that dis-adaptation and readaptation to a learned environment are more rapid
than adaptation to a novel environment [5], implyingthat the human motor control could be performed bya modular structure consisting of multiple controllers
each adapting to a speciﬁc environment.
MOSAIC [1] was originally proposed to model the
motor control system with such a modular structure.
In MOSAIC, each controller is coupled with a corresponding predictor, and a motor command is determined by a weighted mean of outputs of multiple controllers, where the weight for each controller (responsibility) is estimated based on the prediction error of thecorresponding predictor. However, MOSAIC includes©ISAROB 2007

312

some complex and heuristic procedures that make itdiﬃcult to understand the model.
In this study, we re-formulate MOSAIC as a probabilistic model in order to construct an easily understandable framework. Parameters of predictors andcontrollers are estimated by the online EM algorithm[4], which maximizes the log-likelihood of the model,
given the history of control results. We also show results of computer simulations in which behaviors of responsibility and controller learning of p-MOSAIC arecompared with those of MOSAIC.

2

MOSAIC

We consider a situation where the dynamics of the
motor system is given by a discrete-time system:x̃t+1 = Φ(x̃t , ut ),where x̃t and ut are the system state and the applied
motor command, respectively, at time t. The task ofthe motor control is to make the system state x̃t tokeep on a given trajectory x∗t .
To perform the control task, we assume M pairs of acontroller and a predictor. The aim of the controller isto generate an appropriate motor command ut which
produces the desired state x∗t+1 . We assume that an
output of the i-th controller is represented as
ψi,t = ψ(x̃t , x∗t+1 ; vi ),where vi is the parameter of the i-th controller. The
objective of the predictor is to accurately predict thesystem state at the next time step, and an output of
the i-th predictor is given by
φi,t = φ(x̃t−1 , ut−1 ; wi ),where wi is the variable parameter of the i-th predictor. Because there are M pairs of a controller anda predictor, the responsibility for each controller (andpredictor) should be deﬁned. The responsibility signal

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

λi,t for the i-th pair is deﬁned byexp(−|x̃t − φi,t |2 /σ 2 )λ̂i,tλi,t = PM,22
j=1 exp(−|x̃t − φj,t | /σ )λ̂j,t(1)

the set of controllers’ parameters. The motor command ũT −1 is deterministically given by Eq. (2). In
our particular experiments in Section 4, we use a linear
predictor:
φ(xt−1 , ut−1 ; wi ) = wi,x xt−1 + wi,u ut−1 .

where σ is a constant and λ̂i,t is a rough prediction of
the responsibility signal λi,t which is typically given
as a constant (then ignored). The responsibility represents how well each predictor reproduces the target
dynamics, then an overall motor command ũt at time
t is given by a linear combination of outputs ψi,t of
the M controllers as(6)

For a desired trajectory x∗1:T = (x∗1 , · · · , x∗T ) and an
actual trajectory x̃0:T = (x̃0 , · · · , x̃T ), the probabilityof a state sequence x1:T = (x1 , · · · , xT ) of randomvariables is represented asp(x1:T |x̃0:T , x∗1:T ; λ, w, v) =TYp(xt |x̃t−1 , x∗t ; λ, w, v),

t=1

ũt =

M
X

λi,t ψi,t + ufb
t .

(2)

i=1

Here, ufb
t is a feedback motor command, which is assumed to be produced by a PID or PAD controller,
based on the diﬀerence between x∗t and x̃t .
MOSAIC is trained by updating the parameters ofcontrollers and predictors. A learning rule is given by∂ψi,t ∗
(u − ψi,t )∆vi = κλi,t
∂vi t
∂φi,t
∆wi = κλi,t(x̃t − φi,t ),
∂wi

(3)

3

∂ψi,t fb
u .
∂vi t

(5)

p(xt |x̃t−1 , ũt−1 ; λ, w, v)
λi N(xt |φ(x̃t−1 , ũt−1 ; wi ), αi−1 ),log p(xt = x̃t |x̃t−1 , x∗t ; λ, w, v),

by means of the online EM algorithm, in which thecontroller parameters v are ﬁxed. By introducing
a hidden variable ct that indexes predictor-controller
pairs, the online free energy for any distribution of thehidden variable, qp (ct ), is deﬁned as
FT [{qp (ct )}, λ, w]À¿TX
qp (ct ),=ΓT (t) logp(x̃t , ct |x̃t−1 , x∗t , λ, w) qp (ct )t=1
where p(x̃t , ct |x̃t−1 , x∗t , λ, w) = N(x̃t |φct ,t , αc−1
)λct .
t< · >qp (ct ) is the expectation with respect to the distribution qp (ct ), and ΓT (t) is given by‰1(t = T )QTΓT (t) =γ(0≤ t < T ),s=t+1 swhere γs (0 ≤ γs < 1) is called the forgetting factor.
The online free energy is minimized by the online EM
algorithm, in which the following two steps are implemented once after seeing x∗T and x̃T −1 at a time stepT:E-stepi=1where xt is a random variable for the predicted state attime t, λ = (λ1 , · · · , λM ) is the mixing rate vector suchPMthat λi ≥ 0 and i=1 λi = 1, w = (w1 , · · · , wM ) is theset of predictors’ parameters, and v = (v1 , · · · , vM ) is

©ISAROB 2007

Parameters λ and w of the predictors are primarily
estimated so as to maximize the log-likelihood:t=1

With a set of M predictors, x̃t = φ(x̃t−1 , ũt−1 ; wi )+εi , where εi is the noise of the i-th predictor, the stateprediction by integrating those predictions is given
probabilistically as a mixture of normal distributions:

M
X

Learning rule of predictors

TX

p-MOSAIC

=

3.1

(4)

where ∆vi and ∆wi are the updates of parameters
vi , wi in a single learning step, κ is the learning rate,
and u∗t is the desired motor command. Although it isassumed that the desired motor command u∗t is available in Eq. (3), this assumption is not practical. Thus,
the controller learning (3) is approximately performed
using the feedback-error learning [3] as∆vi ≈ κλi,twhere the random variables are assumed to be independent of each other. Given x∗1:T and x̃1:T , the parameters of the predictors and the controllers are determined by the maximum likelihood estimation. In
the following two subsections, we describe learning
rules of the predictors and the controllers.

313

qp (cT ) ∝ p(x̃T , cT |x̃T −1 , x∗T , λ, w)(T −1)−1)∝ N(x̃t |φcT ,T , 1/αcT )λ(T,cT

where the superscript T −1 means the time step, T −1.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

M-stepM-step(T −1)(T )= (1 − ηT )λi(T )= (1 − ηT )∆wi

λi
∆wi

+ ηT qp (cT = i)

(7)

(T )

∆vi

(T −1)+ ηT καi qp (cT = i)(x̃T − φi,T )

∂φi,T
,
∂wi
(8)

where ηT is given by
ηT = 1/NT ,+ ηT κλi

Here, wj,u is the predictor parameter deﬁned in Eq.
(6). Even if the forgetting factor γt is constant atzero, the M-step equation reduces to

NT = γT NT −1 + 1 (N0 = 0).

The above learning rules of p-MOSAIC involve asmoothing eﬀect on the suﬃcient statistics in the Mstep, because of the online free energy. On the other
hand, they become similar to the learning rules of MOSAIC in a special setting of γt = 0(t = 1, · · · , T ),which corresponds to discarding the smoothing eﬀect.
Even in this special setting, however, the learning ruleof p-MOSAIC contains an additional term associatedwith the inverse variance αi of each predictor (Eq.
(8)), which represents the noise level of the predictor.

Learning method of controllers

The controller parameters v are primarily estimated so as to maximize the log-likelihood:
TX

while the predictor parameters, λ and w, are ﬁxed.
According to the online EM algorithm, instead of the
log-likelihood, the online free energy:
FT [{qc (ct )}, v]¿TX=
ΓT (t) log

qc (ct )p(xt = x∗t , ct |x̃t−1 , x∗t , v)

Àqc (ct )for any distribution of the hidden variable, qc (ct ),is minimized, where p(xt = x∗t , ct |x̃t−1 , x∗t , v) =)λct . < · >qc (ct ) is the expectation withN(x∗t |φct ,t , αc−1trespect to the distribution qc (ct ). As an incremental minimization of the online free energy, the following two steps are implemented once, given the desired
state x∗T and the previous state x̃T −1 :E-stepqc (cT ) ∝ p(xT = x∗T , cT |x̃T −1 , x∗T , v)
(T −1)−1)∝ N(x∗T |φcT ,T , 1/αcT )λ(T.
cT

©ISAROB 2007

∆vi

314

= κλi

M
∂ψi,T −1 X
αj q(cT = j)wj,u (x∗T − φj,T ),∂vi j=1

which is obviously diﬀerent from Eq. (3), the learning
rule of controllers in MOSAIC. The controller learningin MOSAIC is deﬁned as a gradient-based feedbackerror learning, which tries to minimize the time-lag
diﬀerence between the previous actual state x̃t−1 andthe previous desired state x∗t−1 . In p-MOSAIC, thecontroller learning tries to minimize the diﬀerence between the current predicted state x̂t and the current
desired state x∗t . Moreover, the learning rule of pMOSAIC includes the inverse variance αj (Eq. (9)).
These two points arise from the diﬀerence in the learning criteria between MOSAIC and p-MOSAIC.

4

log p(xt = x∗t |x̃t−1 , x∗t ; λ, w, v),

t=1

t=1

M
∂ψi,T −1 X
αj q(cT = j)wj,u (x∗T − φj,T ).
∂vi j=1

(9)

(T )

3.2

(T −1)= (1 − ηT )∆vi

Simulation studies

To compare p-MOSAIC with MOSAIC, we simulated the control of a spring-mass-damper system. The
desired trajectory of the object (mass position) followed a mixture of sine waves for 12 seconds. To show
the adaptability of the motor control system, environment (mass of the object M , damping B and spring
constant K) switchs every 4 sec between the following
three settings:
 1.0, 2.0, 8.0 (0 − 4sec)
5.0, 7.0, 4.0 (4 − 8sec)(M, B, K) =
8.0, 3.0, 1.0 (8 − 12sec).
In both MOSAIC and p-MOSAIC, we prepared three
predictor-controller pairs. Observation and control
were performed at 1,000 Hz, and a single trial was
continued for 12 seconds. The predictors (6) were input by the motor command, the state (position andvelocity) of the object at the present time, and output
the predicted acceleration of the object at the next
time. The controllers were input by the state at thepresent time and the desired acceleration at the nexttime, and output a motor command at the present
time. In this simulation, we used a PAD controller
to produce the feedback motor command. Note that

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

-1

ǳ
0
0

2000

4000

6000

8000

10000

12000

RMOSAICMOSAIC1log10 MSERMOSAICMOSAIC

1

ǳ

-3
-4
-5
-6

0

MOSAIC(NoiseVar 0.1)
p-MOSAIC(NoiseVar 0.1)

-2

0

200

400

600

800

1000

-1
2000

4000

6000

8000

10000

12000

RMOSAICMOSAIC

1

ǳ
0
0

2000

4000

6000

8000

10000

12000

log10 MSE

0MOSAIC(NoiseVar 1.0)
p-MOSAIC(NoiseVar 1.0)

-2

-3

-4

Time(msec)
-5

0

200

our task for the spring-mass-damper system is almost
the same as in the previous work [2]. A regularization
term is introduced to the estimation of responsibility
in MOSAIC and p-MOSAIC in order to suppress any
overﬁtting to the noisy environment.

4.1

Responsibility

We ﬁrst examined how the responsibility behaves.
Prior to the experiment, three predictor-controller
pairs were completely adapted to their own environments. Since there is no learning factor, we can compare solely the estimation of the responsibility between
Eq. (7) with the forgetting factor being zero (for comparison), and Eq. (1). Figure 1 shows the result.
Although p-MOSAIC achieved a complete switchingof controllers in response to changes of environments,
MOSAIC sometimes failed.

4.2

400

600

800

1000

Trial

Figure 1: The responsibility along time

Figure 2: Logarithm of the mean square error between
the actual and the desired trajectory vs. number oftrialsslowly due to the control of the inverse variance αi .
Because the environmental noise was large, the adaptive control of the inverse variance made the learning slow but stable, suggesting adaptive adjustment oflearning speed in p-MOSAIC.

5

Summary

In this study, we proposed p-MOSAIC, a probabilistic model of MOSAIC, and derived learning rules
according to the online EM algorithm. P-MOSAIC
achieved an appropriate estimation of responsibility inthe predictor, and accurate control and robust learning when the controllers learned.

Controller learning

We compared the controller learning, Eq. (9) of pMOSAIC, and Eq. (5) of MOSAIC, assuming the predictors were completely trained to adapt to their own
environments. To compare controller learning only,
we used Eq. (7) in both MOSAIC and p-MOSAIC toestimate responsibility, and the forgetting factor was
ﬁxed at zero. We examined the controller learning inparticular when the actual state x̃t is disturbed by anoise.
Figure 2 shows the results for a small noise and a
relatively large one. When the noise level was low (upper panel), p-MOSAIC achieved more accurate control
than MOSAIC. When the noise level was relatively
high (lower panel), on the other hand, the learning by
MOSAIC proceeded faster, but it was substantiallyunstable; hence, the performance improved due to pMOSAIC after about 1,000 trials. In the early learning
phase, the controller learning of p-MOSAIC proceeded©ISAROB 2007

315

References[1] D.M. Wolpert and M. Kawato. (1998). Neural
Networks, 11, 1317-1329.
[2] M. Haruno, D.M. Wolpert, M. Kawato. (2001).
Neural Computation, 13, 2201-2220.
[3] M. Kawato. (1990). Feedback-error-learning neural network for supervised learning. In R. Eckmiller (Ed.), Advanced neural computers (pp. 365372). Amsterdam: North-Holland.
[4] M. Sato and S. Ishii. (2000). Neural Computation,
12(2), 407-432.
[5] R.B. Welch, B. Bridgeman, S. Anand and K.E.
Browman. (1993). Perception and Psychophysics,
54(2), 195-204.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Iterative Learning Control for Linear Time-Variant Continuous Systems
Based on Two-Dimensional System Theory ∗Deyuan Meng and Yingmin Jia
The Seventh Research Division
Beihang University (BUAA)
Beijing 100083, P.R.China
dymeng23@126.com, ymjia@buaa.edu.cn

Abstract
In this paper, an iterative learning control (ILC)
scheme is presented for linear time-variant continuous
multi-variable systems based on two-dimensional (2-D)
system theory. Three ILC schemes are discussed, andthe corresponding convergence and effectiveness areproved where only the structure of 2-D system model,
the property of λ -norm, and the Bellman-Gronwall inequality are employed. Two numerical simulation examples are included to validate the effectiveness of the
proposed ILC procedures.

1. Introduction and Problem Formulation
Iterative learning control (ILC) was firstly introduced in 1984 by Arimoto et al. [1], and it has generated
considerable research interest over the past years. The
objective of ILC is to determine a control input iteratively, resulting in the plant’s ability to track the givenreference signal or the output trajectory over a fixed
time interval. Hence, the most widely used ILC scheme
is the PID-type scheme because this enables the conventional PID-like system for processing the tracking error[1]-[4]. However in [5], Geng et al. pointed that all
PID-type ILC schemes inevitably suffer from a tight restriction. Moreover, the understanding of the structure
and parameters of the unknown systems cannot be directly increased through the PID-type learning scheme
because it is difficult to generalize the obtained resultsfrom a particular task to other similar tasks [6].
In fact, one of the main difficulties that ILC suffers
is to establish a suitable mathematical model to clearly
describe the dynamics of the control system and the behavior of the learning process [5]-[7]. Two-dimensional∗ Supported by the National Science Foundation of China(60374001), and the Ministry of Education of China (20030006003).

©ISAROB 2007

316

(2-D) model provides an excellent mathematical platform due to its two independent dynamic process [7][8], and hence 2-D system theory is introduced to ILC
schemes.
In this paper, we discuss ILC problem for the following linear time-variant continuous system:ẋ(t) = A(t)x(t) + B(t)u(t)y(t) = C(t)x(t)(1)

where x(t) ∈ Rn , u(t) ∈ Rm , y(t) ∈ R p are the state vector, the input vector, the output vector, respectively, andA(t), B(t),C(t) are real time-variant matrices of appropriate dimensions that can be estimated. The boundary
condition is x(0) = x0 .
Then, the ILC problem we are dealing with is statedas follows. Given system (1) with boundary condition
x(0) = x0 , iteratively find an appropriate control input{u(t), 0 ≤ t ≤ T } such that the system output follows
the reference trajectory yd (t) ∈ R p , 0 < t ≤ T , i.e.,
sup ky(t) − yd (t)k < ε0<t≤Twhere ε > 0 is a required tolerance, and yd (0) = C(0)x0 .
Since the system matrices are not fully known, we are
required to derive an ILC technique. In this paper, 2D system theory is used to solve the above-mentioned
problem. Main difficulty we solve is to establish a suitable 2-D system model to describe the dynamics of thecontrol system and the behavior of the learning process,and a 2-D continuous-discrete Roesser’s model is successfully derived by using the derivative of output tracking error instead of direct output tracking error.

2. ILC Schemes for Linear Time-Variant Continuous Systems
Suppose that k denotes the learning iteration, thena general ILC scheme is given as u(t, k + 1) = u(t, k) +

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
∆u(t, k). Sequentially system (1) can be modeled as thefollowing 2-D time-variant form
∂ x(t,k)∂ t = A(t)x(t, k) + B(t)u(t, k)y(t, k) = C(t)x(t, k)(2)

The boundary conditions for system (2) are given asx(0, k) = x0 for k = 0, 1, 2, · · · , and u(t, 0) = u0 (t) fort ∈ [0, T ]. If yd (t) and C(t) are differentiable for t ∈[0, T ], denotee(t, k) = yd (t) − y(t, k)ξ (t, k) = ∂ e(t,k)∂tη (t, k) = x(t, k + 1) − x(t, k)(3)= A(t)η (t, k) + B(t)∆u(t, k)(4)

ξ (t, k + 1) − ξ (t, k) = ∂ (e(t,k+1)−e(t,k))∂t= − ∂ (y(t,k+1)−y(t,k))∂t= −[C(t)A(t) + Ċ(t)]η (t, k)−C(t)B(t)∆u(t, k)(5)Equations (4) and (5) can be written in compact form¸·· ∂ η (t,k) ¸ ·¸A(t)0 η (t, k)∂t=−C(t)A(t) − Ċ(t) I ξ (t, k)ξ (t, k + 1)
·
¸
B(t)
+
∆u(t, k)−C(t)B(t)(6)
Applying the following ILC scheme∆u(t, k) = K(t)ξ (t, k)(7)

we can derive a system with respect to the derivativeof control error in accordance with the 2-D continuousdiscrete Roesser’s model· ∂ η (t,k) ¸∂tξ (t, k + 1)
¸
¸·
·
A(t)
B(t)K(t)η (t, k)=−C(t)A(t) − Ċ(t) I −C(t)B(t)K(t) ξ (t, k)(8)
The boundary conditions for (8) are η (0, k) = 0 for k =0, 1, 2, · · · and finite ξ (t, 0) for t ∈ [0, T ]. Also, supposethat there exists a positive number LT10 such that thefollowing inequality holds:°·¸°° A(t) B(t)K(t) °°° ≤ LTf or t ∈ [0, T ] (9)
10
° 0
°
0
Thus, the following theorem can be proved by employing the structure of 2-D system model, the property of
λ -norm, and the Bellman-Gronwall inequality.

©ISAROB 2007

can ensure that lim e(t, k) = 0 for t ∈ [0, T ].
k→∞Thus, we can obtain∂ η (t,k)= ∂ (x(t,k+1)−x(t,k))∂t∂tTheorem 1. For a 2-D ILC model (2), suppose thatboth the desired output yd (t) and system matrix C(t)are differentiable for t ∈ [0, T ]. If
°·
¸°
°
°
0
0
°
sup °
° −C(t)A(t) − Ċ(t) I −C(t)B(t)K(t) ° < 1
0≤t≤T
(10)
then the ILC scheme¸·dyd (t) ∂ y(t, k)(11)−u(t, k + 1) = u(t, k) + K(t)
∂t
dt

317

According to Theorem 1, algorithm 1 is introduced.
Algorithm 1:1). The system matrices A(t), B(t),C(t), the reference
output trajectory yd (t), the required form φ (t) of thewhole resulting error matrix I − C(t)B(t)K(t), and the
trajectory tolerance ε > 0 are given for t ∈ [0, T ].
2).
Let k = 0, u0 (t) = 0, x(0) = x0 , K(t) =(C(t)B(t))T [C(t)B(t)(C(t)B(t))T ]−1 (I − φ (t)).
3). According to system (2), calculate y(t, k). If
sup ky(t, k) − yd (t)k ≥ ε , then calculate u(t, k + 1) ac0<t≤T

cording to (11), else go to step 5).
4). k = k + 1, return to step 3).
5). End.
To improve the learning efficiency, a modification of learning scheme (7) is given as: ∆u(t, k) =−K1 (t)η (t, k) + K2 (t)ξ (t, k). Then, the system (8) can
be formulated as¸· ∂ η (t,k) ¸ ·
¸·
η
(t,
k)
A
(t)
A
(t)
11
12
∂t
(12)
=
A21 (t) A22 (t) ξ (t, k)ξ (t, k + 1)
where submatrices are given as A11 (t) = A(t) −B(t)K1 (t), A12 (t) = B(t)K2 (t), A21 (t) = −C(t)A(t) −Ċ(t) + C(t)B(t)K1 (t), A22 (t) = I − C(t)B(t)K2 (t). If
C(t)B(t) has uniform full-row rank for t ∈ [0, T ], thenwe haveK̂1 (t) = (C(t)B(t))+ [C(t)A(t) + Ċ(t)]K̂2 (t) = (C(t)B(t))+
so that −C(t)A(t) − Ċ(t) + C(t)B(t)K̂1 (t) = 0 and I −C(t)B(t)K̂2 (t) = 0, where (·)+ represents the MoorePenrose inverse of matrix. Set K1 (t) = K̂1 (t), K2 (t) =K̂2 (t), and hence we have ξ (t, 1) = 0 no matter what
ξ (t, 0) is. Then, we can obtain e(t, 1) = 0 for t ∈ [0, T ]based on the initial condition yd (0) = C(0)x0 .
Theorem 2. For a 2-D ILC model (2), if C(t)B(t) has
uniform full-row rank for t ∈ [0, T ], then there exists an

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3. Examples

ILC schemeu(t, k + 1) = u(t, k) − K̂1 (t)[x(t, k + 1) − x(t, k)]hi+K̂2 (t) dydtd (t) − ∂ y(t,k)∂t(13)
that can drive the control error to zero for the wholereference output trajectory after only one learning trial.
Though Theorem 2 provides an effective ILC scheme(13), x(t, k + 1) is not available, and hence further modification is needed. If system matrices of (1) are accurately known, then from equations (2) and (13), we canderive∂ x(t,k+1)= [A(t) − B(t)K̂1 (t)]x(t, k + 1)∂t

+B(t)[I − K̂2 (t)C(t)B(t)]u(t, k)+B(t)K̂1 (t)x(t, k) + B(t)K̂2 (t)×hidyd (t)−(C(t)A(t)+Ċ(t))x(t,
k)
dt

(14)

Example 1: Consider the ILC problem for the following linear time-variant continuous system:·¸−0.1cos(t 0.2 )3ẋ(t) =x(t)0.02t10sin(t)·¸
0.027t + 1
(18)
+
u(t)
0.12
£
¤
y(t) = 0.45 −0.001t x(t)where x(0) = [0 0]T , and the matrix C(t)B(t) has uniform full-row rank for t ∈ [0, 1]. The desired output isgiven as yd (t) = 12t 2 (1 − t). Using Algorithm 1, we
set the initial input sequence of ILC as u(t, 0) = 0, andlet K(t) = 0.6(C(t)B(t))T [C(t)B(t)(C(t)B(t))T ]−1 . Additionally, the accuracy of tracking is evaluated by thefollowing total square error of tracking:

Therefore, we can apply control law(15)

to the following systemẋ(t) = [A(t) − B(t)K̂1 (t)]x(t) + B(t)û(t)y(t) = C(t)x(t)

S=

(16)

which is the state feedback form of system (1). Hence,
the output of the closed-loop system is identical with thereference output, namely, y(t) = yd (t),t ∈ [0, T ]. This
result can be directly verified by the response formulaof system (16). Thus, the following theorem can beproved.
Theorem 3. For a 2-D ILC model (2), if the matrix
C(t)B(t) has uniform full-row rank for t ∈ [0, T ], thenthe following ILC scheme

Z 10[yd (τ ) − y(τ )]2 d τFigure 1 shows the tracking error performance of the
ILC system output at different time-steps and iterations,and Figure 2 performs the curves of the total square error of tracking in the process of Algorithm 1 being iteratively executed. From Figure 1-2, it can be concluded
that the convergence rate of Algorithm 1 is high and theoutput is capable of tracking the desired trajectory accurately within few iterations.
1.8

desired output
iterition 3
iterition 4
iterition 5

1.6

1.4

1.2

outputû(t) = [I − K̂2 (t)C(t)B(t)]u(t) + K̂1 (t)x(t)ih+K̂2 (t) dydtd (t) − (C(t)A(t) + Ċ(t))x(t)

1

0.8

0.6

0.4

(17)

can drive the control error to zero for the whole reference output trajectory after only one learning iteration,
where x̂(t) is the state vector of system (16).
Similarly, we give the second algorithm based on
Theorem 3.
Algorithm 2:1). For t ∈ [0, T ], the system matrices A(t), B(t),C(t),the reference output trajectory yd (t), the initial input
sequence u(t), and the initial state vector of system
x(0) = x0 are given.
2). Calculate K̂1 (t), K̂2 (t), and measure x(t), y(t) fromsystem (1).
3). Use (15) to calculate û(t), then apply û(t) to system(16) and measure x̂(t).
4). Apply control û(t) − K̂1 (t)x̂(t) to system (1).

©ISAROB 2007

318

0.2

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time tFigure 1: (Example 1) Tracking error performanceof ILC system output using Algorithm 1.

1.4

1.2

1

total square erroru(t) ⇐ û(t) − K̂1 (t)x̂(t)

0.8

0.6

0.4

0.2

0

0

1

2

3

4

5

6

7

8

no. of iteration

Figure 2: (Example 1) Total square error of different iterations using Algorithm 1.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Example 2: To demonstrate Algorithm 2, consider
the following linear time-variant continuous system:
·
¸
·
¸
0.18
0
0.1
ẋ(t) =
x(t) +
u(t)0.02t −0.5
0.01t + 2
(19)£¤y(t) = −0.52 0 x(t)where x(0) = [0 0]T , and the matrix C(t)B(t) has uniform full-row rank for t ∈ [0, 1]. The desired output
is given as yd (t) = sin(π t). Here, provided that theaccurate information on the system parameters in system (19) is unavailable, and only its estimated system isgiven as
·
¸
·
¸
0.2
0
0.13
ẋ(t) =
x(t) +
u(t)0.02t −0.46
0.015t + 2.1£¤
y(t) = −0.54 −0.01 x(t)(20)
Despite of this situation, Algorithm 2 is still effective.
Figure 3 shows the tracking performance of the ILC system output at different time-steps and iterations. Also,
Figure 4 describes the curves of total square error of
tracking in the process of Algorithm 2 being iteratively
executed. From Figure 3-4, it can be noticed that it takes
few iterations for Algorithm 2 to drive the tracking error
to a very low level for the whole desired output. Moreover, this simulation result demonstrates that Algorithm
2 is robust with respect to small perturbations of systemparameters.
1

0.9

0.8

0.7

output

0.6

0.5

0.4

0.3

desired output
iterition 1
iterition 3

0.2

0.1

0

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time tFigure 3: (Example 2) Tracking error performanceof the system output using Algorithm 2.

0.7

0.6

total squarte error

0.5

0.4

0.3

0.2

0.1

0

0

1

2

3

4

5

6

no. of iteration

Figure 4: (Example 2) Total square error of different iterations using Algorithm 2.

©ISAROB 2007

319

4. Conclusions
Main difficulty of ILC is to establish a suitable
mathematical model to describe the dynamics of thecontrol system and the behavior of the learning process.
This paper successfully established the 2-D continuousdiscrete Roesser’s model with respect to the derivativeof the output tracking error. Then, three ILC schemes
were given, and sufficient conditions for convergence of
these three learning schemes were presented. Example
1-2 validated that these ILC procedures were effective
and robust with respect to small perturbations of systemparameters.

References[1] S. Arimoto, S. Kawamura, and F. Miyazaki, Bettering
operation of robots by learning, J.Robot Syst., vol. 1,
1984, pp. 123-140.
[2] Kwang-Hyun Park, An average operator-based PD-type
iterative learning control for variable initial state error,
IEEE Trans.Autom.Contr., vol. 50, no. 6, Jun., 2005, pp.
865-869.
[3] Saner S. Saab, Optimal selection of the forgetting matrix into an iterative learning control algorithm, IEEE
Trans.Autom.Contr., vol. 50, no. 12, Dec., 2005, pp.
2039-2043.
[4] Geng Ji, and Qi Luo, An open-closed-loop PID-type
iterative learning control algorithm for uncertain timedelay systems, Proceedings of the Fourth International
Conference on Machine Learning and Cybernetics,
Guangzhou, 18-21, Aug., 2005, pp. 1154-1159.
[5] Z. Geng, R. Carroll, and J. Xies, Two-dimensionalmodel and algorithm analysis for a class of iterative
learning control systems, Int.J.Control, vol. 52, 1990,
pp. 833-862.
[6] T. W. S. Chow, and Y. Fang, An iterative learning control
method for continuous-time systems based on 2-D system theory, IEEE Trans. Circuits Syst., I Fundam. Theory Appl., vol. 45, no. 4, Apr., 1998, pp. 683-689.
[7] X.-D. Li, T. W. S. Chow, and J. K. L. Ho, 2-D systemtheory based iterative learning control for linear continuous systems with time delays, IEEE Trans. Circuits
Syst., I Regular Papers, vol. 52, no. 7, Jul., 2005, pp.
1421-1430.
[8] X.-D. Li, J. K. L. Ho and T. W. S. Chow, Iterative learning control for linear time-variant discrete systems basedon 2-D system theory, IEEE Proc.-Contr., Theory Appl.,
vol. 152, no. 1, Jan., 2005, pp. 13-18.
[9] J. E. Kurek and M. B. Zaremba, Iterative learning
control synthesis based on 2-D system theory, IEEE
Trans.Autom.Contr., vol. 38, no. 1, Jan., 1993, pp. 121125.
[10] T. Kaczorek, Two-Dimensional Linear Systems. New
York: Spinger-Verlag, 1985.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Solution Searching for Multi-variable Optimization Problems
by GA with Momentum Offspring
Hiroshi KINJO, Sam DUONG CHAU, Moriyoshi MAESHIRO,
Kunihiko NAKAZONO, and Tetsuhiko YAMAMOTO
Faculty of Engineering, University of the Ryukyus
Nishihara, Okinawa 903-0213, Japan
Abstract

the method for neural network training problem. We
conclude this paper in section 5.

Genetic Algorithm (GA) is known as one of themost powerful solution searching mechanism for nonlinear and multi-variable optimization problems. Generally, GA takes a long time to find the solutions andsometimes it can not find the optimum solutions. In
order to improve the search performance, we propose afast algorithm of GA and a mutation method. The fastalgorithm is usage of a momentum offspring (MOS).
The MOS is an individual not the crossover but by
the best individuals of current and past generations.
The MOS is considered to have higher probability fordesired solution and the effect of MOS is fast searching of the optimum solution. Furthermore, we proposed a constant range mutation (CRM) for the GA.
The CRM is considered to have effect of avoiding theineffective individual production. We apply the GA
with MOS and CRM to optimization problems of two
multi-variable functions and neural network training
problem. Simulations show that proposed method has
good performances.
Keywords: Genetic algorithm, fast algorithm, momentum offspring, constant range mutation, optimization problem, neural network training.

1

2
2.1

Fast GANew Method of Recombination

Conventionally, GAs use the recombination, known
as genetic operations or offspring production process,which typically involves crossover and mutation operators to yield offspring at each generation. The conventional recombination is shown in Figure 1 (a). To
improve the performance of GA, we now propose a newrecombination which includes constant range mutation(CRM) and momentum offspring (MOS) operators, as
shown in Figure 1 (b). In this method, MOS and CRM
are applied simultaneously but with different rates of
Rmo and Rmu , respectively.

Crossover

Mutation

Introduction

Genetic algorithms (GAs), with many precious advantages, are now widely used in various fields, especially in solving optimization problems. Generally,
GAs are also time-consuming in computing, however,and sometimes it can not produce the desired results.
In order to improve the search performance, we propose a fast algorithm of GA and a mutation method,i.e. momentum offspring (MOS) and constant range
mutation (CRM).
In section 2, we describe a new method, then, insection 3, we investigate the search performance of themethod for multi-variable functions. Finally, we apply©ISAROB 2007

320(a) Conventional method

CrossoverMomentumoffspring

Constant
mutation(b) Proposed method
Fig. 1 Offspring production process

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.2

3

Momentum Offspring

For each generation, conventional GAs produce offspring by random process. In our proposed method,i.e. MOS, the next best individual will be determinedbased on the best individuals of the past and current
generations. Namely, if the best individuals of the past
generation (g − 1) and current generation (g) are xg−1
best
and xgbest , respectively, then the best individual of next
generation (g + 1) can be determined as:
´
³
g
g
g−1
(1)
xg+1
m = cr xbest − xbest + xbest
where, c is a constant coefficient (we set c =1 here)
and r is a random coefficient in the range of [0, 1].
Figure 2 shows the idea of MOS method.

generation

x

g-1
best

g-1

x
x

g+1
m1

x

g
best

g

g+1
m2

g+1

Fig. 2 Momentum offspring

2.3

Constant range mutation

Optimization of multi-variable functions

We consider optimization problems of following two
multi-variable functions, Sphere function and Rastrigin function,nXf (x) =
x2i
(2)
i=1

f (x) = 10n +

n
X
¡

¢
x2i − 10 cos (2πxi )where, n is number of dimensions. In this research, wewill investigate the search performances of GA with
MOS and CRM for above functions with n = 2. The
ranges of variables are xi ∈ [−5.0, 5.0]. xi = 0. Parameters for GA are as followings: number of population is20, number of generation is 100 and the range parameter is L = 1.0. The search performance is evaluatedby successful evolution rates obtained from the minimum values of the function in the GA. The simulation
results are shown in Figure 5, for Sphere function, andFigure 6, for Rastrigin function. Generally, it can beobserved that the search performances of our proposed
GA increase with Rmo and Rmu and it has better performances than the conventional GA. For Rastriginfunction, it is more difficult to find the optimum value
because this function is more complicated and it has
some local optimum values, the performance in thiscase is therefore lower than the performance obtained
from Sphere function.
f(x1,x2)

Sometimes, GAs has problem when it just can produce the local optimum values. But GAs can also
reduce this possibility by mutation operator. A simple way to achieve mutation would be to alter one ormore genes. For reducing this problem more properly, we propose a new mutation operator that is constant range mutation (CRM). In this CRM, we will
apply mutation by generating random real values in
the range that widens from two parents xp1 and xp2(xp1 < xp2 ) a constant range of L. Namely, the applied range of mutation is [xp1 − L, xp2 + L] as shownin Figure 3.

50
40
30
20
10
0

-5 -4

1
-10
-3 -2
-1 0
-3-2
1 2
-4
x1
3 4
5 -5

45
23
x2

(a) Sphere fucntionf(x1,x2)
80
60
40
20

X p1

X p2

0

-5 -4

L

L

©ISAROB 2007

-3 -2

-1 0
1 2
x1
3 4
5

45
23
1
-10
x2
-3-2
-5-4

(b) Rastrigin function
Fig. 4 Test functions

Fig. 3 Constant range mutation

321

(3)

i=1

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

102

2

10

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

1
fM

−1

10

10
1
fM

10

10−2

10−1
10−2

10−3
10−4

10−3

−5

10

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

0

20

40
60
Generation

80

0

102

2

10

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

1
fM

−1

10

1

10−2

10−2

10−4

10−3

−5

20

40
60
Generation

100

80

100

80

100

10−1

10−3

0

80

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

10
fM

10

40
60
Generation(a) Rmu = 10%(a) Rmu = 10%

10

20

100

80

0

20

100

40
60
Generation(b) Rmu = 20%(b) Rmu = 20%

102

2

10

1
fM

−1

10

1
fM

10

−2

10

10−1

10−3

10−2

10−4

10−3

−5

10

0

20

40
60
Generation

80

100

(c) Rmu = 30%
Fig. 5 Evolution results for Sphere function (n = 2)

©ISAROB 2007

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%10

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

322

0

20

40
60
Generation(c) Rmu = 30%
Fig. 6 Evolution results for Rastrigin function (n =2)The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4102

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

10
1
EM

−1

10

This section presents simulation results of neuralnetwork training problem; known as a nonlinear multivariable optimization problem. In order to evaluate
the performance of our proposed GA, we will use thewell-known exclusive - or problem (XOR). Figure 7
shows the simulation results. It can be seen thatthe proposed method has better training performance
than that of conventional method.

10−2
10−3
10−4
10−5

0

200
400
600
800
Generation (Rmu=10%)

1000

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%

10
1
EM

−1

10

Summary

References−2

10[1] D. E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley,
1989.

10−3
10−4
10−5

0

200
400
600
800
Generation (Rmu=20%)

1000

102

[4] L. J. Eshelman and J. D. Schaffer, “Real-Coded
Genetic Algorithms and Interval-Schemata” Foundations of Genetic Algorithms 2, Edited by L. Darrell Whitley, Morgan Kaufmann Publishers, 1993.

Conventional
Rmo=0%
Rmo=10%Rmo=20%Rmo=40%101
10−1[5] M. Maeshiro, H. Kinjo, K. Nakazono, and T.
Yamamoto, Fast Method of Genetic Algorithm
Searching and Its Application to Neural Network
Training, Technical Reprot of the IEICE NC200622-37, pp. 81-84, 2006. (in Japanese)

10−2
10−3
10−4
10−5

0

200
400
600
800
Generation (Rmu=30%)

1000(c) Rmu = 30%
Fig. 7 Training results of NN©ISAROB 2007

[2] L. Davis, Hand Book of Genetic Algorithms, Van
Nostrand Reinhold, 1991.
[3] M. Gen and R. Cheng, Genetic Algorithm and Engineering Design, Wiley-Interscience, 1997.

(b) Rmu = 20%

EM

5

Improving GAs by design new recombination process may achieve advantages. In this research, wehave presented a new method for improving the performance of GA by using a fast algorithm with momentum offspring (MOS) and constant range mutation (CRM). The simulations show that our proposed
method has a good performance.

(a) Rmu = 10%

102

Neural network training

323[6] H. Okamura, K. Kitasuka and T. Dohi, “Performance Evaluation of Genetic Algorithms Basedon Markovian Analysis - Evaluation by Relaxation
Time and Application to Real-Coded Genetic Algorithms,” Transactions of the ISCIE, Vol. 16, No.
7, pp. 303-312, 2003. (in Japanese)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Improvement of Training Method for Dynamic Neural Network
Kunihiko Nakazono
University of the Ryukyus
Senbaru 1, Nishihara, Okinawa. 903–0213
nakazono@mibai.tec.u-ryukyu.ac.jp

Kouhei Ohnishi
Keio University
Hiyoshi 3–14–1, Yokohama. 222–8522
ohnishi@sd.keio.ac.jp

Hiroshi Kinjo
University of the Ryukyus
Senbaru 1, Nishihara, Okinawa. 903–0213
kinjo@tec.u-ryukyu.ac.jp

Tetsuhiko Yamamoto
University of the Ryukyus
Senbaru 1, Nishihara, Okinawa. 903–0213
yamamoto@tec.u-ryukyu.ac.jp

Abstract
In this research, we propose a dynamic neural network (DNN) with the characteristics of stiffness, viscosity, and inertia and a training algorithm based on
the back-propagation (BP) method to include a momentum term. In a previous research, we proposeda training algorithm for the DNN based on the BP
method or GA-based training method. However, inthe previous method it was necessary to determine
the values of the DNN parameters by trial and error.
So, the modified BP method and GA-based training
method were designed to train not only the connecting
weights but also the property parameters of the DNN.
We develop the BP method to include a momentum term in order to increase the convergence of the
training effect. Simulation results show that the DNN
with characteristics of stiffness, viscosity, and inertia
trained by the modified BP method to include the momentum term obtains good training performances fortime series signals generated from periodic function.
In this paper, we compare the DNN with a conventional training method in order to verify the effectiveness of the DNN.

1Introduction

In recent years, recurrent neural networks and spiking neural networks have attracted more research interest than layered neural networks having simple
structure [1, 2, 3, 4]. The recurrent neural network is apossible candidate for improving the system dynamics
because it takes time delayed inputs into considerationand incorporates a feedback structure in the neuron
unit. Research on spiking neural networks is also on-©ISAROB 2007324

going. Spiking neural networks treat spike trains andprocess the signals based on spike pulses. However,the network structure in recurrent neural networks andspiking neural networks is complex compared to thatin layered neural networks with a training method.
Here, we propose a dynamic neural network (DNN)that realizes a dynamic property and has a network
structure with the properties of stiffness, viscosity, andinertia without time delayed input elements. In a previous research, the DNN was constructed with a training algorithm that used error back-propagation (BP)method [5]. However, the BP method updated only
the connecting weights for the DNN, and the property
parameters for the DNN had to be decided by trial
and error. Therefore, we designed a GA-based training method [6] to train both the connecting weights
and the parameters of the DNN. But the GA-based
training method took the executing time of computer
program that evolved in GA simulation [7, 8]. We
developed the modified BP method to train both theconnecting weights and the parameters [9].
In the present paper, we design the BP method toinclude a momentum term in order to increase theconvergence of the training effect. The effectiveness ofthe proposed DNN is verified by identifying time series
signals [5, 7, 8, 9]. Simulation results show that the
proposed DNN provides higher performance than theconventional method.

2

Structure of DNN

In this research, we propose a DNN using a neuron
unit having the properties of stiffness, viscosity, andinertia without time delayed input elements. In theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

neuron unit, we assume that the output from the neuron possesses the properties of stiffness, viscosity, andinertia, and that the output is propagated in the next
neuron unit. The proposed DNN is composed of threehierarchy layers, and the proposed neuron adopts only
in a hidden layer. The structure of the DNN is shownin Figure 1.
yi

ui

wij

yj

wjk

yk

3

BP-based training method

First, we derive a minimizing sequence of the measurement of error function E:
E=

Fig. 1 Structure of DNN
∆wjk

yi

=

ui ,

(i = 1, 2, · · · , NI )

(1)

yj

=

Kj fj (netj ) + Dj f˙j (netj ) + Mj f¨j (netj )
(2)

netj

=

NI
X

wij yi ,(j = 1, 2, · · · , NJ )

(3)

i=1

yk
netk

=
=

fk (netk )NJX

wjk yj ,(4)(k = 1, 2, · · · , NK )

(5)

j=1

where ui is the input value to the DNN, and yi , yj , andyk are the output values in the input, hidden, and output layers, respectively. The connecting weight fromunit i in the input layer to unit j in the hidden layer isdenoted by wij . Similarly, wjk is a connecting weightfrom unit j in the hidden layer to unit k in the output
layer. The total sum of the products of the connecting
weight wij and wjk and the output value is denoted by
netj and netk , respectively. Mj , Dj , and Kj are theproperty parameters of inertia, viscosity, and stiffness,
respectively, and NI , NJ , and NK are the number ofneurons in the input, hidden, and output layers, respectively. The activation function fj (x) in the hidden
layer uses a sigmoid function in range of [−1, 1]. Also
the activation function fk (x) in the output layer is alinear function.

©ISAROB 2007

325

N

k=1

k=1

(6)

where dk is the desired signal. In order to minimize
the measurement of the error function E of equation(6), both of the connecting weights and the property
parameters of the DNN are modified.
The BP-based training method is shown in section
3. The connecting weights and the property parameters of the DNN are updated sequentially based on
the steepest descent method.
∆wij

The equations for the DNN are expressed as follows:

N

K
K
1X
1X
(dk − yk )2
e2k =
2
2

∆Kj
∆Dj
∆Mj

∂E
(new)
(old)
= wij
− wij
= −ε
∂wij
∂E
(new)
(old)
= wjk
− wjk
= −ε
∂wjk
∂E
(new)
(old)
= Kj
− Kj
= −ε
∂Kj
∂E
(new)
(old)
= Dj
− Dj
= −ε
∂Dj
∂E
(new)
(old)
= Mj
− Mj
= −ε
∂Mj

(7)
(8)
(9)
(10)
(11)

ε is the training rate (ε > 0).
∂E/∂wjk and ∂E/∂wij are described as follows.
∂E
∂wjk
∂E
∂wij

=
=

∂E
∂netk
∂E
· yj
=
·
∂netk
∂netk ∂wjk
∂E
∂netj
∂E
· yi
=
·
∂netj
∂netj ∂wij

(12)
(13)

In the upper expression, the derivations are defined as
δk

=

δj

=

∂E
∂netk
∂E
∂netj

(14)
(15)

and δk and δj are calculated, respectively. First, δk is
expanded asδk==∂E ∂ek ∂yk∂E··=∂ek ∂yk ∂netk∂netk−ek fk0 (netk )δk finally becomes equation (16).

(16)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

When δj is calculated in the same way, it becomesEquation (17).
δj

∂E
∂netj
¶
NK µ
X
∂netk ∂yj
∂E ∂ek ∂yk
·
·
·
·
∂netj
∂yj
∂ek ∂yk ∂netk==

In order to increase the convergence of the training
effect, we modify the equations (7)–(11) to include a
momentum term. These equations are described asfollows.
∆wij (t + 1) =∆wjk (t + 1) =∆Kj (t + 1) =

k=1
NK
X

=

δk · wjk ·

k=1

∂yj
∂netj

∂yj
∂netj
∂ f¨j (netj )∂ f˙j (netj )∂fj (netj )
+ Mj
+ Dj
∂netj
∂netj
∂netj
(18)

∂fj (netj )∂netj∂ f˙j (netj )∂netj¨∂ fj (netj )∂netj∆Mj (t + 1)=

−εf¨j (netj )γj + ηm ∆Mj (t) (29)where t indexes the present time, and η, ηk , ηd , andηm are small constant values.
In this way, the connecting weights and property
parameters of the DNN are updated based on the concept of the modified BP algorithm to include the momentum term.

4

fj0 (netj )

(19)

=

˙ j
fj00 (netj ) · net

(20)

=

˙ j + fj00 (netj ) · net¨ jfj000 (netj ) · net

=

=

(17)

∂yj /∂netj is expressed as equations (18)–(21).

= Kj∆Dj (t + 1)(25)
−εδj yi + η∆wij (t)−εδk yj + η∆wjk (t)(26)−εfj (netj )γj + ηk ∆Kj (t) (27)−εf˙j (netj )γj + ηd ∆Dj (t) (28)

2

(21)

Numerical simulation

The effectiveness of the DNN proposed in the paper
is verified by numerical simulation in order to identify
a periodic function. The DNN is structured to have asingle input and single output (SISO). The method bywhich a time series signal can be identified is shownin Figure 2. The desired signal, namely the training

Next, the derivation of ∂E/∂Kj is described as follows.
∂E
∂Kj

=

NK
X
∂netk ∂yj
∂E
·
·
∂Kj
∂yj
∂netk
NK
X

+

u(t)

k=1

= fj (netj ) ·d(t)Unknown
Systemwjk δke(t)BP method−

k=1= fj (netj )γj

(22)

PNK
where γj = k=1
wjk δk .
When the property parameters Dj and Mj for theerror function E are calculated in the same way, it
becomes Equations (23) and (24).
∂E
∂Dj

=

NK
X
∂netk ∂yj
∂E
·
·
∂Dj
∂yj
∂netk

k=1

= f˙j (netj )γj
∂E
∂Mj

=

NK
X
k=1

∂netk ∂yj
∂E
·
·
∂Mj
∂yj
∂netk

= f¨j (netj )γj©ISAROB 2007

(23)

(24)

326

y(t)

DNN

Fig. 2 Identification of signals
data d(t), is the signal that has passed one sampling
period prior to the input signal u(t).
In order to facilitate analysis, simulation shows thatthe DNN identified the time series signal of the single
sine periodic function with cycle T as
³ 2πt ´.
(30)
u(t) = sin
TThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

In numerical simulation, the number of neurons NJ
in the hidden layer is 5 units, and the cycle T equals
16. The initial range of the connecting weights of the
DNN is set to [−0.3, 0.3] at random, and the initial
range of the property parameters is set to 1.0. The
training rate is set to ε = 0.1, and the momentum
rates are set to η = 0.0005, ηk = ηd = ηm = 0.0,
respectively.
The training involved 1,000 iterations. The resultof the error function E is shown in Figure 3.
10

Conventional method
Proposed method

Error function

101
100

10−2

5 K. Nakazono, K. Ohnishi, H. Kinjo, and T. Yamamoto (2003), Identification of periodic function
using dynamical neural network, AROB 8th ’03,
Vol.2, pp. 633–636.

10−3
10−4
0

200

400
600
Iteration

800

1000

Fig. 3 Iteration result

The result of the simulation shows that the proposed DNN provides good performance compared withthe conventional method without momentum term(η = 0.0, ηk = ηd = ηm = 0.0). The error function E
decreases gradually and E almost converge about 0.01.
The output of the DNN deviated negligibly from thedesired signal.

5

2 R. J. Williams and D. Zipser (1989), A Learningalgorithm for continually running fully recurrent
neural networks, Neural Computation, 1, No.2,
pp.270–280.

4 W. Mass and C. M. Bishop (1999), Pulsed neuralnetworks, pp. 16–53, MIT Press.

10−1

10−5

1 D. E. Rumelhart, J. L. McClelland, and PDP Research Group (1989), Parallel distributed processing, MIT Press.

3 H. Kinjo, K. Nakazono, and T. Yamamoto (1997),
Pattern Recognition for time series signals using
recurrent neural networks by genetic algorithms (inJapanese), Trans. of ISCIE, Vol. 10, No. 6, pp.304–
314.

3

102

References

Conclusion

In the present paper, the proposed DNN, showing
the validity of dynamic neuron with properties of stiffness, viscosity, and inertia, was structured. The training algorithm adopt the modified BP method. We designed a modified BP method to include a momentum
term in order to train both the connecting weights andthe parameters of the DNN. Simulation results showedthat the DNN trained by the BP method realized goodtraining performance compared with the conventional
method for time series signals generated from a periodic function.

©ISAROB 2007

327

6 Edited by L. Davis (1991), Handbook of genetic
algorithms, Van Nostrand Reinhold, New York.
7 K. Nakazono, K. Ohnishi, and H. Kinjo (2004),
System identification using dynamical neural network with GA-based training, AROB 9th ’04,
Vol.1, pp. 75–78.
8 K. Nakazono, K. Ohnishi, and H. Kinjo (2005),
Identification of time series signals using dynamical neural network with GA-based training, AROB
10th ’05, CD-ROM, pp. 25–28.
9 K. Nakazono, K. Ohnishi, and H. Kinjo (2006),
Identification using dynamical neural network withmodified BP method, AROB 11th ’06, CD-ROM,
pp. 126–129.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A learning model of head-direction cells and grid cells by VQ layers
connected via anti-Hebbian synapses
Naoki Oshiro, Koji Kurata and Tetsuhiko Yamamoto
Faculty of Engineering, University of the Ryukyus,
1 Senbaru, Nishihara, Okinawa. 903–0213
oshiro@mibai.tec.u-ryukyu.ac.jp
Abstract
In this paper, we proposed three layered selforganizing model to extract head direction and position of a moving object separetely. The model consistsof three layers, each of which is a self-organizing vector
quantization (VQ) model. The second layer receives
inhibitory input from the first, and the thrid layer receives inhibitory input from the first and the second.
The first layer is to detect head direction and the second and the third are to detect position. The information representation in the second and the third layers
is shown to be multi-ary expression and the units inthe third layer develop receptive field with grid structure as was observed in entorhinal cortex of a rat.

1

Introduction

The self-organizing map (SOM) and the vector
quantization (VQ) are very famous and widely usedamong many self-organizing models. They are usefulfor application and also important as a computationalmodels of neural systems.
SOM algorithm was proposed by Kohonen[1] as amodel of the cerebral cortex and its self-organization.
It was successful at reproducing a functional mapof the visual cortex[2, 3], and was applied to manykinds of data as a statistical tool of nonlinear autoregression[1]. This map can extract major informations from multi-dimensional data[4].
The vector quantization (VQ) is an on-line learningalgorithm to generate reference vectors for a set of
input vectors[1, 5, 6, 7]. For a given input vector, theclosest reference vector is chosen to approximate it.
VQ algorithm can generate a set of reference vectors
which minimizes mean square of approximation error.
The algorithm can be interpreted as a self-organizing
neural network model adopting competitive Hebbian©ISAROB 2007

328

learning rule, i.e. a self-organizing map (SOM) lacking
neighborhood learning. Thus, VQ and SOM are very
similar models closely related to each other.
We studied a model consisting of two SOMs connected via anti-Hebbian connections, and showed thatit can extract two different information components
on the two SOMs each[8]. The model was applied
to extract position and head direction from visual information. In this application, one of the two SOMs
consists of two-dimensional array of cells so that it
can represent two-dimensional positional information,and the other SOM consisting of one-dimensional array of cells is assigned for one-dimensional information of direction of the moving object. Simulational
results showed that position-sensitive and directioninsensitive cells are formed on the two-dimensional
SOM, and direction-sensitive and position-insensitive
cells are formed on the one-dimensional SOM.
In recent study of cortical micro circuits, it was
found that positional information are represented by‘grid cell’ in the dorsocaudal medial entorhinal cortex(dMEC)[9]. Grid cells are a kind of position cells but
their receptive field shows periodic grid structure in thearea within which rats are allowed to walk around. A
receptive field of a position cell is the set consistingof all positions at which the cell fires. It is striking
that the receptive field of the grid cell in dMEC was
not connected but distributed periodically in hexagonal lattice, for in most of the self-organizing models receptive fields of the cells tend to be connected.
We, however, succeeded in reproducing grid-cell-likeposition cells by a self-organizing model consisting of
two VQ layers connected by anti-Hebbian inhibitory
synapses[10]. In the study we sipmplified the problem by using input carring positional information only.
Here, we propose a more natural model consisting of
three VQ layers all of which are trained by 4D inputs carrying both of positional and directional information. Computer simulation showed that also inThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Head direction layer

j = 1, ..., N (2) , k = 1, ..., N (3) , and superscript(1) (2),and (3) stand for the Layers 1, 2 and 3,
respectively. Initialize all inhibitory connections(2)(1)(3)(1)(3)(2)
sj,i , sk,i , sk,j to zero.

Layer 1
Input space(x,y, )
Sensorvalue(VQ-AH2) Set the position (x, y), (x2 + y 2 ≤ 1)
and the head direction θ (−π ≤ θ < π) of theobject randomly w.r.t the uniform distribution.
Calculate input vector x = (x, y, cos θ, sin θ) to
the VQ layers.

Layer 2Anti-Hebbianconnections(VQ-AH3) Find the winner, c(1) in Layer 1 for the
input x:


2
(1)
(1)
c = argmin
mi − x + θ i(1)i

Layer 3
Position layers

Figure 1: Structure of the proposed model. Three layers
receive a same input at a time. Input vector contains twodimensional positional information and one-dimensional
directional information. Layer 1 is for directional information and Layers 2 and 3 are for positional information.

this model grid-cell-like position cells were generatedin Layer 3.

2

Layer composition of connected VQs

Our model consists of three layers. Layer 1 is todetect head direction of the object and Layers 2 and
3 to detect position. Any unit in Layer 2 receives
inhibitory input from all units in Layer 1 via antiHebbian synapses, and any unit in Layer 3 receives
inhibitory input from all units in Layers 1 and 2 via
anti-Hebbian synapses.
All the cells in the three layers receive a same input
vector x which is a function of position (x, y) and headdirection θ of the object moving randomly in a room
(Figure 1). Layer 1 receives no inputs from Layer 2
or 3, so it works just as an usual VQ model for the
input data. On the other hand, the learning processesof Layers 2 and 3 are influenced by the cells of Layer
1 through the anti-Hebbian synapses.
The learning rule is described as follows. In thisdescription, we refer to our algorithm as ‘VQ-AH’:(VQ-AH1) Assign random values for reference vectors of all units in the three lay(1)(2)(3)
ers mi , mj , mk , where i = 1, ..., N (1) ,©ISAROB 2007329

where θi is the threshold or handicap of cell i in
Layer 1. Then find the winner c(2) in Layer 2,(2)(1)
considering the inhibitory input sj,c(1) from thewinner in the superior layer:


2
(2)
(2)(1)(2)
mj − x + sj,c(1)c(2) = argmin
jFinally, find the winner, c(3) in Layer 3 with in(3)(2)(3)(1)
hibitory inputs sk,c(1) , sk,c(2) from the other twolayers2(3)(1)(3)(2)(3)(3)
mk − x + sk,c(1) + sk,c(2) .
c = argminj(3)(VQ-AH4) After finding three winners c(1) , c(2) andc(3) , we assign winners’ information to variable
(1)
(2)
(3)
yi , yj and yk , respectively:
(l)
yi =



1, i = c(l) ,0, otherwise,(l = 1, 2, 3)(4)(VQ-AH5) Update the reference vectors of the winners in the three layers.


(l)
(l)
(l)
(l)
mi := mi + yi α(l) x − mi , (l = 1, 2, 3)(5)where α(l) is the learning parameter of the reference vectors.
(VQ-AH6) Update the threshold values of Layer 1


1
(1)
θi := θi + γ yi − (1)(6)
NThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where γ is the learning parameter of the threshold, and update the inhibitory connections1(1) (2)(2)(1):= sj,i + β (2)(1) yi yj − (1) (2) ,
sj,i
N N


1
(1) (3)(3)(1)
sk,i := sk,i + β (3)(1) yi yk − (1) (3) ,
N N


1
(2) (3)(3)(2)
sk,j := sk,j + β (3)(2) yj yk − (2) (3)N N(7)where β (2)(1) , β (3)(1) , β (3)(2) are the learning parameters of the inhibitory connections.
(VQ-AH7) Return to (VQ-AH2) and repeat (VQAH2)–(VQ-AH6) many times.
As iterative learning proceeds, the learning parametersα(l) (l = 1, 2, 3) and β (2)(1) , β (3)(1) , β (3)(2) are updated
as follows:(l)
α(l) := α0 (1 − t/tmax ), (l = 1, 2, 3)
 (2)(1)(2)(1)β:= β0(1 − t/tmax ),(8)(3)(1)(3)(1)

(1
−
t/t
),
β
:=
β
max

0


(3)(2)
β (3)(2) := β0(1 − t/tmax ).
(l)(2)(1)(3)(1)(3)(2)are initial values of
, β0
, β0
where α0 , β0α(l) and correspondig β, and t, tmax are iterative learning times and maximum of iterative learning times,
respectively. This update leads stability of learning.

3

Input Data

We need additional learning rules to assign directional information to Layer 1 and positional information to Layers 2 and 3. We divide the whole input
sequence into short periods. There are two kinds of periods coming up alternatively. One is a position-fix period and the other direction-fix period. A direction-fix
period consists of some sequential inputs which share a
fixed direction θ but have different positions (x, y). A
position-fix period consists of some sequential inputs
which share a fixed position (x, y) but have different
directions θ. For the first input of a direction-fix period the winner of Layer 1 is defined by (1), but for
the rest of the inputs in the period the winner remains
unchanged, while the winners of Layers 2 and 3 are defined for each inputs by (2) and (3), respectively. For
the first input of a position-fix period the winners ofLayers 2 and 3 are defined by (2), (3), but for the
rest of the inputs in the period the winners remainunchanged, while the winner of Layer 1 is defined for©ISAROB 2007

330

each inputs by (1). In this simulation the either kindof period consists of 15 inputs.

4

Simulation results

In the equilibrium of the learning process, the learning rule (6) assures that the distribution of the winneron Layer 1 should be uniform over the layer. Similarly,
the learning rules (7) assure that the distribution of
the two winners on two of the the three layers should
be statistically pairwise-independent of each other orthe joint probability of the two winners should be uniform. It should be noted this does not mean that the
distributions of the three winners are independent.
Each of the three VQ layers consists of 4 units(N (1) = N (2) = N (3) = 4). Learning parameters of
this experiment are α(1) = 0.01, γ = 0.01; α(2) =0.0008, β (2)(1) = 0.01; α(3) = 0.0005, β (3)(1) = 0.05,
β (3)(2) = 0.005. Also, tmax = 800, 000. Learning
results are shown in Figure 2. For plots in this figure θ was calculated from Layer 3 and fourth components of the reference vector of each unit by θ =arctan(mi3 , mi4 ). In Figure 2 (a), (b) and (c), eachof the four columns corresponds to each unit. The
figure 2 (a) shows that the units in Layer 1 are position insensitive and direction sensitive. The figure
2 (b) and (c) show that Layers 2 and 3 are position
sensitive and direction insensitive. Each of the four
positional receptive fields of the units in Layer 2 isconnected. The whole disk area is divided into four
receptive fields. In Layer 3, however, each of the receptive fields of the units consists of four disconnected
sub-fields, each of which is contained in one of the four
receptive fields of the units in Layer 2. The representation of the positional information in Layers 2 and 3
is similar to two-digit quaternary number system.

5

Conclusion

We showed that three VQ layers connected via antiHebbian synapses can extract directional and positional information separately, and the units in Layer
3 forms disconnected receptive fields, which is similar
to what was observed in dMEC of rats by Hafting etal.[9]. Our model suggests that there should be someother position cells with larger and connected receptive fields in somewhere in rats’ brain.
Unlike our preceding results [10] the sub-fields do
not show clear hexagonal lattice pattern. This is because we used only four units for Layer 3. we assume

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

that with more units in Layer 3 we can reproduce thehexagonal lattice pattern. The model should be tested
with more realistic high-dimensional visual input.

References[1] Kohonen T (1994) Self-organizing maps., Springer,
Berlin[2] Erwin E, Obermayer K, Schulten K (1995) Modelsof orientation and ocular dominance columns in
the visual cortex: A critical comparison. NeuralComput., 7: 425–468(a) Layer 1: The layer in head direction field[3] Shouno H, Kurata K (2001) Formation of a direction map using Kohonen’s self-organization map
by projection learning. Biol Cybern, 85: 241–246[4] Kurata K, Oshiro N (2004) Separating visual information into position and direction by SOM. Artificial Life and Robotics, 8 (1): 5–8[5] Gersho A (1979) Asymptotically Optimal Block
Quantization. IEEE Trans. on Inform. Theory, IT25 (4): 373–380(b) Layer 2: The superior layer in position field[6] Linde Y, Buzo A, Gray R (1980) An algorithm forvector quantizer design. IEEE Trans. on Communications, COM-28: 84–95[7] Gray R (1984) Vector quantization. IEEE ASSPMagazine: 4–29
[8] Oshiro N, Kurata K (2005) Separating visual
information into position and direction by two
inhibitory-connected SOMs. Artificial Life andRobotics, 9 (2): 86–89[9] Hafting T, Fyhn M, Molden S, Moser M, Moser
E (2005) Microstructure of a spatial map in theentorhinal cortex. Nature, 436: 801–806(c) Layer 3: The inferior in position field
Figure 2: Learning results: (a) Each row consistingof three small figures shows the property of each unitin Layer 1. The receptive field in 3D feature space(x, y, θ) is projected in three different ways. The unitsin Layer 1 show direction sensitivity only. (b) The
units in Layer 2 show position sensitivity only. The positional receptive fields are connected. (c) The units in
Layer 3 show position sensitivity only. The positional
receptive fields are divided into four sub-fields.

©ISAROB 2007

331

[10] Oshiro N, Kurata K, Yamamoto T (2006) A
self-organizing model of place cells with periodically distributed receptive fields. Proceedings ofthe International Symposium on Artificial Life andRobotics (AROB) 11, Beppu, Oita, Japan, Jan 23–
25, 2006: OS10-4

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Color system for skin color extraction
Naruaki TOMA
Dept. of Information Engineering
Univ. of the Ryukyus
1 senbaru, Nishihara, Okinawa
903-0213,JAPANEmi TAMAKI
Dept. of Intelligent Interaction Tech.
Univ. of Tsukuba
1-1-1 Tennodai, Tsukuba-shi Ibaraki-ken
305-8577, JAPAN

Abstract
This paper describes a skin color extraction
method, which comes with high-speed and -accuracy.
There were many typical systems to achieve the requirements, however, they were useless due to toughlimitations such as using two cameras to recognize correct coordinates, running only a specific background,wearing some gears, learning the users detailed information in advance, making personalized database andso on. In contrast, our approach which used YIQ color
system with a background estimation method for skincolor extraction, achieved well performance more than
30 fps with strong robustness on any-complicated and-illuminated environments.

1

Introduction

This research concerns an intuitively useable andhigh efficiency interface system, especially be focusinga system based on image processing techniques (visionbased interface).
Recently, vision based interfaces that they were introduced any sensor(s) to use user’s behaviors as inputs into interface system, were studied [1, 2, 3, 4].
These type systems give friendly and efficiently interface for specialized situation. However, their systems
don’t use in general because of strict requirements, forexample, they require two cameras to recognize correct
coordinates, to set a specific background to recognize
correct hand position, to wear some gears (e.g., marker
tracking equipments), to learn the users detailed information (e.g., their color skin, finger form) and makepersonalized database.
This paper describes a skin color extraction
method, which comes with high-speed and -accuracy,
based on YIQ color system with a background estimation method to adjust the extraction parameters. We
preliminarily evaluated the performances comparedwith other color systems (RGB, HSV and HQV [5]),and our method showed well performance (more than

©ISAROB 2007

332

30 fps) with strong robustness (more than 90% accuracy) on any-complicated and -illuminated environments.

2
2.1

Skin color extraction
Basic idea

Our supposed environment requires only one color
movie camera, which must be able to acquire each image with high speed (more than 30 fps). Note that,
we don’t use any kinds of other sensors, don’t careany backgrounds and illuminated environments, don’t
use any database (don’t need to learn detailed information in advance).
Each captured images are processed through 1ststage: translating the color space from RGB space
to the target space, then 2nd stage: calculating thethresholds to extract skin color. We use YIQ color
system in 1st stage, and, if necessary, we use a background estimation method to adjust the thresholds automatically.

2.2

HSV and HQVSome color systems were used for the extraction,and HSV and HQV were typical color systems. HSV
based approach showed very low (fast) calculation
costs and better extraction accuracy than RGB one.
HQV (and also modified HQV) were proposed to improve the accuracy and it was achieved some studies(see [5]), but the calculation costs were too large to
use for real-time processing like an interface system.

2.3

YIQ color system

YIQ is a color space, formerly used in the NTSC
television standard. The YIQ color system is intended
to take advantage of human color-response characteristics. The eye is more sensitive to changes in theorange-blue (I) range than in the purple-green range

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
(Q), therefore less bandwidth is required for Q thanfor I.
There are two stages to translate from RGB space
into YIQ space, gamma correction is performed as
equation 1, and then YIQ space was given by equation 2.

R′ =

′

G =

′

B =

Y

=

I =
Q =

2.4

4.5 × RR < 0.018
1.099 × (R0.45 ) − 0.099 R ≥ 0.018

2.6

(4)

Adjusting thresholds(1)
T hreshold = F (M (R, G, B)) ± β × D(R, G, B) (5)0.299 × R′ + 0.587 × G′ + 0.144 × B ′0.596 × R′ − 0.274 × G′ − 0.322 × B ′0.211 × R′ − 0.522 × G′ + 0.311 × B ′ (2)Filtering with thresholds

Thresholds on each color axis in the color spacemust be calculated in order to extract skin color. In
other words, we assign a color zone on each axis tomeasure the pixel whether skin color or not.

2.5

−
→
→
−
→
−
P (n + 1)i = α × M(n)i + (1 − α) × P (n)i

Our system decides a set of thresholds based on
the estimated background image described above section. The thresholds are calculated by formula 5,
where M (R, G, B) is a set of average (mean) values,
F is translation from RGB to other color system,
D(R, G, B) is a dispersion values and F is a constantvalue.

4.5 × GG < 0.018
1.099 × (G0.45 ) − 0.099 G ≥ 0.018
4.5 × BB < 0.018
1.099 × (B 0.45 ) − 0.099 B ≥ 0.018

background image and α is adding ratio of current→−
image M.

3

Experiments

For evaluation of proposed method, we performed
one preliminary experiment and four experiments withnumerical evaluation, which are compared with traditional color skin extraction methods (based on RGB,
HSV and HQV).
We performed some experiments on table 1, andour system was written in Processing language[6] andJMyron library[7] to process movie images.

Background estimation

When the background image or color is fixed likea blue screen, we can extract our desired objectives
easily. But such extraction methods aren’t functionable in real world (complex background), because themethods require really tough settings on each unknown environments such as different illuminated ones
or really complicated places in advance. In order todecrease user’s preliminary settings, we use a background estimation method to adjust the thresholds for
extraction automatically.
The background estimation calculates a distance on
the color space between current image and a few flamesprevious image. In Exactly, all distances on each pixel→−
are calculated by formula 3, where D is 3-dimentional→−
vectors on the color space, M(n) is movie image at thetime n, and the i is a pixel position.

3.1

−
→
→
−
→
−
D =| M(n)i − M(n − 1)i |

3.2

(3)

→
−
If the D has small values, the estimated background→−are calculated by formula 4, where P is the estimated©ISAROB 2007

333

Table 1: Hardwares.
Video camera iSight1.0.3
OS
MacOSX 10.4.4
CPUDual 2GHz, PowerPC G5Memory4GB DDR SDRAM

Exp1: Background estimation

Left side of figure 1 shows an actual image, which
has hidden background parts with fingers, and rightside shows the estimated image. It is enough result
to decide the set of thresholds, because we use themeanscore only calculated from the image (see also
formula 3).

Exp2: Computational speed

Experiment 2 evaluated computational speeds totranslate of RGB to each color system only, here, weThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
pixels in one frame, and N̄total denotes the number of
total miss-counted pixels.

total miss ratio(%)

Figure 1: An estimated background result (left: actual
image, right:estimated image).

don’t care about the accuracy of skin color. In figure
2, Base denotes a computational time to get and to
display a movie image with Processing. That shows abasic speed without any calculations.

=N̄total /Ntotal × 100 (6)Each recognition-miss ratios are shown in table 3.3,
where Y IQ I denotes a result used I value only in
YIQ color system. The thresholds on each color system were adjusted in order that recognition-miss ratios
become lowest on Black background. Note that, thecorrect skin color pixels are decided by hand, so theseresults have a margin of error with a few percentages.

Table 2: Total-miss ratio on several backgrounds(%).
color
Black
PC Table Complex
RGB
1.69 23.55 56.62
50.41
HSV
2.22 40.08 63.18
67.19
HQV
6.88
6.78 55.42
52.83
YIQ
2.86
1.60
3.41
2.48
YIQ I
5.12
3.61
5.00
2.88

According these results, the accuracies of RGB and
HSV were through 8 to 61 percentages on complicated one. The other, YIQ showed very robust performances, which the accuracies were keeping withthrough 92 to 96 percentages even though the experiment performed in complicated environments.
Figure 2: Computational speed for color translationfrom RGB into the target color space.

As the results, HSV was fastest except for RGB,
and YIQ also was fast enough to achieve 30 fps with
160x120 (19,200 pixels) processing. HQV was too slow
to use real time processing however it was evaluatedas fine color system.

3.3

3.4

Exp4: Extraction accuracy 2

Experiment 4 evaluated the extraction accuracy onseveral illuminated environments: Fluor illuminatedwith fluorescent lamp, Incan illuminated with incandescent lamp, Sun(in) illuminated indoor with sunlight, and Sun(out) illuminated outdoor with sunlight.
In this experiment, the background was fixed on PC
environments.

Exp3: Extraction accuracy 1

Experiment 3 evaluated the extraction accuracy onseveral backgrounds: Black background, PC background experimented on keyboard of note-pc, Table
background experimented on the table with some objects, and Complex background experimented on really complicated situation. We measured three kindsof value. However, due to limitations of space, we
showed one value ‘total recognition-miss ratio’ measured by 6, where Ntotal denotes the number of total©ISAROB 2007

334

Table 3: Total-miss ratio on several illuminated environments(%).
color
Fluor Incan Sun(in) Sun(out)RGB
52.99 78.74
62.82
8.22
HSV
54.68 74.43
67.74
29.08
HQV
32.88 90.17
56.37
65.35
YIQ
3.14 94.40
2.17
13.33
YIQ I
3.15 94.75
2.03
13.33

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
The results (see table 3.4), showed all kinds of color
system didn’t have enough accuracy on incandescentlamp. One of the reasons, I axis of YIQ has similar
characteristics as the light of incandescent lamp eventhough I axis is most important axis to extract skincolor.

3.5

Exp5: Extraction accuracy 3

To resolve the previous problem (weak recognition
rate on incandescent lamp), background estimation
method described in section 2.5 was introduced. Here,
we don’t experiment with RGB, HSV and HQV because they don’t have robustness against the differenceof illumination.
Tables 3.5 showed the accuracies with backgroundestimation, where withoutBS denotes the results
without background estimation (same values on experiment 4), and withBS denotes with backgroundestimation. Figure 3 showed a extracted result without BS in leftside and with BS in rightside, where
black color in hand part shows skin-miss pixels, darklight grey around of hand shows background-miss pixels and dark grey mainly showed in hand part of rightside shows correct recognized pixels.

Table 4: Total-miss ratio with background estimation(%).
color
without BS with BS
YIQ
94.40
2.40
YIQ I
94.75
2.17

4

Conclusion

In case of using only extraction based color system, the accuracy on all color systems were too low to
use for practical. The accuracy of YIQ with proposed
threshold adjusting based on background estimation
was pretty improved through 97 to 98 percentages.
In summary, the proposed method showed well performance with strong robustness for skin color extraction on any-complicated and -illuminated environments. According to this summary, this method so as
to apply widely applications because our method requires only one camera and without any personalized
database.

References[1] Nozomu Matsui and Yoshikazu Yamamoto, “Virtual Keyboard with Single Video Camera (injapanese)“, in Programming Symposium, 1999.
[2] Nozomu Matsui and Yoshikazu Yamamoto, “Virtual Keyboard: Realization of Robust RealTime Fingertip Detection from Video-Image (injapanese)“, in JSSST SPA2000, 2000.
[3] Yishiyuki Kojima, “Hand Interface for Wearable
Mixed Reality - Clip Interface -”, Master s Thesis, Department of Information Processing, Graduate School of Information Science, Nara Instituteof Science and Technology, NAIST-IS-MT0051037,
February 8, 2002.
[4] Takeshi Kurata, Takashi Okuma, Masakatsu
Kourogi, and Katsuhiko Sakaue, “The Hand Mouse:
GMM Hand Color Classification and Mean Shift
Tracking”, In Proc. Second International Workshop
on Recognition, Analysis and Tracking of Faces andGestures in Real-time Systems (RATFG-RTS 2001)
in conjunction with ICCV 2001, pp.119-124.

Figure 3: An extracted result (leftside with BS, rightside with BS).

[5] Satoshi matsuhashi, Osamu Nakamura and Toshi
Minami, “Human-Face Extraction Using Modified HSV Color System and Personal Identification
Through Facial Image Based on Isodensity Maps”,
IEEE CCGEI 1995, Vol 2, No 2, pp.909-912.
[6] Processing.org,
http://www.processing.org/

The accuracy of YIQ with proposed threshold adjusting based on background estimation was pretty improved through 97 to 98 percentages. These resultsshows that our approach has highly robustness not
only complicated backgrounds but also several illuminated environments.

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[7] Myron WebCamXtra,
http://webcamxtra.sourceforge.net/

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τ21

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input
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 %  L 4
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τ12

τ11

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τ23

!

[Nm]

! /      # orientation
error
[rad]

8

input

X [m]

τ31

4

input

0

τ322

-4

input

-8
-8

input

Robot 2

input

Robot 1

τ33

-4

position
error (y)[m]Initial state
4[s]
8[s]
Final state

input

0

position
error (x)[m]

Object

3
0

x10

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2 x10

variable

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constant

-2

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1
0
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3      ! >(GQ>.3 (II(!

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Improvement of the Algorithm for the Search of Periodic Gaits of a Passive
Dynamic Walker
Xuemin Su
Mingguo Zhao
Department of Automation
Department of Automation
Tsinghua University
Tsinghua University
BeiJing China, 100084
BeiJing China, 100084
suxm06@mails.tsinghua.edu.cn mgzhao@mail.tsinghua.edu.cn
Abstract
Periodic gaits of passive dynamic waking are foundby solving nonlinear dynamic equations, and the convergence speed of numerical solving of the equations
is greatly affected by the choice of initial values. This
paper investigates the iterative searching algorithm forperiodic gaits by studying the simplest passive dynamic walking model. An improved algorithm forchoosing the initial values is proposed based on thework of Garcia etc. The effectiveness of the proposed
algorithm is demonstrated by numerical simulations.

1

This method works well when the equations are simple but involves some complicated symbolic manipulation. This article proposes an improved algorithm
which searches in the parameter space whose dimension is relatively low instead of the solution space. The
effectiveness of the algorithm is demonstrated by numerical simulation and comparison with the originalalgorithm.

2

Model

The model used here is the simplest walking model
proposed by Garcia et al. [3], which is shown in Figure 1.

Introduction

Why can human walk? One may focus on the control of nerve and muscle but relatively neglect otherimportant factors. Passive dynamic walking proposedby McGeer [1] presented a novel idea.
Passive dynamic walker is a simple mechanism normally composed of some solid links connected by frictionless hinges. Without motors and controllers, it canwalk down a small slope stably by gravity and have ahuman-like gait.
The key to analyze passive dynamic walking is tofind periodic gaits, normally by numerically solving
nonlinear differential equations with iterative method
like Newton-Raphson method [2].
The iterative
method first selects a set of initial values, then searches
directly in the solution space whose dimension is relatively high for the fixed points of the equations. The
choice of the initial values is quite important since it
directly affects the convergence and the convergence
speed of the iterative method, but how to choose theinitial values is still a problem. Garcia et al. [3]had developed a method to solve this problem. They
first got a low-order approximation to the equations,solved them for the analytical solutions which could
be used as the initial values of the iterative method.

©ISAROB 2007

Mengdi Wang
Department of Automation
Tsinghua University
BeiJing China, 100084
wang.mandy@hotmail.com

340

Figure 1: The simplest model of passive dynamic walking
Assumptions of the model are:
1 Mass: The only mass is at the hip and the feet,and the foot mass m is much smaller than the hip massm
→ 0.
M , satisfying β = M
2 Actuator: No actuators are used.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3 Collision: When the swing foot hits the ground,the collision is completely inelastic (no slip or bounce)
and the double support phase is instantaneous (thestance foot leaves the ground when the swing foot hits
the ground).
4 Ground: The swing foot may be below the groundlevel during the swing phase. To solve this problem,
we allow the swing foot to move below the ground innumerical simulation and use a chessboard-like groundfor real-world experiments.
According to Lagrange function, equations of the
motion for the swing phase are#³ ´T  ·
¸ "
∂g
f¯
M̄
∂q

 q̈ =
(1)
∂2g
∂g
λ
q̇ q̇
− ∂q∂q0∂q

and the relation of the velocities before and after thecollision can be written as follows according to the
angular momentum conservation³ ´T  ·¸ ·¸∂g+M̄ q̇ −
∂q
 q̇
 M̄
=
(2)
∂g
ρ
0
0
∂q

T

where q = (u, v, θ, φ) is the configuration of themodel, x = F (q) the coordinates of the hip and thefeet, g (q) = 0 the constraints of the ground, M thegeneral mass matrix, f the force vector, λ, ρ the
³ ´T
¯
M ∂F
Lagrange multipliers, and M̄ = ∂F∂q∂q , f =³ ´T hi∂F∂2Fq̇ q̇ .
f − M ∂q∂q
∂q
We define the start of a stride as the moment theswing foot hits the ground and the stance foot leaves
the ground, which is also the end of the last stride.
The state at this momentis ´the initial state of the³Tmodel, which is v = θ, θ̇, φ̇ . A Stride Function
vk+1 = S (vk ) is a map from the state of the k th strideth
to the state of the (k + 1) stride.

3

Search of the Fixed Points of theEquation

A periodic gait corresponds to a fixed point of theStride Function, which is normally found by iterative
method. If an initial value v satisfies the equationv = S (v), it is a fixed point of the Stride Function
S, otherwise the iterative method should modify the
initial value by ∆v to make the modified value satisfy
v + ∆v = S (v + ∆v). Since we can’t explicitly write©ISAROB 2007341

the expression of S (v + ∆v), we use the first-orderapproximation
S (v + ∆v) ≈ S (v) + J∆v, where J =

∂S
∂v

(3)

then we get−1∆v = (I − J)

(S (v) − v)

(4)

The iterative method can be described as
repeat−1∆v = (I − J) (S (v) − v)
v = v + ∆v
until |S (v) − v| < ε(5)This is the Newton-Raphson method.

4

Improvement of the Search of the
Fixed Points of the Equation

Since we use the first-order approximation to theStride Function, the initial value v should be near the
fixed point. In a stride, the swing foot first moves
below the ground level, then moves above it, and thenmoves downwards and hits the ground. If the initialvalue is far from the fixed point, the swing foot may
not be able to move above the ground level, and thecollision may not occur. Since we can’t get the state
after heel-strike, S (v) and Jacobian matrix J can’t beobtained. In a word, the initial value should guaranteethat at least one heel-strike can happen.
To solve this problem, we propose an improved
method. We assume that if the parameters of themodel change slightly, the fixed point will not change
too much. Based on this assumption, a fixed point
for one set of parameters can be used as the initialvalue for another set of parameters if these two setsof parameters are near together. On the other hand,an initial value not suitable for one set of parameters
may be a good choice for another set of parameters.
Based on the above observation, we propose an improved method as follows:
1. Given a slope angle γ, choose an initial value v;
2. Search for the fixed point using Newton-Raphson
method; if not found, jump to 3;
3. Keep the initial value v unchanged, let γ = γ +∆γ, and use Newton-Raphson method again; modify∆γ until the fixed point is found. Let the found slope
angle be γ ∗ and the fixed point v ∗ ;4. Use v ∗ as the initial value, let γ ∗ = γ ∗ + ∆γ,
search for the fixed point. Modify the slope angle by

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

∆γ at a time, and search for the fixed point using
the fixed point with the slope angle unchanged as theinitial value. Repeat until γ ∗ = γ.
The dimension of the parameter space is 1 while
the dimension of the state space is 3. The improved
method searches in the parameter space instead of thestate space, making the search easier.

5

An Example of the Algorithm

To demonstrate the effectiveness of the algorithm,
we first give a plot of the fixed points versus the slope
angles, see Figure 2. The same result was shown by
Garcia et al. [4] too.

4. Use the fixed point when γ = 0.013[rad] as
the initial value, search for the fixed point when γ =0.014[rad]; Use the fixed point when γ = 0.014[rad]as the initial value, search for the fixed point whenγ = 0.015[rad]; finally we get the fixed point when γ =0.016[rad] using the fixed point when γ = 0.015[rad]as the initial value


 
θ
0.2259204891316
v =  θ̇  =  −0.22237603365099 −0.022316588434412
φ̇.
The procedure is shown in Table 1.
slope angle
γ (rad)
0.016
0.015
0.014
0.013
0.014
0.015
0.016

0.4
short period fixed points
long period fixed points
0.35

fixed point θ (rad)

0.3

0.25

initialvalue
θ (rad)
0.1
0.1
0.1
0.1
0.2259204891316
0.23144913545946
0.23671218331329

fixed
point
θ (rad)not found
not found
not found
0.2259204891316
0.23144913545946
0.23671218331329
0.24173855862323

0.2

Table 1: An Example of the Algorithm
0.15

0.1

0.05

6
0

0.005

0.01

0.015

0.02 0.025 0.03
slope angle γ (rad)

0.035

0.04

Figure 2: Fixed points of the stride function
We give an example here to demonstrate that the
improved method can find the fixed point even when
the initial value is not suitable.
1. Given a slope angle γ = 0.016[rad], choose an³´TT= (0.1, −0.01, −0.002) ;initial vale v = θ, θ̇, φ̇
2. Search for the fixed point using Newton-Raphson
method; we can’t find the fixed point after 80 iterations;
3. Keep the initial value v unchanged, let γ = γ −0.001[rad] , and use Newton-Raphson method again;
we find the fixed point

 

θ
0.2259204891316
v =  θ̇  =  −0.22237603365099 −0.022316588434412
φ̇
when γ = 0.013[rad];©ISAROB 2007

Conclusion

0.045

342

This paper investigates the iterative searching algorithm for periodic gaits by studying the simplest passive dynamic walking model. An improved algorithm
for choosing the initial values is proposed based on thework of Garcia etc. The effectiveness of the proposed
algorithm is demonstrated by numerical simulations.

References[1] T. McGeer, “Passive dynamic walking,” Intern. J.
Robot. Res., Vol. 9, pp. 62-82, 1990.
[2] W. Martijn, “Essentials of dynamic walking; Analysis and design of two-legged robots,” Doctor thesis, Delft University of Technology, Mechanical Engineering, 2004.
[3] M. Garcia et al., “The simplest walking model:
Stability, complexity, and scaling,” ASME J.
Biomech. Eng.,Vol. 120, pp. 281-288, 1998.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[4] M. Garcia et al., “ Speed, efficiency, and stability ofsmall-slope 2d passive-dynamic bipedal walking,”
In Proc., IEEE Int. Conf. on Robotics and Automation, pp. 2351-2356, 1998.

©ISAROB 2007

343

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Clothes Manipulation by Inchworm Robot Grippers
Khairul Salleh

Hiroaki Seki

Yoshitsugu Kamiya

Masatoshi Hikizu

Department of Mechanical Systems Engineering
Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192 JAPANE-mail: hseki@t.kanazawa-u.ac.jp

Abstract

Hand A

Deformable objects manipulation by robot is very
challenging. We focused on clothes manipulation as an
example and applied edge tracing method in order to searchfor corners of particular clothes. Tracing in this paper
context involves tracing the towel’s edge, with the robot arm
movement based on feedback from sensors. Development ofspecial tools for deformable object manipulation is also
considered to be important as well and must consider theproperties of the deformable objects. We designed inchworm
type grippers for the purpose of clothes manipulation. This
paper proposes a unique tracing method for towel spreading
using the inchworm grippers. Experimental results havedemonstrated the effectiveness of both the proposed method
and the grippers.

Keywords
Edge tracing, deformable object, robot gripper, inchworm,
spreading of clothes, home service robot

1. Introduction
The world is full of deformable objects. Let's just lookaround us. Clothes, papers, cables, plastic bags; they are all
deformable objects. Even in today's robotics, researches
concerning deformable object manipulation are becoming
more and more popular. The presence of these objects meansthat home service robots need to be dexterous in object
handling [1]. In handling clothes, for example, the robot
must first recognize the shape of clothes. Due to contact andgravity, clothes rarely show their original shape. In this case,edge tracing is important in object recognition [2]. We use it
here to spread clothes, which is important in folding or
hanging clothes out to dry. Much research has been done on
deformable object manipulation [3,4,5,6,7,8] but very little
on clothes manipulation. Even that on clothes manipulationis largely theoretical [9,10] rather than practical. We propose
practical research on towel spreading as an example ofclothes manipulation using sensor-based edge tracing.
Spreading clothes basically involves holding two corners of
the clothes to spread or fold them [10]. The problem isfinding the appropriate corners. Vision sensors help findcorners [11], but corners are not necessarily visible or
exposed to the camera, causing the robot to hold the clothes
unsatisfactorily (Figure 1). Even when a camera detects a
corner, the corner may not be appropriate. By using tracing
manipulation to find the second corner, it is proven that the©ISAROB 2007

344

First corner

Hand A

Hand B(ii)
towel
Hand B(i)
SecondcornerHand B’

Hand A

Hand B’(i) Example of an improper clothes spreading(ii) Example of a proper clothes spreading

Figure 1 - Problems in clothes spreading
second corner found is that next to the initially found cornerand not the one apposite it [12,13]. But there are also
problems concerning tracing manipulation. One of the major
problems is how to retrieve the fabric when it is in danger ofslipping away from the gripper. The robot may be able todetect that the fabric is about to slip but it is hard to retrieve
or prevent it. If the robot tries to regrasp the fabric, it would
probably slip away. If the robot tries to retrieve the fabricwithout regrasping it, it would most probably drag the fabricalong, flexing it instead of retrieving it. This is due to the factthat deformable objects are sensitive to contact forces. The
robot can be programmed to retrace the fabric from the
beginning but this will take time. The solution to thisproblem is to design a special tool that can trace the fabricand does not release it. This paper proposes tracing using
inchworm type grippers as a solution.

2. Clothes Manipulation System

Figure 2 - Clothes spreading scene

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 2 shows a scene in towel spreading. In this research,
we use two robot arms, Js2 (Kawasaki Heavy Industries)
with 6 degrees of freedom (DOF) and RCH-40 (Yamaha)with 5 DOF. Both arms are equipped with grippers designed
for clothes manipulation. A CCD camera that detects andconfirms corners is located in front of the two robot arms.
The images taken by the CCD camera are in 8-bit gray scale
and 640x480 pixels in size. TRV-CPW5 image processing
board (Fujitsu) speeds up image processing.
The robot grippers designed for fabric manipulation play
important roles in clothes manipulation. Figure 3 shows thedetails of the inchworm grippers attached to the Js2’s wrist.
There are two pairs of gripper, one is a fixed gripper and theother one capable of sliding sideways using a Maxon motorwith rotary encoder (18V, 3.0W, lift-torque 10.8mNm, max
rpm 16000rpm) via 2 spur gears of the same diameter and aball screw with a 1mm lead. The encoder’s resolution is
100pulse/rotation. The maximum distance between the twopairs of grippers is 60mm.The grippers both have 3 pairs of
infrared sensors to check whether or not the fabric is insidethem. Infrared LED OP298B (Optek) and phototransistor
TPS601A (Toshiba) is used. The grippers can also detect
force being applied to the fabric via strain gauges attached tothem. The thick part of the gripper just behind the thin partacts as a stopper to protect the thin part from breaking due toexcessive force. The gap between the two fingers of agripper (gripper gap) is controlled using an RC servomotor
from Futaba.

8mm

0.5mm
Strain gage Phototransistors

14mm

Spur
gear

10mm

Slide gripper Fixed gripper Servo motor
4mm Infrared LEDs
max.
60mm

Ball screw
M otor with rotary encoder

Inchwormtype grippers Rch-40 gripper
(Js2)

u2

1

u1 Fixed gripper ∆s1
Slide gripper
2
z y
x

− u1
3

Towel

4

1.Slide gripper opens
and traces towel(tracing vector u1 )2.Slide gripper stops when∆s1 = ∆r or IR sensors
feedback changes

3.Slide gripper grasps
towel and returns toorigin4.Fixed gripper opens
and traces towel (tracing
vector u 2 , ∆s 2 = ∆s 1 )Figure 4 - Inchworm movement during tracing
The gripper movement during tracing is like the movementof an inchworm (Figure 5). The slide gripper will trace thetowel until the slide gripper reaches the maximum sliding
distance (maximum tracing distance ∆r) during infrared
feedback patterns A and B or until the infrared sensors
feedback changes while the fixed gripper maintain its firm
hold of the towel. During this process, Js2 will remain static.
The actual tracing distance is ∆s1. The relation between ∆r
and ∆s1 can be written as follows:∆s1 ≤ ∆r12.25mm(1)

The tracing vector for the sliding gripper u1 is perpendicularwith the orientation of the inchworm type grippers on thexz-plane. The tracing vectors u1 can be written as follows:

78.5mmFigure 3 - Inchworm robot grippers

u1 = (− sin θ ,− cosθ ) , | u 1 |= 1

4. Finding a Corner using Edge Tracing
Manipulation
Let’s assume that the RCH-40 gripper is grasping the firstcorner. This can be achieved using the CCD camera [12].
After Js2 gripper is positioned at the edge of the towel justbeneath the first corner, it will start tracing the edge of the
towel by smartly using the feedbacks from the infrared
sensors (Figure 4) with the fixed gripper positioned on top
and the slide gripper below. This movement pattern is basedon the feedbacks from Js2 gripper’s infrared sensors andshould enable Js2 to find a corner next to the one being heldand not the one across it. Since the inchworm type robot
grippers have two sets of grippers, at least one of the grippers
can be made to grasp the towel while the other gripper traces
the edge of the towel. The advantage of using the inchworm©ISAROB 2007

grippers is that Js2 will be grasping the fabric all the time,
reducing the probability for the towel to slip away during
tracing besides no precise force control is required during
tracing. This is because the two grippers of the inchworm
type grippers are near to each other and in case the tracing
gripper accidentally releases the towel, it can regrasp andretrace the towel from under the other gripper without theJs2 having to trace the towel back from the start.

345

(2)

After either ∆s1=∆r or the infrared sensors feedback changes
from pattern A to pattern B (Figure 4) or vice versa, the slide
gripper will grasp the towel and then return to its origin. Thismeans that during this process, both grippers are supposed tofirmly hold the towel. The new tracing vector for the slide
gripper during this process u1new can be written as follows:
u1new = −u1 = (sin θ , cosθ )(3)

The sliding distance is ∆s1' where:∆s1 ' = ∆s 1

(4)

After returning to its origin, the fixed gripper will open
slightly and then the Js2 will move downwards. The fixedgripper will be tracing the towel’s edge during this process

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Move outwards the towel
Js2 gripper

Move inwards the towel

Rch-40 gripper
Pattern B

Pattern A

Rch-40 gripper

Js2 gripper

ST ARTθ

Rotation center

θ

θ+α

Towel

Slide gripper

θnew = θ + α

θnew = θ - αu1∆s1

Slide gripper traces towel∆s1 = ∆r

Infrared sensor
feedback on slide
gripper changes topattern A

Infrared sensor
feedback on slide
gripper changes topatterns C or D

u 2 = (-sin θnew, -cos θnew)∆s 2 = ∆s1

u1
Pattern D
Slide gripper

Confirmation(n)n=2 times
Not a corneru2
Corner confirmation

Fixed gripper traces towel∆s 2 = ∆s1 = ∆r

Rotate Js2 gripper ccw on
xz-plane

Pattern C

u1 = (-sin θnew, -cos θnew)

Towel

Slide gripper

Check infrared sensors
feedback on slide gripper

Rotate Js2 gripper cw on
xz-plane

Rotation center θ - αSlide gripper traces towel

Infrared sensor
feedback on slide
u1 = (-sin θnew, -cos θnew)gripper changes topatterns C or D Infrared sensor
feedback on slide∆s1 = ∆r
gripper changes topattern B
u2

Fixed gripper traces towel

Confirmation(n)n=2 times
Corner confirmed

Fixed gripper
Slide gripper
Confirmation(n)n<2 times

END

∆s1

Fixed gripper
Slide gripper

u 2 = (-sin θnew, -cos θnew)∆s 2 = ∆s1∆s 2 = ∆s1 = ∆rFigure 5 – Tracing algorithm using inchworm grippers(tracing vector for the fixed gripper u2 and tracing distance∆s2). u2 and ∆s2 can be written as follows:u 2 = u1 = (− sin θ ,− cosθ ) , | u 2 |=| u 1 |= 1
∆s 2 = ∆s1

(5)
(6)

Both grippers will then grasp the fabric firmly before Js2changes the inchworm grippers’ orientation θ based on the
final infrared feedback pattern of the slide gripper when it
first traced the towel. In case of pattern A, the robot will
open the slide gripper slightly before Js2 rotates the
inchworm grippers counter clockwise (rotating angle α) on
the xz-plane so that the tracing direction will be facing
deeper into the towel. In case of pattern B, the robot will
open the fixed gripper slightly before Js2 rotates clockwise(rotating angle α) instead on the xz-plane so that the tracing
direction will be facing away from the towel. The rotation
center is the center of the fixed gripper’s fingertip. The slide
gripper will then again starts tracing the towel. The newtracing vector u1new can be written as follows:
u1new = (− sin(θ − α ),− cos(θ − α ) ,(7)

u1new = (− sin(θ + α ),− cos(θ + α ) ,(8)where eq.(7) represents the new tracing vector for pattern A
and eq.(8) represents the new tracing vector for pattern B.
Js2 will trace the edge of the towel until the infrared sensors
feedback on the sliding gripper turns to patterns C or D,
indicating that Js2 is holding a possible corner, and cornerconfirmation process will take place. This is done bychanging the orientation of the Js2 gripper θ and checking it
the feedback stays at pattern C, D or not. The corner©ISAROB 2007346

confirmation process is done two times. If the second corneris reached and confirmed, Js2 gripper will firmly grasp thecorner and the towel is then spread.

5. Experimental Results
Experiments were conducted 20 times to evaluate theproposed edge-tracing algorithm using a towel with thefollowing properties: size 32cm x 32cm, thickness 2.28mm,
mass per unit 0.037g/cm², coefficient of friction 0.615, andstretch rate 0.005cm/gf. All experiments were started with
the Rch-40 gripper holding a corner of the towel. ∆r and α
are set at 5mm and 5deg respectively.
Figure 6 shows the data acquired during one of the tracing
experiments. From Figure 6 (c), we can see that at the end of
tracing, the infrared sensors feedback on the slide gripper
turned to patterns C or D. This proves that the robot knows
when it is approaching a corner. We can also see from Figure
6 (c) that corner confirmation process took place based on
the number of times the feedback turned to patterns C or D atthe end of tracing. Figure 6 (a) shows the trajectory forinchworm grippers. Figure 6 (b) shows the orientation of the
inchworm grippers on the xz-plane. The percentage for Js2
finding a corner is 80%, proving that our proposed tracing
manipulation method has been successful. The average time
is approximately 58sec. Failures are mainly due to the Js2
gripper failing to trace along the edge of the towel. This
happens when Js2 gripper does not hold the edge of the towel
at the beginning of the tracing manipulation.

6. Conclusion
A method for finding a corner of a towel using a unique edge

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Tracing’s
starting point

References

0.55

0.45
0.40
Rch-40gripper 0.35

Corner confirmation

Z-axis coordinate [m]

0.50

0.30

End of tracing(grasped corner)

0.25-0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00
X-axis coordinate [m]Js2 gripper angle [deg](a) Js2 gripper trajectory
180
160
140
120
100
80
60
40
20
0

0

10

20

30
40
Time [s]

50

60

70

(b) Js2 gripper orientation θ on the xz-plane

Infrared sensor feedback

Pattern B

Pattern A
Pattern C
Towel detected
Towel not detected
0

10

20

Pattern F
Pattern E
30
4050Time [s]6070(c) Infrared sensors feedback from slide gripper
Figure 6 - Experimental data obtained during edge
tracing of towel by Js2 with inchworm type grippers
tracing method by inchworm grippers is presented. The
gripper has enabled Js2 to trace the edge of the towel withmore stability and also successfully even without the help of
a vision sensor.
Projected plans include the improvement in both speed andreliability of the process. Collaboration with the existingvision sensor might solve this problem. Applying the current
algorithm to other clothes such as shirts and skirts is also
important to check the robustness of the method to otherkinds of clothes as well.

©ISAROB 2007

347

[1] Hirai, S. 1998. Deformable object manipulation. Journalof the Robotics Society of Japan 16(2): 136-139.
[2] Arslan, A. and Turkoglu, I. 1999. An edge tracing
method for object recognition. In Proceedings ofInternational Conference in Central Europe on
Computer Graphics, Visualization and Interactive
Digital Media, Vol 3. Citing Internet sources URL
http://wscg.zcu.cz/wscg99/papers/H53-final.pdf[3] Wakamatsu, H. and Wada, T. 1998. Modeling of string
objects for their manipulation. Journal of the Robotics
Society of Japan 16(2): 145-148.
[4] Nakagaki, H. 1998. Insertion task of a flexible beam or a
flexible wire. Journal of the Robotics Society of Japan
16(2): 159-162.
[5] Abegg, F., Henrich, D., and Worn, H. 1999.
Manipulating deformable linear objects –Vision-based
recognition of contact state transitions-. In Proceedingsof the 9th International Conference on Advanced
Robotics.
CitingInternet
sources
URL
http://wwwipr.ira.uka.de/de/Publikationen/detailed_pu
blication.htm?id=976207040.
[6] Balkcom, D.J. and Mason, M.T. 2004. Introducing
robotic origami folding. In Proceedings of IEEE
International Conference on Robotics and Automation,
3245-3250.
[7] Kraus Jr., W. and McCarragher, B.J. 1997. Case studies
in the manipulation of flexible parts using a hybrid
position/force approach. In Proceedings of IEEE
International Conference on Robotics and Automation,
367-372.
[8] Smith, P.W., Nandhakumar, N. and Tamadorai, A.K.,
1996. Vision based manipulation of non rigid objects. In
Proceedings of IEEE International Conference Automat,
Vol.4, 3191-3196.
[9] Desbrun, M., Schroder, P. and Barr, A. 1999. Interactive
animation of structured deformable objects. In
Proceedings of the Graphics Interface Conference, 1-8.
[10] Hamajima, K. and Kakikura, M. 1998. Planning
strategy for task of unfolding clothes. In Proceedings ofthe 16th Annual Conference of the Robotics Society ofJapan, 389-390.
[11] Ono, E., Kita, N. and Sakane, S. 1997. Unfolding afolded using information of outline with vision andtouch sensor. Journal of the Robotics Society of Japan
15(2): 113-121.
[12] Salleh, K., Seki, H., Kamiya, Y. and Hikizu, M. 2004.
Spreading of clothes by robot arms using tracing
method. In Proceedings of the 5th International
Conference on Machine Automation, 63-68.
[13] Salleh, K., Seki, H., Kamiya, Y. and Hikizu, M. 2006.
Tracing manipulation by robot grippers equipped withgear tips. In Proceedings of IEEE International
Conference on Robotics, Automation & Mechatronics,
278-283.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of an obstacle recognition system
for autonomous robots in indoor environments
Hiroshi Mizoguchi
Eiji Hayashi
Department of Mechanical Information Science and Technology
Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka Prefecture, Japan
Key Words: Robot, Recognition, Autonomous, Obstacle

Abstract
We are currently developing an autonomous robotthat can work in human living environments and thatcan carry out users’ basic instructions. When such arobot moves and works in a human living space, therobot should be able to recognize elements of its
environment.
Thus, we are engaged in research aimed at theacquisition of a sense of distance from based on image
information, utilizing sight information and notnecessitating remodeling of the human living space.
We give our robot a “finite space map.” This map
contains information describing a floor plan including
obstacles (desk and sofa, etc.), and the robot’s position.
The robot searches for a route based on the finite space
map, and moves along that route. However, numerous
obstacles that would disturb the movement of the robot
can exist in a human living environment; in addition,these objects are often unknown to the robot. The
robot thus needs a system to recognize unknown
obstacles not provided by the finite space map
beforehand.
Thus, we developed an obstacle recognition
system that discovers detects obstacles, and detects
determines the distance to the obstacle and obtains
three-dimensional data describing the obstacle.

intended to serve.
Our robot’s only sensor is a CCD camera installedin the robot‘s head, which can rotate and tilt forward
and backward. Our robot moves on two motorized
wheels (front wheels) and two castor wheels (rearwheels). Moreover, the robot has two arms with whichto perform simple work (e.g., grasping an object). The
PC installed in the main body executes control of thedevice and runs an autonomous program. An installedwireless LAN provides the option of remote control
for humans. Lead batteries supply the robot’s electric
power. [1] [2]
In order to detect obstacles, the system extracts
the pixel groups whose shapes are much different fromthe main background pixels and interprets them as
obstacles. The system can predict the distances to the
obstacles and also the obstacle’s height and width byusing the extracted information and attached drive
encoders.
In addition, the position and the size of thedetected object are reflected in a finite space map, andthe robot evades the obstacle by calculating an evasionroute.
In this paper, we explain our robot’s processing
mechanism. In addition, we describe the details of the
obstacle recognition system, and show the results of anexperiment using this system.

1. Introduction
Recently, the chances of seeing a working robot
in our surroundings have increased. Robots withvarious functions have been developed based on recent
technological advances. However, a robot capable ofhelping us autonomously has not yet appeared. Thus,
our laboratory is developing an autonomous personal
robot that can work in the home and the hospital while
being easy to command.
In order to allow the robot to work autonomously,
it is necessary to create a program for the robot thatallows it a variety of actions.
We are advancing the development of a robot thatacts autonomous by giving the robot functions thatallow it to work and move easily in a room, and thatallow easy communication with the person it is©ISAROB 2007

348

Fig. 1

Robot appearance

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Self-drive control for robot
Our robot has an action determination programthat enables it to act autonomously. The obstacle
recognition system explained in this paper is part ofthis program. The robot has a system that enables it tocommunicate with humans and recognize moving
objects. These action determination programs use thefeedback values returned from the CCD camera andthe motor encoders. In addition, these programs
control the arm and the wheels and command thedriving motors. [3]ＣＣＤ camera

Image processing

Finite-space map

Feedback system

Fig.3 Extract the obstacle
Labeling is used to make one set of each group ofimage pixels extracted from an image. This isprocessed from the extraction result of the floor region.
The group of image pixels that leads to the detectionof an obstacle is distinguished by this process. Figure
4 shows an example of labeling.

Action determination program

Drive mechanism
encoder

Motor drivermotor

Fig.2 Self drive control for robot
Fig.4 Example of labeling

3. Obstacle recognition system
3.1 Outline of the system
In order to ensure that the robot can drive safely
in an indoor environment, the system must detectobstacles. Thus, data that can allow it to avoid
obstacles is needed. We have developed a system ofrecognizing obstacles that uses only image data
captured by a CCD camera. The purpose of this system
is to enable the robot to detect obstacles and roughly
recognize an obstacle's size and position. In addition,
the position and the size of the detected object arereflected in the finite space map, and the robot evades
the obstacle by calculating an evasion route.

Next, the system analyzes the object that has
been determined to be an obstacle. We define thelower side of a group of image pixels as the distanceto the object, and the system presumes the width of the
obstacle based on the width of a group of image pixels.
In addition, we defined the uppermost part of a groupof image pixels as the height or a depth of the object.
The robot runs on the floor side, and presumes
determines the height of the obstacle based on theposition of the change in the image pixel group, thecamera position, and the angle between the two
images.

3.2 Method of extracting obstacles
First of all, the system converts 24bit RGB image
data into HSV data. HSV data shows the image
elements of hue, saturation, and value. The processingof the image data could be simplified by using HSV.
The system samples a group of image pixels in arectangular region at the bottom center of an image.
The system uses the group of image data inside thisregion as its sample image data an then uses thedeflection calculated by the sample data. The system
extracts the floor region in terms of the difference ofall pixels in the image. Figure 3 shows an extracted
obstacle.

position 1

position 2

Fig.5 Estimation of an obstacle’s data

Fig.6 Object recognition from motion stereo©ISAROB 2007

349

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
We process the calculation of We process the dataof these obstacles in consideration of the focus andgeometric calculation of the camera. Lastly, we show
the flow of the obstacle estimation processing in
Figure.7. [4]CCD camera image acquisition
Convert RGB image to HSV image

Table.3 Calculation result of object data (ball)
Actual measurement Calculation result
Distance to obstacle

96cm

93cm

Obstacleposition(horizontal
direction)
HeightRight 29cm

Right 33cm

22cm

18cm

width

22cm

27cm

depth

22cm

8cm

Sampling of floor region information
Extract the floor region in an image
The process of labeling
Estimate the data of obstacle

Fig.8 Estimation of obstacle displayed in 3-D

Fig.7 Flow of processing

3.3 Evaluation of the obstacle extraction
process
We show the results of a test regarding theaccuracy of the estimation of an object. When thesystem is actually used, processing will be repeated
while the robot is moving. However, two images
acquired to accurately measure the distance when the
robot was in a fixed position were used to verify theaccuracy of the data describing the obstacle (width,height, and depth). Table.1～3 shows an example ofthe verified obstacle data. Figure 8 shows the results in
3-D space.
Table.1 Calculation result of object data (box)Actual measurement Calculation result
Distance to obstacle

100cm

98cm

Obstacleposition(horizontal
direction)
HeightRight 1cm

Right 3cm

31cm

34cm

width

21cm

30cm

depth

21cm

17cm

Based on these results, it was judged that thesystem collects obstacle data at a sufficient level ofaccuracy to evade an obstacle.

3.4 Method of evading obstacle
The robot evades an obstacle by using the resultsof obstacle estimation. When the obstacle is initiallyestimated, the system saves the camera image (the firstimage). As the robot advances in a regular fashion, thesystem obtains a second image that is used by the
motion stereo process. The distance from the firstimage to the second image can be obtained according
to the value of the encoder installed in the robot’s
drive wheel. Obstacle data is obtained by using these
two images and the method of obstacle extraction.
When the height of an obstacle is detected (if theheight of the obstacle is 0, the system determines thatthe robot need not evade the obstacle), the obstacle’s
data (width and depth) is written into the finite space
map.

Table.2 Calculation result of object data (plate)
Actual measurement Calculation result
Distance to obstacle

130cm

128cm

Obstacleposition(horizontal
direction)
HeightLeft 69cm

Left 78cm

0.5cm

0cm

width

50cm

63cm

depth

50cm

57cm

Fig.9 Finite space map©ISAROB 2007

350

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The finite space map is one of the knowledge
databases that the robot has beforehand. This map
contains information regarding the floor plan
including obstacles (desk and sofa, etc.), and therobot’s own position. The robot searches for a routebased on the finite space map, and moves along thatroute.
In addition, the route for evading the obstacle iscalculated by using the route searching systeminstalled in the robot, and the robot then performs anevasive action. The route searching system calculates
and controls the route of the robot by using the finite
space map. Figure 10 shows the flow of the evasive
action.
The robot drives[1] Toru Azuma, Yoshihiro Takenaga, Eiji Hayashi,
Development of a self-driven personal robot、Artificial
Life and Robotics(AROB 8th ’03)、pp.1-3、Oita, Japan,
2003[2] Eiji Hayashi, Keisuke Ikeda, Development of asystem for self-driving by an autonomous robot,
proceeding of 10th international symposium on
artificial life and robotics, Vol.10th, pp.4, Beppu, Oita,
Japan, 2005
[3] Takuma Umeno, Eiji Hayashi, Development of anautonomous personal robot “The visual processing
system for autonomous driving”, proceedings of the10th international symposium on artificial life androbotics, Vol.10th, pp.44, Beppu, Oita, Japan, 2005

The process of extracting obstacles

No
Were obstacles extracted?

[4] Takuma Umeno, Eiji Hayashi, Navigation system
for an autonomous robot using an ocellus camera inindoor environment, proceedings of the 11th
international symposium on artificial life and robotics,
Vol1.11th, pp.229, Beppu, Oita, Japan, 2006

Yes
The obstacle inputs into the finite space map

Calculation of evasion route

Evasive action of robot
Fig.10 Flow of evasive action processing

4. Conclusion
We developed an obstacle recognition systemthat allows a robot to drive safely in an indoor
environment. This system is able to roughly recognize
a three-dimensional object. In addition, the obstacle
could be successfully avoided by using data describing
the recognized obstacle.
However, the current method for calculating the
obstacle's height and depth cannot accurately calculate
an obstacle having an intricate shape. Our next focusof study is to find a solution to this problem and to
make the system more stable by improving its
processing speed and efficiency.

©ISAROB 2007

References

351

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Autonomous control of a robot arm based on contact with an object
Motoki Shimono

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
shimono@imcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract
In this paper, I focused on developing a robot arm withthree degrees of freedom and an autonomous function. I
modeled my research on the actions of animals, which adjust
well to environmental change, and aimed for control of anautonomous robot that would imitate the action of an animal.
Our laboratory developed a software architecture to control
the action of the robot by introducing an evaluation function
for action choice into the hierarchically structured model.
This is the element that specifically connects theconsciousness of the robot with its action, and I named it
Consciousness-based Architecture (CBA). In this research, I
ran an experiment on a search action for a robot arm
executing a detour around obstacles and arriving at a target
position. An experiment was performed to evaluate theeffectiveness of CBA, and its results are discussed. I let the
robot arm imitate the groping movement of a human being
and allowed a search action to proceed. In the case of an
outward trip, the robot arm recognized the positions ofobstacles by touching them, and it avoided these obstacles
by executing detours. As a result, the robot arm arrived at
the target position while memorizing the course of detours
around the obstacles that it recognized.

arm imitate the groping movement of a human being andallowed a search action to proceed. This paper describes
control of a joint angle by a computed torque method, theconsciousness architecture incorporated in the robot armthat allows it to act on a search, and an experiment of acourse search.

Fig. 1-1 Schematic diagram of the system structure forthe robot arm
Joint2

Key words: robot arm, evaluation function,
Consciousness-based Architecture, autonomous function

1. Introduction

Joint1The Japanese robots industry developed for the field of
manufacturing has played a key role in producing robots
that can assemble cars and operate precision instruments.
[1]
Hence, in this research, I focused on developing arobot arm with three degrees of freedom and an
autonomous function. Fig.1-1 shows the systemconstitution of this research; Fig.1-2 presents a diagram
showing the placement of the degrees of freedom of therobot arm.
This arm is not equipped with sensors to the outside
world and cannot take in nearby environmental
information before touching it. Therefore, I let the robot©ISAROB 2007

θ2

352

Joint3
θ3

θ1

Fig. 1-2 Overview of the 3 DOF of the robot arm

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. Positioning of a robot arm by acomputed torque method

3. An autonomous action
3-1 Consciousness-based Architecture (CBA)

A computed torque method is a control technique tomake compensations by a reverse dynamics calculationthat does not consider centrifugal force and Coriolis
force to be agitation. [2] Expression (2-1) below is an
exercise equation for motors; expression (2-2) is an
exercise equation for a robot arm by the Lagrange
method; and expression (2-3) is a law of control.
Jθ&& + Cθ& = τ

Fig. 3 shows a summary of a search action in thisresearch. I applied CBA to a robot arm at level 5 thistime. The robot arm moves the shoulder (B) and elbow(C) from (S), which is in an appropriate initial state,
and it leads to the aim state (G), while detouring aroundan obstacle.
Target

J：Rotor inertia [ kgm 2 ]C：Braking constant [ kgm2 / sec]

( )

C

(2-2)

M (θ )：inertia matrixh(θ ,θ&)：considering centrifugal force and coriolis force

TargetA

B

A

τ ：Torque [ N m ]τ = M (θ )v(t ) + h θ ,θ& + g (θ )Goal(G)

Start(S)(2-1)

B

ObstacleC
Obstacle

Obstacle

Obstacleg (θ )：gravity(v (t ) = θ&&r + K P (θ r − θ ) + K d θ&r − θ&Fig. 3 Schematic diagram of the experimental environment

)

(2-3)

θ r ：aim angle [deg ree]
Each feedback gain is derived with a pole assignment
technique. [3] Two poles constitute conjugate imaginary
poles, and I assume s = −α ± jβ And when I
2assume α = ζωn , β = ω n 1 − ζ , each feedback gain isfound according to expressions (2-4) and (2-5):
Kd =2 Jωn − C
M (θ )Kp =Jω n
M (θ )

Table 3-1 Details of consciousness, behavior, and deterrent at each
level

(2-4)

2

(2-5)

In addition, each parameter is demanded throughconsideration of the response and the greatest electric
current which a motor permits experimentally. I show aresult for a shoulder joint in Fig.2-1. I assumed
parameters which determined feedback
gain α = 30，ζ = 0.90 .
θ2 (actual)θr2 (target)

In addition to the robot arm shown here, our laboratory
constructed a hierarchically structured model based on
an expression mechanism model of consciousness andthe action of an animal. Table 3-1 shows aconsciousness module and a restraint object, aspects ofan action module applied in this research .

Level

Consciousness

5

Memory for an obstacle

think

Memory for an obstacle
Confirmation of an assistant goal
position
Confirmation of fingers position
Confirmation of an aim position
Confirmation of the obstacle
position where a robot arm
Confirmation of fingers position
Confirmation of an aim position

detour4

3
2

Behavior

search
avoid
move

1

************

reflex0************

sleep

Deterrent
A startup to an
initial position
A stop of link AB,bc
by contact
A stop of link AB,bc
by contact
A stop of link AB,bc
by contact
A stop of link AB,bc
by contact************

6

In explanation of this hierarchical structure model, a
consciousness level first emerges at 1 when a performed
action is restrained. A robot arm then makes the best
choice from actions on a lower level.

5Angle[degree]

4

3

2

3-2 An evaluation function for behavior choice
1

0
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Time[sec]

Fig. 2-1 Response of©ISAROB 2007

Joint2

353

Various elements participate in the ground rule of ananimal. The thing which a function makes inside thestate of an animal is "an evaluation function." In thisexperiment; an evaluation function assumes an outsideenvironmental state to be a component. Expression (3-1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
defines the strength of consciousness Ci of level i in atime t ;expression (3-2) defines an evaluation functionthat chooses an action:NE(i = 0 K p ) (3-1)
Ci (t ) =β ij (t )

∑
j =1
NE

I i (t ) =∑ β (t )

(3-2)

ij

j =1

where N E =the number of things outside perception,and βij = the degree of the physical outside perception.

4. An autonomy action experiment
I applied an action module and an autonomy action
algorithm that I built into a robot arm. I thenexperimented with a diplomatic shuttle action using these.
I tested the action in the environment shown in Fig. 3. In
the test, I gave the robot arm only coordinate information
about the aim position, none about obstacles. When thefingers arrived at the aim position, the robot arm changed
its initial position to the new aim position and moved.

consciousness level fell to 2, a robot arm reopens anaction to go to an aim position. Just like in the sequence
above, the robot arm chooses a reflection/detour action at
T3 - T4 and moves towards an aim position. The robot
arm judges that the finger reached the target position andstops at T5.
At T4, the robot arm begins a movement to return to its
startup posture. In the return journey, the robot arm will
trace the most suitable course. Therefore, theconsciousness level rises to 5, and it refers to theassistant goal information memorized in the outward
trip given it at level 4. Assistant goals given then are thefingers position when the robot arm judged that a detour
action was completed at T2 and T4. The robot arm acts
on a search in level 4 and starts movement towards agiven assistant goal. At T6 and T7, the fingers of a robot
arm arrive at an assistant goal. Therefore, theconsciousness level rises to 5 and is given at level 4 thenext assistant goal information. The robot arm judgesthat the finger reached the initial position and stops at
T9.

0.45

T0 T1

T2

T3

level 3level 2level 1
0.4

T4

T5

T6

T7

level 5level 4

T8

T9

0.35
0.3

C value

0.25
0.2

0.15
0.1

(a) T0(b) T1(c) T20.05
0Temporaldata
showingfor 7autonomous
0Fig. 4-1
1
2
3
4
5 C values
6
8
9
behavior
Time [sec]

10

-0.05

Fig.4-1 and 4-2I show the experimental result. Fig. 4-1
shows the consciousness evaluation function in CBAwhen the arm moved to a destination. The vertical axis
shows the size of the evaluation function at each level,
and the horizontal axle shows time. The figure indicatesa series in consciousness from the beginning of
movement to arrival at the aim position and an action. Ti(i=0…9) in an explanation helps to support Fig. 4-1
The consciousness level rises to 2 when the program of
the robot arm is at T0. When the robot arm comes incontact with an obstacle at T1 and causes a reflection, theconsciousness level falls to 1. When the consciousness oflevel 1 fades away afterwards, a restraint produces anaction at level 2 with the memory of the obstacle
remaining, and then the consciousness level stops at 2, 3,
and 4 sequentially.
Because the consciousness level rises to 4, the robot arm
chooses an action to detour around an obstacle. The
robot arm judges the obstacle to be a detour in T2, andthe consciousness level goes down to 2. Because the©ISAROB 2007354(d) T3(g) T7(e) T4(h) T8(f) T5, T6(i) T9Fig. 4-2 Robot arm postures for each time

5. Conclusion
I report performing the positioning of a robot arm by acomputed torque method and also an experiment

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
regarding a search action. I will aim at the realization of
more practical feel movements in three dimensions in the
future. However, a search action recognizes an obstacle
only after it is restrained in an action bringing it into
contact with the obstacle. Therefore, when an obstacle issoft, there is the danger that it might be damaged. This is
a problem for the future.

6. Acknowledgment
The authors would like to acknowledge the advice andassistance of Professor Tadashi Kitamura of Kyushu
Institute of Technology who died in 2006.

References[1] The Japan Machinery Federation, “Technical strategic
working papers for robot society creation in the 21st
century,” 2001, pp.10.
[2] Kobayashi, N., Masuda, R., “Real robot control,”Corona Publishing Co., 1997, pp.127.
[3] The Japan Society of Mechanical Engineers, “JSME
text series, Control Engineering,” 2002, pp.160.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Real-Time Target Detection Using Infrared Camera
M. Sugisaka1*, Erma Rahayu Mohd. Faizal2
Department of Electrical Engineering, Oita University, Japan(Tel: +81-097-554-7841; Email: ermarahayu81@yahoo.co.jp)

Abstract
Infrared (IR) cameras are often used in a vehicle

system is not hardware expensive system, however thebased multi-sensor platform for landmine detection.

human requirement to monitor 24 hours per day is

Additional to thermal contrasts, an IR polarimetric

expensive and source of inaccuracy. Some of the systems

sensor also measures surface properties and therefore has

may include recording media to capture all the events forthe potential of increased detection performance. Thisa later viewing. The problem with this is it turns thepaper describes all the steps to reach detection

surveillance system into a forensic tool to solve a

performance. The first step is the acquisition of thepossible robbery or illegal action that has alreadypolarimetric IR image data. The next step is thehappened nowadays.

pre-processing to (re)construct polarimetric images. A

Instead, a better solution is to have real-time

subsequent segmentation step is made to identify objects.

continuous and automated surveillance systems to detect

Features, like intensity, reflectivity and shape, of theseand alert a potential threat in progress. The need for

objects are measured. For independent performance

automated surveillance is very important in medical,

analyses, the data set is divided into a training andcommercial, law enforcement and transportation.

evaluation section. A classifier is trained on the training

The computational power required for a real-time video

section and evaluated on the classification section.

processing is large. The field of computer vision is

Keywords: Infrared camera; Infrared thermography;

taking advantage of the computational power available

Dataand monitors the applications into real-time automatedanalysis;

Image

Processing,

Thermograhy

processing.

surveillance for reasonable cost. As the fields ofcomputer vision continues to grow, new technologies

1. Introduction

will make it possible for reliable and cost effective

1.1 Computer Vision and Surveillancemeans of implementing better smart surveillancesystems.

Many current surveillance systems consist ofcameras and monitors with human dependence toanalyze and report possible problems. This type of©ISAROB 2007

356

1.2 Purpose and Objectives

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

For

this

research,

we

have

done

pixels as found by the edge detector and the actual

severaledge should be as small as possible.

experiments to achieve a few objectives. The objectives
are the system should be able to detect any movement3.

Response – The edge detector should not identify

object in real-time. As far as we concern, any movement

multiple edge pixels where only a single edge

detect by the camera could be in fast or slow motion. If

exists.

the image taken while the object is moving fast, theimage could be blur or hardly been seen.

2.2 Filtering

Therefore the objectives, we are proposing a

To sharpen the blur image taken we propose a new

method to overcome such problem. Whenever we use

method in filtering which used the capability of Gaussian

such technique of the image processing, it is guaranteed

equation to filter images. In one dimension, the responsethat the image after going through image processing, theof the filter f to an edge G is given by a convolution

image produce will be in better result. In image

integral:processing we can’t run from detecting edges. Therefore
the proposed method will also has the ability to detectW

H = ∫ G (− x ) f ( x )dx−Wedges precisely to easily recognize the object taken

The Canny detector method attempts to find thewhether in fast or slow moving object. Beside, it is alsofilter f that maximizes the product SNR x localization

feature one of the important techniques in image

subject to the multiple-response constraint, and while theprocessing, which is filtering. The filter use will be able

result is too complex to be solve analytically, an efficient

to sharpen the image taken.

approximation turns out to be the first derivative of a
Gaussian function. Recall that a Gaussian has the form:

2. Image Processing

G (x ) = e

2.1 Edge Detection

− x2

2σ 2

The derivative with respect to x is therefore:
In this research, the method proposed for edge
detection is using the Canny Edge Detection Method. In
this method, there are three issues that an edge detector⎛ x2 ⎞⎟⎜⎛ x ⎞ −⎜ 2 ⎟G ′(x ) = ⎜ − 2 ⎟ e ⎝ 2σ ⎠⎝ σ ⎠In two dimension, a Gaussian is given by:must be address.

G ( x, y ) = σ e
2

1.

2.

Error rate – The edge detector should respond⎛ x2 + y2 ⎞⎟−⎜⎜ 2σ 2 ⎟⎠⎝only to edges, and should find all of them; no edges

and G has derivatives in both the x and y directions. The

should be missed.

approximation to Canny’s optimal filter for edge

Localization – The distance between the edges

detection is G’, and so by convolving the input image©ISAROB 2007

357

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

with G’, the result obtain is an image E that hasenhanced edges, even in the presence of noise, which has
been incorporated in to the model of the edge image.
A convolution with two- dimensional Gaussian canbe separated into two convolutions with one-dimensional

Figure 2.1: Image taken in two frame of different time

Gaussian, and the differentiation can be done afterwards.

However, we purposely did not remove the noises

Indeed, the differentiation can also be done byas necessary information is obtained, in order to saveconvolutions in one dimension, giving the two images:

time computing. As long as we achieved the required

one is the x component of the convolution with G’ andgoal, extra computing process should be eliminated tothe other is the y component.

save time computing and moving detection in real timerequired immediate response. Illumination changes canpossibly give an error results in detecting changes.

2.3 Difference Picture Technique

The most obvious method of detecting edges
between the two frames is to directly compare thecorresponding pixels of the two frames to determine
whether they correspond to the same pixels value. In thesimplest form, difference picture DPjk ( x, y ) between

Figure 2.2: Image after going through edge detection

frames F ( x, y , j ) and F ( x, y , k ) is obtained by:

3. Conclusion

DPjk (x, y ) = F (x, y, j ) − F ( x, y, k )Infrared (IR) cameras are often used in a vehicle
based multi-sensor platform for landmine detection.

where x, y are the spatial coordinates in the frame, j

Additional to thermal contrast, an IR polarimetric sensor

j and frame k taken at

also measures surface properties and therefore has theand k represent frame
different

time

sequence.

In

ourexperiment,

potential of increased detection performance. This paper

DPjk ( x, y ) output is displayed as indicator for motion

describes all the steps to reach detection performance.

study. After a differentiate checking using detection of

The first step is the acquisition of the polarimetric IR

edges and pixel luminance comparison, we managed toimage data. The next step is the pre-processing todifferentiate whether the output is moving object orreconstructnoises.

segmentation step is made to identify objects. Features

polarimetricimages.

A

subsequent

like intensity, reflectivity and shape, of these objects are©ISAROB 2007

358

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

measured. For independent performances analyses, theReferences

data set is divided into a training and evaluation section.

[1] “Introduction to the Special Section on Video

A classifier is trained on the training section andSurveillance”, Robert T. Collins, Alan J. Lipton,

evaluated on the classification section.

and Takeo Kanade, IEEE Transactions on Pattern

Utilizing a desktop PC and a infrared thermal

Analysis and Machine Intelligence, Aug 2000

imaging camera, a real-time vision system was able to[2] “Recent Advances in Computer Vision”, M.

detect and collects targets within the field of view of the

Piccardi, The Industrial Physicist, Feb/Mar 2003

camera. Using fast image processing tools the system[3] Practical Computer Vision Using C, J.R. Parker,

detects regions of heat and collects attributes about theJohn Wiley and Sons, 1994

objects. Using this information about each object overlay[4] J. Ruffner, K. Woodward, Computer-based andgraphics are drawn on the image as rectangles around the

web-based applications for night vision goggle

object. The implementation of this project is in Visualtraining, Proc. SPIE Vol. 4361, p. 148-158,

C++ with all functions for image processing andHelmet- and Head-Mounted Displays VI, 2001.

real-time detection. Beside the method of Canny Edge

[5] B.

Tingzhu,N.

Li, Digital simulation for

Detector is proposed in this research. It implies the way

low-light-level night vision imaging system, Proc.

to detect edges of the image more relevant and effective.

SPIE Vol. 4222, p. 100-104, Process Control andThe

Inspection for Industry, 2000.

speed

of

the

application

allowsreal-timecomputation and functionality with the capability for[6] J. Ruffner, K. Woodward, Development of a night

integration with a larger surveillance system in thevision device driving training aid, Proc. SPIE Vol.

capacity of target detection with or without human

3691, p. 184-194, Enhanced and Synthetic Visioninteraction.

1999, 1999.

There are so many directions to take this to develop
a new system for automated surveillance applications.
This application of the thermal imaging camera could be
integrated with other CCD camera. Once the infraredand system like this detects and marks potential regionof heat, it could trigger the activation and position ofthese cameras to continue with the analysis. Such asystemwould

integratethedifferent

hardwaretechnologies for a more complete automated computer
vision based surveillance system.

©ISAROB 2007

359

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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(End point)Y[m]The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Simulating Crowd Motion with Shape Preference and Fuzzy Rules
Tsai-Yen Li
Computer Science Department
National Chengchi University
64, Sec. 2, Zhi-Na Rd., Taipei, Taiwan 116

Jen-Yao Chang
Computer Science Department
National Chengchi University
64, Sec. 2, Zhi-Na Rd., Taipei, Taiwan 116
Abstract
Crowd simulation is becoming indispensable in computer games and animations. Several approaches to thisproblem have succeeded in generating flocking
behaviors with virtual forces but it remains difficult tomanipulate the collective behaviors of a crowd to respect
certain desired shape. In this paper, we propose a crowd
simulation system that can generate a feasible path taking the crowd to the goal while maintaining auser-specified shape as much as possible. The system
consists of two key components: a path planner that cansearch for a feasible path for a region of flexible shapeand a local motion controller based on three classes offuzzy rules for generating desired agent behaviors. We
will demonstrate the implemented system with several
examples to show the generated path and the corresponding crowd motions adhering to a desired shape.
Keyword: Motion planning, computer animation,
multi-agent system, crowd simulation

1

Introduction

Group formation has many applications in the entertainment industry. For example, formation of a group in
a battle game like the one shown Figure 1 could be a key
factor in defeating an enemy. Different shapes formed bya group could provide different functions for distinct
physical and visual effects. We often see animations
showing a crowd of congregated animals through ashape of elephant, geometry, or a death’s-head in traditional cartoons to exaggerate the visual effects and toconvey special contextual meanings to the audience.
In traditional animation production, animators manually arrange the position of every animated object ineach key-frame in order to show the final flocking behavior. This process is very tedious and time-consuming
even with the helps of animation tools available today. In
the literature of computer animation, some motion-planning techniques have been proposed to generatea path for a rectangle shape and then constrain motionsof the agents in a group in this shape. However, the rigid
shape greatly limits the applicability of this technique ina real animation production. In this paper, we propose a
new crowd animation tool, in which a new motion planner is designed to take the requirement of flexible shape
into account. Given the geometric description of environment, the planner is capable of generating a collision-free path for the deformable object while account-©ISAROB 2007

364

Figure 1. Snapshot of group formation in a battle gameing for the preference of a user on the shape of the crowd.
The final crowd motion is generated with the help of
fuzzy rules in a agent-based simulation system to show
the desired visual effect.
Next, we will first describe some research pertaining
to our work and then show how to formulate this newplanning problem and the algorithm for generating sucha path automatically. Several experimental results showing how the shape of the crowd changes during a pathwill also be given at the end.

2

Related Work

Simulating emergent behaviors for virtual agents is acommon topic in computer animation. Reynolds proposed a virtual force model (separation, cohesion, andalignment forces) for the simulation of flocking
behaviors [1][2]. The main advantage of this approach isease of use. However, it is also difficult to tune theweights of these forces directly in order to achieve a
specific effect. Anderson used a simple force model in [3]to create a flocking behavior for a crowd with an expected appearance by continuously adjusting the positionand velocity of the agents according to the estimatedglobal shape. This method can generate a crowd motion
respecting the given desired appearance but the timespent in adjusting the positions and velocities of theagents is also too large for on-line applications.
In the literature of Robotics, it has been a classicalproblem to generate a feasible path for a robot movingfrom its initial configuration to a goal configuration. One
can find many approaches to this problem in Latombe’sbook [4]. However, most the traditional motion planner

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Environment initialization
Setting initial and goal configurations
Setting the parameters of user preference
Discretizing shape into a set of cells
CInnerCBoundary

Motion planning
Searching for feasible path

Figure 3. Example of shape configuration and configuration coverage map

Crowd simulation
Collecting path information
Fuzzy logic controller
Figure 2. System architecture
considers the robot as a rigid body or chains of rigid
bodies whose shape cannot be changed during the trajectory. In [5], Bayazit added some global information in
the roadmap of the environment to facilitate more sophisticated flocking behaviors, and proposed three group
behaviors exploiting global knowledge of the environment.
Kamphuis modeled a group of crowd with a deformable shape, and planned the global motion of the shapeby Probabilistic Roadmap Method (PRM) [6]. He used agroup potential field to control the local motion of entities in the shape. This method can solve the problem ofcontrolling the appearance of a moving crowd, whichcannot be solved by applying behavior rules only. However, the bottleneck of this method is that the crowd can
only move along the path consisting of simple shapes
and constrained by narrow passages.
Adaptive fuzzy logic controller (FLC) provides a goodsolution for autonomous robot to move in an uncertain
environment. The main advantage of FLC is that it ishighly adaptive and nimble in tackling the uncertainty ofcontrol system through linguistic presentation. Tunstel
used the idea of FLC to design an autonomous robot
having three types of basic behaviors as described in [7]:goal-seek, route-follow, and localize. Corresponding
primitive actions were also designed for each behavior.

3

Problem Description

The architecture of the system proposed in this work,
as shown in Figure 2, consists of two key components: apath planner to search for a feasible path for a flexible
region and a local motion controller based on fuzzy logics. We assume that we are given a geometric description of the environment as well as the initial configuration and a goal position of the virtual crowd. We hopethat that proposed motion planner can generate a feasible
path for the region of flexible shape automatically. While©ISAROB 2007

CCFreeCCInner
CCBoundary

365

accounting for the length of the path, the planner should
generate a path that can allow the crowd to maintainsome preferred shape as much as possible during the
movement of the crowd along the path.
Our system first searches for a feasible path for theflexible region of a fixed volume. We use a set of cells
called shape configuration to describe the region of theflexible shape. The system generates new shape configurations in the neighborhood of the current shape configuration in the process of motion planning until thegoal configuration is reached. The path returned by theplanner consists of a sequence of shape configurations inwhich the crowd will be put into simulation using fuzzy
logic controllers to control the motions of the agents fordesired flocking effects.

4

Path Planning for a Flexible Shape

4.1

Configuration and coverage map

We represent a region of flexible shape with a conserved volume by a fixed number of connected discrete
cells in the workspace. The obstacles in the environment
are also represented as forbidden cells in the workspace. ,A shape configuration, denoted by S, is collision-free if
and only if all of the cells in S do not overlap with the
obstacle cells.
In order to facilitate the measure of incremental
movement of S, we distinguish two types of cells in S:
inner cells and boundary cells. A boundary cell for ashape configuration, denoted by CBoundary, is defined as
the cell having at least a free cell in its neighbors. An
inner cell for a shape configuration, denoted by CInner, isthe cell inside the region of shape configuration but is
not a boundary cell. Examples of these two types of cells
are illustrated on the left of Figure 3. Note that these two
types of cells are for a shape configuration. Since eachcell consists of two degrees of freedom, the complexityof the overall configuration space, growing exponential
in the overall degrees of freedom is rather large. However, the spatial and temporal relations of these cells are
not without constraints. We assume that the region needs
to move as a connected component and the speed of the
movement for each cell is limited. In the planner, we
ensure that the region moves continuously by allowing

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Procedure: Generate_New_Shape_Configuration
Input. A configuration S.
Output. A new shape configuration S’.
1. Clone S’ from S
2. if all cells in CBoundary(S’) have no collision-freeneighbors then return nil
3. Randomly choose Cf in CBoundary such that aneighbor of Cf must be collision-free
4. let v be the vector from Cf to the free neighbor
5. Add all CBoundary cells of S’ into a list Q sorted
according to the distance to Cf
6. while Q is not Empty
7. begin8.
poll the front element Ci from Q
9.
if Ci + v is free in workspace then10.
poll the rear element Cj from Q
11.
remove Cj from S’
12.
add Ci + v into S’
12. end
13. return S’Figure 4. Procedure for the generation of a new shapeconfiguration
only one cell of difference in two consecutive configurations. Hence, we only need to consider the variation of
CBoundary in generating a new shape configuration.
We use a data structure called Configuration
Coverage Map (CC-Map) to record the trace of regions
covered by the generated shape configurations. This
coverage map contains three types of cells: CCInner,
CCBoundary, and CCFree as shown on the right of Figure 3.
CCFree are the unvisited cells in the freespace. Similar tothe definition of the region in shape configuration, CCInner are the set of inner cells in the covered region so far
while CCBoundary is the set at the boundary. In order toensure that the motion planner makes progress in eachstep of the search process, a new shape configuration isconsidered valid only if it can expand the coverage region by at least one cell. In other words, the difference ofthe region for a new shape configuration, Snew, and theexisting coverage map must be at least one cell.

4.2

Generating a new shape configuration

The procedure used for generating a new valid shapeconfiguration is shown in Figure 4. The procedure starts
by duplicating the given shape configuration S to S’.
Then the system checks if there exists at least one cell in
CBoundary of S’ that is collision-free. The system randomly
chooses a seed cell Cf and a moving direction from theintersection of CCBoundary and CBoundary of S’. If the newneighboring cell along the chosen direction iscollision-free, then we move other cells in CBoundary along
the same direction by one cell if the new cells are collision-free. The way we move the whole region is by removing the cells at the other side of the boundary andput them in the new cells along the moving direction.

©ISAROB 2007

366

Since the number of cells is not changed, the volume of
the region is also conserved.

4.3

Motion planning for flexible shape

In the planner, we use a Best-First Search (BFS) algorithm, commonly used in the motion planners for low
dimensional search space, to search for a feasible path.
In the algorithm, the planner keeps track of the exploredvalid configurations that can be further expanded, called
OPEN, and selects the best configuration in it to explore
further according to some criteria such as an objectivefunction. In this work, we define the objective function
as the weighted sum of the following two scores. One isthe distance score computed according to the average
distance between the cells of the current configurationand the goal position. The other is the shape score determined by the difference of the ideal shape and thecurrent shape.
When a shape configuration is selected for exploration,
all new configurations that can extend the region of
CC-Map further are inserted into the OPEN list for future exploration. The system will continue to generate
new shape configurations, update the CC-Map until thegenerated shape configuration covered the goal position(success) or the OPEN list becomes empty (failure). If
the goal configuration (any configuration that can contain the goal position) is found, we can then backtrack
the search tree to obtain the path for the flexible shape.

5

Fuzzy Rules for Crowd Simulation

According to our observation of the interaction between people in our real life, we model the behaviors of
the agents in a crowd with a three-layer fuzzy logic: typeof model, behavior model, and primitive action. Each
type of models contains several behavior models, andeach behavior model contains primitive actions. Threetypes of behaviors are defined in our system: Intra-agent,
reactive, and inter-agent. The fuzzy behaviors use rules
and linguistic variables to describe the relation between
sensation and actions. Each primitive action is represented by a distinct control policy governed by fuzzy
inference. We use a fuzzy knowledge base (FKB) to
store the fuzzy rules and linguistic variables and use afuzzy logic controller (FLC) to control the motions of
the agents. In each control loop, the system fuzzifies the
position and velocity of the agents and some environmental information for the processing of FKB. Then it
defuzzifies the results to obtain the control parameters
driving the motions of the agents.

6

Experimental result

We use the scene in Figure 5 to demonstrate how theselection of different weights on distance and shape affect the paths generated by the planner. Key snapshots,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 6. Applying the result generated by the planner tothe simulation of liquid motion
can also be applied to liquid simulation as show in
Figure 6. We have also designed a fuzzy-behavior model
to facilitate the motion control of the agents inside themoving shape. More examples created with these fuzzycontrol rules will be reported in the future.

Acknowledgement
This research was founded in part by the National
Science Council (NSC) of Taiwan under contract no.
NSC 93-2213-E-004-001
Figure 5. Key snapshots along the paths of a flexible
object in various experiments
arranged from the top to the bottom, of these experiments are shown in Figure 5. The weights on the distance score and shape score are the only parameters thatare different among these experiments. The weights(shape/distance) for experiments A, B, and C are(0%/100%), (25%/75%), and (75%,25%), respectively.
Since distance is the only criteria used in searching forthe path in experiment A, a shortest path passing throughthe narrowest passage was generated.

7

Conclusion

It is a time-consuming and challenging task to create
the animation of a crowd motion that conforms to theenvironment and respects a specific shape. In this paper,
we have designed a motion planner to address the problem of how to generate the path for a region with flexible
shape and fixed volume. We use the concept of coverage
map in workspace to ensure the completeness of thebest-first search algorithm. The system allows a user tospecify his/her preference on path length or ideal shapewith weights on the corresponding evaluation scores. In
addition to the application of crowd simulation, the configurations and paths generated by the motion planner©ISAROB 2007

367

References[1] Reynolds CW (1987), Flocks, Herds, and Schools: A
Distributed Behavioral Model. Computer Graphics:
ACM SIGGRAPH, pp.25-34[2] Reynolds CW (1999), Steering behaviors forautonomous characters. In Porc. of 1999 Game Developers Conference, pp.763-782[3] Anderson M, McDaniel E, Chenney S (2003), Constrained Animation of Flocks. In Proc. of ACM Eurographics/SIGGRAPH Symp. on Computer Animation 2003
[4] Latombe JC (1991), Robot Motion Planning, Kluwer,
Boston, MA[5] Bayazit OB, Lien JM, Amato NM (2002), Better
Flocking Behaviors in Complex Environments Using
Global Roadmaps. In Proc. of the 2002 Artificial Life
(ALIFE), pp.528-534[6] Kamphuis A and Overmars MH (2004), Finding Path
for Coherent Groups using Clearance. In ACM SIGGRAPH/Eurographics Symposium on Computer
Animation, pp.193-202[7] Tunstel E, Danny H, Lippincott T, Jamshidi M(1997), Autonomous Navigation using an Adaptive
Hierarchy of Multiple Fuzzy-Behaviors. In Proc. ofIntl Symp on Computational Intelligence in Robotics
and Automation, pp. 276-281

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of an autonomous driving personal robot“Development of arm’s mechanism and control program”
Makoto Tone
Eiji Hayashi
Department of Mechanical Information Science and Technology
Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka Prefecture, Japan
Key Words: Personal robot, Autonomous driving, control

1. Introduction
Japan is facing a declining birthrate and a growing
proportion of elderly people in its population. In such asociety, the lack of a sufficient workforce is almost
guaranteed. Specifically, the lack of caregivers to
support the lives of senior citizens and handicapped
persons will become a problem in the future. Therefore,
efforts are underway to develop personal robots toprovide independent life support for patients or perform
light work in the home or office and thus to reduce
caregivers’ work.
This research was aimed at the development of apersonal robot that acts with users in a human living
environment to assist the work of humans. Specifically,
I designed an arm mechanism that could perform lightwork such as taking a glass or newspaper or turning on awall switch, and I created a control program for it.

2. Basic policy of the personal robot
Up to now, robots have primarily been developed towork in a factory or at the scene of a disaster. These
robots can operate in a severe environment and cancontinuously perform simple work at high power andhigh speeds and with a high degree of accuracy.
However, a skilled operator with special knowledge isnecessary for their use. They are not easy for the average
person to use.
Conversely, a personal robot is designed to live together
with humans in a human living environment, andcommunications skills with humans are demanded.
Moreover, because the situation in the surroundings ofthe robot is always changing, programming that isautonomous and flexible is demanded.
However, at present it is difficult to meet all of thevarious work instructions that may arise in various
environments. Thus, in this research the range in which
the robot acts was limited to the home, the office, andthe hospital, and the type of work was limited to lightwork.

3.1 Hardware composition of the robot©ISAROB 2007

368

Parts of the personal robot under development are thehead that carries a CCD camera, the running drive part,the frame chassis, the arm part, and the hand. The
camera on the head recognizes the external world.
Because it can move in both horizontal and verticaldirections, a wide-ranging search is possible both up anddown and right and left. A personal computer, battery,
ACDC inverter, and various drivers have been placed in
the frame chassis.
The driving part is composed of the DC servo motor in
the front of the robot, and the caster in the back of therobot. With the pulse, the DC servo motor can control
the position of the robot. As for the arm part, five
stepping motors are used and make possible an
operation that brings the point part of the arm as close to
the target object as possible. Hands have three fingers,
and three servo motors are in the center finger and four
servo motors are in the outside finger. The two outside
fingers can move the position of the root and haveachieved a flexible correspondence matched to theshape of an object.

3.2 Software composition of the robot
The processing system of the robot is divided chiefly
into four parts. It is composed of a space recognition
part that consists of the CCD camera, an operation
decision part that consists of a limited space map and amovement pattern database, a driving control part thatconsists of a driver and amplifier, and a driving part thatconsists of various actuators for arm, hand, andmovement mechanisms. With the space recognition part,the robot that receives instruction from a human sets
information obtained from the CCD camera against a
shape pattern DB. Moreover, the coordinates for thesituation and the work object where the robot is being
placed are now understood based on information in alimited space map. This information is given to theoperation decision part, and the operation that should beexecuted is determined. For the movement, the bestroute to a destination is found from the limited space
map. The operation that is decided upon is sent to thedriving control part, and each actuator is driven.
Realtime processing is done by repeating these

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
operations, and the instructions are executed.

Fig.2 Schematic view of hand

4.1 Development of the arm partI assumed that this mechanism would mainly aim thehand to grip an object. And on this basis I designed this.
Figure 1 shows a schematic view of an arm.
A multi-jointed arm that was able to do complex work
was adopted as the mechanism in the development of thearm part. The parameters for three postures and threepositions are necessary to describe the position of an
object in three-dimensional space. I designed the armwith five degrees of freedom in this research. The robot
is moved up to a distance from which an object can beeasily gripped and then grips the object. Such an
operation involves only a small degree of freedom.
Moreover, motors and gears are constructed in the arm.
This method makes for a simpler mechanism than one
with wires, and it can increase the accuracy of positional
control. The arm does become heavy because of theweight of the motor, and therefore the weight that it canhandle decreases. However, it is not a problem in thisresearch to limit the content of work to light work.

Fig.1 Schematic view of the arm

Form 1

Form 2Form 3

Fig.3 The hand that was seenfrom the tip of a finger.

5.1 About the control of the arm and hand
Operations that stop the hand in a target location anddirection for a particular purpose are called positional
control and posture control. By these operations a work
object can be grasped and placed. It is necessary to
calculate how many degrees each motor must be rotatedto control a position. This calculation is called a reverse
kinematics calculation.
Before this calculation, it is necessary to calculate thevalue of the hand’s position and posture from the angleof each motor. This calculation is called a direct
kinematics calculation. I created the control program
based on this principle.

5.2
Coordinates
calculationtransformation matrix

4.2 Development of the hand partIn this research, the work is limited to light work suchas gripping an object or pushing a button. The work
object could be something like a glass. The size of eachhand part was designed based on the size of the humanhand. The weight of the work object is assumed to beabout 1kg. The outside two fingers can move the root
position through a feature of this hand. Form 1 is thebasic arrangement. When a spheroidal object is gripped,
Form 2 is used because it is the steadiest one that can
add power from three points. When the work object isgripped from the right and left, Form 3 is used. Figure 2
shows the schematic view of the arm. And in Figure 3
the hand was seen from the tip of a finger.

byaFirst, I defined a relative position of a link andcoordinate system like in Figure 4.

Fig.4 The relative position of the link
and the definition of the coordinate system
These relative positions are shown by multiplying thefollowing by the transformation matrix of 4×4.

 A a
T =

 000 1 
A is a procession which shows the rotation of thethree-dimension procession coordinate system, and A isa three-dimension vector in which the movement is©ISAROB 2007

369

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
shown. If the positional vector shown in certain
three-dimension coordinate systems is r, by thetransformation matrix T the new positional vector r' is

 r'
r 
 1  = T 1  The driving corner of joint i is θi. The transformation
matrix Ti when the distance between joint j and joint j+1
is assumed to be Lj. When O1 is the reference point andO1 is joint i is θi. The transformation matrix Ti when the
distance between joint j and joint j+1 is assumed to be Lj.
T
When O1 is reference point and O1 is (0001) ,positional vector Oi of joint i is・ ・ ・・・ ・

coordinates of the target by using the reverse kinematics
calculation is calculated. The condition of the drive that
locks the direction of the wrist at this time can be added.
Figure 6 and Figure 7 show the appearance of thedriving experiment that uses an existing arm.

Fig6 Program before it drives andOi = T1 T2 T3
Ti −1 O1
As a result, the simultaneous equations to calculate
coordinates of each joint can be derived. If the driving
corner is determined, the coordinates of each joint canbe calculated.

5.3 Calculation of the joint corner using
reverse kinematics
The driving corner to drive the hand to the coordinatesof the target can be derived when you solve theabove-mentioned simultaneous equations about thedriving corner θ. At this time, the driving corner can becalculated by imposing the conditions that the angle andthe height of the hand be kept constant. The control
program was made based on these assumptions.

the appearance of the arm
Fig.7 Program after it drives andthe appearance of the arm
There is a difference in time from the start to the stopof each motor. However, the minion was almost able tobe driven to coordinates of the target. As a result, there
is no problem regarding its practical use.

6. Recognition of the work object

5.4 Control program of the arm
The control program was made based on theabove-mentioned theory. Figure 5 shows the execution
screen of the control program.

A plane that is of a similar color is extracted from one
image obtained from the CCD camera, and the desk andthe floor are recognized. A plane part on the desk and thefloor is extracted using the HSV conversion. Next, thepart not extracted is considered to be a part of the object,and it is extracted. Figure 8 shows the appearance of theobject extraction.
Fig.8 The appearance of the object extraction
The relative coordinates of the work object and therough size of the object can be calculated by the main
body of the robot. These data is sent to the control
program of the arm and using it, the robot can grip thework object. Figure 9 shows the size and the relativeFig.5 The execution screenof the control program
The left figure displays the present appearance of thearm and the parameter of the angle of each arm joint.
The target and the minion's coordinates are displayed on
the right side. First, the coordinates of minion's target
are input. Next, the joint corner of the arm that fills the©ISAROB 2007370

coordinates of the work object on the output screen.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig.9 The screen for the outputof the parameters of the work object

7. Conclusions
In this research, the robot arm that met the demandrequired by a personal robot was designed. It is
possible to create the various conditions that will drive
the hand to a target position by the driving control
program, and it is thought that the work object can be
gripped by the hand.

References[1] Shinya Fujiwara, Eiji Hayashi, Development of anautonomous driving personal robot “Design ofmechanism and control system”[2] Yoshitaka Hane, Eiji Hayashi, Development of anautonomous driving personal robot “Design ofmechanism of hand”©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Knowledge Evolution in a Dynamic Environmentof RoboCup Simulation
T. Nakashima, H. Ishibuchi
Graduate School of Engineering
Osaka Prefecture University
1-1 Gakuencho, Sakai, Osaka 599-8531, Japan{nakashi, hisaoi}@cs.osakafu-u.ac.jp
Abstract
In this paper we examine the performance of theevolutionary algorithm for a dynamic environment
where the opponent team changes during the evolutionof strategies. In the dynamic environment, the dashpower of opponent players in ball intercept behavior
is adjusted. We consider two adjustment modes of thedash power in our experiments. In one mode the dashpower of the opponent players is gradually increased
over generation of the evolutionary algorithm. The
other mode monitors the performance of the evolved
team strategies to increase or decrease the dash powerof opponent players accordingly. We compare the evolution of team strategies between the two modes anddiscuss for the future extension to the current experimental settings. We also discuss the possibility ofknowledge extraction from the obtained team strategies by the evolutionary algorithm.

1

two adjustment modes of the dash power in our experiments. In one mode the dash power of the opponent players is gradually increased over generationof the evolutionary algorithm. The other mode monitors the performance of the evolved team strategies toincrease or decrease the dash power of opponent players accordingly. We compare the evolution of teamstrategies between the two modes and discuss for the
future extension to the current experimental settings.
We also discuss the possibility of knowledge extraction
from the obtained team strategies by the evolutionary
algorithm.

2

Team Setup

We use the following action rules to determine
player’s action
Rj :Introduction

RoboCup soccer is a competition between soccer
robots/agents. Its ultimate aim is to win against thehuman soccer champion team by the year 2050 [1].
Developing RoboCup teams typically involves solving the cooperation of multiple agents, the learningof adaptive behavior, and the problem of noisy data
handling. Many approaches have been presented that
try to tackle these problems, an example is the application of soft computing techniques.
In this paper we examine the performance of theevolutionary algorithm for a dynamic environmentof RoboCup simulation where the opponent teamchanges during the evolution of strategies. In the dynamic environment, the dash power of opponent players in ball intercept behavior is adjusted. We consider©ISAROB 2007

M. Nii
Department of Computer Engineering
University of Hyogo
2167 Shosha, Himeji, Hyogo 671-2201, Japannii@eng.u-hyogo.ac.jp372

If Agent is in Area Aj andthe nearest opponent is Bj
then the action is Cj , j = 1, . . . , N,(1)where Rj is the rule index, Aj is the antecedent integer value, Bj is the antecedent linguistic value, Cj isthe consequent action, and N is the number of action
rules. In this paper we evolve action rule sets to finda competitive soccer team strategy.
The antecedent integer value Aj , j = 1, . . . , N refers
to a subarea of the soccer field. We divide the soccer
field into 48 subareas as in Fig. 1.
Each subarea is indicated by an integer value. The
antecedent value Aj of the action rule Rj is hence aninteger value in the interval [1, 48]. In this paper 12
actions are available for the consequent action Cj .
Note that each player has a set of action rules. Since
there are 48 subareas in the soccer field and near andThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1

7

13

19

25

31

37 43

2

8

14

20

26

32

38 44

3

9

15

21

27

33

39 45

4

10

16

22

28

34

40 46

5

11

17

23

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35

41 47

6

12

18

24

30

36

42 48

3.2

Figure 1: Soccer field
not near are available for the second antecedent partin action rules (i.e., Bj ), the number of action rules
for a single player is 48 × 2 = 96. There are 96 × 10 =960 action rules in total for a single team with ten
field players. Action rules for a goal keeper are notconsidered in this paper.
If the ball cannot be kicked by a player, there are
two kinds of actions. One is ball intercept where theplayer move toward the ball in order to be able to kickit. The other action is positioning where the agent
keep its position that is determined from the ball position and its home position. The player determines
which action to take based on the positional relation
among all the objects including teammates, opponent
players, and the ball.

3
3.1

Evolutionary Computation
Encoding

The action of the agents is specified by the action
rules in (1) when they keep the ball. Considering thatthe soccer field is divided into 48 subareas (see Fig. 1)
and the position of the nearest opponent agent (i.e., itis near the agent or not near) is taken into account in
the antecedent part of the action rules, we can see that
there are 48 × 2 = 96 action rules for each player. We
apply our evolutionary method to ten soccer agents
excluding the goal keeper. Thus, the total number ofaction rules for a single team is 96 × 10 = 960. We
use an integer string of length 960 to represent a rule
set of action rules for ten players. The task of our
proposed evolutionary method is then to evolve theinteger strings of length 960 to obtain team strategies
with high performance.
On the other hand, the actions of the agent in thecase where the nearest opponent agent is not near theagent are shown in the other 48 integers. The valueof each integer ranges from an integer interval of [1,12] as the number of possible actions for each rule istwelve.

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Dynamically Changing Opponent

In our previous studies on the evolutionary computation for RoboCup soccer, the opponent team wasfixed during the course of the evolutionary algorithm.
Obtained strategies by the evolutionary algorithm are
therefore able to successfully perform against the fixed
opponent team. However, it is not neccessarily said
that the obtained strategies are successfully able toplay the soccer game against different team strategies
as successfully as against the fixed opponent team. In
this paper we consider a dynamically changing environment in order to solve this problem.
In our implementation of the dynamically changing environment, dash power is used as a parameter ofdynamically changing opponent strategies. The higher
the dash power of opponent agents is, the harder it isfor evolutionary teams to defeat the opponent team.
In the beginning of the evolutionary algorithm thedash power of the opponent is zero and linearly increase as the number of generations increases.

3.3

Evolutionary Operation

We use one-point crossover, mutation, and ES-type
selection as evolutionary operations in our evolutionary method. New integer strings are generated bycrossover and mutation, and selection is used for generation update.
In the crossover operation, we first randomly selecttwo integer strings. Then latter part of both strings is
exchanged with each other from a randomly selected
cut-point. Note that we do not consider any evaluation results when two integer strings for the crossover
operation are selected from the current population. In
the mutation operation, the value of each integer isreplaced with a randomly specified integer value inthe interval [1, 12] with a prespecified mutation probability. It is possible that the replaced value is thesame as the one before the mutation operation. Itshould be noted that new integer strings generated by
the crossover and the mutation operations do not havetheir match history. Thus the fitness evaluation of the
new integer strings are made by using the game resultof only a single game.
Generation update is performed by using ES-type
selection in our method. We use a so-called (µ + λ)ES [3] for our generation update scheme. By iterating
the crossover and the mutation operations we produce
the same number of new integer strings as that of current strings. Then the best half integer strings fromthe merged set of the current and the new strings arechosen as the next population. The selection is based

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Step 1. Initialization. A prespecified number of integer strings of length 960 are generated byrandomly assigning an integer value from theinterval [1, 12] for each integer.
Step 2. Generation of new integer strings. First randomly select two integer strings from the current population. Then the one-point crossoverand the integer-change mutation operations
are performed to generate new integer strings.
This process is iterated until a prespecified
number of new integer strings are generated.
Step 3. Performance evaluation. The performance ofboth the current integer strings and the new
integer strings generated by Step 2 is evaluated through the results of soccer games. Notethat the performance of current integer strings
is also evaluated every generation because thegame results are not constant but different
game by game.
Step 4. Dynamic environmental change. The dashpower of the opponent team is increased. The
schedule of the change of the dash power ismake so that at the first generation it is zeroand becomes full (i.e., 100%) at the final generation.
Step 5. Generation update. From the merged set ofthe current integer strings and new ones, select best integer strings according to the performance evaluation. In the performance evaluation goals for are used as the first criterion.
If multiple individuals have the same goals
for, then goals against are used as the second
performance criterion. The selected integer
strings form the next generation.
Step 6. Termination of the procedure. If a prespecified termination condition is satisfied, stop theprocedure. Otherwise go to Step 2.

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4

Computer Simulations

The following parameter specifications were used
for all the computer simulations in this paper:
The number of integer strings in a population: 5,
The probability of crossover: 1.0,
The probability of mutation for each integer: 5/96,
Generation update: 500.
The population size is specified as five. This is a small
number comparing to commonly used parameter specifications. This is because it takes at least five minutes to complete a single soccer game. If the population size is specified large, it is difficult to performthe evolutionary method for a large number of generations. Currently we use a 16-node cluster system forthe computational experiments in this paper. It stilltakes several days to perform a single run of the evolutionary process. The population size will be increased
when more powerful computational environments areequippped.
We show the evolution of the soccer teams in Fig. 2.
Total scores of goals for and goals against are plottedin Fig. 2. From this figure we can see that the offensive
performance degrades as the evolution proceeds as thedash power of the opponent teams increases during thecourse of the evolution.
30

Goals for25

Total goals

on the match results. Note that the current strings
are also evaluated in this selection process. Thus, it
is possible that a current integer string with the best
performance at the previous generation update is notselected in the next generation update because the average goals of the integer string after the next performance evaluation may become lower if the result of
the game at the next evaluation is poor.
To summarize, our proposed evolutionary method
is written as follows:[Procedure of the proposed evolutionary
method]

20
15
10
5

Goals against

0

0

100

200

300

400

500

Generations
Figure 2: Performance of evolutionary teams.
Now we closely investigate obtained strategy by theevolutionary algorithm. Figure 3 shows the trajectoryof the ball by an individual at the initial population.
We can see from Fig. 3 that the ball is somewhat kicked
randomly at different directions. This is because theinitial individual was generated by randomly assigningan action to each action rule. Next we show in Fig. 4
the trajectory of the ball by an individual at the final population (i.e., at the 500-th generation). From

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 4, we can see that the evolved team obtained offensive knowledge where the side forward drives theball toward the opponent area from the side of the fieldand place the ball to the center when the ball is near
the opponent goal. This strategy is intuitively understandable because human soccer teams normally takethis strategy. It is shown that the evolutionary algorithm automatically obtained the human-like strategy
from the initial population that was randomly generated.

Figure 3: Ball trajectory at the first generation.

In the evolutionary process of this paper the actionof soccer players that keep the ball is determined bya set of action rules. The antecedent part of the action rules includes the positions of the agent and its
nearest opponent. The soccer field is divided into 48
subareas. The action of the agent is specified for each
subareas. The candidate actions for the consequent
part of the action rules form a set of 12 basic actions
such as dribble and kick. The strategy of a soccer
team is represented by an integer string of the consequent actions. In the evolutionary process, one-pointcrossover, replacement mutation, and ES-type generation update are used as evolutionary operators. The
generation update is performed in a similar mannerto the (µ + λ)-ES of evolution strategy. That is, thebest integer strings are selected from a merged set ofcurrent integer strings and new integer strings thatare generated from the current integer strings by thecrossover and mutation operations. The performanceof the soccer team strategies becomes better over generation. For example, the average goals at the endof the evolution process is larger than in the initial
population. We also observed that the average goals
against did not increase as the evolutionary computation progressed.
In a series of computer simulations, we examined the performance of our evaluation method. We
showed that the evolutionary algorithm automaticallyobtained a human-like strategy from random strategies through the process of evolutional trial-and error.
This paper focused on offensive strategy rather thandefensive one. Developing a method for the defensive
strategy is left for our future work.

Acknowledgment
Figure 4: Ball trajectory at the final generation.

5

Conclusions

In this paper we proposed an evaluation method ofsoccer team strategies by using match history. The
match history is used to calculate the average goals
and the average goals against. Those teams with highaverage goals are evaluated as better than those withlow average goals. The average goals against are used
when the average goals are the same among more than
one soccer team strategies. This method avoids the
problem caused by uncertainty in the RoboCup soccer such as noise in object movement and the sensing
information.

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This work was partially supported by Grant-in-Aid
for Scientific Research on Priority Areas: KAKENHI(18049065) and KAKENHI (1770241).

References[1] RoboCup official page, http://www.robocup.org/.
[2] T. Nakashima, M. Takatani, M. Udo, H. Ishibuchi,
and M. Nii, “Performance Evaluation of an Evolutionary Method for RoboCup Soccer Strategies,”
RoboCup 2005: Robot Soccer World Cup IX, inpress.
[3] T. Bäck, Evolutionary Algorithms in Theory andPractice, Oxford University Press, New York,
1996.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Control of Autonomous Mobile Robot throughEnvironment Recognition with Photoelectric Sensor
Takahiro Kurita, Norihiro Abe,
Kazuaki Tanaka
Kyushu Institute of Technology
680-4 Kawazu, Iizuka-shi,
Fukuoka, 820-8502, JAPANE-mail:kurita@sein.mse.kyutech.ac.jp
Abstract
With the advancement of computing technology,
robot industry has grown substantially in the past few
years. However, most of the current robots are still
simply following through the pre-defined procedures andmoving in a well-defined workspace. The control of anautonomous mobile robot remains challenging. A truly
autonomous mobile robot will definitely benefit our life
in many areas, including but not limited to nursing careservices and office guide.
How to get to know the current location is themost difficult issue that an autonomous mobile robot has
to deal with. Without knowing its current location, it is
impossible for the mobile robot to move towards its
destination. Let us think how we identify our location
when we are put in an unfamiliar place. The mostcommon things that we do will be to collect thegeographical features and then match what we havecollected with the information on a map. The method is
considered applicable to an autonomous mobile robot as
well. This paper discusses how to collect thegeographical features with a photoelectric sensor andhow to identify the current location by comparing thesensor information and the map information.

Shoujie He
Eastman Kodak Company
Plano, Texas, USA
Hirokazu Taki
Wakayama University
930 Sakaedani, Wakayama-shi, Wakayama,680-8510, JAPANit sounds unavoidable to stop in order to avoid obstacles,
sensor information integration and fast-speed route
planning will be used so that stops are not needed
anymore.
With the control program experimented in thisstudy, a mobile robot could move autonomously to thedestination specified by a person.

2

System Organization

2.1

Communication subsystem

Key Words
Autonomous Mobile Robot,
Recognition, Photoelectric Sensor,

1

Environment

Introduction

Mobile robots in use are still moving within awell-defined static environment. This indicates that the
research and development of autonomous robots still
have a long way to go. Some day in the future, if amobile robot could autonomously move in a dynamicallychanging environment, it would definitely have a lot ofapplications in many areas of our daily life.
At the current stage, however, our very first goal is
to come up with a practical approach to the control of amobile robot so that it will move autonomously towards
its destination within a static environment. Our second
goal is basically along the same line, which is to keep themobile robot moving smoothly with no stops. Although©ISAROB 2007

376

Fig.1. Communication configuration
A laptop computer is put on the mobile robot forthe communication with a motor and a photoelectric
sensor. At the beginning, the laptop computer sends anauthentication code over to the photoelectric sensor.
Once the code has been authenticated by thephotoelectric sensor, will the laptop computer start
receiving the distance data from the photoelectric sensor.
The laptop computer recognizes the environment of its
current location through the analysis of the distance data.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Based on the result of environment recognition, thelaptop issues a voltage command to the servo packthrough the DA board. The servo pack will apply asteady voltage to motors, which control the wheels forthe movement.

2.2

Driving subsystem

The mobile robot in this study can move forward
as well as backward, turn right as well as left, and caneven change its facing directions through rotation
without turning right or left. Changing the facing
directions of a mobile robot could of course be achievedby turning right or left. But instead of turning, using thefreedom of rotation could keep the photoelectric sensor
staying at the same location and the same direction. Thisis because the photoelectric sensor is mounted between
the front wheels, which are the driving wheels. The
benefit is that the environment recognition results
obtained before the rotation stay valid even after therotation.

3

Control program

The environment recognition is accomplished byapplying the minimum mean square algorithm to the
distance data. As a result, a set of feature points are
extracted. The feature points here are the uneven points
on the walls. Figure 2 shows the flow chart of the
program.

distance data every 1.8 degrees. If all the samples are
directly used for the feature point extraction, an
overwhelmingly large number of feature points will be
extracted. This is because the existing variance among
the samples leads to the extraction of uneven points froma flat wall. The minimum mean square algorithm is used
for the approximation of straight lines so that no featurepoint is extracted from a flat wall.

Fig.3. Minimum mean square method

3.2

Extraction of feature points

Environment recognition is solely based on theextraction of feature points, which makes the extractionof feature points extremely important. The feature points
here are the uneven points on the walls. Let us see thetriangle in Figure 4, where the vertexes, A, B, and C are
the points that three approximated straight lines intersect.
As soon as the length of all the three sides of the triangle
a, b, c is calculated, could the angle θ at B be
calculated with the formula (1).

a 2 + b2 − c2
θ = arccos(
)
2ab
point

Fig.2. Flow chart of the program

3.1

Minimum mean square method
The photoelectric sensor generates 100 samples of©ISAROB 2007

377

(1)

If θ is smaller than a pre-determined threshold,
B will be extracted as a feature point.

Fig.4. Extraction of feature points
Although the minimum mean square algorithm has
already been applied, it is still possible to extract extra
feature points. One solution to this problem is to
dynamically reduce the threshold from 150 to 140

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

degrees in the process of selecting the feature points.
Another criterion is to choose one when two or morefeature points are nearby. Since the two criteria areapplied to the feature pointed extracted, the removed
extra feature points will not be re-selected.

3.3

Environment recognition

The length of the wall is used as a reference forthe environment recognition. Since the feature points
correspond to the joint points of different walls, thelength of a wall could be measured by finding the
distance between the feature points. The current location
is identified by comparing the length of walls with theinformation given in a map.

3.4

When the robot moves, the control program
compares the distance data sampled at 35 degrees on theright side with those at 145 degrees on the left side thistime. When there is an obstacle that doesn't exist in thegiven map on the road, the robot may collide with it.
How to avoid collision against an obstacle is shown here.
To detect unknown obstacle, it examines distancedata captured with the photoelectric sensor from 35 to
145 degrees. When an obstacle is found on the road, thedistance between the obstacle and the right wall iscompared with that between the obstacle and the left
wall. Then the robot changes the moving direction
toward the center of the wider space and successfully
passes over the obstacle. After the robot recognizes thatit has passed over the obstacle, it switches back themoving direction to the previous one.

Autonomous movement

When the robot moves, the control program
compares the distance data sampled at 35 degrees on theright side with the distance data at 145 degrees on theleft side. In order to minimize the potential collision, therobot is kept moving along the centerline of the road. To
keep it on the centerline, the wheels with shorter distancedata will be controlled to move forward while the wheelson the opposite side move backward. This adjustment
keeps going until the robot moves to the point on thecenterline. When the distance data sampled at 35 and145 degrees are equal, wheels on both sides will becontrolled to move forward. The data sampling and thecontrol of wheel rotation are constantly happening
during the entire movement until the robot arrives at thedestination.

Fig.6. Evading obstacle

4

Experiment

The autonomous mobile robot has been
experimented in a narrow indoor environment. Simply
with the distance data collected by the photoelectric
sensor, the mobile robot is moving autonomously.

4.1

Test cases

The following two experiments are conducted:
Experiment 1: the centerline of the road is specified
as the destination.
• Experiment 2: the destination is specified to be on
the side of a road.
In Experiment 2, when the robot arrives at a point withthe same y coordinates as the destination, it rotates by
90 degrees and then move forward to the specified
destination. The moving speed of the robot is set tobe 0.5km / h .

•Fig.5. Move to the center

3.5

Experimental result

4.2.1

Experiment 1

Figure 7 shows the console of our control programin execution. It indicates that the robot has arrived at the

Obstacle avoidance©ISAROB 2007

4.2

378

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

specified destination. Point d is the destination. Pointe is the current location and c is the wall that was
identified through the environment recognition by pointa and b . The error of the x coordinate was -45mm
in the destination and the error of the y coordinate was-20mm. The total execution time was 21360msec.

Fig.7. Result of experiment 1

4.2.2

substantially. As a future solution, a counter board can beused to measure the cycle of wheels so that the rotation
could be well controlled to be close to 90 degrees.
The control program in the experiments is tocontrol the robot move along the centerline while doing
the environment recognition. It stops the movement only
when both the environment recognition is achieved aswell as the destination is reached. In another words, inthe case that the robot arrived at the destination with noenvironment recognition completed, the robot may keep
going and pass the destination. One solution could be tokeep track of the previous locations and match with thecurrent location. This way, the speed of robot could be
reduced as soon as it is identified that it getting close to
the destination. With the reduced speed, the robot will
have more distance data collected for the environment
recognition. As a result, the robot will stops at thedestination more frequently.

5

Experiment 2

Figure 8 shows the console of our control programin execution. It indicates that the robot has arrived at the
specified destination. Point e is the destination. Pointf is the current location and d is the wall that was
identified through the environment recognition by point
b and c . The error of the x coordinate was +10mm
in the destination and the error of the y coordinate was-90mm. The total execution time was 15000msec.

Conclusion

In this research the environment recognition wasachieved by processing the distance data obtained
through the photoelectric sensor. The control of anautonomous mobile robot through the environment
recognition has been experimented. The goal ofcontrolling the mobile robot so that it moves
autonomously to the destination in a static environment
was achieved. The second goal that the robot moves withno stops was achieved.
The approach in this study is going to be enhanced
to deal with the issues that an autonomous mobile robot
may encounter in a dynamically changing environment.
A dynamically changing environment could be anenvironment with people walking around or some othermoving objects. In a dynamically changing environment,
the robot needs to predict the movement of other objects
including people and try not to interfere with othermoving objects.

Acknowledgment
Fig.8. Result of experiment 2

4.3

We greatly appreciate the aid of Ministry ofInternal Affairs and Communications (MIC) and theGrant-in-Aid for Scientific Research.

Result analysis

The experimental results in Experiment 1 showed
us that the error of each coordinates could be 50 mm or
less, which indicates that the destination set at centerlineof a road could be easily reached with very highaccuracy. The experimental results in Experiment 2,
however, showed big errors. The reason is because therobot is controlled to move along the centerline of theroad all the way to the point with the same ycoordinate as the destination and then rotates by 90
degrees and move towards the destination. In the casethat 90 degrees rotation is not accurate enough, the error
occurred along the y axis will be increased©ISAROB 2007

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Reference document[1] Jean-Claude Latombe, “Robot Motion Planning”,
Kluwer Academic Publishers[2] Robin R. Murphy, “Introduction to Ai Robotics”,
MIT Press[3] Chrystopher L. Nehaniv, Kerstin Dautenhahn,“Imitation in Animals and Artifacts”, MIT Press[4] John M. Holland, “Designing Mobile Autonomous
Robots”, Elsevier

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Interactive System for Creating a 3-D Graphical Road Map
Kouhei Tou

Tohoru Irie

Joo Kooi Tan

Seiji Ishikawa

Department of Mechanicaland Control Engineering
Kyushu Institute ofTechnology
Sensuicho 1-1, Tobata
Kitakyushu 804-8550
JAPANResearch & Development
Department
GEO TechnicalLaboratory
Hakataekihigashi 3-1-26
Fukuoka 812-0013
JAPAN

Department of Mechanicaland Control Engineering
Kyushu Institute ofTechnology
Sensuicho 1-1, Tobata
Kitakyushu 804-8550
JAPAN

Department of Mechanicaland Control Engineering
Kyushu Institute ofTechnology
Sensuicho 1-1, Tobata
Kitakyushu 804-8550
JAPAN

Abstract
Recently, Information Technology has been introduced
to social systems for various purposes. The map that was
the paper medium has been changed into an electronic
form and digital maps have become familiar in our life
such as the map for car navigation. They are also
introduced in the Internet and mobile phones. Now, thedemand on a 3-D map has rapidly risen to the digital map
users. This is because a 3-D map excels in the visibility
and is intuitively understood when the location
information is passed on to a user three-dimensionally inthe digital map.
This paper proposes a novel technique for creating a 3-D
road map. It includes proposal of a vehicle that collects
ground-view information of a road environment and aninteractive graphic system for producing a 3-D road map
employing the collected information. The function of the
proposed technique is to make a 3-D map electronically byfitting measured data to a given 2-D digital map. In orderto evaluate the proposed technique, we performedexperiments at Paris (France) and Fukuoka (Japan), andproduced a highly realistic 3-D graphical map of the roadenvironment. This confirms the availability of the
proposed technique.
Keywords: 3-D maps, road maps, car navigation, GIS,
ITS, image processing

1. Introduction
Recently, Information Technology (IT) has been
introduced to social systems for various purposes. The
map provided by the paper medium has changed its style
to an electronic and digital form. It has been introduced to
car navigation. The Internet and mobile phones also
employ digital maps. In this way, the digital map has
become much familiar in our life.
A 3-D road map is the present concern of major maprelated companies, since it may be easily accepted topeople because of its realistic and therefore©ISAROB 2007

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understandable nature, particularly in car navigation.
Although lots of researches on creating 3-D road maps
have been done so far using aerial or satellite photographresources, methods of collecting geographic information
without using such aerial view resources has a strong
demand from map-related industries, since certain kinds ofgeographic information (roads in tunnels, roads hidden bystreet trees, building texture, etc.) are difficult or even
impossible to collect their information using theseresources. Particularly, in the field of car navigation,
frequent map update is indispensable for providing adriver with most recent and exact geographic information
having a driver’s view.
In this circumstance, some studies have proposed
methods of measuring 3-D shape of buildings and objects
from a driver’s view. There are established techniquessuch as the stereo vision [1] and the factorization [2] thatuse video images or still images measured by a camera. A
method of using aerial photos [3] also exists as a passive
technique. The literature [4] employs a laser range finder
as a sensor. These techniques can recover actual 3-D
shape, but they need much complicated post-processing tohandle the obtained large amount of data and their noise
reduction.
If these kinds of measurement information are arrangedon the 2-D map with latitude and longitude information
obtained from the Global Positioning System (GPS)receiver, it causes some difficulty in its accuracy becauseof the noise contaminated in the GPS signal.
In this paper, we propose a novel technique for creating
3-D graphical road maps that contain road environments.
The technique includes a vehicle that collects roadenvironmental information and an interactive graphic
system for producing a 3-D road map from theinformation. The interactive system assumes theemployment of a 2-D electronic map and aerial
photographs as reference and creates a 3-D road map fromthe video images the vehicle provides. This procedure isdone on the computer display by man-machinecollaborative work. Visual operation of a user on theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

display supported by some automated procedures bycomputer is much effective. This system will certainly
open up a new application field in the development ofman-machine systems.
The paper is organized as follows. Section 2 outlines amethod of data collection. Section 3 outlines a mapping
method of measured data to a 2-D map. Section 4 explains
how to make a 3-D map from the measured data. Section 5shows experimental results on the data collection and the
produced 3-D maps of the streets in Fukuoka (Japan) andParis (France) using the proposed method. Discussion andconclusions are given in Section 6.

2. Data Collection
It is assumed in the proposed technique that the
information obtained from the real world contains
horizontal surfaces and vertical surfaces. We obtain theinformation on road surfaces as horizontal surfaces andthe information on the walls of buildings, sign boards, etc.,
as vertical surfaces. It is also assumed that a 2-D electronicroad map is available. In the technique, the measurement
position on the 2-D map is specified by using horizontal
geographic information, and after that, 3-D information ismapped by using vertical geographic information.The
outline of the entire procedure on the proposed system is
described in three parts (See Fig. 1).
The principal data obtained by the proposed vehicle
measurement system is as follows;
A) Road image data for detecting the car position andcollecting road surface information.
B) Scene image data for collecting geographic objects
information such as buildings, traffic signals, etc.
C) Distance data for detecting the car position.
Collection of road environmental data
Two dimensional processing1) Allocation of road images onto a 2-D map
2) Placement of road surface data on the 2-D map.
Three dimensional processing
1) Placement of 3-D buildings on the 2-D map
2) Allocation of 3-D objects along the road in theFig. 1 Overview of the procedure.
pulse
generator

In this data collection, synchronization between the car
position/time and the sensor data is important. Therefore,
we propose the measurement system as shown in Fig. 2.
In Fig. 2, multiple digital video cameras are employed toobtain the data A and data B above. The images of thecameras are recorded on DV tapes, and time code isrecorded on the personal computer to synchronize thepulse generator. The pulse generator that is connected tothe computer through the pulse counter makes
equidistance information (The pulse generator is set up at
the rear wheel of the measurement vehicle). GPS isconnected to the computer to obtain the rough position of
the car. All the data obtained from these sensors (DVcameras, a GPS receiver, and the computer time)synchronize by the signal of the pulse generator. Herewith,
the DV time code (i.e., DV images) and the GPS data (i.e.,rough position of the car) are obtained at an equidistanceinterval.

3. Two-dimensional Processing
A method of producing initial state of the 3-D map frommeasurement data by two dimensional processing is
described in this section.

3.1 Data improvement & allocation of road
images onto a 2-D map
3.1.1 Geometrical conversion of road images
In order to obtain the road surface images from theoriginal road images in the video taken by the camera in
the vehicle, geometrical conversion is applied to theoriginal image. Here, we assume that the road has a flat
surface, and the gradient value (roll) of the car isvanishingly small.
In the first place, the point marks are arranged in frontof the car in the form of the lattice and having equalintervals to obtain geometrical conversion parameters, and
it is recorded with a DV camera (See Fig. 3). In the second
place, coordinates of each quadrangle that consists of four
point marks in the image are obtained and geometricalconversion parameter (pseudo affine transformation
parameter) is calculated. The pseudo affine transformation
is given as follows;(1a)x ′ = axy + bx + cy + d ,.
(1b)′y = exy + fx + gy + h

GPS
DV Camera
ReceiverRS-232C

Pulse Counter
USB

IEEE1394…

PC

Fig. 2 Configuration of the devices employed in themeasurement vehicle.

©ISAROB 2007

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The result of having applied Eq.(1) to the quadrangle
image in Fig. 3(a) is given in (b). It is the image of thelattice observed from above and is called an ortho-image.
Referring to the time code, the original road image in thevideo is separated horizontally to make rectangle patcheson the computer display, to each of which Eq.(1) isapplied and the rectangle patch is transformed into an

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ortho-image. The separation of the road is done by a user,
whereas the transformation to the ortho-image isperformed by computer. In this way, the ground-view road
images obtained from the frontal camera in the vehicle
measurement system is converted into corresponding
ortho-images which are then placed on the 2-D road map.

3.1.2 Extraction of crossroads
In this technique, crossroads are considered to be acheckpoint on the 2-D map. Checkpoints are basic pointsof measurement data at the position on the 2-D map.
In this section, the technique is explained for extracting
the candidate of the checkpoint by recognizing thecrosswalk from the ortho-image.
Extraction of white line edges
To extract white line edges of a crosswalk, some image
processing algorithms are combined. The following four
processed images are examined whether or not there are
frequent white areas on the images (See Fig. 4).
1) Emphasis of Contrast
2) Thresholding - Emphasis of Contrast
3) Emphasis of Contrast - Minimizing Average Error Median Filter (5 times)
4) Thresholding - Emphasis of Contrast - Minimizing
Average Error - Median Filter (5 times)

curve along the road. The road patches are then rearrangedon the NURBS curve automatically to get their exact
locations. This map is the initial state of the 3-D map
produced and is called an initial 3-D map.

3.2 Placement of road surface data on the 2-D
map
Road features are obtained from road surface images.
We input stop lines, lane lines, traffic lights, crosswalks,etc., to the initial 3-D map by referring to the road surface
images on the 2-D map and aerial photo.

4. Three-dimensional Processing
A method of producing a 3-D map employing theobtained video data and the initial 3-D map is described inthis section. Here, the 3-D map does not contain exact 3-D
shapes of real buildings, trees, or others, but it contains
their pseudo shapes registered in the system.

4.1 Placement of 3-D buildings on the 2-D map
A 3-D object is displayed by 3-D volume data that is
made from building 2-D frames of the object (See Fig. 6).
The wall texture obtained as vertical geographic
information is put on the building.

The Sobel filter in the horizontal direction is applied tothe above four images and vertical edges are extracted in
the images.
Recognition of crosswalk
A crosswalk is judged by the number of intersections of
the horizontal straight line that passes the center of theimage and the extracted vertical edge. It is recognized as acrosswalk if the number of the intersection is larger than athreshold on at least one of the above four preprocessed
images.

(a)(b)
Fig. 3 Image of the lattice in front of a vehicle and its
ortho-image.

3.1.3 Allocation of road images onto a 2-D map
Road surface images (See Fig. 5, for example) can bemade, using Eq.(1), from the ground-view images
obtained from the frontal downward camera equipped in
the vehicle and distance data. A road interested isseparated into successive rectangle patches as shown in
Fig. 5. They are transformed ortho-images. The patches
are roughly arranged onto the corresponding road on the
2-D map automatically according to the GPS information.
Since the GPS information is not very exact, crossroads at
the junctions along the road are detected by the techniquestated in 3.1.2. These crossroads specify check points on
the 2-D map. Two check points between the adjacentcrossroads are combined on the 2-D map by computer by
fitting a NURBS (Non-Uniformed Rational B-Spline) [5]©ISAROB 2007

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Fig. 4 Ortho-Image (left), and the extracted white area
image (right)

Fig. 5 Road surface image segmented into rectangle
parts.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4.2 Allocating traffic objects and plants along theroad on the map
There are some typical objects along a road such as
street-lights, trees, traffic signals, traffic signs, etc. The
developed system stores their 3-D models in a database.
Once they are observed along the road in the video image,their models are chosen by a user and placed at
appropriate positions in the 3-D map.

5. Experimental Results
Experiments were performed to show the performanceof the proposed technique.

5.1 Experimental environment
Five cameras were installed in the vehicle. Two of them
(Cam 1, Cam 2) were used to measure the road surface
data, and other two (Cam4, Cam5) were used to measure
the wall of buildings, etc. The last one was used tomeasure the signs, signals, etc. Also, to obtain distancedata, the magnetic pulse generator was set in the vehicle.
Additionally, GPS and note PC were set. The road images
were taken in Paris, France, and in Fukuoka, Japan, withthese devices. We rented a vehicle in the locale, and all thedevices were set up in the vehicle.

References[1] M. Okutomi and T. Kanade, “A multiple-baseline s
tereo”, IEEE Trans. on Pattern Analysis and MachineIntelligence, vol.15, no.4, 1993.
[2] C. Tomasi and T. Kanade, “Shape and motion fromimage streams under orthography: A factorization
method”, Int. J. Computer Vision, vol.9, no.2, pp.137154, 1992.
[3] K. Azmi, S. Ono, M. Kagesawa, and K. Ikeuchi,“Automatic reconstruction of large-scale virtual environment for intelligent transportation systems s
imulation”, Proc. The 11th ITS World Conference,
October, 2004.
[4] H. Zhao and R. Shibasaki, “Reconstructing atextured CAD model of an urban environment using
v ehicle- b orne, l aser r ange s canners and l ine c
ameras”, Machine Vision and Applications, Vol. 14,
No. 1, pp. 35-41, 2003.
[5] T. Hayashi, K. Kato: 3D-CG Programming Using
Open GL, Corona Publishing, 2003. (in Japanese)

5.2 Results
Figure 7(a) shows a photo of a road environment in
Paris and (b) is the created 3-D map. Satisfactory
correspondence is obtained between the 3-D map and theactual road environment. It may provide enoughinformation for driving this route.
Similar result was obtained with Fukuoka.

Fig. 6 Buildings on the 3-D map produced
automatically

6. Discussion and Conclusions
In this research, we proposed a simple and useful
method of producing a 3-D geographical map by mapping
onto a given 2-D map the data measured in the street using
video cameras and several sensors on the vehicle. In theexperiment, it was confirmed that allocating themeasurement road surface data that are difficult or even
impossible to collect using the aerial or satellite
photograph resources in the 2-D map was possible.
Moreover, we were able to produce a highly realistic three-dimensional map with the acquired data. The work
efficiency has been raised 30% than before with respect tothe production time.
In the future, to raise the work efficiency of the postprocessing of the measurement data, we plan to introduce
image recognition techniques more in the procedure and a
new measurement method, so that we can develop an easyand simple 3-D map production application.

©ISAROB 2007

383

(a)

(b)
Fig. 7 Photo of a road in Paris and its 3-D Map

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ITERATIVE DENOISING USING NON LINEAR FILTER IN WAVELET
DOMAINM. Hariharan1, Paulraj M P1, Sazali Yaacob1, M. Rizon1, M. Sugisaka2 and D. Hazry2.

1

School of Mechatronics Engineering, Northern Malaysia University College of Engineering (KUKUM), Malaysia
Department of Electrical and Electronic Engineering, Oita University, 700 Oaza Dannoharu, Oita 870-1192, Japan2

good-quality image can be obtained forvarious applications. This paper investigates
the problem of image denoising when theimage is corrupted by additive white
Gaussian noise. Traditionally, linear
processing methods such as wiener filter,mean and median filters are employed forthis purpose. However, mean filters tend toblur the edges in the images and medianfilters are ineffective in dealing with nonimpulse noise components. To preserve theedges, non-linear methods have become themainstream approaches in the field of image
denoising.

Abstract

Digital images are prone to different
noise during acquisition and transmission.
Noise reduction is a traditional problem
in image processing. This paper explores
a novel neural network-based non-linear
filter in wavelet transform domain forimage denoising. In this method, using
wavelet transformation a noisy image isdecomposed into four subbands. A simple
procedure is suggested to extract thetraining patterns from these four
subbands. Using a layered neural networkarchitecture denoising is performed. The
denoised image is thereafter obtained
through the inverse transform on thenoise-removed
waveletcoefficients.
Simulation results demonstrate that thismethod is very efficient in removing thenoise. Compared with other methods
performed in wavelet domain, it requires
no a priori knowledge about the noise andneeds only one level of signal
decomposition to obtain very gooddenoising results.
Keywords: Wavelet transform, Neural Network
Filter, White Gaussian noise,Iimage denoising
1. INTRODUCTION

An image is degraded by noise due tovarious factors during its acquisition andtransmission phases. Image denoising is
aimed to remove or reduce the noise so that a©ISAROB 2007

384

Wavelet transform provides excellent
properties for image processing. Non-linear
denoising methods performed in the wavelet
transform domain have received wide
research attention. One of the standard non
linear methods is wavelet thresholding. In
this method a threshold value is chosen foreach subband of the image. The better result
depends on the choice of the thresholding
parameters. It is difficult to find the optimum
threshold value. In wavelet thresholding, thelocal space-scale information is notconsidered adaptively. In order to overcome
these drawbacks here a Neural network
based Non linear method is developed forimage denoising.
A layered neural network (LNN) is
properly designed and trained to explore thelearning capability of the neural network tolearn the correlation among the noisy

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

wavelet coefficients, thus removing noisefrom them. The LNN filter method generates
better results than the linear processing
methods. Traditional wavelet thresholding
methods which usually require three or morelevels of wavelet decomposition and need
the accurate estimate of the noise to obtain
good denoising results. But this methodneeds only one level of wavelet
decomposition and can adapt itself to thevarious noise environments by learning.
2.DENOISING IN IMAGES
Applied scientists and engineers who
work with data obtained from the real world
know that signals do not exist without noise.
There are several fact that noise into an
image depending on how image is created.
For example:
If the image is scanned from a
photographic made on film, the film
grain is source of noise. Noise canalso be the result of damage to thefilm, or be introduced by the scanner
itself.
If the image is acquired directly in adigital format, the mechanism forgathering the data (such as a CCD
detector) can introduce noise.
Electronic transmission of image data
can introduce noise.
There are different types of noises likegaussian noise, salt and pepper noise andspeckle noise.
2.1.IMAGE DENOISING
Denoising is the process ofremoving/reducing noise from the noisy
image and retaining the original image. The
general additive noise reduction problem canbe formulated as follows[1]:y=x+vwhere, y is the corrupted image.
x is the original uncorrupted imagev is the additive noise©ISAROB 2007

385

The objective of noise reduction is to reduceΛ

the noise in y and to make the estimate x
from y which is as close to x as possible.
Here it is assumed that the noise is Gaussian
zero mean and variance σ2 ,ie., N (0,σ2).
3.LAYERED NEURAL NETWORK FOR
IMAGE DENOISING
An artificial neural network (ANN or
NN for short) is an artificial intelligenceclosely modeled after a human brain[5].
Such a neural network is composed ofcomputer-programming objects called nodes.
These nodes closely correspond in both form
and function to their organic counterparts,
neurons.
Individually,
nodes
are
programmed to perform asimplemathematical function, or to process a small
portion of data. A node has othercomponents, called weights, which are an
integral part of the neural network. Weights
are variables applied to the data that eachnode outputs. By adjusting a weight on anode, the data output is changed, and the
behavior of the neural network can be alteredand controlled. By careful adjustment ofweights, the network can learn. Networks
learn their initial behavior by being exposedto training data. The network processes thedata, and a controlling algorithm adjusts eachweight to arrive at the correct or final
answer(s) to the data. These algorithms orprocedures are called learning algorithms.
Neural networks are often used forimage denoising and compression. Their
adaptability and learning capabilities makethem excellent choices for removing noisy
coefficients. Image processing using neuralnetworks is a very broad field, but one of thecommon use for an NN is image denoising.
This neural network based non linear filter
removes the noise effectively than thestandard filters. There are different types
algorithms to train the network. Here weThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

input layer and n-th neuron in the hidden
layer and is initialized randomly between -1
to 1. wn is the weight connecting the n-th
neuron in the hidden layer to the neuron in
the output layer and is initialized as
randomly between -1 to 1.

used the Back propagation Neural Network
for image denoising.
In image denoising, filtering is acommon technique. In this paper, neuralnetwork is used as a non linear filter. Their
adaptability and learning capabilities makethem excellent choices for removing noisy
coefficients.
The LNN filter is a three-layer neuralnetwork with inputs derived from an NxNneighborhood of the transformed image andappropriately selected neuron activation
functions. As shown in Figure(1), thenetwork takes YP and ∆Yk as the inputs,
where YP is the wavelet transform coefficientunder consideration, which is the center of a
N x N processing window, and ∆Yk = Yk YP is the difference value between YP and
the coefficient
Yk (k=0,1,2………N2 -1,k≠p ) of the other points in the N x N
window, Figure(2) shows an example of aprocessing window with a size of a 5 x 5pixels. In this example, Y12 is the center ofthe window, and ∆Yk = Yk – Y12(k=0,1,……..24, k≠ 12).

Y2

Y3

Y4

Y5

Y6

Y7

Y8

Y9

Y10

Y11

Y12

Y13

Y14

Y15

Y16

Y17

Y18

Y19

Y20

Y21

Y22

Y24

Y25

3.1 TRAINING THE LAYEREDNEURAL NETWORK

Wn

∆Yp
1
Y
p

Y1

Fig.2 5 x 5 processing window

Vmn

∆Y

Y0

Λ

Xp

In this paper, neural network is
trained using back propagation algorithm. In
this algorithm, first error is calculated
between the noise free coefficients and theoutput of the neural network. Error is backpropagated to output and hidden layer.
According to the error weights are updated.
This algorithm minimizes the output error by
updating the weights based on the gradientsof the output error. The proposed algorithm
is as follows: The input 25 elements arenormalized using the formula as given:2( y − y min )
(1)
NV =
−1
y max − y min∆Yp+1

Where ymin is minimum value among the 25
values. ymax is the maximum value amongthe 25 values. NV is the normalized value
between -1 to 1. The net input to the hidden
layer is calculated as[7]:∆YN2-1Yp

N 2 +1

net hn = ∑ i m v mn

Figure.1 Structure of the neural network

m =0

In this figure vmn is the weight of theconnection between the m-th neuron in the©ISAROB 2007

386

(2)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The output of the hidden layer is calculated
as:z n = f h ( net hn )(3)where im is the m-th input of the input layer
and
takes
its
from
set
(∆y0,
∆y1,………∆y24,yP,1),
and activation
function of the hidden layer is selected as:
2− 1 λ >01 + e − λxIn this work, the λ value is taken as 1.
f h ( x) =(4)

The net input to the output layer is calculatedas[7]N 2 +1

net o = ∑ z n wn

(5)

n =0

The output of the output layer can beformulated as:Λ

X p = f 0 ( net o )(6)ΛWhere X p is the output of the neuralnetwork at pixel under consideration and theactivation function of the neuron in the
output layer is of the following form:f o ( x) =2
−1
1 + e − λx

λ >0

(7)

The value of the λ here is 1.
The update equations of the weights andfunction coefficients are as follows[7] :
k +1
k
vmn
= vmn
−α

k
= v mn−α∂E∂vmn∂E ∂o ∂net o ∂z n ∂net hnk∂o ∂neto ∂z n ∂net hn ∂vmnk
= v mn+ α ( d − o ) f o' ( net o ) w n f h' ( net hn )i m

©ISAROB 2007

(8)

387

k

w nk +1 = w n − β∂Ek=+ β ( d − o ) f o' ( net o ) z n∂w n w n(9)

In the above equations, E = (di – oi)2 /2 is thesquare error, di is the Noise free coefficients,
oi is the output of the neural network, and α,
β are the learning factors. Here α, β are
initialized as 0.8 and 0.6. Weights are
updated based on error accordingly. Finallyneural network will give the noise removed
coefficients. They are value between -1 to 1.
It should be denormalized to obtain theoriginal value.
The equation for
denormalization is as follows:( NV +1.0) * ( xmax − xmin )+ xmin (10)2.0Where DNV is the denormalized value. NV
is the noise free coefficients from the neuralnetwork.The LNN filter explores the strong
correlation between the pixel underconsideration and its neighbors and utilizes
the mapping function of the layered network
to remove the white Gaussian noise.
DNV =3.2. PROCEDURAL STEPS INVOLVEDIN LNN FILTER METHOD
The steps involved in this method are as
follows[6] :Step 1: A noisy image is decomposed into
four subbands using a wavelet
transform. Here Daubechies wavelets
family is applied.
Step 2: For each subband, a layered neuralnetwork is properly designed.
Step 3: Each of the LNNs is trained using
one of the four subbands of thedecomposed image. All the four LNN
have same structure.
Step 4: The inputs to the neural network arederived from an NxN neighborhoodof the transformed image. Here wehave taken 5x5 window. It consists ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 200725 elements. They are given as inputs
to the neural network.
Beforeapplying to the network the inputs arenormalized.
Step5: Back propagation is used to train the
network. The activation functions
used are bipolar activation for bothhidden and output layer. Weights are
updated according to the error
criterion. This algorithm minimizes
the error by adjusting weights
accordingly.
Step 6: After the training process, the fourLNNs
areappliedtothecorresponding subband of the
wavelet transformed noisy image.
Step 7: The outputs of the networks arenormalized
noise-removed
coefficients. Hence it is denormalizedusing original image to obtain
original value of the noise removed
wavelet coefficients.
Step 8: The denoised image is obtained by
performing an inverse wavelet
transform on the noise free wavelet
coefficients.

M

N∑∑ Original image2

i =1 j =1

PSNR = 10 * log 10 M

N∑∑ (Original − noise )i =1 j =12......(11)Where NxN is the size of the image.
Table 1 - shows the PSNR values of the
Noisy image of cameraman image and
PSNR value obtained from proposed method
and standard denoising methods. It showsthat the PSNRs of the images are improvedat all of the noise levels. The table also
shows that the proposed method outperforms
all of the standard methods.
The Figure 3 shows that output
images obtained for cameraman image whennoise level is 20 dB.
Figure 4 shows that output images
obtained for parrots image when the noiselevel is 25 dB.
Simulations demonstrate that the
proposed method can remove the noise
efficiently, and improve the visual quality ofthe degraded image.

4. SIMULATION AND RESULTS

5. CONCLUSION AND FUTURE WORKThis algorithm is implemented using
Matlab6.5. The LNN filter was implemented
and the computer simulation results arepresented. We used cameraman and parrots
image of size 256 x 256 are used as the test
image. Noises of different decibels are added
and the denoised images are produced. The
Daubechies wavelet family(db2) is used andone level 2D DWT of noisy image is taken.
The noisy images of above are used
for training the neural network.PSNR(in dB)
is used as performance measure forcomparing the quality of denoising. The
PSNR is calculated using the formula

In this paper, a neural network based
non linear filter in wavelet domain isconstructed for the removal of noises indigital images. In this method LNN learn thecorrelation of the wavelet coefficients andgenerate the noise–removed values from
their
noisy
versions.
Simulation
demonstrates that it can efficiently remove
the noise, and improve the visual quality ofthe degraded image. The LNN filter method
requires no priori knowledge about the noise
and needs only one level of signal
decomposition to obtain very good denoising
result. In terms of PSNR, this method
outperforms the standard denoising methods.
This work can be extended by using©ISAROB 2007

388

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
ORIGINAL IMAGENOISY IMAGELNN FILTER METHOD

functional link neural network to reduce thecomputation complexity and time.
REFERENCES:[1]. Xiao-Ping Zhang, “Thresholding Neural
Network for Adaptive noise Reduction”,
IEEE, 2001.
[2]. R.C. Gonzalez and R.E Woods, “Digital
Image Processing” , Addison Wesleyreading, MA, 1992.
[3].Raghuveer M. Rao, and Ajit S.
Bopardikar, “ Wavelet Transforms”,
Pearson Education”, 2004.
[4]. K.P. Soman and K.I Ramachandran,“Insight into Wavelets”, Prentice-hall
India, 2004.
[5] Dr. S.N. Sivanandam, and Dr. Paulraj,“Introduction to Neural networks” ,Vikas Publications, 2003.
[6].S. Zhang and E. Salari, “Image
Denoising using a Neural Network based
Nonlinear filter in Wavelet domain”,
ICASSP 2005.
[7]. S. Zhang and E. Salari, “A Neural
Network
based Nonlinear filter for
Image Enhancement”, Wiley Periodicals,
2002.

SOFT THRESHOLDING HARD THRESHOLDING WIENER FILTER

Figure 3.Output images obtained for cameraman image when the noisy level is 20 dB
ORIGINAL IMAGENOISY IMAGELNN FILTER METHODSOFT THRESHOLDING

HARD THRESHOLDINGWIENER FILTER

Figure 4 Output images obtained for parrot image when the noisy level is 25 dB

TABLE 1 PSNR VALUES OF THE NOISY IMAGE OF CAMERAMAN AND PARROTS BY THE LNN
FILTER METHOD AND STANDARD METHODS©ISAROB 2007

INPUT IMAGE
AT VARIOUS
NOISE
LEVELS(dB)

NOISY
IMAGE
(PSNR)

Cameramanσ = 10NOISE REDUCED PSNR(dB)

LNN FILTERMETHODSOFT
THRESHOLDINGHARD
THRESHOLDINGWIENER FILTER

28.0358

49.2130

27.0826

28.218829.0423σ = 1524.542149.1069

25.6131

26.191128.1453σ = 2022.0035

49.0588

24.472424.7728

27.1147

σ = 2520.1172

48.9956

23.4326

23.5968

26.2004

Parrots
σ = 1028.1397

49.9619

29.210330.277132.3015

σ = 1524.622050.841227.271027.762830.7722σ = 2022.161850.7787

25.874226.1187

29.4985

σ = 2520.180746.6144

24.5230

24.629328.2893389

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Computer Aided Diagnosis System for Pulmonary Nodules
Using Hierarchical Feature Extraction
K. Takei
Grad. Sch. of Eng.
Tohoku Univ.
Sendai, Aoba
980-8579

N. Homma, T. Ishibashi
Faculty of Med.
Tohoku Univ.
Sendai, Seiryo-machi
980-8575

M. SakaiGrad. Sch. of Info. Sci.
Tohoku Univ.
Sendai, Kawauchi
980-8576

Abstract
In this paper, we propose a new diagnosis methodof pulmonary nodules in CT images to reduce false
positive rate (FP) for a high true positive rate (TP)conditions. An essential core of the method is in its
hierarchical feature extraction. In the 1st stage, novel
orientation features of nodules in a small region of interest (ROI) are extracted in addition to several conventional features, while a more structural feature ofa surrounding area of the ROI is extracted in the 2ndstage. Without the orientation features, when TP was
90%, FP was about 65% and 55% in the 1st and 2ndstage, respectively. On the other hand, using the orientation features, FP was about 15% and only 5% in
the 1st and 2nd stages, respectively. These improvement of the discrimination rate clearly demonstrates
the eﬀectiveness of the proposed hierarchical methodon the nodules diagnosis.

1

Introduction

With the increasing of the mortality rate for lungcancer, computed tomography (CT) has been used for
detection of lung cancer at early stages [1]. However, using CT may exhaust for radiologists because
CT generates a large number of images (over 30 per
patient) and they must read all of them. Therefore, some computer-aided diagnosis (CAD) systems
have been developed. Okumura et al. proposed a NQuoit ﬁlter [2]. Lee et al. proposed a nodule detection system using a genetic algorithm [3]. However,
although these CAD systems can automatically detect pulmonary nodules with a high true positive rate(TP), the false positive rate (FP) is also high. This
is not appropriate for clinical use since such high FP
may disturb radiologist’s diagnosis process. To reduceFP, several methods have been proposed. Suzuki et al.
proposed a massive training artiﬁcial neural network(MTANN) for reduction of FP [4]. Nakamura et al.

©ISAROB 2007390

M. Yoshizawa
Info. Syn. Center
Tohoku Univ.
Sendai, Aoba
980-8579

K. AbeCol. of Eng.
Nihon Univ.
Koriyama, Tamura
963-8642

proposed a nodule recognition system using subspacemethod [5].
In this paper, we propose a new method to diagnose pulmonary nodules in CT images. Especially,
compared to the methods mentioned above, we aim
to further reduce FP for a high TP conditions by extracting novel orientation and structural features of
the nodules in a hierarchical manner.

2

Discrimination algorithm

The proposed method for discrimination between
shadows of nodules and non-nodules mainly consistsof 2 stages. In the ﬁrst stage, nodule candidates are
diﬀerentiated from the non-nodule ones using novel
orientation features of a small size image of region ofinterest (ROI). On the other hand, in the second stage,a structural feature of surrounding area of the ROI isused for further discriminating the ﬁnal nodule candidates from the nodule ones in the ﬁrst stage.

2.1

Discrimination using orientation features of shadows

Orientation information can be very important torecognize shapes of subjects, but as we know, there
is no CAD system using eﬀective orientation featuresof nodules. To extracted the orientation features, we
may use a gabor ﬁlter whose impulse response is deﬁned by a harmonic function multiplied by a Gaussian function. Indeed, due to its orientation selectivity,
the gabor ﬁlter has been applied to feature extraction
problems for various image recognition systems, suchas face recognition, ﬁngerprint recognition and so on[6] [7].
2.1.1

Feature extraction

In the ﬁrst stage, to extract features for nodules
and non-nodules recognition, we ﬁrstly binarized theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where D(X, µj ) denotes the Euclidean distance of
X and µj .

0o 45o 90o 135o

3. Based on the classiﬁcation, update the cluster
centroids as㪝㫀㫃㫋㪼㫉㫀㫅㪾

Binarizing

1 ∑ (j)X
nj i=1 injµj =㪝㫀㫃㫋㪼㫉㫀㫅㪾

Binarizing

where nj , j = 1, 2, · · · , k, are the numbers of nod(j)ules or non-nodules in clusters j, and Xi are theith feature vectors in cluster j.

Figure 1: Examples of 4 orientaion features.

images in ROI. Then, we extracted M1 orientation features from the binarized image by using the gabor ﬁlter
given as( 02
) ()
x + γ 2 y 02
2πx0g(x, y, σ, λ, γ, θ) = exp −
cos
(1)
2σ 2
λ

4. If any clusters centroid is changed, go to step 2.,otherwise stop the algorithm.
In determination of the number of clusters k, we employed the cluster validity analysis [8]. The cluster
separation measure ρ(k) is deﬁned aswhere θ is the angle of orientation, σ is the bandwidth,
γ is the aspect ratio, and λ is the wave length. x0 andy 0 are, respectively, given by
x0 = x cos θ + y sin θ,

y 0 = −x sin θ + y cos θ

Clustering

Using C1 dominant principal components of thefeature vectors X of training data, we made noduleand non-nodule clusters of the binarized images by Kmeans method [8]. The K-means method algorithm isimplemented as
1. Initialize vectors of cluster centroids µ1 , · · · , µk .
2. Classify each feature vector X to the cluster p̂
with the smallest distancep̂ = arg min D(X, µj )1≤j≤k©ISAROB 2007

(6)

1 ∑
(j)
ηj =
D(Xi , µj )nj i=1(7)ξij = D(µi , µj )

(8)

nj

and

(3)

Figure 1 shows examples of ﬁltered image of 4 orientaions. For the original binarized and each orientation
images, we calculated three features (mean, variance,and entropy of intensity). Consequently, by adding
3 × M1 features to the 3 original features, we got total
3 × (1 + M1 ) features of the binarized image. Then wedeﬁned a feature vector X of 3 × (1 + M1 ) features,
X = [x1 , x2 , · · · , x3(1+M1 ) ]T , of nodule or non-nodule
image.
2.1.2

)
(
k¡1
ηi + ηj1 ∑max
ρ(k) =
k − 1 i=1 i<j≤k
ξij
where

(2)

The orientation features are obtained from the convolution of the binarized image Iβ (x, y) of the original
image I(x, y) and g(x, y, σ, λ, γ, θ) given as
O(x, y) = Iβ (x, y) ∗ g(x, y, σ, λ, γ, θ)

(5)

(4)

391

ηj is the intra-cluster distance of cluster j, while ξij
is the inter-cluster distance of clusters i and j. The
optimal number of clusters k̂ is selected ask̂ = min ρ(k)
1≤k≤L

(9)

In other words, the K-means algorithm is ﬁrstly tested
for all candidates k = 1, 2, · · · , L, and after that kwhich gives the lowest value of ρ(k) is chosen.
However, since ρ(k) decreases as L increases, it is
not appropriate to determine the number of clusters
by the minimum value of ρ(k) simply. Therefore, inthis paper, we employed the local minimum [9]. In
other words, we determined L when ρ(L + 1) − ρ(L)is maximum, and chose k̂ which gives minimum valueof ρ(k), k = 1, 2, · · · , L.
2.1.3

Determination of candidate clusters

Using the dominant C1 principal components, wemade, respectively, P1 and Q1 clusters of nodule andnon-nodule images by the method in Section 2.1.2.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Then, we calculated Euclidean distances between test
image and all the clusters. Let us consider (P1 + Q1 )distances dA1
p1 , p1 = 1, 2, · · · , P1 , from P1 nodule1
clusters and dN
q1 , q1 = 1, 2, · · · , Q1 , from Q1 nonnodule ones. The discrimination in this ﬁrst stage
was conducted by comparing the minimum distancesdA1, p⁄1 ∈ p1 , from the nearest nodule cluster to thep∗11⁄
distances dN
q1∗ , q1 ∈ q1 , from the non-nodule one. That
1
is, if the ratio d1 = dA1/dN
p∗q1∗ is less than a threshold
1
α1 , then the test image can be a nodule candidate,otherwise a non-nodule one.

2.2

nodule(a) a small size of ROI

Discrimination using structural feature of shadowsIn the second stage, we further discriminated theﬁnal nodule candidates from the nodule candidates inthe ﬁrst stage by using a structural feature of surrounding area of the ROI which includes nodule or
non-nodule shadows. The reason why we pay attention to the surrounding feature is that there are differences between surrounding images of nodules andnon-nodules as shown in Fig. 2. That is, even there isno big diﬀerence between nodule and non-nodule images in the small ROI, signiﬁcant structural diﬀerences
between surrounding areas of them can be found: The
nodule tends to exist in isolation comparatively, while
the non-nodule tends to exist with other shadows (vessel etc.).
To extract such structural feature, we used the fractal dimension of the surrounding area. The fractal
dimension is a statistical quantity that gives an indication how completely a fractal appears to ﬁll space.
In fact, usefulness of the fractal dimension to quantify
image structures has widely been reported in CAD systems [10][11]. The brief explanation of the algorithm
in this second stage is as follow.
We binarized the surrounding images of shadowsand calculated the three features of mean, varianceand entropy by the same method as in the ﬁrst stage.
We also calculated the new feature of the fractal dimension using box counting algorithm [10][11]. For
the original and M2 orientation, i.e, (1 + M2 ) images,
three and one new (3 + 1 = 4) features are calculated. Consequently, we got total 4 × (1 + M2 ) features of the surrounding binarized image. Using dominant C2 principal components of the 4 × (1 + M2 )features of nodule and non-nodule training images, wemade, respectively, P2 nodule and Q2 non-nodule clusters. Let us denote the minimum distance dA2, p⁄2 ∈p∗2
p2 = 1, 2, · · · , P2 , from the nearest nodule cluster and2⁄
dN
q2∗ , q2 ∈ q2 = 1, 2, · · · , Q2 , from the nearest non-

©ISAROB 2007

392

non-nodule(b) surrounding area of the ROI
Figure 2: Examples of the surrounding images.

2
nodule cluster. If the ratio d2 = dA2/dN
p∗q2∗ is less than
2
a threshold α2 , then the test image can be a nodule,
otherwise a non-nodule.

3

Experimental results

We used 97 nodule data (59 training image and 38
test image) and 174 non-nodule data (104 training image and 70 test image) from the database [12]. Image’s
size was 33 × 33 and 99 × 99 pixels in the ﬁrst andsecond stages, respectively. Gabor ﬁlter’s parametersσ, λ, and γ were 1.5, 2.6 and 1, respectively. The numbers of clusters Pi and Qi , i ∈ {1, 2}, orientations Mi ,principal components Ci are shown in Table 1. The
Mi was determined empirically, and the Ci was determined
the minimum value that satisﬁes the con∑Casiuj > 0.95 , where uj is the contribution
dition j=1
ratio of principal component j.
Figure 3 shows the 4 receiver operating characteristic (ROC) curves by 4 diﬀerent methods. Without 12

Table 1: Experimental conditions.
㪥㫌㫄㪹㪼㫉㩷㫆㪽

1st stage

2nd stage

Nodule clusters

P1=3

P2=2

Non-nodule clusters

Q1=15

Q2=3

Orientations

M1=4

M2=4

Principal componets

C1=5

C2=4

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

References[1] T. Iinuma et al.,“Preliminary speciﬁcation of X-ray
CT for lung cancer screening (LSCT) and its evaluation on risk-cost-eﬀectiveness,” Nippon Acta Radiologica, Vol. 52, pp.182-190, 1992. (in Japanese)[2] T. Okumura et al., “Variable-N-Quoit ﬁter applied for automatic detection of lung cancer by Xray CT,” Proc. of Computer-Assisted Radiology,
pp.242-247, 1998. (in Japanese)[3] Y. Lee et al.,“Nodule detection on chest helical
CT scans by using a genetic algorithm,” Proc. of
IASTED International Conference on Intelligent
Information Systems, pp.67-70, 1997.
Figure 3: Comparison of ROC curves by using (a)conventional, (b) fractal, (c) orientation, and (d) orientation and fractal features.

features of 4 orientation outputs extracted by the gabor ﬁlter, FP was, respectively, about 65% and 55% in
the ﬁrst and second stages when TP was 90%. On theother hand, by using gabor ﬁlter, FP was about 15%(ﬁrst stage) and 5% (second stage). The improvementof the discrimination rate, i.e, from 65% to 15% andfrom 55% to 5% in the ﬁrst and second stages, respectively, clearly demonstrates eﬀectiveness of the orientation and surrounding features on the pulmonary
nodules diagnosis. In addition to this, FP was about
35% under the same condition by using a MTANN [4].
Although this rate can be improved if we could choose
more suitable settings for the MTANN, we may claimthat the discrimination rate of the proposed method
is the same level or more than that of the MTANN.

4[5] Y. Nakamura et al., “Recognition of X-ray CT
image using subspace method considering translation and rotation of pulmonary nodules,” TECHNICAL REPORT OF IEICE, MI2004-102, pp.119124, 2005. (in Japanese)[6] K. C. Chung et al.,“Face recognition using principal component analysis of gabor ﬁlter responses,”
Proceedings. International Workshop on 26-27
Sept , pp.53-57, 1999.
[7] C. J. Lee et al.,“A Gabor ﬁlter-based approach toﬁngerprint recognition,” IEEE Workshop on 20-22
Oct, Vol. 52, pp.371-378, 1999.
[8] C. W. Ngo et al.,“On clustering and retrieval ofvideo shots through temporal slice analysis,” IEEE
Trans. Mlt., Vol. 4, No.4 pp.446-458, 2002.
[9] S. Hattori et al.,“Video classiﬁcation based onvideo description space,” TECHNICAL REPORTOF IEICE, CS2003-150, pp. 55-60, 2003. (inJapanese)

Conclusion

We have proposed a new diagnosis method of pulmonary nodules in CT images. The results demonstrated that the proposed method can further reduce
FP under a high TP condition compared to the conventional ones. This improvement has been achievedby extracting new orientation features of nodules inthe small ROI using the gabor ﬁlter and by the hierarchical combination with the structural feature quantiﬁed by the fractal dimension of the larger area surrounding the ROI.

©ISAROB 2007

[4] K. Suzuki et al.,“Massive training artiﬁcial neuralnetwork (MTANN) for reduction of false-positives
in computerized detection of lung nodules in lowdose computed tomography,” Med.Phys, Vol. 30(7), pp.1602-1617, 2003.

393[10] S. Kido et al.,“Fractal analysis of interstitial lung.
abnormalities in chest radiography,” Radiographics, Vol. 15, pp. 1457-1464, 1995.
[11] T. Ishida et al.,“Trabecular Pattern Analysis using Fractal Dimension,” Japanese Journal of Applied Physics, 32 Part1(4), pp. 1867-1871, 1993.
[12] National Cancer Imaging Archive (NCIA),https://imaging.nci.nih.gov/ncia/faces/baseDef.tiles

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Segmentation and tracking of cells in the image sequences
Tomasz Kubik††

Masanori Sugisaka∗

Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology,
Janiszewskiego 11/17, Wroclaw 50-372, Poland
E-mail: Tomasz.Kubik@pwr.wroc.pl∗
Department of Electrical and Electronic Engineering, Oita University,
700 Dannoharu, Oita 870-1124, Japan
E-mail: msugi@cc.oita-u.ac.jp

Keywords: cells tracking, image processing

Abstract
In the paper the method of automated cells tracking
in the sequence of images is presented. The method
starts with a sequence of images processing by the useof an adaptive threshold operator. Then the cells aredetected, labeled and parameterized by their size, position, orientation, bounding box, etc. Each pair of
two consecutive images in the sequence is analyzed
next in order to find corresponding (i.e. matching)cells. In that way all observed cells might be recognized and traced. The tracking, associated with cells
parameters measurements, provides data in a form oftime series. This data can be used for a particular cell
activity observation, complex interaction modeling ofmulticellular organisms, etc. Such analysis can givedetailed information about cells time-space dynamics.
The method proposed is demonstrated on a set of biological data.

1

Introduction

In the biological research in vivo live confocal microscopy has enabled scientists to perform observation and store their results for further analysis, [1].
Usually the data is available in the form of sequencesof images with time stamps assigned. Especially the
GFP (Green Fluorescent Protein) images became thesources of valuable data. It comes from the fact thatGPF intensity may reflect protein concentration andthus GFP intensity observation within a cell can serve
as a method of its inspection. In the consequence
the data provided can be used for cells activity observation, complex interaction modeling of multicellular organisms, cell growth and proliferation examination, gene expression modeling, etc. But to draw
valid conclusions or build models with a great accuracy the amount and quality of collected data should©ISAROB 2007394

be significant enough. Because the amount of data to
be analyzed is huge usually, there is a need for automated data processing. The problems of GFP fluorescence intensities measurements and cells tracking wereconcern in [2, 3].
In this paper we present the method of automatedcells tracking in the sequence of images, which isbased on image segmentation. The method proposed
assumes that sampling rate of image taking is fastenough to assure that a new cell positions and oldcell positions on two consecutive images are relatively
closed. The methods consist of some computation
steps, which are repeated for each image in the sequence analyzed. Thus, the computation starts with
an image processing by the use of an adaptive threshold operator. Then the cell detection is done. The
cells detected are labeled and parameterized by theirsize, position, orientation, centroid, bounding box, etc.
(recognition of cell splits and merges events is not addressed here, for the details on this problem please refer to [4]). Two consecutive images in the sequence are
analyzed then in order to find corresponding cells.The
result of tracking can be used for many purposes. It
can serve, for example, as a base for the changes in thecells color intensities detection. The averaged intensity within a cell can be interpreted as a relative protein concentration, while the number of a particular
image corresponds to the time of observation. Other
cell attributes (such as: position, size) can be used
to observe the cell movement and growing dynamics.
The method is demonstrated on a set of biological data
which is a sequence of images with living cells colonies.

2

Cell tracking algorithm

The cell tracking problem can be formulated as follows: Given a sequence of images with species, de-The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

termine the positions of each cell at each time point(track cells through time). In the method proposed weassumed that the images are captured from a video
stream with a preset sampling rate, and stored in asequence for the further processing. Thus the idea of
the algorithm proposed can be expressed as follows:• segment each image in the sequence in order tofind the areas covered by the cells (create a segmented image for each original image in the sequence);• analyze the original images within areas corresponding to the areas found (analyze the interiorsof the cells detected, and extract individual cell
attributes such as: cell placement, shape, averageof lightness, etc.);

so, the varying lighting intensities would cause filtering out underexposed cells, or accepting overexposed
background as an area of interests. The solution tothis problem can be an application of an adaptive
threshold operator.
The adaptive threshold operator uses the threshold level which is calculated on the base of the histogram of the re-quantized original image. It appeared(see Figure 2) that all histograms of the re-quantizedimages in the sequence analyzed were of the similar
shape. The maximums of histograms were observed
for the values which were standing in correspondence
to the lighting intensities. Thus the threshold value inthe adaptive threshold operator can be calculated asfollows:
vt = xM AX + d(1)• find the matching cells for every two consecutive
images in the sequence and evaluate the dynamics of parameters changes and cells movements(identify the same cells on two consecutive images using cells attributes just extracted, and estimate dynamics of changes knowing the timeslice’s length between two image captures).

where vt is the threshold value, xM AX is the mostoften observed pixel intensity in the re-quantized original image, d is a parameter which assure the proper
background elimination (in the experiments performed
this value was set to 4).

In a confocal microscopy the observations can be captured by different video capturing devices. It may
happen, that a lighting conditions in the experimental setup are varying due to some reasons (voltage instabilities for example may produce different lighting
intensities). More over, the distribution of the lighting can be non uniform (most often the distribution
is radial as a result of the light source’s characteristic). Thus before performing any observation somepreliminary steps are needed. These steps can include
a lighting distribution measuring in an experimental
setup without species. The result of such steps can beused later to adjust the observed light intensities byremoving a non uniform background. The influence ofthe varying lighting intensity can be canceled by theintensity tuning during image processing. The following subsection provides some clues on the way it canbe done.

The images obtained after adaptive threshold operator application contains areas of interest. These areas
are groups of pixels but not objects yet. To find theobjects the following algorithm is proposed:

2.1

Adaptive threshold operator

The cell detection method discussed in the next section requires that the images provided are monochromatic with different colors for the background andthe areas of interest. The simplest way to createsuch images is to apply the threshold operator on there-quantized original images (images with a reduced
number of colors). But the threshold value can not be
kept on the same level for all images. If it would be©ISAROB 2007

395

2.2

Cell detection

let A, B, T , O be the colors of pixels belonging to,respectively, the area of interest, the background,the temporary area, and the area of an object.
1. trace an image starting from the upper left pixelin order to find a pixel whose color is A
2. beginning with the pixel found fill the area witha color T
3. extract parameters of the area filled in 2 (size, position, orientation, centroid, bounding box, etc.)
4. if the area filled in 2 is interesting (is a cell), fill
it with a color O and memorize the parameters of
it in a list, otherwise fill this area with a color B
5. repeat 2, 3, 4 until the lower right pixel is notreachedThe fill procedure (step 2) tries to mark all connected
points having the same color A with a color T , startingfrom a given pixel. This procedure returns the bounding box of the area filled. There are, of course, several
more general algorithms for region filling, which are

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

working with non monochromatic images. In these algorithms some criterions are defined, which are used
to classify the neighboring pixels as belonging to theregion being filled or not. In our case we have an image obtained after adaptive threshold operator use, so
the areas of interest are already filled. Now we want toknown, what kind of the object is represented by eacharea, and what are the objects parameters. Thus ourfill method is used mainly to find the bounding box ofthe area filled. Having bounding box we can simply
extract objects parameters by looking at pixels in thisbox and at corresponding pixels in the original image(step 3).
There might be several objects parameters. One of
the most important parameters of the object is the position of its geometric center. This parameter is usedin the corresponding objects finding (see section 2.3).
The coordinates of the object center expressed in thecoordinate frame associated with a bounding box canbe calculated from the formula:
xc =

M10
M01
, yc =
M00
M00

(2)

P
where: Mji =x,y σ(xj , yi )xj · yi is a spacial moment; σ(xj , yi ) is an indicator function which returns
1 if a pixel at position (xj , yi ) has color T , and 0 otherwise. The average intensity of the cell, avg, can becalculated from the formula:
X I(x + dx, y + dy)
avg =
(3)
M00
x,y
where dx, dy are the coordinates of the upper left corner of the bounding box expressed in the image coordinate frame. Several other parameters can be obtainedin similar manner.
It is not known, weather objects detected are cells
or uninteresting parts of the observed field. Once theobjects parameters are extracted, the task of objects
identification becomes a problem of classification withextracted parameters as features. Finding the correspondence between cells at different time points simplifies then to finding the correspondence between cells
detected on the consecutive images.

2.3

Cell tracking

The problem of finding correspondences between
image objects is well known in the computer image
analysis field. It can be NP-complete, if an object
in one image potentially match every object in another image. To limit the number of possible matches
we propose to consider the distance between matching candidates. The assumption we have made about©ISAROB 2007396

the sampling rate allows as to define matching candidates as objects from two consecutive images which aremost closely, and lying within a distance which does
not exceed an assumed limit. Lets assume that thedetection of cells ended successfully and the cell parameters have been collected. Thus to find the matching
candidates for an object from one image it is enough tosearch for them in the second image within a circulararea centered at the object center. The matching object should be the closest one (and most similar one,
if we take into account extracted earlier objects parameters). If there is no matching objects it means,that cell ”disappeared”. The correspondence analysis
can be summarized with some information about cells
movements, appearance, etc.

3

Experiments

The experiments were performed on the sequence of
captured images, which was processed off-line. Eachcaptured image was, in fact, a frame of 672×512 pixel
size, stored in 48 bit grayscale image format. The
images were converted into 8 bit grayscale and thenprocessed by the method described. In the Figure 1
the characteristics of the lighting conditions we met
can be observed. It is clear, that the radial background intensity should be considered. The use of an

Figure 1: Three pictures representing the same image
after filtering with gradual increased threshold.
adaptive threshold operator was connected with histogram’s analysis. In the Figure 2 two histograms (ina logarithmic scale) are shown. In most cases the histograms were similar. Thus on the base of observations the value of d parameter (equation (2) was set to
3. After thresholding the objects detection was done.
For each object detected different parameters were extracted (this included object’s area and position of its
center). The objects detected with the are of 20 pixels
were filtered out. The rest of the objects was classified as cells. The cells centers calculated are shown in
the Figure 3b) as black dots within cells interior. The
part a) of this figure represent the first, original image
in the sequence analyzed. The part b) represents final

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 3: The first image in the sequence of 238 original images analyzed, a); and results of 21 cells tracking withcells detected for the first image in the background, b).
ters found were extensively used there (cells parameters are calculated on the base of cells detected in a
thresholded image and image intensities inside corresponding areas of the original image).
The data collected during processing can be used
for different purposes, for example for a particular cell
activity observation, intercellular processes modeling,
complex interaction modeling of multicellular organisms, etc.
Aknowledgement: We would like to thank Lukasz
Miroslaw for his contribution to this work.
Figure 2: Histogram examples of images analyzed,
drown in the logarithmic scale.
results of cell tracking. For better visualization the
tracks of 21 chosen cells are shown only.

4

Conclusions

In the paper a method of automated cells tracking
was presented. The method was successfully applied
to the sequence of GFP images of biological species.
For the good method’s performance method parameters tuning might be necessary.
The data used in the experiment performed were
processed off-line, but processing on-line is also possible. The experiments showed, that adaptive thresholding shortens the time necessary for cells detection
and their parametrization coming next (the thresholding depends on the image histogram shape). The celltracking was implemented as a problem of cell matching on two consecutive images, while the sampling time
provided the clue about time line. The cells parame-©ISAROB 2007

397

References[1] V. Gor, T. Bacarian, M. Elowitz, and E. Mjolsness: Tracking Cell Signals in Fluorescent Images.
In Proceedings of the 2005 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR05), (2005).
[2] L. Vincent and P. Soille: Watersheds in digitalspaces: an efficient algorithm based on immersion
simulations. Trans. Patt. Anal. Mach. Intell., 13(6),(1991).
[3] H. Shen, G. Nelson, S. Kennedy, D. Nelson,
J. Johnson, D. Spiller, M. R. White, and D. B.
Kell: Automatic tracking of biological cells andcompartments using particle filters and active contours. Chemometrics and Intelligent Laboratory
Systems, 82(1-2):276–282, (2006).
[4] J. Withers and K. A. Robbins: Tracking cell splits
and merges. In Proceedings of the IEEE Southwest
Symposium on Image Analysis and Interpretation,(1996).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Level Set Methods and Auto-Relation for Detection of Objects
Xin Wang1;21

Masanori Sugisaka2

Jin Wang3

Department of Electrical and Information, Niihama National College of Technology, Japan2
Department of Electrical and Electronic Engineering, Oita University, Japan
3
College of Textile & Garments, Hebei University of Science and Technology, China

Abstract
This paper presents a framework for detecting objects
in images. The motive of this research can be found inonsite requirements. We focus on the practical need on
distinguishing salt called purity from impurities, which
are sand, soil, and other substance in the heaped salt
on a conveyer belt. In this work, basing on image energy, we formulate the auto-relation function on image
energy to construct a piloting set which includes possible elements to classify objects and the impurity object
in order to lead the front propagation. We use levelset method to detect the topologic changes for evolution curves and to catch the objects/impurity.

1. Introduction
Image vision technology has matured substantially inthe last decade to be successfully applied to a variety of industrial tasks. Three methods, optical devicebased, algorithm-based technology and special image
deice-based are the main in industrial applications. In
the åeld of factory automation, successful applicationsof image technology are roughly divided into assembling and inspection. For example to decide a 2D positions as analogical robot sensor or 3D positions whenmeasuring automobile's body, surface inspections, inspecting LSI pattern, and mask and printing board
etc [1][2][3][4]. With the development of cheaper color
cameras, more people have been more and more interested in digital image application or algorithm technologies [5][6][9]. There are many successful industrial applications in the past years, but the past works
mostly depend on the constraints of possible patternmatching. Image technologies for applications, especially in industry, are strong case-dependence. Because we want to solve our problem by image technology instead of paying more cost to some special
material inspection sensor device, it is our intelligent
selection to develop the image algorithm.
The aim of this paper is to introduce level set methods based on the auto-relation techniques and provide
a basic framework for applications. The key idea ofLevel set methods in image plane is implicit curve evolution in the planer image. We notice the fact thatwhen purity and impurity are mixed in an images,
their diãerent textures and gray values are certainly
bringing about gradients changes. These changes show

©ISAROB 2007

398

us a lot of clues for image classiåcation and recognition. We induce the propagating interfaces by thosehigh image energy parts to label the object from theirbackground. The evolving surface of impurity is presented as the zero level function. To reduce the computation cost required by level set formulation scheme, a
new approach exploited by image auto-relation is proposed. Making a piloting set is a process calculating
image energy. It supports level set function a limited
domain and speed up evolving front eãectively.
The present approach is described as followings. The
image pre-process is at the årst. This will take us theadvantages that the changes we are interested in will
not be suppressed by some smoothing, which tends tosuppress the eãects of noise. We introduce the important auto-correlation method to pilot the interest
point in an image. Such a fact has been noticed thatthe border between and soil certainly cause an obviously image energy changes i.e. gray value changes.
We will illustrate either how the auto-correlation algorithm catches these changes or how its results give
us a coarse pilot on the objectives we are interested
in. We want to classify the coarse positions by somelocal windows, to inspect the detail changes and compare the results with our preset models, which are thefeatures of their Gaussian distributions. Based on thesimilarities between the results and the models, we
judge whether a class is accepted as an object soil ornot [14] and these are our following works.
In this paper, we give the auto-relation model in next
section. The pre-process and classiåcation are also
introduced in the section 2. Section 3 describes theprinciple of curve evolution based on level set methods. Some experimental results and discussions are at
the last.

2. Auto-relation Model
2.1 Image pre-precess
In general, any change of signiåcance to us has effects over a pool of pixels. For many kinds of noisemodel, large image derivatives due to noise are an essentially local event. This means that smoothing adiãerentiated image tends to support the changes weare interested in and to suppress the eãects of noise.
In a pre-process, the smoothing ålter can be chosen
by taking a model of an edge and using some set of

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
where (xk ; yk ) are the points in the working local widow
w . Based on the Taylor expansion:I(xk + Åx; yk + Åy)= I(xk ; yk ) + Ix (xk ; yk )Åx + Iy (xk ; yk )Åy+; ÅÅÅ
í ì
Ä
Å Åx
ô I(xk ; yk ) + Ix (xk ; yk ) Iy (xk ; yk )(2)Åywhere Ix =@I(X)=@x, Iy =@I(X)=@y. Substituting theabove approximation (2) into Eq.(1), we obtain:f (x; y)
†
í ì!2
X Ä
Å Åx
Ix (xk ; yk ) Iy (xk ; yk )
=
Åy
w
ìí
ì
í
X
I 2 Ix IyÅx(Åx Åy) x=
Ix Iy Iy2
Åy
w
#í ì
"
í
ì
X I 2 Ix IyÅxx= (Åx Åy)Ix Iy Iy2
Åy
w
í ì
Ç
É Åx
= (Åx Åy) Ä(x; y)ÅyFig. 1: The soil grains image for test.

Fig. 2: The horizontal is the horizontal coordinate in Fig.1, the vertical isM(X) in Eq. (6).
criteria to choose a ålter that gives the best responseto that model. It is diécult to pose this problem as a
two-dimensional problem because edges in 2D can be
curved. Conventionally, the smoothing ålter is chosen
by formulating a one-dimensional problem and thenusing a rotationally symmetric version of the ålter
in 2D. In our case, we select a nonlinear rank-value
median ålter for image pre-process. We take all thegray values of the pixels which lie within the ålter
mask and sort them by ascending gray value. The
rank-value ålter only diãers by the position in the list
from which the gray value is picked out and written
back to the center pixel, well known as median ålter. Let M1 =fM1 (n; n)g (n is odd) be those grayvalues around a pixel. To an array M2 = fM2 (k)g
(k = 1; 2; . . . ; S = n Ç n), this ålter use the value
M2 (S=2) as it responses. This made us easily adjust
the smoothing scales to diãerent size of objects.
2.2 Auto-relation on image energy
Let I(X)(also denoted as I) be the image function in
an image frame. Given a shift (Åx; Åy) and X=(x,y), X2 R2 . The auto-correlation function is deåned
as:f (x; y)XÄÅ2
I(xk ; yk ) Ä I(xk + Åx; yk + Åy)
=

(1)

w

©ISAROB 2007

(4)

To I(X)=G ÉI, É is the convolution operation, wechange Ä(x; y) as 5I(5I)T , build up a transform relation H(X) in a local window about X.
Xàâ
H(X) = T (X)É
5I(5I)TX ö (Gx ÉI)2 (Gx ÉI)(Gy ÉI)õ= T (X)É(Gx ÉI)(Gy ÉI) (Gy ÉI)2
(5)

where G is a Gaussian with standard deviation one,
Gx =@G/@x, Gy =@G/@y. T (X) is a weight mask toweight the derivatives over the window. In Eq. (5),there relations @I/@x=@/@xÉI, @=@xÉ(GÉI)=(@=@xÉG)ÉI=@G/@xÉI. This matrix captures the local structure. The eigenvectors of this matrix are the principal
curvatures of the auto-correlation function. We consider a cost function M(X) :M(X) = E[H(X)] + C[H(X)](6)

where E[É], C[É] are the determinant and trace of H(X )respectively. For example, M(X) is shown in Fig. 2.
2.3 Image classiåcation
M(X) in Eq. (6) gives the distributions of image energy clearly. We classify those points by M (X) further. Assume the ith point Pi (X) be presented by acomplex OPi , the jth point Pj (X) by OPj , to a constant è1 , if it is true thatjOPi Ä OPj j < è1

(7)

Pi (X(i)) and Pj (X(j)) are put into same set C k , C k ö
C. C is deåned as the classiåcation set.
s
[
Ck
(8)
C=
k=1

399

(3)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 3: 1: the initial curve; 2 and 3: the evaluating curve; 4: The entire objects are caught.
where s is a preset constant to decide the subsets in
C. The elements in C k are coarse results classiåed.
Assume the center of gravity of the elements in C k
be Pc (X), M (X) will be recalculated by Eq. (5) andEq. (6) with a smaller preset constant èt (èt < ètÄ 1 ,t î constant) around Pc (X) in a smaller local window
several times. If the results under èt will be treated as
the part of the soil, the calculations ånish. The reason
we did this is that it is hardly to get complete pixels
about the object, for the reasons that the surface of
any object reçects light in all direction, smaller èt canuse more åne resolutions to analyze objectives.

3. Skeleton of Level Set Methods
Level set methods add dynamics to implicit surfaces.
The key idea that started the level set fanfare was the
Hamilton-Jacobi approach to numerical solutions of atime-dependent equation for a moving implicit surface.
Given a moving closed hypersurface G(t), we wish to
produce an Eulerian formulation for the motion of thehypersurface propagating along its normal direction
with speed F, where F can be a function of various
arguments, including the curvature, normal direction,etc. This propagating interface is embed as the zerolevel set of a higher dimensional function û(x, t) (alsodenoted as û in this paper). Let û(x, t=0), where x is
a n-dimension space, be deåned byû(x; t = 0) = D(9)where D is the signed distance from x to G(t=0),
and plus/minus sign is chosen if the point x is outside/inside the initial hypersurface G(t=0). Thus, wehave an initial function û(x, t=0) with the propertythat
G(t = 0) = (xjû(x; t = 0) = 0)

(10)

Our goal is to produce an equation for the evolving
function û(x; t) which contains the embedded motionof G as the level set û = 0. Let x be the path of apoint on the propagating front. That is, x(t=0) is apoint on the initial front G(t=0), and dx/dt=F (x)with the vector dx/dt normal to the front at x. Since
the evolving function û(x, t) is always zero on the
propagating hypersurface, we must have the constraint
û(x; t) = 0©ISAROB 2007

(11)

400

By the chain rule,ût + 5(x; t)xt = 0(12)

We then have the evolution equation for û(x, t)
ût + F j 5 ûj = 0(13)with a given value of û(x, t=0). This is referred as
Hamilton Jacobi \type" equation because, for certain
forms of the speed function F , we obtain standard
Hamilton Jacobi equation. Because û(x, t) remains afunction as it evolves, we may use a discrete grid in thedomain of x and substitute ånite diãerence approximations for the spatial and temporal derivatives. We
use a uniform mesh of spacing h, with grid nodes ij,
and employing the standard notation that ûnij is theapproximation to the solution û(ih; jh; nét), where étis the time step, we may writeÄ ûnijûn+1
ij(14)+ (F )(5ij ûnij ) = 0
étHere, we have used forward diãerences in time, and letûnij be some appropriate ånite diãerences in time, andlet 5ij ûnij be some appropriate ånite diãerence operator for the spatial derivative. To a given speed function
F , we update the front by the modiåed version of an
Engquist-Osher scheme [11]. The front propagation is
illustrated in Fig. 3.

4. Experiments and Discussions
We compute a practical images, which was taken onsite, by the proposed algorithm. In Fig. 4, using theoriginal images on the left, we indicated the processes.
Based on the result of Eq. (5) and Eq. (6), the positions of image energy are detected. We have gotten
two positions or two signiåcant M(X). Locating thetwo positions, we give the closed initial front curve for
evolution. This decrease the computation cost obviously. The results in Fig. 4 also show us that M(X)bring us less image noise in the closed front curve and,this is very helpful for the recognition using the ånished evolution results. Fig. 4-4 show that the contour of the objects are caught perfectly. This is one ofsuccessful applications by means of the advantage ofthe level set based active contour technique.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 4: 1: starting contour based on the image auto-relation; 2 and 3: the evaluating
curve; 4: the objects are caught.

5. Conclusions
We proposed in this work a piloted level set methods.
This approach uses traditional rank-value median ålter
as pre-processor, creates an image energy auto-relation
function to lead an initial front propagation in orderto perform classiåcation and calculate features about
their textures and so on for the purpose of recognition.
When diãerent kinds of objects/grains appear on thesame image, most of them will bring about image energy changes presented by the form of gray gradients.
The auto-relation function is excellent way to describe
these features, especially in the case of objectives havethe global dominant positions in an image, just like thecase of the soil-in-salt. The ånished front propagation
can give more information for recognition. We can adjust the cost constants from coarse to åne in a widow
around piloted positions dynamically. Then those features can be compared with their models made in advance. Not limited by this application, the developed
technique will be also available when the environment
is changed, with some modiåcation. Though this approach faces the problem of computation cost, it still
is a basic frame work.
In this algorithm, if the objects don't have an obvious energy features in a global detection, it will cause sin Eq.(8) increased, and hard to be classiåed. We suggest s should be maximum three, number constrain
companies it, or it is an intelligent way to consider
this problem from other bases.
Acknowledgements
This research was also done while Xin Wang was employed at Graduate School of Engineering, the University of Tokushima.

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[3] G. Carneiro, and A. Jepson, \Phase-based local
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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Motion trace in real-time processing
Eiji GOUBARU

Masanori SUGISAKA

Department of Electrical and Electric Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan.

E-mail: msugi@cc.oita-u.ac.jp

Abstract
Recent years, researches about visual tracking
are extensively studied. If we can visual tracking,we can apply the skills of automotive control andso on. For example, if obstacle avoidance ofmoving object instead of obstacle avoidance of thestatic object becomes possible, the control
becomes possible as for a more complex situation.
Facial recognition was researched in mylaboratory, however, this research is weak ofchange in light, so that my main research
improve that and to motion trace through head
control of mobile robot.

The mobile robot is equipped on the right and left withthe driving wheel, auxiliary caster rings at the front andback, a driving wheel on either side rotates by DC motor.
Equipped with the rotary encoder of resolution 80 (Pulse
Per Resolution) beside the driving wheel, and counting
the number of pulses, the right-and-left independence
can be achieved and a wheel can be controlled.
The difference in the rotation speed of a right-and-leftdriving wheel performs a steering function. The CCD
camera (EVI-G20: Sony) is carried by the height ofabout 130 [mm] at 55 degrees of perpendicular directions
at the head. The picture obtained from the camera is
taken in by the memory on an image-processing board(FDM-PCI3: FOTORON).

1 Introduction
Recent years, the introduction of robot was beanconsidered in various fields. Various functions were beanrequired and the robot use was expanded. One of thefunctions requires environmental recognition andavoidance of an obstacle. In order to develop such robot,
this laboratory, for some time, has mainly respond to thefield of welfare and nursing. A mobile recognition robot
has been researched and developed [1][2][3].
Researches about visual tracking are extensively
studied. In this research, the following are conditions.
(1) Moving object is red circle marks.
(2) Moving object is near the CCD camera.
We used our laboratory robot. Image import from CCD
camera, we compute feature point and center point totrack moving object, and implement motion control of
the head.

2 Experiment System
The robot which is being used in this research was
manufactured by incorporated company DENKEN in
2000.The figure is shown in Fig.1.It consists of 2 drive 2
caster (2DC2W) systems.

©ISAROB 2007

402

Fig. 1 View of The Mobile robot

3 Image Processing
3.1 Pixel Skipping
When the size of image is 300 by 300 [pixel], it isdifficult to implement in real time processing, when thesize of picture is small, moving object will be notdetected.
In order to make realize real time processing, image
processing time must be shortened. Therefore, by setting
the area of pixel which to be removed and to be taken theimage is processing again by re-sizing and showing only

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
the taken pixel area. In this research, re-sizing of image
is 60 by 60. Fig. 2 shows that gray color is taken pixel
area.

I

120[°]
G
R 0[°]
B
240[°](b) Skipping Image

H(a) Original Image
Fig. 2S

Pixel Skipping

Fig. 3 Coordinates About RGB & HSI

3.2 Conversion From RGB To HSI

However, if HSI value is not threshold area, thecamera will not recognize the object.

The image is taken in from a CCD camera; it is a
picture expressed with a total of 24 bits of 8 bits each of
RGB. Then, in order to treat the feature from a picture

3.3 Equalization Processing

independently as much as possible, the following
equation performs HSI conversion of the hue,saturation and intensity. Each of R, G and B is
standardized to take values between 0 and 1, with amaximum total intensity of 1. Following equations are

When the light is strong or weak, visual tracking can
not be performed by using color information. Then,converse from RGB to HSI. We improve to be constantcurve of cumulative frequency of I(intensity).
Equalization processing is shown in Fig.4.

Hue, Saturation and Intensity.

Brfore equalization

12000

I = max(R, G, B )(1)

I − min( R, G, B)
S = 255 ×
I

(2)

10000
8000

4000

(max(R, G, B) = R)
2000

60 ×

R −G
+ 240 (max(R, G, B ) = B )I − min( R, G, B)

252

240

228

216

204

192

180

168

156

144

132

120

96

108

84

72

60

48

0
36

(3)

24

B−R
60 ×
+ 120 (max(R, G, B ) = G )I − min( R, G, B)

0

H=

G−B
I − min(R, G, B)

12

60 ×

6000

(a) Original Image

G

After equalization

8000
7000
6000

(0,255,0)

5000
4000
3000
2000
1000

(0,0,255)

Fig. 4 Equalization Processing

403

252

240

228

216

204

192

180

168

156

144

132

120

108

96

84

72

60

48

36

24

0

(b) Equalization

B©ISAROB 2007

12

0

R
(255,0,0)The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.4 Fuzzy Control
To perform the visual tracking, there is thresholdprocessing. In many cases, human decided threshold.
However, there are some problems. For example,
difference object of same saturation, threshold cannot be
detected. In this research, to prevent this problem, we
perform fuzzy control of saturation. Input and output
fuzzy set is shown in Fig. 5. Result is shown in Fig. 6.
The green point is center of circle.

(a) Original Image

Hue

-30

0
(a) Input Hue

30

[°]

(b) Human decided Threshold

Saturation

0

50

100

[%]

(b) Input Saturation(c) Fuzzy Control Of Threshold
Fig. 6 Fuzzy Control

3.5 Labeling Technique
The label represents number. The number, which
represents value from 0 to 255, is boundary value. The
processed image is labeled separate boundary value.
Labeling-propagation processing by using rasterscanning is shown in Fig. 7. Fig. 7 shows that the label
represents by using alphabet value. As a result,characteristics of the moving object are calculated.

Saturation

0

50

100

[%]

(c) Output Saturation
Fig. 5 Fuzzy Set©ISAROB 2007

404

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A
1
1
1
1

1

1

1

1

1

1

1
1
1
1
1

(a) Labeling Start(c) Result Image

A
A
A
A
A

A

A

A

B

B

B

Fig. 8 Motion Detect Process

A
A
A
A
A

Since the image (c) is shown in Fig. 8, center of thecircle is calculated. Therefore, head of the robot rotate tocenter of the circle. However, moving object is difficult
to detect in strong light. When moving object and otherobject are similar saturation, other object is detected, too.

(b) Labeling End
Fig. 7 labeling-propagation processing by using rasterscanning

4 Motion Detect Experiment
Motion detect experiment was actual conducted in theenvironment. Tracking moving object motion trace using
red circle mark is carried out. After the red circle mark is
detected, the head of the robot will move following thedirection of the red circle mark movement. As the firststep of processing is pixel skipping, second step isconversion from RGB to HSI, equalization and third stepis labeling. The green point of the image (c) is shown in
Fig. 8 is center of circle.

5 Conclusions
In this research, we are able to improve threshold inlight. However, moving object is difficult to detect instrong light. When moving object and other object are
similar saturation, other object is detected, too. Therefore,
we would like to improve than this research in strong
light and weak light, and we would like to consider aboutocclusion.

References[1]Syuiti Otsu, Masanori Sugisaka “ Research on theenvironmental recognition in a mobile robot “, Oita
University Graduate School Master Paper , 2003.
[2]Syohei Kuriyama, Masanori Sugisaka “ Developmentof the mobile robot run system using dead reckoning andvision information ”, Oita University Graduate School
Master Paper , 2003.
[3]Toshio Maeyama , Masanori Sugisaka “ The obstacle
avoidance run in the mobile robot using the CCD
camera ”, Oita University Graduate School Master Paper ,2002.

(a) Original Image[4]

http://www.cse.unr.edu/~looney/cs674/ch6html.htm(b) Skipping Image©ISAROB 2007

Chapter 6. Processing Color Images.

405

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

On the Application of Lip Features in Classifying Human Emotions
M. Karthigayan1, M. Rizon1,2, S. Yaacob1, R. Nagarajan1, M. Sugisaka2,3, M. Rozailan4 and D. Hazry2
1

School of Mechatronics Engineering, Kolej Universiti Kejuruteraan Utara Malaysia (KUKUM), Jejawi, Perlis, Malaysia
2
Deparment of Electrical and Electronics Engineering, Oita University, 700 Oaza Dannoharu, Oita 870-1192, Japan
3
Advance Research Institute for Science and Engineering, Waseda University, 2-2 Hibikino,
Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan
4
Terengganu Advanced Techical Institute,Kampus TATI, Jalan Panchor, Teluk Kalong, 24000 Kemaman, Terengganu, Malaysia
Email: karthigayan@ieee.org

Abstract
In this paper, lip features are applied to classify the humanemotion using a set of irregular ellipse fitting equations
using Genetic algorithm. As South East Asian (SEA), is
considered in this study. All six universally accepted
emotions are considered for classifications. Lip is usuallyconsidered as one of the features for recognizing theemotion. In this work, three feature extraction methods areproposed and their respective performances are comparedfor determining the feature of the lips. The method which isfastest in extracting lip features is adopted in this study.
Observation of various emotions of the subject lead to uniquecharacteristic of lips. GA is adopted to optimize suchirregular ellipse characteristics of the lip features in eachemotion. That is, the top portion of lip configuration is a partof one ellipse and the bottom of different ellipse. Two ellipse
based fitness equations are proposed for the lip
configuration and relevant parameters that define theemotion. This has given reasonably successful emotion
classifications.

Keywords: Feature extraction, Irregular ellipse fitness
function, Genetic algorithm, Face emotion recognition.
1. Introduction
In recent years, there has been a growing interest in
improving all aspects of interaction between humans andcomputers especially in the area of human emotion
recognition by observing facial expression. Ekman andFriesen developed the most comprehensive system forsynthesizing facial expression based on what they call as
action units [1]. In the early 1990’s the engineeringcommunity started to use these results to construct automatic
methods of recognizing emotion from facial expression in
still or video images [2]. Human being possesses an ability ofcommunication through facial emotions in day to day
interactions with others. Some emotions attracted most of theinterest in human computer interaction environments. Double
structured neural network has been applied in the methods offace detection and emotional extraction. In this, two methods
are proposed and carried out; they are lip detection neuralnetwork and skin distinction neural network [3]. Facial
action coding is given to every facial points. For example,
code 23 is given for lip funnel, code 4 for eye brow lower,
code 10 for chin raise etc. The cods are grouped for a
specific facial emotion. In order to determine the category of
emotion, 15 facial points in a face-profile sequence has been
recommended. The algorithm performs both automatic©ISAROB 2007406

segmentation of an input video images of facial expressions
and recognition of 27 AUs occurring alone or in combinations in the input face-profile video. A recognition rate of
87% is reported [4]. The motion signatures produced are thenclassified using Support Vector Machines as either nonexpressive or as one of the six basic emotions. The
completed system is demonstrated in two simple but effecttive computing applications that respond in real-time to thefacial expressions of the user, thereby providing the potential
for improvements in the interaction between a computer user
and technology [5]. The method uses edge counting andimage correlation optical flow techniques to calculate thelocal motion vectors of facial feature [6]. Cauchy Naïve
Bayes classifier is introduced in classifying the face emotion.
The person dependent and Person-independent experimentsshows that the Cauchy distribution assumption typicallyprovides better results that the Gaussian distribution
assumption [7]. The universally accepted categories of
emotion, as applied in human computer interaction are: Sad,
Anger, Joy, Fear, Disgust (or Dislike) and Surprise. In thispaper, image preprocessing, filtering, edge detection methods
that are suitable for feature extraction are presented andcompared towards applying it to get lip features. A set of
fitness function for the GA methods are also proposed as
suitable for face emotion recognition. Such an approach ofdetermining the emotions are highly suited for a personified
face and cannot be common to all faces of all human being.
A subject (South East Asian) as shown in Figure 1 has beenused for emotion classification. The generalized process flow
for the image processing, feature extraction and classifying
the emotions is shown in Figure 2.

2. Face Image Processing
As the first step in image processing, the region of intesrest(ROI) has been selected in the acquired image. The ROI
image is converted into grayscale image (0-256).

Figure 1. The Angry Emotion SEA

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Before obtaining the filtered grayscale image, a histogram
equalization method has been applied.Histogram equalization[8] improves contrast in the grayscale and the goal of
histogram equalization is to obtain an uniform histogram.
The histogram equalization method also helps the image toredistribute the intensity distributions as shown in Figure 3.
New intensities will not be introduced into the image.
Existing intensities values will be mapped to new values but
the actual number of intensities in the resulting image will beequal or less than the original number of intensities. In theimage sequence, the histogram equalized image is filteredusing average and median filters in order to make the image
smoother. Finally, Sobel edge detection method is applied to
the filtered image. The cropped ROI lip region is shown in
Figure 4.
Noises are added to the cropped ROI lip region. The salt andpepper noise are added to the image. This type of noise
consists of random pixels being set to black or white. The
application of the filter such as average filter and medianfilter to the noise added image is to remove the unwanted
noise. This method creates a two-dimensional filter andreturns with a correlation kernel. Median filtering [9] is that

Region ofInterest (ROI)&
Histogram
Equalization

Lip ImageAcquisition

EmotionClassification
based on Lip
Parameters

Genetic
Algorithm for
Optimizing
Lip Contour
by Irregular
EllipseThreshold has been performed to the filtered image by
selecting a suitable threshold value. Various edge detection
methods such as Sobel, Prewitt, Canny, Roberts and Log
have been applied to the image. A comparison has been
made among the edge detection methods and it is found that
the Sobel edge detection method [9] performed wellcompared to other four methods. The sobel edge detection
region of lip region is shown in Figure 5

Cropping the
Lip Region

Figure 5. Sobel Edge Detected Lip Region

Image
Processing &Lip Feature
Extraction

Figure 2. Process Flow of Image Processing

3. Feature Extraction
A feature extraction method is now to be applied to the edge
detected image to extract features. Three feature extraction
methods are considered and their capabilities are comparedin order for adopting the one that is suitable for the proposed
face emotion recognition problem. They are projection
profile, contour profile and moments.
The performance of each of the above described feature
extracting methods can be compared with respect toprocessing time using the edge detected image of the lips.
The processing time includes the image reading,preprocessing, filtering, edge detection and feature extraction
processes. Table 1 shows the processing time for obtaining
the lip features of all three feature extraction methods. The
projection profile is found to perform well in feature
extraction with regards to the processing time and is adopted
here. The projection profile has been also found to have
performed well in earlier works [10,11].
Table 1. Processing Time for Lip Feature Extraction

Figure 3. Histogram Equalization of Face ROI

Figure 4. Cropped ROI Lip©ISAROB 2007

each output pixel is set to an average of the pixel values in
the neighborhood of the corresponding input pixel. However,with median filtering, the value of an output pixel isdetermined by the median of the neighborhood pixels, rather
than the mean. The median is much less sensitive than themean to extreme values (called outliers). Median filtering istherefore better in the ability to remove these outliers without
reducing the sharpness of the image. The median filter withvarious matrix sizes such as 3*3, 4*4, 5*5, 6*6, 7*7 and 8*8
are applied. The 5*5 size matrix has been found to besuitable in removing unwanted noise in the image.

407

Feature Extraction
MethodProcessing Time
(Seconds)

Projection Profile

0.691 – 0.862

Moments0.865 – 0.892

Contour Profile

23.07 – 33.31

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

b1

4. Face Emotion Recognition using Genetic
Algorithm
In the early 1970s, John Holland, one of the founders
evolutionary computations, introduced the concept of genetic
algorithm [12]. Genetic algorithm (GA), or GA is a heuristic
method used to find approximate solutions to solve problems
through application of the principles of evolutionary biology.
GA adopts biologically-derived techniques such as
inheritance, mutation, natural selection, and recombination(or crossover). GA is a particular class of evolutionary
algorithms. A population containing a number of trial
solutions each of which is evaluated (to yield fitness) and a
new generation is created from the better of them. The
process is continued through a number of generations withthe aim that the population should evolve to contain an
acceptable solution. GA is well known for optimization of
nonlinear functions. It offers the best optimized value for any
fitness or objective functions suitably selected for particular
problems.
GA has been applied in various applications which include
in image processing, control, design of aircraft, robot
trajectory
generation,
air-injectedhydrocycloneoptimization, multiple fault diagnosis, a hybrid technique forengineering design optimization the traveling salesman,sequence scheduling and quality solutions where nonlinear
optimization are required [13] Some aspects of vision system
and image processing methodologies have been discussed
towards approximating the face as a best ellipse using GA. In
the feature extraction stage, the GA is applied to extract the
facial features such as the eyes, nose and mouth, in a set ofpredefined sub regions. Some simulation has been carried out[9]. A method that extracts region of eyes out of facial image
by genetic algorithm has been suggested recently [14].
The human lip shape is more of towards combination of two
ellipse and we call this is as irregular ellipse. The word
‘irregular’ means that the ellipse has two different minor
axes wherein a major axes remains the same.
The
preprocessed lip image is considered as a irregular ellipse.
Lengths of minor axes of the lip feature for each emotion arecomputed. In Figure 6, the whitened area of edge detected
lip image for a particular emotion of SEA is shown. The
major axis “2a” (considered to be fixed) and two minor axes
are “2b1” and “2b2” (to be computed). This is shown in
Figure7. A general form of the regular ellipse is defined by

x2 y2
+
=1
a2 b2

(1)

b2

Figure 7 Irregular Ellipse with Minor and Major Axis
A fitness equation is suggested in this work in order to findthe two minor axes so that the emotion changes can berecognized. A fitness function, to be discussed latter, forapplying GA, is derived to optimally compute semi-minor
axes, b1 and b2, for the top lip area and bottom lip area
respectively; emotions can thus be related to the values ofb1 and b2.
4.2 Fitness Function
A fitness function is a particular type of objective functionthat quantifies the optimality of a solution (that is, achromosome) in a GA problem so that that particularchromosome may be ranked against all the otherchromosomes. A fitness value reflecting the amount ofoverlapping between the regions covered by the overlaid
boundaries is computed for each chromosome. A pair ofindividuals are selected with a probability proportional totheir fitness and mated to reproduce their next generation.
The process is repeatedly performed with the same numberof individuals of the previous epoch. The fitness function
equation, Equation (2) and Equation (3) with b1 and b2 arederived based on the general ellipse equation, Equation (1).

m nrow(i) 2f ( x) = ( ∑ ∑ col ( j ) − 2 X 12 (1 −
a2
i j

)2

(2)

)2

(3)

ifX 1 ≥ 0m nrow(i) 2f (x ) = ( ∑ ∑ col( j) − 2 X 22 (1 −i ja2
ifX 2 ≤ 0

In Equation (2) and Equation (3), X1 and X2 are variables
which finally converged to optimized values as b1 and b2
respectively. White pixels of the preprocessed lip feature foreach column are calculated. In Equation (2) and Equation(3), col(j) is sum of white pixels occupied by jth column androw(i) is number of rows of white pixels in ith row. Equation(2) and Equation (3) are fitness equations of top and bottom
lip respectively.
4.3 Results and Discussion
In this study on a SEA subject, six emotions and one neutral
have been considered. The lip features have been given as
input to the genetic algorithm to find the optimized values.
The selected values of GA parameters are indicated in the
Table 2. The process of optimization has been carried out for
5 times for each emotion. This process of optimization isfound to be giving favorable two minor axis value b1 and b2
for top and bottom areas of the lips. Table 3 indicates the
manually measured values of b1 and b2 and the

Figure 6 Image Processed Lip for SEA©ISAROB 2007a408

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

corresponding optimized values of X1 and X2. The emotion
based on minor axes of the lip feature can now be estimatedfor the subject. The experiment result shows that the twominor axis (b1 and b2) of the lip feature are different for eachemotion there by distinctions are possible.
Table 2 Parameter Settings
Generation
250
Population size
20
Fitness scaling
Rank
Selection Function Roulette
Mutation
Gaussian
Crossover
Scattered
Stall generation
50
Stall time
20
Table 3 Classification of Emotion (SEA)
Emotions
Manually
Optimized
Mean Value by GA
Computed
Value
b1
b2
X1
X2
Neutral
44
56
49.929447.7074Fear
38
57
40.841054.9664
Happy
31
178
22.5574
74.1890Sad
44
1645.5745
17.9480
Angry
1961
12.621864.5633
Dislike76
4737.9206
41.9958
Surprise60
75
52.1640
77.4762

5. Conclusion
In this paper, a set of suitable sequences in image processing
and new fitness function for the GA application have been
proposed for determining the top-lip and bottom-lip features.
The face and lip are obtained under uneven lighting. In
image processing task, the average and median filters areapplied to smoothen the image. The Sobel edge detection isfound to perform well for edge detection. The suggested
sequence of image processing offers acceptable lip images
compared to other methods suggested in this work. The lips
are considered as an irregular ellipse. The GA is then applied
to get the optimized values of the minor axes, b1 and b2, of
the irregular ellipse by using a set of proposed fitness
functions. These optimized values of minor axes indicate thatthe values b1 and b2 can be regarded as identifying theemotion. The new method has shown successfulclassifications emotion. The proposed method of emotion
classification is a general approach to lips of any face. The
parameters listed in Table 3 are for a personified face. Such atable look-up can be applied to a particular bed-ridden
disabled dumb patient for identifying the patient’s reaction.

References
1) Haibo Li, “Computer Recognition of Human Emotion”,
Proceedings of International Symposium on Intelligent
Multimedia, Video and Speech Processing, Hong Kong,
2-4 May 2001, pp. 490-493.

©ISAROB 2007409

2) Nicu Sebe, Michael S. Lew, Ira Cohen, Ashutosh Garg
and Thomas S. Huang, “Emotion Recognition Using a
Cauchy Naïve Bayes Classifier”, Proc. of SixteenthInternational Conference on Pattern Recognition, vol. 1,
11-15 August 2002, pp. 17-20.
3) Hironori Takimoto, Yasue Mitsukura, Minoru Fukumi
and Norio Akamatsu, “Face Detection and Emotional
Extraction System Using Double Structure Neural
Network”, Proc. of the International Joint Conference
on Neural Networks, vol. 2, pp. 53-1257, 20-24 July
2003.
4) Maja Panti and Ioannis Patras, “Dynamics of Facial
Expression: Recognition of Facial Actions and Their
Temporal Segments from Face Profile Image Sequences,
IEEE Transactions on Systems, Man, and Cybernetics—
Part B: Cybernetics, Vol. 36, No. 2, pp. 433-449, April
20065) Keith Anderson and Peter W. McOwan, “A Real-Time
Automated System for the Recognition of Human Facial
Expressions”, IEEE Transactions on Systems, Man, andCybernetics—Part B: Cybernetics, Vol. 36, No. 1, pp.
96-105, February 2006.
6) Liyanage C De Silva and Suen Chun Hui, “Real-time
Facial Feature Extraction and Emotion Recognition”,
Proceedings ofFourth Pacific RIM Conference on
Multimedia, vol. 3, 15-18 Dec 2003, pp. 1310-1314.
7) Nicu Sebe, Miachel S. Lew, Ira Cohen, Ashutosh Garg
and Thomas S. Huang , “Emotion Recogmnition Using a
Cauchy Bayes Classsifier”. Proceeding of 16th
International Conference on pattern Recognition, vol. 1,
11-15 August 2002, pp. 17-20.
8) Rafael C Gonzalez and Richard E Woods, “Digital
Image processing”. Pearson Education, Inc, India, 2002.
9) Gary G. Yen and Nethrie Nithianandan, “Facial Feature
Extraction Using Genetic Algorithm”, Proceedings ofCongress on Evolutionary computation, vol. 2, 12-17
May 2002, pp. 1895-1900.
10) M. Karthigayan, Mohammed Rizon, Sazali Yaacob and
R. Nagarajan, “An Edge Detection and Feature
Extraction Method Suitable for Face Emotion Detection
under Uneven Lighting”, The 2nd Regional Conferenceon Artificial Life and Robotics (AROB’06), July 14 July 15, 2006, Hatyai, Thailand.
11) R. Nagarajan, Sazali Yaacob, Paulraj Pandiyan, M.
Karthigayan, Marzuki Khalid and Shamsudin H Amin,“Real Time Marking Inspection Scheme for
Semiconductor Industries”, International Journal ofAdvance Manufacturing Technology (IJAMT), Springer
Publisher, 2006. (Accepted for publication).
12) Michael Negnevitsky, “Artificial Intelligence”, Addison
Wesley, Pearson education Limited, England, 2002.
13) http://neo.lcc.uma.es/TutorialEA/semEC/cap03/cap_3.ht
ml, 1997.
14) Hideaki Tani, Kenji Terada, Shunichiro Oe and Junichi
Yamaguchi, “Detecting of One’s Eye from facial Imageby Using Genetic Algorithm”, The 27thAnnual
Conference of the IEEE Industrial Electronics Society,2001, pp. 1937-1940.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Face localization for facial features extraction using symmetrical filter and linear
Hough transform.
Hamzah Arof
Dep. of Electrical Engineering
University Malaya,
Malaysia

Fauzan Ahmad
Fac. of Engineering and Industrial Technology
IUCTT, Malaysia

Abstract
In this paper, face localization for facial feature
extraction is presented. The method consist threesteps: (1)facial features enhancement using
symmetrical filter, then the morphologicalprocess is applied to examine the edge, peaks
and valley fields. (2) Line construction using
linear Hough transform and (3) localization offace region based on the constructed lines andthe elimination of excess area outside of faceboundary.

Noraisyah M. Shah
Dep. of Electrical Engineering
University Malaya
Malaysia

interocular distance which is constant amongpeople and not easily effected by moustache orbeard.

1.1 Face region extraction and facial
features enhancement
Our algorithm extracts the face region using asimilar method to the one shown in [4].

Key words: face localization, symmetrical filter,
linear Hough transform.

1. Introduction
Face detection defined by [1] is the following:
given an arbitrary input image, find the locationand size of the human face in the image. There is
no assumption made regarding the number of thefaces in the image. In face localization it isnormally assumed that the input image only
contains one face in the foreground. Facesegmentation as defined by [2] is described as aprocess as finding the location and actual shapeof face(s) appearing in a given image. Facesegmentation is more challenging because it not
only locates the position of human face, but also
segments the actual shape of complete humanface [3].In this paper, we will discuss ourapproach for face localization, which will startwith coarse face region extraction. Ouralgorithm tries to localize the face with thecomplete facial features and if possible, it willsegment the face to get the actual shape ofcomplete human face. The localized or
segmented face could be further processed for
facial features extraction such as eyes, nose andmouth detection, which is a key requirement forface recognition. Among those facial features,eyes are the most salient feature due to its©ISAROB 2007

410

Fig1. A head-shoulder image

Fig.2 The face region extracted from the image of Fig.1

Then the estimated face area is further cropped byremoving the top region of the face image where
hair lies. If hair is not found in the top region theimage is left uncropped

Figure3 Cropped face region.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

After that, a symmetrical filter is applied to thecropped face region to remove sharp edges andoccluded dark patches while maintaining thesalient features of the eyes0-2 -4 -2
0
-2
2
6
2
-2
-4
6
8
6
-4
-2
2
6
2
-2
0
-2 -4 -20
Figure 4 Symmetrical mask

reduced by linking process and therefore betterpeak and valley images are obtainable.

Figure 6 The output after linking process
After the linking process, we obtain the final
enhanced image by combining the linked image,the edge image, the peak and valley fields in thefollowing way.

H (i, j ) = L(i, j ) + E (i, j ) + I (i, j ) − V (i, j )Where H (i, j ) is the enhanced image, L(i, j ) theimage after linking process, I (i, j ) the original
image, V (i, j ) the valley image. The output of the

Figure 5 The face region after we apply thesymmetrical mask on figure 3.

enhanced image is shown in figure 7 below.

1.2 EXTRACTION OF THE EDGE,
VALLEY AND PEAK INFORMATIONLet I(x,y) be the image obtained by cutting off
the face region from the original intensity image
given as input. We produce an edge image
E(x,y), a valley V(x,y) and a peak image P(x,y)
in the following ways. E(x,y) is given by
applying the Sobel operator to I(x,y). The valley
V(x,y) and peaks P(x,y) images are obtained
from mathematical morphology as follows.LetG(x,y) denote the image obtained by applying agrayscale closing [6] to I(x,y) and H(x,y) be theimage obtained by applying a grayscale opening[6] to I(x,y). Then, the valley image V(x,y) is
given by G(x,y) minus I(x,y) and the peak image
P(x,y) is given by I(x,y) minus H(x,y). The
mathematical morphology is followed by linking
process based on the gradient of the edge field.
This process is useful to combine isolated edge
pixels in the filtered image while reducing theeffect of white spots on irises. In figure 5, we
could observe that there are broken edges andisolated pixels where the edges are notconnected and they are visibly different fromtheir surrounding neighbours. The best example
to illustrate this condition is by observing theeye area. We notice that the eyeball is not totallyfull of black pixels and it contains dots of white
pixels due to reflection. This effect can be©ISAROB 2007

411

Figure 7 Enhanced image
Figure 8 compare the valley and peaks for theimage before enhancement and after we apply thesymmetrical filter, linking process and image
enhancement.

(a)

(b)

(c)

(d)

Figure 8 Comparison the valley image (a) before image
enhancement (b) after image enhancement. Comparisonof peaks image (c) before image enhancement (d) afterimage enhancement

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

check if the area is less than 30% of the total area.

1.3 Linear Hough transform
The edge field E(x,y) obtained in the last section
is reused in the linear Hough transformation. For
every edge pixel, the line angle can be varied
from 0° to 180° with an increment of 1°. The
distance from the origin to the nearest point on
the line is calculated. Both the angle and thediscretized distance will become an index to the
2-D accumulator array where its frequency is
accumulated. Then an accumulator array is used
to store the frequency of various lines for every
single edge pixel. After the accumulation
process, a data reduction scheme is employed toreduce the size of the accumulator array andtherefore avoid redundancy. The accumulator
array is divided into 2x2 subarrays. For each 2x2
subarray only one major value is retained (out offour) while the remaining three values are set tozero. In our experiment θ is restricted until from0°-30° and 0°-45°. By applying this approach,the size of array will not be a major drawback inthe execution time of our program. Figure 9
show the edges and the line constructed using
Linear Hough transform.

(a)(b)
Figure 9 (a) Edges of the image (b) Linesconstructed using Hough transform.

Major area
Figure 10 One of the line divided the image into minor
area.

From our experiment, if the percentage of theminor area over the total area is higher than 30%,
it will dissect part of the face region and therefore
erroneous. For each line with area less than 30%,
we will examine the pixel distribution lying on therespective line and if the black pixels are greaterthan 70% of the total pixels on the line, the pixelin the minor area will all be set to zero. The
second threshold is used to indicate that the line
should lie on the hair. The process stops when
there is no more line to consider.

1.5 Experimental results
The efficiency of this method was tested onuniversity of Bern face database which contains
300 frontal faces under controlled lighting
conditions. The database is characterized by smallchanges in facial expression and immediatechanges ( ± 30° ) in head pose, with two images
for each of the poses right, left, up, down andstraight. Using the 300 face image in the database,line constructed by varying the angles (θ) from 0°30° and from 0°-45° are examined. For simplicity,let θ 1 to represents bounded angles of 0°- 30° andθ 2 for bounded angles of 0°-45°. Table 1 showsthe experimental result for θ 1 and θ 2 . For θ 1 , themaximum line investigated is 30° and for θ 2 the

1.4 Using constructed line for facelocalization and segmentation
Finally, selected lines will be constructed aroundthe face region based on the information fromthe accumulator array. The constructed lines will
be used to localize face region by eliminatingareas outside the face boundary. The
examination of each line involves inspecting theminor area covered by the line and the intensity
pixel distribution lying on the respective line.
The line will divide the image into two areas,
namely minor area and major area as shown infigure 10. We will examine the minor area to©ISAROB 2007

Minor area412

maximum line investigated is 45°.
Facial features
Eyes
All (eyes,mouth,nose)
Mouth eliminated
Mouth and nose eliminatedEye corner eliminated

θ1

θ2

100%
85%
8%
1.33%
0.33%

100%
85%
8%
1.33%
0.66%

Table 1 Comparison of the facial features contains in thelocalizes face for θ 1 and θ 2

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

outside of the face boundary. For the two bounded
angles, the face localization rate which contains all
facial features are 85%, while the rate for images
that contain both eyes is 100% and the future
works will concentrate on eye detection or eye
feature extraction.

References

θ1

θ2

Figure 11 Differences localize face between θ 1
and θ 2(a)(b)
Figure 12 sample of the localize face with(a)Mouth eliminated (b)Mouth and nose
eliminatedFigure 13 Sample of the eliminated eye cornerfor θ 2

Summary
In this paper, face localization for facial feature
extraction is presented. The coarse region of theface is extracted, and then the facial features areenhanced using symmetrical filter. The
morphological process is applied to produce theedge, peaks and valley fields. Lines areconstructed in the face region using Linear
Hough transform and the constructed lines areused to localize face region by eliminating areas©ISAROB 2007413

[1]M. Yan, D.J Kreigman ,N Ahuja (2002).
Detecting faces in image: a survey. PAMI 24(1)34-58.
[2] F. Marques ,V.Vilaplana (2002). Facesegmentation and tracking based on connected
operator and partition projection. Patternrecognition 35,601-604.
[3] Z. Liu,J. Yang, N.S Peng(2005). An efficient
face segmentation algorithm based on binary
partition tree. Signal processing: Image
communication 20, 295-314.
[4] T. Kawaguchi, M. Rizon (2003).Iris detection
using intensity and edge information. Pattern
Recognition 36, 549-562.
[6] S.R Sternberg (1986).Grayscale morphology.
Comput. Vision Graphics and Image Process,
35,333-335.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Imitation of Human Action Intelligence for the Environment of Desktop Teleoperation
Tao Shang
School of Information Science andEngineering
Shenyang University of Technology
Shenyang, 110023, P.R.China
shangtao@sut.edu.cn

Shuoyu Wang
Department of Intelligent Mechanical System
Engineering
Kochi University of Technology
Kochi, 782-8502,Japanwang@mech. kochi-tech.ac.jp

Abstract

research, we believe that human action can remain in form of
direct data and human action intelligence is certainly hidden in
the remaining data. Thus we address to extract action
knowledge representing human action intelligence fromhuman action data and then imitate human action by
employing the extracted action knowledge. Based on theeffectiveness of previously proposed imitation method in
simulation environment [4], we need to further verify theeffectiveness of proposed imitation method and achieve theimitation of action intelligence on a real mobile robot.

It is well known that humans have high-level action
intelligence. If such action intelligence could be represented
in certain form, it could contribute to the path-planningproblem of autonomous mobile robot. Therefore we shalladdress to transfer human action intelligence to mobile robot
by means of imitation and acquire such action intelligence.
Based on the effectiveness of previously proposed imitation
method in simulation environment, we need to further verify
the effectiveness of proposed imitation method and achieve
the imitation of action intelligence on a real mobile robot. In
this paper, the imitation of human action intelligence for theenvironment of desktop teleoperation, including experimental
platform, research methods, and experimental procedures,
was discussed. Firstly, imitation problem was clarified fromthe viewpoint of problem solving system, which provides
guidance to the construction of imitation system. As anexperimental platform, a desktop teleoperation system,
including mobile robot and manipulation interface, wasintroduced. Through the developed teleoperation system, theintention representing human action intelligence can be easilytransferred into mobile robot and meanwhile thecorresponding action data can also be obtained for modeling.
Then, based on the teleoperation system, the imitation
procedure of human action intelligence was described. Finally,
the effectiveness of proposed imitation method was
illuminated by experiment.

1 Introduction
It is well known that humans have high-level action
intelligence. If such action intelligence could be acquired incertain form, it can be applied to various fields such as thedevelopment of autonomous mobile robot, which can freely
move even under complex environment. Naturally, if desired
actions were appropriately achieved by imitating human action,such imitation methods would transfer human action
intelligence into practical application and facilitate theacquisition of human action intelligence. So far, many
imitation approaches have been popularly developed in thefield of both robotics and artificial intelligence [1-3]. For our©ISAROB 2007

414

The construction of this paper is organized as follows:
imitation problem is clarified from the viewpoint of problem
solving system in section 2. The objective imitation system isverified to construct in term of reasoning method, knowledge
acquisition and knowledge base. As an experimental platform,
the teleoperation system is described in section 3. Then an
imitation procedure is proposed to construct objective
imitation system. Here, learning method and reasoning method
emphasizing knowledge usage are considered to implement
the imitation of action intelligence. To illustrate thecharacteristics of proposed method, the experiments are
conducted in section 4. The paper finishes with a conclusion insection 5.

2 Problem solving system for imitation
As the basic definition of imitation, imitation is not to create
an original act or instance, but to try to reproduce an existing
one so that same or similar act or instance could happen in
imitator. Consequently, it can be said that if an imitation
system (virtual imitator) does same or similar act as human,such system can be thought to be as intelligent as human.
According to the development of problem solving system [5],the imitation system of action intelligence can be constructedjust as in figure 1 by means of acquiring action knowledge,
memorizing action knowledge, and reproducing the action.
Knowledge acquisition method can be adopted to acquire
action knowledge; knowledge base can be constructed tomemorize knowledge representing human action intelligence;
based on the acquired and memorized knowledge, not only
precise action, but also predictive action for environmental
variation is needed to reproduce, so fuzzy reasoning method
can be adopted to implement this function.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Input

OutputHuman

Imitation
evaluation
Knowledge acquisition
Knowledge
Knowledge baseReasoning method

OutputFigure 3. Photo of the mobile robot

Imitation system

Table 1 Specification of the mobile robot
Item
HeightLength/Width
WeightMoving velocity

Figure 1. Construction of imitation system

3 Teleoperation system

Detail600mm
800mm/800mm
about 50kg
about 0.3m/sec

ATX Power source

Supersonic sensor

DC-AC inverter

PC

RIF-

Battery 12V 5.0Ah
Board

Figure 2. Teleoperation system

TITECH-motor

Just as in the figure 2, the developed teleoperation system
connects the interaction between human and corresponding
environmental space and provides a platform to fully employ
human’s obstacle avoidance ability. The platform mainly
includes two parts: mobile robot equipped with digital cameraand supersonic sensor, manipulation interface with thefunctions of both manual operation and autonomous operation.
Facing the visual environmental space of manipulationinterface, an operator can control the speed and direction of amobile robot by joystick. By judging the goal and surrounding
obstacles, operator can try to avoid obstacle and arrive at thegoal. As a result, operation is treated as successful operation,
only if the robot controlled by operator arrives at a goal from astarting point with free collision during a specified timeinterval; otherwise, it is treated as failed one.

3.1

CameraPC for camera

DC-Motor

DC-MotordriverOmniwheel

Omniwheel

Figure 4. Hardware of the mobile robot

3.2

Manipulation interface

Manipulation interface was developed by means of VisualC++ 6.0 under the environment of windows XP. Figure 5
shows the software of manipulation interface. The left part of
Figure 5 is the visual surrounding environment of mobile
robot from camera (Creative technology Corp., WEBCAMNX Pro), while the right part of figure 5 are the experimental
module such as teleoperation, learning and reasoning forimitation.

Mobile robot

The mobile robot is equipped with ball actuator so as to beable to move on any direction of 0~360 degree. Figure 3 is theappearance of the mobile robot, and table 1 shows the
specification of the mobile robot. Figure 4 shows the
Hardware construction of the mobile robot. DC motor andomni-wheels are evenly arranged every 90 degrees on thelower part of robot. The regular output of DC motor is 90W
and its regular torque is 45kg.cm. Five supersonic sensors areevenly equipped on the upper part of robot to measure thedistance between robot and surrounding object. Its maximum
measure distance is 3000mm.

©ISAROB 2007

415

Teleoperation

Learning
Reasoning

Figure 5. Manipulation interface
According to the structure of imitation system in section 2,
these modules are necessary components for implementing
imitation. Teleoperation module is used for manual operation

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007of mobile robot to obtain the action data of operators. Bothlearning module and reasoning module are for autonomous
operation of mobile robot. Learning module is used to acquire
action knowledge from data, while reasoning module is used
to reproduce the action of human.
Concrete learning and reasoning algorithms have been
described in the literature [4]. Based on the effectiveness ofpreviously proposed imitation method in simulation
environment, we need to further verify the effectiveness ofproposed imitation method and achieve the imitation of action
intelligence on a real mobile robot. Compared with most of
methods focusing on how to acquire knowledge better such as
the acquirement and adjustment of fuzzy rules andmembership function [6-8], alternatively, we are taking it into
account that it will be more meaningful to pay attention to theimportance of knowledge usage for imitation performance,
according to the theorem that the SAM (Standard Additive
Model) architecture allows independent rules to function
cooperatively and the credibility of knowledge source isequivalent [9]. Also, considering the fact that humanselectively employ knowledge for reasoning, i.e., not allknowledge in one’s brain is used for reasoning. Considering
the quantifiable characteristics of the relationship between thefact and rule, we shall adopt the distance between fuzzy set as
the criterion of knowledge selection to strengthen knowledge
usage during reasoning process. Concretely, the concept ofknowledge radius[11] is introduced for knowledge selection.
Knowledge radius q is the number of rules with the nearest
distance between fact and antecedent of a rule. Where, q is an
integer, [2, L , n ] , n is the number of rules.
The Distance-Type Fuzzy Reasoning method withknowledge radius is adopted to develop manipulation interfaceof teleoperation system. The fuzzy reasoning method isdeveloped on the Distance-Type Fuzzy Reasoning (DTFR)
method, which is a kind of fuzzy reasoning method
considering the distance between fuzzy sets [10]. It features
two main characteristics: asymptotic and decomposition
characteristics (Modus Ponens is strictly satisfied). Thus thephysical meaning of rules for reasoning is clear and reasoning
tendency can be predicted appropriately.
Also, an improved learning algorithm for Distance-Type
Fuzzy Reasoning is adopted for knowledge acquisition [4],because it is strongly related to the above reasoning method.
Compared with conventional data learning methods for thegeneration of fuzzy rule such as GA or NN, this algorithm isvery fast and simple with an arbitrarily specified error forlearning, especially, it is suited for common numerical data
learning. Meanwhile it also considers the statisticalcharacteristics of teacher data to improve the selection ofconsequent for contradictive rule.

©ISAROB 2007

416

3.3

Imitation procedure

Based on the reasoning engine of the Distance-Type Fuzzy
Reasoning method with knowledge radius, and the acquired
knowledge by the learning algorithm for Distance-Type Fuzzy
Reasoning, optimum knowledge radius is verified to
implement the imitation of action intelligence. The imitation
procedure consists of the following four steps:
Step1: Acquire the action data of human by means of
teleoperation module of manipulation interface;
Step2: Acquire knowledge from the remaining action data bymeans of learning module of manipulation interface;
Step3: Calculate the evaluation value of knowledge radius andset the optimum value of knowledge radius;
Step4: Treat the reasoning performance of manipulationinterface based on optimum knowledge radius as the imitation
performance.

4 Experiments
4.1

Learning procedure

In order to describe variable environment for knowledge
acquisition, the physical units between robot and objects,
including goal and obstacle, are given by (1).
(1)
if S1 S 2 S 3 S 4 S 5then X Y Z
Where
S1 S 2 S 3 S 4 S 5 : respectively denotes the distance
between robot and the nearest point of an obstacle/goal, itsvalue varies in;
X Y Z : denotes the control behavior in form of joystick’s
position, X ∈ [− 790,627 ] Y ∈ [− 930,215] Z ∈ [− 961,96] .

Figure 6. Operation path
As can be observed in figure 6, an operator conductedobstacle avoidance and successfully arrived at the goal point.
Then by using the learning module of manipulation interface,action rules were generated. Concretely, table 2 lists theparameters for learning procedure.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 2. Learning parameters

4.2

4.3

Environment

Two Static obstacles
o1(250,100) o2(20,90)

Number of data

435

Learning error
Learning time
Number of rules

50
1.8 Second
96

Validation

The above experiment demonstrated the good performancebased on the acquired rules with optimal knowledge radius 20,
which does not mean the only rules within knowledge radius
are useful, but all the more illuminate the effectiveness ofboth knowledge radius and current rules. We can draw thesame conclusion as simulation that all of rules are useful, andgood performance can be achieved by focusing on local
knowledge, i.e., the control path can be effectively imitatedby emphasizing knowledge selection.

Search of effective knowledge radius

5 Conclusion
In this paper, we discussed the imitation performance ofproposed imitation method based on a developed
teleoperation system and proposed corresponding imitation
procedure. Through this teleoperation system, the data
representing human action intelligence can be acquired andfurther be used for imitation. The effectiveness of imitation
can be verified by experiment. Furthermore, the identificationof knowledge radius is still needed to discuss for theguideline to specific problem.

(a)without knowledge radius

References(b) with knowledge radius( q = 20)
Figure 8. Reasoning results
Y(cm)

[1]

Stefan Schall: Is Imitation Learning The Route to Humanoid Robots?,
Trends in Cognitive Science, Vol. 3, pp.233-242 ,1999.

[2]

Dautenhahn,K. and Nehaniv,C.L. : Imitation in animals and artifacts,
Cambridge, Massachusetts: the MIT press, 2002.

[3]

A. Billard and M. Mataric: Learning human arm movements byimitation: Evaluation of a biologically-inspired connectionist
architecture, Robotics & Autonomous Systems 941. pp.1-16. 2001.

[4]

Tao Shang and Shuoyu Wang : Knowledge Acquisition and Evolution
Methods for Human Driving Intelligence, International Journal of
Innovative Computing, Information and Control, Vol.2,No.1, pp.221236,2006.

[5]

K. Forbus and J. de Kleer:Building Problem Solvers,MIT Press,1993.

[6]

H. Ishibuchi and M. Nii: Generating Fuzzy Classification Rules fromTrained Neural Networks, Journal of Japan Society for Fuzzy Theory
and systems, Vol. 9,No.4, pp. 512-524,1997.

[7]

K. Tachibana and T. Furuhashi: A Proposal of Fuzzy Modeling Methodwith Generation/Deletion of Membership Functions, Journal of Japan
Society for Fuzzy Theory and systems, Vol. 11, No.6, pp. 10781088,1999.

[8]

H. Inoue,L. Hng, K. Miyasaka, and M. Tsukamoto: An Acquisition
Method of Fuzzy Classification Rules using Hyper-cone Membership
Function by Gas, Proceedings of 19th Fuzzy system symposium, pp.
445-448, Sakai, 2003.

[9]

B. Kosko: Fuzzy Engineering,Prentice hall, New Jersey, 1997.

2
3
4
1

5
(20,90)(250,100)

8
9

6

7

10
(0,0)

X(cm)

Figure 8. Control path and reasoning path
(1:control path 2: without knowledge radius 3: q = 30 4: q =20 5: q
=16 6: q =15 7: q =14 8: q =12 9: q =10 10: q = 5)

For convenient analysis of imitation performance, path area
error S between human’s control path and reasoning path iscalculated as criterion. Considering the practical problem ofrobot, the search scope of q is limited in [5, 10, 12, 14, 15, 16,
20, 30]. Figure 7 and 8 show the imitation performancecorresponding to different knowledge radiuses. We can verify
that the only optimum knowledge radius q is 20 with theminimal path area error.

©ISAROB 2007417

[10] S.Y.Wang, T. Tsuchiya, and M. Mizumoto: Distance-Type Fuzzy
Reasoning Method, Journal of Biomedical Fuzzy System Association,
vol.1, no.1, pp.61-78, 1999.

[11] S.Y. Wang: The Development of Human Reasoning Engine, 2002 IPA
Project,2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Introduction of Multi-Step K-means Clustering
Applied to Rice Microarray Data†

Daehoon Park, †Yountae Kim, ††Ok-Kyung Ham, †Sungshin Kim, and ††Choon-Hwan Lee†

School of Electrical and Computer Engineering, Pusan National University, Busan, Korea
Pusan National University 30 Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: dhsmile@pusan.ac.kr, dream0561@pusan.ac.kr, sskim@pusan.ac.kr††

School of Molecular Biology, Pusan National University, Busan, Korea
Pusan National University 30 Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: 82dhrrud@pusan.ac.kr, chlee@pusan.ac.kr
Abstract − Long gene sequences and their products
have been studied by many methods. The use ofDNA(Deoxyribonucleic acid) microarray technology
has resulted in an enormous amount of data, which has
been difficult to analyze using typical researchmethods. This paper proposes that mass data beanalyzed using division clustering with the K-meansclustering algorithm. To demonstrate the superiority ofthe proposed method, it was used to analyze themicroarray data from rice DNA. The results werecompared to those of the existing K-means method
establishing that the proposed method is more useful inspite of the effective reduction of performance time.
Keywords: K-means clustering, Microarray, Rice
1. Introduction
Since the inception of biotechnology, large amountsof new genetic information have been amassed fororganisms varying from bacteria to human beings. This
information has provided many clues for solving
biological problems. However, new technologies areurgently needed because most genetics engineering
methods have limitations. One method that has beendeveloped to overcome the problems of the existing
methods is searching genetic material using a DNA
chip. The DNA chip is divided into a cDNA chip andoligonucleotide chip depending on the size of thegenetic material. A large amount of genetic
information can be obtained by using this DNA chipanalysis technique [1]. The development of an efficient
clustering algorithm for DNA microarray data willcontribute to research in several important fields suchas functional genomics and genetic networks.
Furthermore this information can be analyzed byvarious data mining methods, and these results can beevaluated by many methods. A literature review offormer data mining methods reveals a data clustering
algorithm using graph theory and an algorithm by
Hartuy and Ben-Dor et al. [2] [3], and Tamayo et al.
developed the Self-Organizing Maps (SOM) algorithm[4]. And Eisen et al. proposed and developed a method
using hierarchical clustering [5].

©ISAROB 2007

418

The most typical method of analysis for microarray isclustering. Clustering analysis classifies a large amountof genetic information into several groups that have
similar properties, so it is effective for analyzing data.
The hard clustering method has been especially useful
because it is intuitive and its ease of use.
In this paper division clustering using a two step
structure K-means clustering method is proposed inorder to treat process the thousands of pieces of
microarray data effectively. The proposed method
processes enormous amounts of microarray data fasterbecause it classifies and clusters data twice. The
reliability of these clusters has been evaluated bycomparing these results with those of the earlier Kmeans clustering method.
2. Microarry
People usually think that enormous number of genes
and their products of a living organism create themystery of life. Most molecular biological methods areused to study one gene during one experiment.
Consequently, the DNA microarray method has attracted
wide-spread attention from biologists to monitor thewhole genome on a single chip so that researchers canbetter monitor the results of the simultaneousinteractions of the numerous genes [6]. The DNA
microarray technique is comprised of the cDNA
microarray and oligonucleotide microarray techniques.
Microarray data from an experiment on the
manifestations of a gene can be classified using the dataclustering method. Thus results can be used in many
fields including the development of drugs andtoxicological research. In this paper we used themicroarray data from rice genes consisting of 17,000
units.
3. Clustering Algorithm
3.1 Several Clustering Algorithms
The clustering algorithm classifies a whole data set
into several clusters that have similar characteristics.
This algorithm has been used in a variety of fields suchas pattern analysis and classification, grouping, decision
making, machine-learning situations, and data mining [7].
This clustering algorithm has been developed by experts
from various scientific fields such as statistics, computer

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

science and biology. Now the application of theclustering algorithm is more actively studied than thealgorithm itself. The clustering algorithm is largely
divided into a hierarchical clustering algorithm and apartitional clustering algorithm [7].
In the clustering algorithm, it is important to define
the similarity of two clusters. Similarity wasdetermined by Euclidian distance and measured using
equation (1).

Start
X l s d a ta
Read DataFirst Clustering

Second Clustering⎧⎫d ( xi , x j ) = ⎨∑ ( xi , k − x j , k ) 2 ⎬⎩ k =1
⎭
d

1
2

(1)
Data Extract

3.2 K-means Clustering Algorithm
The K-means clustering algorithm used in this paper
is the most frequently used of the partitional clustering
algorithms. K-means clustering differs fromhierarchical clustering in that the number of clusters, k,needs to be determined at the outset. The goal of the Kmeans clustering algorithm is to divide the objects into
k clusters such that some metric relative to thecentroids of the clusters is minimized. Two procedures
are available to search for the optimum set of clusters.
The first assigns each object to a cluster and thesecond sets initial positions for the cluster centroids.
The K-means algorithm consists of the K-means
method and K-medoid method. In this paper we used
the average value as the center of the cluster.

Save Result

E ndFig. 2. Flow chart of the proposed algorithm.
START

Obtain absolute values frommaximum value of each rows
YES
V al ue > 1

4. Implementation of the Division System Using the
Two-Step Structure K-means Algorithm
In this paper, we used Matlab 7.1 and GUI is shownin Figure 1. GUI is divided into three parts: input,
clustering, and results. On the left-side of GUI, theresults are shown in the graph window and character
window. On the right-side of the GUI, the button thatare used to operate the algorithm in the order they areneeded.

NO

V al ues of eac h r ow
/ The value wePrint the result

END

Fig. 3. Normalization flow chart of normalization.

Fig. 1. GUI of the divided clustering system.

4.1 Normalization
The microarray data used in this paper has a value
between -1 and 1, but a few datum are out of this range.
The clustering algorithm is based on the similarities
between the data, so if the data is outside of the normal
range, that value determines its total similarity. Therefore
this data needs to be adjusted so that it is between -1 and1. Consequently it is divided by the maximum or
minimum value to normalization. Figure 3 shows thenormalization flow chart.

Figure 2 shows the flow chart of the program usedin this paper, as can be seen, the rice gene microarraydata has been normalized and clustered twice. The
clustering step ends when the data has been clustered
for the second time then the data from the center valueof each cluster can be gathered, and the results saved.

4.2 Division Clustering Algorithm
The data consisted of 17,000 units, so it took too long
to cluster the entire data set at one time. First, the 17,000
units were divided into 17 groups consisting of 1,000
units, and then each group was clustered. In this paper, k
was set at 36. The clustering algorithm was performed asecond time using the typical values of the first©ISAROB 2007

419

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

clustering. Thus the data was processed twice, and
17,000 units of data were clustered.

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Fig. 6. The clustering results using the previous method.

1 7 * 3 6 u n i ts

1 7 0 0 0 u n i ts

Fig. 4. Process of dividing the clustering.
4.3 Data Analysis and Saved Data
The results of the clustering determined the data thatwas saved. The graph file of typical values and the text
file of the gene numbers were saved from theclustering results.
5. Simulation and Results
In this paper the clustering method for rice
microarray data was analyzed and a new clustering
method for a large amount of data like that in the rice
microarray data was proposed. Figure 5 shows theresults of the clustering of the proposed algorithm.
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0
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Table 1. Comparison of the performance of the previous
method and proposed method.
clustering method
evaluation standards

formermethodproposedmethod

2 4 6

performance time (sec)

251

46.45

2 4 6

standard deviation

0.7702

0.7987

2 4 6

Figure 7 compares two typical values of the two
methods when k is 36. As can be seen, the clusteringresults are similar. Figure 8 compares the methods when
k is 16, and the result are also similar.

2 4 6

2 4 6

2 4 6

Fig. 5. The clustering results using the proposed
method.

©ISAROB 2007

Figure 6 shows the result of the previous method thatclustered the entire data set of 17,000 units at one time.
Table 1 compares the results of both method using twovalues. First, the time each method needed to cluster
each data set was compared, and the shorter time isconsidered to be better. And second, the standard
deviation between the typical values resulting from eachalgorithm was compared, and the larger standard
deviation is an indication of a cleaver classification of
the data. A computer with a 3GHz CPU and 512 MB
RAM was used to run the algorithm. The previous
method required 251 seconds to cluster the data and the
proposed method, 47.76 seconds. Therefore, theproposed method sharply reduced the clustering time andis better than the previous method. The standard
deviation of the previous method is 0.7702, and thestandard deviation is proposed method, 0.7987.
Consequently it can be concluded that the proposed
method is better.

420

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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methods using 17 groups, and a k of 36.
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using 17 groups, and a k of 16.
6. Conclusion
Most of the study methods used previously have
limits. For example, the hierarchical clustering
methods cannot process mass data. The previous
partitional clustering method can takes more time thanproposed method to classify data. In this paper,
clustering was divided into two steps using K-meansclustering. This method allowed a large amount of data
to be processed more quickly than the previous Kmeans method but with similar results. The speed ofthe proposed algorithm needs to be verified using otherlarge data sets.
ACKNOWLEGEMENT
This work was supported by the Second-stage of theBrain Korea 21 project in 2006.
References[1] Hwang, Seung Yong: DNA chip technology. Korea
Information Science Society, 18(8).(2000), 23- 28©ISAROB 2007

421

(in Korean)[2] Hartuv, E., Schumitt, A., Lange, J., Meier-Ewert, S.,
Legrach, H. and Shamir, R.: An Algorithm for
Clustering cDNAs for Gene Expression Analysis.
Proceed ings of the Third International Conference
on Computational Molecular Biology (RECOMB99), (1999), 188- 197[3] Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering
Gene
Expression
Patterns.
JournalofComputational Biology, 6. (1999), 281- 297[4] Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q.,
Kitareewan, S., Dmitrovsky, E., Lander, E. S. andGolub, T. R.: Interpreting patterns of gene
expression with self-organizing maps : Methods andapplication to Hematopoeitic differentiation.
Proceedings of National Academy of Sciences of the
USA , 96. (1999), 2907- 2912
[5] Eisen, M. B., Spellman, P. T., Brown, P. O. and
Botstein, D.: Cluster analysis and display ofgenome-wide expression patterns. Proceedings of
National Academy of Sciences of the USA , 95.
(1998), 14863- 14868[6] http://gene-chips.com[7] Jain, A. K., Murty, M. N. and Flynn, P.J.: Data
clustering: A review. ACM Computing Surveys,
31(3). (1999), 264-323

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Cost-Based Fuzzy System
for Pattern Classification with Class Importance
T. Nakashima, Y. Yokota, H. Ishibuchi
Graduate School of Engineering
Osaka Prefecture University
Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan{nakashi, hisaoi}@cs.osakafu-u.ac.jp
yokota@ci.cs.osakafu-u.ac.jp
Abstract
This paper proposes a cost-based fuzzy classification system for pattern classification problems withan order class importance. The task here is to minimize the misclassification of patterns from an important class. It is assumed in this paper that the classification importance is given for each class, not for eachpattern. Another assumtion in this paper is that only
the order of importance is given for given classes without any numerical measures of importance. We show
the performance of the proposed cost-based fuzzy classification system for a real-world pattern classification
problem.

1

Introduction

Fuzzy rule-based systems have been mainly applied
to control problems [1]-[3] while more recently they
have also been applied to pattern classification problems. One advantage of fuzzy rule-based systems is its
interpretability. There are many approaches to the automatic generation of fuzzy if-then rules from numerical data for pattern classification problems [4]-[11].
There are several cases where misclassification of aparticular input pattern causes extra costs. For example, in the medical diagnosis of cancer diagnosing malignant tumors as benign and hence mistaking a cancer
patient as healthy could be penalized more than interpreting benign tumors as malignant. In [12] a pattern classification problem is re-formulated as a cost
minimization problem. The concept of a weight is introduced for each training pattern in order to handle
this situation. The weight of an input pattern can beviewed as the cost of misclassification of the pattern.
Fuzzy if-then rules were generated by considering the©ISAROB 2007

422

G. Schaefer
School of Engineering and Applied Science
Aston University
Birmingham B4 7ET, U.K.
g.schaefer@aston.ac.uk

weights as well as the compatibility of training patterns.
In many cases it is difficult to specify an exact valueof the cost for misclassification. Contrary to this, theorder of importance is easily available for a problem
domain. For example, in the medical diagnosis of cancer it is reasonable to consider that the misclassficationof malignant tumors as benign incurs higher cost thanthe other misclassification even if we do not know how
high the cost is for both types of misclassification (i.e.,
misclassifying malignant tumors as benign and benign
ones as malignant).
In this paper we propose a method for constructing
a fuzzy classification system that considers the orderof class importance. The assumption here is that a
set of training patterns and the order of class importance is given a priori. We compare the performance ofthe proposed fuzzy classification systems and the conventional ones for a real-world pattern classification
problem.

2

Classification Problem

Without loss of generality, we assume thatgiven training patterns are distributed over an ndimensional pattern space [0, 1]n . We also assume
that m training patterns xp = (xp1 , xp2 , . . . , xpn ),p = 1, 2, . . . , m, from C classes are given a priori.
In many real-world problems, the misclassification
cost is different for different patterns. It is often thecase that misclassification cost is different class byclass. As an example let us consider the medical diagnosis of cancer where the task is to diagnose a tumor as
benign (not cancerous) or malignant (cancerous). Although any misclassification should be avoided, more

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Membership

Membership

attention should be put into the correct diagnosis ofmalignant than that of benign tumors because the misdiagnosis of the cancer patient as not having the disease leads to much higher costs than the other misclassification. In this paper, we handle this situationby specifying different misclassification costs for different classes and put more emphasis on minimizing
the misclassification costs rather than minimizing thenumber of misclassified input patterns.

1.0

1.0

0.0

Input value

1.0

Membership

Membership

1.0

1.0

Input value

1.0

0.0

Input value

1.0(c) L = 5

Figure 1: Triangular-type membership function used
for antecedent fuzzy sets.

The proposed method is based on a fuzzy rule-basedclassification system that was proposed by Ishibuchi etal.[6]. A fuzzy rule-based classification system is composed of a set of fuzzy if-then rules. The antecedent
part of a fuzzy if-then rule specifies a fuzzy subarea inthe pattern space while the consequent part describes
the class and the degree of certainty for the specified
fuzzy subarea. A fuzzy if-then rule is automaticallygenerated from numerical data that are given as a setof training patterns. This section explains the generation of fuzzy if-then rules from given training patterns.
The following type of fuzzy if-then rules is usedin the fuzzy rule-based classification system for an ndimensional C-class pattern classification problem:
Rule Rj : If x1 is Aj1 and . . . and xn is Ajn
then Class Cj with CFj , j = 1, 2, . . . , N,(1)where Rj is the label of the j-th fuzzy if-then rule,
Aj1 , . . . , Ajn are antecedent fuzzy sets on the unit interval [0, 1], Cj is a consequent class (i.e. one of the
C given classes), CFj is the grade of certainty of rule
Rj , and N is the total number of fuzzy rules. We use
triangular-type membership functions in Fig. 1 for antecedent fuzzy sets Aj1 , . . . , Ajn . Figure 1 shows four
fuzzy partitions. We denote the number of fuzzy setsin the unit interval as L. The total number of generated fuzzy if-then rules N depends on the dimensionality of a pattern classification problem n and the number of fuzzy partitions L. For a ten-dimensional pattern classification problem for instance, the total number of generated fuzzy if-then rules is N = 210 = 1024
when the number of fuzzy sets for each attribute is
specified as L = 2. While we use a homogeneous fuzzy
partition as in Fig. 1, it is also possible to use a heterogeneous fuzzy partition that reflects the distributionof training patterns. In this paper only homogeneous
fuzzy partition is used because of the interpretabilityof the fuzzy rule-based classification systems.

©ISAROB 2007

1.0(b) L = 3(c) L = 4

Fuzzy Rule-Based Classification

Input value(a) L = 2

0.0

3

0.0

423

The generation procedure of fuzzy if-then rules consists of three steps: specification of the antecedent
part, determination of the consequent class, and thecalculation of the grade of certainty. Once the numberof fuzzy sets L for each attribute is specified by an expert with the domain knowledge, the pattern space isdivided into Ln fuzzy subareas where n is the dimensionality of a pattern classification problem at hand.
Thus the specification of the antecedent part of a fuzzy
if-then rule is already performed once a fuzzy subarea
to generate a fuzzy if-then rule is fixed. The consequent part (i.e., the consequent class and the grade ofcertainty) is determined from the given training patterns [6]. In [15] it is shown that the use of the gradeof certainty in fuzzy if-then rules allows us to generatecomprehensible fuzzy rule-based classification systems
with high classification performance. In the following
we describe the remaining two steps in detail.

3.1

Determination of Consequent Class

The consequent class Cj for the fuzzy if-then rule
Rj is determined from a set of the given training patterns xp , p = 1, 2, . . . , m. In [13] first the sum of thecompatibility is calculated for each class. Then theclass with the largest value is taken as the consequentclass of the fuzzy if-then rule. The procedure of determining the consequent class in the conventional fuzzy
rule-based classification systems is summarized as follows:[Conventional determination of Cj ]Step 1: Calculate βClass h (j) for Class h as
βClass h (j) =X
xp ∈Class hµj (xp ),(2)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where

where
µj (xp ) = µj1 (xp1 ) · . . . · µjn (xpn ),(3)

Step 2: Find Class ĥ that has the maximum value ofβClass h (j):
1≤k≤C

(4)

Since the above procedure does not consider misclassification costs nor class importance, we proposea cost-based determination method. In this approachthe consequent class of a fuzzy if-then rule is determined from its compatible training patterns. Let us
define the number of compatible training patterns forClass k with the fuzzy if-then rule Rj as nkj . A pattern x = (x1 , x2 , . . . , xn ) is compatible with the fuzzy
if-then rule Rj if the following condition holds:µj1 (x1 ) · µj2 (x2 ) · . . . · µjn (xn ) > 0,

3.3

Fuzzy Inference for Classification

Using the rule generation procedure outlined above
we can generate N fuzzy if-then rules as in Equation (1). After both the consequent class Cj and thegrade of certainty CFj are determined for all N rules,
a new pattern x = (x1 , . . . , xn ) is classified by the following procedure:
Step 1: Calculate αClass h (x) for Class h, j
1, . . . , C, as=

αClass h (x) = max{µj (x) · CFj |Cj = h}. (9)(5)

where µj1 (·), µj2 (·), . . . , µjn (·) are the membershipfunctions of antecedent fuzzy sets Aj1 , Aj2 , . . . , Ajn of
Rj . The consequent class of Rj is determined as theclass with the maximum misclassification cost among
the nkj training patterns. We use a multiplication operator to calculate the compatibility of an n-dimensional
pattern with a fuzzy if-then rule in this paper.
The cost-based class determination procedure is
performed as follows:

βClass h (j)

h6=ĥ

.
(8)
C −1
On the other hand, the proportion of βClass ĥ (j)over the sum of βClass ĥ (j), 1 ≤ h ≤ C, is used in thispaper. This is because the grade of certainty sometimes becomes an invalid value (e.g., negative) withthe conventional determination.

β̄ =µj1 (·), µj2 (·), . . . , µj (·) are the membershipfunction of the fuzzy sets Aj1 , Aj2 , . . . , Ajn ,respectively.

βClass ĥ (j) = max {βClass k (j)}.

X

Step 2: Find Class h0 that has the maximum value ofαClass h (x):αClass h0 (x) = max {αClass k (x)}.
1≤k≤C

(10)

If two or more classes take the maximal value, thenthe classification of x is rejected (i.e. x is left as an
unclassifiable pattern), otherwise x is assigned to Classh0 .

[Proposed determination of Cj ]Step 1: Calculate nkj for Class k, k = 1, 2, . . . , C.

4

Step 2: Find Class ĥ that is the most important (i.e.,
has the highest misclassification cost) among
those classes with nkj as follows:

In the computational experiments performed toevaluate our proposed classifier we use Appendix data
set. The appendix represents a seven-dimensional twoclass pattern classification problem. There are 106
patterns in the data set, 21 patterns from Class 1 and85 from Class 2. We assume in the computational experiments in this paper that Class 1 is more important
than Class 2.
We apply both the cost-based fuzzy classification
system and the conventional to the three data sets.
First we examine the performance of both fuzzy classification systems on the training data. That is, theperformance is measured on the whole data set thatis used to generate fuzzy classification systems. We
specified the number of fuzzy partitions L = 3 foreach axis. We show the experimental results in Fig. 2.

Costĥj = max Costkj .
1≤k≤C
nk >0
j

3.2

(6)

Calculation of the Grade of Certainty

The procedure of calculating the grade of certainty
in this paper is different from that in [13]. In [13]the grade of certainty CFj of rule Rj is determined asfollows:
βClass ĥ (j) − β̄,(7)CFj = X
βClass h (j)h©ISAROB 2007

424

Computational Experiments

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

tems”, IEEE Trans. on Fuzzy Systems, Vol. 4,
No. 3:238-250.
[5] G.J. Klir, B. Yuan. 1995, Fuzzy Sets and Fuzzy
Logic, Prentice-Hall.
[6] H. Ishibuchi, K. Nozaki, H. Tanaka. 1992, “Distributed representation of fuzzy rules and its application to pattern classification”, Fuzzy Sets andSystems, Vol. 52, No. 1:21-32.
[7] M. Grabisch. 1996, “The representation of importance and interaction of features by fuzzy measures”, Pattern Recognition Letters, Vol. 17:567575.

Figure 2: Results for training data.

[8] M. Grabisch, F. Dispot. 1992, “A comparisonof some methods of fuzzy classification on realdata”, Proc. of 2nd Int. Conf. on Fuzzy Logic and
Neural Networks, 659-662.
[9] M. Grabisch, and J.-M. Nicolas. 1994, “Classification by fuzzy integral: performance and tests”,
Fuzzy Sets and Systems, Vol. 65, No. 2/3:255-271.
[10] G.G. Yen, P. Meesad. 2001, “Constructing a fuzzy
rule-based system using the ILFN network andgenetic algorithm”, International Journal of Neural Systems, Vol. 11, No. 5:427-443.

Figure 3: Results for test data.

5

Conclusions

In this paper we have proposed a cost-based fuzzyclassification systems for pattern classification problems with an order of class importance. The task is
to minimize the total misclassification cost. Experimental results demonstrated the effectiveness of the
proposed method compared to a conventional fuzzyrule classification system.

References[1] C. C. Lee. 1990, “Fuzzy Logic in Control Systems:
Fuzzy Logic Controller Part I and Part II”, IEEE
Trans. Syst., Man, Cyberetics, Vol. 20:404-435.
[2] C. T. Leondes. 1999, “Fuzzy theory Systems”,
Techniques and Applications. Academic Press,
San Diego, Vol.1-4, 1999.
[3] M. Sugeno. 1985, “An Introductory Survey of
Fuzzy Control”, Information Science, Vol. 30, No.
1/2:59-83.
[4] K. Nozaki, H. Ishibuchi, H. Tanaka. 1996,“Adaptive fuzzy rule-based classification sys-

©ISAROB 2007425

[11] Y. Chen, J.Z. Wang. 2003, “Support Vector
Learning for Fuzzy Rule-Based Classification Systems”, IEEE Transactions on Fuzzy Systems, Vol.
11, No. 6:716–728.
[12] T. Nakashima, Y. Yokota, H. Ishibuchi, A.
Bargiela. 2005, “Constructing Fuzzy Classification Systems from Weighted Training Patterns,”
Proc. of European Simulation Conference, 23862391.
[13] H. Ishibuchi, K. Nozaki, N. Yamamoto, H.
Tanaka. 1995, “Selecting fuzzy if-then rules forclassification problems using genetic algorithms”,
IEEE Trans. on Fuzzy Systems, Vol. 3, No. 3:260270.
[14] H. Ishibuchi, T. Nakashima. 1999b, “Improving
the performance of fuzzy classifier systems forpattern classification problems with continuous
attributes”, IEEE Trans. on Industrial Electronics, Vol. 46, No. 6:1057- 1068.
[15] H. Ishibuchi, T. Nakashima. 2001, “Effect ofrule weights in fuzzy rule-based classification systems”, IEEE Trans. on Fuzzy Systems, Vol. 9, No.
4:506-515.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Verification of Normalization Results using Variable Clustering
Methods in cDNA Microarray Data†

Gyeongdong Baek, †Yountae Kim, ††Ji-Young Kim, †Sungshin Kim, and ††Choon-Hwan Lee†

School of Electrical and Computer Engineering, Pusan National University, Busan, Korea
Pusan National University 30 Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: gdbaek@pusan.ac.kr, dream0561@pusan.ac.kr, sskim@pusan.ac.kr††

School of Molecular Biology, Pusan National University, Busan, Korea
Pusan National University 30 Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: dryoung82@pusan.ac.kr, chlee@pusan.ac.kr
Abstract − cDNA microarray analysis has enabled themeasurement of thousands of gene expressions at thesame time. The gene expression levels are monitored
using log ratios between green and red fluorescentintensities. However, imbalances can be caused bydifferent incorporations of dyes, amounts of mRNA,and scanning parameters and these biases result inincorrect conclusions. Normalization makes gene
expression data more accurate by removing thesesystematic variations. Therefore, the study ofnormalization is important for clustering and also
profitable by making groups that show similar
expression patterns. We tried to certify the results ofnormalization by comparing the operation time of kmeans and fuzzy c-means clustering methods. When it
takes less time for k-means clustering and more timefor fuzzy c-means clustering relatively, we can say the
result of the normalization is good. In addition, we
analyzed characteristics of standard normalization
using two clustering methods. These two methods will
be used as the verification methods of any
normalization in cDNA microarray.
Keywords: fuzzy clustering, fuzzy c-means algorithm,
k-means clustering, microarray, normalization
1. Introduction
The latest advancements in genetics has made manythings possible [1]. They ascertain the facts how thegene expressed in body and what to do. cDNA
microarray maps and sequences all the genes on asmall chip. It looks over gene expression patterns. For
example, a disease happens because of an interaction
between genes and not simply because of one gene.
cDNA microarray is useful to observe the wholeexpression pattern in this case[2]. The cDNA
microarray data needs normalization steps before
classification steps are possible [3]. Because there are
more noise than any experimentation. The difference
the physical properties of two dyes accounts for thatnoise. This noise is the exact intensity of temperatureor fluorescence, sometimes the green dye seems tohave high fluorescence intensity. It doesn`t have the©ISAROB 2007426

same dye intensity for this reason [4]. There are otherproblems which the efficiency of dye incorporation, datacollection, the data scanning process, the difference
between pin-groups and slide heterogeneity. All of these
problems generate noise. The quality of data is poorer as
the noise adds up. The noise differs in degree due to an
experimenter`s skill or chemical materials used. The
purpose of normalization is to correct errors in thepatterns among the kinds of noise. cDNA microarraynormalization means the revision of fluorescenceintensity and the comparison of gene expression level
through experiments or slides.
cDNA microarraynormalization is divided into three divisions of selection
methods. In the first method, let us suppose that a small
ratio is expressed in all genes. This normalization applies
to almost all of the genes. The second method, genes arenormalized based on constant expression. This method
applies to a subset of genes instead of all genes. In thethird method, genes are either arranged by spiked control
or the titration series of control sequence is used. This
paper uses the first method in which all genes in the
cDNA microarray are used.
This paper verifies the normalization results of thecDNA microarray data. It uses the eigen feature of kmeans clustering and the fuzzy c-means clustering
algorithm [5-8]. The eigen feature makes rules according
to experiment data. It influences operation time of thetermination tolerance. The notion of cDNA microarray
analysis decreased the average operation time of kmeans clustering and increased the time of fuzzy cmeans clustering. The cause of the operation time is anappraised method of cluster position selection. Section 2
explains the difference and features of the two clustering
algorithms. Section 3 explains the structure of theexperiment data. Section 4 explains the noise reduction
method and normalization to be used. Section 5 shows
the result based on the proposal theory, and shows theproper objective. The last section presents conclusions
and suggests future study.
2. The Clustering Characteristic Analysis
Clustering analysis uses general Euclidean distance
between an independent entity. The calculated distanceindicates similarity and non-similarity. For k-means

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

clustering, it makes using (1) and (2).

∑uj1≤ i ≤ c( l −1)

(1)

ij

i =1

(

)

(

)

⎧1 if d m j , ci( l ) = min d mj , ck( l )1≤ k ≤ c(l ) ⎪
uij = ⎨
(2)⎪⎩0 otherwise

3. Experiment Data Structure Analysis
cDNA microarray data is generally represented inmatrix form. The matrix expresses the genes in rows,
and the cDNA chips or samples in columns. This paper
uses the cDNA microarray data from 17,000 genes and 7
chips. The microarray data matrix form is as follows.

When it satisfies (3), it stops the operation.

Table. 1 DNA microarray matrix

1

x1,1

x1,2

…

x1,7

2

x2,1

x2,2

…

x2,7
…

Chip 7…

…

…

Chip 2…

U , which is a real number [ 0,1] , but not {0,1} .

Chip 1

17,000x17000,1x17000,2…

x17000,7…

This means the change of U , the cluster
membership set, is confined to the limits of thetermination tolerance. Fuzzy c-means clustering uses
Euclidean distance to estimate similarity, too. It uses

Gene

…

(3)

…

If U (l ) − U ( l −1) ≤ δ then END…

cli = i =1d

l −1
ij

…

∑ u( )m…

dmeans clustering time is shortened after normalization.
This result can be explained by the cluster membershiperror effects decreased. If there are more samples, it
becomes a remarkable large difference. Unlike fuzzy cmeans clustering, k-means clustering takes longer andthe variance becomes larger after normalization.

For example, x1,1 is the expression value of sample 1
and gene 1, and x3,2 is the expression value of sample 2uij( ) =1l∑ ( d ( m , c( ) ) / d ( m , c( ) ) )
c

i

2
A

j

k

2
A

1/ ( ω −1)(4)jand gene 3. Each expression is a log-ratio value. The
microarray software uses this matrix form in the majorityof cases.

k =1

The membership matrix changes can be explained
by Figure 1.

C1

Clusters



……

Cl

C2

d
⎧ d1
2

4. Noise Removal and Normalization
4.1 Noise Removal
When the flag which means reliability level is -50 or 100, it is cut out. In addition, when the sum of genechange rate doesn`t rise above the threshold, it is also cut
out. The threshold this paper used is 2. It was able toremove 300 genes.

IF xi ,1 + xi ,2 + ⋅⋅⋅ + xi ,n < 2⎪⎪samples ⎨
⎪
⎪⎩
dn
…

X ( i,:) = [ ]END

Fig. 1 Membership matrix.

{

Sample D = di i = 1, ⋅⋅⋅ N} belongs to eachcluster with membership values. This means that eachsample has an overlap, so it gives the problem carefulconsideration. For the last section, the normalization
corrects fluorescent intensity imbalances and gene
expression levels between slides. When it corrects two
factors, the cDNA microarray data removes absolute
fluorescence intensity and only the variation between
samples remains. So it is difficult to divide clusters.
We may be thought that it is more convergent than kmeans clustering, because fuzzy c-means clustering
fully considers cluster membership. However, fuzzy c-©ISAROB 2007

427

4.2. 1 Generalization of Normalization
The normalization method is illustrated in figure 2.
Single-slide

GlobalLocation

Intensity dependent

Scale

Within-print-tip-group

Within-slide

Normalization
MethodPaired-slide

Multiple slide

Fig. 2 Normalization Method.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Common normalization methods correct errors
using means and variances of the slide by matching the
red and green intensities. That is to say, the gene
expression level corrects as many errors as α .

04Values

2
1.5
1
0.5

2
none

normalization

Values

15
10
5
none

normalization

K-means Clustering Normalization Result
i

n −1

i ,n

i

(6)

1.2
1
0.8
0.6
0.4
0.2

10
0
none
normalization
Data size:8000, cluster:30
60

30
Values

(7)

20

none
normalization
Data size:8000, cluster:15

The value of each normalized is updated on the
cDNA microarray data matrix.

xi ,1 − xi xi ,2 − xix −x,, ⋅⋅⋅, i ,n i
si
si
si

Valuesi ,2Data size:8000, cluster:10

2

Values2

Data size:8000, cluster:5
Valuesinonenormalization
Data size:2000, cluster:30

Fig. 3 The result of k-means clustering (size:2000).

( x − x ) + ( x − x ) + ⋅⋅⋅ + ( x − x )
i ,1

none
normalization
Data size:2000, cluster:15

(5)

n
2

si =

2.5
2
1.5
1
0.5

none
normalization
Data size:2000, cluster:60

4.2.2 The Normalization Equation to be used
This paper uses the standardization method[3]. The
sample is updated to calculate a mean and standard
variation. (5) is the mean of each sample, (6) is thestandard variation of sample.

xi ,1 + xi ,2 + ⋅⋅⋅ + xi ,nData size:2000, cluster:10
ValuesValuesα = Normalization method

α can build up to several normalization. If a
special α is made, there will be need to verify.

xi =

1
0.5

Values⎛R⎞log ⎜ ⎟ − α⎝G⎠K-means Clustering Normalization Result
Data size:2000, cluster:5

20
10
0

40
20
0

none
normalization
Data size:8000, cluster:60

none

normalization

Values

150
100
50
none

Fuzzy c-means Clustering Normalization Result
Data size:2000, cluster:5©ISAROB 2007

428

Data size:2000, cluster:10

0.8

ValuesValues

2
0.6

1.5

0.4
none
normalization
Data size:2000, cluster:15

none

2.8
normalization

normalization

Data size:2000, cluster:30
Values

3

none

6

5.5
none

normalization

Data size:2000, cluster:60
13Values

5.2.1 The Result of Fuzzy c-means Clustering
The features of k-means clustering are certainly
observed in fuzzy c-means clustering. When the valueof the median is similar, the rate of interquartile takes
longer, as in figure 5 and figure 6. Figure 5 isambiguous, but figure 6 shows that the operation does
not take long, because fuzzy c-means clustering has asensibility of cluster membership.

normalization

Fig. 4 The result of k-means clustering (size:8000).

Values

5. The Experimentation Result
The result of experimentation is drawn in box-plot,changing the sample size and the cluster number. A
box-plot can reasonably show the feature affected by
the first random centers.
5.1. The Result of k-means Clustering
If the normalization of the cDNA microarray goes
well, a operation time of k-means clustering takes
longer. The interquartile range of the box-plot has 50
percent of the observered data. When the value of themedian is similar, the large rate of interquartile takers
longer, as in figure 3 and figure 4. After thenormalization, k-means clustering takes longer. That is
to say, the rate of change is more emphatic than theabsolute fluorescent intensity, because k-meansclustering has a sensibility for uclidean distance. Thisfeature of normalization also occurs in fuzzy c-meansclustering

12
none

normalization

Fig. 5 The result of fuzzy c-means clustering (size:2000)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ACKNOWLEGEMENT
This work was supported by the Second-stage of theBrain Korea 21 project in 2006.

Fuzzy c-means Clustering Normalization Result
Data size:8000, cluster:10
94ValuesValuesData size:8000, cluster:5

3
2

8
7

none
normalization
Data size:8000, cluster:15

none
normalization
Data size:8000, cluster:30
ValuesValues

28
14
12

26
24

Valuesnone
normalization
Data size:8000, cluster:60

none

normalization

56
55
54
none

normalization

Fig. 6 The result of fuzzy c-means clustering(size:8000).

5.2.2 A High Spot
A high spot is found in figure 6. Figure 7 zooms
cluster:30 and cluster:60 in figure 6. It is difficult toshow the change of time before and after. Although thenormalization emphasizes sample expression, it isdifficult to classify to the number of clusters, so it canbe assumed that the 8000 samples have 15~30 clusters.
When the interquartile rate of fuzzy c-mean clustering
keeps their distance, the cluster of gene expression isdetermined.
Data size:8000, cluster:30

Data size:8000, cluster:60
56.5

28
56

27.5
27

55.5
ValuesValues

26.5
26

55
25.5
25

54.5

24.5
24

54
none

normalizationnone

normalization

Fig. 7 Zoom in Fig. 6 (cluster:30 and 60).

6. Conclusion
This paper proposes an appraised method of
normalization in the cDNA microarray. The proposedmodel uses the difference of cluster membership
update method. However, in this model, it is difficult
to determine optimized data-size and cluster number inthe microarray. Therefore, in subsequent rearch, thecluster number of gene expression by the interquartile
rate is expected to be determined.

©ISAROB 2007

429

References[1] Brown PO, Botstein D. 1999. Exploring the newworld of the genome with DNA microarrays. Nat
Genet 1999 Jan;21(1 Suppl):33-7[2] D. Wishart, "Efficient hierarchical cluster analysis
for data mining and knowledge discovery."Computing Science and Statistics. pp. 257-263,
1998.
[3] Willian Shannon, Robert Culverhouse, Jill
Duncan." Analyzing microarray data using cluster
analysis",2003.
[4] J. Derisi, V. Iyer and P. Brosn, "Exploring themetabolic and genetic control of gene expression ona genomic scale," Science, vol. 278, pp. 680-686,
1997.
[5] Kjersti Aas, "Microarray Data Mining: A Survey",
2001.
[6] E. Hartuv, A. Schumitt, J. Lange, S. Meier-Ewert,H.
Legrach and R. Shamir , “An Algorithm for
Clustering cDNAs for Gene Expression Analysis,”
Proceedings of the Third International Conference
on Computational Molecular Biology (RECOMB99),pp.188- 197, 1999.
[7] J. C. Bezdek, Pattern recognition with fuzzy
objective function algorithms, Plenum Press, 1981.
[8] F. Hoppner, F. Klawonn, R. Kruse and T. Runkler,
Fuzzy cluster analysis, Wiley, 2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Improvement of Color Images Halftoning with Simulated Annealing
Ken-ichi Tanaka

Ryouta Isoi

Meiji University
1-1-1, Higashimita, Tama, Kawasaki, Kanagawa, 214-8571,Japan
E-mail: {tanaken, ce66003}@isc.meiji.ac.jp
Keyword : halftoning, simulated annealing, color image, quantization, cost function

Abstract

2. Extension to color image of halftoning used
SA in gray scale image

In this paper, it introduced simulated annealing (SA)as a method of halftoning in the color image. 3 methods
called Type1, Type2 and Type3 are research in the paper.
Type1 is using the SA with resolving of color image into
three primary colors ingredient of Red (R), Green (G) andBlue (B). Those applied the halftoning by SA respectively,
and composed process result. In the Type2, a color regards
as the 3 dimensions vector of R, G and B. Not only size oferror vector but also angle error added to cost. In the Type3,
the basis of above two results, it made not to consider thecost of angle error at Type2. The appropriation pixel
becomes R=G at the process image when R=G of original
image was formed. Similarly, in case of becoming R=B
and B=G, it were assumed to a restriction.
In case of SA is adapted to the halftoning in color
image. we could get result the superior image quality using
Type3. Therefore, availability to the halftoning to the color
image of SA became clear.

1.Introduction
Halftoning 1) ~ 3) has an important role in the hard copyof a printer and facsimile communication system. The
digital halftoning of the gray scale images has dither
method 1) , error diffusion method 2 ) , application of thesolution of combinatorial optimization proble 3),6 ) andvarious methods.
In case halftoning is thought as combinatorial optimization
problem, ideal image to visual sensation is searched. It has
been proposed that genetic algorithms (GA) on the process
to the gray scale image. String size is not taken greatly,
there is influence by the non-continuity in the boundary 3) .
For this reason, we introduce makes to hasten calculation
speed substantially clear at using SA 4 )~6 ) . In this paper, it
introduces Simulated Annealing (SA) at the halftoning in
the color image. We can get result that halftoning of color
image is good visual sensation and when input image
became a gray scale image, gray scale image can beobtained in process image.

Fig. 1 Procedure of the halftoning with deviding the three primary colors©ISAROB 2007

430

It is mentioned procedure for halftoning of color
image based on halftoning 6 ) for gray scale image as
mentioned using SA. At the color image the case of
Windows Bit Map, the three primary colors of R, G and B
has density information of 0-255 respectively. These 3
plains would be piled with regarding R, G and B as threegray scale images. It stands for the flow of this process to
Fig. 1, and states the process to the following. This method
is called Type1.
1) The color image is resolved into 3 planes on three
primary colors of R, G and B.
2) It uses Cost function that is the same as the case of thegray scale image and carries out SA on eachplanes.
3) Process image can be obtained by each solution.
Cost function used in the SA is assumed to stand by linear
combination with cost Em on present of gray level andcost Ec on contrast of edge, those showed by following
equation.
E = α m Em + α c Ec
(1)Here, α m and α c are each weight. The relation is filled
αm + αc = 1 .
Cost Em on present of gray level showed by following
equation.
1(2)
Em = ∑F ( x, y ) − q F ( x, y )( x , y )∈block N p
Here, gray level density of original image regards
F ( x, y ) and gray level density of process image regards
Q( x, y ) in coordinates (x,y). Then qF ( x, y ) is graylevel density of binary pattern in Gaussian filter convolute,N p is pixel number in block . In case of changing colorof a pixel, block in this equation is domain where effectcost function. It indicates the pixel and 24 neighborhood
pixels here. Cost Ec about the contrast shown by
following equation.

Ec =1F ′( x, y ) − Q′( x, y )( x , y )∈block N p∑(3)⎧ F ′( x, y ) = F ( x, y ) − f s ( x, y )(4)⎨⎩Q′( x, y ) = Q( x, y ) − M 2
Here, f s ( x, y ) is local average of F ( x, y ) in 5× 5
pixels. M is gray level number in original image.
The process image could be got by the method that isshown a Fig. 2.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(a) Original image(b) α m = 0.0(a) Original image(b) α m = 0.0(c) α m = 0.2(d) α m = 0.5(c) α m = 0.2(d) α m = 0.5(e) α m = 0.8(f) α m = 1.0(e) α m = 0.8(f) α m = 1.0

Fig. 2 Halftone Images using the Procedure Type1 (Original images is

Fig. 3 Halftone Images using the Procedure Type1 (Original images isfull color image)gray scale image

These images are 256( H ) × 256(V ) pixels, 24bit
images. Therefore, α m shows cost on weight about thegray level density, α c shows cost on weight about thecontrast, (α c = 1 − α m ) . In SA, initial temperature T0 is
equal to 100, decreasing coefficient α is equal to 0.995.
The chapter is explained again about Compromise type of
the initial temperature and the temperature fall coefficient.
The gray scale image is well known 3) 6 ) for getting the best
result at α m = 0.2 . Also at the result of Fig. 2(b)-(f), theoutline is clear and also the reappearance of color is goodwhen the color image is used in case of α m = 0.2 .
Therefore, halftoning in the color image also consider ratio
of weight about gray level density α m and weight about
contrast α c is the same.
The same process is applied to the gray scale image(It is RGB24bit but calculates brightness information, and
R=G=B is consisted). This is shown a Fig. 3. The color
other than white and black is breaking out. The reason, it isbecause same solution can't get necessarily in R plane, G
plane and B plane by multimodal of a solution in SA. And
the result that convoluted them appeared the color otherthan white and black. Inherence the gray scale image isdesirable that the color other than white and black is not tobreak out. Therefore, the color image could get good result.
However, the gray scale image could get bad result.

3.1 Process procedure
Fig. 4 is shown flow diagram in the quantization ofcolor images on SA 6 ) . It is not resolved into 3 planes on R,
G, B, it process same procedure that halftoning used SA in
the gray scale image. The procedure is shown a following.
1) Random pattern of 8 colors is selected as initialprocess image.
2) The element in each pixel of color is changed. Itchanges color by using random number of 0 form 7.
3) Cost Eold conducts before step 2 and cost Enew
conducts after step 2 calculate. Change volume of costfunction gets dE .
dE = Enew − Eold(5)4) The turning over can accept at probability p .

3. Primary color process when it doesn't
resolve into 3 primary colors ingredient
The color image is resolved into each ingredient of R,
G, B, after did halftoning on SA, it was adapted to reset©ISAROB 2007

method in previous chapter. Original image is case of fullcolor image, it became comparatively good result.
However, Original image is case of gray scale image, theresult is necessary to improve that the color other thanwhite and black break out. Because in this chapter uses
method that it is not resolved into three primary colors of R,
G, B. This method is called Type2.

431

1
⎧
p=⎨
dE
⎩exp( − kT ( n ) )( dE ≤ 0 )( dE > 0 )(6)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

obtained in a foregoing chapter. It considers that lengthof vector becomes 0 in seven colors of other either ifcolor of process image is black (R=G=B=0), an angle
error θ is taken 0 too.
4) Cost function is shown by following equation fromabove result.

E = (1.0 + βθ )(α m Em + α c Ec )Fig. 4 Flow diagram

Fig. 5 Treatment of thevector for color

Here, T ( n) is temperature in the calculation, k is
Boltzmann constant.
5) Trial of the above step2-4 does as order of raster
operation against all pixels. After trial in all pixels
finished, temperature T ( n) is let to fall, shown by
following equation.
T (n) = αT (n − 1)(7)Here, α is constant how many 0 and over less than 1.
6) Step2-5 is repeated by change value of cost function
becomes small.

3.2 Treating of cost function like the vector
The process method is shown in previous sectionneeds to think about the cost function newly. Because it is
not divided into the plane of 3 primary color. Treatment of
the vector for color shows Fig. 5. Color vector c shows by
following equation.
c = ri + gj + bk
(8)Here, i, j , k are unit vector to each R, G, B direction. Andr , g , b are shown size of vector to each R, G, B direction.
Therefore, cost function E is considered as follow.
1) F ( x, y ), f s ( x, y ), Q( x, y ), q F ( x, y ) is replaced in aforegoing chapter with eachF ( x, y ), f s ( x, y ), Q ( x, y ), q F ( x, y ) . And
calculation of cost E that related to expression of graylevel is shown follow equation.

Em =
(9)

r
1 r
F ( x, y ) − q F ( x, y )∑( x , y )∈block N p2) Similarly, cost Ec about the contrast shown by
following equation.

r
1 r′′( x, y )
−
F
(
x
,
y
)
Q
∑
( x , y )∈block N prrr⎧ F ′( x, y ) = F ( x, y ) − f s ( x, y )rr⎨r⎩Q′( x, y ) = Q( x, y ) − M 2
Ec =

(10)

(11)

3) An angle error θ with color vector of original image
and process image is considered because only using theabove (1), (2) can be the result that is same as the one©ISAROB 2007

432

(12)

In this expression, there is a constant 1.0 to prevent thatthe cost become 0 used an angle error θ , β is an angle
error.
5) Calculation of cost in equation (19) is used α m = 0.2
and α c = 0.8 .
Process image using SA above procedure is shown by
Fig. 6 and Fig. 7. Fig. 6 is process image using color image
as original image. Here, It follows that it is similar to theFig. 2(c) that is process image in a foregoing chapter in thecase of β = 0 . However, it is become color reappearance
such as achromatic color when β value is much. This isconsidered a ratio of angle error θ grows big and error ofgray level density of color of each is made light of one.
The above, β = 0 is considered that color image iseffective and introduction of an angle error is meaningless.
Fig. 7 is process image by use of gray scale image (Itis RGB24bit, but R = G = B is taken advantage of only
intensity information) as original image. Here, in case of
β = 0 follows that it is similar to Fig. 3(f) that it isprocess image a foregoing chapter. The reason isconsidered to make neighboring color vector average and it
should become same color. However, there is not outbreak
except for white color and black color when weight of anangle error θ is heavy. In this way the color except whiteand black does not appear in gray scale image when theangle error into the cost function introduced, but it becomecolor reproduction such as achromatic color in the color
image. Therefore, even this method is the method that ispoor in versatility for color image and gray scale image.

4. Introduction of a limitation condition inchoice of a color
Type1 is suitable to halftoning of color image, but it isa problem to occur that the color except white and blackwhen gray scale image is extended. Type2 dose not occurthat the color except white and black in gray scale image, it
is a problem of color reproduction such as achromatic color
in the color image. Among both of a trade-off is connectedwith each other. Therefore, it must be think about a method
to satisfy the following conditions at the same time.
1) Color image should be natural color reproduction
visually.
2) Gray scale image should appear the color that except
white and black.
To satisfy this condition, the method use the versatility togray scale image of Type2 with good point of Type1.
Therefore, there is following method.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(a) β = 0.0(b) β = 0.03(a) β = 0.0(b) β = 0.03(c) β = 0.1(d) β = 0.3(c) β = 0.1(d) β = 0.3

Fig. 6 Halftone Images using the Procedure Type2 (Original images is

Fig. 7 Halftone Images using the Procedure Type1 (Original images isfull color image)gray scale image)1) Weight β = 0 for angle error θ as cost function isfollowing equation in Type2.
(13)
E = α m E m + α c Ec
Here, it is assumed α m = 0.2 , α c = 0.8 from anargument to a foregoing chapter.
2) If there is a relationship of R=G in original image, thechoice of color in process image should be restrictedwithin the relationship of R=G.
3) If there is a relationship of G=B in original image, thechoice of color in process image should be restrictedwithin the relationship of G=B.
4) If there is a relationship of R=B in original image, thechoice of color in process image should be restrictedwithin the relationship of R=B.
The above method is called Type3. Process image obtainedby this Type3 is shown as Fig. 8. This result is same as
result of process image of Type1 with α m = 0 in color
image. Only white and black appear in gray scale image
and it is well result.

(a) Original image(d) gray scale image

Fig. 8 Halftone Images using (Type3)

Therefore, in case of SA is adapted to the halftoning incolor image, we could get result good as the visualsensation at using Type3. Therefore, availability to thehalftoning to the color image of SA became clear. Methodof SA is calculated 15 minutes degree at CPU is 2GHz ofPentium4, Memory capacity is 1.5GB and OS is
WindowsXP. Mention as assignment, the cutting of
calculation cost and the shortening of process time.

6. References
5. Conclusions
Type1 was the same as the knowledge in the grayscale image of SA, and when the weight of cost Em on
present of gray level, contrast Ec on contrast decides each
0.2, 0.8, the image understood to have become theexpression were prominent subjectively by this method.
In case of using this method, It is adapted to the gray scale
image that consist of R=G=B. Then the color other thanblack and white is breaking out by multimodal of asolution, as a result, process image does not become thegray scale image. Type2 could prevent the occurrence ofothers color in gray scale image. However, it became theprocess image of little saturation in color image. Follow,
Type3 made the basis of above two results. When it madenot to consider the angle error at Type2, the same process
image could get by color image. And in case of gray scale
image, the color other than black and white doesn't appear
by process image.

©ISAROB 2007

433

[1] B.E. Bayer, “An optimum method for two-level
rendition of continuous-tone pictures”, Ineternl. Conf.
On Comm., vol.50, pp.69-77 (1976).
[2] R. W. Floyd and L. Steinberg, “An adaptive algorithm
for spatial gray-scale,” Proc. Soc. Inf. Disp., vol.17,
pp.75-77 (1976).
[3] N. kobayashi and H. saito, “Halftoning TechniqueUsing Genetic Algorithm”, Trans. IEICE., J78-D-II,
pp.1450-1459 (1995).
[4] S. Kirkpatick, C. D. Gellatt and M. P. Vecchi,“Optimization of simulated annealing”, Science,
vol.220, pp.671-680 (1983).
[5] H. Szu and R. Hartley, “Fast simulated annealing”,
Phys. Lett., vol.122, pp.157-162 (1987).
[6] Ken-ichi Tanaka, “Improvement of Halftone Images
Using SA”, Journal of ITE., vol.54, no.3, pp.11041106 (2000).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Optimization of area traﬃc control parameter using a GA
Haruka Sasaki Manami Shinohar
Graduate School of Engineering
Utsunomiya Univ.
Utsunomiya-city,
321-8585 Japan

Kenji Shoji Toyama Fubito Juichi Miyamichi
Faculty of Engineering
Utsunomiya Univ.
Utsunomiya-city,
321-8585 Japan

Abstract
In this paper, we propose a method for optimizing
common cycle length and oﬀsets among signal control
parameters in area traﬃc control of a grid street network using a genetic algorithm. The gene of each oﬀset
is expressed by three bits, and that of a common cycle
length by six bits. Each individual (a candidate of solution) is evaluated based on the weighted sum of delay
time and stop percentage using CORSIM simulator in
TSIS traﬃc integration software. The results of simulation experiments for regular and irregular grid street
networks show that the optimal solutions obtained by
the proposed method are better than theoretical ones
based on a linear model in traﬃc signal control.

of signals on a grid street network. The time cost of
the method is small but it is diﬃcult to optimize them
globally. Therefore, we have to solve the problem bysome heuristic method. Abu-Levdeh and Benekohal[2] examined several genetic algorithms (GAs) applied
to the optimization of traﬃc control parameters. GA
is metaheuristics applied in versatile as a methodology for optimization, adaptation, and learning. Theirtarget system in experiments was a signalized arterial
road. In this paper, we propose a method for optimizing common cycle length and oﬀsets among signal control parameters in area traﬃc control of a grid street
network using a GA.

2
1

Area traﬃc control

Introduction

Increasing number of vehicles in use generates serious problems such as traﬃc accidents and congestion.
It is deeply desired that automobile traﬃc safety andsmooth traﬃc ﬂow are realized by appropriate trafﬁc light control. There are three types of traﬃc lightcontrol: independent control handling a single signal,
coordinated control in which two or more signals on aarterial road work in association with each other, andarea traﬃc control which is a two dimensional versionof coordinated control. Especially, large eﬀects can be
expected in coordinated control and area traﬃc control if they are optimized.
The object of this study is to improve the performance in area traﬃc control. Among the parametersof area traﬃc control, common cycle length, splits,
and oﬀsets, we focus attention on the common cycle length and oﬀsets. The decision of common cycle length and oﬀsets is considered as a combinatorial
problem. If the number of intersections increases, thenumber of combinations becomes huge, and it is difﬁcult to ﬁnd an optimal solution. Hisai and Sakai [1]proposed analytical method for optimizing the oﬀsets©ISAROB 2007

434

Coordinated control is an approach to manipulating two or more signals on an arterial road in association with each other. Area traﬃc control (ATC)is a method by which a traﬃc signal group placed ona street network spreading two-dimensionally is controlled concentratedly. It can be considered as a twodimensional version of coordinated control. Common
cycle length and oﬀsets are particular parameters ofarea traﬃc control. Common cycle length is a total
time to complete one cycle in common with all signals. Oﬀset is the delay from the start of the green phaseof a reference signal to that of the signal concerned.
According to the manual of JSTE [3], if round trip
time T of a link in overall speed V is multiple integerof cycle length C, that is,
T =

2D
= nC,
V

(1)

vehicle delay time can be minimal. In the equationof (1), D is the link length. In the method adopted
by present Japan, a region to be controlled by ATC
is manually divided into several blocks based on theamount of traﬃc ﬂow for every traﬃc situation con-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
sidered. The control parameters are decided in every
block and they are integreted. For a traﬃc situation,a set of control parameters corresponding with the situation is chosen from among the several sets of themobtained in advance and applied. However, because
the frequent renewal of the several sets of them is diﬃcult in this system, the system can not deal with
unexpected traﬃc situations immediately. And, it isdiﬃcult to automate such work. So, we aim at optimizing ATC parameters of all signals in the region ofinterest by a heuristic method. In this study, we limit
street networks to grid ones as the simplest case, andpropose an optimization method of the control parameters.

3

Applying a GA to ATC

In this study, a combinatorial optimization problem of the set of control parameters, the common cycle length and oﬀsets, is considered for a grid street
network which consists of the streets of L rows and R
columns and the intersections of L × R. Each intersection points is called a ’signal’ and each connecting
street between two adjacent intersections is called a’link’. Using the traﬃc micro simulator CORSIM, we
simulate the street network with the sets of control
parameters represented by viable solutions generated
by a GA. Evaluating the results of the simulations,
we use the output data of CORSIM; Vt (Vehicle Trips :the number of vehicles that have been discharged
from the link), Tt (Total Time : total time on the link
for all vehicles), Dt (Delay Time : the time that vehicles are delayed if they cannot travel at the free ﬂow
speed), Sp (StopPercentage : the ratio of the numberof vehicles that have stopped at least once on the linkto the total link trips) where the link means unidirectional one. We calculate a performance index (P I) as
follow:

N 

Dtn
Vtn
Vtn
+ K × Spn ×
×
(2)
PI =
Ttn
Td
Td
n=1
where K is the weight coeﬃcient of Sp, T d is thesimulation time, and N is the number of unidirectional
links, N = 2L(R − 1) + 2(L − 1)R. The P I is theweighted sum of delay and stop. So, the smaller the
P I the better the evaluation.
This optimization is carried out by a GA. GA is ametaheuristics applied in versatile as a methodology
for optimization, adaptation and learning. The coding method of the GA is as follows. A chromosome for
the GA consists of 30 bits as shown in Figure 1, where©ISAROB 2007

435

Main streets

Minor streets

Intersection
number②

individual 1

0

②

③

④

⑤

①

⑥

⑦

⑧

⑨

③
0

0

link

reference intersection（offset=0）

④

1

0

0

1

⑨
1

・・・

0

0

Common cycle length IN

10

3-bit binary number

1

0

1

1

0

6-bit binary number

Figure 1: Coding method of cycle length and oﬀsets
an oﬀset is represented by a 3-bit binary number, andcommon cycle length is represented by a 6-bit binary number. Each intersection is numbered as shownin Figure 1. Actual oﬀset values represented by 3-bit
binary numbers Ik are taken as center values of equally divided 8 intervals between 0 and a common cycle
length C, as shown in Figure 2. Actual common cycle
length C(M IN ≤ C ≤ M AX) represented by a 6-bit
binary number IN is ranging from 40 to 150 seconds,
and obtained as
M AX − M ININ .
(3)
C = M IN +26 − 1②

Intersection
numberindividual 1 0

Ik

0

0…

③
0

0

1

1

2

④
0

0

1

⑨
1

2

30…37…44

・・・

3

0

0

4

IN
1

0

5

1

6

0

1

1

0

7

…119

Figure 2: Calculating method of oﬀsets
The fundamental algorithm of the GA is expressedin the following. First, M individuals (the initial population) are generated using uniform random numbers.
All individuals of the current generation are evaluatedby the ﬁtness F deﬁned by the inverse number of P I
as
1
.
(4)
F =
PI
Secondly, the operations based on genetic rules are applied to them for the construction of the next generation. As the genetic rules, we adopt elitism, and apply
crossover and mutation to the individuals selected byroulette. Elitism is a mechanism that protects thechromosomes of most-highly-ﬁt population from thegenetic operations. In experiments, the top of the individuals becomes elite. Crossover is the main mechanism of the GA which expects the more-highly-ﬁt children than their parents. For crossover, M/2 pairs ofparents with permitted duplication are chosen from

fitness

fitness
fitnesscommon cycle length [s]common cycle length [s]The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
M individuals based on the roulette selection at the
The reference signal of the grid network is madeprobability proportional to the F value. From M/2
to be the center one and its oﬀset to be 0. In thepairs of parents, their copies are made as M children,
linear model, the oﬀset of each signal connected to theand one-point crossover is applied for the children inreference one by the link is calculated by D/V , where
the probability of 1.0, and the last child is exchanged
V is the free ﬂow speed. The oﬀset of each of thefor elite. The mutation inverts a randomly chosen bit
rest of 4 signals are calculated by adding D/V for theamong 30 bits in each chromosome according to thevertical link to the oﬀset of the signal connected by theprobability of 0.1. The set of oﬀsets with most highly
link. And the common cycle length in the linear modelﬁtness at the last (T th) generation is made to be theis equal to the round trip time T of a link according
best solution. Considering the calculation eﬃciency,
to the equation (1).
the GA optimizes the oﬀset of each intersection in the160
resolution of 3 bits. Then, we apply a coarse-to-ﬁne
160
140
140
approach to raise the resolution of the oﬀset. In the
120
120
100
ﬁrst stage, the interval between 0 and C is coarsely
100
80
80
divided into 8 segments, each oﬀset is represented by
60
60
40
40
the segment number 0 to 7 which corresponds to the20
20
0
3-bit binary number, and the oﬀset of all signals are
0 0
5
10
15
20
25
30
0
5
10generation
15number 20
25
30
optimized by the GA. In the second stage, for eachgeneration number
oﬀset the interval is diminished by one quarter, its
Figure 3: Distribution of the common cycle length as
center is set to the center of the segment obtained ina function of generation in the ﬁrst stage of the GA
ﬁrst stage, and the same procedure as the ﬁrst stage is(regular grid network)
carried out. After the third stage performed as sameas the second one, each oﬀset can be obtained at the
1st stage
2nd stage
3rd stageaccuracy of 1/4×1/4×1/8 = 0.78% in the interval be0.08
tween 0 and C. Whereas, the common cycle length C
0.07
0.08
0.06
0.07
is optimized in the ﬁrst stage, and it is ﬁxed in the0.05
1st stage
2nd stage
3rd stage
0.06
0.04
0.08 0.05
second and third ones.
0.03

4

Results of simulation experiments

4.1

Regular grid network

First, we compare the solution of a linear modelwith that of the proposed method under the situation
where the actual traﬃc condition is simpliﬁed. Thatis, the street network is made to be the regular grid
one with 3 columns of main streets and 3 lines of minor ones and having 9 intersections and 24 unidirectional links (12 bidirectional links) of equal length D= 1000[ft]. In the GA, the size of population is madeto be 50 individuals and the number of generations to
be 30. Traﬃc and signal conditions of the simulator
CORSIM is set as follows.
• Free ﬂow speed : 27[ml/h]• traﬃc ﬂow : (main street)

0.35[vehicles/s](minor street) 0.20[vehicles/s]• phase probability : 0.50
• right and left turn : no turn• yellow time : 3[s]• all red time : 2[s]©ISAROB 2007

0.07 0.04
0.02
0.06 0.03
0.01
0.05 0.02
0.04 0.01 0
0
10(1)
20(1)
30(1)
10(2)
20(2)
30(2)
10(3)
20(3)
30(3)
0.03
0
generation(stage number)
0
10(1)
20(1)
30(1)
10(2)number
20(2)
30(2)
10(3)
20(3)
30(3)
0.02
0.01
generation number (stage number)
0
0
10(1)
20(1)
30(1)
10(2)
20(2)
30(2)
10(3)
20(3)
30(3)
generation number (stage number)

Figure 4: Distribution of the ﬁtness value of each individual as a function of generation (regular grid network)
Table 1: The best solution of the GA and the linear
model (regular grid network)GA
linear model

cycle length
oﬀsets of 
1 to 
9ﬁtness57[s]0 2 31 1 28 28 1 31 1 0.0754
50[s]
0 0 25 0 25 25 0 25 0 0.0279

As experimental results, Figure 3 shows the distribution of the common cycle length as a function ofgeneration in the ﬁrst stage of the GA. Figure 4 shows
the distribution of the ﬁtness value of each individual
as a function of generation. Table 1 shows the bestsolution of the GA and the linear model.

4.2

Irregular grid network

Next, we examine the linear model and the proposed method under more actual situation than theabove. That is, the street network is made to be theirregular grid one with 3 columns of main roads and 3

436

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
lines of minor ones and having 9 intersections and 24
unidirectional links (12 bidirectional links) of unequal
length as shown in Figure 5.
Main streets②

1737[ft]⑤

1303[ft]

1300[ft]1099[ft]⑦1096[ft]①

1374[ft]⑧1246[ft]

1182[ft]

1538[ft]④⑥

1178[ft]

600[ft]⑨

Table 2: The best solution of the GA and the linear
model (irregular grid network)
Minor streets

715[ft]③GAlinear model

5
Figure 5: The setting of the link lengths

commoncommoncycle
cyclelength
length[s][s]The parameters of the GA and the traﬃc and signal
conditions of CORSIM are same as the above experiments. The reference signal and its oﬀset are set assame as the above. In the linear model, the oﬀset ofeach signal is also calculated as same manner as theabove. And the common cycle length in the linear
model is equal to the average of the round trip timesof all links.
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
00

55

10
15
20
10
15
20
generationnumber
numbergeneration

25
25

30
30

Figure 6: Distribution of the common cycle length asa function of generation in the ﬁrst stage of the GA(irregular grid network)

fitnessfitness
fitness

0.03
0.03
0.025
0.025
0.02
0.03
0.02
0.015
0.025
0.015
0.01
0.02
0.01
0.005
0.015
0.005
0.010
0
0.0050 000

1st stage

1st stage

2nd stage

2nd stage

3rd stage

Discussion

In Tables 1 and 2, the ﬁtness values of the optimal
parameters obtained by the GA are higher than thoseby the linear model on the regular and irregular grid
street networks. Figures 3 and 6 show the convergence
to constant values of the common cycle length in thecourse of generation. In Figures 4 and 7, we can seethat the ﬁtness values become high and converge, inthe course of generation. And, the eﬀect of the coarse
to ﬁne approach of the 3 stage GA can be seen. Afterall, on both of the regular and irregular grid street
networks the availability of the proposed method is
conﬁrmed.

600

cycle length
oﬀsets of 
1 to 
9ﬁtness70[s]0 15 43 12 35 45 69 30 10 0.0279
61[s]0 51 33 0 33 30 0 39 60 0.0196

Conclusion

In this paper, we propose an optimization method
for common cycle length and oﬀsets among signal control parameters in area traﬃc control of a grid street
network using a GA. As future work, we intend toinvestigate the goodness of the proposed method bycomparing it with the other methods, and to apply
other metaheuristics such as ant colony optimizationin order to raise the eﬃciency of the proposed method.

3rd stage

References
10(1)
10(1)

20(1)
20(1)

10(1)

20(1)

30(1)
10(2)
20(2)
30(2)
10(3)
20(3) 30(3)30(1)20(2) number)
30(2)
10(3)
20(3) 30(3)generation10(2)number (stage
generation number (stage number)
30(1)
10(2)
20(2)
30(2)
10(3)
20(3) 30(3)generation number (stage number)

Figure 7: Distribution of the ﬁtness value of each individual as a function of generation (irregular grid network)
As experimental results, Figure 6 shows the distribution of the common cycle length as a function ofgeneration in the ﬁrst stage of the GA. Figure 7 shows
the distribution of the ﬁtness value of each individual
as a function of generation. Table 2 shows the bestsolution of the GA and the linear model.

©ISAROB 2007

[1] Abu-Levdeh and Benekohal, “Convergence variability and population sizing in micro-genetic algorithms”, Computer-Aided Civil and Infrastructureengineering, 14, pp. 321-34,1999.
[2] Masakazu Odawara and Mamoru Hisai, “A Studyon Common Cycle Length of Coordinated Signal
System through Genetic Algorithm”, The Infrastructure Planning Committee, vo.20(2), pp. 815818,1997 (in Japanese).
[3] Japan Society of Traﬃc Engineers et al., em Manual on Traﬃc Signal Control, Japan Society of Trafﬁc Engineers, 1994 (in Japanese).

437

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Three dimensional medical image recognition of the lungs
using artificial neural network
Takaomi Matsuki
School of Health Sciences
The University of Tokushima
3-18-15 Kuramoto-cho,
Tokushima 770-8509, Japan
takao_dq69@yahoo.co.jp

Tadashi Kondo
School of Health Sciences
The University of Tokushima
3-18-15 Kuramoto-cho,
Tokushima 770-8509 Japan

Abstractui = x i

In this study, we propose a method of
3-dimentional medical image recognition of the lungs,
pulmonary vessels and bronchial trees by using an
artificial neural network. Two neural networks trainedusing the back propagation algorithm are applied to
medical image recognition. First, the neural network
recognizes the outline of the lungs and outputs the lungregions and 3-dimensional images of the lungs are
displayed. Then, second neural network recognizes
outline of the pulmonary vessels and bronchial trees andoutputs the regions of them and 3-dimensional images
are displayed accurately. These image processing
proposed in this study can be easily applied to anothermedical image such as MRI image.
Keywords: Neural network, lungs, 3-dimensional image

1

Introduction

Neural network used in this study has a threelayered neural network architecture that is constructedwith the input layer, the hidden layer and the output layer.
Weights of the neural network are estimated using theback propagation algorithm. Two neural networks areapplied to 3-dimentional medical image recognition of
the lungs, pulmonary vessels and bronchial trees. First,
the neural network is organized in order to recognize theoutline of the lungs and extract the lung regions. Then,the subtraction images of the lungs are extracted and areused to organize the second neural network. Second
neural network is recognized and extracts the pulmonary
vessels and bronchial trees regions. These image
processing are carried out for all slices of multi detectorrow computed tomography (MDCT) images and 3dimensional images of pulmonary vessels and bronchialtrees are displayed with the volume rendering software.

2

Artificial neural network architecture

The neural network, which has 3-layered
architecture with the input layer, the hidden layer and theoutput layer, is trained using back propagation algorithm.
Architecture of the neural network is shown as follows:(1) Input layer©ISAROB 2007

438

Junji Ueno
School of Health Sciences
The University of Tokushima
3-18-15 Kuramoto-cho,
Tokushima, 770-8509, Japan(i=1, 2,…, p)(1)

Here, xj is input variables and p is the number of the
input variables.
(2) Hidden layerPy j = ∑ wi ui − θ , ( j = 1,2, L , g )

(2)

i

uj =

1
， ( j = 1,2, L , g )1 + exp(− y j )(3)Here, wi (i=1,2,…, p) is the weights between the input
and the hidden layer and g is the number of neurons in
the hidden layer.
(3) Output layergy k = ∑ w j u j − θ ， (k = 1,2, L r )(4)

1， (k = 1,2, L r )1 + exp(− y k )

(5)

j

uk =

Here, wi (i=1,2,…, g) is the weights between the hidden
and the output layer and r is the number of neurons in the
output layer.

3 Application to medical image recognitionof the lungs
The neural network trained using the backpropagation algorithm was applied to the medical image
recognition of the lungs. MDCT images of the lungs
were used in this study.

3.1 Image recognition of the lung regions
An original image shown in Fig.1 was used forlearning weights of the artificial neural network. The
statistics of the image densities in the neighboring region,
the N×N pixel regions, were used as the image features.
Three parameters namely, mean, standard deviation andvariance were used as the useful input variables [1]. The
output value of the neural network was zero or one.
When N×N pixel region was contained in the region of
the lungs, the neural network set the pixel value at thecenter of the N×N pixel region to one and this pixel was
shown as the white point. In this study, we set the valueof N to 4. Figure 2 shows the output image of the neural

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

network after the post-processing. In the post-processing,small isolated regions were eliminated by the image
processing such as the dilatation and the erosion. Then,outlines of lung regions were expanded by N/2 pixels
and outline of the lungs were extracted. In order to checkmatching between original and output image of theneural network. The output image was overlapped on
original image after the post-processing. Overlapped
image is shown in Fig.3. Gray scale image (Fig.4) of thelungs was subtracted from the original image (Fig.1)
using the output image (Fig.2) of the neural network.
These image processing were carried out for all slices of
MDCT images and 3- dimensional images of the lungs
are displayed with volume rendering software as shownin Fig.5.

pulmonary vessels and bronchial trees and outputs theregions of them. Figure 6 shows the output image of
second neural network. Then, the gray scale images(Fig.7) of the pulmonary vessels and bronchial trees are
extracted from the new original image (Fig.4) using theoutput image (Fig.6) of the neural network. These image
processing were carry out again to all slice images of the
MDCT. Then, 3-dimensional images were displayed by
the volume rendering software using these gray scale
images of the pulmonary vessels and bronchial trees. By
these procedures, the regions of pulmonary vessels andbronchial trees were recognized and extracted accuratelyby the neural network.

Fig.6 Output image(2)Fig.1 Original image

Fig.2 Output image(1)Fig. 3 Overlapped image

Fig.4 Subtraction image(1)Fig.7 Subtraction image(2)Fig.8 3-dimensional image of the pulmonary vessels
and bronchial trees

4 Conclusion
In this study, the neural networks trained using theback propagation algorithm were applied to the
3-dimensional medical image recognition of the lungs,
the pulmonary vessels and bronchial trees and3-dimensional images were displayed accurately. These
image recognition procedures using the artificial neuralnetworks can be applied easily to another medical image
such as the MRI.
Fig.5 3-dimensional image of the lungs

References

3.2 Image recognition of the pulmonary vessel
and bronchial tree regions
Gray scale image shown in Fig.4 was used as a
new original image to organize second neural network.
The organized neural network recognizes the outlines of©ISAROB 2007

439

[1] T.Kondo, J.Ueno and K.kondo, “Revised
GMDH-type neural networks using AIC or PSS criterion
and their application to the medical image recognition”,
Journal of advanced computational intelligence andintelligent informatics, Vol.9, No.3, pp.257-267,2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Design of Rough Set Processor for Knowledge Discovery
Matsumoto Mitsuhiro
Dept. of Electronic Engineering
Tokyo Denki Univ.
2-2, Kanda-Nishiki-cho, Chiyoda-ku,
Tokyo, Japan 101-8457
E-mail: 05gmd17@ed.cck.dendai.ac.jp

Kanasugi Akinori
Dept. of Electronic Engineering
Tokyo Denki Univ.
2-2, Kanda-Nishiki-cho, Chiyoda-ku,
Tokyo, Japan 101-8457
E-mail: kanasugi@d.dendai.ac.jp

Abstract

CoreData

Function DataCore
Selector

This paper propose a basic design for rough set processor for knowledge discovery. In this paper, the architecture
and the algorithm, the simulation, the experiment of thededicated processor are shown.

Function Data

CoreData

Core
Data

Covering R educed Data Function
Unit
Data

1 Introduction
Rough set theory is widely paid to attention in recent
years. This theory is very effective to the database including rough expressions and contradictions [1]. The processing of rough set is simple, but it is difficult to obtain thequick response. The design of the hardware based on therough set theory have been reported, but the dedicated processor for the large-scale data mining has not been reported
yet. The proposed design can be applied in the field of
robotics, medical science, industry, and so on.

2 Architecture and Design

E0
E0
E

F
F0
F0

G
G
G

H
H0
H0

I
I
I

J0 K )
J0 K0 )
J K )

0
B C
D

0
E

0
F

G

0
H

I

0J

Core

0 )
K

Figure 1: Core-Data Selection and Data Reduction
Unit” repeats the processing of the matrix data that is the
reduced function data. Figure 2 shows the discovery of theimpormant rules in the post-processing. “Reconstruction2,000 columns…

1,000,000 rows

…

…
…

.

………..…….

…

………..…….

…

………..…….

X1㺢X2㺢X4㺢X38………..…….

0 3㺣X4㺣 …………. 㺣X
0 1㺣X2㺣X
0 2030㺣X2031 )F = 㺢 ( X0㺣X
0 2㺣X3㺣X
0 4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X1㺣X
0 3㺣X4㺣 …………. 㺣X2030㺣X
0 2031 )0 1㺣X2㺣X
0 0㺣X
㺢(X0 2030㺣X2031 )0 2㺣X3㺣X4㺣 …………. 㺣X
㺢 ( X0㺣X1㺣X
0 2030㺣X
0 2031 )0 1㺣X2㺣X3㺣X
0 4㺣 …………. 㺣X
㺢 ( X0㺣X
0 1㺣X2㺣X
0 3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X
0 2030㺣X2031 )0 0㺣X1㺣X
0 2㺣X3㺣X
0 4㺣 …………. 㺣X
㺢(X㺢 ( X0㺣X1㺣X2㺣X3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X1㺣X2㺣X3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X1㺣X2㺣X3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X1㺣X2㺣X3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X1㺣X2㺣X3㺣X4㺣 …………. 㺣X2030㺣X2031 )0 2㺣X3㺣X
0 4㺣 …………. 㺣X2030㺣X
0 2031 )0 0㺣X1㺣X
㺢(X0 1㺣X2㺣X
0 3㺣X4㺣 …………. 㺣X2030㺣X2031 )㺢 ( X0㺣X
0 2㺣X3㺣X
0 4㺣 …………. 㺣X
0 2030㺣X2031 )㺢 ( X0㺣X1㺣X………..…….

440

0
C
D
0
C
D
C D

………..…….

©ISAROB 2007

0
F = (A………..…….

In the case of the huge database, it is necessary totreat the logical function that consists of almost two thousand variables. In order to process huge logical functions, three units named “Core-Selector”, “Covering-Unit”,
and “Reconstruction-Unit” were designed [2]. “CoreSelector” and “Covering-Unit” reduce the data in the preprocessing, and “Reconstruction-Unit” extracts the rulesin the post-processing. “Core-Selector” selects some coredata and transfers the selected core data to reduct the logical function data. On the other hand, “Covering-Unit”
deletes the data that can be deleted by using the selected
core data. Figure 1 shows the core-data selection andthe function-data reduction. The post-processing time of“Reconstruction-Unit” is reduced by the pre-processing by“Core-Selector” and “Covering-Unit”. “ReconstructionUnit” discovers the important rules from the reduced logical function. In the post-processing, ”Reconstruction-

F= (A B
0
(A
B
0
(A
B

X567㺢X826㺢X2030㺢X3031

Figure 2: Discovery of the Important RulesUnit” consists of “Reconstruct-Controller”, “Parallel-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Counter”, and “Sorter”. Figure 3 shows the block diagram of “Reconstruction-Unit”. It is necessary forunit nameCore-Selector (16 core-data)
Covering-Unit (16 core-data)
Reconstruction-Unit (4 rules)

Rough Set Processor
Data Bus

Reconstruction-Unit

Address Bus

Control Signal

Controller

Table 1: Gate Count for Design and Processing Timegate count (gates)
9,400
2,100
36,200time (µs)
82.5
263.1
4,152.7

Reconstruct-Controller

the FPGA evaluation board (Xilinx Spartan-3E Starter Kit,
500 thousand gates). Table 1 shows the number of gates
and the processing time of the proposed processor. Figure 5 shows the experiment of ”Reconstruction-Unit” in the
post-processing.

Parallel-Counter
SorterRegister 㬍 4
Internal-Memory

Figure 3: Block Diagram of “Reconstruction-Unit”“Reconstruction-Unit” to repeat three steps to discover theimportant rules. The step 1 is the parallel count process tofind the number of each variable in the logical function.
The step 2 is the sort process to select the variable thathas the largest counts. The step 3 is the delete process todelete the row that has the selected variable. The discoveryof the most important rules is realized by repeating thesethree steps until all rows are deleted. Fugure 4 shows theschematic of “Reconstruction-Unit”.

Processing Time
Resultclk

Reconstruction-Unit

Internal-Memory

Count-Cashdi_ar4a addr_cc

addr

di_cc

addr_fr

dididi_cr4aaddr_ft

clk

clk

di_ft

addr_im

we

we

do

addr

Figure 5: Experiment of “Reconstruction-Unit”

dodi_im addr_ac4a
clk

en
rst

do_ar4a

di_fr

do_cc

en

do_cr4a

rst

do_ft

Attribute-Cash
addr

do_fr

di_ar

ru_end

di_cr

we_cc

clk

we_fr

we

4 Conclusion

Flag-Register
addr

do_ar

do

di
clk
we
do_cr

we_ft
we_ar4aaddr_ft

Function-Table
or

addr

do

do_ft

di
clk
we

ru_endIn this paper, the design and the experiment of rough set
processor is described. The future works are the experiment with the actual database and the improvement of theprocessing speed.
This research is patically supported by Research Institute for Science and Technology of Tokyo Denki University under Grant Q03-04.

Figure 4: Schematic of “Reconstruction-Unit”

References

3 Experiments
The authors performed the design and the simulation using the logic synthesis tool (Xilinx ISE WebPACK 8.2i)
and HDL simulator (Mentor Graphics ModelSim XE 6.1e).
The experiment of the designed processor is performed on©ISAROB 2007

441

[1] Pal S K and Skowron A (1999), Rough Fuzzy Hybridization - A New Trend in Decision - Making,Springer Verlag.
[2] Kanasugi A (ed. by Inuguchi M et al.) (2002), RoughSet Theory and Granular Computing, Springer-Verlag,
pp. 273-280.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Outdoor Autonomously Moving Robot Using a Distributed Image Processing System
Hiroki GOTO, Norihiro ABE
Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Email: goto@sein.mse.kyutech.ac.jp
Hirokazu TAKI
Wakayama University
930 Sakaedani, Wakayama-shi
Wakayama 680-8510, Japan

Shoujie He
Eastman Kodak Company,
Plano, Texas, USA

Abstract

scene requires running a lot of complicated image
processing algorithms, which consumes a lot of
resources in terms of memory and CPU usages. The
computer carried by the robot is overwhelmed by dataprocessing needs.
Over the past few years, network based
communication technologies have made remarkableprogress. Sharing the computational resources such as
memory and CPU power and load balancing could easilybe achieved through the network. This makes us think
the possibility for the mobile robot to have its
computational burden shared with others through the
network, as well. With the computational burden shared,
real-time processing of all the data could be possible andthus an autonomously moving robot could be expected.
Based on the above-mentioned idea, we mounted a
network camera on the mobile robot so that the robot
could have the image processing load shared by a coupleof high performance computers through a wireless LAN.
The feasibility has been verified and the experimental
results will be reported in this article.

This study aims at building an autonomous robotthat is capable of comprehending complicated image
processing algorithms rapidly and concurrently via awireless communication network. The entire system
includes a mobile robot, a network camera mounted on
the mobile robot, a wireless LAN, and a distributed
image processing system. Images captured by the
network camera are first transmitted to one of theclustered computers through the wireless LAN. The
computer receiving the images then distributes theimages to the other computers in the cluster. The image
processing results will be collected and reported back to
the robot through the wireless LAN also by the same
computer. With the clustered computers, multiple
image-processing algorithms could be applied to a single
image in a concurrent fashion. As an experiment, themobile robot has been put on a street. In order for themobile robot to move autonomously on the street, threetargets including white lines on the road, static andmoving obstacles, have been identified necessary for the
distributed image processing system to detect. The
experimental results indicate that the distributed image
processing system successfully meet the needs for theautonomous movement of the mobile robot.
Keywords: Network, Distributed Image Processing,Autonomously Moving Robot

1

2

System Organization

As shown in Figure 1, the entire system could be
divided into the following three subsystems.
・ Mobile robot
・ Network communication
・ Data processing for the autonomous movement

Introduction

This study aims at the research and development of
an autonomously moving robot so that the quality of ourdaily living could be further improved. The potential
applications include the automated car driving, ITS(Intelligent Transport System), environmental and safetyissues, and social welfare improvements such as thereplacement of the guide dogs.
The fundamental problem with an autonomously
moving robot is the computational intensiveness. While
moving autonomously, the robot needs to process all
kinds of information such as the dynamically changing
scene, sensor data, path finding, control of the movement,
and so on. Recognition of the dynamically changing©ISAROB 2007

442

Figure 1: System organization of the autonomously
moving robot.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.1

Mobile Robot

The mobile robot used in this research is 90 cm longand 60 cm wide. A network camera is mounted on thefront of the mobile robot. The network camera keeps
transmitting images through the wireless LAN to ahigh-performance server machine. The network camera
has its own IP address, with which the communication
with the remote server machine will not consume any of
the computational resources on the computer carried by
the robot. Images are transmitted through an IEEE
802.11a wireless router that is mounted on the robot as
well.
In addition to the network camera, the mobile robot
is equipped with photoelectric sensor, GPS (Global
Positioning System) sensor, and 3D motion sensor. The
computer carried by the robot processes the data from allthese sensors. The same computer will also control the
servomotor of the robot. Since the remotehigh-performance computers have shared the heavy-loaddata-processing tasks such as image processing and pathfinding, the computer on the robot could have morecomputational resources for the overall control of the
movement. Visual Studio.NET from Microsoft has beenused for the software development. The image
processing tasks are executed with HALCON, an image
processing software package from MVTec, a German
company.

2.2

Network Communication

Fig. 2. Architecture of the network organization.
The needs for the network communication could be
divided into two major areas. One is the area where themobile robot is moving around. The other is the servermachine and the other high performance computers.
Considering the limited transmission performance of
wireless LAN, the usage of wireless LAN is minimized
by using the VPN (Virtual Private Network) so that theexisting wired LAN could be used as much as possible.
It could be a challenge to discover the existing wired
LAN when the robot is put into operation in the realworld. The benefit of using wireless LAN, however,includes the possibility of extending the area that the©ISAROB 2007

443

mobile robot could move around by the relay of thewireless LAN.
The network communication on the server side, on
the other hand, involves large amount of data processing.
It requires high-speed and high throughput
communication network. SCRAMNet, an optical
network is used to reduce the communication delay.
With the above optimization of the network
resources, real-time processing could be expected.

2.3

Distributed Image Processing

Fig. 3. Distributed Image Processing System
Figure 3 shows the organization of the distributed
image processing subsystem. The server machine that isreceiving images from the network camera will write theimage data into the shared memory. The same server will
read the results processed by the other computers andreport the results back to the robot.
All the computers involved in the image processing
basically share the shared memory. The synchronizationis achieved through the SCRAMNet. The server machineis receiving images at the speed of 10 images per second.
It will write the image data to the shared memory at thesame speed. The other computers will read the image
data from the shared memory and start processing. Since
the robot is put on a street, at the very least three targets,
white lines, static and moving obstacles, need to be
detected through the image processing algorithms. Three
high-performance computers accomplish the detection of
the three targets independently and concurrently. The
benefits of this system architecture include thescalability and the maintainability.

3

Image Processing

The following three image processing tasks are
assigned to the distributed image processing system.
・ White line recognition
・ Static obstacle identification
・ Moving obstacle identification

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Based on the feature analysis of each of the target
objects, a corresponding image processing algorithm has
been developed. The outdoor scene is unstable due to thesunlight, shadow, and the vibration caused by the
movement of the robot. This requires high robustness tothe image processing algorithms.

3.1

White Line Recognition

Basically, two approaches have been taken toward
the extraction of white lines. One is color-based. The
other is edge element based. The white lines are finallyidentified by integrating the information from the two
approaches. With the information integration, thelighting effects could be reduced to the minimum level.
Furthermore, real-time straight line approximation
algorithm is introduced to improve the robustness ofwhite line recognition even if when the white lines areshaded by other objects. Figure 4 shows the entire
process of extracting white lines.

Present Image

Edge Processing

Color Information

White line extraction

The person's movement forecast
Fig. 5. Extraction of moving obstacles
Similar to the extraction of white lines, both color
information and edge elements are used for the
extraction of the static and moving obstacles. With aproperly selected threshold, the boundary of obstacles
could be extracted. If the area of the enclosed region islarger than a preset threshold, the enclosed region isconsidered as the existence of an obstacle. The
coordinates of the center of the obstacle are persisted tothe memory on the computer that is processing the image.
As soon as the center of an obstacle extracted from thesubsequent image is measured, the results will becompared to the previously persisted data. Whether the
obstacle is a static one or a moving one is decided by thedifference. The difference could also tell the moving
direction and speed of a moving obstacle. The
information regarding the moving obstacle is useful forthe robot to decide the next movement.

4

The experiments are focused on the distributed
image processing subsystem and the overall network
communication. The main purpose is to verify the
response time from the distributed image processing
system to the robot. The experimental results havesuccessfully verify the feasibility of the robot system
proposed in this study.

Approximate deriving
Fig. 4. Results of white line extraction

3.2

White Line Recognition

Figure 5 shows the entire process of extracting amoving obstacle.

Edge Processing©ISAROB 2007

Experiments

Making Up

444

4.1

Methodology

1. Capture images with the network camera.
2. Transmit the images to the server machine through thewireless LAN.
3. Distribute the images to the computers dedicated toimage processing through the shared memory.
4. Process the images with the algorithms described in
Section 3.
5. Suggest the next movement of the robot based on theimage processing results by turning on or off the flagsin the shared memory.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

6. Server machine will check the flags and decide
whether and what to report back to the robot.
7. Measure the time from the transmission of image data
to the server machine and the response from the servermachine.
8. Measure the response time through steps 1 to 7. Also
measure the time spent for processing an image by asingle computer. Do the comparison between the two.

4.2

Experimental Results
TABLE I
Experimental Result ( Time of image processing )Possible

image processing

frequency（1 sec）

Necessary time for image dataprocessing of one time

Necessary time
only for

8.5 times

image data

0.118 sec

response time, we are investigating the possible ofconverting image format during the transmission. With
the improvement, more complicated image processing
algorithms requiring image processing 10 times per
second could be achieved.
Furthermore, edge detection, shape features could
be further improved so that the entire response time
could be substantially shortened. However, in order todeal with the outdoor scene, a lot more different type of
obstacles needs to be taken into account. Accordingly,
the image processing algorithms need to besophisticating enough to handle the complicated scene.
Algorithms such as obstacle identification based on
pattern matching, accurate distance measurement basedon stereo matching, and the processing high-resolution
images are being considered. With all the availability of
all these algorithms, the truly autonomously moving
robot could be expected.

processing

TABLEⅡ

6

Future Work

EXPERIMENTAL RESULT ( RESPONSE TIME )Reception of result for every

Time necessary for reception

100 seconds frequency

once(reactive speed)

Robot reaction time

480 times

0.208 sec

In order to measure the response time, one image
processing computer is used for the experiment. The
experimental results in table 1 and 2 are all collectedfrom the same computer. In the case that more than one
computer are used for more image processing algorithms,
the response time will increase. Considering the possible
number of images transmitted per second, approximately
8 computers will be possibly used for the image
processing. The response time will increase to 0.95
seconds at most. The time needed for processing one
image will not exceed 0.2 second. The data in Tables 1
and 2, however, are collected from the indoor scene. To
measure the outdoor scene, we have to extend thecoverage of the wireless LAN, the response time will forsure increase.

5

Acknowledgements
We greatly appreciate the aid of Ministry of Internal
Affairs and Communications (MIC) and theGrant-in-Aid for Scientific Research.

References

Discussion

The experimental results indicate that the proposedsystem architecture for supporting autonomously moving
robot provides fast enough image processing capability.
Assume that the robot is moving at the speed of 1 m
per second. It will be considered necessary to do theimage processing 10 times per second. The network
camera mounted on the robot is capable of transmitting
10 images per second to the server machine. The
response time measured from the experiments is 0.2
second. Therefore, doing image processing 5 times per
second is possible. In order to further improve the©ISAROB 2007

In the current distributed image processing system,
one computer is dedicated to process an entire image. It
is possible that the lack of responsiveness is due to thecompletion of a complicated image-processing algorithm.
If it is possible to divide the image into 3 portions andhave three computers to apply the same image
processing algorithm only to one third of the image data.
The entire processing time will be reduced by one third.
It is also possible that the movement of the robot at thenext moment has been decided already while finishingprocessing only one third of an image. In that case, theimage processing algorithms should be applied to thesubsequent incoming images. This divide-and-conquer
approach is promising and still has a lot of uncertainties
that need to be resolved in the near future.

445[1].Kazushi Hirano,"Research on remote control systems
for half autonomous movement robot in outdoor",
Kyushu Institute of Technology Information
Department of Engineering graduation thesis,2003[2]. J. Miura and Y. Shirai,"Vision and Motion Planning
for a Mobile Robot under Uncertainty",Int. J. of
Robotics Research, Vol. 16, No. 6, pp.806-825, 1997
[3].T.TAOKA,
M.MANABE,
M.KANBAYASHI,
Y.OHNISHI, M.FUKUI ”An Efficient Lane
Recognition
Algorithm
forAutomobile
Applications” Information Processing Society ofJapan Research Report, Vol.2006, No.111,
2006-SLDM-126, pp.63-68

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Lateral Control Design of an Autonomous Land Vehicle Using RTK-DGPSHyung Gyu Park, Jae Heon Ryu
and Seong Taek Hwang
Department of Mechanical andIntelligent Systems Engineering
Pusan National University
Busan, 609-735, KOREA

Man Hyung Lee
School of Mechanical Engineering,
Pusan National University
Busan, 609-735, KOREA

Abstract

the information to the GPS receiver so it can use the datato correct its own measurements and calculations. Thisdifferential correction technique applies to GPS receivers
performing code-phase navigation.
When a receiver navigates in carrier-phase mode, it ismeasuring a different GPS observable, namely the GPS
carrier wave. In order to obtain high accuracy withcarrier-phase measurements, it is necessary to computethe number of GPS wavelengths between the roving GPS
receiver's antenna and the satellites using the information(i.e., carrier-phase measurements) from a base receiver.
This technique yields accuracy to the cm-level indynamic environments called RTK-DGPS. So, themethod is used to make a digital road map and computes
vectors for navigation.
In this paper, the H ∞ control by the feedback of theyaw angle error was used to design a robust lateralcontrol against modeling uncertainty [4]. Furthermore,
its property is compared that of the PID controller.

This paper describes the development of the H ∞lateral control system for an autonomous ground vehicle
in a limited area using the RTK-DGPS (Real Time
Kinematic - Differential Global Positioning System).
Before engaging in autonomous driving, map data are
acquired by using the RTK-DGPS. The data are thereference trajectory for autonomous vehicles. The
navigation system with the map data computes thereference yaw angle of the vehicle using its present
position and next position. Also, the yaw angle of thevehicle is controlled by the H ∞ controller. A prototypeof the autonomous vehicle by the navigation method isdeveloped. In addition, the performance of the vehicle isevaluated by experiment. The experimental results showthat the H ∞ controller and the RTK-DGPS basednavigation system can sufficiently track the map at low
speed. We expect that this navigation system can bemade more accurate by augmenting other sensors.

2 Lateral Control

1 Introduction

2.1 Lateral Vehicle Model

Research centered on ITS (Intelligent Transportation
System) and PATH (Partners for Advanced Transit and
Highways) has led to the development of theautonomous vehicle. Generally, it has been realized using
MR sensors and vision sensors, among othertechnologies. Though the autonomous vehicle wascreated in part by using them, it is difficult to operateautonomous vehicles in all conditions [1]. For example,
if we use MR sensors, we must mark magnet points on
the road and stochastic error occur. With vision sensors,
the problem is that they are sensitive to weather
conditions or light [2]. Now, concerning about thenavigation systems by using the absolute coordinate
system has progressed [3]. So, we realized anautonomous vehicle using the absolute coordinate systemwith an RTK-DGPS (Real Time Kinematic-DGPS)Generally, a differential GPS has been used for
improving the accuracy of GPS. All the DGPS reference
stations have transmitters to forward the error factors to
DGPS receivers by radio or other methods, which gives

This section considers a classic linearized bicycle
model with two degrees of freedom for the lateral andyaw dynamics of a vehicle. We used the PATH car model(Fig. 1) in order to design controller. Because our maininterest is with steering control, we ignored the roll, pitch,and vertical movement of the vehicle. And if we supposethat the vehicle runs on a flat road, we can regard thelateral slip angle and the yaw angle as small [5]. Also, if
the speed of the vehicle is constant, the complex car©ISAROB 2007

446

δf

CG
j

Lr
i

Ψ
Lf
yr

Fig. 1 Integration concept of operation

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

model equation can be simplified. The curvature of theroad was not considered in this study for simplifying themodel, since the effect of the curvature on vehicle
control at the low speed and in the large curvature is lessthan that of other uncertainties and external disturbances.
The linearized dynamic equation can be simple, as
follows:⎡0⎡y ⎤ ⎢⎢ ⎥ ⎢0d y⎢ ⎥=⎢dt ⎢ Ψ ⎥ ⎢ 0⎢ ⎥ ⎢⎣Ψ ⎦ ⎢
0
⎣⎢

1

r

A1

r

V

r

0

r

A3
V

0 ⎤

0
− A1
0

A2 ⎥ ⎡ y r ⎤⎥⎢ ⎥V ⎥ y r⎡0⎤⎢B ⎥⎢ ⎥ + ⎢ ⎥δ f ,1 ⎥ ⎢Ψ ⎥ ⎢ 0 ⎥⎥⎢ ⎥ ⎢ ⎥ ⎦ ⎣B ⎦A ⎥ ⎣Ψ
⎥
⎦
V

(1)

1

A3 =

C f + Cr
m

− A3

,

( −C f L f + Cr Lr )

B1 =

J
Cf
m

and

Ψr

y r


Ψ
r

θ m ⎤⎦

Tis obtained asfollows:
⎡0
⎢
⎢0
⎢
⎢0
X = ⎢
⎢
⎢0
⎢
⎢
⎢0
⎣

1
A1
V
0

0
− A1
0

0
A2
V
1

A3
V

− A3

A4
V

0

0

0

0 ⎤
⎥
⎡0⎤
B1 ⋅ n ⎥
⎢0⎥
⎥
⎢ ⎥
⎥
⎢0⎥ .
0
⎥ X + ⎢ ⎥uf⎥⎢0⎥
B2 ⋅ n ⎥
⎢1⎥
⎥
⎢ ⎥
1 ⎥
⎣⎢ Tm ⎦⎥
− ⎥
Tm ⎦

(4)

2.2 Lateral Control of an Autonomous Vehicle2r

A2 = −, A4 = −B2 =(C f L f − Cr Lr )(C f L2f + Cr L2r )C f Lf
J

m

J

,

,

.

The front wheel steering actuator is assumed to be
dominated by the first order delay.
θm =X = ⎡⎣ yrr4

where A1 , A2 , A3 , A4 , B1 , and B2 are defined as
A1 = −

states1
uf ,Tm s + 1(2)

where, Tm is the time constant of the motor and u f is
the control input and θ m is the motor (or steering wheel)
angle.
The steering system from the motor (or steering
wheel) angle θ m to the wheel angle δ f is modeled as agear with the gear ratio of n . So, δ f is given byδ f = nθ m .

Fig. 2 is general a block diagram of H ∞ control.
The components of w are all the exogenous inputs tothe system [6]. Typically, these consist of disturbances,sensor noise reference commands and fictitious signalsthat drive frequency weights and models of theuncertainty in the dynamics of the system. The
components of z are all the variables we wish tocontrol like the tracking errors and the actuator signals.
The inputs generated by the controller are denoted u .
The sensor measurements that are used by the feedback
controller are denoted y .
The generalized plant P , which is assumed to belinear and time-invariant, contains all the information adesigner would like to incorporate into the synthesis of
the controller, K . System dynamics, models of theuncertainty in the system’s dynamics, frequency weights
to influence the controller synthesis, actuator dynamics,
sensor dynamics, and implementation hardware
dynamics are all included in P .
The relation of the variable z and the exogenous
input is z = Tzw w . So, the H ∞ control is represented as
Tzw ∞ ≤ γ .

(5)

The H ∞ control is represented as (6) with themethod of Glever and Doyle [7].
(3)Combining the dynamics of the actuator and thevehicle dynamics, a 5th order state space model with

W1S
≤γ .
W3T ∞

(6)

Regarding the mixed-sensitivity problem, W1 and

z

P

y

W3 are weighting functions for improving performanceof the system. In addition, S and T are the sensitivity
function and the loop transfer function of the system,
respectively. Based on the time domain performance
specifications, the corresponding requirements in afrequency domain in terms of the bandwidth ωb and thewuK
Fig. 2 The basic block diagram of H ∞ controller©ISAROB 2007

447

peak sensitivity M s can be determined. This assumes
that the steady state error of the step response ε has toThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

satisfy W1 (0) ≥ 1/ ε . A possible choice of W1 can beobtained by modifying the weighting function as follows:
W1 =s / M S + ωb.
s + ωbε

Additionally, the magnitude of KS(7)in the low-frequency range is essentially limited by the allowable
cost of control effort and saturation limit of the actuators;
hence, in general, the maximum gain M T of KS canbe fairly large, while the high-frequency gain isessentially limited by the controller bandwidth ( ωbc ) andthe sensor noise frequencies. A candidate weight W3
would beW3 =s + ωbc / M T
ε1s + ωbc

(8)

for a small ε1 > 0 .
Fig. 3 is the bode plot of each weighting function.

(9)

Pt −1Pt ⋅ PMtt +2

Also, the sign of direction is solved by using the signof the cross product of vectors. So, we can acquire ψ ref
as follows:Gψ ref = sign(k ( Pt −1Pt × PMtt + 2 − Pt −1Pt × PMtt + 2 ) × ψ ref . (10)
x

y

y

x

For determining the target point, r is calculated byusing velocity V of the vehicle, sampling time (t) of the
RTK-DGPS and α , the coefficient considered curvatureof the road. α is used for determining the proper target
point. If target points are calculated at 1 second intervals,
the variation of yaw severely increases. So, angle data
λ of the vectors are added to the path data by postprocessing. The data are calculated using n − 1 th, n thand n + 1 th data in the path data. The coefficient α isacquired by using equation (11). It is optimized bysimulation and experiments as follows:
α =1⎧λ > 0.1 radian,
⎨
<
=
λ
0.1
radian,α180
λ 2 − 37λ + 3.
⎩

3 Navigation Algorithm
Fig. 4 shows the target points are determined using
map data. When the unmanned vehicle navigates theroad, it compares current position with map data.
However, the navigation system cannot compare all data
for searching target points. So, it calculates maximum
moving distance r of the vehicle by using velocity ofthe vehicle until acquiring the next set of RTK-DGPS
data. It compares the current position with maximum
distance position M n + 2 within the circle that has aradius r . It calculates the yaw reference ψ ref

JJJJJG JJJJJJJG
P P ⋅ PMt −1 ttt +2
ψ ref = cos −1 JJJJJG
JJJJJJJG .

by thedot product of vectors. It uses the prior position Pt −1 , thecurrent position Pt and the target position M n + 2 , whichis shown in equation (9) as follows:(11)

4 Experiment
4.1 Experimental System
The experiment vehicle is SPORTAGE of KIA
motors. The RTK-DGPS system is from the Z-family of
Ashtech. The accuracy of the synchronized RTK mode is
0.5cm+1ppm. In addition, the maximum position output
rate at the remote receiver is 1Hz. The communication
between the base station and the remote station in thevehicle was accomplished by using a PDL (Positioning
Data Link) system that uses an RF connection. Also, weused the digital compass of Robot electronics. Itsabsolute error is 3˚, with a resolution of 0.1˚. The
navigation computer was PXI-1002 from National
Instrument. Finally, the navigation software was coded

M n +3
M n −2
Pt −1

M n+2

M n −1Mn
Pt −−11

M n +1

Pt +1

Ψ ref

Pt

r = α ×V × t

Fig. 4 Navigation concept

Fig. 3 Selected weighting functions©ISAROB 2007

448

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

by using LabVIEW 6.1.

4.2 Experiment Results
The width of the test road was a minimum 3.6 m andthe vehicle width was 1.735 m . Before the test, the pathdata were acquired by using the RTK-DGPS. Fig. 8 is thepath data. The full lap of it was 160 m .
The velocity of the vehicle is about 2.778 m / s(about 10 km / h ). Fig. 6 shows the test results when theparameter is similar to that used in designing thecontroller. In Fig. 7, the velocity and mass of the vehicle
are changed in the test driving condition. This was donein order to compare performance of each controller
considering modeling uncertainty. The performance ofthe H ∞ controller is more robust than a PID controller
for a change of system parameters. Table 1 presents the
maximum and mean of the errors of each experiment.

a simple navigation algorithm. To control the yaw of thevehicle, an H ∞ control algorithm was introduced. To
cope with the sensor noise and the modeling uncertainty,
a robust lateral controller was designed by the feedbackof the yaw angle error of the vehicle.
The position and attitude of the vehicle wascalculated by using the RTK-DGPS and a digitalcompass. The vehicle was steered by DC motors whichwere controlled by a real time module. The
experimentation for the real scale vehicle showed thatthe proposed lateral controller improved the tracking
performance.
We also considered current related work concernedwith improving the precision of dead reckoning bycorrection of the diameters of tires. In such cases, thevehicle navigates for a longer time by dead reckoning inan urban canyon environment. We expect suchapplications will be useful in the future to create a wheel
chair system for blind.

5 Conclusion

Acknowledgment

In this paper, the real vehicle was implemented toverify the performance of a proposed lateral control andThis work was supported by the Brain Korea 21
Project.

Reference[1] Huei, P., Wei-Bin, Z., Alan, A., Ye, L., Thomas, H.,
Peter, D., Masayoshi, T., and Steven, S, Experimental
Automatic Lateral Control System for an Automobile,
PATH Research Report, 1992.
(a) PID Controller[2] Maurer, M., Behringer, R., Furst, S., Thomanek,
F. and Dickmanns, E. D., "A Compact Vision
System for Road Vehicle Guidance Pattern
Reconition," in Proceedings of the 13th(b) H ∞ Controller

Fig. 5 Trajectories for each controller(a) PID Controller(b) H ∞ Controller

Fig. 6 Trajectories for each controller(+2m/s,+100kg)
Table 1. Results of driving test

Test result

Maximum
error[m]Meanerror[m]

PID

0.447

0.276

H∞

0.521

0.220

PID(+2m/s,+100kg)

0.914

0.483

H ∞ (+2m/s,+100kg)

0.732

0.310

©ISAROB 2007

449

International Conference, Vol. 4, pp. 2243-2247,
1998.
[3] Han Shue, Farrell, T., A., and Yunchun, Y.,"Automatic Vehicle Steering Controls: DGPS/INS
and Magnetic Markers," in Proceedings of American
Control Conference, Vol. 1, pp. 60-65, 2003.
[4] Shladover, S., "Automated Vehicle Control
Developments in the PATH Program," IEEE
Transaction on vehicular technology, Vol. 40, No. 1,
pp. 114-130, 1991.
[5] Huei, P., and Masayoshi, T., Lateral Control of FrontWheel-Steering Rubber-Tire Vehicles, PATH
Research Report, 1990.
[6] Kemin, Z., Essential of Robust Control, The firstedition, Prentice-Hall, New Jersey, 1998.
[7] John, C., Keith, G., Pramod, P., and Bruce, A., "StateSpace Solutions to Standard H 2 and H ∞ Control
Problems," IEEE Transactions on Automatic Control,Vol. 34, No. 8, pp. 831-847, 1998.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Navigation system for a mobile robot
using an omni-directional camera
Tatsuya Mochizuki

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
hiki@mmcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp
movement to pursue a moving object were developed.

Abstract
This study developed a self-localization system thatuses landmarks and movement to pursue a moving object.
Specifically, an omni-directional camera was adopted as
a visual sensor and was installed on a robot.
The image from the omni-directional camera wasprocessed to detect a landmark (an object), and therobot’s distance from that landmark was then measured.
These functions allowed the mobile robot to pursue amoving object. The measurement of distance based on
the omni-directional image depends on the precise
inclination of the camera. Consideration of this factor
enabled the measurement of distance of relatively close
objects with a high degree of precision.
In this study, the effectiveness of this system wasinvestigated by various experiments evaluating themeasurement of distance, the movement to pursuit amoving object, and self-localization. The robot
demonstrated its ability to recognize a moving object andto measure its distance and its angle in relation to theobject. Based on these abilities, the robot could
successfully follow a moving object. The robot’s ability
to recognize its location was seen to be based on its
ability to recognize certain landmarks, the functions ofwhich are mentioned above.

2. Omni-directional camera
2.1 Background of an Omni-directional camera
Fig. 1 shows the omni-directional camera. It can shoot acircumference of 360 degrees. Fig. 2 is an image thatwas shot by the omni-directional camera.

Fig. 1 The omni-directional camera

Fig. 2 Omni-directional image

2.2 Principle of the omni-directional camera
An omni-directional camera consists of a
hyperboloidal mirror and an upward camera. It shoots thecircumjacent environment that is reflected in the mirror.
Thus, it acquires an omni-directional image (Fig. 3).

Z

Focus of a mirror
Perspective projection image sideY

Hyperboloid mirror

1. Introduction
With the increase of the elderly population comes anincrease in individuals who need attendant care. These
increases will also bring about an increase in the burdenof caregivers.
This study aimed to develop technology for a personalcare robot that will reduce the burden of the caregiver.
Specifically, an omni-directional camera was adopted as
a visual sensor and was installed on a robot.
The image of the omni-directional camera wasprocessed to detect a landmark (an object), and therobot’s distance to that landmark was then measured.
Thus, a self-localization system that uses landmarks and©ISAROB 2007

450

X

Omni image side

P(X,Y,Z)p(x,y)
Lens center

CameraFig. 3 Structure of visual system

The arbitrary point P(X,Y,Z) in 3-dimensional spacecorresponds with point p(x,y) on the omni-directional
image.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(b 2 − c 2 )x = X × f ×(b
y = Y × f ×2+ c ) Z − 2 bc
2

(b

2

X 2 +Y 2 + Z 2−c )2( b 2 + c 2 ) Z − 2 bc

(1)

X 2 +Y 2 + Z 2

degree intervals. Therefore, it interpolated the distanceusing an interpolating spline, and was able to derive a
relation expression that calculated real distances. Fig.
6 shows a graph of the relation expression.
The omni-directional camera was strapped into a
tripod stand, and measured real distances. As a result,the value of the average error was 13[mm].

3. Detecting a landmark
The image processing applied to the image from theomni-directional camera (Fig. 4) used color
information.
When detecting a landmark by use of color
information, an RGB color model has to discriminate acolor based on three values (R, G, and B). This makes
detection challenging. However, a HSV color model isable to discriminate a color by one value. Accordingly,
the RGB color model was converted to a HSV color
model, and thereby detected the landmark by HSVvalues.
When detecting a landmark using HSV values, somenoise is present. In such cases, the image processing
removes the noise. Also, it discriminates an object
using labeling.
Fig. 4 shows an omni-directional image and Fig. 5
shows how it was processed by means of image
processing.

Fig. 4 The picture before theimage data processing

Fig. 5 The picture after theimage data processing

4. Measuring the distance to the landmark
The distance between the omni-directional camera
and a landmark was measured by means of theomni-directional image. This study derived relations
between the distance on the omni-directional image
and the real distance. And it directly calculated the
distance from the image.
Concretely speaking, we experimentally derived
relations between the distances on an omni-directional
image and real distances. From this experiment result,
we were able to derive relations between the distanceof an image in the directions of 0, 45, 90, 135, 180,
225, 270, and 315 degrees and the real distance. But
the system could not measure distances in between
these directions because it only derived relations of 45©ISAROB 2007

451

8000
7000

real distance[mm]The relation can be expressed by expression (1).

6000
5000
4000
3000

7000-8000
6000-7000
5000-6000
4000-5000
3000-4000
2000-3000
1000-2000
0-1000

2000
1000

ang
le[
deg
r ee
]

0 cel]dista

e[p iximagnannc e oFig. 6 The spline surface

5. Installing on a mobile robot
5.1 Mobile robot KITASAP2
In this study, we used a mobile robot that was
fabricated in this laboratory. Fig. 7 shows a picture of
the mobile robot. The robot we adopted has three wheels.
The two front wheels have independent drives while therear wheel is free.
The following devices were adopted in the mobile
robot.
・PC
・H8 microcomputer・Sensor① Omni-directional camera② Encoders

Omni-directional
cameraHDD

PC

BatteryMotor/Encoder

Fig. 7 The mobile robot

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5.2 Compensation of slippage by an incline
The omni-directional camera was installed in themobile robot, and it measured distance as before.
While the error in the case described above was
13[mm], the error in this case was 53.52[mm].
It is thought that cause of this greater error was theslippage on the incline that occurred when we installed
the camera. Therefore, we revised the slippage.
In Fig. 8, the dashed line indicates the direction inwhich the omni-directional camera inclines, and thecontinuous line is the horizontal.

calculates a circle such that the direction of the robot’s
progress is a tangent line and the circle passes throughthe position of the object (Fig. 9)This circle is unique. Therefore, the mobile robot
assumes this circle as its movement course. The radiusof this circle can be calculated with expression (3). By
performing these calculations and movements
repeatedly, we succeeded in having the robot follow amobile object.

Z Z’ Omni-directional camera
objectInclined_angle
YX(Tx,Ty)camera_height

r

X’

θp

course of movement
θrmobile robot

X

direction of robot(Rx,Ry)mark

Fig. 9 Course of movement
real_distancemeasure_distancer=(Tx − Rx )2 + (Ty − Ry )22{cos θ p (Tx − Rx ) + sin θ p (Ty − Ry )}(3)

7. Self-localization

Fig. 8 Inclinations of the omni-directional camera

Assuming the height of the camera is given, we can
express the incline (the angle between arrows) in thefollowing expression.
 camera_height  camera_height  − tan −1 inclined_angle = tan −1 measure_di
stance real_distance 

We applied the above-described detection of alandmark and measurement of the distance to thelandmark in creating a self-localization system.
Here, I explain the method of self-localization. The
mobile robot calculates circles using the distance tothe landmark (Fig.10). The point where three circles
cross becomes its self-position.

(2)
We calculated the inclines of each direction from thisexpression (2), and revised the data using calculated
inclines and expression (1).
The average error before revising was 53.32[mm],while that after revising was 9.72[mm]mobile robot

distance of landmark

6. Following a mobile object

landmark

Applying the above-described detection of alandmark and measurement of the distance to thelandmark, we attempted to have the mobile robot
follow a mobile object. Mobile robots calculate themovement course by measuring distance and angle
information. Here, we explain the calculation methodof the movement course of the robot. The mobile robot©ISAROB 2007

452

Fig.10 Self-localization

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
In addition, we tested self-localization in anenvironment such as that shown in Fig.11. We moved
the robot from the A spot (-500,-500) to the B spot
(500,-500) and moved it again afterwards to the A spot(-500,-500). During movement, we tested the robot’s
self-localization. The result is shown in Fig. 12．

Therefore in the future, we will aim at improving
self-localization. Concretely, in considering the shapeof landmarks, the robot will be able to detect its
position more precisely.

8. Acknowledgment

Ｙ

The authors would like to acknowledge the advice
and assistance of Professor Tadashi Kitamura of
Kyushu Institute of Technology who died in 2006.

landmark

References
Ｘ

(0,0)

[1] Kazumasa Yamazawa: "HyperOmni Vision: Visual
Navigation with an Omnidirectional Image Sensor",

mobile robot[2] Akira Sakurai: Introduction to spline function

A(-500,-500)

B(500,-500)

Y coordinates[mm]Fig. 11 Result of experiment

-600

-400

0
-200-100 0

200

400

600

-200
-300
-400
-500
-600
X coordinates[mm]measured_value(outward)
measured_value(homeward)
true_value

Fig. 12 Result of experiment

8. Conclusion
In this paper, as elements of a navigation system for amobile robot using an omni-directional camera, wedeveloped systems for following a mobile object andfor self-localization using a landmark. The robot
demonstrated its ability to recognize a moving object andto measure its distance and its angle in relation to theobject. Based on these abilities, the robot could
successfully follow a moving object. However, theprecision decreased sharply when the distance of thelandmark became remote.

©ISAROB 2007

453

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Adaptation of a distributed controller depending on morphology
Nathan Labhart and Shuhei Miyashita
Artificial Intelligence Laboratory
University of Zurich
Zurich, Switzerland{labhart,miya}@ifi.unizh.ch
Abstract
In this paper we investigate the influence of anagent’s morphology on its neural controller. Ourmodel consists of a number of identical modules, eachof which comprises of two half-wheels for movement
and a Central Pattern Generator (CPG) as its own
neural control. Based on a series of simulation experiments, we conclude that one single type of CPG canadapt well to different types of morphologies, and that
there seems to be a suitable or optimal morphology
depending on the environmental givens.
Keywords: Morphology, Central Pattern
Generator, evolutionary algorithm

1

Introduction

It is a widely accepted fact that the neural controller of an agent has to match the complexity of its
body as well as the complexity of its task environment(“Principle of Ecological Balance”, [1]). Normally,
controller and morphology evolve in parallel, mutually influencing each another, and taking into accountthe interaction with the environment. In this paper,
we investigate the influence of an agent’s morphology
on the evolution of its neural control – inspired by thecentipede, where locomotion is achieved by “synchronizing” a number of two-legged body segments.
In the present research, a series of simulation experiments are carried out with modular robots, where
each module consists of a body and a Central Pattern
Generator (CPG).
J. C. Bongard and R. Pfeifer tested hypotheses
about the behavioral effect of specific morphological features by keeping the neural controller constantacross different body sizes, masses, and morphologies[2]. The essential issues of how to develop a cellular
robotic system were described by Y. Kawauchi et al.
Their project “CEBOT” included optimization meth-©ISAROB 2007454

ods for the structure of both hardware and software[3]. S. Murata et al designed a modular robotic system
in hardware, which could metamorphose into desiredconfigurations, and showed results of changes in morphology [4]. In the “Conro” project by A. Castano, A.
Behar, and P. M. Will, each module was self-contained(it included its own processor, power supply, communication system, sensors, and actuators). These modules were designed to work in groups as part of a large
configuration [5]. A similar project is presented by
M. W. Jørgensen et al with “ATRON”, which consists of several fully self-contained robot modules [6].
Using nonlinear coupled oscillators for the CPG was
described by A. Ijspeert and J.-M. Cabelguen. Theirexperiments on salamander movement show how configurations could combine global couplings from thesegment oscillators in order to achieve “realistic” locomotion data [7].

2
2.1

Model
Single Module

In this first set of experiments, the agents are simulated in the physics engine ODE. In its base form, amodule consists only of a motor, rigid joints to stickseveral modules together, one set of half-wheels, anda neural control mechanism (Fig. 1).
The half-wheels of one module are connected by afixed axis (i.e. they always turn in parallel) and are
constrained in movement from − π2 to + π2 . This restriction is introduced to exploit the interaction withthe environment: by taking into account the staticand dynamic friction on the ground, the back-andforth movement of the half-wheels causes the whole
configuration to move.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

controlling mechanism, we implement the same typeof neural control in two different configurations andcompare the CPGs as well as the traveled distances.

A
B
C

module mass
module L×W×H
half-wheel radius

D
F

1.4kg
1×0.5×0.5m
0.2m

τ
θlow
θhigh

0.2
−0.3
0.3

E

Table 1: Simulation properties

Figure 1: A single module. A: connector to neighbor
module; B: CPG; C: input from neighbor module; D:
forward/backward command; E: stopper (to restrict
movement from − π2 to + π2 ); F: half-wheel angle input.
2.1.1

CPG

The CPGs are modeled as nonlinear oscillators in x,
v space, taking the angles of the corresponding halfwheels as inputs and returning a “forward” or “backward” command depending on a threshold θ.
The controller inside every module is connected not
only to its own half-wheels, but also to the immediately neighboring modules. Thus, every CPG has alimited knowledge about the state of its neighborhood,but not about the entire configuration:

This model is deliberately kept simple and easily
extensible for future experiments. In addition, it allows a relatively straightforward way of implementing
it in hardware in order to test the outcome of the simulation in the real world.

3

Simulation

We implement two configurations with 8 modules
each, which differ only in the distribution of the four
top modules, so that the center of gravity is shifted to
one end of the robot (Fig. 2).

(a) “Hat-shaped”

X
x2 + vi2 − Eivi − xi +τ v̇i = − i
wj sj
Ei
j

(1)

τ ẋi = vi

(2)where i is the number of the current module, j is thenumber of the neighbor module, wj are the weights forthe connection to the neighbors s, and τ and Ei areconstants.
This CPG is an oscillator with amplitude√
Ei and period 2πτ .

x + v > θhigh “backward”(3)
x + v < θlow “forward”

2.2

Configurations

Like building blocks, these modules are attachedto each another to form various morphologies. In order to understand the influence of morphology on the©ISAROB 2007

455(b) “Slanted hat”

Figure 2: Two configurations simulated in ODE.

In order to understand the influence of morphology
on the controlling mechanism, we let the two configurations evolve the Ei parameters of their CPGs over1000 generations and then compare their performance.
Minimal Generation Gap [8] evolution is applied toadapt this controller to the respective morphology (i.e.
configuration of the modules). The fitness function
is defined as the distance a configuration can cover
within a limited time frame. In addition, we analyzed
the trajectories of the CPG parameters from the first,
the 500th, and the 1000th generation.
For each generation, the simulation runs for 2000
time steps. In every 50th step, the positions of thehalf-wheels are recorded, and the CPG values (x, v)
are calculated for 1000 iterations (∆t steps), taking
into account the half-wheel positions. The resulting
CPG values define whether a “forward” or “backward”

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

command is issued to the corresponding half-wheel.
Once the movement command has been executed, theconfiguration “slides” on the ground for a few (lessthan 50) timesteps, and the next CPG calculation cycle takes place.

4

Results and discussion

As can be seen from the distance plots (Fig. 3),
the asymmetrical configuration clearly “makes use” ofthe shifted center of gravity with an average distanceof 0.8387m, compared to 0.4482m in the symmetrical configuration. The different morphology is also
reflected in the CPG trajectories (Fig. 4, 5).

(a) “Hat-shaped” morphology(b) Magnified trajectories from the bottom-left module of generation 1 (left) and 1000 (right)

Figure 4: CPG trajectories of the symmetrical configuration.
(a) “Hat-shaped”(b) “Slanted hat”

Figure 3: Performance (traveled distance) of both configurations.

The experiments show that a slight change in morphology results in different performances. The average
distance covered by the asymmetrical configuration isabout 6 times as large as that of the symmetrical one;furthermore, the symmetrical morphology takes much
longer to evolve its CPG parameters into a set of values that performs well.

By comparing the CPG’s x, v trajectories of one
morphology over time we find that the further theconfiguration moves, the more distinct the trajectories become (Fig. 4(b), 5(b)). In contrast to the symmetrical “hat-shaped” morphology, the CPG adapts
to the asymmetrical morphology of the “slanted hat”
configuration more quickly, and the CPG trajectories
are more distinct.
A comparison of the two morphologies suggeststhat evolution apparently exploits the friction on the
ground, taking into account the asymmetrical weight
distribution of the “slanted hat” morphology where
the weight on the left side is higher than on the right.

©ISAROB 2007

456(a) “Slanted hat” morphology(b) Magnified trajectories from the bottom-left module of generation 1 (left) and 1000 (right)

Figure 5: CPG trajectories of the asymmetrical configuration.

These results imply two things: On the one hand,
one single CPG can adapt well to different types ofThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

morphologies; on the other hand, there seem to besuitable or optimal morphologies depending on the environmental givens (in this case, friction).

A[3] Kawauchi Y, Inaba M, Fukuda T (1994), A
Study on Cellular Robotic System (A Realization of a Robotic System Capable of Adaptation, Self-organization, and Self-evolution). JRSJ
12(1):116–132

B[4] Murata S, Tomita K, Yoshida E et al (1999), SelfReconfigurable Robot – Module Design and Simulation. Proceedings of 6th International Conference on Intelligent Autonomous Systems (IAS-6),
pp. 911–917

CFigure 6: Mean values of all CPGs in the 1000th generation of the symmetrical configuration. The four
modules which touch the ground are marked with A
and C, the four upper modules are marked with B.

Furthermore, we observe in the symmetrical configuration that the four modules which touch the groundshift their CPG’s center (mean value) away from thezero point in an asymmetric way (Fig. 6). This enables the “hat shaped” morphology to move at all,rather than staying at the same position (as could be
expected from its symmetrical shape). The top four
modules, where the half-wheels do not directly influence the movement of the configuration, keep their
CPG centers very close to the zero point. The “slanted
hat” morphology does not exhibit such a behavior, as
it is already asymmetric by design.

Acknowledgments
This research is partially supported by the
Swiss National Science Foundation, project #200021105634/1 and project #200021-109864/1.

References[1] Pfeifer R, Scheier C (1999), Understanding Intelligence. MIT Press, Cambridge, pp. 312–315©ISAROB 2007

[2] Bongard JC, Pfeifer R (2002), A Method for
Isolating Morphological Effects on Evolved Behaviour. In: Hallam B, Floreano D et al (ed),Proceedings of the Seventh International Conference on the Simulation of Adaptive Behaviour(SAB2002), MIT Press, pp. 305–311

457[5] Castano A, Behar A, Will PM (2002), The
Conro Modules for Reconfigurable Robots.
IEEE/ASME Transactions on Mechatronics,
7(4):403–409[6] Jørgensen MW, Østergaard EH, Lund HH(2004), Modular ATRON: Modules for aself-reconfigurable robot. Proceedings of 2004
IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2068–2073[7] Ijspeert A, Cabelguen J-M (2003), Gait Transition from Swimming to Walking: Investigation of
Salamander Locomotion Control Using Nonlinear
Oscillators. In: Kimura H, Tsuchiya K, Ishiguro
A et al (ed), Adaptive Motion of Animals andMachines. Springer, Tokyo, pp. 177–188[8] Satoh H, Yamamura M, Kobayashi S (1996), Minimal Generation Gap Model for GAs Considering
Both Exploration and Exploitation. Proceedingsof IIZUKA ’96, pp. 494–497

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

On-line Variational PCA for Adaptive Visual Tracking
Torazo Date∗

Takashi Bando∗

Tomohiro Shibata∗Shin Ishii∗∗ Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
Takayama 8916-5, Ikoma, Nara 630-0192, Japan
Abstract
In the research area of visual tracking as well
as recognition, there have been numerous attempts
to use appearance-based object representation which
does not require explicit knowledge or precise geometric representation of the object. In particular, theeigenspace representation has been widely used for visual tracking of a target, because of its rich representational power based on clear mathematical properties.
There is, however, a big problem that the appearanceof a target object may vary in time due to various factors such as changes in the lighting condition, and inlocation, pose, scale, and shape of the object. Online algorithms that incrementally update eigenvectors (basis images) can be an answer to this problem.
In this study, we propose an adaptive visual tracking
method by combining an on-line variational Bayesianversion of principal component analysis (PCA) andparticle ﬁltering. Computer simulations demonstrate
our method enables robust visual tracking of a target
whose appearance varies in noisy environments.

1

Introduction

In the research area of visual tracking as well
as recognition, there have been numerous attempts
to use appearance-based object representation which
does not require explicit knowledge or precise geometric representation of the object. In particular, theeigenspace representation has been widely used (e.g.,[6, 5]) because of its computational eﬃciency based on
clear and established mathematical properties. Thereis, however, a serious problem that the conventional
eigenspace method is not robust against changes in
the appearance of a target, such as lighting condition,pose, scale, shape, and so on. This problem can be
mediated if every appearance variation of the target
object is prepared as basis images, but this is quite©ISAROB 2007

458

impractical. An algorithm which updates eigenvectors (basis images) on-line can be an answer to thisproblem. Lim et al. (2004) proposed such a visualtracking algorithm [2] that employed a sequential algorithm based on singular value decomposition (SVD),
called R-SVD method, and demonstrated robust facetracking of a person walking in cluttered backgrounds.
In this article, we propose an adaptive visualtracking method by combining an on-line variational
Bayesian version of principal component analysis(PCA) and particle ﬁltering [4]. Computer simulations demonstrate our method enables robust visualtracking of a target whose appearance varies in noisy
environments.

2

Algorithm

2.1

Overview

Our visual tracking algorithm is basically combination of on-line variational PCA and particle ﬁlter. Particle Filter (PF) [7] is an approach to making Bayesian estimation of intractable posterior distributions from time-series observation signals disturbed
by non-Gaussian noises. The eﬀectiveness of PFs has
been reported in various research area, such as realtime visual tracking in Computer Vision (e.g., [4]).
Under an assumption that the target dynamics form
temporal Markov chains and observations are independent, incremental Bayesian estimation of the hidden
target state Xt at time t is computed by∫p(Xt |It ) ∝ p(It |Xt ) p(Xt |Xt−1 )p(Xt−1 |It−1 )dXt−1(1)where It = {I1 , . . . , It } is a set of observed images.
In this study, smoothness is assumed for target motions: Xt −Xt−1 = Xt−1 −Xt−2 +ξt , where ξ denotes aprocess noise. In the next subsection, we describe theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

likelihood term p(It |Xt ), based on on-line variational
PCA.

2.2

On-line Variational PCA

Principal component analysis (PCA) is a wellestablished method of multivariate analysis to perform
feature extraction from a data point or a set of data.
A data space is reduced by a projection to a comparatively low-dimensional subspace, a feature space.
In PCA, this projection is linear. PCA can be reformulated into a probabilistic model including latentvariables, which is called Probabilistic Principal Component Analysis (PPCA).
Our proposed method is an on-line updating
method whose generative model is given by PPCA, toconstruct the basis images. This method assumes observed data include noise, thereby robust basis images
can be obtained in a real-world environment which includes various distracters or occlusion.
An observed image vector It is given by
It =µ + W st + nt
=W̃ s̃t + nt ,nt ∼ NN d (nt |0, Σt ),(2)

where µ ∈ RN d is the mean of observed images,
W ∈ RN d×N b is the basis images (the eigen-images),W̃ = (W, µ), st ∈ RN b is basis score, s̃ = (sTt , 1)T , N dis the number of pixels, and N b is the number of bases.
ND (I|µ, Σ) denotes a D-dimensional Gaussian distribution with the mean µ and the variance Σ. Then, we
obtain the following formulation:p(It |W̃ , st ) =NN d (It |W̃ s̃t , Σt ).

(3)

We also assume the prior distribution of st is given byp(st ) =NN b (st |0, EN b ),(4)

where EN b is a N b × N b unit matrix. Using the Bayes
theorem, we havep(It , St |W̃ ) =t∏p(sτ )p(Iτ |sτ , W̃ ).

(5)

τ =1

For the prior distribution of the eigen-vectors, we assume natural conjugate:p(W̃ ) =D∏

NNb (θ d |ed , Σθ ),

(6)

d=1
(1)

(Ñ b)

where θ d = (wd , ..., wd )T ∈ RÑ b , ed =(δ1,d , ..., δÑ b,d )T ∈ RÑ b , Σθ = γ −1 EÑ b ∈ RÑ b×Ñ b ,©ISAROB 2007

459

Ñ b = N b + 1, and then (θ 1 , ..., θ D )T = W̃ . δi,j is the
Kronecker delta:{
1 (i = j)
δi,j =(7)0 (i 6= j).
Then, the posterior distribution of the basis scores andthe parameter (basis images) is given byq(W̃ , St ) =q(St )q(W̃ ),q(St ) =t∏q(sτ ),(8)τ =1where St = {s1 , ..., st }.
In the variational Bayes, the free energy is deﬁned
and maximized. The following is an on-line versionof the free energy in which a forgetting factor is introduced in order to be insensitive to inaccurate pastdata, and to cope with sudden changes of a target appearance or changes in the environment.
In our on-line variational PCA, the forgetting factor λs (0 5 λs 5 1) is introduced in order to forget(discount) gradually the inﬂuence of the past incorrect(∑)−1testimation. ηt =Λ(τ,t)
is a normalization
τ =1∏t
coeﬃcient, Λ(τ, t) = s=τ +1 λs , and then the on-line
free energy is given by
Ftλ [q] =ηtt∑Λ(τ, t){log p(Iτ )

(9)

τ =1

− KL(q(W̃ , Sτ )||p(Sτ , W̃ |Iτ ))}.
The maximization of the free energy is equivalent
to the minimization of the discounted KL divergence
between q(St , W̃ ) and p(St , W̃ |It ).
On variational Bayes estimation, q(St ) and q(W̃ )that maximize the free energy are computed by repeating a VB-E step and a VB-M step. After convergence,
we obtain approximated solutions of factor score matrix S and eigen-vector matrix W̃ . Then, eigen-vector
matrix W̃ can be used as basis images.
The tracking is achieved by particle ﬁlter governedby the observation model p(It |Xt ), which is the likelihood of Xt given It , and the dynamics model between
two states p(Xt |Xt−1 ). The observation model is given
by using the estimated basis W̃ as
)
(
1
2
(10)
p(It |Xt ) ∝ exp − 2 kIt (Xt ) − W̃ s̃t k ,2σwhere It (Xt ) is an observed image at the target state(position) Xt , and σ 2 denotes the measurement noise
variance assumed to be a constant for simplify. In
this study, the motion of the target object between

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

occlusion

target object

occlusion

target object

noisy datatime step

Figure 2: Sample images in simulation

Figure 1: Simulation environment
two consecutive frames is assumed to be linear. The
target state is deﬁned as Xt = (xt , yt ), where xt andyt denote the two-dimensional position on the image
plane at time t.

3
3.1

Figure 3: Time course of basis images when the target
object was reversed (for the normal condition).

Experiments
Setup

We conducted tracking experiments through computer simulations to examine the eﬀectiveness of our
proposed method, that is, adaptive basis image updates and robust visual tracking.
Figure 1 illustrates the simulation environment. A
target, a coﬀee cup, moved from the left side of theimage toward the right side with a velocity of 2 [pixels/frame], and turned back to the right side, as depicted by an allow. During this course, the appearanceof the target was reversed twice with speciﬁc intervals
indicated by gray lines. There were four experimental conditions; normal condition, noise condition inwhich pixel-wise Gaussian noise was added, occlusion
condition in which a stationary occluder depicted asa shade was added, and noise-occlusion condition inwhich both noise and the occluder were added (cf. Figure 2). In each condition, 10 times of simulations wereconducted to estimate statistically the performance ofour method.
200 particles were used. The image stream was 110
frames of 120×160 gray scale images. The image ofthe target coﬀee cup was of 20×20 pixels. The widthof occlusion was 15 pixels. The pixel-wise Gaussian©ISAROB 2007

460

noise was zero-mean with variance 302 . The maximum
number of basis images was set to 10.

3.2

Results

Figure 3 demonstrates how the basis images were
updated. This ﬁgure shows the time course of ﬁve basis images of the tracked object every three ﬂames justafter the object was reversed. Each basis was quickly
and successfully adapted to the reversed images, inwhich the handgrip of the cup was a salient feature in
the appearance of the cup. This result suggests threeor four bases would be enough to represent this speciﬁctarget object.
Figure 4 presents an example time course of the online free energy. Shaded areas and unshaded areas correspond to the two diﬀerent appearances of the coﬀeecup (see Figure 1). The free energy sharply dropped
down just after the target appearance was reversed,
followed by quick recoveries. Because the free energy
strongly correlates with the reliability of the estimation of basis images, this result indicates our successful
implementation of the on-line variational PPCA. The
time course shown in Figure 4 was for the normal con-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4

-2.85

for the performance comparison, and for making thediﬀerence salient.
One of the most interesting and useful extensions
would be on-line control of the forgetting factor. Hirayama et al. proposed a method in which the forgetting factor was controlled by the free energy calculated
based on their probabilistic model, and demonstrated
the eﬀective detection of abrupt changes in face images [1]. As shown in their study, probabilistic models
diﬀerent from the conventional PCA model can be considered to improve the tracking performance in realworld environments.

x 10-2.855

Free energy-2.86-2.865-2.87-2.875-2.88-2.885-2.89-2.895
-2.9

1

10

20

30

40 50 60 70 80
Time step

90 100 110

Figure 4: Example time course of on-line free energy
Table 1: Average tracking error over 10 trials
Task
normalnoisy
occlusion
noisy and occlusion

error(MSE)
4.29±2.68
4.42±2.63
4.39±2.67
4.52±2.62

Conclusion and Future WorkIn this study, we proposed an adaptive visualtracking method by combining the on-line variational
Bayesian PCA and particle ﬁltering. Simulation experiments demonstrated that the algorithm realized
accurate and robust visual tracking of a target whose
appearance was suddenly reversed, even in the casewhere Gaussian noise was artiﬁcially added and an
occluder was placed in the pathway of the target.
There are many issues opened for future work. Firstof all, the performance comparison with existing methods like R-SVD [2] is necessary. Our preliminary experiments using the same simulation environment suggests that the tracking performance of R-SVD is ashigh as our method. Further exploration is required©ISAROB 2007

[1] J. Hirayama and J. Yoshimoto and S. Ishii,“Bayesian representation learning in the cortex regulated by acetylcholine”, Neural Netw,
Vol.17(10), pp.1391-1400, 2004.
[2] J. Lim and D. Ross and R.-S. Lin and M.-H. Yang,“Incremental Learning for Visual Tracking”, Advances in Neural Information Processing Systems,
2004.

dition, but we conﬁrmed that proﬁles in other cases
were almost consistent to this time course.
The tracking performance in the four conditions issummarized in Table 1. The tracking errors are similar. Increasing the number of particles from 200 should
decrease the errors.

4

References461[3] Z. Khan and T. Balch and F. Dellaert, “A RaoBlackwellized Particle Filter for EigenTracking”,
Proc Comput Vis Pattern Recogn, Vol.2, pp.980986, 2004.
[4] M. Isard and A. Blake, “CONDENSATIONConditional Density Propagation for Visual Tracking”, Int J Comput Vis, Vol.29(1), pp.5-28, 1998.
[5] M.J. Black and A.D. Jepson, “EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation”, Int J
Comput Vis, Vol.26(1), pp.63-84, 1998.
[6] H. Murase and S. Nayar, “Visual Learning andRecognition of 3-D Objects from Appearance”, Int
J Comput Vis, Vol.14, pp.5-24, 1995.
[7] N.J. Gordon and D.J. Salmond and A.F.M
Smith, “Novel approach to nonlinear non-Gaussian
Bayesian state estimation”, IEEE Proc Radar Signal Processing, Vol.140, pp.107-113, 1993.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Development of an Indoor Positioning System
Using Incident Angle Detection of Infrared Emitters
Seong Taek Hwang
and Jea Heon Ryu

Department of Mechanical andIntelligent System Engineering
Pusan National University,
Busan, 609-735, KOREA

Chun Han Lee

Department of Electric Control
Volvo Construction
Equipment-KOREA, Ltd.,
Changwon, 641-430, KOREA

Abstract
In this paper, a new indoor positioning system
based on incident angle measurement of infrared
lights has been suggested. Though there have beenvarious researches on indoor positioning methods
using vision sensor or ultrasonic sensor, they have
not only advantages, but also disadvantages. To
minimize the disadvantages they have, this new
method using incident angle of infrared light has
been invented. In a new positioning system, there
are three infrared emitters on fixed known positions.
An incident angle sensor measures the angle
differences between each two emitters. Measured
angle differences determine a position. This method
is available only inside the triangle which iscomposed of three emitters. Mathematical problems
to determine the position with angle differences andposition information of emitters has been solved. To
solve the non-linear equations without prior position
information, iterative linearization process has beenused. Simulations and experiments have been
implemented to show the performance of this new
positioning system.

1

Introduction

Positioning is a major problem for mobile robots
and autonomous vehicles. So, various positioning
systems and algorithms have been researched todevelop a navigation algorithm. GPS (Global
Positioning System) is a precise absolute positioning
system for the outdoors [1]. But, several indoor
absolute positioning systems have been researched.
The ultrasonic positioning system is very similar to
GPS [2]. It measures distances from emitters to ameasuring point. Then it solves the equations todetermine its position. Since ultrasonic waves aremuch slower than electromagnetic waves, it is easier
to count the spent time that diffused waves need toreach the measuring points than it does for GPS.
However, waves radiated from other emittersinterfere with each other. Thus, only one emitter
can radiate ultrasonic waves at a time. Since all the©ISAROB 2007

462

Man Hyung Lee

School of MechanicalEngineering,
Pusan National University,
Busan, 609-735, KOREA

transmitters radiate their waves by turns, it takes
more time to measure all the distances fromdifferent transmitters.
An absolute position measurement system basedon incident angle detection of infrared light is
proposed [3]. Infrared light sources are mounted ona robot as markers. Then an observation agent
mounted on a ceiling detects incident angles ofinfrared light emitted from markers. Though much
infrared light is also contained in sunlight, it isrejected using filters. Emitters and a receiver use
the same carrier frequency. And all the otherfrequencies are filtered out while passing the
bandwidth filter.
In this paper, an absolute position measurement
system based on incident angle detection of infrared
light is proposed. Three infrared light sources aremounted in fixed positions as emitters. And three
infrared incident angle sensors measure the incident
angles of the infrared light from each of the threeemitters. Then anyone who tries to measure theirposition can estimate their absolute position without
external assistance.

2 Mathematical Problems of the System
Consider three points. A  point is the origin,B   point is on the x-axis, and the C    point is a point on the upper part of the right-halfplane. Assume that all the coordinates of the threepoints are given as shown in Fig. 1. The relativeR( x, y)Fig. 1 Concept of new positioning system

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

position R   to the origin can be determined.
First, we can obtain the following equations todetermine the position with given angle differences,  and   .   is calculated by using an innerproduct of  and . And also,   is solved
by using it for  and .
∙ 
  ∙    







 ∙     ∙   

(1)
(2)



         (3)           ∙                      ∙ 



 

(4)

Equations (3), (4) are linearized by using Fourier
Transformation.





 
 
          ′
            

Where(5)              ′
        (6)      is the point of linearization,   ′ represents the higher-order terms in the

expansion, and   and   are the functions asfollows:           (7)           ∙                   ∙  Finally the equation (9) isrepresentation of vectors as follows:           ′ obtained

(8)
in
(9)

where   is the initial value of  ,






,

 



be

are linearlysolved by       ,



(10)

with error      ′ .
When measurements from more than 3 emitters
are available, the least-squares solution is          .
To solve the equations (1), (2) about   and   :Assuming that the rows of
independent, equation (9) caninversion of the  matrix:

With knowledge of the position error   , the
actual position is determined as(12)      Assuming no prior position information, theiterative linearization process will be initialized at a
fixed point in the triangle consisting of the given
three points.

3 Simulations
In order to examine the performance of thepositioning algorithm, Matlab 6.1 is used.
Consider the process shown in Fig. 3. The threepoints are  ,   , and    respectively
so that they compose a regular triangle. The fixed
initial point to determine the position without priorposition information is   . So, that is thecentroid of the given regular triangle shown in Fig.
2.
First, test the algorithm without the prior position
information. To determine the exact position without
it, iterative calculations are necessary. Using theiterative linearization process based on equation(12), position values are converged very fast, asshown in Fig. 2. The angle differences used in thissimulation are 80 and 130 degrees. The position
values are converged within 4 steps with very small
errors.
Secondly, test the algorithm along the path, not apoint. The test path is a horizontal straight line
passing through the centroid in the range of ≤  ≤ . In order to simulate the algorithm,
angle differences   and   of the test path are

 


,
 



and
     

 


           ©ISAROB 2007

Fig. 2

463(11)

The iterative linearization process

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

needed. Fig. 3 hows the result of the simulation
Fig. 4 is the result of the curvature test.

4 Experimental and Result
To determine the incident angle, current passing
through the sensor has to be measured. The incident
angle sensor is composed of two photodiodes. And
the incident angle is determined by two current
values from each photodiodes as follows: 
ANGLE   × 
 

(13)

where  and  are output currents from the two
photodiodes.
The picture of the incident angle sensor(HI-M600H3-2) and its characteristic is shown in
Fig. 5. It detects tuned signals at 32kHz. In therange of ± , the value of the incident angle
from the sensor is absolutely linear.
Figure 6 shows the circuit to convert the current
value to voltage value. The following relation issatisfied in the circuit:    × 

the intensity of the emitters and infrared LED has
been limited and we have had difficulties in signal
conditioning. Figure 7 is the system configuration
for the test. The system consists of 3 emitters and
1 receiver. Each receiver has a slit. It forbids thesurrounding signals to interfere. The receiver isrotated by using a 400 pulse stepping motor. Itsangular rate is 360deg/sec. Therefore the proposedsystem can get position data with 1Hz resolution. If
the angular rate of the motor and signal processing
time is faster, we can get higher resolution data.
Figure 8 shows the emitter and receiver systems.
For each test, 11 points and 9 points are selectedon the straight and curved path and the mean data
are calculated using data every 10sec.
As shown in Fig. 9, the experimental result isvery similar to the simulation result in Fig. 3
except for the limitation of ranges. The ranges of x
and y in the experiment has been limited is becauseof the emitting shape of the infrared LED. Most ofall, the infrared LED is produced for remote(14)

where   is the output voltage, and  is current
from photodiode.
To measure the incident angles of the infrared
lights, it is very important to reject the interferences
and to amplify the signal. Bandwidth filter rejects
all the frequencies except the carrier frequency.
Op-Amps amplify the signal to a suitable voltage
level for an A/D converter.
Experiments have been implemented inside theregular triangle of 2.36m for each side length, sinceFig. 5 Incident angle sensors

Fig. 6 Circuit to convert the currents to voltage

SensorStepping
Motor

Fig. 3 Simulation result in a straight path

Transmitter

ReceiverTransmitter
Transmitter

Fig. 7 System configuration for test

Fig. 4 Simulation result in a curve path

©ISAROB 2007

Fig. 8 Emitter and receiver

464

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

controllers or wireless communications. Therefore,
the infrared light radiated from the LED is acutely
focused on the center region.
Figure 10 shows the test result in a curved path.

Acknowledgment

5 Conclusions

References

In this paper, the new indoor positioning method
using infrared lights has been suggested. Users are
independent of the emitting devices so there is nolimitation of the number of users provided in thispositioning service. Since this method has nointeractive process between emitters and receivers,
the measuring time is less than any otherpositioning system. Mathematical problems necessary
for the determination of the position havebeen
derived. To solve these non-linear problems, aniterative linearization process has been used.
The algorithm to determine the position has been
simulated and experimented. Results of thesimulations were reasonable. Experiments have hadmany problems, such as the limitation of intensityof emitters and signal conditioning problems. Thus,
the experiments have been implemented in a very
bounded area, inside the regular triangle with 2.36m
side length. However, the experimental result was
acceptable.
This new positioning method can be applied toany indoor system that needs absolute position
information. To apply this method to mobile robots
or for industrial purposes, emitters should be
improved to a more suitable form and signals fromthe sensors should be well-conditioned. Moreemitters can achieve more accurate positions using
the Least Mean Square method.

[1]

Fig. 9 Test results in a straight path

Fig. 10 Test results in a curved path

©ISAROB 2007

465

This work was supported by the Brain Korea 21
Project.

[2][3]

Farrell, Jay and Barth, Matthew, The Global
Positioning System & Inertial Navigation,
McGraw-Hill, 1998.
Lee, Jung-Min et al.,󰡒Ultrasonic Satellite
System for the Positioning of Mobile Robots,
󰡓in Proceedings of Industrial Electronics
Society (IECON 2004), Vol. 1, pp. 448-453,
2004.
ARAI, Y., SEKIAI, M., 󰡒Absolute Position
Measurement System for Mobile Robot basedon Incident Angle Detection of Infrared Light,
󰡓in Proceedings of the 2003 IEEE/RSJ
International Conference on Intelligent Robots
and Systems(IROS2003), pp. 986-991, 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Efficient Localization Algorithm in the RFID Sensor Space for Mobile Robot
Localization
EunTae Lyu
Dept. of Electronics Eng.
Pusan Nat. Univ.
pbman02@pusan.ac.kr

HyungSoo Lim
Dept. of Electronics Eng.
Pusan Nat. Univ.
chojja_tpz @pusan.ac.kr

Abstract
This paper proposes an efficient localization algorithm in
the RFID sensor space for the precise localization of a mobile
robot. The RFID sensor space consists of embedded sensors
and a mobile robot. The embedded sensors, that is, tags areholding the absolute it is called as antenna usually, getsseveral tag data at the as position data and provide them to therobot which carries a reader and requests the absolute
position for localization. The reader, me time within its
readable range. It takes time to read all the tags and to process
the data to estimate the position, which is a major factor todeteriorate the localization accuracy. In this paper, anefficient algorithm to estimate the position and orientation of
the mobile robot as quickly as possible has been proposed.
Along with the algorithm, a new allocation of the tags in the
RFID sensor space is also proposed to improve thelocalization accuracy. The proposed algorithms aredemonstrated and verified through the real experiments.
Keyword: RFID, Tag allocation, Localization, Mobile robot,
Estimation error.

1. Introduction
An RFID (Radio Frequency IDentification) technology is a
non-touching recognition system that transmits and processes
the information on events and environments using a wirelessfrequency and small chips [1]. The RFID system can read thetags at high-speed and send data within various distances.
Therefore, the application of the RFID technology has
recently been increased and an RFID has been applied for the
robot technology [2]. With the development of the personal
robot and advanced ubiquitous network robots, it is essential
for the robots to recognize their own location andenvironment and to keep high security in a common spacewith people. If an RFID technology is properly applied to therobot, the services for the users can be provided by the service
robot at anytime at any place.
The passive RFID technology has been utilized for the
researchers to recognize the position of the service robot [3].
The method is that RFID passive tags are arranged on thefloor to provide the absolute position data, which are free

©ISAROB 2007

466

JangMyung Lee
Dept. of Electronics Eng.
Pusan Nat. Univ.
jmlee@pusan.ac.kr

from the problems of conventional systems [4]. Note thatdead reckoning sensors suffer from accumulating errors, laser
and ultra-sonic sensor from line-of-sight errors, and CCDfrom the illumination. The absolute location of the robot canbe obtained robustly, with the RFID tag and reader in sensor
network space [5] where sensors are properly embedded
several places to provide the absolute position information to
the service robot. However some shortcomings are found in
the localization systems using the RFID [6]. The antenna
detects several tags within its detecting range and thenumbers of detected tags are not constant at all times, whichcauses the position estimation error.
On account of this, the precise localization is not feasibleusing the RFID tags unless it is located many tags in a very
short interval ignoring economics. Also, in the posture
estimation of the mobile robot, two antennas are necessary torecognize the orientation of the robot since the orientation
cannot be detected by using only one antenna.
In this paper, the problem of acquiring position
information, including the orientation of the robot, isintroduced in the robot localization using the RFID system.
Also, the algorithms to reduce estimation error of the robot
and to achieve more efficient localization are newly
proposed.

2. Position Estimation of a Mobile Robot
In order to estimate the location of the robot using RFID
system, RFID tags are arranged in a fixed pattern on the floor.
Absolute coordinates of the location have been stored in eachtag to provide the position data to the mobile robot. An RFID
reader (antenna) has been installed to read the tag data on thebottom of the mobile robot. If the robot moves and stays on
any tag, the RF field is formed by RFID reader antenna. All
the tags within the circle of radius, r , which are under theeffective area of RFID antenna, are activated.
When the localization process starts, the RFID reader
gathers the position data of the tags under the effective area ofantenna. The RFID reader repeatedly gathers the tag
information sequentially when there are more than one tag in
the RF field, since it can recognize only one tag signal at atime. In order to receive other tag data within the effective
area of the RF reader, the tag data previously read are stored

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

to the PC. Then, the reader gathers the next tag information,andmax{x1 , L, x N } + min{x1 ,L, x N }2max{y1 , L, y N } + min{y1 , L, y N }yest =
2

xest =(1)(2)

where N represents the number of tags detected by the reader
and x1 , x 2 , x 3 , y1 , y 2 , L represents the coordinates

Fig. 1. Configuration of the Experimental System..
repeats this procedure until there is no unread tag left within
the RF field. The configuration of the experimental system isshown in Fig. 1.
The orientation of the robot can be estimated using themultiple sets of the tag position data. The initial orientation
can be estimated using the sets of position readings. And thetraveling orientation can be estimated by the two sets ofposition data, the previous and current position data,assuming that the mobile robot is moving forward all thetimes.

3. Uncertainty of Position Estimation
In the passive RFID localization system, the utilization of
tag information is dependent on the system characteristics. In
other words, even though the RFID reader detects RFID tags
within the recognition area, it cannot obtain a precise location
value from the tags since there is no position information
between tags. The distance between RFID reader and tag isalso a variable to be considered in the localization process.
Therefore the classical localization system based on thetriangulation technique with three distance data has too big
error to be used for the mobile robot navigation.
The estimation error is unavoidable when the robot
location is estimated by the coordinates of tags within therecognition area of the reader. The estimation error ismodeled in this research to minimize this estimation error.
When the tags are arranged in a square pattern and the
distance between them does not exceed the range of reader,the recognition area of the reader for the tags can be
represented as a circle as shown in Fig. 2.
The position of the mobile robot ( x est , y est ) that carries areader antenna on the bottom, can be obtained through theposition data of the tags that are located within therecognition area of the reader as©ISAROB 2007

467

information of the tags.
The position of antenna --coordinates of the mobile robot-is estimated by Eqs. (1) and (2) using the absolute coordinatesin the tags. However, the real position of mobile robot has theestimation error because of the gap between the tags.
Therefore the estimation error is proportional to the gap
between the RFID tags. Figure 2 illustrates the relationshipbetween the estimation error and the gap between the RFID
tags where only X-dimensional tags are considered. Each tagfrom left to right has coordinates, a1 , a 2 , a 3 , L , a n −1 , a n , anda n +1 , and the gap between the tags is d tag .
RFID Tag

R1

R2
X

a1

a2

a3

L

an −1

an

an +1

RFID reader
recognition aread tag

Fig. 2. Estimation error and the gap of tags.
The left boundary of the reader recognition area is denoted
as R1 and the right as R 2 . That is, the RFID reader can detecttags located between R1 and R 2 . The estimated coordinates,
R est _ x , and the real center position of the reader R real _ x , arerepresented as follows:

a 2 + a n −12
R1 + R 2.
Rreal _ x =2Rest _ x =

(3)

(4)

The estimation error, e est , is defined aseest _ x = Rest _ x − Rreal _ x=

R1 + R2 a 2 + a n −1
−
2
2

(5)

where the ranges of R1 and R 2 can be described as⎧ a1 < R1 < a 2⎨⎩− d tag < R1 − a 2 < 0⎧ a n −1 < R 2 < a n⎨⎩0 < R 2 − a n −1 < d tag

(6-a)

(6-b)

From (5) and (6), now the range of estimation error can be
represented as

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

( R1 − a 2 ) + ( R2 − a n −1 )2
1
≤ d tag .
2eest _ x =(7)eest _ x = Rest _ x − Rreal _ x

Equation (7) shows that the estimation error is proportional
to the gap between the tags and the maximum value is half ofthe gap. Therefore, the maximum estimation error in the X-Y
Cartesian coordinates is represented aseest _ max =(1 / 4) d tag 2 + (1 / 4) d tag 2(8)≅ 0.707 d tag .

4. Algorithm for Reduction and Compensation

For the localization process of the mobile robot in the
RFID sensor space, two schemes are introduced to reduce theerror in this paper.
When the gap between the tags is reduced, the accuracy ofthe estimation is improved as described in the previous
section. But, this solution increases costs because it increases
the number of tags. The optimal allocation of the RFID tags in
the sensor space proposed in this paper aims at improving theaccuracy of the position estimation without increasing thenumber of tags. Traditionally tags have been allocated in asquare pattern (Fig. 3), but in this paper a triangular pattern(Fig. 4) is proposed to decrease the estimation error without
increasing the number of tags.
Figure 5 illustrates the decrease of the estimation error in
the triangular pattern space.
RFID Tag

R1

R2
X

a3

a2
b1

d tag / 2

b2

b3

an

an −1

L
L

bn −1

an +1

bn

X1

bn+1

RFID reader
recognition area

Fig. 3. Estimation error decrease in triangular pattern.
The coordinates of R1 and R 2 are represented as follows:
b1 < R1 < a 2
⎧
⎪
d
⎨ tag
⎪⎩ − 2 < R1 − a 2 < 0⎧ bn −1 < R 2 < a n⎪
d tag .
⎨
R
b
<
−
<
0
2
−
1
n
⎪⎩
2

©ISAROB 2007=

R1 + R2 a 2 + bn −1
−
2
2

(9-a)

(9-b)

468

(10)

( R1 − a 2 ) + ( R2 − bn −1 )2
(11)
1
≤ d tag .
4
Therefore, the maximum estimation error in X-Y Cartesian
coordinates can be represented aseest _ x =eest _ max =(1 / 2) d tag 2 + (1 / 4) d tag 2≅ 0.58 d tag .

of the Error

a1

When the RFID tags are arranged in the triangular pattern,
the estimation error in x-direction can be decreased asfollows:(12)

5. Experiments and Results
For the localization of a mobile robot, it is assumed that themobile robot moves along the designated path.
5.1 Experimental environment
The main frequency of the RFID is 13.56 MHz, The
positions of the tags are pre-stored, and the tags are regularly
allocated following a designed pattern. To show thesuperiority of the triangular pattern, the tags are allocated atevery 0.05m in a row for both patterns. The size of the RFID
reader antenna is 0.1m*0.1m and that of epoxy tags is
3cm*3cm. The mobile robot moves in the 1m*1m spacealong the designed path. The velocities of right and left
wheels are sent in radio frequency from the main computer tothe differential-driving mobile robot. The reader antenna is at
the bottom of the robot, and is connected to the reader. The
reader and main computer are linked through the RS-232
serial communication channel.
5.2 Experimental results
The first experiment aims at the comparison of robot
localization accuracies in triangular and rectangular patternsof tag allocation. The mobile robot follows the path-1 andpath-2 in Fig. 4 with the velocity of 0.25 m/s and thesampling time of 0.04 sec.
The estimation errors are illustrated in Fig. 5 for path-1. As
it can be seen by comparing the errors, the estimation errors
for the triangular pattern are a lot smaller than the square
pattern. For the Path-2, the same results are obtained.
To express correct values, the average position error andorientation error is represented in Table 1. The fact that thetriangular pattern of RFID tag allocation reduces theestimation error is described in section 4 by using the error
model and it is demonstrated by the experiment.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

This paper proposes a new localization scheme in an RFID
based sensor space, which is derived from the new ideas on
the tag allocation and on the accurate and efficientestimations. This scheme overcomes the shortcomings of theconventional absolute position estimations and improves the
localization efficiency and accuracy. The main ideas aredemonstrated by the experiments of a mobile robot
navigating over the RFID based sensor space. To illustrate
the improved accuracy and efficiency of the position
estimation scheme, the square and triangular tag patterns havebeen compared. The triangular pattern has shown better
performance than the square pattern for position estimation ofa mobile robot. When the robot moves in the RFID sensor
space, the velocity and position of the robot are estimated andcompensated to reduce the estimation error according to the
localization scheme developed in this paper. Therefore, thisscheme is very effective for the position estimation of any
object in the sensor space and it could be a good tool to form aubiquitous environment for a mobile robot.

Fig. 4. Two different paths (Path-1 and Path-2).

Acknowledgment
This work was supported by the Regional ResearchCenters Program(Research Center for Logistics Information
Technology ), granted by the Korean Ministry of Education& Human Resources Development.

(a) Position estimation error.

References(b) Orientation estimation error.
Fig. 5. Estimation errors of Path-1.
Table1. Average of the position and orientation error
Path-1
Path-2

SquarePattern
Triangle
Pattern

PositionError(m)Orientation
Error(deg)PositionError(m)Orientation
Error(deg)

0.02

1.72

0.02

1.42

0.016

1.12

0.015

0.89

6. Conclusions©ISAROB 2007

469

[1] D. Hahnel, W. Burgard, D. Fox, K. Fishkin, and M.
Philipose, “Mapping and localization with RFID
technology,” Proc. IEEE Int. Conf. on Robotics andAutomation, vol. 1, pp. 1015-1020, 2004.
[2] V. Kulyukin, C. Gharpure, J. Nicholson, and S. Pavithran,“RFID in robot-assisted indoor navigation for the visually
impaired,” Proc. IEEE/RSJ Int. Conf. Intelligent Robots,
Systems, pp. 1979-1984, 2004.
[3] Kentaro Yamano, Kanji Tanaka, Mitsuru Hirayma, Eiji
Kondo, Yoshihiko Kimuro and Michito Matsumoto,“Self-localization of mobile robots with RFID system byusing support vector machine,” Proc. IEEE/RSJ Int. Conf.
on Intelligent Robots and Systems, vol. 4, pp. 3756-3761,
Sep. 28-Oct. 2, 2004.
[4] K. Nagatani, Y. Iwai, and Y. Tanaka, “Sensor basednavigation for car-like mobile robots using generalized
Voronoi graph,” Proc. IEEE Int. Conf. on Robotics andAuto., vol. 3, pp. 3068-3073, 2002.
[5] Joo-Ho Lee and Hideki Hashimoto, “Controlling Mobile
Robots in Distributed Intelligent Sensor Network,”
IEEE Transaction on Industrial Electronics, Vol. 50, No.
5, pp. 890-902, Oct. 2003.
[6] Heesung Chae and Kyuseo Han, “Combination of RFID
and Vision for Mobile Robot Localization,” Proc. IEEE
Int. Conf. on Intelligent Sensors, Sensor Networks andInform. Processing, pp. 75-80, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of an autonomous mobile robot:
Self-Localization and Mapping using Odometry and a Laser Range Sensor

Keisuke Niimoto

Eiji Hayashi

Graduate School of Computer Scienceand System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
niimoto@imcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstr act

We are attempting to develop an autonomous mobile
robot that has the ability to perform practical tasks in ahuman living environment by using information derived
from sensors and a knowledge database. When a robot ismade to adjust to a human environment, robots require
systems that can recognize the external world andperform correct driving control. We have developed aself-localization method for an autonomous robot.
Odometori offers a self-localization method that is of
the greatest use, providing a method of calculating anddetermining the tracks of a robot. However, there is alimit to the accuracy of this self-localization because acumulative error occurs regarding the self-position
determined by Odometori. To solve this problem, thispaper describes the development of a self-localization
method based on a fusion of Odometori and LRS (Laser
Range Sensor) data.
Key words: autonomous mobile robot, LRS (Laser Range
Sensor), Odometori, ICP (Iter ative Closest Point) algorithm,
Self localization, mapping.

1. Introduction
In our country now, the population is aging, and thenumber of people who receive nursing in hospitals andwelfare facilities is increasing. At the same time, theproportion of the working population continues todecrease. Robots are expected to make a contribution in
helping to solve the problem of the labor shortage this iscreating. Indoor robots intended for use in a general life
environment include, among others, cleaning robots,house-sitting robots, and guard robots. Indoor mobile
robots that have movement functions like these robots
are expected to evolve.
Environmental recognition is required so that the
robot may coexist among humans. An autonomous
mobile robot requires map information regarding its
range of action. This includes a grasp of the position
where the robot is located on the map. By recognizing©ISAROB 2007

470

this environmental information, the robot can search fora route to its destination and can accurately reach thedestination. Therefore, the robot’s knowledge of its
position on the map is one of the importantenvironmental recognitions that is required.
Odometori is the self-localization method that has
been of the greatest use; it can calculate distances anddetermine the tracks of the robot. However, there is alimit to the accuracy of this self-localization because acumulative error occurs regarding the calculated tracks
by encoder data [1][2]. To solve this problem, wedeveloped a self-localization method based on a fusionof Odometori and LRS (Laser Range Sensor) data.
In our research up to last year, in which the dataobtained from the LRS made a straight line, self-position
was determined by matching the robot with analready-known obstacle. However, the inability toestablish a correspondence to obstacles other than by astraight line (because in this technique the LRS datarequired a straight line) meant that the robustness was
low. In addition, there was the problem that it wasnecessary to give information about the obstacle to the
database before the position of the robot could bedetermined. Therefore, in our new system the LRS data
does not require a straight line. The earlier problems
were solved using an ICP algorithm to match LRS datawith the database. Moreover, by using this method, therobot becomes able to construct a map without being
supplied with a map beforehand. We mounted thissystem on the robot in an indoor environment andconducted an experiment involving self-localization andmapping.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. System Architecture

3.2 Self-localization method

In this research, we used a mobile robot, Kitasap2,
developed in our laboratory. Figure 1 shows the externalsof Kitasap2. It is composed of three wheels; the two front
wheels are independent drives, and the rear wheel uses aball. An encoder is installed on the front wheel, and a
LRS is installed in front of the bottom of the robot. The
robot is equipped with a computer that controls all
devices; lithium-ion batteries supply the electric power.

The procedure for self-localization is shown below(See Figure 2).

Fig. 1 Mobile robot

3. Self-Localization and Mapping
3.1 ICP algor ithm
The ICP algorithm is a technique of the optimizationof overlapping point sets [3]. For each point of point sets,
the nearest point is looked for and related mutually. The
least square method concerning the distance is calculated
based on them (expression 1). And points are updated
using a homogeneous transformation (expression 2). It
then optimizes matching by repeating these procedures.
The ICP algorithm has the following advantages. It isstrong in suppressing noise; it need not be given
correspondence points; it can be matched even if two setsof points are off to some degree; it does not require
location information; and it can deal with overlapping ofcomplex shapes. In this research, by applying thisalgorithm to match the LRS data with map information,
the position of the robot is presumed.

F Xg , Y
Xg i( m 1)

1 N
m
Xg i
N i1
Trans m2

Yi

Rot m X i

(1)
m

(2)

N ：Number of points
Xg i ：LRS Data in global coordinates system
Yi ：The map data of database
Xi ：Scanning data of LRS
Trans ：Translation procession
Rot ：Rotation procession©ISAROB 2007

471

Ⅰ. The rotation angle of the tire is acquired from theencoder. Tracks of the robot are calculated, and theposition of the robot is presumed.
Ⅱ. Acquired data from LRS is transformed into a
global coordinate system based on the self-position
presumed from encoder.
Ⅲ. The map information (database) and the LRS datathat transformed into a global coordinate system arecompared. The nearest points are related mutually.
Ⅳ . The related point data is substituted for theevaluation function of the ICP algorithm(expression 1).
Ⅴ. If the value of the evaluation function is less than
the threshold, it is considered to match. The valueof the transformation matrix at that time is the
position of the robot. (End)If the value of the evaluation function is greaterthan the threshold, the transformation matrix is
calculated.
Ⅵ. A geometric transformation is performed using thehomogeneous transformation (expression 2).
(Return to Ⅲ)

Encoder data

LRS data

Tracks calculation

Geometric transformation
ICP algorithm

Relating points

DatabaseYes

F Xg , Y

Geometric transformationNo

Calculation oftransformation matrix

Self-localization

Fig. 2 Procedure for self-localization

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.3 Mapping method
The mapping procedure is shown below(See Figure 3).
Ⅰ.~ Ⅳ.
See the corresponding steps in the procedureof self-localization.
Ⅴ. If the value of the evaluation function is less than
the threshold, the transformation matrix at thattime is the position of the robot. (Go to Ⅶ)
If the value of the evaluation function is greaterthan the threshold, the transformation matrix is
calculated.
Ⅵ. A geometric transformation is performed using thehomogeneous transformation (expression 2).
(Return to Ⅲ)
Ⅶ. If the value of the evaluation function is less than
the threshold and converges, the map isconstructed, and the database is updated based on
this map.
Ⅷ. (Return to Ⅰ)

margins of self-localization are shown in Figures 5 and 6,
respectively. The average error, the maximum error, andthe standard deviations of the results are shown in
Tables 1 and 2.
From Figure 5 and Table 1, it can be confirmed thatthe error of X coordinates and the error of Y coordinates
are within ±3.0cm. Moreover, we confirmed that theorientation of the robot was able to be presumed within
an error of ±0.01rad. Therefore it is proven that accuracyof this self-localization method is high.
From Figure 6 and Table 2, in the case where therobot is compulsorily moved, it can be confirmed that theestimated position converges in the correct vicinity of
coordinates. And, it can be confirmed that the accuracyof self-localization is high. Therefore, it is proven thatrobustness improves with the accuracy maintained.
01m①
LRS data

Encoder data

: robot

Tracks calculation: wall②

Geometric transformationy

ICP algorithm

Database
Yes

converge &
Fmin
min

x
No

Fig. 4 Environment of the self-localization experimentmapping10

Y coordinates(cm)Fig. 3 Mapping procedure

4. Experiment
4.1 Exper iment of self-localization
We performed experiments to verify the
self-localization method proposed in this research. In
experiment 1, the robot (Kitasap2) is arranged on ① inthe environment of Figure 4, and robot presumes its
self-position.
In experiment 2, to verify the robustness, the robot islifted and is moved to position ② of Figure 4
compulsorily. Again, the robot presumes its self-position.
The amount at this time of the movement is -1m to thedirection of Y coordinates, and the robot orientation isrotated by 90 degrees.
We conducted each experiment 100 times. The error©ISAROB 2007

472

5
0
-5
-10
-10

-5

0

5

10

X coordinates(cm)Fig. 5 Localization result of experiment 1
Table 1 Results of experiment 1
X

Y

Average [cm]

1.4

-1.0

Maximum [cm]

2.7

-1.9

Standard deviation

0.4

0.3

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
0

1m

150

Y coordinates(cm)

100
50
0
-50
-100
-150
-150

-100

-50

0

50

100

150

X coordinates(cm)(a) Map of experimental
environment

Fig. 5 Localization results of experiment 2

Fig. 7 Map of the mapping experiment andexperimental results

Table 2 Results of experiment 2
X

Y

Average [cm]

0.1

-1.2

Maximum [cm]

0.7

-3.1

Standard deviation

0.3

0.4

(b) Results of the mapping
experiment

5. Conclusion

4.2 Mapping exper iment
We also performed an experiment to verify themapping method proposed in this research. In thisexperiment, no map information is given to the robot
beforehand, and the robot is arranged in an unknown
environment (Figure 6). Figure 7 (a) is a map of theenvironment of Figure 6. The robot moves on the tracks
shown by the arrow in Figure 7 (a), and constructs themap. The movement speed of the robot is about
20cm/sec. The map the robot generated is shown in
Figure 7 (b).
From the experimental results, it can be confirmedthat even if no map information is given to the robot
beforehand, the robot is able to construct a map.
However, the constructed map includes an error. And thisexperiment occasionally failed in mapping.

Our research has developed a self-localization
method based on a fusion of Odometori and LRS (Laser
Range Sensor) data. To solve the problems in our
research up to last year, we used the ICP algorithm tomatch the LRS data with the database.
By using this method, the robot has improved
robustness, and becomes able to construct a map without
being supplied with a map beforehand.
The next problem is to improve the accuracy and thesuccess probability of the mapping.

ThanksI’d like to acknowledge the advice and assistance ofthe late Prof. Tadashi Kitamura of Kyushu Institute ofTechnology and pray for the repose of his soul.

References[1] Masato Tashiro: “Interface development forcooperation work with a semi-autonomous mobile robot(2nd report)”, Proc. of the 2004 JSME Conference on
Robotics Mechatronics, 2P2-H-16(CD-ROM), 2004
[2] Shinya Nakao: “Development of a control system
based on imitative leaning animal behavior forsemi-autonomous mobile robot: Search for a detour byusing subgoals,” Proc. of the 2004 JSME Conference on
Robotics Mechatronics, 1P2-N-56(CD-ROM), 2005
[3] P.J.Besl and N.D.Mckay: “A Method of Registrationof 3-D Shapes”, IEEE Trans. on PAMI, Vol.14, No.2,
pp.239-256 (1992)

Fig. 6 Environment of the mapping experiment©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Linux-based Real Time Monitoring System of Mobile Robots
SeungHo Cho
Dept. of Electronics Eng.
Pusan Nat. Univ.
Sungho123@pusan.ac.kr

HyoSik Choi
Dept. of Electronics Eng.
Pusan Nat. Univ.
chs3040 @pusan.ac.kr

Abstract
Real time monitoring is necessary for the dynamic
obstacle avoidance and trajectory tracking of mobile
robots. However there are several problems in
implementing a real time operating system: 1. It is
expensive to develop since a high license fee is requiredand 2. Standards for the real-time systems are not wellestablished yet. For an educational system under these
difficulties, a Linux-based real time system is possible toreceive the display data with the minimum time delay by
the kernel compiling to fit to the user system. Notice that
Windows OS is suffering from code bloating phenomena,
which may result in a long operation time. An i-BOT isdeveloped for a demonstration system to show the
localization schemes using the RFID and ultrasonicsensors, named as iGS (indoor GPS). An RFID isdesignated to synchronize the transmitter and receiver ofthe ultrasonic signal, and the traveling time of theultrasonic signal has been used to calculate the distancefrom the iGS to a mobile robot.
Keywords: real-time, Linux, localization, RFID

1. introduction
In the near future, various new services come to ourdaily lives through the ubiquitous computing andnetwork environment. Especially for a mobile service
robot, it is very important to estimate the position and torecognize human beings and obstacles at any instance.
The localization and recognition techniques are very
important to provide various services to human beings invarious environments [1].
Since the mobile robot is able to move in the working
area, it can be used for various tasks unless it causes
collisions to the obstacles in the environment. On
account of its mobility, the mobile robot can replace
human beings for the hard and dangerous tasks. To
execute the given tasks successfully, the mobile robot
needs to identify its own position for a certain task
including sensing the environment and controlling the
motors. Unexpected disasters may happen through themalfunction of the mobile robot in synchronizing to theenvironment. Therefore the mobile robot is a real timesystem to move to the goal as well as to avoid obstacles
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JangMyung Lee
Dept. of Electronics Eng.
Pusan Nat. Univ.
jmlee@pusan.ac.kr

Most of the robot control systems are being developed
using Windows O/S. Windows has many advantages ofeasy developing environment, such as, supporting
abundant device drivers and multitasking. However itcannot support hard real time capabilities since theWindows is not designed for the real-time system
originally, which emphasizes the graphic display.
Moreover it is difficult and takes a lot of time to develop
the device drivers [2] in Windows O/S.
Basically a real time O/S is suitable to implement areal time control system. The commercial QNX and
VxWorks have the hard real time capabilities. However,the price is too high to be used for education andresearch purposes. Their support of device drivers forcustom designed controllers is so weak that theexpendability is very low [3].
In this paper, for the autonomous navigation of theintelligent robot, i-BOT, a real time control software isdeveloped and evaluated. To keep the hard real time
capabilities, Linux with RTAI has been used.
In section 2, the development environments of realtime Linux and Windows O/S are analyzed to show thedifferences. Section 3 illustrates the hardware platformof i-BOT and the constitution of iGS (indoor GPS), andsection 4 describes the operating platform for theautonomous navigation and the position estimation
algorithm of iGS, and the simulation and experimental
results follow in section 5. The ideas and contributionsof this paper are summarized in section 6.

2. Comparison of Real Time Linux andWindows
2.1 Stability of Linux control system
Linux is a next generation O/S which can replace theWindows O/S which has code bloat caused by thecontinuous addition of functions when they are required.
That is, the unnecessary code increase (Actually
Windows has about 35 to 40 million unnecessary lines.)degrades the operating performance in the real time
control system. On the contrary, the real time Linux canbe ported on the small memory such as a floppy disk
since it can be reconfigured by the complier to fit to theuser system. On this reason, the Linux has been widely
utilized for embedded systems.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

two stepping motors are used. The mobile robot can becontrolled wirelessly from a PC through Bluetooth.

2.2 Flexibility of development environment
There are several reasons that the Windows platform
has been dominant so far. It provides a user friendly
interface and various services, and the educational cost isvery low. Now the Linux also has almost all theadvantages of Windows by the continuous development.
Figure 1 is a screen shot of Visual Studio which is the
Windows development environment, and Fig. 2 is ascreen shot of QT which is the Linux development
environment. Since both of these two environments
support C and C++ as a basic programming language, thedeveloping environments are almost the same.

Fig. 1. Visual Studio Screen shot.

Fig 2. QT screen shot.
Linux also enables to form the tele-debugging
environment by a terminal, which is very rare in
Windows. Moreover the source codes of Linux are open
to public. Therefore it is possible to modify the source
codes according to developing objects and application
environments promptly and easily. However the
performance of the real time system in the Windows
degrades on account of the additional codes for thevarious functions.
Therefore in this paper a mobile robot controller isdeveloped based on a real time Linux, and theperformance is compared to the Window based system.
The modularity and real time performance of the Linux
based system are emphasized to show the superiority ofLinux to Windows.

3. i-BOT Platform and iGS
3.1 i-BOT Platform
The mobile robot used in this research, i-BOT, ismade by Ninety Corp., which is shown in Fig. 3. For the
power supply, 12 V rechargeable batteries are used. Twoactive wheels and one auxiliary wheel are driving themobile robot. For the simplicity of driving mechanism,©ISAROB 2007

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Fig. 3. i-BOT system.

3.2 Structure of the iGS system
The proposed localization scheme can be applied forany of moving objects, such as, home robot, service
robot, humanoid robot, etc. The indoor environment
consists of columns, corners, and two dimensional flatwalls which can include desks, tables, and computersdepending on the size of the objects.
There are four beacons in the workspace of i-BOT.
Each beacon is located at a specific corner with a
specific coordinates and it transmits the ultrasonicsignal when it is requested. Notice that most of cases, it
is convenient to install the beacon at the corner ofceiling. On the mobile robot, there is a receiver which
detects the arrival time of the ultrasonic signal from thebeacon. The receiver has a RF transmitter in the same
body, which sends out an ID for a specific beacon
assigned to the ID to request the ultrasonic transmission.
To identify the orientation of the mobile robot, there are
two receivers on the robot. The beacon receives an
RFID from the receiver, and checks whether the ID
matches to itself or not. When the matching succeeds, it
sends out the ultrasonic signal to the receiver on themobile robot.

4. O/S platform for autonomous navigation
4.1 Real-Time System
To incorporate various functions to i-BOT, not only
improvement of the mechanical structure but also design
and implementation of real-time software structures areimportant [4]. The real-time software governs theefficient flow of resources and defines data-flows among
the elements, for the real-time control system. The realtime position estimation and object recognition arenecessary for higher level functions, such as, theautonomous navigation and recharge. For the precise
estimation and recognition, the data which have different
physical dimensions from various sensors need to beprocessed and fused efficiently. The autonomous mobile
robot may confront with unwanted collisions to obstacles
when one of these elements does not keep the time
constraint.
For the autonomous navigation of i-BOT, many tasks,
such as, motor control, sensing, position estimationcommands, need to be processed in real time. Each task

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

has its own control period, priority, execution time, andcomputational complexity. Since the hard real-time
properties are required in the control of motors using
sensor data in these tasks, a real-time operating system
which manages the resources efficiently is essential to
implement the real-time control system.
The most important feature for the controller is realtime capability. Notice that the real time operating
system is a higher level program to make the tasks being
executed in real time. The operating system does not
solely aim at the high speed operation. Within theallowed time slot, it provides the desired output utilizing
the predictable system functions. Both of Windows
and Linux are not possible to execute hard real time
tasks, by themselves. Therefore, each operating systemsuggests real time kernels with the basic operating
system to be used for the necessary real time system. For
Windows, RTX is the most popularly used kernel. There
are several on-going projects to make the Linux a realtime operating system, such as, RTLinux and RTAI. The
real time operating system supports the programs of
multi-tasking structure with time tags, and also provides
communication synchronization among tasks andscheduling mechanisms to implement the real timesystems.
Linux is a performance-oriented O/S, which has theround-robin scheduler. That is, each task holds the token
in a fixed period. Therefore the real time processing
cannot be guaranteed. Even though a preemptionfunction is added in the recent kernel version 2.6x, it isstill not suitable for the hard-real time system, such as, amobile robot system. There is a Linux based real-time
operating system, RTLinux which is commerciallyavailable but applicable to very limited types ofprocessors.
As a sort of interface program, RTAI is developed and
it is free of charge. The RTAI provides most of the hard
real-time properties, such as, task pre-emption andpriority setup/inheritance. Therefore it is suitable for the
real-time systems under Linux operation system.
Figure 4 shows the basic structure of RTAI. The Linuxkernel is treated as a task. RTAI and Linux kernel can beinterfaced to the hardware by HAL (Hardware Abstract
Layer). HAL manages to execute the real time tasks
preferentially, while it leaves Linux kernel and theprocesses working in the kernel as the lower priority
tasks in RTAI [5].

There are three levels in the architecture depending on
the execution period and characteristics: 1. The highest
level is the task goal which sets up the goal of thenavigation, 2. The second level is the path planner which
generates a path to the goal, and 3. The lowest level isthe trajectory generator which specifies location andtime pairs of the mobile robot on the path.
When the final goal for the autonomous navigation isdetermined, the highest level monitoring system plans
the trajectory to the goal by selecting appropriate via andthrough points for the mobile robot to follow.
The path planner decides the existence of obstacles
and changes the path to avoid the obstacles. For the
obstacle detection, the ultrasonic transmitter sends out
the signal every 100 msec, and the corresponding
receiver computes the TOF (time of flight) of theultrasonic signal and detects the obstacles within thedangerous range. After the collision avoidance, thecurrent and goal positions are newly set in themonitoring system to execute the trajectory planningagain.
In the lowest level, the stepping motors are controlled
to follow the trajectory by the timer interrupt service
routine in every control cycle, which provides afrequency output to control the motor.
The obstacle avoidance routine is activated when there
are some static or dynamic obstacles in the path of themobile robot, i-BOT, which senses and avoids thepossible collisions using the ultrasonic sensors and thetwo stepping motors, respectively. When it is notpossible to avoid the collision, the routine may send out
STOP command to the motor controllers. After the
successful avoidance, the current mobile robot follows
the desired trajectory to the goal.

4.3

iGS position estimation algorithm

The localizer starts to count when it sends out an
RFID signal assuming that the transmission time to abeacon is negligible. When a beacon receives its RFID,
it sends out the ultrasonic signal immediately. The
counting at the localizer continues until it receives theultrasonic signal. After the distance computation using
the counter value which corresponds to the TOF of theultrasonic signal, the localizer sends out another RFID to
the next beacon. When the distances to all the expectednearby beacons are measured, the coordinates of themobile robot is going to be calculated.
The TOF which can be calculated by the counter isbasis for the computation of the distance between theultrasonic transmitter (beacon) and receiver (localizer) as
follows:.
(1)
v = 331.5 + 0.6 × T [m/sec]
[sec]
(2)
s = n × C − td[m](3)
r = v⋅swhere T is the room temperature, C is the period ofcounter clock, n is the number of counter, t d is theFig. 4. Structure of RTAI.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

time delay for ultrasonic signal detection, s is the total
freight time of the ultrasonic signal, and r is the
distance between the beacon and the mobile robot.
The synchronization between the beacon and thereceiver on the mobile robot is very important inmeasuring the TOF of the ultrasonic signal without
interferences. To select a beacon at a time, an RFID istransmitted to the beacon from the mobile robot. At the
moment of the RFID receipt, the corresponding beacon
transmits the ultrasonic signal to the receiver. Therefore
there is an RF receiver with a specific ID and an
ultrasonic transmitter at each beacon; there is an RFtransmitter and an ultrasonic receiver on the mobile
robot.
For the localization of the mobile robot using thetriangulation technique, at least three beacons arenecessary for a mobile robot in the 3D space..

5. Simulations and experiments
To implement and to analyze the performance of amobile robot control system under Linux O/S, a test-bed
is implemented using iGS and i-BOT as shown in Fig. 5.
The height, length, and width of the iGS space are 3.0 m,
3.0 m, and 2.0 m, respectively. The speed of i-BOT is
kept below 35cm/sec considering the localization
accuracy and safety of the mobile robot.

Figure 6(a) shows experimental trajectories of themobile robot: the black solid line represents the actual
trajectory while the rectangular marks represent theestimated trajectory by the triangulation method. In the
middle of the path, there are filled rectangular marks
which represent the positions estimated by only two
sensor data. Fig. 13 represents the trajectory obtained by
the EKF (extended Kalman filter) using the data set fromthe triangulation method. By applying the extended
Kalman filter, the error between the real and estimatedtrajectories has been reduced to 20 % as it is clearlyrecognized by comparing Fig. 6(a) and Fig. 6(b).

6. Conclusion
A flexible controller is designed to satisfy the various
requirements of customers working on the Linux
environment. RTAI has been utilized to form a real time
control system based on Linux, since its source codes areopen to public and it has higher flexibility than Windows
to satisfy the controller specifications and environment.
Through the various experiments, RTAI is proved to beefficient for modular and real-time programming. A
monitoring system is specifically designed for the
improvement of i-BOT control precision by taking
advantage of this Linux-based real time O/S. It is
concluded that the i-BOT working in the Linux
environment developed in this research, is useful andflexible for education and researches.

Acknowledgements
This Work was supported in part by MIC&IITA
through IT Leading R&D Support Project

REFERENCES
Fig. 5. Experiment environment.
Monitoring program manages the display of theposition data measured by iGS and the counter values ofthe stepping motors sent through the Bluetooth, and it
also sends out the motor control commands. In this paper,the monitoring program is implemented on a PC with theLinux using QT program, which gathers the data through
Serial-COM and displays on the monitor.
To make the ultrasonic receiver free from reflectedinterference signals. An RFID is sent out every 20 ms foreach beacon. The total data processing time of iGS toobtain the location coordinates is 90 ms.
1800

1800

1600

Center Y Position[mm]C e n t e r Y P o s it io n [ m m ]

1600

1400
1200
1000

800
600
1000

1400

1200

1000

800

1200

1400 1600 1800
Center X Position[mm]

2000

600
1000

1200

1400

1600

1800

2000

Center X Pos ition[mm](a) Trajectory estimation.
(b) EKF results.
Fig. 6. Trajectory of a moving object.

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[1] S. Singh, “Obstacle detection for high speed
autonomous navigation,” Proc. Of IEEE Int. Conf.
on Robotics and Automation, pp. 2798-2805, 1991.
[2] C. H. Lee and C. Mavroidis, ”PC based control ofand mechatronic systems under MS-windows NT
workstation,” IEEE/ASME Trans. on Mechatronics,
vol.6, no. 3, pp. 311-321, 2001.
[3] W. F. Lages and E. M. Hemerly, “Linux as asoftware platform for mobile robots,” submitted toIEEE Transactions on Software Engineering, 2000.
[4] I. S. Song and F. Karray, “Software architecture
for realtime autonomous agents : a case study for
digital train system,” Proc. of IEEE International
Symp. on Intelligent Control, pp. 403-408, 2002.
[5] L. Dozio and P. Mantegazza, “Linux real time
application interface (RTAI) in low cost highperformance motion control,” Motion Control 2003,
a Conference of ANIPLA, Italy, March, 2003.
[6] SeungBu Kim, Gi-Gun Nam and JangMyung Lee,
“Indoor Localization Scheme of a Mobile Robot
Using RFID” international robot Week, Deajeon,
Korea, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Ontological Representation of Vision-based 3D Spatio-Temporal Context for Mobile
Robot Applications
Jung Hwa Choi and Young Tack ParkGi Hyun Lim, Jongleul Chung,Gwang Geun Ryu, Jong Bok Kim,
Sang Hyoung Lee, Sanghoon Lee,and Il Hong Suh

School of Computing
Soongsil University, Seoul, 156-743, Korea

College of Information and Communications
Hanyang University, Seoul, 133-791, Korea
Abstract
In this paper, we propose an ontology-base context
model that consists of high level context as well as
primitive spatial and temporal context. Moreover reasoning tools are used to find out not only simple contextual information such as object location, movementand distance but also hidden contextual informationsuch as some objects disappeared by moving behindbigger objects. Also we use axiomatic rules for resolving uncertainties which might be caused by themismatches of 3D SIFT key points. Some practicalexamples will be provided to show the validities of our
proposed ontology-based context model.

1

Introduction

The intelligent robot needs high level perceptualtasks - context awareness[1][4][5][6], SLAM[10], object recognition. Especially contextual informationis necessary for robot intelligence with which robots
can recognize environments and plan their behaviors
to complete missions while adapting to their environments. Such perceptual tasks are often required to be
implemented by relatively inexpensive vision sensorsof which visual data can be made very informativeby employing many data processing algorithms. We
note that vision-based context understanding system
requires not only recognition of objects in the scene,
but also contextual interpretation of the scene. SpatioTemporal (ST) context is the basis for high level context understanding. However, visual data could be almost partial and occult in real environment. And suchvisual data processing knowledge have been speciallydesigned for the domain-specific application. Thus,
there can be hardly shared knowledge such as data

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structure, data processing mechanisms and rules. Using ontological representation makes it easy for intelligent robot to share its knowledge and common
concepts[3][4][5][7]. Therefore, ontological representation and reasoning tools will open possibility for robots
to find hidden knowledge and/or to make that knowledge is growing and reusable.
In this paper, we propose an ontology-base context
model that consists of high level context as well as
primitive spatial and temporal context. Moreover reasoning tools are used to find not only simple contextual information such as object location, movementand distance but also hidden contextual informationsuch as some objects disappeared by moving behindbigger objects. Also we use axiomatic rules for resolving uncertainties which might be caused by the mismatches of 3D SIFT key points. For example, objects
cannot float in the air by themselves. Instance of our
proposed ontological context model will be generated
based on 3D SIFT features. Specifically, after recognition of objects using SIFT features[2], primitive spatial
data including location of objects, distances between
objects, movement of objects are created. When theprimitive spatial data are generated, an approximatedcenter of an object is selected as a representative point
for fast and efficient data processing. The compensated primitive spatial data are then clustered to instantiate primitive ST contexts. Then, higher level
ST contexts are also instantiated by classifying theseprimitive ST contexts according to ontological taxonomy of context model. The instance is stored with atime-tag . Instances are then used to extract somemissing information and/or to resolve uncertain relations by inference tools. Some practical examples
will be provided to show the validities of our proposed
ontology-base context model.
In the following sections, robot-centered ontology

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
is described. Robot-centered ontology includes ontological model of perception, model, context and activity. Second, it will shown how context ontology
instances are generated. Third, our ontology model
will be tested on home service environment. Finally,a conclusion and consideration for future research areprovided.

2

Robot-centered ontology
Figure 3: The Definition of Interval Relation by Allen

Robot-centered ontology is an ontological representation of robot knowledge that supports intelligent
robot to perceive the environment, to model the stateof the world, to plan the sequence of job, to perform the selected activity and to aware a given situation - context. Robot ontology is necessary for
robot to share and reuse its knowledge, because robot
perceives the environment and puts into action in adifferent way as human do[3]. It requires that ontological representation of robot knowledge should
be suitable for its own sensors, behaviors as well as
their coordination. Fig. 1 shows the architecture of
ontology-based multi-layered robot knowledge framework (OMRKF), which includes 4 dimensions; KLevel,
KLayer, OLayer and time and 4 levels of knowledge(KLeveli ); Perception(Pi ), Model(Mi ), Context(Ci )and activity(Ai ). And those knowledge are representated by ontology.

2.1

4 Levels of Robot ontology

479

Context ontology

The context KLevel (KLevel4 ) of OMRKF has 3
knowledge layers (KLayerij ); spatial context (C1 or
KLayer41), temporal context (C2 or KLayer42 ) andhigh level context (C3 or KLayer43 ) as shown in
Fig.1. C1 is is generated by primitive spatial dataand includes spatial concept such as on, in, near,far, left and right. C2 is clustered with compensated primitive spatial contest and includes spatialconcepts; object-fixed, move-near, temp-moving. And
C3 is more abstracted context in specific domain withrules such as dinner, appetizer, main dish and dessert.
And, basic ontological elements of OMRKF is 3 ontology layer (OLayerijk ) such as meta ontology layer(OLayerij1 ), ontology layer (OLayerij2 ) and ontology
instance layer (OLayerij3 ). Fig. 2 shows an example
of OMRKF.

2.3

Robot perceives objects with its sensors, models
world, plans some sequence of task, performs the taskwith behavior and perceive again, or robot behaves
through its sensor values not with planning but withpre-programmed behaviors[11]. But, those sensor data
are uncertain and partial information[10]. And service
robot needs context-awareness[4], [5], [6], so that they
can adapt themselves to changing situations. Context
offers a few clues of the proper action selection mechanism for robot. According to the advance of context, it is necessary to develop formal context models
to facilitate context representation, context sharing.
Thus, OMRKF is composed of KBoards and rules, andKBoards is composed of 4 levels of knowledge perception, modeling, activity and context. each level anddimension are connected by association rules as in Fig.
1.

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2.2

Temporal ontology

Time information is absolute measure which is obtained regardless of location and movement[9]. For
temporal ontology, we reference thirteen interval relations as shown in Fig. 3 to define relations among
actions which were proposed by J. Allen to formalize time[8]. Temporal ontology makes it possible toobtain knowledge not defined in ontology by inferring
relations among locations using interval relation.

3

Instantiation of Context ontology

In this paper, vision-based objects recognition is
performed by SIFT features. After recognizing objects, some instance of spatial context ontology including location of objects, distances between objects,
movement of objects are created. When the spatial

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
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Ontology
Meta
Ontology
Instance
Ontology

Meta
Ontology
Ontology
Ontology
Instance

C11 C12

KL
ay
er
(R H
)

Low Level
Middle Level

C21 C22High Level

C31 C32

C23

C13

t1

C33

OLayer (Axiom)

Time (RT)t2

KLevel(RL)…

Association (RA)tn

Perception(KLevel1)

Model(KLevel2)

Activity(KLevel3)

Context(KLevel1)

Figure 1: 4 Dimentional OMRKF

Figure 2: An Example of Robot-centered ontology Schema©ISAROB 2007

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contexts are instantiated (OLayer413 or C13 ), an apTable 1: Definition for Primitive-ST Data
proximated center of an object is selected as a repreDefinitions
Description
sentative point with axioms such as ”Solid objects cants
start time
not penetrate other solid objects” and ”Objects cantend timee
not float in the air by themselves”. The compensated
t
=
(t
−
t
)
time
intervalof interestdesspatial context are then clustered to generate tempot
sampling
timefreq
ral context instances(OLayer423 or C23 ). Finally, wettime
acquiringi-th datai
can get high context instances(OLayer433 or C33 ) bytd
N=maximum
numberofavailable datamax
tf reqdomain specific rules. And also we can get hidden STethresholdof
position
errorR
contexts by inferencing pre-instantiated ST contexts.
eD
tolerance of distance errorTtrustability
3.1 Instantiation of Spatial Context On-

tology (OLayer413 or C13 )In order to recognize objects, we use a model-based
approach. First of all, we use a 3D OFM (Object
Feature Model) as the reference model which includes
images of object, their corresponding SIFT keypoints,
and representative point. By simply matching of 3D
keypoints between OFM and input image, we can identify objects and obtain positions of objects with respect to robot. After identifying objects, primitive
spatial data which describe geometric relations of recognized objects and are composed of location, distance, and movement data are generated. Also, theprimitive spatial data are instantiated one of spatial
context ontology (OLayer413 or C13 ).
Location Data: Location data for an object ob isdefined as R(ob) = (t, obj, R(ob)), where t is a time
when the location data is created, obj is the name ofobject ob, and R(ob) is a position vector of object obwith respect to the robot coordinate.
Distance Data:
Distance data for objects ob1 and ob2 is defined as D(ob1, ob2) =(t, obj1, obj2, D(ob1, ob2)), where t is a time when
the distance data is created, obj1 and obj2 represent
names of object ob1 and ob2, respectively. And,D(ob1, ob2) is a position vector from object ob1 toobject ob2 with respect to the robot coordinate.
Movement Data: Movement data for an object
ob is defined as M (ob) = (t, obj, M (ob)), where t is atime when the movement data is created, and obj represents the name of object ob. M (ob) is the vector of
movement which can be simply obtained by the vector
difference Rt (ob) and R(t−1) (ob).
Table 1 shows definitions used in generation ofprimitive-ST data.

distance, and movement data.
Primitive location data of object ob(pR(ob)) showsthat the object ob stays at the same location for agiven period of time td. And pR(ob) is defined aspR(ob) = (id, ts , te , obj, R(ob), T ).The primitive location data can be generated as follows;
For any time ti ∈ {0 < ts ≤ ti ≤ te }3.2

4

Instantiation of Temporal Context
Ontology (OLayer423 or C23 )Temporal context ontology is instantiated by inferring spatial context ontology instance such as location,©ISAROB 2007

481

1: If abs(R(ob) − Rti (ob)) ≤ eR then count = count + 1
2: T = count / Nmax
3: Generate pR(ob)Temporal context ontology are instantiated with one
or more spatial context ontology instance about the same
objects. Table 2 show the spatial context and temporalcontext ontolgy and their FOL[7] rules.

3.3

Reasoning of context ontology

We represent OMRFK with FOL. Moreover, OMRKF
includes sub-symbolic data that are seldom utilized by conventional ontology system. The data generated from robot
perception or activities are numerical data, which are partial and incomplete. The probabilistic approach has dominated the solution of that case [2]. However, those systems
may be application-specific, which is difficult to reuse andrequires verification of the procedures. OMRKF applies
sub-symbolic data to ontology-based knowledge representation, so that OMRKF can deduce hidden knowledge thatis generated by a partial observation or an observation error, and make it easy to reuse and verify. Moreover, OMRKF needs rules that associate each level of knowledge,
these rules enable robot to query not only unidirectional
reasoning but also bidirectional reasoning. Table 2 show
rules for the generation of context represented by FOL.

Experimental result

The ST context generation experiment was performed
for objects in a refrigerator. For sequentially changing context in the refrigerator as shown in Fig.4. Mobile robot

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(a) Context
Time 1 1

Time 2 2(b) Context(c)(c) Context
Time 3 3(d)(d)Context
Time 4 4

Table 2: Rules for spatial context and temporal context ontology
Layer Context Logical representation

C1
C1

SC1
SC3

C1

SC4

C1

SC6

C2

T C1

C2

T C2

C2

T C3

C2

T C5

C2

T C7

©ISAROB 2007

∀o1 , o2 , tlocation(o1 , t) ∧ location(o2 , t) ∧positive((o1 .x − o2 .x), t) ⇒ lef t∀o1 , o2 , tlocation(o1 , t) ∧ location(o2 , t) ∧−o2 .x), t, eD )∧dis err((o1 .x
dis err((o1 .y−
o2 .y), t, eD )∧dis err((o1 .z − o2 .z), t, eD ) ⇒ near∀o1 , o2 , tlocation(o1 , t) ∧ location(o2 , t) ∧positive((o1 .y−
o2 .y), t)∧near(o1 , o2 , t) ⇒ over
∀o1 , o2 , tover(o1 , o2 , t)∧equal((o1 .y−
o2 .y), (o1 .height + o2 .height)) ∧dis err((o1 .y − o2 .y), t, eD ) ⇒ on∀o, t, t1
location(o, t) ∧ location(o, t1 ) ∧loc err(o, t, t1 , eD ) ⇒ object − f ixed
∀o1 , o2 , t, t1
distance(o1 , o2 , t)∧distance(o1 , o2 , t1 )∧dis err(o1 , o2 , t, t1 , eD )⇒f ixed − distance∀o1 , o2 , t, t1
distance(o1 , o2 , t)∧distance(o1 , o2 , t1 )∧dis near(o1 , o2 , t, t1 ) ⇒ object − near∀o1 , o2 , t, t1
contain(o1 , o2 , t)∧contain(o1 , o2 , t1 ) ⇒inside∀o1 , o2 , t, t1 , dis1 , dis2
distance(o1 , o2 , t)∧distance(o1 , o2 , t1 )∧positive(dis1 , dis2 ) ⇒ move − near

482

Figure 4: Sequentially Changing Contexts

may generate primitive spatial data and instantiate spatial
context and temporal context ontology. At t = t1 , there
are two objects (obj2 and obj3) apart from each other ina refrigerator(obj1). At t = t2 and t = t3 , obj3 moves left
and forward, respectively. Finally, a large object(obj4) appears in front of obj2 and occludes it.
Fig.5 shows the display of our context generation system. In the left upper corner, images captured by stereo
camera attached on top of the mobile robot are displayed
continuously. In the right upper corner, the location of
objects, where the location is obtained by our proposed
point-based approach and compensated by our proposed
axiomatic rules, can be also observed. In the lower part of
Fig.5, ST Context predicates generated at experiment are
displayed. And ST Context Predicates for situations inside
refrigerator are listed in Table 3. Note that the proposedsystem can extract the relationship between objects which
are not directly visible by inferencing relationship among
the instance of spatial context and temporal contexts created at previous times. In this experiment, although obj2
is occluded by obj4, contexts such as f ront(obj4, obj2) andf ront(obj2, obj3) were generated.

5

Conclusion

We proposed ontology-based context model for thehousehold service robot, and the ontology is represented
by FOL. This model allow robot to find contexts in spite ofhidden and partial data. Moreover OMRKF enable robot
to query through any directional reasoning between eachlayer as well as between each level of knowledge with small
number of clues.
For a future work, we would like to extend our context
model to include knowledge to handle uncertain and partial
data. We also would like to represent our knowledge modelas OWL/SWRL which is known to be decidable.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5.1

Acknowledgements

This work was performed by a grant of the Second Stageof BK21 Research Division for Advanced IT Education
Program on Industrial Demand. and for the Intelligent
Robotics Development Program, one of the 21st Century
Frontier R&D Programs funded by Korea Ministry of Commerce, Industry and Energy.

obj1
obj3
obj2

References

obj4
Robot’s view

Top view in refrigerator[1] J. Chung, H.W. Kim, S. Lee et al, ”Vision-based
3D Spatio-Temporal Context Generation for Mobile
Robot Applications,” The 21st International Technical Conference on Circuits/Systems, Computers andCommunications(ITC-CSCC2006), pp I-77 I80, 2006

Logical expressions for created ST Context[2] D. Lowe, ”Distinctive image features from scaleinvariant keypoints,” International Journal of Computer Vision, Vol.60, pp.91-110, 2004.

Figure 5: Shapshot of Experiments: Inside a Refrigerator[3] E. Bozsak, M. Ehrig, S. Handschuh et al, ”KAON ?
Towards a Large Scale Semantic Web,” Lecture notesin computer science, No. 2455, 2002, pp. 304-313.
[4] W. Hwang, J. Park, H. Suh et al. ”Ontology-based
Framework of Robot Context Modeling and Reasoning for Object Recognition,” to appear in /em Lecture
Notes in Computer Science, 2006.
[5] X.H. Wang, D.Q Zhang, T. Gu and H.K. Pung, ”Ontology Based Context Modeling and Reasoning using
OWL,” Proc. of the Second IEEE Annual Conference
on Pervasive Computing and Communication Workshops (PERCOMW04), 2004.

Table 3: ST Context Predicates generated at Experiment
Time ST Context Predicates for situations
inside refrigerator
t1 ∼ t2 move-near(obj2, obj3), temp-moving(obj3)
t1 ∼ t4 object-fixed(obj1), object-fixed(obj2), fixed-t4

distance(obj1, obj2), object-near(obj1, obj2),
object-near(obj1, obj3), object-near(obj2,obj3), inside(obj2, obj1), inside(obj3, obj1)
visible(obj1), visible(obj2), visible(obj3),
visible(obj4), left(obj2, obj3), left(obj4,obj2), left(obj4, obj3), front(obj2, obj3),
front(obj4,obj2),
front(obj4,obj3),
near(obj1), near(obj2), near(obj3), near(obj4)[6] K. Henricksen, J. Indulska, and A. Rakotonirainy,”Modeling Context Information in Pervasive Computing Systmems,” Lecture notes in computer science,
2002, no. 241, pp. 167-180.
[7] I. Bratko, Prolog programming for artificial intelligence, 3rd ed. Pearson education, 2001, pp. 57.
[8] J.F. Allen, and H.A. Kautz, A model of naive temporal reasoning, In Formal Theories of the Commonsense
World. J. R. Hobbs and R. C. Moore, eds., Ablex.,
pp.251-268, 1985.
[9] J.R. Hobbs, J. Pustejovsky, ”Annotating and reasoning about time and events,” In Proceedings of AAAI
Spring Symposium on Logical Formalizations of Commonsense Reasoning, 2003.
[10] S. Thrun, W. Burgard and D. Fox, Probabilistic
Robotics, MIT Press 2005, ch. 1.
[11] R.C Arkin, Behavior-based Robotics, MIT Press, 1998.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Cortical current sources for processing visual target motion revealed by MEG and fMRI
D. Kawawaki
T. Shibata
Y. Fujiwara
Theoretical Life-Sciences Lab.
NAIST
8916-5, Takayama, Ikoma
Nara 630-0192, JAPAN
Abstract
Experiments of a magnetoencephalography (MEG) andan functional magnetic resonance imaging (fMRI) wereconducted to reveal the cortical mechanisms related tocovert pursuit to a moving visual target. Subject was asked
to gaze a fixation point at the center of screen and to trackcovertly a horizontally moving target. The MEG was measured when the subjects were tracking the target covertly.
Current sources of about 7,000 dipoles on the cortical surface were estimated from the MEG data by a hierarchical
Bayesian method incorporating the fMRI data. We investigated whether the target velocity can be reconstructed fromestimated current sources. One of the datasets was used fortraining of the weight parameter, and validation tests wereconducted using other two datasets. The result showed thattarget velocities could be reconstructed from the current
sources in the cortical areas, related to processing target
motion in eye movements, such as primary visual cortex,
lateral occipito-temporal cortex, parietal cortex, and prefrontal cortex. This result suggested that these areas were
responsible for tracking a moving target, in consistent withprevious studies using noninvasive recording of brain function.

1 Introduction
The purpose of smooth pursuit eye movements is successively gazing on a smoothly moving object with thecentral fovea. It has been known that the main computational goal of smooth pursuit is to minimize the retinal
slip, i.e. target velocity projected onto the retina. It has
been known that eyes were able to pursue a sinusoidal target motion (its frequency up to around 1Hz) without delays in primates’ smooth pursuit (e.g., in humans [1], inmonkeys [2]), whereas there is a significant delay (60ms inmonkeys [3], more than 70ms in humans [4]) in the control
loop of smooth pursuit. Thus, the central nervous systems
of primates predict the target motion for smooth pursuit eye
movements [5].

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484

O. Yamashita
M. SatoM. Kawato
Computational NeuroScience Lab.
ATR
2-2-2, Hikaridai, “Keihanna Science City”(Seika, Soraku), Kyoto 619-0288, JAPAN
There are many reports that reveal predictive mechanisms for smooth pursuit eye movements by noninvasive
functional brain imaging methods, e.g. functional magnetic resonance imaging (fMRI) [6, 7, 8, 9]. But, Cortical activity patterns with millisecond-order temporal resolution related to predictive smooth pursuit eye movements
cannot be obtained by fMRI in principle. In contrast, magnetoencepharogram (MEG) can record cortical activities
with high temporal resolution. Georgopoulus et al. [10]reported that trajectories controlled by joystick were predicted from signals recorded by 248 MEG channels. The
result suggested that MEG signals represented information related joystick trajectories. However, it was not clear
which areas were important for predicting joystick trajectory because of small number of MEG channels.
In this study, we analyzed current sources on cerebral
cortex with high spatiotemporal resolution, which was estimated by hierarchical Bayesian method [11] incorporating
fMRI activities. We conducted an fMRI-based MEG experiment for investigating the neural mechanisms supporting
predictive smooth pursuit eye movements and for dissociating neural signals related to prediction from response
signals derived from visual inputs. In this experiment, we
performed Covert pursuit task in which subject was asked
to orient their attention to a target and to pursue the target
motion in mind because the electrooculogram (EOG) component contaminates MEG signals when subject’s eyes are
moved actually. Note that there is agreement that Covert
pursuit is based on common mechanisms with eye movements [12].

2 Methods
2.1 Subject
A healthy male human volunteer with normal visionparticipated in this study. The subject gave informed consent in writing and the study was approved by the Ethics
and Safety Committee of Advanced Telecommunications

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Research Institute International (ATR).

2.2 Stimuli and apparatus
MEG recorded for 4096ms at 1000Hz by 201-channel
sensors of whole-head biomagnetic imaging system (Shimazu). A white target (with a diameter of 0.3deg and a luminance of 1.30cd/cm2 ) and a red fixation point (0.3deg
and 1.68cd/cm2 ) were projected by DLA-G11 (Victor) at
a 60Hz refresh rate. The screen (a background luminanceof 1.30cd/cm2 ) was placed 100cm in front of the subject’s
eyes.
Target motion patterns were generated as output signalsof a second order linear system with Gaussian noise were
input. The second order linear system was defined in the
Laplace domain as
H(s) =

ω2
s2 + ω 2,

ω = 2π(1)

the pole of H(s) was s = 0 ± ωi. Therefore the cycleof target motion patterns was about 1Hz (2π/ω). These
parameters were determined through trial and fault for that
subject can perform smooth pursuit eye movements to thetarget. Three patterns were picked up randomly from thesignals generated by the second order linear system with
Gaussian noise input for our experiment.

2.3 Procedures
Subject sat in front of a projector screen. An MEG
dewar was put on the subject’s head. His head was held
on a chin supporter. Two sessions of covert pursuit task
were conducted. In each a session, each target motion pattern was presented at 30-trials respectively, and additional
30 exploratory trials were performed in random order. In
each trial, the target was stationary at a center of screen for
100ms, and moved for 3996ms. Then, it was extinguishedand subject was required their eyes blinked for about 2s.

2.4 Current source estimation
A baseline correction, drift removal, and invalid sensors/trials removal were applied to the recorded MEG data.
Surviving MEG data were collected up with respect to eachsession and each target pattern, and were used for current
source estimation.
The three-dimensional cerebral cortex model was madefrom the high-resolution MRI structure image by using
Brain Voyager (Brain Innovation), and used for current
source estimation and result presentation. The number ofvertices on the brain surface model was 27513 points.

©ISAROB 2007

Figure 1: Result of statistical analysis of fMRI data. Color(contrasting density) bar represents magnitude of t-value.

485

Figure 2: Colored area shows areas in which reconstruction
error was significantly smaller than errors calculated frompseudo data.

Cortical areas in which current sources were assumed
were decided by the statistical result of the fMRI experiment in which the same task as this MEG experiment was
performed. In detail, current sources were assumed at eachvertices perpendicular to cortical surface. The areas weresignificantly activated (p < 0.001, uncorrected) when subject pursued a moving target in mind with gazing a fixation point compared with when subject just gazed a fixation
point. The fMRI data were analyzed by SPM5 (The Wellcome Department of Cognitive Neurology). This process
limited the number of vertices on the surface of cerebral
cortex to 7266 points. The statistical result of fMRI experiment was shown in figure-1.
The time courses of current sources were estimated bya hierarchical Bayesian method [11]. In order to apply thismethod, the leadfield was calculated by a sphere model,
a Gaussian filter of 6mm FWHM was applied to spatial
filtering, and the fMRI information (t-value) was used as aprior.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 3: Results of reconstructing target velocities from current time course in right lateral occipito-temporal cortex. Solidand dashed lines denote actual and reconstructed target velocity, respectively. Weight parameters were trained with training
dataset.

3 Results
We assumed that current time courses on each vertices
x and target velocities y were explained by the following equation with parameter aτ , (τ = 1, 2, · · · 150), andthe parameter aτ was estimated by using training dataset.
The number of 150-parameters was determined throughour trial and error process.
yt =

150
X

aτ xt−τ + ²t

(2)

τ =1

Target velocities were reconstructed from the current timecourse of test datasets by using the estimated parametersaτ , and reconstruction error was calculated.
ŷt =

150
X

aτ xt−τ

(3)

τ =1

s
e=

X

2

(ŷt − yt )(4)t

The error e was statistically evaluated by null hypothesis that the current sources could reconstruct any patternsof target velocity. First, fifty pseudo-datasets were prepared by the same way as presented target velocity patterns.

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Here, the power spectrum of the pseudo target velocities
was same as the original target velocities but the patterns
were different from the original velocities. Next, Parameters of the pseudo datasets were estimated by the same way
as the original datasets. Finally, the reconstruction tests
were performed by using the estimated parameters, and reconstruction errors were calculated. We performed statistical test whether the original reconstruction error was statistically smaller than the pseudo reconstruction errors. Here,
we assumed that distribution of the pseudo reconstruction
errors follows normal distribution, and the original error
was transformed into Z-score using the mean and standard
deviation of the pseudo-errors. Figure-2 shows areas inwhich the original error was statistically small [Z < 3.09,
p < 0.001, one-sided, uncorrected for multiple comparison].
Figure-2 shows that the reconstruction errors were significantly small in V1 of right hemisphere, lateral occipitotemporal and intra-parietal cortex of both hemispheres,right pre-central cortex, and left supra-marginal gyrus.
Figure-3 shows reconstructed target velocities from current time course in the right lateral occipito-temporal cortex. This result showed that target velocity was able to bereconstructed from the estimated current in test dataset byusing weight parameters estimated in training dataset.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4 Discussions

dependence on target saliency”, Exp Brain Res, 141,2, pp. 242–249 (2001).

In this research, we recorded MEG data during covert
pursuit task and estimated current sources on cerebral cortex from the MEG data by hierarchical Bayesian method[11] incorporating fMRI data recorded during the sametask. We also reconstructed the target velocities from current time course and to perform statistical test for the reconstruction error. The result showed that target velocities were able to be reconstructed from the current in V1of right hemisphere, lateral occipito-temporal and intraparietal cortex of both hemispheres, right pre-central cortex, and left supra-marginal gyrus.
Previous studies related to Brain Machine Interface(BMI) reported that actions intended by subjects were able
to be reconstructed and predicted by the biomedical signals that were brought out from electrodes embedded in
the brain or from EEG/MEG channels. However, global
cortical activations are hard to be captured by current electrophysiology. Also, signals driven by cortical neurons
are mixed and recorded by EEG/MEG channels. In contrast, this study employing a hierarchical Bayesian method
showed that the current signals contributing to reconstruction of the target velocities localized in the cortical areas
related to processing of target motion in eye movements.
This result suggested that these areas were responsible fortracking a moving target, in consistent with previous studies using noninvasive of brain function.

Acknowledgements[5] T. Shibata, H. Tabata, S. Schaal and M. Kawato: “Amodel of smooth pursuit in primates based on learning the target dynamics”, Neural Netw, 18, 3, pp.
213–224 (2005).
[6] R. Lencer, M. Nagel, A. Sprenger, S. Zapf, C. Erdmann, W. Heide and F. Binkofski: “Cortical mechanisms of smooth pursuit eye movements with target
blanking. an fmri study”, Eur J Neurosci, 19, 5, pp.
1430–1436 (2004).
[7] J. J. Barton, T. Simpson, E. Kiriakopoulos, C. Stewart, A. Crawley, B. Guthrie, M. Wood and D. Mikulis:“Functional mri of lateral occipitotemporal cortex
during pursuit and motion perception”, Ann Neurol,40, 3, pp. 387–398 (1996).
[8] S. P. Dukelow, J. F. DeSouza, J. C. Culham, A. V.
van den Berg, R. S. Menon and T. Vilis: “Distinguishing subregions of the human mt+ complex using visual fields and pursuit eye movements”, J Neurophysiol, 86, 4, pp. 1991–2000 (2001).
[9] D. Kawawaki, T. Shibata, N. Goda, K. Doya and
M. Kawato: “Anterior and superior lateral occipitotemporal cortex responsible for target motion prediction during overt and covert visual pursuit”, Neurosci
Res, 54, 2, pp. 112–123 (2006).

This research was supported in part by the National
Institute of Information and Communications Technology(NICT-KARC).

[10] A. P. Georgopoulos, F. J. P. Langheim, A. C. Leuthold
and A. N. Merkle: “Magnetoencephalographic signals predict movement trajectory in space”, Exp
Brain Res, 167, 1, pp. 132–135 (2005).

References[11] M. A. Sato, T. Yoshioka, S. Kajihara, K. Toyama,
N. Goda, K. Doya and M. Kawato: “Hierarchicalbayesian estimation for meg inverse problem”, Neuroimage, 23, 3, pp. 806–826 (2004).

[1] G. Westheimer: “Eye movement responses to a horizontally moving visual stimulus”, AMA Arch Ophthalmol, 52, 6, pp. 932–941 (1954).
[2] K. Fukushima, T. Yamanobe, Y. Shinmei andJ. Fukushima: “Predictive responses of periarcuate
pursuit neurons to visual target motion”, Exp Brain
Res, 145, 1, pp. 104–120 (2002).
[3] M. M. Churchland and S. G. Lisberger: “Experimental and computational analysis of monkey smooth
pursuit eye movements”, J Neurophysiol, 86, 2, pp.
741–759 (2001).
[4] K. Miura, K. Suehiro, M. Yamamoto, Y. Kodaka and
K. Kawano: “Initiation of smooth pursuit in humans.

©ISAROB 2007487

[12] M. Corbetta, E. Akbudak, T. E. Conturo, A. Z. Snyder, J. M. Ollinger, H. A. Drury, M. R. Linenweber,
S. E. Petersen, M. E. Raichle, D. C. Van Essen and
G. L. Shulman: “A common network of functional
areas for attention and eye movements”, Neuron, 21,4, pp. 761–773 (1998).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Robust Tracking Control Based on Neural Network for
Nonholonomic Mobile Robot
Yaonan WANG and Jinzhu PENG
College of Electrical and Information Engineering
Hunan University
Changsha Hunan, P.R. China, 410082{wang_yaonan@hotmail.com and pengjinzhu1980@126.com}

Abstract
To deal with the unknown factors of nonholonomic
mobile robot, such as model uncertainties and external
disturbances, a robust tracking controller with bounded
estimation based on neural network is proposed. A neuralnetwork is to approximate the uncertainties terms, theinterconnection weights of the neural network can betuned online. And the robust controller is designed tocompensate for the external uncertainties and theapproximation error. Moreover, an adaptive estimation
algorithm is employed to estimate the bound of theapproximation error. The stability of the proposed
controller is proven by Lyapunov function. The proposedrobust tracking controller based on neural network canovercome the uncertainties and the disturbances. The
simulation results demonstrate that the proposed method
has good robustness.
Keywords: Robust Control; Neural Network; Bounded
Estimation; Mobile Robot; Nonholonomic Constrain

1

Introduction

The tracking control of nonholonomic mobile robot has
been a topic of research during recent years. The
characteristic of the nonholonomic system is that theconstraints, which are imposed on the motion, are notintegratable, i.e., the constraints cannot be written as time
derivatives of some functions of the generalized
co-ordinates. It is a typical nonholonomic mechanicalsystem with high nonlinearity and its control is very
difficult. It is also a typical nonlinear uncertain systemwith both the parametric uncertainty in the dynamic modelof the robot including motor dynamics and disturbancesfrom the external environment or unmodelled dynamics.
For the tracking control problem of the mobile robot,
lots of control methods have been applied. J. M. Yang andJ. H. Kim [1] proposed a robust tracking controller fornonholonomic wheeled mobile robots using sliding modetechnique. Y. Kanayama et al. [2] developed smooth static
time invariant state feedback for a velocity-controlled
mobile robot with nonholonomic constraint. In [3-8], thebackstepping technique was used to design the adaptive
and robust controller for the nonholonomic system. M.S.

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488

Kim et al. [9] applied a robust adaptive dynamic controller
for a nonholonomic mobile robot with modelinguncertainty and disturbances. In recent years, intelligent
systems, such as fuzzy logic [10] and neural network [5,
11-13], have been applied to approximate the models or todeal with the disturbances and dynamic uncertainties ofdynamic systems [14, 15]. F. M. Raimondi, M. Melluso[10] developed a new theoretical control method based on
the dynamic behavior of a wheeled vehicle, where amechanism of fuzzy inference for designing a robust
control system was present. In [5], a robust motion
controller based neural network and backstepping
technique is proposed for a two-DOF low-quality mobile
robot. In [11-13], the neural network controllers in the
proposed control structure were to deal with unmodeledbounded disturbances and unstructured unmodeled
dynamics in the vehicle.
In this paper, we proposed a robust tracking controller
based on neural network for a mobile robot withnonholonomic constrains. The proposed controller canguarantee robustness to parametric and dynamics
uncertainties and also rejects any bounded, immeasurable
disturbances entering the system. The stability is provedby the Lyapunov theory.
The rest of this paper is organized as follows. In Section
II, a mobile robot with nonholonomic constraints is
introduced. An robust controller based on neural networkwith bounded estimation for the mobile robot is designed
in Section III, and the stability is proven using the
Lyapunov method the velocity tracking error, the neuralnetwork weights error and the bounded estimation error
are all bounded. Section IV gives some simulation results
and conclusions are given in section V.

2
Dynamic Model of a Nonholonomic
Mobile Robot
2.1

Preliminary definitions

A mobile robot is shown in Fig. 1, which contains two
driven wheels mounted on the same axis and a castor. It isa typical example of a nonholonomic mechanical system.
An inertial Cartesian frame {O, X , Y } linked to theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

world and {C , X C , YC } linked to the mobile platform

such thatq& = S (q )v (t )(6)TMultiplying both sides by S and using (5), we have
S T MSv& + S T MS& + CS v + S T F + S T τ d

are used here. It is assumed that the centre of mass of themobile robot is local in C . The pose of the mobile robot
is completely specified by:(

q = [x, y, θ ]T(1)
The nonholonomic constraint states that the mobile
robot satisfies the conditions of pure rolling andnon-slipping, i.e., the mobile robot can only move in the
direction normal to the axis of the driving wheels:

y& cos θ − x& sin θ − dθ& = 0

(2)

YC

Y

d

•
G

b

•

C

ω

angle

M = S T MS ,C = S T MS& + CS , F = S T F , τ d = S T τ d ,
is

where

(

the

)

velocity,

M is a symmetric positive definite matrix.

norm.

Fig. 1 A nonholonomic mobile robot

2.2 Dynamic model of a mobile robot
Consider a nonholonomic mobile robot system with ngeneralized coordinate q and subject to m constrains canbe described by [12]:M (q )q&& + C (q,q& )q& + F (q,q& ) + τ d= B (q )τ − A (q )λA(q )q& = 0
T(4)

M (q ) ∈ ℜ
is a symmetric, positive definite
n× n
inertia matrix, C (q,q& ) ∈ ℜ
is the centripetal andncoriolis matrix, F (q,q& ) ∈ ℜ
denotes the surfacenfriction and the gravitational vector, τ d ∈ ℜ denotesn× nbounded unknown disturbances including unstructured

B (q ) ∈ ℜ n×r
is the inputrtransformation matrix, τ ∈ ℜ is the input vector,
AT (q ) ∈ ℜ m×n is the matrix associated with themconstrains, λ ∈ ℜ is the vector of constrain forces.
Let S (q ) = [s1 (q ), L , s n − m (q )] be a set of smooth
unmodeled dynamics,

and linearly independent vector fields in N(A), the nullspace of A q , i.e.,
It is possible to find a velocity vector v (t ) ∈ ℜ⋅ denotes Euclid(&)

Property 3. The matrix M (q ) − 2C (q, q& ) is
skew-symmetric.
Assumption 1. The friction and gravity are bounded by

F (q, q& ) ≤ ξ 0 + ξ1 q& , where ξ 0 and ξ1 are somepositive constants.
Assumption 2. Disturbance is bounded by

(3)

(5)
n−m

,

489

(9)

M min , M max , C b are some positive constants

that assumed to be unknown. andX©ISAROB 2007

]T , v is the velocity of mobile robot,

M min ≤ M (q ) ≤ M max , C (q, q& ) ≤ C b q&

O

S T (q ) AT (q ) = 0(8)

Where v = v, ωProperty 1.
Property 2.

θ

( )

(7)

τ = S Bτ .

b

where

= S T Bτ
Mv& + C v + F + τ d = τ

T

XC

2r

[

)

τd ≤τ D ,where τ D is a positive constant.
For a two-wheeled mobile robot, the kinematic model
can be given as [2]: x&  cos θ y&  =  sin θ  θ&   0

− d sin θ v
d cos θ   ω
1   (10)

In order to simplify the problem formulation, it isassumed that d = 0 . The alternative formulations can bereadily deduced when d ≠ 0 [5].
Suppose the mobile robot is required to follow areference trajectory, with position and velocity areq r = [x r , y r , θ r ]TT
 v r = [v r , ω r ](11)Then the tracking error expressed with respect to aframe fixed on the mobile robot are given as [2] e1   cos θ
e q = e2  = − sin θe3   0

sin θ
cos θ
0

0  x r − x 0  y r − y  (12)1 θ r − θ 

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The velocity control law (15) may achieve theoreticalstability with respect to a reference trajectory. In practice,
however, the velocity v d cannot be generated directly by

The Lyapunov candidate is Chosen as(1 − cos e3 )1
L1 = e12 + e22 +2
k2
Differentiating L1 , then we obtain
L&1 = e1e&1 + e2 e&2 + sin e3 ⋅ e&3()= e1 (− v + v r cos e3 ) +(13)

the motors. Instead, the motor provide a control torque tothe wheels, which will result in an actual velocity v . So
it is necessary to design the torque for the robot system.

sin e3(ω r − ω + k 2 e2 vr )k2

The velocity control law v d

3 Robust Control Based on Neural Networkwith Bound Estimation(14)
achieves stable tracking

Dynamics of mobile robotic are highly nonlinear andmay contain uncertain elements. Many efforts have been
made in developing control schemes to achieve the precise
tracking control of mobile robot [8]. In order to control themobile robot effectively, a neural network-based robust
controller with bound estimation is proposed in this paper.
The structure for the tracking control system is presented
in Fig. 2.

of the mobile robot for the kinematic model (10) as:

k1e1 + v r cos e3
vd = 
(15)ω r + k 2 e2 v r + k 3 sin e3 
where k 1 > 0, k 2 > 0, k 3 > 0 are the controller gains.
Then, the equation (14) can be rewritten ask
L&1 = − k1e12 − 3 sin 2 e3 ≤ 0
k2

(16)

vr

qr
q

M

d dt

Reference
Trajectory

M

M

U NN
τd

&
Wˆ
e q Kinematic
Coordinate
Transformation
Controller−

Adaptation
Laws

vd
−

e

k4

r

&
Eˆ

v

∫

q&

RobustController

M −1

τ

(S B )T−1

τ Mobile
Robot

v

UR

Estimation
Algorithm

S (q )

Fig. 2 The structure of the control systemf ( x ) = − M −1 (C v + F ) − v& dFirst, we define the velocity tacking error ase = v − vd

(17)Then, define a filtered tracking error asr = k4e

(18)

(20)

In this paper, we want to approximate this unknown
function using a two-layer neural network, where thevector[x can be defined as x = v T

v dT

v&dT

] .
T

where k 4 is a positive coefficient vector. The time
derivative of the filtered tracking error can be written as

Therefore, by the universal approximation theorem, there
exist ideal vector W such that [11, 13]

(19)

(21)
where the neural network approximation error ε isr& = − M −1 (C v + F + τ d ) + M −1τ − v& d

In general, the inertia matrix is known while
uncertainties in the centripetal and coriolis matrix are
sometimes difficult to compute. So, a new control vector−1

is defined as U = M τ , and the unknown term of
the equation (19), denoted by f , is an unknown smoothfunction, that is©ISAROB 2007

490

f = W T σ (V T x ) + ε

assumed to be bounded byε ≤ ∆ . σ (⋅)

is acontinuous sigmoid activation function. The first layer
weights V are selected randomly and will not be tuned
while the second layer weights W are tunable. The ideal

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

neural network weights in vectors W that are needed tobest approximate the given function f are difficult todetermine. All one needs to know for control purposes isthat, for a specified value of E some ideal
approximating neural network weights exist. Then, an
estimate value of f can be given byfˆ = Wˆ T σ (V T x )Substituting (16), (24), and (25)-(28) into (30), we canobtain:

(

(

(22)

where Ŵ is the estimated value of W .
Choose the tracking control law as

(

)

(23)

where U R is robust controller.
Then, equation (19) can be rewritten as

where

is

the

)

uncertain

)

(

(

term

(24)
error,
oftheapproximation error and the external disturbances.
According to (21) and Assumption 2, we can know thatthe uncertain term is bounded, that is,ε d ≤ ε + M τ d ≤ ∆ + M maxτ D = E≤ − r (E − ε d ) = −α r ≤ 0

where α = E − ε dL& ≤ 0 , it can be inferred that the filtered error r ,~the neural network weights error W and the bounded~
estimation error E are all bounded. Let function
Ξ(t ) = − L& = α r , and integrate function Ξ(t ) with(25)

respect to time[14, 15]∫ Ξ(τ )dτ ≤ L(r (0),W , E (0)) − L(r (t ),W , E (t ))
t

~ ~

(

)

(

)

t →∞ 0where Γ > 0 is the learning rate of the neural network.
The robust controller is designed as

U R = − Ê sgn (r )

(27)

~&
&
Eˆ = − E = ηr sgn (r )(28)

where Ê is the estimated value of E , sgn (⋅) is astandard sign function. And the bound estimation law ischoose as~
where E = E − Eˆ is the estimation error, η is apositive constant.
Then, the closed-loop system (8) and (23) is
asymptotically stable, the filtered error r , the neural~network weights error W and the bounded estimationerror~
E are all bounded.

Proof: Choose Lyapunov function candidate as

L = L1 +1 2 1 ~ T −1 ~ 1 ~ T −1 ~r + W Γ W+ E η E
2
2
2

(29)

Differentiating yields

~
~& ~
~&
L& = L&1 + rr& + W T Γ −1W + E T η −1 E

©ISAROB 2007

(30)

491

(32)

~ ~
BecauseL r (0 ), W , E (0 )isbounded,and~ ~L r (t ),W , E (t ) is nonincreasing and bounded, the

(26)

)

~ ~

0

following result is obtained

(

(31)

> 0 is a small positive constant.

Theorem: Given the system (8), choose the velocity
control law (15) the tracking control law (23), and theadaptation law of the neural network as~&&
Wˆ = −W = Γrσ V T x

)

)

Since

~
r& = Wσ V T x − U R + ε d~
W = W − Wˆ
is the estimation

ε d = ε − Mτ d

)

)

d

U = −Wˆ T σ V T x − U R((~
L& = L&1 + r − W T σ V T x − U R + ε d~~& ~+ W T Γ −1W − E T r sgn (r )~= L&1 + r ε d − Eˆ sgn (r ) − E T r sgn (r )~~&− W T rσ (V T x ) + Γ −1W≤ rε − Eˆ r sgn (r ) − E − Eˆ r sgn (r )lim ∫ Ξ(τ )dτ ≤ 0t(33)& (t ) is bounded, by Barbalat’s Lemma, it
In addition, Ξ

can be show that lim Ξ(τ ) = 0 . That is, r (t ) → 0 as
t →∞t → 0 . As a result, the closed-loop system (8) and (23) is
asymptotically stable.

4 Simulation Results
In order to verify the validity of the proposed controller,a nonholonomic mobile robot is used for illustration in thispaper, as shown in Fig. 1. The dynamical equations of themobile robot can be expressed in (1) where [12]0md sin θ  mM (q ) =  0m− md cos θ md sin θ − md cos θI0 0 mdθ& cos θ C (q, q& ) = 0 0 mdθ& sin θ  ,
0 0

0



The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

k 2 = 5 , k 3 = 4 , and k 4 = diag{10,10}.

cos θ
1
B(q ) =  sin θr b

sin θ cos θ − b 
− sin θ τ r 
Tτ =   , A (q ) =  cos θ  ,τ l 
 − d 
λ = − m( x& c cos θ + y& c sin θ )θ&Defining a straight line, starting fromT

The mobile robot, however, is initially atT

m = 10kg , I = 5kg ⋅ m 2 , b = 0.25m ,r = 0.05m , and v r = 0.5 m s . The externalτ d ≤ 3 .0i

is a random noise with themagnitude bounded.
The initial values of neural network weights W are
selected randomly in [-1, 1], and the estimations areT](a) Trajectory in the (x, y) plane(b) Position errors
Fig. 3 Results by computed torque controller(a) Trajectory in the (x, y) plane(b) Position errors

492

T

Fig. 3 shows the simulation results for tracking astraight line using computed torque method. Since there
are the uncertainties and disturbance, the mobile robot
cannot track the trajectory and exhibit a steady state error.
Under the same conditions, Fig. 4 shows the results for
tracking a straight line using the proposed method. As it
can be seen from the figure, the mobile robot can reach theline quickly and continues to track it.

TEˆ (0 ) = [0,0] , The controller gains are k1 = 10 ,©ISAROB 2007[]

q(0) = [x(0 ) y (0) θ (0)] = 1 0 0 oowhere θ (0 ) = 0 indicates that the robot is heading toward
positive direction of x .

wheredisturbance[q r (0) = [x r (0) y r (0 ) θ r (0)] = 0 1 45o

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007(c) Neural network outputs(d) Torques
Fig. 4 Results by the proposed method

5 Conclusions
Using the robust and neural network methods, a robust
tracking controller with bounded estimation based onneural network is proposed for a nonholonomic mobile
robot. This controller can guarantee robustness toparametric and dynamics uncertainties and also rejects any
bounded, immeasurable disturbances entering the system.
The stability is proven using the Lyapunov method. The
velocity error, the neural network weights error and thebounded estimation error are all bounded. Finally, somesimulation examples are utilized to illustrate the control
performance.

Acknowledgements
The author would like to acknowledge the support of
the National Natural Science Foundation of China, No.
60375001.

References
[1]

[2]

[3]
[4]

J. M. Yang and J. H. Kim. “Sliding Mode Control for
Trajectory of Nonholonomic Wheeled Mobile Robots,”
IEEE Trans. on Robotics and Automation, Vol. 15, no. 3,
pp. 578-587, 1999.
Y Kanayama, Y Kimura, F. Miyazaki, T. Noguchi. “A
stable tracking control method for an autonomous mobile
robot,” Proc. IEEE Int. Conf Robot. Automation, pp.
384-389, 1990.
Z.P. Jiang, H. Nijmeijer. “Tracking control of mobile
robots: a case study in backstepping,” Automatica, Vol. 33,
no. 7 pp. 1393-1399, 1997.
R. Fierro, F.L. Lewis. “Control of a nonholonomic mobile©ISAROB 2007493

robot: backstepping kinematics into dynamics,” Journal of
Robotic Systems, Vol. 14, no. 3 pp. 149-163, 1997.
[5] Q. Zhang, J. Shippen, B. Jones. “Robust backstepping andneural network control of a low-quality nonholonomic
mobile robot,” Int. J. of machine Tools & manufacture, Vol.
39 pp. 1117-1134, 1999.
[6] T. C. Lee, K. T. Song C. H. Lee and C. C. Teng. “Tracking
Control of Mobile Robots Using Saturation Feedback
Controller,” Proc. of IEEE International Conf. on Robotics& Automation, pp. 2639-2644, 1999.
[7] M.S. Kim, J.H. Shin, S.G. Hong et al. “Designing a robust
adaptive dynamic controller for nonholonomic mobile
robots under modeling uncertainty and disturbances,”
Mechatronics, Vol. 13, pp. 507-519, 2003.
[8] N. Sarker, X. Yun and V. Kumar. “Control of mechanical
System with Rolling Constraints: Application to Dynamic
Control of Mobile Robots,” Int. J. Robot. Res., Vol. 13, no.
1 pp. 55-69, 1994.
[9] S. Yildirim. “Adaptive robust neural controller for robots,”
Robotics and Autonomous Systems, Vol. 46, pp. 175-184,
2004.
[10] F. M. Raimondi and M. Melluso. “A new fuzzy robust
dynamic controller for autonomous vehicles withnonholonomic constraints,” Robotics and Autonomous
Systems, Vol. 52, pp. 115-131, 2005.
[11] S. Lin and A. A. Goldenberg. “Neural-Network Control of
Mobile Manipulators,” IEEE Trans. on Neural Network,
Vol. 12, no. 5, pp. 1121-1133,2001.
[12] R. Fierro and F. L. Lewis. “Control of a Nonholonomic
Mobile Robot Using Neural Networks,” IEEE Trans. on
Neural Network, vol. 9, no. 4, pp. 589-600, 1998.
[13] C. Oh, M. S. Kim, J. Y. Lee et al. “Control of Mobile
Robots using RBF Network,” Proc. IEEE/RSJ Conf. onIntelligent Robots and Systems, pp. 3528-3533, 2003.
[14] C. M. Lin and C. F. Hsu. “Neural-Network-Based Adaptive
Control for Induction Servomotor Drive System,” IEEE
Trans. on Industrial Electronics, vol. 49, no. 1 pp. 115-123,
2002[15] R. J. Wai. “Tracking control based on neural network
strategy for robot manipulator,” Neurocomputing, vol. 51,
pp. 425-445, 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Parallel Software Architectures Analysis for Implementing P Systems
Luis Fernández
Natural Computing Group
Universidad Politécnica de Madrid
Madrid (Spain), Campus Sur. 28031

Fernando Arroyo
Natural Computing Group
Universidad Politécnica de Madrid
Madrid (Spain), Campus Sur. 28031

Iván Garcı́aNatural Computing Group
Universidad Politécnica de Madrid
Madrid (Spain), Campus Sur. 28031

Abraham Rodriguez
Natural Computing Group
Universidad Politécnica de Madrid
Madrid (Spain), Campus Sur. 28031

Abstract
This paper presents software processes architectures for implementing P systems on parallel hardware architectures. In order to analysis three different
software architectures present in literature, two parameters are established: parallelisms degree and computational overload. This parameters will allow us tocompare the three different studied software architectures. Finally, this analysis determines the candidate
software architecture depending on available hardware
architecture and the P system structure.

1

Introduction

P systems are a new computational model basedon the membrane structure of living cells [1]. Thismodel has become, during last years, a powerfulframework for developing new ideas in theoreticalcomputation. P systems with simple ingredients
are Turing complete. In particular, P systems area class of distributed, massively parallel and nondeterministic systems. ”As there do not exist, up tonow, implementations in laboratories (neither in vitro
or in vivo nor in any electronic medium), it seems
natural to look for software tools that can be used as
assistants that are able to simulate computations of
P systems” [2].
There are many simulators, but ”the next generation of simulators may be oriented to solve (atleast partially) the problems of storage of informationand massive parallelism by using parallel language
programming or by using multiprocessor computers”[2]. ”Several authors have implemented the first ver-

©ISAROB 2007494

sions of simulators based on parallel and distributed
architectures, which is close to the membrane computing paradigm” [2]. From them, we emphasize thefollowing ones: Ciobanu presents an implementation
based on a computer cluster that ”consists of 64 dual
processor nodes. [...] The rules are implemented as
threads” [3], this implies that a one to one relationshipis established between processes and evolution rules,
we refer his parallel software architecture as evolution
rules oriented ; Syropoulos presents a simulationthat ”is characterized as distributed in the sensethat a number of objects -modeling membranesexecute code on different machines” [4], we will refer
this as parallel software architecture as membraneoriented ; finally, Tejedor presents in [5] a new parallel
software architecture in which only exists one process
per processor establishing a one to one relationshipbetween processes and processors, we refer to it asprocessor oriented.
The aim of this work is to determine the appropriate parallel software architecture for a given P
system and a hardware architecture. So, it pretends
to determine the set of process and their relationships (parallel software architecture, P SA) that areappropriate to be executed over a set of connected
processors (hardware architecture). In order to do it,
it is necessary to establish the evaluation parameters
for the analysis of parallel software architectures
independently of software and hardware technologies.
This paper is structured as following: first, they are
established the evaluation parameters for the analysis of a given parallel software architecture; the threefollowing points evaluate the different parallel soft-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ware architectures in bibliography; next, it is presented a comparative of the three previous architectures through the established evolution parameters;
finally, we present our conclusions.

do not depend on the number of processes. Therefore,
we get following equations:∆CS (Na ) = k.∆CS (Nb ) where Na = k.Nb
∆BS (Na ) = k.∆BS (Nb ) where Na = k.Nb

2

Evaluation Parameters

Evaluation of parallel software architecture must
be independent of particular hardware technology inwhich it is implemented on. This abstraction process
is only dependent on the number of processors available on the hardware architecture; we will refer to thisnumber as attribute P from now on. As first parameter for evaluating a parallel software architecture, we
define the parallelism degree, P DP SA as follows:

(3)
(4)

Then, our second evaluation parameter for theparallel software architecture is defined as computational overload, ∆P SA (N, P ), being N the number ofprocesses and P the number of processors, this parameter is expressed by.
∆P SA (N, P ) = ∆P M (N, P ) + ∆CS (N ) +∆M E (N ) + ∆BS (N )

(5)

P
· 100
(1)
R
where R is the number of evolution rules. The
range of values for P DP SA belongs to (0 − 100].

Let C be the sequential execution cost of a P systemon a monoprocessor hardware architecture. Then it
could be define the performance of a parallel softwarearchitecture with N processes on a hardware architecture with P processors by the following equation:

On the other hand, each process will be executed
sequentially on its own processor. But, in case of
there is less processors than processes, multiprogramming technique gets concurrency of several processes
assigned to a same processor but it does not get real
parallelism. However ”while the general motivation
for demand-driven concurrency is laudable, the implementations [...] may not produce optimal results. To
understand why, we must consider the subtleties ofprocess creation and scheduling” [6]. Consequently,
in multiprogramming cases, it is required a computational overload of the operating system to manage
processes. Process management costs, ∆P M (N, P ), is
increased when the number of processes, N , increase
keeping the same number of processors, P . Hence,C + ∆P SA (N, P )(6)PNext three sections are devoted to the analysis ofevaluation parameters over the three parallel softwarearchitectures mentioned above.

P DP SA =∆P M (Na , P ) < ∆P M (Nb , P ) ⇔ Na < Nb

PSA Evolution Rules Oriented

First architecture establish one process per evolution rule of the P system. Therefore, the number ofprocesses is R and each one of them is responsible
for applying one evolution rule over the multiset ofits membrane. So, parallelism is given when eachone of the R processes progresses over the membranemultiset.

(2)

Moreover, concurrent and/or parallel processes interrelate between each other by two types of behaviour: cooperating and/or competing. These relationships require synchronization and communication interactions between processes. At the end, these interactions become conditional and barrier synchronizations and mutual exclusions when there are sharedresources. These interactions fall into a new computational overload. In particular, costs of conditional
synchronization, ∆CS (N ), and barrier synchronization, ∆BS (N ), are linear dependent on the numberof processes while costs of mutual exclusion ∆M E (N )©ISAROB 2007

3

495This parallel software architecture reaches a parallelism degree equal to 100% when P = R. But
when P < R, it does not exists a total parallelism because, necessary, it is made use of multiprogramming
techniques. Thereby, its parallelism degree decays to
P/R × 100 %. Moreover, computational costs of the P
system evolution is overloaded by multiprogrammingof R processes over the P processors, that we willrefer as ∆P M (R, P ). If P << R, this overload
degrades the system because of ∆P M (R, P ) → ∞ asthese architectures ”do not bound concurrency are arisk in an environment that presents a heavy load.
Concurrency can increase until operating system

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

becomes swamped with processes” [6].
On the other hand, algorithms for evolution rules
application over a membrane multiset gathered in literature [2] always answer to the same scheme: ”Sincemany rules are executing concurrently and they aresharing resources, a mutual exclusion algorithm isnecessary to ensure integrity” [3]. So, ”when morethan one rule can be applied in the same conditions,the simulator randomly picks one among the candidates” [2]. Therefore, processes include pre-protocols
and post-protocols for critical sections of their codethat necessary must work under mutual exclusion.
We will refer to this overloaded computational cost as∆M E (R).
At this point, it has to be highlighted one aspectof synchronization between processes that is determinant for the parallelism degree of this architecture:
granularity of critical sections referred above. Thus,
we have coarse-grained critical sections when majority of the process of evolution rule application
over its multiset is located in a critical section andfined-grained critical sections otherwise.
For the case of coarse-grained critical sections,concurrent execution of subsets of processes of rules ofa same membrane offers a sequential behavior. Therefore, repercussion of coarse-grain critical sections inprocesses implicates in a direct way that parallelism
degree is reduced to a M/R × 100 %, where M is thenumber of membranes.
Last, we would like to highlight the following synchronization between processes and the communication phases in the P system evolution:• A barrier synchronization that ensures that every
process ends its corresponding evolution rule application before that it begins the communicationof current evolution transfer.
• A conditional synchronization that ensures thatevery process waits to apply its evolution rule until it ends the communication phase of the previous evolution step ends.
So, it has to be considered a new computational
overload that ensures these synchronizations for the
R processes, that we will refer ∆CS (R) and ∆BS (R)respectively.
Figure 1 summarizes the parallelism degrees presented in this point for parallel software architecture©ISAROB 2007

496

evolution rule oriented. Thus, it brings in as determinant parameters: number of processors and critical section of process granularity. Moreover, on every
case, it is attached the computational added costs tothe evolution of the P system.

4

PSA Membranes Oriented

In this architecture it is established one process per
membrane of the P system. Therefore, the numberof processes is M and each one of these processes is
responsible for applying sequentially every evolution
rule of a membrane over the multiset of its region.
So, parallelism is reached when each one of the
M processes progresses over the multiset and theevolution rules of its membrane.
This architecture is bounded by a degree ofparallelism equal to M/R × 100 % when P = M .
But, when P < M , it is necessary to use multiprogramation techniques , in a similar way to thearchitecture presented above. In similar manner, its
parallelism degree decay to P/R × 100 %. Moreover, computational costs of the P system evolution
are overloaded with the multiprogramming of M
processes over the P processors, that we will referas ∆P M (M, P ). In the case of de P << M , thisoverload degrade the system due to ∆P M (M, P ) → ∞.
On the other hand, sequential algorithms for theapplication of evolution rules does not use concurrent
access to shared information because only one processapplies every evolution rule over the multiset sequentially.
Finally, synchronizations between processes andcommunication phases in the evolution of the P system are the same than in the previous architecture:barrier synchronization respect to the communication of the current evolution step and conditional
synchronization respect to the communication ofprevious evolution step. So, it is also necessary toconsider the computational overload that ensuresthese synchronizations for the M processes, that wewill refer as ∆CS (M ) and ∆BS (M ) respectively.
Figure 1 summarizes the parallelism degrees presented in this point of membrane oriented parallel software architecture. So, it brings in just one parameter:
the number of processors. Moreover, on every case,
it is attached the computational added costs to theevolution of the P system.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 1: Parallel Software Architecture Analysis

5

PSA Processors Oriented

Last proposed architecture establishes a process
per available processor in hardware architecture.
Therefore, every one of these processors is responsible
for applying sequentially every evolution rule over themultisets of a subset of membranes. These subsets
of membranes assigned to processes define a wrapper
over the set of membranes. Hence, parallelism isreached when each one of the P processors progresses
over the multisets and the evolution rules of themembranes of each subset.
This architecture is bounded by a parallelism
degree equal to P/R × 100 %. On the other hand,
it has to be highlighted that it does not exist
computational overload for processes multiprogramming, because it exists a one to one ratio a priori
established. ”In fact, because a single-process implementation requires less switching between processcontexts, it may be able to handle a slightly higher
load than an implementation that uses multiple
processes” [6].
In an analogous manner to theprevious architecture, given the sequential characterof the processes of evolution rules application, neither
exist computational overload for the mutual exclusion.
Last, synchronizations between processes andcommunication phases are the same as previous
architectures. So, it is also necessary to consider thecomputational overload that ensures these synchronizations for the P processes, that we will refer as∆CS (P ) and ∆BS (P ) respectively.

©ISAROB 2007

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Figure 1 summarizes the parallelisms degree presented in this point of processors oriented parallel software architecture. Moreover, on every case, it is attached the computational added costs to the evolutionof the P system.

6

PSA Comparative

In this point, we show a comparative of the threeparallel software architectures presented above. In order to this, it is presented a detailed study of parallel
software architectures behavior respect to the different states that a P system evolution can be found. So,
considering the relation of P with M and R as thedeterminant condition, following cases are contrasted:a) R ≤ P ; b) M ≤ P < R; c) P < M . Subsequently,
obtained general conclusions are presented:
A. R ≤ P . On evolution rules oriented architecture
it is obtained the same computational overloadin (A) and (D) of figure 1, but with a lesser
parallelism degree with coarse-grained criticalsections. This fact, lead us to avoid evolution
rules oriented architecture with coarse-grained
critical sections.
On the other hand, in membranes oriented andprocessors oriented architectures, it is obtained
the same parallelism degree and computational
overload in (G) and (H) of figure 1. This is due
to, being R ≤ P and M < R, processors oriented

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

architecture makes responsible for a subset of an
only one membrane to each process. Which isan analogous situation to the membrane oriented
architecture.
From (3), (4) and (6) equations and (A) and (G)equations of figure 1, it is obtained the necessary
condition (7) for assuring that processors oriented
architecture with fined-grained critical section has
better performance than membranes and processors oriented architectures.
C>M· ∆M E (R)
R−M

(7)

In case (7) is not fulfilled, membranes and processors oriented architectures will give a better performance despite of their lesser parallelism degree.
B. M ≤ P < R. In this second case, we find same
situations than the point above: the convenienceof fined-grained critical sections over coarsegross granularity in evolution rules oriented
architecture. Equivalent results for membranes
and processors oriented architectures have beenobtained.
From (3), (4) and (6) equations and equations(B) and (G) from figure 1, it is obtained the newnecessary condition (8) for assuring that processors oriented architecture with fined-grained critical sections has better performance than membranes and processors oriented architectures.

C>M· (∆P M (R, P ) + ∆M E (R)) + (8)P −M
R−P· (∆CS (M ) + ∆BS (M ))P −M

It occurs the same that in previous case, if (8) is
not fulfilled, parallel software architectures membranes and processors oriented will give a better
performance despite of their lesser parallelism degree.
C. P < M . In this third case, it can be noticed that every parallel software architectureoffers the same parallelism degree.
But,
considering that P < M < R and so it is∆CS (P ) < ∆CS (M ) < ∆CS (R) and that
∆BS (P ) < ∆BS (M ) < ∆BS (R), lesser computational overload is always obtained with processors©ISAROB 2007

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oriented architecture.
Moreover, in the most restrictive case in which
P << M < R, computational overload of∆P M (M, P ) and ∆P M (R, P ) degrade the system.
Detailed study permits us to conclude that:• The parallel software architecture membranes oriented always has a worse behavior than processors
oriented. In particular, it is observed that membrane oriented is a particular case of processors
oriented architecture where the membranes subset assigned to each processor is always equal to
one membrane. Therefore, flexibility of processors oriented architecture allows suiting to different conditions always balancing or improving tothe other more restrictive architecture.
• In cases in which evolution rules oriented architecture is suitable, it always need parallel algorithms
of rules application with fined-grained critical sections. Otherwise, it never raises as a parallel software architecture candidate.
• Most appropriate parallel software architecture
for a given hardware architecture, it is not always
the one with a better parallelism degree. For cases
where P > M , it has to be fulfilled (7) or (8) conditions.
• Moreover, for these cases, for a given hardware
architecture there will not be only one suitableparallel software architecture. It also depends on
the number of evolution rules and membranes of
the P system. Therefore, combination of a hardware architecture and a particular P system will
determine the parallel software architecture suitable for its implementation.
It is important to remarks the goodness of parallelism opposite to totally sequential implementation.
”In particular, one must consider the cost of concurrency as well as its benefits” [6]. So, in order to guarantee that parallelism offers better results, it must be
ensured that computational overload costs is taken by
a bigger number of processors and that it reduces theevolution time of the P system with respect to be taken
by just one processor. Therefore, it must be fulfilled:C + ∆P SA (n, P )<C(9)P
In particular, the processors oriented parallel software architecture must fulfill:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

∆CS (P ) + ∆BS (P )
(10)
C>
P −1
While, the evolution rules oriented parallel softwarearchitecture must fulfill:C>

7∆CS (R) + ∆M E (R) + ∆BS (R) + ∆P M (R, P )
R−1
(11)

Conclusions

In this work, we present mechanisms for evaluating
parallel software architectures for the evolution of a
P system over a given hardware architecture. We
propose the parallelism degree and the computational
overload for processes management and synchronization as evaluation parameters.
With these parameters and the appropriate set of
equations based on them, we have analyzed evolution
rules, membranes and processors parallel software architectures. Each one of these architectures proposes
different number of processes depending and the P
system. But, obtained results in this study showsthat the best performance is not parallel softwarearchitecture independent, neither always offered by
the higher parallelism degree.
In particular, we show that membranes oriented
architecture never improves the other two studied
parallel software architectures. Moreover, evolution
rules oriented architecture requires the design ofprocesses with fined-grained critical sections to offer
better results than processors oriented one. Finally,
we present here a set of equations establishing theconditions for determining the appropriate parallel
software architecture, and the conditions for theparallelism to overcome a totally sequential implementation.
Analysis of these equations permits to conclude thatthe candidate parallel software architecture is dependent on the number of processors of the hardware architecture and on the P system structure.

References[1] Gh. Păun, ”Computing with Membranes”, Journalof Computer and System Sciences, 61(2000).

©ISAROB 2007

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[2] G. Ciobanu, M. Pérez-Jiménez, Gh. Păun, ”Applications of Membrane Computing”. Natural Computing Series, Springer Verlag, (October, 2006).
[3] G. Ciobanu, G. Wenyuan, ”A P system runningon a cluster of computers”, Proceedings of Membrane Computing. International Workshop, Tarragona (Spain). Lecture Notes in Computer Science, vol 2933, Springer Verlag (2004), 123-150.
[4] A. Syropoulos, E.G. Mamatas, P.C. Allilomes,
K.T. Sotiriades, ”A distributed simulation of P
systems”. Preproceedings of theWorkshop on Membrane Computing (A.Alhazov, C.Martin-Vide andGh.Paun, eds); Tarragona, vol July 17-22 (2003),
455-460.
[5] J.A. Tejedor, G. Bravo, L. Fernández, F. Arroyo,”An Architecture for Attacking the Bottleneck
Communication in P System”. AROB 12th. International Symposium on Artificial Life and Robotics, Oita (january, 2006) (accepted).
[6] D.E. Cormer, D.L. Stevens, ”Internet networking
with TCP/IP. Vol 3”’. Prentice Hall, International
Editions (1993).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Architecture for Attacking the Bottleneck Communication in P Systems
J. A. Tejedor
Natural Computing Group
Universidad Politécnica de Madrid
Madrid, Spain

L. Fernández
Natural Computing Group
Universidad Politécnica de Madrid
Madrid, Spain

F. Arroyo
Natural Computing Group
Universidad Politécnica de Madrid
Madrid, Spain

G. Bravo
Natural Computing Group
Universidad Politécnica de Madrid
Madrid, Spain

Abstract

may experience a series of chemical reactions (evolution rules) and produce other elements. Inside theregion limited by a membrane may be, at the sametime, other membranes creating a complex hierarchical structure that may be represented by a tree. The
products generated by the chemical reactions may stayin the same region or travel to the container region or
to the regions contained by a membrane. As a result of such reaction, a membrane may dissolve itself(its chemical elements transfer to the container membrane) or inhibit itself (the membrane becomes impermeable and does not allow any object to pass).

The distributed implementation of P system on
a cluster of processors has met with the bottleneck
communications problem.
When the number ofmembranes grows in the system, the network getscongested and the times to execute an evolution stepdegrades.
In this paper, we suggest a sotfware architecture denominated “partially parallel evolution withpartially parallel” communication where some membranes are located in each processor, proxys areused to communicate with membranes located indifferent processors and a policy of access control to
the network communications is mandatory. With all
this, we get a certain parallelism in the system andan acceptable functioning in the communications. In
addition to this, it establishes a series of equations
that allows us to determine in the architecture theoptimum number of processors needed, the required
time to execute an evolution step, the number ofmembranes to be located in each processor and theconditions to determine when it is best to use the
distributed solution or the sequential one.
keywords: architecture, bottleneck, communication, P systems

1

Introduction

The transition P systems were presented by Gheorghe Păun in 1998 [1] who based his work on basic
features of biological membranes. A membrane definesa region where a series of chemical elements (multisets)©ISAROB 2007

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The membranes systems are dynamic as the chemical reactions produce elements that cross the frontiersof the membranes to travel to other regions in orderto produce new reactions. This dynamic behaviour
can be sequenced in a series of evolution steps between one and another configuration system that will
be determined by the membrane structure and multisets present inside membranes. In the transition P
systems formal model two phases are distinguished ineach evolution step: rules application and communication. In the rules application phase inside each membrane its rules are applied to the multisets in parallel.
Once the previously described phase has concluded,
the communication phase begins and the generated
multisets travel towards the target membranes. Thesesystems perform a computation through transition between two consecutives configurations, transforming
into computational device with the same capacities
that Turing machines.
The power of this computation model lies in thefact that the process is massively parallel in the rules
application phase as well as in the objects communication phase. The challenge for the researchers is

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

to get hardware or software implementations of P
systems with a high parallelism degree.
A natural and intuitive implementation of P systems in electronic devices is carried out based on thefollowing:
1. To locate in each processor a membrane where
the evolution rules are applied parallelly on theirmultiset.
2. To carry out the communication between 2 membranes (2 processors) using 4 interfaces and 2
communications buses. The data travel in eachdirection (father-son or son-father) using a communication interface in the outgoing processor
and another one in the incoming processor thatare connected by a data bus.
The main problem of this implementation model isthat it is unfeasible. Nowadays technologies do notallow for a processor to have as many communicationinterfaces as membranes are connected to it.
The aim of this work is to establish a communications architecture that will adapt to the special features of P systems. For this, it is structured in thefollowing way: in the first place, the related works
are enumerated analyzing the proposed architectures,next a communication architecture model is introduced stating its economical and computational cost
as well as its viability, to continue with a more detailed
analysis of the model, to end describing the conclusions obtained.

2

Related Works

The implementation of P system in digital hardware device is being carried out from the point ofview of Hardware as well as Software. Most of thesolutions have been focused, mainly, in the first phaseof the P system evolution describing digital circuits
or software architectures/designs that have allowed
the application of the defined evolution rules inside
the membranes. The phase of multisets membranes
communication has not been contemplated or it
has simply been performed by shared memory,
except Syropoulos [2] and Ciobanu [3] that in theirdistributed implementations of P systems use Java
Remote Method Invocation (RMI) and the Message
Passing Interface (MPI) respectively, on a cluster of
PC connected by Ethernet. These last authors do not©ISAROB 2007

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carry out a detailed analysis of the importance of thetime used during communication phase in the total
time of P system evolution, although Ciobanu affirmsthat “the response time of the program has been
acceptable. There are however executions that could
take a rather long time due to unexpected network
congestion” [3].
A model of impementacion of P system simplifying
and generalizing the ideas of the works of Syropoulos
and Ciobanu would be the following one:
1. In each processor a membrane is located where its
rules will be applied. A processor can be a digital circuit implemented by Field-Programmable
Gate Arrays (FPGAs), a microcontroller or a microprocessor.
2. All processors are connected to a common bus
through a communication interface governed by aprotocol.
In this model, all the membranes apply its rules inparallel for later communicating among them. Dueto the fact that the communication line is common
to all of them, at a particular time there will only bea membrane or processor communicating. Then, thecommunication becomes sequentially. The problem ismore complex as Ciobanu advices [3], because if nospecial measures are taken, there may be more than
one processor trying to communicate at a particulartime and collisions will produce that will delay thewhole communication process.
Assuming that no collisions happen in the networkand taking into consideration that the informationtransfer between 2 membranes is made in both directions (father-son/son-father), the total number ofcommunications made up during a transition step is2(M − 1). Therefore, the total time to perform theevolution step is:
T = Tapl + 2(M − 1)Tcom(1)

where M is the number of membranes of the
P system, Tapl is the maximum time used by theslowest membrane in applying its rules in the wholesystem and Tcom is the maximum value of all timesof communication between 2 nodes. Meaning thatthe time T is linear dependent on the number ofmembranes that the P system has.
The system throughput (processors and communications) can be expressed in accordance with the following:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

T hevo =Tapl
Tapl + 2(M − 1)Tcom(2)

T hcom =2(M − 1)Tcom
Tapl + 2(M − 1)Tcom(3)

So if the number of membranes grows, the processors throughput will approach zero, while thecommunications will practically be working 100% of
the time.
Considering the system costs, this model is expensive because it needs as many processors and communication interfaces as membranes in the system:
C = M Cpro + M Ccom

(4)

The analysis of this model makes us reach the following conclusions:• The time used in a step of P system evolution
is huge and it grows lineally with the number ofmembranes.
• The system cost is very high with the processors
throughput practically nil.

1. Membranes Distribution: In each processor,
K membranes are located that will evolve, atworst, sequentially. The value of K is determinedby the relation between the number of membranes
M and processors P , where K ≥ 1.
The benefit obtained is that the number of theexternal communications decreases. The total
number of communications splits in two classes:a group of internal communications for pairs ofmembranes located in the same processor and another group of external communications to interchange information among pairs of membranes located in different processors. Therefore, the number of external communications against the previous model will always be smaller. Moreover, thisis an important fact because the run time to carryout the internal comunications will be negligible.
For example, the 22 external communications
performed by an architecture with a membrane
located in each processor (Figure 1) have been
reduced to 10 in the architecture that has located
3 membranes in 4 processors (Figure 2).

• Considering that Tapl is normaly smaller than
Tcom , it complies with:M Tapl < Tapl + 2(M − 1)Tcom(5)

The time used in a step of P system evolution isworse than the one obtained using a single processor. Consequently, it is useless to choose thissoftware architecture for the P systems implementation.
Therefore, it is necessary to carry out a study of thecommunications problem in a processors distributed
architecture in order to determine the viability implementation of a P system.

3

Partially parallel evolution with partially parallel communication Model

The communication model that is being introducedin this section allows a certain parallelism degree inthe rules application phase, as well as in a P systemcommunication phase. In this way, the system costdecreases and the acceptable time used in a P systemevolution step is obtained. In short, this new model
is denominated “partially parallel evolution with partially parallel communication” and it is based on thefollowing:©ISAROB 2007

502

Figure 1: P system communications
2. Proxy for processor: When a membrane wants
to communicate with another one located at a different processor, the first one uses a proxy (programs or device located in the processor that carries out an action in representation of another),instead of doing it directly. Therefore, the communications that use the common line (external
communications to the processor) are carried out
between proxys, not between membranes. This
intermediate element located between the bus andthe membranes concentrates the information in
two stages:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3. Tree topology of processors: In graph theory it is established that P − 1 connections is theminimum number required to interconnect a connected graph of P processors. This restriction
imposes on the graph a tree topology. The ben-Figure 2: Communications with membranes distribution(a) N multisets of N membranes located in aprocessor that has a common father membrane in another processor, becoming integrated in a single multiset that is the onethat will be sent.
(b) The S communication packet of L length
necessary to communicate between S pairsof membranes located in 2 different processors are reduced to one single packet of S.L
length.
The benefit of using proxys in the communication
among membranes against direct communication
is double:(a) Due to the first stage previously described,
the amount of information sent is smaller.
This is produced by the fact that the N
packet necessary to communicate N membranes with the same father, are transformed
into a single packet of the length of a single
multiset.

Figure 3: Communications with a proxy for processor
efit obtained with the tree topology of processor
is that it minimizes the total number of external
communications made as the proxys interchange
information only with its direct predecessor andits direct successors, and therefore the total number of external communications in each evolution
step is 2(P − 1).
Figure 4 shows that external communications are
reduced to 6 when a tree topology of procesors isused to connect them.

(b) Due to the second stage, the number of external communications is smaller although
packets are bigger. But, considering thatthe communication protocols penalize thetransmission of small packets because to thedata encapsulated processes and to the timesafety intervals between future transmission,
it is better to send one packet of length equal
S.L than S packets of length equal L.
Figure 3 shows that if proxys are introduced in
the processors, then the number of external communications are reduced to 8.

©ISAROB 2007

503

Figure 4: Communications using a tree topology

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4. Token passing in the communication: No
proxy can start a communication until it is invited to do it by means of token passing. This token travels through a depth first serarch sequencein the topology of processors tree. In this way,
when a X proxy receives a communication fromits father proxy acquires the token and then sends
information to its first child proxy (C1) passing
the token and keeps waiting. When C1 sends information in answer to X, the later acquires thetoken again and sends information to its second
son proxy (C2) passing the token and keeps waiting, and so on until all father-childrens communications have been carried out. Finally, X proxysends its answer to its father returning the token.
The whole process starts with the root proxy ofprocessors hierarchy.
This communication policy prevents that morethan one proxy are trying to transmit information at the same time. Therefore, there are no
collisions and no congestion in the line. Figure
5 shows the communication sequence of the four
processors proposed in the sample.

Due to the establishment of tree topology of processors, the number of external communications in each
evolution step of the P system will be:2(P − 1)
Therefore, the required time to perform a complete
evolution step will be:
T = KTapl + 2(P − 1)Tcom pro(6)

Being Tcom pro the maximum time required to communicate 2 proxys using the common bus. Tcom pro
value will depend on the topology of P system and also
the distribution that has been made of M membranes
in P processors. In the most favourable case, when aunique membrane located in a processor has to communicate with a unique membrane located in anotherprocessor, Tcom pro is similar to Tcom . The worst casetakes place when K membranes located in a processor
send information to other K membranes located in adifferent processor. However, keeping in mind that the
protocol penalizes the short packets (there is not muchdifference sending 10 or 10000bytes through TCP/IP
according to the experimental data obtained), that theencapsulation processes take its time and that the information can be compressed before sending it, we canassume that Tcom pro is similar to the product of a constant by Tcom , being this constant much smaller that
K when K is big. Therefore,
Tcom pro = cTcom where c >= 1 << K

(7)

Once it is known the required time to perform an
evolution step, we can determine the number of membranes that should be located in each processor in order to minimize the time:
Figure 5: Communication sequence&s
Kopt =

4

Detailed analysis of the partially parallel evolution with partially parallel
communication model

2cM Tcom
Tapl'
where 1 <= Kopt <= M (8)This Kopt value allows to calculate the number ofprocessors necessary to run the P system minimizing
the necessary time to carry out an evolution step.

In this software architecture K membranes havebeen located in each processor. At the worst, the application of the rules in each one of these membranes
will be made sequentially in each processor. Therefore, the run time to carry out the application of therules of M membranes will be:

From the values Kopt and Popt the minimum timerequired to perform an evolution step is:

KTapl

p
Tmin = 2 2cM Tapl Tcom − 2cTcom©ISAROB 2007

504&rPopt =

M.Tapl2cTcom'where 1 <= Popt <= M

(9)

(10)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Therefore, the system evolution time is obtained byadding twice the square root of the result of multiplying the number of membranes M by Tapl and by Tcom .
The processors and the communications throughput is calculated as follows:p2cM Tapl Tcom
T hpro = p(11)
2 2cM Tapl Tcom − 2cTcomp
2cM Tapl Tcom − 2cTcom
T hcom = p(12)
2 2cM Tapl Tcom − 2cTcom

an evolution step
√ would improve approximately withthe same factor N .
Finally it is important to know, when a distributed
architecture is better than a momoprocessor architecture to perform a computation. Therefore we will haveto determine under which conditions the following is
fullfilled:p(17)
2 2cM Tapl Tcom − 2cTcom < M Tapl
Then, the several processors solution is better
when:

If we disregard the 2cTcom term, the value got inboth cases is 0.5, therefore, a more balanced system
has been obtained (50% working the processors, 50%
working the communications) than the one obtainedwith the other software architectures.

M>

5With regards to the cost of this architecture, we
can assure that it is moderate against the other architectures proposed, as P processors are needed with P
communication interfaces and the value of P is aroundthe square root of the number of membranes M .
C = P Cpro + P Ccom

(13)

About the main factors that influence in the timeused in a step of P system evolution in this software architecture (Tapl and Tcom ) we really can influence only
on the first one in order to improve this time. The software engineers can make that the K membranes of aprocessor apply faster the evolution rules, thus developing faster sequential or parallel algorithms. Nevertheless, it is difficult to get faster communicate amongprocessors as it is necessary to invest many resourcesthat are only within reach of the telecommunicationsindustry. If it is possible to make that Tapl be N fastertimes the values of Kopt , Topt and Tmin will be:&s
Kopt ='
2cM N Tcom
where 1 <= K <= M (14)
Tapl

&r

'
M.TaplPopt =
where 1 <= P <= M
2cN Tcomr
2cM Tapl Tcom
Tmin = 2− 2cTcom
N

(15)

(16)

Therefore, the number of membranes that would√
be runned in a processor would be multiplied by N ,the number of required processors would be divided
by the same factor and the time required to perform©ISAROB 2007505

2cTcomTapl(18)

Conclusions

In this paper a communications architecture to implement P system has been introduced. This architecture is based on the location of several membranes
in the same processors, the use of proxys for communicating processors placed in a tree topology andtoken passing in the communication. This solution
avoids communication collisions, reduces the number
and length of the external communications. All this,allows to obtain a better step evolution time thanin other suggested architectures congested quickly by
the network collisions when the number of membranes
grows. Also, our architecture is highly scaleable withmoderate costs.

References[1] Gh. Păun (2000), “Computing with membranes”,
Journal of Computer and System Sciences, 61, 1,
108-143[2] A. Syropoulos, E.G. Mamatas, P.C. Allilomes et al(2003), “A distributed simulation of P systems”,
A. Alhazov, C. Martin-Vide and Gh. Păun (Editors): Preproceedings of the Workshop on Membrane Computing; Tarragona, July 17-22, 455-460[3] G.Ciobanu, W.Guo (2004), “P Systems Runningon a Cluster of Computers”. Workshop on Membrane Computing (Gh. Păun, G. Rozenberg, A.
Salomaa Eds.), LNCS 2933, Springer, 123-139.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Dynamics of an abstract chemical system with few molecular
Yasuhiro Suzuki
Department of Complex Systems Science
School of Information Science, Nagoya University
Furocho Chikusa Nagoya, 464-8603, JAPAN
Abstract
Under the mass action law of chemical reactions,the calculations of Abstract Rewriting System on Multisets can be regarded as linear maps. Therefore, inorder to analyze the dynamical behaviors of the system, we use the method of analyzing linear dynamicalsystems. We consider the brusselator model and analyze its dynamical stability.

1

introduction

Abstract Rewriting System on Multisets (ARMS) isa class of P Systems[6]. Since in ARMS we can model asystem intuitively, it has been applied in various ﬁelds,such as systems biology (modeling the signaling pathway of P53 protein in a cell[4], modeling inﬂammation
response, etc.); bio-chemistry and the science of complexity (modeling and simulation of proto-cells whichis composed of membranes, modeling proto-enzyme
networks and their evolution, etc.).
ARMS is not only a model of computation based oncomputational algebra (rewriting systems) but also ahybrid model that connects between discrete systems
and continuous systems; under the mass action law ofchemistry, ARMS can be regarded as a discrete expression of the master equation[?][?], which describes
chemical reactions[6]. Furthermore, if we assume thesystem size is large enough, we have a continuous approximation of the ARMS[6].
In this paper, we consider the ARMS under themass action law, where calculations of ARMS can be
regarded as sequences of linear maps and where, inorder to analyze the system, we will use methods oflinear dynamical systems[3].

2Abstract Rewriting System on Multisets, ARMS
ARMS is a model of computation of chemical re-©ISAROB 2007506

actions, in which ﬂoating molecules can interact witheach other according to given reaction rules. Technically, in ARMS a chemical solution is a ﬁnite multisetof elements denoted by symbols from a given alphabet, A = {a, b, . . . , }; these elements correspond tomolecules.Reaction rules that act on the molecules are
speciﬁed in ARMS by reaction rules.
Let A be an alphabet (a ﬁnite set of abstract symbols). A multiset over a set of objects A is a mapping
M : A 7→ N, where N is the set of natural numbers,
N, 0, 1, 2,. . . .The number M (a), for a ∈ A, is the multiplicity of object a in the multiset M . We denote by
A# the set of all multisets over A, including the empty
multiset, ∅, deﬁned by ∅(a) = 0 for all a ∈ A. A multiset M : A 7→ N, for A = {a1 , . . . , an } is represented
by the vector w =(M (a1 ) M (a2 ) . . . M (an )). The
union of two multisets M1 , M2 : A 7→ N is addition ofvectors w1 and w2 that represent the each multisets
respectivelly. If M1 (a) ≤ M2 (a) for all a ∈ A, then we
say that multiset M1 is included in multiset M2 andwe write M1 ⊆ M2 .
Since we consider population dynamics of molecules
and a reaction rule denotes the population change inthis paper, we deﬁne a reaction rule u → v, u, v ∈ A#
is deﬁned as a vector r, r = −u + v (it cannot express
catalytic reaction such as A+C → B +C). In general,a reaction rule is deﬁned as the pair of vectors, (u, v)(in general case see [?]).
A reaction is the addition of vectors M ∈ A# andr ∈ R, and it can be deﬁned only when r ⊆ M. We candeﬁne over A# a relation: (→): for M, M ′ ∈ A# , r ∈R we write M → M ′ iﬀ M ′ = (M + r) ≥ 0.
m times of reactions from S0 ∈ A# corresponds
to m times of vector addition, Sm = S0 + ai ri +aj rj +ak rk +· · · , (ai , aj , ak , · · · ∈ {1, 2, 3, · · ·}, ai +aj +m∑ak + · · · = m, ri , rj , rk , · · · ∈ R) = S0 +rj , m =1, 2, 3, · · · , rj ∈ R.

i=1

Deﬁnition (cycle) The sequence of reactions such

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

that Si+m = Si +is the period.

m∑rj , m ≥ 2 is called cycle and mi=1

as the Belousov Zhabotinsky reaction (BZ reaction)[2].
The brusselator model is given by:

Deﬁnition (Rule Matrix) The rule matrix A is composed of the transpose of all rule vectors. For example,
the rule matrix of the rule vectors of {(-1 1),(1 -1)} is
(
)
−1 1
A=
.
1 −1

A
B
2X

+X

k

→1
k2

→
k3

+Y

→

X

→4

k

X

r1

Y

+D

3X

r3

E

r4

r2

Figure 1: Brusselator

3

Theoretical Remark on ARMS

Remark
If the rules of R of an ARMS are linearly independent,
there are no cycles in any sequence of reactions.
proof: If the rules of an ARMS r1 , r2 , · · · , rn are linearly independent, there does not exist any sequencem∑of reactions such that Si+m = Si +rj , m ≥ 2. If
i=1such a sequence existed, it would requires ai ri +aj rj +ak rk +· · · = 0, but since we assumed that the rule vectors are linearly independent, it is satisﬁed only whenai = aj = ak = · · · = 0.
This remark also claims that if sub sets of rule vectors are not linearly independent, there can exist cycles
in the sequence of reaction. 1
It is noted that even if a set of reaction rules arelinearly independent, there can exist cycles in its subspace. The rule vector
  
 

a
−3
5
 b  ≡  −1  ,  1  ,
(1)
c
1
−1

where A and B are input and are continuously supplied to or richly exist in the system. Since we areinterested in the behavior of the number of X and Y,
we will use the two-dimensional rule vector x=(x y)where:
r 1 = (1, 0), r 2 = (−1, 1), r 3 = (1, −1), r 4 = (−1, 0),(2)respectively. Although there are some of simulation
based works on this model by using various models,
basically they follow:
(

xn+1
yn+1

)

(
(
(
)
)
)
( )
bxn −1xn −1ax2n yn
1
1
+
+
.
=
+
−1
1
0
0
C
C
C

where C is a constant for normalization and deﬁned
by axx yn + bxn + xn = C. For simplicity, we assume
that k1 and k4 are equal to 1, k2 = b and k3 = b.
The moleculaes A and B are assumed to be in large
excess so that their concentrations do not change withtime. Furthermore, in analyzing dynamics we ignore
the constant C (we can ignore it without loss of generality).

Equilibria

are linearly independent, but since the subspace of band c is not linearly independent, there can exist cyclic
reaction sequences in the b − c space so that the trajectory of reaction sequences will spiral in the a − b − cspace.

Since (1 0) + (1 1) + (-1 1) + (-1 0) = (0 0) whenax2 y = bx = x = 1, obviously these rule vectors are
not linearly independent and there can exist cyclic reaction sequences. It is apparent that ax2 y = bx = x =1 is satisﬁed only when (x, y) = (1, ab ), so this is the
only equilibrium of the system.

4

Stability

Analysis of the dynamics of Brusselator

The brusselator model is a mathematical model ofan autocatalytic, oscillating chemical reaction, known
1 rank(R) illustrates the ARMS may have cycles or not in its
sequences of reactions©ISAROB 2007

507

Since X, Y ∈ A# , by calculating partial diﬀerence
we obtain the Jacobian of the system:()−bϵ − ϵ + 2axyϵ + ayϵ2 ax2 y
Jf (x, y) =, (3)bϵ − 2axϵ − ayϵ2
−ax2 ϵThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where ϵ is given as;ϵ=([X] + [Y ]) + δ, (0 < δ < [X] + [Y ])[X] + [Y ]Acknowledgments(4)

where δ denotes the change of concentration of x ∈ Xor y ∈ Y . While δ is ﬁxed, if [X] + [Y ] (system size)
is getting larger (macroscopic), δ is getting smaller in
relation to the system size, on the other hand, if [X] +[Y ] is getting smaller (mesoscopic), the δ is relatively
getting larger (ϵ > 1). It is noted that ϵ ∈ the set of
quotient, Q.
Evaluated at (x, y) = (1, ab ),()
b−bϵ + bϵ2 aϵ
Jf (1, ) =.
(5)−bϵ − bϵ2 −aϵaThus the trace, τ and determinant, det are(b )= T race Jf (1, ) = (b − a − 1 + bϵ)ϵ, (6)a(b )det = Det Df (a, ) = aϵ2 ,(7)aτSince a > 0, ϵ > 0, this implies that (1, ab ) is not a1(a + 1), then τ < 0 and thesaddle point. If b < ϵ+11equilibrium is an attractor, while if b > ϵ+1(a+1), it isa repellor. This shows that when a system is macroscopic, its stability is close to the model of diﬀerentional equations, while when the system is mesoscopic,
1
ϵ+1 (a + 1), ϵ >> is getting smaller and the unstableregion is expanding. Thus, the behavior of a systemon a mesoscopic scale is likely to be destabilized by
ﬂuctuations.

5

Conclusion

On the simulation of the brusselator model, especially, on a mesoscopic scale, Vladimir[7], using
the Monte-Carlo simulation on two-dimensional lattice, reported that decreasing the system size down tomesoscopic may result in the periodic kinetic oscillations becoming aperiodic and disappearing. We havealso found this behavior throughout simulations by using ARMS [5]. We believe that it is the descritizationthat makes the system unstable. However, its physicalsigniﬁcance is still open to discussion.
Since under the mass action law ARMS can be regarded as a linear map, we attempted to use methodsof analyzing linear dynamical systems in our investigation of the stability of the system. It is a challenge toapply this method to P Systems or the reaction map
systems[1] will be the subject of our future study.

©ISAROB 2007

508

The authors are grateful to prof. Yuki Sugiyama,
Ryosuke Ishiwata for discussions and Julia Wilson for
discussion and English corrections. This research was
supported by Grants-in-Aid for Scientiﬁc Research No.
14085205.

References[1] L.Bianco, F. Fontana, V. Manca, P systems withreaction maps. Int. J. Found. Comput. Sci. 17(1):27-48 (2006)[2] G. Nicolis and I. Prigogine, Exploring Complexity, An Introduction. San Francisco: Freeman andCompany (1989).
[3] H.W.
Hirsch, S Smale, Diﬀerential equations,dynamicalsystem and linear algebra, AcademicPress, (1974).
[4] Y Suzuki and H. Tanaka, Modeling p53 Signaling Pathways by Using Multiset Processing, Applications of Membrane Computing Springer-Verlag,
Berlin, (August 2005).
[5] Y. Suzuki and H. Tanaka. 1997. Chemical oscillation on symbolic chemical systems and its behavioral pattern, In Proceedings of the International
Conference on Complex Systems, Nashua, NH, 2126 Sept (1997).
[6] Y. Suzuki, Y.Fujiwara, J.Takabayashi and
H.Tanaka, Artiﬁcial Life Applications of a Classof P Systems: Abstract Rewriting System on Multisets, Multiset Processing, LNCS2235, pp299-346,
Springer Verlag Berlin Heidelberg (2001).
[7] P. Vladimir, Disappearance of oscillations in theBrusselator on the mesoscopic scale, Phsy. Chem.
Chem. Phys., 3, 1432-1434, (2001).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Spiking Neural P Systems with Inhibitory Axons
Rudolf FREUND
Marion OSWALD
Faculty of Informatics
Vienna University of Technology
Vienna, Austria
rudi@emcc.at
marion@emcc.at
Abstract
We extend the original model of spiking neural P systems by adding inhibitory axons. We show how computational completeness can already be obtained withextended spiking neural P system with inhibitory axons, and we also exhibit that …nite extended spiking
neural P systems with inhibitory axons characterize
the regular sets. As a speci…c application example, weshow how logical gates can be modelled with a static
simple variant of (extended) spiking neural P systems
with inhibitory axons.

1

Introduction

Based on the biological background of neurons sending electrical impulses (also called spikes) along axons
to other neurons, in the area of neural computation
several new models have been introduced, e.g., see [4],[6], [7]. P systems (membrane systems) have been introduced as a formal model implementing the hierarchical structure of membranes in living organisms andthe biological processes in and between cells (an introduction to this …eld can be found in [9], for theactual state of the art in this area see [12]). Just recently, combining the ideas of P systems and spiking
neurons, this led to a new variant of tissue P systems(see [3]) called spiking neural P systems, e.g., see [5],[10]. An extended version of spiking neural P systems
allowing to send di¤erent informations along the axons
between two neurons was investigated in [1].
In spiking neural P systems (see [5]), the contents
of a neuron consists of a number of so-called spikes.
The rules assigned to a cell allow us to send information to other neurons in the form of electrical impulses – spikes – which are summed up at the target
cell; the application of the rules depends on the contents of the neuron. As inspired from biology, thecell sending out spikes may be “closed” for a speci…c©ISAROB 2007

509

time period corresponding to the refraction period of
a neuron; during this refraction period, the neuron isclosed for new input and cannot get excited (“…re”) for
spiking again. In [1], an extended version of this original model of spiking neural P systems was introduced
based on some other observations from biology: forexample, the length of the axon may cause a time delay before a spike arrives at the target; moreover, thespikes coming along di¤erent axons may cause e¤ectsof di¤erent magnitude.
Another quite natural feature found in biology andalso used in the area of neural computation is thatof inhibitory neurons or connections between neurons.
Hence, in this paper we consider spiking neural P systems with inhibitory axons, thus extending again themodel of extended spiking neural P systems by considering inhibitory axons that allow for “closing” a neuron for one step by sending a spike along such an inhibitory axon to this neuron from another one.
The rest of the paper is organized as follows: In thenext section, after giving some preliminary de…nitions,
we introduce the model of extended spiking neural P
system with inhibitory axons. In Section 3, we exhibit
some theoretical results for this new model of extended
spiking neural P system with inhibitory axons: …nitespiking neural P systems with inhibitory axons (where
the number of spikes that may be stored in each neuron can be bounded) characterize the regular sets inthe same way as the well-known McCulloch-Pitt networks (e.g., see [8]); without any bounds on the number of spikes in the neurons we obtain computational
completeness as already shown for the other models of
spiking neural P systems. As a speci…c application, in
Section 4 we show how spiking neural P systems with
inhibitory axons can be used to specify/simulate logical gates. A short summary and an outlook to future
research conclude the paper.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

De…nitions

F [1::m] speci…es the set of neurons which store
the output.

For the basic elements of formal language theory
needed in the following, we refer to any monographin this area, e.g., to [2] and [11]. We just list a few notions and notations: V is the free monoid generated
by the alphabet V under the operation of concatenation and the empty string, denoted by , as unit
element; for any w 2 V , jwj denotes the number ofsymbols in w (the length of w). N+ denotes the setof positive integers (natural numbers), N is the set of
non-negative integers, i.e., N = N+ [ f0g. The intervalof non-negative integers between k and m is denotedby [k::m]. For any k 2 N, RE Nk and REG Nk
denote the sets of recursively enumerable and regularsubsets of Nk , respectively. REG (fag) denotes the setof regular languages over the alphabet fag (observethat there is a one-to-one correspondence between thesets in REG (fag) and the sets in REG (N)).
Extended spiking neural P systems with inhibitory axons
For the motivation and the biological backgroundof spiking neural P systems we refer the reader to [5].
An extended spiking neural P system with inhibitory
axons is a construct= (m; S; R; F )wherem is the number of cells (or neurons); the neurons
are uniquely identi…ed by a number between 1 andm (obviously, we could instead use an alphabet
with m symbols to identify the neurons);
S describes the initial con…guration by assigning
an initial value (of spikes) to each neuron;
R is a …nite set of rules of the formi; E=ak ! P; d such that i 2 [1::m] (specifying that this rule is assigned to cell i), E
REG (fag) is the checking set (the current number of spikes in the neuron has to be from E if
this rule shall be executed), k 2 N is the “number of spikes”(the energy) consumed by this rule,
P is a (possibly empty) set of productions of theform (l; w) where l 2 [1::m] (thus specifying thetarget cell), and w = a (we also call a inhibitor )or w 2 fag is the weight of the energy sent along
the axon from neuron i to neuron l, and d is thedelay.

©ISAROB 2007

510

Starting from the initial con…guration of the systemthat is given by S; a transition from one con…guration
to another one now works as follows: for each neuroni, we …rst check whether we …nd an applicable rulei; E=ak ! P; d (i.e., the number of spikes in neuron
i coincides with the regular checking set E). If this is
the case and the neuron is not blocked due to the delayof a previously applied rule, then the neuron “…res”,
i.e., for every production (l; w) occurring in the set
P the corresponding package (l; w) is sent from i toneuron l; if d > 0, then the neuron is blocked for the
next d steps, i.e., it cannot apply another rule and,moreover, all inputs arriving during the next d 1
steps are ignored. Now for every neuron we have toconsider the following two cases:
If in any of the packages just having arrived ina neuron we …nd an inhibitor a; then neuron l isblocked for one step, i.e., no rule can be appliedin neuron l and no input from other cells is takenin this step.
On the other hand, if in the packages just having
arrived in a neuron we …nd no inhibitor, for theother packages (l; w) with w 2 fag , the weight
w in such package is added to the corresponding
number of spikes in neuron l (provided the neuron
is not closed for input).
A computation is a sequence of con…gurations starting with the initial con…guration given by S. The
result of a halting computation (where no neuron isblocked, no rule can be applied anymore) then can befound in the output neurons speci…ed by F .
Note that in the system de…ned above, we did not
introduce any delay for the packages along the axons,as e.g. done in [1]. The original version of spiking
neural P systems as de…ned in [5] corresponds witha very restricted variant of extended spiking neural
P system with inhibitory axons where we do not use
inhibitors and, moreover, the number of spikes sentfrom a neuron i to others is always …xed and (i)either no spikes are emitted, which corresponds tothe case P = f g in the rule i; E=ak ! P – sucha rule is called a forgetting rule –, or (ii) the rule
is of the form i; E=ak ! f(l; a) j l 2 Mi g for someMi [1::m] only depending on i –such a rule is calleda spiking rule. On the other hand, this variant caneasily be extended to spiking neural P systems with
inhibitory axons by also allowing rules of the formi; E=ak ! f(l; a) j l 2 Mi g [ f(l; a) j l 2 Ni g

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

for some Ni
Ni = f g :[1::m] only depending on i, with Mi \If we only allow the rules to be of the formi; E=ak ! P with E = al for some l1 andwith all productions (l; w) being of the form w = aj
for some j 0 or w = a, then such an extended spiking neural P system with inhibitory axons is called…nite.

3

Theoretical Results

Theorem 1. For every set L in RE Nk , we canconstruct an extended spiking neural P systems with
inhibitory axons generating L.
It remains a challenging research topic for future
theoretical investigations whether inhibitory axons
could be used to obtain computational completenesswith various restrictions thereby trading inhibitory axons for some other features like forgetting rules.
Finite extended spiking neural P systems with inhibitory axons (where we only allow the rules to beof the form i; E=ak ! P with E = al for somel 1 and with all productions (l; w) being of the formw = aj for some j
0 or w = a) can only generate regular sets, because the number of spikes to be
stored in each neuron can be bounded (following theconstruction given in [1] for extended spiking neural P
systems), i.e., we obtain the following result:
Theorem 2. Finite extended spiking neural P systems (with inhibitory axons) characterize REG Nk .

Simulating Logical Gates

In this section we restrict ourselves to spiking neural
P systems with inhibitory axons, i.e., all the rules areof one of the following forms:
i; E=ak ! f g ; such a rule is called a forgetting
rule;i; E=ak ! f(l; a) j l 2 Mi g [ f(l; a) j l 2 Ni g forsome Mi ; Ni
[1::m], Mi \ Ni = f g, only depending on i; such a rule is called a spiking rule.

©ISAROB 2007

the neurons are represented by the nodes of thegraph;
the spiking rules and the forgetting rules are speci…ed in the nodes;
for each j 2 Mi we draw a directed edge from ito j and mark it as excitatory edge;

Extended spiking neural P systems without inhibitory
axons were shown to be computationally complete [1]even if not using any delay in the neurons or axons,
which also proves the computational power of the systems introduced in this paper. Hence, we obtain thefollowing result:4

The restriction to these kinds of rules allows us
to represent such a spiking neural P system with inhibitory axons by a directed graph as follows:511

for each j 2 Ni we draw a directed edge from i toj and mark it as inhibitory edge.
Using spiking neural P systems with inhibitory axons, we now can easily represent (simulate) logical
gates. For example, the simulation of a NAND-gate
by a spiking neural P systems with inhibitory axons
can be seen in Figure 1; it corresponds to a system(3; f(1; a) ; (2; ) ; (3; ) ; R; f3gg)
with R containing the rules(1; fa2 g=a ! f(3; a)g); (1; fag=a ! f g);(2; fag=a ! f(2; a); (3; a)g), and(3; fag=a ! f(out; a)g) –out speci…es that the signal coming from neuron 3 is the output of the system,
but it can be taken as input signal for another NANDsystem.
A

B

?
?
$
'
$
a
(fag=a ! f(2; a);(fa2 g=a ! f(3; a)g)(3; a)g)(fag=a ! f g);
1%
2
&
%
&
@
@
@
@
r
R
@
'
$

?
'

(fag=a ! f(i; a)g)&3%

?A N AN D B

Figure 1: NAND-gate.
In each step, neuron 2 sends one spike to neuron 3:
Neuron 1 has two input axons (marked by A and B).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

If a spike arrives from only one of these axons, then therule (fag=a ! fg) is executed, and the output neuron
3 spikes. Only if neuron 1 gets a spike from both its
input axons, then the rule (fa2 g=a ! f(3; a)g) will
be applied, which now inhibits neuron 3 from …ring
because the inhibitory axon (indicated by a dot at theend of the directed edge) from neuron 1 to neuron 3 isactivated.
As a speci…c feature of the spiking neural P systemwith inhibitory axons described above, we can see thatfor every neuron j in this system, either Mj or Nj is
empty.
As is well known, every boolean function can be
represented by just using NAND-gates. Hence, combining such systems as described above, we can easily
represent every boolean function by a corresponding
spiking neural P system with inhibitory axons. In thiscase, the answer to an input arrives in 2k steps where kis the depth of the logical network of NAND-gates representing the given function. Moreover, in this case we
do not consider halting computations, yet instead observe the spike train (the sequence of zeroes and onesin the output neuron) taking into account the delay of
2k.

5

References[1] A. Alhazov, R. Freund, M. Oswald, M. Slavkovik,
Extended Spiking Neural P Systems Generating
Strings and Vectors of Non-Negative Integers,in H. J. Hoogeboom, Gh. Paun, G. Rozenberg(Eds.), Pre-proceedings of the 7th Workshop on
Membrane Computing WMC7, 88-101 (2006)[2] Dassow J, P¼
aun Gh (1989) Regulated Rewritingin Formal Language Theory. Springer, Berlin[3] Martín-Vide C, Pazos J, P¼
aun Gh, RodríguezPatón A (2002) A new class of symbolic abstract
neural nets: Tissue P systems. In: Proceedingsof COCOON 2002, Singapore, Lecture Notes in
Computer Science 2387, Springer-Verlag, Berlin,
290–299[4] Gerstner W, Kistler W (2002) Spiking Neuron
Models. Single Neurons, Populations, Plasticity.
Cambridge Univ. Press[5] Ionescu M, P¼
aun Gh, Yokomori T (2006) Spiking
neural P systems. Fundamenta Informaticae 71,
2–3:279–308[6] Maass W (2002) Computing with spikes. Special
Issue on Foundations of Information Processingof TELEMATIK 8, 1:32–36

Conclusion

We have introduced the model of extended spiking
neural P systems with inhibitory axons. Based on
the theoretical results already proved in [1] and [5],we have exhibited that …nite extended spiking neural
P systems with inhibitory axons characterize the regular sets; on the other hand, already very restrictedvariants of extended spiking neural P systems with inhibitory axons allow us to obtain computational completeness, i.e., to characterize the recursively enumerable sets.
Spiking neural P systems with inhibitory axons canbe used to specify logical gates, but they also promise
to be interesting for specifying other models of computation; for example, in the future we shall also investigate the relation between extended spiking neural
P systems with inhibitory axons and Petri nets.

[7] Maass W, Bishop C (eds) (1999) Pulsed Neural
Networks. MIT Press, Cambridge[8] Minsky M L (1967) Computation: Finite and In…nite Machines. Prentice Hall, Englewood Cli¤s,
New Jersey[9] P¼
aun Gh (2002) Computing with Membranes: An
Introduction. Springer, Berlin[10] P¼
aun Gh, Pérez-Jiménez MJ, Rozenberg G (2006)
Spike trains in spiking neural P systems, Intern J
Found Computer Sci, to appear (also available at[12])[11] Rozenberg G, Salomaa A (eds) (1997) Handbook of Formal Languages (3 volumes). Springer,
Berlin[12] The P Systems Web Page,
http://psystems.disco.unimib.it

Acknowledgements
The work of Marion Oswald is supported by FWFproject T225-N04.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Genetic Algorithms for Buffer Size and Work Stations Capacity in
Serial-Parallel Production Lines
Abu Qudeiri Jaber1, Rizauddin Ramli2 and Yamamoto Hidehiko3
Intelligent Manufacturing Systems Laboratory, Gifu University
1-1 Yanagido, Gifu Shi, 501-1193, Japan1{ k3812203, 2k3812205}@edu.gifu-u.ac.jp, 3yam-h@gifu-u.ac.jp
Abstract
Recently, many production lines that havecomplicated structures such as parallel, reworks,
feed-forward, etc. are widely used in high volumeindustries. Among them, the serial-parallel production
line (S-PPL) is one of the more common production
styles in many modern industries. One of the methods
used for studying the S-PPL design is through genetic
algorithms (GA). One of the important jobs to use GA is
how to express a chromosome. In this paper, we attempt
to find the nearest optimal design of an S-PPL that will
maximize production efficiency by optimizing thefollowing 3 decision variables: buffer size between eachpair of work stations, machine numbers in each of thework stations; and, machine types. In order to do this we
present a new GA-simulation based method to find thenearest optimal design for our proposed S-PPL. For
efficient use of GA, our GA methodology is based on atechnique that is called gene family arrangement method(GFAM) which arranges the genes inside individuals. An
application example shows that after a number ofoperations based on the proposed simulator, the nearest
optimal design of S-PPL can be found.

Almost all researchers assumed that the machine
numbers are fixed and only concentrate on finding thebuffer size. In this paper, we attempt to find the nearest
optimal design of an S-PPL that will maximize
production efficiency by optimizing the following 3
decision variables: buffer size between each pair of work
stations, machine numbers in each of the work stations;and, machine types. In order to do this we present a new
GA-simulation based method to find the nearest optimal
design for our proposed S-PPL. One of the importanttaskes in using GA is how to express a chromosome. For
the efficient use of GA, our GA methodology is based ona technique that is called gene family arrangement
method (GFAM) which arranges the genes inside
individuals.
In evaluating the S-PPL, each work station thatconsists of multiple parallel machines is combined into
one equivalent single machine that turns the S-PPL into a
serial production line. Then, the serial production line
includes equivalent machines which can beapproximated by using a well known decomposition
approach.

2. Problem Description
Keywords: Serial-parallel production line, buffer size,
Genetic algorithms, Throughput evaluation.

1. Introduction
Production lines that have complicated structures
such as parallel, reworks, feed-forward, etc. are widelyused in high volume industries [1, 2]. Among them, theserial-parallel production line (S-PPL) [1, 3] is one of themore common production styles in many modern
industries. S-PPL is mainly used to increase thecapacity of one work station that has a lower speed thanother work stations by reducing the variation of material
flow speed through the overall production line.
Furthermore, S-PPL also reduces the effect of machine
failure during processing time. Despite manymethodologies developed to study S-PPL, several
researchers have described the optimization of
production lines using various optimization methods,
such as functional approximation and evaluation [4],knowledge-based methods [5], simulated annealing [6],heuristics algorithm [7], dynamic programming method[8], and other search methods. One of the methods used
for studying the buffer size in production lines is genetic
algorithms (GA) [9, 10].

©ISAROB 2007

513

Consider a series-parallel production line with K
work stations (S1, S２, … , SK) and K-1 buffers. Eachwork station i (i = 1, 2, … , K) in the S-PPL can containseveral multiple parallel machines as shown in Figure 1.
IN

S1

B1

S2

...

Bk-1

Sk

Out

M11
M12

..
.

M1(j1)

Figure1: S-PPL Model
Before formulating the problem, notations are
introduced as follows.
P
the total production rate
B{Bi} buffer size between stations i and i+1
S
maximum capacity of buffer
C{Ci} number of parallel machines in work
station i
T{Tij} type of machine j that is located in

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

station ia
M groupthe available types of machines,
M

a
group

=

{M

a
1 , M

a
2 ,K

,M

a
l

}.

The problem can be formulated mathematically as
follows:
Maximize P(B C,T)
(1)
Subjected to 1 ≤ B ≤ S(2)

T ij ∈{

M 1a , M 2a , K , M la}

(3)

In order to evaluate the S-PPL, the multiple
parallel machines in each work station are combined into
one equivalent single machine. In this way the S-PPL
can be converted into a serial production line where eachmachine in the serial line is equivalent to one set of
multiple parallel machines. Then, the serial production
line includes equivalent machines which can beapproximated by using a well known decomposition
approach [11].

3.1. Replacing Each Work Station by anequivalent machine
Let us consider a work station i in the S-PPL
model with machines Mij, j= 1, …, j1 as shown in Figure
1. Assume that the uptime and the downtime of theaggregated machines Mij are randomly variable anddistributed exponentially with parameters Pi and Ri. Then,the parameters Pe and Re for the equivalent machine canbe calculated as follows.
K

Pe = S∑ (P i 1 + R i 1 )
i= 2
K

K

∑ SR
i

i1

i=1

∑ (P

j1

+ R j1,

uptime parameter p 2f is defined as follows.
r2f = r2 [1 - Q(p1 , r1 , p 2 , r2 , N 1 )] ,
p2f

(6)

(7)
Where Q( p1 , r1 , p 2 , r2 , N 1 ) is the probability that
= p2 + r2Q(p1 , r1 , p2 , r2 , N1 )machine M 2e is starved and is defined by [12] asfollows:

3. Throughput Evaluation of the S-PPL

P11 R 11aggregation procedure is a good approximation andresults in good accuracy. Consider a serial line withmachines ( M 1e , M 2e ,K , M Ke ) and buffer size B1 to
BK-1. The first two machines aggregate into a single
machine M 2f with downtime parameter r2f and the

(4)

)

p
p
 (1 - e1 )( 1 - Φ ),if 1 ≠ 2

- βN
r
r2
Φ
1
e
r
+
1
2

p1(pp1 )(rr1 ),Q(p1 , r1 , p2 , r2 , N) = (p + r )[ (pp1 )(rr1 ) + p2 r1( pp1 + rr1 )N ]
 1 1
 p1 p2
=
if
r2
 r1

ei =

ri
, i = 1,2
pi + ri

rri = ri + ri +1 and β =,Φ=e1 (1 - e 2 )
e 2 (1 - e1 ),(8)

ppi = pi + pi +1(p1 + p2 + r1 + r2 )( p1r2 - p2r1 ).
(p1 + p2 )(r1 + r2 )Next, M 2f is aggregated with M 3e to result in M 3f , andso on until all K machines are aggregated in a single one,
M nf . This constitutes forward aggregation. Then, inbackward aggregation, the last machine, M Ke , isaggregated with M Ke −1 to result in M nb-1 and so on until
all machines are aggregated in a single machine, M 1b .
The procedure is repeated until convergence is satisfied.
By following the aggregation procedure, the production
rate can be approximated as,
PR(p1 , r1 , ... , pn , rn , N1 ,..., Nn -1 ) =rNffpN + rNf=

r1bb
p1 + r1b(9)

4. Gene Family Arrangement Method

i=1, j≠ i

K

P11 R 11
Re = S∑ (P + R )
i1

i1

i= 2
K

K

∑ S P ∑ (P
i

i1

i =1

j1

+ R j1(5))

i =1, j ≠ i

KWhere S = ∑ S i
i =1

3.2. Serial Line Aggregation
Previously, no closed form expression for
performance of a serial line with more than two(non-identical) machines has been available. Therefore,
many approximation approaches have been used toevaluate the production line based on aggregation anddecomposition. In this paper, we introduce theaggregation procedure proposed by Li [11], which
modifies the machine downtime parameter toaccommodate starving and blocking information. This

©ISAROB 2007

514

One of the important tasks in using GA is how to
express a chromosome. As we described above, thisresearch solves three different decision variables. To
represent these variables, we propose GFAM as a newarrangement method which arranges the genes in eachindividual. Furthermore, GFAM adopts two groups ofgenes in each individual. The first group represents thebuffer size and is located in the even positions of theindividual (G2, G4, … , G2k-2, G2k), where k is the numberof work stations. The second group represents thenumber of machines in each work station and it islocated in the odd positions of the individual (G1, G3, … ,G2k-3, G2k-1). Each of the items in the odd group includes
a family of genes which represent the machine types ineach work station; each family of genes is coded asfollows.
 G i1  (10)
G i =  M  ∀ i = 1, 3 K 2 k - 3, 2 k - 1
 G ij  The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Where j is the number of parallel machines in work
station i. The number of items in the odd and evengroups is not limited, which means that any production
line with any number of work stations that include any
number of machines and any buffer size between eachpair of work stations can be dealt with.

5. Genetic Algorithms for the Optimal
Design of S-PPL
GA is a global optimization technique used forvarious optimization problems [9, 10]. In this paper, we
present the determination of a near optimal design for an
S-PPL. Since the developed GFAM is different fromconventional individual expressions, the operational
procedure for our GA is also different. The
characteristics of the GA are described in sections 5-1
and 5-2.

5.1. CrossoverIn this research, the encoding method to express
each individual using GFAM is different from thatobtained using conventional encoding methods. The
crossover operations for our GA system operate by using
two processes. The first crossover is similar to theconventional crossover method, i.e., the genes after the
crossover point are swapped between two individuals.
On the other hand, the second crossover method swaps
the genes between two families for each of theindividuals that results from the first process.
The crossover operations are generated by using
the following steps:
Step 1: Randomly select two individuals from thecurrent population.
Step 2: Randomly select a crossover point and swap thegenes after that crossover point.
Step 3: Randomly select two items (families), I1 and I2
from the odd group in the first individual generated fromstep 2.
Step 4: Randomly select a family crossover point.
Step 5: Randomly select the number of genes after thefamily crossover point to be included in the crossover,
NF. NF < I1 and NF < I2.
Step 6: Replace the genes after the family crossover
point one by one. If one of the two families reaches its
last gene, continue the replacement from the first gene in
the family.
Step 7: Repeat steps 3-7 for the second individual
generated from step 2.
Figure 2 shows the crossover process graphically.

5.2. Mutation
The mutation of our GA system is different fromthe traditional mutation operator because the gene
expression adopts GFAM. The mutation is carried out by
using the following steps:©ISAROB 2007

515

1

[3 8 7 15 5 12 2][4 8 9 12 3 8 4]

[4 8 9 12 5 12 2][3 8 7 15 3 8 4]

 M 11 M  12 
 M 13  M 14 2
 M 11 M  53 
 M 54  M 14  M 51 M  52 
 M 53  M 54  M 55  M 51 M  52 
 M 12  M 13  M 55  M 51 M  52 
 M 53 

 M 71 M  72 
 M 73  M 74  M 73 M 
 74 
 M 71 

 M 53 M  72 
 M 51  M 52 

Figure 2: Crossover process
Step 1: Select one individual randomly from the current
population.
Step 2: Randomly select one of the genes of a single
gene type (first group).
Step 3: Change the value of the selected gene to a newvalue, which can also be selected randomly between(1-S).
Step 4: Randomly select one gene family (Second
group).
Step 5: Change the value of the selected gene to a newvalue, which can also be selected randomly between (1the number of the selected family members).
Step 6: Randomly select one gene family, again.
Step 7: Randomly select one member of the selectedgene family.
Step 8: Change the type of the selected family member
to a new value, which can also be selected randomly.

5.3. Implementation of the GA
Before describing our implementation of the genetic
algorithm, the following notations are defined.
Notations:
PS
Population size.
Pc
Crossover rate.
Pm
Mutation rate.
Pi
Selection probability of the individual i.
Fi
Fitness of individual i.
N
Number of individuals in the population.
Di
Individual i in the population.
The implementation of GA is presented below.
Step1 [Initialization] Randomly generate an initial
population.
Step2 [Evaluation] Evaluate the fitness for eachindividual in the population.
Step3 [Selection] Calculate the roulette selection
probability Pi, ∀ i = 1 , 2 , ..., N by using equation (11).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
i

Pi ←

∑

N

( Fj

j =1

∑F )

(11)

k

k =1

Step4 [Create new population] For i ≤ (Pc × N ) , create
D1…i using crossover operations. Set the other individuals
using the roulette selection process.
Step5 [Carried out mutation] Apply mutation operations
on (Pm × N ) individuals.
Step6 [Keep fittest using elitist selection strategy]
Randomly select one individual from the generated
population. Replace the selected individual with the bestindividual in the current generation if it has not been
selected through the roulette selection process.
Step7 [Loop]: Loop until fitness reaches its maximum
value.

6. Numerical Experiments

References[1] Dallery, Y. and Gershwin, S. B., “Manufacturing
Flow Line Systems: A Review of Models and Analytical
Results”, Queueing Systems theory and Applications,
Special Issue on Queueing Models of Manufacturing
Systems, Vol 12, No. (1-2), pp. 3-94, 1992.
[2] Gershwin, S.B., “Manufacturing systems engineering“, Prentice-Hall, 1993

6.1. Simulation model
The simulation model of our S-PPL was developed
using C++. The uptimes and the downtimes of themachines were all assumed to be randomly variable anddistributed exponentially. The model was run until thefitness (throughput) attained maximum value. In eachgeneration the parallel machines, each work station is
replaced by an equivalent machine. Then, thethroughputs of the serial line of the equivalent machines
are evaluated. GA operations improve the throughput
until the fitness becomes constant.

6.2. Results
We applied our algorithm to an example of an
S-PPL with 10 work stations. The maximum allowable
number of machines to be connected in parallel is 5. The
algorithm was tested by performing many trials. Figure 3
shows the (fitness) throughput versus the number ofgenerations.
87
86
Fitness (%)

is based on a new technique of gene expression that iscalled gene family arrangement method (GFAM) whicharranges the genes inside individuals. We used the new
GA-simulation based method to determine some S-PPL
designs. After a number of generations, the nearest
optimal S-PPL could be determined. The results of thisstudy can be used to improve S-PPL design, andproduction engineers can use these results when they
design a new S-PPL.

[3] Burman, M.H., “New Results in Flow Line Analysis”,
Ph. D. Thesis, MIT, Cambridge MA, 1995.
[4] Enginarlar, E., Li, J., Meerkov, S. M. and Zhang, R.
Q., “Buffer capacity for accommodating machine
downtime in serial production lines”, International
Journal of Production Research, Vol. 40 No. 3, pp.
601-624, 2002.
[5] Vouros, G. A., and Papadopoulos, H. T., “Buffer
allocation in unreliable production lines using aknowledge based system”, Computers and Operations
Research, Vol. 25, No. 12, pp. 1055-1067, 1998.
[6] Spinellis, D., Papadopoulos, C.T., and MacGregor, J.,“Large production line optimization using simulated
annealing”, International Journal of Production
Research, Vol. 38, No.3, pp. 509–541, 2000.
[7] Papadopoulos, H.T. and Vidalis, M.I., “A heuristic
algorithm for the buffer allocation in unreliableunbalanced production lines”, Computers & Industrial
Engineering, Vol. 41, No. 3, pp. 261–277, 2001.
[8] Jafari, M. A. and Shanthikumar, J. G., “Determinationof optimal buffer storage capacities and optimal
allocation in multistage automatic transfer lines”, IIE
Transactions, Vol. 21, No.2, pp. 130-135, 1989.

85
84
83
82
81
80
0

1000

2000

3000

4000

5000

Ge ne ra tions[10] Goldberg, D. E., “Genetic Algorithms: In Search of
Optimization & Machine Learning”, Addison-Wesley,
1989.

Figure 3: Best fitness curve

7. Conclusion
This study describes a new GA-simulation based
method to find the nearest optimal design for a S-PPL.
Instead of using buffer size as a single decision variable,
this paper proposes an optimal design for the S-PPL byusing three decision variables: buffer size between eachpair of work stations, machine numbers inside theworkstations, and machine types. The GA methodology©ISAROB 2007

[9] Forrest, S., “Genetic Algorithms”, ACM Computing
Surveys, Vol. 28, No. 1, pp. 77-83, 1996.

516[11] Jingshan Li, “Overlapping Decomposition: A
System-Theoretic Method for Modeling and Analysis ofComplex Production Systems”, Technical Report,
General Motors Research & Development Center, 2003.
[12] Chiang S.Y., Kuo C.-T. and Meerkov S.M.,“DT-Bottlenecks in Serial Production Line: Theory andApplication”, IEEE Transactions on Robotics andAutomation, Vol. 16, pp. 567-580, 2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Implementation of a GA Driven Programming Training Support System
Eiji Nunohiro, Kenneth J.Mackin, Masanori Ohshiro, Kotaro Matsushita, Kazuko Yamasaki
Department of Information Systems, Tokyo University of Information Sciences1200-2 Yatoh-cho, Wakaba-ku, Chiba 265-8501, Japan{nunohiro, mackin, ohshiro, matsushita, yamasaki}@rsch.tuis.ac.jp
Abstract
Based on the analogy that the process for building a program is similar to configuring a puzzle, we
propose a programming training method in which theuser reconfigures program fragments or program puzzle pieces, and improves program structure and program flow comprehension skills. The proposed training system applies genetic algorithm (GA) to realize
the problem creation feature which creates a program
puzzle which best matches the user’s level of comprehension. The GA process evaluates both the level ofcomprehension of the user and the estimated difficultyof the puzzle, to determine the target problem difficulty, i.e. the difficulty of the puzzle. With this GA
feature, we developed a programming training support
system which automatically creates appropriate programming training problems matching the user’s comprehension level. In this paper, we first describe thefeatures and functions of the developed puzzle styleprogramming training support system. Next, we describe the results of implementing the developed system to a C language programming course at Tokyo
University of Information Sciences. Finally, we apply
statistical tests to the results of changes in the students programming skills, to show the validity of the
proposed system.
key words: genetic algorithm, e-leaning, programming education, T test.

1

introduction

Computer education, such as Information literacy
and Programming training, has received much interest recently in a wide range of academic programs,
including high schools and liberal arts undergraduate
programs. In the case of early programming training,
there are many topics that need to be studied together,i.e. programming language syntax, programming design methods for object oriented design, and problem©ISAROB 2007517

solving algorithms.
In this research we aim at creating a training support system which targets an introductory or beginnerlevel programming user, and allows the user to develop
and improve programming skills through a game-likelearning process.
There has been previous research on automatic generation of programming exercises [1]. In this system
proposed by Suganuma et. al., in order to modify
the difficulty of the created exercise, various parameters must be modified by the administrator. On theother hand, our proposed system determines the difficulty of the exercise by evaluating the current levelof the user from the user’s training history, and automatically creates appropriate programming exercises
matching the user’s level of understanding. The programming training support system developed in thisresearch is designed upon the similarity between thebuilding process of a software program and a jig-saw
puzzle. The proposed system trains the programmingskill of the user by providing exercises in which theuser must rearrange source code fragments, similar toa puzzle building task. By providing an interface tosolve programming exercises much like puzzle solving,it allows users who are weak in programming to continue training through a game-like experience. In our
proposed system, the difficulty of the problem is adjusting by modifying the granularity of the source codefragments, and the location of the breakpoints of thefragments or puzzle pieces. For this system, we developed an algorithm applying genetic algorithm (GA)to automatically generate exercises of appropriate difficulty that match the user’s level of proficiency[2].
We used the proposed training system in a beginner level class for a C language programming coursein Tokyo University of Information Sciences. We analyzed the changes in the students’ programming skill
in order to evaluate the validity and effectiveness ofthe proposed system.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

Outline of Programming Training
Support System

In order to improve programming skills, it is important to practice coding programs to fulfill a given problem specifications. But it is also important to learn toanalyze a given program code to (a) understand thealgorithm flow, (b) confirm that the source code correctly fulfills the requirements, and (c) suggest possible improvements in performance or maintenance.
The proposed programming training support systemprovides a training method to improve (1) program
flow analyzing skills, and (2) program structures comprehension skills. The proposed system trains these
aspects by having the user reconstruct programs fromprogram fragment pieces, much like building a puzzle
by finding the right puzzle pieces. The proposed system evaluates the student’s current level of progressfrom past training history, and automatically generates programming exercises which match the student’s
level of progress.
The programming exercise is automatically generated using the following 2 features.
(a) The progress management feature calculates thestudent’s current level of progress from the user’s
history of training with the system.
(b) The puzzle creation feature generates the program puzzle exercise from a completed programsource code. The puzzle creation feature can create different patterns of puzzle exercises from thesame source code, so that the user can repeatedly try solving different exercises with the same
correct answer. The different patterns of puzzle
exercises generated are selected to be of the correct level of difficulty for the user’s current level ofprogress. The level of difficulty of the generated
puzzle is determined by the number fragments andlocation of break points. Genetic algorithm is applied in the algorithm to determine the number offragments, and the location of the break points todivide the program into fragments.
Processing flow of programming training support
system is described in the following.
(1) System shows a problem to user according to thelearning contents such as a syllabus of program
practice or algorithm that user wishes.
(2) User selects a problem to answer from the problem that system showed(3) System breaks up a program into puzzle pieces depending on user level by using genetic algorithm©ISAROB 2007518

to determine the location and number of separation for the program.
(4) User reconstructs the program by selecting thecorrect program puzzle pieces in the correct order.
(5) System estimates user’s result, and accumulates
as the user history.
The basic component of this system to achieve theabove mentioned processing is shown in Fig.1.
1 exercise display section indicates the problem°
which is proposed by system using the learning
item and the user’s proficiency.
2 puzzle generation section breaks up a program°
into puzzle pieces depending on user progress levelby using genetic algorithm.
3 exercise selection section selects a problem°
from the learning item that user whishes or user
proficiency.
4 program management section manages the°
program used as a problem.
5 progress display section indicates°progress status from user proficiency.

user

6 progress management section manages user°
progress status.
7 progress evaluation section evaluates user°
progress from the user proficiency.
8 result display section indicates a result of user.
°
9 answer evaluation section estimates user’s an°
swer.

3

Programming problem creation

In this research, we created programming exercises
targeted towards beginner learners. The exercise required the student to arrange program fragments orpieces in the correct order to complete the program
described. In general, if the exercise given is muchtoo difficult or too easy for the student, the exercise
will not assist the learning process of the student, andmay adversely affect the motivation to study the subject. Therefore, it is necessary to create program puzzle exercises of appropriate difficulty according to thelevel of comprehension of the student. To realize this,we considered the following method to create program
puzzle exercises.
【step1】 Analyze the program source code by determining for each program statement, a) the control

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 1: Configuration of programming training support system
structure depth (control information) and b) variable reference (reference information).
【step2】 Calculate the difficulty level for each statement using the above control and reference information.
【step3】 Apply genetic algorithm (GA) search to findthe optimal combination of partition-points which
best match the progress level of the user.

In the following sections, we describe the genetic
algorithm search applied.

3.1

Possible granularity of program fragments or divisions are at identifier, expression, statements, or blocklevels. For this research we create program fragments
between lines of code. Program fragment granularity at the expression level will be implemented in the
future.

3.2

Figure 2: Example of program

Puzzle fragment granularity

Source code analysis

Each line of code is analyzed for information to be
used in determining the difficulty of the partition. For
each line of code, the control depth, and variable reference is calculated to measure the complexity of thesource code.
【step a】 control information analysis
The ”control depth” for each statement is calculated. The control depth is the depth of the nestof control statements, such as if, for, and whilestatements.

©ISAROB 2007

519

【step b】 reference information analysis
The ”variable reference” count for each statement
is calculated. The following criteria are used todetermine the variable reference for statement S:- a variable is defined in statement S- a variable is used in statement S
The variable reference count for a statement is thetotal number of times a variable is defined or usedin the statement.

An example of source code analysis is shown in Fig.
2.

3.3

Relation between Program partition
points and difficulty of puzzle

The program puzzle pieces are created by selecting
partition points between two lines of code. The difficulty of the puzzle is dependent on the number ofpieces and the location of the partition points. If aThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

partition occurs at a location with high control depth
and high reference count, the puzzle can be assumed
to have a high difficulty. From this assumption, we
define the following function to evaluate the difficultyof the partition. Partition point difficulty k is the evaluated difficulty when a partition occurs after programcode statement k. Partition point difficulty P pdk is
defined as:
P pdk = control depth ∗ ref erence count(1)

The difficulty of the puzzle is also affected by thedifficulty of the program algorithm used in the given
program source code, but this degree difficulty is dependent on the prior knowledge of the individual user.
For this research we let the user decide on the degreeof difficulty for an algorithm.

3.4

1 language specification which is being used by a°program.
2 algorithm which is used in a program.
°3 partition difficulty for a program.
°

Program partition by GA

The partition pattern (number of partitions andlocation of partitions) is selected using the user’s
progress level and puzzle difficulty. This puzzle difficulty is calculated using algorithm and language specification that is used in problem and partition difficulty
which is total value of partition point difficulty for eachpartition point. We apply genetic algorithm (GA) toselect the partition pattern.

3.5

Figure 3: Example of program

1 and °
2 are difficulty about a compoThe above °nent of a program, and this these are divided into 10
stages as following.

* Level of language specification : g(1 ≤ g ≤ 10)* Level of algorithm : a(1 ≤ a ≤ 10)
The partition difficulty is the sum of the partition
point difficulty value for the given program puzzle
chromosome, calculated with the following equation.

Chromosome expression
partition dif f iculty(pd) =The partition pattern expressed as a binary stringis used as the chromosome in the genetic algorithm.
The length of the chromosome is 1 less than the lines ofcode (statements) in the program. In the chromosome,the value 1 indicates a partition at that location (line),
and 0 indicates no partition at that location. Fig. 3
illustrates the relationship between chromosome andpartition points. In Fig. 3 an example of a 15 line
program is shown, and the partition points are thelocations where the chromosome value is 1, i.e. afterlines 2,5,6,8,10.

3.6

Fitness evaluation

Fitness is evaluated using the user’s level ofprogress, target problem difficulty, and difficulty of thecreated program puzzle. Below we describe the fitness
evaluation method.
【puzzle difficulty】
Puzzle difficulty is decided by 3 factors.

©ISAROB 2007n
X

(P pdk ∗ Chrk ) (2)k=1

where: n is number of program, P pdk is the puzzle
difficulty at position k, Chrk is the value of the chromosome at position k.
Puzzle difficulty is calculated as follows using theabove g, a and pd.
puzzle dif f iculty(d) = g ∗ a ∗ pd

【User’s level of progress】
The user’s level of progress is defined as the puzzle
difficulty of the last cleared problem.
【fitness function】
The value of the degree of adaptation is to estimate
whether degree of difficulty of a problem is suitable
for the intelligibility of the learner, and it’s calculatedusing the degree of fitness function.
The fitness function is defined as the following:f itness = |p − d|

520

(3)

(4)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where : p is user’s level of progress, d is the puzzle
difficulty.
The chromosome with the fitness value closest to 0
is selected as the optimal program puzzle combination
for the user.

4
4.1

Training Implementation and System Evaluation
Course procedure
methodandevaluation

The proposed system was implemented in a beginner level class for a C language programming coursefor 1st year students in the environmental information department curriculum at Tokyo University ofInformation Sciences. In the programming course, allstudents take a preliminary questionnaire in order todivide the course in 4 classes depending on past programming experience and knowledge. The four classes
are higher level, standard level, and 2 beginner levelclasses. The beginner level students are divided into 2classes which are beginner class A and beginner class
B from the questionnaire taken at the beginning of thecourse. Fig. 4 shows the course and evaluation flowof the beginner classes. The number in the figure represents the number of 90 min lectures. The questionnaire includes short quizzes which evaluate aptitude in
logical thinking, and students with higher scores wereplaced in the beginner class A, and students with lower
scores were placed in the beginner class B. As a result,
31 students were placed in beginner A, and 24 students
were placed in beginner class B. The proposed training system was applied only to the beginner class B
after the first mid-term exam, and changes in the exam
scores between the mid-term and end-of-term exams
were compared and evaluated.

4.2

Course Content

Table 1 shows the lecture content for the beginnerclass. Basic programming language topics were explained through lecture, and students create simple
programs applying the selected topic. The contentsof the lecture were identical for both beginner class A
and beginner class B, and the educational experienceof the lecturers for both classes were roughly the same.

4.3

Evaluation Results

Table 2 shows the results of the mid-term exam, theend-of-term exam, and the statistical T test. From

©ISAROB 2007

521

Figure 4: Course and Evaluation Flow
Table 1: Lecture content
lecture topic- constants and variables
・I/O statements (printf, scanf )・if statement (include nesting)・for statement (include nesting)・array (1 dimensional array)

the results of the mid-term exam, there was a large
difference between the average scores of the 2 beginner classes, and at 5% significance level, a statisticalsignificance was noted between beginner class A and
beginner class B. From the results of the end-of-term
exam, the difference in average scores between the 2classes is much smaller, and a statistical significancewas not found between beginner class A and beginnerclass B.
In order to collect the subjective views of the students towards the proposed system, an anonymousquestionnaire (5 being the highest rating) was taken
after the end-of-term exam. Table 3 shows the results

Table 2: Exam and T Test results
class
without system
beginner A
with system
beginner Bt test of beginner A& beginner B

averagestandard deviation
averagestandard deviation
t valuesignificance

mid
73.7
20.3
61.9
23.0
2.02
*p<0.05

end
66.3
19.6
63.3
20.3
0.56
none

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 3: Questionnaire results
question
1. The system waseffective in improving
programming skill.
2. The system will be
useful in futurestudies.

results[%]
2
3

1

22.7

0.0

0.0

17.4

0.0

0.0

5

4

18.2

59.1

30.4

52.2

of the anonymous questionnaire.
The questionnaire also asked for free opinions regarding the system in general. For positive views,
comments such as ”It was fun to study using the system.” and ”It was easy to understand programming
using the system” were received. On the other handcomments such as ”The operability was poor” gave
feedback on points to be considered in the future.

4.4

Discussion of Results

From the exam results in Table 2, it can be seenthat the beginner class B which had much lower average scores at mid-term compared to beginner classA, had improved to similar scores by the end of term.
From Table 3 showing the results of the questionnaire,
77.3% of the students marked 4 or higher to the question ”The system was effective in improving programming skill”. Also, 82.6% of the students marked 4 orhigher to the question ”The system will be useful infuture studies”. From the free opinion question, many
positive comments such as ”It was fun to study using
the system” and ”It was easy to understand programming using the system” were received. From these results, we conclude that the proposed training system
had been effective in supporting programming education.
On the other hand, points to be considered in the
future were raised through the questionnaire. There
were comments on the poor operability of the system,
and we feel that the man-machine interface and screen
layout needs to be reconsidered for improvement.

5

Conclusion

For software programming classes and other lab
based courses, there is an inherent problem of how©ISAROB 2007522

to address the needs of the beginner and slower learning students. Especially when the size of the classbecomes larger, it becomes more difficult to supervise every student individually. The effectiveness ofthe training course can be improved by providing aself learning environment in which these students canstudy at their own paces.
In this research, we proposed a programming training support system which targeted beginner and slower
learners. The system aims at improving programmingskill through a game-like interface. We applied the
proposed system in a C language course, and evaluatedthe educational effectiveness of the system. From theresearch results, we were able to show that this system was effective in supporting the learning of beginner and slower students. We feel that similar positive
results can be expected in programming courses forstudents without strong mathematical background.
For future works, we plan to redesign the manmachine interface and screen layout to improve theoperability of the system. We also plan to add features to enable algorithm training and object-oriented
programming training, in order to expand the application area of the proposed training system.

References[1] AKIRA SUGANUMA, TSUNENORI MINE,
TAKAYOSHI SHOUDA, Automatic Exercise Generating System That Dynamically Evaluates bothStudents and Questions Levels, IPSJ(Information
Processing Society of Japan) Journal, pp.18101817, Vol.46 No.7 July 2005
[2] Eiji Nunohiro，Kenneth J. Mackin, Masanori
Ohshiro, Kazuko Yamasaki, Applying Genetic Algorithm to a Programming Training Support System, The eleventh International Symposium on
Artificial Life and Robotics(AROB 11th
06)
Program p75，Proceeding Index GS10-1, January
2006

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ESTIMATION OF SOURCE-FILTER MODEL VIA ACOUSTICAL FEATURE
EXTRACTION BY GA-LIKE ALGORITHM
Mizuki Ihara, Shin-ichi Maeda and Shin Ishii
Graduate School of Information Science,
Nara Institute of Science and Technology
8916-5 Takayama Ikoma, 630-0192 JAPAN
Abstract
This study presents an estimation method for a sourcefilter model, which takes a temporal continuity of pitch
and amplitude into account and is useful, for example,
for instrument identification.
We assume pitch and amplitude as hidden variables
that tend to change continuously in time while theresonant property is fixed in order to reduce inherent indeterminacy in the source-filter model. In theobservation process of this dynamical system, whichmodels the generation of sound spectra from the hidden variables of the dynamics, pitch and amplitude
are highly nonlinear and non-Gaussian, i.e., a nonlinear dynamical system. Therefore, it is intractable toanalytically estimate the hidden variables as well as
the model parameters which define the resonant property. For this parameter estimation, we employed aGA (Genetic Algorithm)-like algorithm. After the parameter of each instrument was estimated from isolated notes, we verified the possibility of this system
identification method by reconstructing the spectrum
and by whether synthesized log-spectrum are close to
the original log-spectrum.
Index Terms – State space methods, Nonlinear
acoustics, Nonlinear dynamics, Acoustic filters

1

Introduction

The estimation of elements in sound such as pitch, amplitude and timbre has many applications, including
audio encoding with a small number of parameters,sound synthesis, extraction of instrument properties
and music transcription. In this study, we in particular focus on one of those applications, instrument identification.
As an example of existing studies of instrument
identification, Eronen used instrument features to create an instrument classifier in a hierarchical structureand evaluated it on solo tones from 30 instruments,©ISAROB 2007

527

achieving an identification rate for individual instruments of approximately 80 percent [1]. Brown presented a classifier with Gaussian mixture models, andobrained 75-85 percent accuracy in monophonic music
instrument identification [2]. However, these studies
could still not identify the instruments perfectly, especially for time-varying monophonic music.
For estimation of sound source or filter, Fant’s
source-filter model was sometimes used, which is originally used for modeling production processes of soundand speech [4]. In this model, it is assumed that the
combination of a sound-source generation pattern andthe filter that represents the resonant property of thetarget instrument produces observable power spectra.
Simultaneous estimation of the time-varying soundsource pattern and the resonant property, however,
suffers from the problem of indeterminacy; that is, observable spectra can be expressed in various ways. To
determine the source-filter model, therefore, some kindof constraint is necessary.
Itakura and Saito attempted to solve this problem
by identifying the filter part first. They modeled theshort-term speech signal as a stationary Gaussian process and estimated the filter using maximum likelihood
spectrum estimation [5]. The assumption of the stationary Gaussian process, however, ignores the effectof time-varying pitch and amplitude. Because of thisassumption, the model does not include the continuityof pitch and amplitude, and then, it is not enough toexpress real sound-source characteristics or resonant
properties.
In this study, we aim at simultaneous estimationof a sound-source and a filter with a less number ofparameters. We propose an estimation method forthe source-filter model that takes a temporal continuity into account by constructing a dynamical systemmodel for the sound-source. In particular, dynamics
of pitch and amplitude are considered. Additionally,
we assume that the resonant property does not vary intime from the fact that the body of instrument itself

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
should be consistent in time.
Here, we define the time-fixed function of the resonant property as

2

The sound generative modelω̃ Q(cos ω̃ − cos bk )2
H(ω̃) = 21−p {sin22 k=2,4,··· ,pωQ+ cos2(cos ω̃ − cos bk )2 }−2 , (5)2 k=1,3,··· ,p−1

Using the source-filter model, unknown sound source
generation Gt and resonant property (the filter part)
Ht are both estimated from observable power spectraof sound st , whose generation process is described as
st = φ(xt ) = Gt

Ht ,which follows the one of Line Spectrum Pair (LSP)in LPC [6]. In equation (5), ω̃ represents normalized
frequencies ω̃ = ωFs
2π , where Fs is the sampling frequency, and bk (k = 1, · · · , p) is the parameter of H.
The sound-source is time-dependent and represented
as the sum of Gaussians whose peaks are located atharmonic frequencies:(1)

where st is a d-dimensional vector representing
the spectrum amplitude of each digitized frequency,
and the operationmeans the Hadamard product(element-by-element product). The representation ofsource-filter model has an inherent indeterminacy that
we cannot identify Gt or Ht without additional constraints. To solve this problem, therefore, we introduce
the continuity in sound source Gt as a nonlinear dynamical system and assume the resonant property Htdoes not vary in time, written as H in the followings.

2.1

Gt (ωi ; at , ft , K, σp , τ ) ω P
i
K
2
N
(ω
|kf
,
σ
)
. (6)
= exp at + A exp −
i
t
p
k
τ
Here, − ωτi indicates exponential decay in frequency,
A is an adjusting parameter for power, and N (x|µ, σ)denotes the Gaussian distribution of x with mean µ
and variance σ. K and σp are the number of Gaussians in the resonant property and the variance of each
Gaussian, respectively.

Nonlinear dynamical system

The nonlinear dynamical system with Markov properties consists of the observation process and the statetransition process:st = φ(xt ) n1 ,xt = ψ(xt−1 ) + n2 ,

(2)
(3)

2.3

In addition to the constraint of time-invariance in theresonant property H, the hidden variables for soundsource xt = {at , ft } are assumed to change either continuously or discontinuously in time [7]. Whether continuous or discontinuous is modeled by means of a twocomponent Gaussian mixture:

where the function ψ(·) describes acoustical dynamics.
In these equations, xt = {at , ft } is a two-dimensional
hidden vector representing internal acoustical dynamics, where at and ft denote log-amplitude and pitch,
respectively. We express these equations as probabilistic models, p(st |xt , θ) and p(xt |xt−1 , θ).

2.2p(xt |xt−1 , θ) = η̄N (xt ; m1 , σ12 ) + (1 − η̄)N (xt ; m2 , σ22 )= η̄ N (at ; at−1 + log ρ, σ12a )N (ft ; ft−1 , σ12f )+ (1 − η̄) N (at ; m2a , σ22a )N (ft ; m2f , σ22f ) ,(7)

Observation process

When the sound is assumed to be stationary Gaussian as in Linear Predictive Coding (LPC), Gaussian
noise in the time domain is closely represented as multiplicative Chi-square distribution with γ = 3 in thefrequency domain [5], so we employ that for the noise
distribution. Since observable spectra can be written
as the multiplication of noise and estimated with Gt
and H: st = ŝt n where ŝt = Gt H, we obtain

where ρ is an attenuation constant ranging from 0 to1, m1 and m2 are mean vectors of at and ft . Covariance matrices for continuous transition are σ12 ={σ12a , σ12f }, and those for discontinuous transitions are
σ22 = {σ22a , σ22f }. The first term corresponds to thecontinuous transition where amplitude decreases exponentially and pitch does not change much. The second transition corresponds to the discontinuous one,
approximated as a Gaussian process with a large variance σ2 . Under these assumptions, the proportion of
the state transition being either continuous or discontinuous is represented by η̄ that takes a value from 0
to 1.

log p(st |xt , θ)



N 
X
1
st (i)st (i)log−
4Γ(1.5)σo ŝt (i) i=1
2σo ŝt (i)σo ŝt (i)




N
X
1
st (i)st (i)'log−.
4Γ(1.5)σo st (i) i=1
2σo ŝt (i)σo ŝt (i)
=

(4)
2

©ISAROB 2007

State transition

528

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
2.4 Joint distribution
EM algorithm when we employ strict alternate minimization of the free energy. Instead of the intractable
For time-series of the observable variable st and thecalculation of posterior distribution, however, we rehidden variable xt during t = 1, · · · , T , which arelax the strict alternate minimization as to restrict thewholely expressed as S1:T = {s1 , s2 , · · · , sT } andtrial distribution q(X1:T ) being a single Gaussian disX1:T = {x1 , x2 , · · · , xT }, respectively, the joint distribution, and then use a GA-like algorithm for thetribution for X1:T and S1:T is given byparameter estimation.

3.2

p(X1:T , S1:T )= p(s1 |x1 , θ)p(x1 |θ)QT

t=2 p(st |xt , θ)p(xt |xt−1 , θ),(8)

For the estimation of optimal parameters, we used GAlike algorithm known as a simplex method [8]. Thismethod looks for an optimal point by moving to a
new vertex whose function value is equal to or better
than that of the previous vertex. When there is nosuch vertex, the current vertex is the locally optimal
solution.

where θ is the parameter vector that defines the functions of Gt and H. Likelihood p(S1:T ) can be calculated by integrating this joint distribution with respect
to X1:T .

3

Parameter estimation

4

When the model has hidden variables, the EM algorithm has often been used for the parameter estimation. It requires the posterior probability of hidden
variables to be calculated, but this calculation is often
intractable in many nonlinear dynamical systems, likeour case. Therefore, instead of the EM algorithm, weused an equivalent but practically different methodology, a coordinate descent of free energy.

3.1

4.2

F (q(X1:T ), θ)= − log p(S1:T |θ) + KL [q(X1 :T )||p(X1 :T |S1 :T , θ)] .
(9)

A sound-source and a filter estimation

The performance of the proposed system was first examined by seeing if a sound-source and a filter can bewell estimated. Fig. 1 shows the original log-spectrum
and the log-spectrum synthesized by the identified system for cello. In the log-spectrum after learning parameters, the characteristic of pitch expressed as peaks
with constant intervals and the gradually decaying amplitude were well reconstructed.

Rq(x)where KL[q||p] = q(x) log p(x)dx is the KullbuckLeibler divergence. Apparently, minimizing the freeenergy with respect to the trial distribution q(X1:T )yields the negative log-likelihood − log p(S1:T |θ), andin that case, q(X1:T ) is equal to p(X1:T |S1:T , θ) because of the positivity of the Kullbuck-Leibler divergence. Therefore, the maximum likelihood (ML) estimation is achieved by the simultaneous minimization
with respect to q(X1:T ) and θ:  

θ̂ = arg maxθ log p(S1:T |θ)(10)

In this optimization, we can employ alternate minimization of the free energy with respect to q(X1:T ) andθ, and it is known that the minimization of the freeenergy becomes identical to the ML estimation by the           











= arg minθ minq(X1:T ) (F (q(X1:T ), θ)) .

Sound database

To verify the performance of the proposed method as
an instrument identification application, we used isolated notes from five kinds of instruments, taken fromthe University of Iowa Electronic Music Studios samples [9]. The dataset consists of samples of flute, horn,
trumpet, viola and cello. For each instrument, we prepared fifteen training data which consists of three datafrom five different tones, and six test data which contain the different tones with training data.

Free energy is defined for any trial distribution of thehidden variable, q(X1:T ), as





   











   

Figure 1: original log-spectrum and synthesized logspectrum

3©ISAROB 2007

Experimental evaluation

4.1

The EM algorithm and free energyGA-like algorithm approximation

529

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
4.3 Tracking of pitch and amplitude
For the practical use of this model for instrument
identification, we should evaluate this model with
We next evaluated whether the model can track themonophonic and polyphonic music to know the abilpitch and amplitude of the original cello sound. The
ity to identify instruments being played. Although theresult of pitch and amplitude tracking is shown in Fig.
GA-like algorithm used for parameter estimation does
2. In the left panel(frequency), since we used three
not require explicit gradients of the objective function, it does not guarantee global minimization. In our
future work, we also consider more strict free-energy
minimization for the parameter estimation.
15.5

600

amplitudefrequency

700

500

400

16

16.5
300
200
0

2

6
4
time frame

8

17

10

0

2

4
6
time frame

8

References

10

Figure 2: Pitch and amplitude tracking[1] A. Eronen and A. Klapuri, ”Musical instrument
recognition using cepstral coefficients and temporal features,” ICASSP, 2000.

data for each tone, frequency in each group of three
are almost the same. That means the pitch tracking
was successful.

[2] J.C. Brown, O. Houix and S. McAdams, ”Featuredependence in the automatic identification of musical woodwind instrument,” J. Acoust. Soc. Am.,
Vol.109(3), Mar. 2001.

4.4

Feature extraction[3] L.J. Lee, H. Attias, L. Deng, and P. Fieguth,”A multimodal variational approach to learningand inference in switching state space models,”
ICASSP, 2004.

In addition, the classification performance was plotted with Local Fisher Discriminant Analysis(LFDA)[10]. From the results in Fig. 3 that each instrument
tends to group, the system has the possibility to be
used in instrument identification.

[4] G. Fant, Acoustical Theory of Speech Production,
The Hague, 1960.
[5] F. Itakura, and S. Saito, ”A statistical method
for estimation of speech spectral density and formant frequencies (in Japanese),” Trans. IECE
Jpn., Vol.53A, pp.36-43, 1967.

2

1

0

−1
−2

[6] N. Sugamura, and F. Itakura, ”Speech Data
Compression by LSP Speech Analysis-Synthesis
Technique (in Japanese),” Trans. IECE Jpn.,
Vol.64A(8), pp.599-606, 1981.

viola
fl
horn
tr
cello

−7

−6

−5

Figure 3: Instrument classification of isolated notes bylearned characteristics of H

[7] R.A. Irizarry, ”Local Harmonic Estimation in Musical Sound Signals,” Journal of American Statistical Association, Vol.96(454), 2001.

5[8] J.C. Lagarias, J.A. Reeds, M.H. Wright, and P.E.
Wright, ”Convergence Properties of the NelderMead Simplex Method in Low Dimensions,” SIAM
Journal of Optimization, Vol.9(1), pp.112-147,
1998.

Conclusion and future work

We presented a system identification approach to theestimation of sound-source generation and resonant
properties. To consider the time-varying phenomena,a nonlinear dynamical system was employed, while thefilter representing the resonant property was fixed intime but estimated. A GA-like algorithm was used for
identification of this complex model based on available data. This model well reconstructed the original
sounds from the estimated sound source generation Gt
and the resonant property filter Ht .

[9] The University of Iowa Electronic Music Studios:
MIS, http://theremin.music.uiowa.edu/[10] M. Sugiyama, Dimensionality reduction of multimodal labeled data by local Fisher discriminant
analysis, Technical Report TR06-0008, Department of Computer Science, Tokyo Institute ofTechnology, Japan, 2006.

4©ISAROB 2007

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      .  *          .     ,     
 

 L   

) 678

     

   "# $%        "&'(%     !  678    

9        

/           

   -      

      2L   

      L .                  




 



The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Gene in Population 1 g1

0 1 0 1 0

A Gene in Population 2 g2

1 0 0 0 1

p = g1 XOR g2

1 1 0 1 1

0101100 Mutation
0100001
Crossover
1101001
1101100
0100100
1101001
0101001
0100100
Crossover 1100100
1101101 Mutation

p1 = Shifted p

1 1 0 1 1

q = p XOR p1

0 1 1 0: $0 ) 

: #0        

L   L    .  

      



5          



 







 





   

                 ,  .   

  -78-78          

















   -78


 L



           !  

 1010    

2           01101001

 

 <$               ©ISAROB 2007




#

$

532



  -78 M    

 #

$

m1 = 0,<

m2 = l 2 ,&

m3 = 2l3  ,    

 m4 = 0  (3)     

L!2l1 8l2 2l3l1 !l2 !l3 !
  ,/ l1

l2  l3  -   ( 

l2 + 2l3 = 0
l1 + l2 + l3 = L

!         

L = l1 + l2 + l3 + l4 ,M = m1 + m2 + m3 + m4 ,       9     
0101    $ &  <  L   
l2 l3  l4      M  m1 m2 m3  m4   ) 

<#

 

   l1  0      
 q 

 1

  

      8                             (l − 1) =5>      p  #$ 678              p                     l  

 <

& 6   

5

         p      q             

        
  678

 

)

  l  =5>        #        )  : $0 !             

      .    678  

 

 

 <

     



 &     * -78 ;      -78

   

3
?

0 ≤ l3 ≤ L/2  (2) (4)   

 

2L L/2
1  

24L

M=1 l3 =0  @  

  m4 = 0 

M L!2L+2M−4l3,(L − M + l3 )!(M − 2l3 )!l3 !

4The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007    ν̃1 

l3  l4 

5  ,/ l1      !  

l2

 



  /       .  L!
l1 !l2 !l3 !l4 !

#A

     
l4 −1 Cm4 





  

    <   





 <#  







 
##
 l1

l2
(4)  m4 ≤   l3 l4  m4      (2) 
l4 − 1        
  
2L
1 

24L





 

M=1 l1 ,l2 ,l3 ,l4 ,m4

 



:   

  -  
  
-  678

  L

 LB    











       678 1    4 × l /        

  6      pc (l)   pm (l) ;pc (l) + pm (l) = 24l  6   rc (l, j)5   

j = 1, . . . , pc (l)   -78    ;  
rm (l, i) i = 1, . . . , pm (l)  ν̃  /  
qc (l) + qm (l)
,
24l

ν̃ =

#(

 qc (l)  qm (l)  , pc (l)

qc (l) =        ;   8  

 6       ,   

 K = 2 8      #

678     



1    -78

#&ν̂       

  ,        /    678                 678 ν̃ = ν̃1 + ν̃2 .

 

  l  /  

M L!2l1 8l2 2l3 2l4 +1 l4 −1 Cm4, #$
l1 !l2 !l3 !l4 !

     ν̃2            Πl

L!2 8 2 2
l4 −1 Cm4.
l1 !l2 !l3 !l4 !
-        l1 l2 l3 l4 +1       

 K 

 K > 2            

 <    <$    

  K > 2   /             

rc (l, j),

#3

j=0



pm (l)

qm (l) = 

rm (l, i).

#?

i=0

L

ν̂



&

$&&(4

A??@3@

3((A#

A@#@@



#&#A@@?

A@&?(#@#($(&

A@<?<$@$<AAAA@(?#<@<#(AAA@3<33@(4AAAA@3?3 #08        

 678 ν̂  

2            

ν̃ =



/

 ! ©ISAROB 2007

C    

 LB  <   
 
#<
2L

 L   l + 1    

 #3#@     .  )2N ν + LÑ

 

⎞ ⎛
⎞ ⎛
⎞
⎛
⎞
0
0
0
1
⎜ 0⎟ ⎜ 0⎟ ⎜ 0⎟⎜⎟⎜Πl ⎟ , ⎜Πl ⎟ , ⎜Πl ⎟ , . . . , ⎜Πl 1⎟ .
⎝ 0⎠ ⎝ 0⎠ ⎝ 1⎠
⎝ 1⎠
0
1
0
1

  ν    678  Ñ ≡ 2

/

⎛

           678 ν̃  "#A ##% )



533

pc (l + 1) = 16pc(l) + 4pm (l),pm (l + 1) = 12pm (l),#4qc (l + 1) = 16qc (l) + 4qm (l) + 14pc (l),qm (l + 1) = 12qm (l) + 12pm (l),

$#

$A

$$

      

pc (1) = 4,
qc (1) = 0,pm (1) = 12,
qm (1) = 12.

$&
$<

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

- "#A%   

    

  (13)  /   D  


 

 

  

 678          



7/8  

 < -       ;   

       

      <  




 

/ 

, 

 678     -78               (18)            "$% F 9  



(13)

           

    -78

 

<#

 <$

Πl     /     

7/8        (13)     7/8          K > 2       678  

 



             

     

              

= 2     678

        

 

 

/ 678   K

    678    
    O(L)    
5

  0 -  678  -78              

        5 






       


 


         
         -, >  #(?AA#&A #@&AAA?@   E   

#         C ; 7 #4?("#% E ) ) ©ISAROB 2007"<% II G         

        H

534

  !%   $@   #  4('#A$ #44@
"(% ) ;J  > )K   H

G        

&   !    #&  #  #'$? $AA(
"3% F E 1 - ) - *

 L 

B 



H(A4'(#$ #444

       K   

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 !"  #!$   $(   <  3(('   H

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Function-discovery-system by the evolutional strategy using thesearch-accumulation
Mitsutoshi SAITO and Seiichi SERIKAWA
Graduate School of Electric Engineering
Kyushu Institute of Technology
1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka, 804-8550, JAPAN
Abstract
Recently, the system using bug type of artificial life
was proposed for discovering the function, and it was
improved. It is one of the extended models of Genetic
Algorithm (GA) and Genetic Programming (GP).
However, the function is not found sometimes when theobservation data is very complicated.
In this study, a new concept is introduced so that thefunction-search can be applicable to the complicatedobservation data. The function-search by S-System is
executed two or more times as a basic idea. This is called“search-accumulation”. To confirm the validity ofsearch-accumulation, equal-loudness-level contours(ISO226) are used as the observation data. Since thecontours are very complicated, it is very difficult to
express the counters as a function. In reality, thefunction-search was tried by the conventional S-System,
but the function does not agree well with the data. By theuse of the strategy of search-accumulation, the function
in agreement well with the contours is found successfully.
Thus, the validity is conformed.
Keywords: search accumulation, genetic programming

1. Introduction
Recently, a variety of problem-solving systems thatuse genetic algorithm (GA) and genetic programming(GP) are proposed. The purpose of GA is mainly the
optimization of the numerical value, while that of GP is
the generation of the symbol. Koza proposed thefunction-discovery-system using GP [1]. It generates theapproximate function that shows the relationshipbetween input data and output data in observation data.
In GP, the chromosome which represents a function isexpressed by the tree structure. It is optimized by thecrossover and mutation. However, there are problems
that the schema is destroyed and the length of generatedfunction becomes extremely long. To solve the problem,©ISAROB 2007535

the function-discovery-system that used the concept ofan artificial life was proposed [2]. This is called“S-System”, because it is the system that uses theconcept of sexual and asexual reproduction. The
improved model was also proposed [3]. However, both
GP and S-System cannot discover the approximate
function, when the observation data is complex.
In this study, a new concept is introduced togenerate the approximate function corresponding withthe complicated data. A basic idea is the execution of thefunction-search by S-System two or more times. This
strategy is called “search-accumulation” in this study. As
the result, the generated function agrees well with thecomplicated observation data.

2. The model for discovering function
2.1 Summary of S-System
The S-System is one of the evolutional strategies,
and the mail flow is the same as that of GA and GP. The
chromosome and the fitness are designed according to
the purpose of applied field. In general, the fitness isdefined as an evaluation-function in which the quality of
the chromosome is expressed. The main flow of
evolutional strategy is as follows.
(i) A lot of chromosomes are generated at random
as the first stage. (ii) The chromosome is selected. The
selected probability of the chromosome with high fitness
is high than of the chromosome with low fitness. (iii)
The crossover and the mutation are practiced between
two selected chromosomes. As a result, a new
chromosome is born. (iv) The procedures of (ii) and (iii)
are repeated until the fitness exceeds a certain thresholdvalue.
As the result, the chromosome changes and thevalue of fitness increases, according as the generation
proceeds.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

+
*
k

/
y

x

k

Fig.1 The example of chromosome for GP(tree structure of k  y  x / k ).

Start
i = 0, Di = D(1)

Function search fi (2)Yes

Di+1 = Di – fi

(3)i= i+ 1

(4)

i< n

(5)

No
End
Fig. 2 The flowchart of function-discovery-systemusing search-accumulation.
The length of chromosome for GA is fixed, but thatfor GP changes. The purpose of GA is the optimum of
the numerical value. On the other hand, that of GP is thegeneration of the symbols. The tree structure is regarded
as the chromosome for GP as shown in Fig. 1. The
S-System also represents the tree structure as thechromosome. The crossover of GP is different from thatof S-System. For GP, it is practiced between thechromosomes with different tree structure. Therefore, theform of tree structure dramatically changes. For example,
when the chromosome shows a function as shown in
Fig.1, the form of function greatly changes by thecrossover. Thus, the solution is changeable. On the other
hand, the function form doesn't change by the crossoverin S-System. The concepts of homogeneity andheterogeneity have been introduced into S-System. If
two individuals have the same tree structure, they arecalled homogeneity. The individual with a different tree
structure is called heterogeneity. The crossover ispracticed between homogeneities. It is called “sexual
reproduction”. The tree structure is not broken by the©ISAROB 2007

536

sexual reproduction, but only the constant value includedin the function changes. This is because the tree structureof homogeneity is equal. Therefore, the constant value is
optimized as the generation proceeds. On the other hand,
the individual without homogeneity generates its twocopies. This is called “asexual reproduction”. By thestrategy, the sexual reproduction can be practiced to thenext generation, because two homogeneities are born by
the asexual reproduction. In S-System, the change of tree
structure occurs at the mutation. As the result, theoptimization of the numerical value is practiced, thelength of function doesn't become long compared withGP, and the solution is stabilized. For more details, refer
to Ref [2].
2.2Function-discovery by search-accumulation
In this study, a new concept is introduced so that thefunction-search can be applicable to the complicatedobservation data. The function-search by S-System is
executed two or more times. This is called“search-accumulation”. Here, the observation data is
assumed to be D, and its approximate function isassumed to be f. Figure 2 shows the flowchart of thefunction-discovery-system using search-accumulation.
The details are described as follows.
(1) The counter i is set to be 0, and observation data
D is regarded as the initial data D0.
(2) An approximate function that agrees with theobservation data Di is searched by S-System.
The obtained function is assumed to be fi.
(3) The difference (i.e. Di - fi) between Di and fi isregarded as the new observation data Di+1.
(4) One is added to the value of the counter i.
(5) The search finishes when the value of counter ireaches a threshold. Otherwise, jump to (2).
After the search, the approximate function f is
obtained by the following equation.
nf   fi

(1)

i 0

When n = 0 in Eq. (1), the equation corresponds tothe function discovered by the conventional S-System.
Thus, the proposed search-accumulation is regarded as
the extended model of conventional S-System.

3. Experimental Result and investigation
To confirm the validity of the proposal method,
equal-loudness-level contours (ISO226) [4] are used as
an observation data. From the data, good approximate

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

Observation data
Approximate function

270 phone

2

40 phone

0

0
-1

-2
-2

-1

0

1

1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5

10 phone
-2

phon

dB

dB

1

2

-1

0

1

3
Hz
Fig.3 Equal-loudness-level contours about sound3

Fig.4 Example of approximate function by
Condition I (fitness=0.781).

(ISO226) [4].
3Table 1. The value of parameters for S-System.

Observation data
Approximate function

2

900
4000
0.5
20

70 phone

1dB

40 phone
0

-1

200

10 phone
-2

2
0.2
30

-1

0

1

2

3

Hz

Fig.5 Example of approximate function by
Condition II (fitness=0.918).

300
0.01
0.3
0.2
0.2
8
1

3

Observation data
Approximate function
70 phone

2

1

40 phonedB

Number of chromosomes
Maximum generation
Generation gap
Number of homogeneities at
next generation
Number of gates at next
generation
Tournament size
Crossover rate
Limitation of the number ofhomogeneities
Maximum number of gates
Threshold for going backMutation rate of virus
Mutation rate of bugs
Mutation rate of constant
Maximum depth of tree
Threshold of fitness for ending
program

2

Hz

0

-1

10 phonefunction was not obtained by the conventional S-System.
Figure 3 shows the contours. The loudness level[dB] that man psychologically perceives changes byfrequency [Hz] and the sound pressure level [phone]. In
another word, the loudness [dB] is represented as afunction f of the frequency [Hz] and the sound pressurelevel [phone]. In this study, the frequency [Hz] and thesound pressure level [phone] are regarded as the input
data, and the loudness level [dB] is regarded as theoutput data. The approximate function f(Hz, phone) is
obtained from those data. The search-parameter of
S-System is shown in Table 1. The search is tried under©ISAROB 2007

537

-2
-1

0

1

2

3

Hz

Fig.6 Example of approximate function by
Condition III (fitness=0.927).
the following three conditions.
Condition I: Conventional method.
Condition II: Search-accumulation I.
Condition III: Search-accumulation II.
As for Condition II and Condition III, the methodof the accumulation is different. In Condition II, after theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4. Conclusions

Condition III

0.95

Condition II
0.90

Fitness

0.85
0.80

Condition I

0.75
0.70
0.65
0

1000

2000

3000

4000

Generation
Fig. 7 Relationship between the generation and thefitness in Condition A, B, and C (average of 30
trials).
search of 1000th generation, the accumulation ispracticed. It is repeated every 1000 generation. Total
generation is 4000 (  1000  4 ). In Condition III, thefirst search is practiced until the 2000th generation. After
that, the accumulation is repeated every 200 generation.
Total generation is 4000 (  2000  200  10 ). The
examples of the experimental result are shown in Fig. 4,5, and 6. To avoid the confusion of drawing curves, only
the results of 10, 40, and 70 phones are shown. As
understood from these Figs, the waveform by Condition
II and Condition III are corresponding with theobservation data well compared with that of Condition I.
Figure 7 shows the relationship between the generationand the fitness in Condition I, II, and III. There is little
increase in fitness, when the generation exceeds 2000 for
Condition I. As for Condition II and III, it is understoodthat the fitness has improved whenever the
search-accumulation is repeated. Therefore, the validityof the proposal method is mentioned.
The fitness rapidly increases in Condition II after
the 1000th generation. This is corresponding to thegeneration when the first search-accumulation started.
On Condition III, the fitness rapidly increases after the
2000th generation is over. This is also corresponding to
the generation when the first search-accumulation started.
Thus, it is understood that fitness increases by thesearch-accumulation. The final fitness of Condition III ishigher than that of condition II. The frequency of thesearch-accumulation of condition III is more than that ofcondition II. From the above-mentioned, even if thenumber of total generations is the same, it is understoodthat the search-ability is different if theaccumulation-frequency is different.

©ISAROB 2007

538

The search-accumulation of the function-discovery
by S-System has been proposed in this study. When thismethod is used, the function corresponding well with thecomplicated observation data is generated. To confirm
thevalidityoftheproposedmethod,equal-loudness-level contours (ISO226) are used as anobservation data. In conventional method, the function inagreement with this data is not obtained. On the other
hand, the function corresponding well with theobservation data has been discovered by proposed
method.
As the problem in the future, there is the reductionof the length of discovered function. In addition, theeffectiveness should be verified by applying to morepractical problems.

References[1] J. Koza, Genetic Programming Ⅱ, Auto Discovery
of Reusable Subprograms, MIT Press, p.109, (1994).
[2] S. Serikawa and T. Shimomura, “Proposal of a
System of Function-Discovery Using a Bug Type ofArtificial Life”, Trans. IEE, Vol.118-C, 2, p.170,(1998).
[3] S. Serikawa and T. Shimomura,“Improvement of the
Search Ability of S-System (A Function-Discovery
System)”, Trans. IEE, Vol.120-C, 8, p.1281, (2000).
[4] National Astronomical Observatory, Chronological
Scientific Tables 2006, Maruzen Co., Ltd, p.424,(2005).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Development of Satellite Image Searching using Distributed Genetic Algorithmwith Normalized Correlation
Kotaro Matsushita[1], Kei Katayama[2], Kenneth J.Mackin[1], Eiji Nunohiro[1][1]Tokyo University of Information Sciences[2]Gracuate School of Tokyo University of Information Sciences1200-2 Yatoh-cho, Wakaba-ku, Chiba 265-8501, Japan{matusita, mackin, nunohiro}@rsch.tuis.ac.jp, g05004kk@edu.tuis.ac.jp
Abstract
Tokyo University of Information Sciences maintains
and distributes MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data as part of theresearch output for Frontier project. An intelligent
image search system is being developed as part of the
project, in order to retrieve requested images suchas matching images patterns or forest and field fires
extraction. The intelligent image search system applies GA (Genetic Algorithm) in the search algorithm.
When searching for a target image area within the
MODIS image database, it is possible that the searchalgorithm cannot match the optimal location when thebrightness of the search image data and MODIS data
image are very different. In order to solve this problem, we applied normalized correction to the GA fitness function to improve the matching accuracy. Further, we implemented the image search as distributed
genetic algorithm search over a PC cluster network,in order to increase the search speed within the satellite image database. We tested the proposed system
and verified the effectiveness of distributed genetic algorithm for the distributed MODIS satellite databasesearch process.
Keyword: Satellite image data, Image search system,
Brightness, Distributed genetic algorithm, nornalized
correction

1

to monitor changes in land cover and land use for extended periods of time.
In order to effectively utilize the large scale satellite
image database, an efficient search algorithm allowing
quick retrieval of the required information from thelarge database is required. An intelligent image searchsystem is being developed as part of the research output for the Frontier project, in order to retrieve requested images such as matching images patterns orforest and field fires extraction. The intelligent image
search system applies GA in the search algorithm.
In our previous work, we have proposed applying
GA to match partial images from MODIS image data[2]. But when searching for a target image area within
the MODIS image database, it is possible that the
search algorithm cannot match the optimal location
when the brightness of the search image and MODIS
data image are very different due to differences in timeof day of the satellite image retrieval. In order to solve
this problem, we applied normalized correction, whichis effective in matching images with different relative
brightness, to the GA fitness function to improve thematching accuracy. Further, we implemented the image search as distributed genetic algorithm search overa PC cluster network, in order to increase the search
speed within the satellite image database.
We applied our proposed method in our MODIS
data image matching system, and verified that the proposed method improved matching accuracy for images
with different relative brightness.

Introduction

Tokyo University of Information Sciences receives
MODIS image data from NASA satellites Terra andAqua, and distributes collected data to universities
and research institutions. MODIS sensors are the key
sensors aboard the Terra and Aqua satellites. Moderate resolution remote sensing allows the quantifyingof land surface type and extent, which can be used

©ISAROB 2007

539

2

Outline of
Image Match Search Processing

We develop an image search system which retrieves
matching images with similar features and characteristics to the search image, from the MODIS image
database. When searching for a matching image area

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

within the MODIS image database, it is possible thatthe search algorithm cannot match the optimal location for the following reasons.
(1) The MODIS image data for the correct locationof the search may be masked by clouds, or changed
from land surface alterations.
(2) The range of brightness for the requested search
image and compared MODIS image data may bedifferent due to difference in data retrieval time ofthe satellite data.
For the solution to problem (1), we proposed using
multiple time-series data for the same search space,and applying GA within the time-series data to search
for the requested image. For the solution to problem(2), we propose applying normalized correction, whichis effective in matching images with different relative
brightness, to the GA fitness function to improve thematching accuracy. We describe the basic flow of the
proposed search process below. Figure 1 shows theimage match search system outline.
(1) At the server, the requested search image, searcharea, and search time span is specified.
(2) The server distributes the search image, searcharea, and search time span for each individual
client machine.
(3) The requested search image and MODIS search
space image data are converted from color images
to grayscale images by each client. Each client
applies distributed genetic algorithm to search
for the requested image pattern among the distributed search space. Brightness of the gray-scale
images is adjusted by the distributed genetic algorithm.
(4) Clients which finished the search relays the search
result to other clients.
(5) When the search location is determined, eachclient analyzes the image data for the same location, and evaluates the land cover change forthe time span.

3

Image Search Processusing Distributed Genetic Algorithm

Recently, GA has attracted much attention as aneffective method for solving large scale complex problems [1]. GA searches for the optimum solution by
applying genetic operations of ’mutation’, ’crossover’,©ISAROB 2007

540

Figure 1: Outline of the image match search system’evaluation’ and ’selection’ to the population. In the
distributed GA model, the main population is divided
into several subsets, and genetic operations are repeated among the distributed sub-population.

3.1

Distibuted Genetic Algorithm

DGA (Distributed Genetic Algorithm) is a parallelmodel of GA. In DGA, the main population is divided
into sub-populations. DGA is also called the ’islandmodel’ from to this feature.
The divided sub-population is distributed into several clusters for parallel processing, and each cluster executes the GA search on the sub-population received. At each predefined interval or number of generations, each cluster exchanges individual solutions
or chromosomes. This is defined as a ’migration’ operation.
For this research, time-series image data are distributed to PC clusters, and in each cluster the GA
search is processed in parallel. The parallel search process is completed when any one of the clusters finds astrong match to the requested image pattern.
Figure 2 shows the outline of DGA model, executedthat preprocessing brightness confronts.

3.2

Brightness Adjustment Method

MODIS data is the value of the strength of reflected wavelengths for specified bands. This meansthat the value of MODIS data (i.e. brightness) forThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 3: Contents of the chromosome

3.3

Chromosome Expression

The image matching problem is defined as an optimization problem of finding the highest matching rate
image by modifying the 4 parameters: 1)x-axis location, 2)y-axis location for the center of the matching
image, 3)the magnification rate, and 4)the rotation
angle of the matching image. The GA chromosome isdesigned to encode the 4 parameters, and the fitness
is defined as the matching rate of the compared image
pixels. Figure 3 shows contents of the chromosome.

3.4

Figure 2: Outline of DGA model

the same location vary depending on the time of day.
When searching for a matching image area within the
MODIS image database, it is possible that the searchalgorithm cannot match the optimal location when thebrightness of the search image and MODIS data image
are very different.

Selection, Crossover,
Mutation Operations

In this paper, we evaluated the following genetic
operation methods.
(a) Selection Operation
The elite selection strategy was applied, in which
the individuals with the highest fitness will be selected for the next generation.

MODIS data is the value of the strength of reflected wavelengths for specified bands. This meansthat the value of MODIS data (i.e. brightness) for
the same location vary depending on the time of day.
When searching for a matching image area within the
MODIS image database, it is possible that the searchalgorithm cannot match the optimal location when thebrightness of the search image and MODIS data image are very different. The pattern matching algorithm, such as GA, must be able to match similar location images with different brightness. In this research,
we applied normalized correction, which is effective inmatching images with different relative brightness, tothe GA fitness function to improve the matching accuracy. Details of the fitness function is described in
section 3.6. In order to shorten the computation timeto acceptable limits for actual use, we first convert the
RGB color image data for the search image to grayscale images using the following equation [3].

(b) Crossover Operation
For the selection operation, a single locus method
is applied, in which a random locus or break pointis selected and two parents chromosomes swap one
side of the chromosome up to the break point.
This creates two child chromosomes of the samelength combining information of both parents.

p = 0.299 ∗ R + 0.587 ∗ G + 0.144 ∗ B(1) Receive the search image, search area and searchtime span, and read the specified date and location MODIS data from the MODIS database.

(1)

where p is the image pixel of the gray-scale image,
R,G,B are the respective red, green, and blue pixel
data of the color image at the same pixel location.

©ISAROB 2007541(c) Mutation Operation
For the mutation operation, 2 points in a single
chromosome are selected at random, and the bit
information between these 2 points are replaced
in reverse order.

3.5

Distributed Genetic Algorithm
ProcedureIn this research, DGA is applied to the image matchsearch system. The procedure for each client is thefollowing.

(2) Convert the color search image and color MODIS
image data to gray-scale images.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

(3) Create initial population
Create an initial population of chromosomes withrandom parameters, with coordinates (x,y) for
center of search region, magnification m, and rotation r.
(4) Calculate the fitness of the chromosomes.
(5) Evaluate the search completion condition.
(6) If the search completion condition is met, notify
the result to other clients.
(7) If the completion condition is not met, continue
with (8).
(8) Selection
Select individuals based on fitness, and create mpairs randomly.
(9) Crossover
Create new child chromosomes from the selectedpairs as parents, applying the specified crossover
operation strategy.
(10) Mutation
Applying the specified mutation strategy, randomly modify a part of the selected chromosome.
(11) Migration
At each specified number of generations, migrate
selected chromosomes according to the specified
migration strategy.
(12) repeat from (3).

3.6

Fitness Evaluation

The fitness is evaluated by calculating the matchingrate of the selected image pixel data that is specified
by the search parameters encoded in the chromosome.
The fitness function (C) is shown in equation (2),where the compared image size is M(pixel)*N(pixel),the brightness value (color value) for the template(MODIS) image and compared search image at coordinates(x,y) are T(x,y) and I(x,y), respectively.
N X
MX
x=1 y=1

C=v
uN M
N X
M
X
uX X
t
{I(x, y) − µI }2 ·{T (x, y) − µT }2x=1 y=1

(2)
where
N

M

1 XX
I(x, y)µI =N M x=1 y=1©ISAROB 2007

N

µT =

(3)

542

M

1 XX
T (x, y)N M x=1 y=1(4)

The fitness function is evaluated, and when the fitness result exceeds a predefined value, the specified
regions is selected as an optimal match of the search.

4

System Configuration

Figure 4 shows the image searching system configuration. The responsibilities of the data management
server are 1)Managing the MODIS Image database,2)Transmission of search information (MODIS satellite images and search space), 3)Configuration andtransmission of DGA parameters. Each PC clusteringclient receives the search information and GA parameters from the server, and processes the image data
search. Taking into account the ease of portability
for a multiplatform cluster environment, the system
was implemented using Java programming language.
Java RMI (Remote Method Invocation) architecture
was used to implement the network messaging andcontrol features.

5{I(x, y) − µI } · {T (x, y) − µT }x=1 y=1

Figure 4: Outline of Sysem Configuration

Expreiment Results

In this experiment, the effectiveness of the proposedbrightness correction method in the image search system is evaluated. The system configuration of the experiment is shown in table 1. Table 2 shows the DGA
parameters applied. Figure 5 shows the image of chromosome migration between islands (clients).
The mean retrieval times of 100 independent trials
with different random initial populations were used

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 1: Specification of PC Cluster system
Item
Number of PC
Specifications of PC

Contents
16
CPU: Intel Celeron D 310(2.13GHz)
Memory: PC2700 DDRSDRAM 512MB

Table 2: DGA’s parameter
Parameters
Population
Basic GA
Number of generations
Basic GA Parameters
Selection
Crossover type
Crossover rate
Mutation rate
Maximum fitness value
Strategy of selection
Number of island
Interval of migrations
Number of migrations
DGA Parameter
Method of migrations
Strategy of migrations

Contents
200
2500
Comparing fitness value of each generation
1 point
0.5
0.05
0.6
Elite
16
20
1 (population of maximum fitness value)
Neighbor (Migration to neighbor island)
The best chromosome is migration to the worst chromosome of neighbor island.

Figure 5: Example of migration between islands

Figure 6: Evaluation resultsto evaluate the performance of the proposed system.
Figure 6 shows the results.

6

The measurement of each trial uses the elapsed timeof when the performance measurement started searching and found the similarity image data. We wereable to verify that by applying normalized correction,
the matching accuracy for images with different relative brightness had improved compared to previous
research[2]. Parallel efficiency is not enough in comparison with cluster number. However, the searchingtime of DGA decrease as increasing number of cluster and we confirmed the requested search image thathave range of brightness is difference compared with
MODIS image data can be searched.

We developed a distributed parallel processing system which searches for requested image patterns
within a large-scale satellite image database. We applied normalized correction to the DGA fitness function used for the image search algorithm.
The tested results showed that the developed system with the proposed normalized correction method
improved the matching accuracy for images with different relative brightness, compared to previous research. From this result we were able to verify thatthe proposed method was effective for matching images within templates of different relative brightness.

©ISAROB 2007

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Conclusion

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

For future works, we plan to research algorithms toeffectively search for complex formations and features,
as well as investigate different methods to evaluate thetime series change in land formations.

References[1] Akio YAMAMOTO, Hiroshi HASHIMOTO, Application of the Distributed Genetic Algorithm for
IN-Core Fuel Optimization Problems under Parallel Computation Environment, Journal of NUCLEAR SCIENCE and TECHNOLOGY, Vol.39,
No.12, P.1281-1288, December 2002.
[2] Eiji Nunohiro, Kei Katayama, Kenneth J. Mackin,
Masanori Ohshiro, Kazuko Yamasaki, Image
Match Search System using Distributed Genetic Algorithm, SCIS&ISIS 2006, pp.1536-1541,
September 2006.
[3] Kunio Kashino, Takayuki Kurozumi, Hiroshi
Murase, Quick AND/OR Search for Multimedia
Signals Based on Histogram Features, The transactions of the institute of electronics, informationand communication engineers D-II Vol.J83-D-II
No.12 pp.2735-2744, December 2000.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Job-shop Scheduling Problems Based on Immune Ant Colony Optimization
Ming-lei SUI

Yong-li ZHANG

Engineering Institute of Engineering CorpsEngineering Institute of Engineering Corps

PLA Univ. of Science and Technology

PLA Univ. of Science and Technology

Nanjing 210007, Jiang Su China

Nanjing 210007, Jiang Su China

Email:good823@163.com; zyl19810723@yahoo.com.cn

processing order, another is time subject, which is the

Abstract
Mathematic

model

of

Job-shopschedulingsubject that process’s time wasting.

problem was established, and a kind of immune antnmin ∑ max{cih }colony algorithm is introduced to dispose Job-shop

i =1

scheduling problem. Through introducing the mechanismof immunity into the operations of genetic algorithm, thes.t. cik-tik+M0(1-aihk)≥cih

vaccines is obtained and updated in those operations.

i,j=1,2,…,n; h,k=1,2,…,m

Then, the immune operation is used on the evolution of

cjk-cik+M0(1-xijk) ≥tjk

populations. And the problems on easy appeared

i,j=1,2, …,n; h=1,2, …,mprecocity, low searching efficiency can be avoided when
immune operation takes effect. The simulations showthat the algorithm is feasible and efficient.

(1)

cik≥0;

i,

(2)

(3)

i, j=1,2, …,n; h=1,2, …,mDifine aihk and xijk as follows:

Keywords: job-shop scheduling; Ant colony algorithm;
artificial immune algorithm

1 Introduction
The job shop scheduling problem (JSP) is a central
NP-hard problem in Operations Research and Computer⎧1 machinei, if machinei before kaihk = ⎨
else
⎩0

(4)

⎧1 workpiecei before j to reach machinek
xijk = ⎨
else
⎩0

(5)

M0 is a maximum, which is used as a punishment forthe subjects.

Science [1] that has been studied extensively from avariety of perspectives in the last thirty years. Genetic
algorithm has been heavily employed in JSP [2]. Aiming
to GA’s inefficiency and pre-mature, this paper combines
the ideals of the Ant colony algorithm and Immune
algorithm together, and applies the proposed algorithm
into JSP. The main ideal is to distill and inject thevaccines from and in the evolving colony, in order thatthe evolution will be more steady-going and quickly.

3 Ant colony optimization and Immune algorithm
Ant algorithm was first proposed by Dorigo andcolleagues [3, 4]. Its essential idea of ACA is that: theindividual searching behavior which is directed by thedifferent probabilities to the next node forms colony
intelligent. It is versatile and robust in solving integer
programming such as NP problem[5].
However, due to its inherent defects it needs more

2 Description for the problem and its math modeltime to find the global optimal, its complexity canreflects that. It is likely to be trapped in local optimal andJob-shop scheduling problem’s math model is atypical integer programming model. To this problem,
there are two subjects: machine subject, which is©ISAROB 2007545

stop evolving, and can’t exploit new search space. Thisphenomenon can be seen in many simulations.
Immune algorithm (IA) is a bionic algorithm as a

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

simulation of immune system, which combines theEvery stage’s start time is max (MacTime_Tag ,certain and random together and has the capacity to

JobTime_Tag).

reconnaissance and exploit. It’s a developmental random

The steps to operate are as follows:

search algorithm. Immune algorithm is inspired of cellstagej of jobi, Set S_Time＝J_T_Tag

theory and net theory, and it realized the function ofChoose machine

self-adjust and creation of different antibody. It’s a newFree？

multi-peak value and the capacity of global searching [6].
Through the combination of ACA and IA, we canN

E_Time=S_Time+ci,kJobi has been done？

only be directed by the pheromone, but also by the
immune system. We combined the random factor and

MakeSpan＝M_T_Tag

certain factor together, so we can improve the search

E_Time=M_T_Tag+ci,k

J_T_Tag=E_Time; M_T_Tag=E_TimeYdiversify the colony and the colony’s evolution will notNY

algorithm which is characteristic of searching in

Fig. 1. sketch map for decoding

efficiency and quality, enrich the diversity and resist the
pre-mature.

4.3 Steps for immune ACA
The procedure of the ACO algorithm manages the

4 Job shop scheduling based on immune ACA

scheduling of three activities: ants’ generation, actions
and pheromone update. The steps of Immune Ant Colony
Algorithm in this are follows:

4.1 Initialize the antibodies
The antibodies are initialized as a series of random
permutations of the integers from 1 to m×n, which standfor the ants’ routing order. We obtain the antibodies as
Ref.7, after the steps as follows.
Step1: Create a random permutation of the integers
from 1 to 9: such as [5 2 8 4 3 9 7 6 1].
Step2: array=array-1: [5 2 8 4 3 9 7 6 1]-1＝[4 1 7
3 2 8 5 0].
Step3: divide matrix by m, and then add 1:

int([4 1

7 3 2 8 5 0]/3)+1=[2 1 3 2 1 3 3 2 1].
Then we obtain an antibody as Ref.7, where: 1 stands
for job 1, and the same to 2, 3. The first 2 stands for stage
1 of job 2, the same to 1, 3.

1) Select list replace tabu list
The way in tabu list results in that it’s hard to selectnext node. If some nodes have been selected, there will
be some vacancies. It’s more complex to handle the data,so change the tabu list to select list. The operations are
explained as follows:
If the node has been selected, erase it from the selectlist, and the following nodes move forward. For example:
if node.1 has been selected, change the list to:{2→3→4→5→6→7→8→0},

then{2→3→4→5→6→8→0→0},

until

the
the

node.7:last:{5→0→0→0→0→0→0→0}.
2) Pick-up vaccines from memory storeroom
Memory storage is used for storing the best ant, the4.2 Decoding scheme
The crucial of the procedure is to guarantee two
steps:(1) The same stage can’t be done on two different
machines;(2) The same machine can’t do two job at the sametime.
In programming, two tags are set: job time tag(JobTime_Tag) and machine time tag (Mac_Time_Tag).

©ISAROB 2007

546

better ant and the poor ant. Vaccines are picked–up fromthe memory storage through the follow formulas at a
certain probability.
The antibodies of higher thickness and lower
appetency will be restrained, and the antibodies of lower
thickness and higher appetency will be promoted. So theant colony will be more diversiform. The ith antibody
will be selected as [8]:The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

i = 1, 2," , N 0∑ f (x ) − f (x )
j =1

i

(6)

Mutation1

D( X i ) = N0

j

A=[1

2

3

4

5

6 7

8

]

B=[1

2

8

4

5

6 7

3

]

Fig. 3. Sketch map for mutationN01
f ( xi ) − f ( x j )∑D( X i )j =1, i = 1, 2," , N 0= N0 N0p( xi ) = N0
1
f ( xi ) − f ( x j )∑∑∑i =1 D( xi )i =1 j =1(7)

After mutation, compute the new ants’ f i , select thebetter to save.
Crossover is operated after all the ants finished theirrouts. Randomly select two ants and operate as Fig.4
illustrates:

3) Decide the next one to be processed
Take advantage of Roulette select scheme in GAs:

Crossovser

A= [ 1 3 5 7 4 2 8 6 ]select operation is based on the individual’s adaptive
value. The most well-known scheme is Roulette select

B=[2 4 6 5 1 3 8 7]
A= [ 1 2 5 8 4 3 7 6 ]
B=[3 4 6 5 1 2 7 8]scheme proposed by Holland [9].
psi = f i

∑fi =1Fig. 4. Sketch map for crossover

(8)

N

i

After crossover, compute the new individuals’ f i , andN is the number of nodes that are can be selected, f i

the better individuals survive to the next repetition.

＝ [τ ij (t )]α ⋅ [η ij ]β . The steps are as follows: Compute thevalue according to f i ; accumulate every element; thearray divides the sum of every element.

5 Instances simulation
Literature [12] gives the study conclusion on how to
select α, β, ρ. In this paper, after some tries, the4) Injection vaccine
Select a piece of information segment from theselected antibody at a certain probability, and inject theparameters

are

set

as

follow:

α=1,

β=5,

ρ=0.6,Q=1000,and set iterations=500.

vaccine to the ant. Just as fig.2 shows:
5.1 Instance 1
This is a Job-shop scheduling problem select from
Ref.13: LA01(10×5). All of the basic ACA and IACA
obtain the optimum: 666, the comparisons between them
are showed as follows:
Fig. 2. sketch map for vaccinating

5) Introduce mutation, crossover
In order to avoid the pre-maturity of the ACA,
mutation, crossover[10] are introduced into this way
which can guarantee the diversity of solutions, exploit
new search space, andthen the phenomena trapped in

the local optimal can be avoid to a certain extent.
Mutation is operated when the route has been doneby an ant. Randomly select two nodes in its path, swap
them, and then replace them to the rout.

©ISAROB 2007

547

Fig. 5. Comparison in solving LA01

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5.2 Instance 2This is a Job-shop scheduling problem select from
Ref.13: LA06(10×10). All of the basic ACA and IACA
haven’t obtained the optimum; basic ACA: 990, IACA:
945. The comparisons between them are showed asfollows:

Fig. 6. Comparison in solving LA06

6 Conclusions
This paper applies ant colony algorithm to JSP, andthen introduces immune algorithm to the ACA, anddesigned the selection and the injection criterions for
vaccines. From the simulations we know that the searchefficiency and quality are improved, and the diversity isenriched, the pre-mature is refrained.

References[1] Dimitris Bertsimas, Jay Sethuraman (2002), From fluid
relaxations to practical algorithms for job shop scheduling:
the makespan objective. Math. Program., Ser. A 92:
61–102[2] WANG Ling, ZHENG Da-zhong (2001), Advances in Job
Shop Scheduling Based on Genetic Algorithm. Control
and Decision, 11(16):641-646.
[3] M Dorigo,V Maniezzo and A Colorni (1991). The Ant
System：An Autocalytic Optimizing Process[R]. Technical
Report No.91-016Revised,Politecnicodi Milano,Italy.
[4] Marco Dorigo (1996). The Ant System：Optimization by acolony of cooperating agents[J]. IEEE Transactionson
System,Man,and Cybernetics-part B, 26(1)：pp29-41.
[5] A.Colorni, M.Dorigo, V.Maniezzo, M.Trubian (1994), Antsystem for Job–Shop Scheduling, Belgian Journal ofOperations Research, Statistics and Computer Science.
[6] GE Hong (2002). Immune Algorithm: A Survey,
JOURNAL
OF
SOUTH
CHINA
NORMAL
UNIVERSITY (NATURAL SCIENCE EDITION),©ISAROB 2007

548

3:12-126[7] CHEN Xiong, LI Hai-gang, WU Qi-di (2002). Study on
Job - shop Scheduling Problem Based on Genetic
Algorithm. JOURNAL OF TONG JI UNIVERSITY,
30(1):88-91.
[8] Lovbjerg M, Rasmussen T K, Krink T (2001), Hybrid
particle swarm optimization with breeding andsubpopulations[C]. In: Proc of third Gentic andEvolutionary computation conference, San Francisco,USA.
[9] D. Goldberg and R. Lingle (1985), Alleles, loci and thetraveling salesman problem, The first International
Conference on Genetic Algorithm, pp. 154-159.
[10] Bierwirth C, Mattfeld D C (1999). Production Schedulingand Rescheduling with Genetic Algorithms. Evolutionary
Computation , 7(1) :1-17[11] Zne-Jung Lee, Chou-Yuan Lee and Shun-Feng Su (2002),
An immunity-based ant colony optimization algorithm forsolving weapon-target assignment problem. Applied Soft
Computing. (2) pp:39-47.
[12] YE Zhi-wei, ZHENG Zhao-bao (2004), Configuration of
Parameters α、β、ρ in Ant Algorithm. Geomatics andInformation Science of Wuhan University. 07 pp:597-601.
(In Chinese)[13] WANG Ling (2001), Intelligence Optimal Algorithm &Application. Beijing: Tsinghua press.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Features of action potential initiation in cortical neurons
with multiple internal states
Miki Matsuo† and Kazuyuki Aihara†,‡†Aihara Complexity Modeling Project
ERATO, JST, The University of Tokyo
4-6-1, Meguro-ku, Komaba, Tokyo, 153-8505, Japan.
‡
Institute of Industrial Science, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Abstract
Analyzing the dynamics of action potential initiation in cortical neurons in vitro, some features of theinitiation dynamics of cortical neuron action potentials are shown to be outside the range of behaviors
described by the Hodgkin-Huxley formalism. We propose a new model which describes the dynamics of
sodium channel, where we are based on the hypothesis that the gate subunits are interacting each other.
This model can reproduce one of the features of action
potential initiation in cortical neurons, variant onset
potential.

1

Introduction

Hodgkin-Huxley formalism is a great guidepointin physiology, because it showed that a mathematical study can contribute to understand the functionof nerve for the ﬁrst time. This formalism not only
describe the electrical phenomena of nerve excitation
well as data ﬁtting but also describes nature of electrically excitable membrane. Hodgkin-Huxley formalism
considers excitable membrane as a population of ionicchannels, and provides us various insights about functional structures of ionic channels [1].
Hodgkin-Huxley formalism has several assumptionsin the starting point of the consideration. Here we give
three assumptions of those.
• The state of an ionic channel is determined by
conformation of subunits called gates.
• A gate can be in either of two conformations
which are uniquely determined by membrane potentials.
• Gates are independent of each other.

©ISAROB 2007

549

In terms of structure of channel, these assumptions
means that channel is not fundamental element itself
but is composed of more fundamental gate elements,
the gate obeys to very simple rules, and the actions of
the gates are aﬀected by voltage sensor. Thus we seethat these assumptions provide rigorous descriptions
about structure of ionic channel. These descriptions
have been veriﬁed by cloning or X-ray structure analysis [2, 3], but even now these are fundamental concepts
in understanding ionic channel.

On the other hand, the phenomena which are notdescribed by H-H formalism are also discussed. About
twenty years ago, Matsumoto insisted that, in orderto describe action potentials in squid axon, it is necessary to assume that the conformation changes of gatesare not only determined by membrane potentials but
also the time after onset [4]. Recently Naundorf et
al. insisted that two characteristic features of action
potential initiation in neurons in cat visual cortex invitro and in vivo, rapid onset of action potential andvariance of onset potential, cannot be described by HH formalism [5]. As shown in Fig.(1), action potential
initiation recorded from rat hippocampal CA3 pyramidal cell in vitro also shows variance of onset potential.
Thus detailed analysis have revealed features that are
impossible to be described by H-H formalism. How
those features are are made by ionic channel, and how
should we change our recognition of ionic channel? In
this paper, we especially note on one of the features,variant onset potential. We propose that this variancecan be produced by gates which interacts each other,while gates are assumed to be independent in the HH formalism. With this proposition, we will see thationic channel is not a simple but a complicated object
which is composed of mutually communicating several
subunits.

rate of membrane potential change(mV/msec)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007Now we restrict the catalytic reaction to ﬁrst order, sothat the transition rates become

20

15

10

(5)
(6)

0
βm (V, n) ≃ βm
(V ), βn (V, m) ≃ βn0 (V ),(7)

where m0 , n0 are constants which imply gate states
where catalytic function works well. Thus the rateequations are linearized as

5

0

-50

-45

-40

dm
0
0
1
= αm
(V )(1 − m) − βm
(V )m + αm
(V )(n − n0 )dtdn
= αn0 (V )(1 − n) − βn0 (V )n + αn1 (V )(m − m0 )dt

-35

membrane potential (mV)

Figure 1: Phase plot (dV/dt versus V) of the action
potential around the initial phase of the action potential in cortical neurons recorded in vitro. Arrows indicate three sample action potentials. The experimental
data was provided by Hujisawa et al. [6].

201αm (V, n) ≃ αm
(V ) + αm(V )(n − n0 ),01αn (V, m) ≃ αn (V ) + αn (V )(m − m0 ),Model

In order to understand the features of the action potential initiation, we consider a sodium channel composed of several gates that are not independent. (Herewe call fundamental subunit which take two state”gate”, even when it is not related to open-close of
the channel.) This model is shown to be able to produce variation of onset potential.
The model is deﬁned as following. We assume asodium channel is composed of three types of gates,
M, N, and H, and their gate variables are m, n, and h.
Among these gates, M and H are activation and inhibition gates for each as given in the H-H formalism, andN is another gate that we introduce here. We assume
that H gate does not work, that is h = h0 (const), in
this paper.
Conformation changes of gates M and N are assumed to mutually catalyze asgate M :gate N :S0M
S0N

αm (V,n)⇀↽βm (V,n)αn (V,m)⇀↽βn (V,m)

S1M

(1)

S1N ,

(2)

where catalytic relation is included in state dependence of transition rate of gate state. The rate equations of these reactions are given bydm= αm (V, n)(1 − m) − βm (V, n)mdtdn= αn (V, m)(1 − n) − βn (V, m)ndt©ISAROB 2007

(3)
.

(4)

550

(8),
(9).

The ionic current caused by the sodium channel is assumed to be aﬀected gate M and N as
IN a = −g N a mh0 (V − VN a ).

(10)

Eqs.(8)(9)(10) and the balance equation between
membrane potential and ionic current are taken as ourmodel of action potential. In our numerical simulations, we assume that the transition rates takes following sigmoid forms,−10(V ) = τm0αm/(1 + exp(−(V − Vm0 )/km0 ))−10(V ) = τm0βm/(1 + exp((V − Vm0 )/km0 )),−10αn (V ) = τn0 /(1 + exp(−(V − Vn0 )/kn0 ))−1
βn0 (V ) = τn0/(1 + exp((V − Vn0 )/kn0 )),−11
αm (V ) = −τm1 /(1 + exp(−(V − Vm1 )/km1 ))−1
αn1 (V ) = τn1/(1 + exp(−(V − Vn1 )/kn1 )).

(11)
(12)
(13)
(14)
(15)
(16)

The concept of our model is illustrated in Fig.(2), but
detailed explanation is given latter.
XQNVCIGUGPUQTICVG0

ICVG/

Figure 2: Illustration of the concept of the interacting
gates model. Voltage sensor aﬀects to the actions ofgate M and gate N, while these gates interact eachother.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

Result






O

Now we investigate behavior of the model. In our
simulation, values of the parameters are assigned referring parameters used in the H-H equations, except
for the parameters used in Eqs. (11)∼(16). We usedparameter values as τm0 = 0.1ms, Vm0 = −35mV,
km0 = 6mV, τn0 = 60ms, Vn0 = 80mV, kn0 = 4mV,
τm1 = τn1 = 0.1ms, Vm1 = Vn1 = −30mV, km1 =kn1 = 6mV. Phase plot that graph the rate of changeof the membrane potential dV /dt against the instantaneous membrane potential V is shown in Fig.(3).
Although the onset is seen to be slow compared toexperimental data, we can see the variance of onset
potential. (Note that it is necessary to consider another mechanism in order to get a rapid onset [5].)This variance of onset potential is considered to be







̂




















P

Figure 4: Orbit of the channel state (m, n). Excitationof gate M is not happened at constant value of gate
variable n, but excitations can be initiated at various
values of n.

TCVGaQHaOGODTCPGaRQVGPVKCNaEJCPIGO8OUGE











̂

̂

̂

̂

̂

̂

̂

OGODTCPGaRQVGPVKCNa O8

Figure 3: Phase plot calculated with the interacting
gates model. Although the onset is seen to be slow
compared to experimental data, the variant onset potential is seen.
made by internal dynamics of the state of the sodium
channel. In Fig.(4), we show the orbit of the channel
state (m, n). As we see, the channel is closed (m = 0)
and the value of n gradually decreases before action
potential initiation, and excitation follows (m > 0) afterward. Here we note that excitation happens not
at a constant value of n but at various values of n.
And we can show with Eq.(8) that the onset value of
potential depends on the value of n. Thus it can besaid that the channel with interacting gates as shownin Fig.(4) can produce the onset potential variation.
Lastly we say about relation between dynamical
feature supposed in the interacting gates model andstructure of ionic channel. In the H-H formalism, theaction of voltage sensor is transmitted to each selective©ISAROB 2007

551

ﬁlter (gate), and each selective ﬁlter independently
open or close. In interacting gates model, voltage sensor aﬀects to not only selective ﬁlter but also hidden
gate component, and this hidden gate component mutually interacts with the selective ﬁlter (Fig.(2)). Thismight be related to paddle hypothesis recently proposed by MacKinnon et al. [3]. In the conventional
idea, the action of voltage sensor aﬀects only to local structure around sensor, while paddle hypothesis
consider that voltage sensor can change global formof the channel. The change of global structure canbe interpreted as the case that the local subunits arestrongly correlated. In our study, we have seen that
ﬂuctuation of action potential includes features which
does not emerge in the case that channel is a simple
compound of local units. This would also imply thatanalysis of ﬂuctuation is useful to generally consider
structure and function of channel.

4

Summary

In order to understand the features of the action
potential initiation, we proposed a new model which
describes dynamics of sodium channel, where we assumed the channel is composed of several gates thatare not independent. One of the features of action
potential initiation, ﬂuctuating onset potential, wereshown to be able to be reproduced by this new model.
This implies that the ionic channels are not simple
compound of local units but global and complex object
whose subunits are strongly correlated. This would

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

mean that analysis of ﬂuctuation is useful to generally
consider structure and function of channels.

Acknowledgements
We thanks to S. Fujisawa to kindly provide us experimental data of action potential.

References[1] A. L. Hodgkin and A. F. Huxley, J. Physiol.
(Lond.), vol. 117, pp. 500-544, 1952.
[2] M. Noda, H. Takahashi, T. Tanabe, M. Toyosato,
S. Kikyotani, Y. Furutani, T. Hirose, H.
Takashima, S. Inayama, T. Miyata, S. Numa, Nature vol. 302, pp. 528 - 532 1983.
[3] Y. Jiang, A. Lee, J. Chen, V. Ruta, M. Cadene, B
T. Chait and R. MacKinnon, Nature, vol. 423, pp.
33-41, 2003.
[4] G. Matsumoto, J. theor. Biol., vol. 107, pp. 649666, 1984.
[5] B. Naundorf, F. Wolf, and F. Volgushev, Nature,
vol. 440, 1060-1063, 2006.
[6] S. Fujisawa, N. Matsuki and Y. Ikegata, em Cerebral Cortex, vol. 16, pp. 639-654, 2006.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Signal Processing With Spikes
Michael Gutmann
Grad. School of Inf. Science and Technology
The University of Tokyo
gutmann@sat.t.u-tokyo.ac.jp

Abstract
In the past, notable advances in the understanding of neural processing have been made when sensory
systems were investigated from the viewpoint of adaptation to the statistical structure of its input space.
Here, we point out that emphasis on the input structure has happened at cost of the biological plausibilityof the corresponding neuron models which process thenatural stimuli. Hence, we propose a spiking neuronmodel to process natural stimuli for which we derive
here a learning rule to estimate its parameters.

1

Introduction

Science is about exploring structure and functionof incompletely understood systems or phenomena.
What concerns the system “brain” or the phenomenaof “learning” or, say, “vision”, great advances havebeen made since the debates in the early 20th century
whether individual neurons are the basic elements of
the nervous system or not (keyword neuron doctrine).
Since then, much emphasis has been on structure, i.e.
on individual neurons or on how distinct classes ofneurons are connected with each other. However, thefunctional aspect of these networks of neurons cannot
be fully understood by its structure alone: How are
the interconnected neurons marshaled to give rise to
behavior? Why are the neurons as they are? Why are
they connected they way they are?

Kazuyuki Aihara
Institute of Industrial Science
The University of Tokyo,& Aihara Complexity Modelling Project
ERATO, JSTaihara@sat.t.u-tokyo.ac.jp
some parts of the brain (redundancy reduction hypothesis) [2]. As information theory requires knowledge about the statistical structure of the information
source, this approach triggered research into properties of the sensory environment, especially with respect
to vision [4, 6, 14, 16], and its link to neural processing[1, 3, 5, 15, 17]. What regards vision, in addition to theprinciples of information theory, other principles wereused to explain its function in the form of “The earlyvisual system might be optimized for . . . ”, including
energy expenditure [18], minimal wiring among neurons [8], or minimal number of active neurons [12].
The early visual system comprises the retina, thalamus
and the primary visual cortex. But higher visual areas have also been investigated in this manner, making
interesting predictions on yet undiscovered cell types[9, 10].

3

Research question

What regards the early visual system in the retina,
multiple “principles of operation” for its function havethus been formulated. Which is the right one? We
think that this question is ill posed since it is unlikelythat the forces of evolution can be reduced to a single
optimization scheme. But it is worth rememberingthat the above theories for the retina use the following
assumptions which we might summarize as linear ratecoding assumption.
• Information is conveyed using a firing rate code.

2

Background• Retinal processing is described by a linear filter.

These kind of questions were mostly addressed fromthe second half of the 20th century onwards. The
brain was considered as a information processing system, and principles of signal processing and information theory were used to understand the function of©ISAROB 2007

553

• The statistics of the natural scenes is describedby the power spectrum.
Although these assumptions are of course well justified as a first approximation to reality, we feel it isThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

time to re-consider some of them. Motivation comesfrom the finding that the linear rate-coding assumption becomes a limiting factor for the deeper understanding of the retina – as explained above, actuallydifferent optimization schemes give the same results
under that assumption – and for the link to experimental results. First, single spikes were found to be
information carrier in the early visual system of forexample the fly [13], second, significant redundancy
was found between retinal ganglion cells (also for cellswith non-overlapping receptive fields) [11]. Hence, inthe following, we re-address the neural processing of
the retina. Specifically, we make the modification thatneurons are modeled as integrate and fire elements, sothat the research question becomes
Which properties of the neural system “retina” can be
explained with the function-hypothesis that neurons in
the retina encode the input with spikes such that it canbe linearly reconstructed from the spike times with aminimal reconstruction error.

with an exponential kernel with refractory time constant τr . The neuron is driven by external input I,
which is obtained through linear filtering of the natural stimulus Y with the encoding filter w. The kernelκ models the soma impulse response function with an
exponential kernel with time constant τm .
From the obtained spike times {tf }, we linearly reconstruct the stimulus Y via
XŶ (t) =Φ(t − tf ),(4)
f :tf <t+Td

with the decoding filter Φ and estimation time delay
Td .
Both the encoding filter w and the decoding filterΦ are unknown, and have to be found in order to minimize the average reconstruction error J
+
* Z
1 Tt
2
e(t) dt(5)J =2 0
where e(t)4Processing
spikesnaturalstimuliwith

Here, we present a learning rule for the minimization of the reconstruction error. The learned filters
and their relation to the early visual system will be
discussed elsewhere. Further, we limit ourselves to asingle neuron. In Section 4.1, we present the model, in
Section 4.2 we derive a learning rule for the minimization of the reconstruction error, and in Section 4.3, we
discuss the obtained learning rule.

4.1

Model

A neuron is modeled with the SM R0 -model [7]Z tκ(t − s)I(s)ds,

(1)



t − tf
η0 exp −τr(2)u(t) = η(t) +
0

where
η(t)

X

=

f :tf <tZ sw(s − v)Y (v)dv.

I(s) =

4.2

Learning rule to minimize the reconstruction error

Above, we have silently assumed that we know
the spike timings {tf } exactly. However, both in experiments as well as in computer simulations, spikes
can only be tracked to a maximal temporal precision ∆. This implies that the integration in Eq. (1)has to be replaced by a summation, and all variables take values in bins of size h ≥ ∆. This meansthat only finitely many parameters w[1], . . . , w[N ] andΦ[−N d], . . . , Φ[N t − 1] with N = N d + N t = (Td +Tt )/h have to be learned.
For a steepest descent learning algorithm, direct
calculation shows that the gradient for the encoding
filter is given by
* Nt
+
X
∂J
=
he[k]Ψn [k](7)∂w[n]hk=1
XΨn [k] =Φ[k − k f ]cfn
(8)
kf ∈T (n,k+N d)

0

The spike times {t ; f = 1, 2, . . .} are defined by the
instant of time where u reaches the threshold θ. Each
spike triggers the reset of u from θ to θ − η0 , and theneuron enters a time of reduces excitability modeled©ISAROB 2007

554

(6)

and <> denotes the sample average over the databaseof natural stimuli ΩY .

(3)f

= Ŷ (t) − Y (t),

cfn

=

1
Ȳ [k f − n] +(9)α(u[k f ] − u[k f − 1])!
X(k f − 1 − pm )hmcn η0 exp −
,
τr
m
f

p ∈T (n,k )The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where n = 1, . . . , N . The term T (x, y) denotes theset of spike times k f satisfying x ≤ k f < y, and theconstant α is a real number slightly less than 1. Ȳ isthe natural stimulus Y input after filtering with thesoma response kernel κ.
Similarly, for n = −N d, . . . , N t − 1 the gradient forthe decoding filter is given by
*
+
X
∂J
f
=
e[k + n](10)
∂Φ[n]hfk ∈Tn

for Tn

4.3=

T (max(1, 1 + n) − n, N t − n). (11)Interpretation

We discuss the obtained formulae for the gradientsfor the decoding and encoding filters.
4.3.1

Decoding filter

From Eq. (4), we see that the reconstruction kernel
Φ[n] bears a a different interpretation for n > 0 andn < 0. That is why we obtain slightly different formulae for the gradient of the decoding filter Φ.
For n = k − k f < 0, the spike k f happens after thetime step k for which we try to reconstruct Y . Thus,by Φ[n] we try to estimate the stimulus which precedes
the spike at k f by n units. Here, Tn becomes
Tn = T (1 + |n|, N t + |n|),(12)

so that for, say, n = −N d, we consider spikes happening from N d + 1 till the end N = N d + N t, andthe gradient is given by the sum of errors which weremade N d time steps before the spike event.
For n = k − k f > 0, however, the spike k f happens
before the stimulus Y [k] which we try to approximate
with Φ[n]. Thus Φ[n] is used to predict from the spiketime k f the stimulus which happens n time units afterwards. Here, Tn becomes

filters. The weighting coefficient cfn is determined by
the input Ȳ [k f −n] and the prior coefficients c1n . . . cfn−1
as well as the slope with which the membrane voltage
u crosses the threshold θ. The influence of the priorcoefficients decays exponentially with the distance between the the spike times. Since η0 is a constant lessthan zero, the influence of the prior coefficients is subtractive for spike times which are close together.

5

Summary

In the ongoing search for understanding of the earlyvisual system great advances have been made throughinvestigations in the statistical properties of natural
stimuli. We pointed out that the corresponding neuron models, which process the natural stimuli, tend tobe however too simple and might be a limiting factor
for further advances. Here, we made the step to biologically more plausible models and considered processingof natural stimuli by means of spiking neurons. Requiring linear reconstructability of the input from the
spike train, we derived a learning rule for both encoding and decoding filter, showing thus how parametersof a spiking neuron model can be learned from natural
stimuli.

Acknowledgments
This study is partially supported by Grant-in-Aid
No. 17022012 for Scientific Research on Priority Areas System study on higher-order brain functions from
the Ministry of Education, Culture, Sports, Scienceand Technology of Japan. MG is further supported
by MEXT grant No. 040680.

References(13)[1] J.J. Atick and A.N. Redlich. Towards a theory of
early visual processing. Neural Computation, 2:308–
320, 1990.

so that for, say, n = 1, we consider spikes happening
from time step 1 till N t − 1, and the gradient is given
by sum of errors made one time step after the spike
event.

[2] H.B. Barlow. Possible principles underlying the transformations of sensory messages. In W. Rosenblith,
editor, Sensory Communication, pages 217–234. MIT
Press, 1961.

4.3.2[3] Yang Dan, Joseph J. Atick, and R. Clay Reid. Efficient coding of natural scenes in the lateral geniculate
nucleus: Experimental test of a computational theory.
J. Neurosci., 16(10):3351–3362, May 1996.

Tn = T (1, N t − n),

Encoding filter

The gradient of the encoding filter w is given by the inner product between the reconstruction error e and thefunction Ψn . From the formulae, we see that this function is a superposition of shifted, weighted decoding©ISAROB 2007

555

[4] Dawei Dong and Joseph Atick. Statistics of natural
time-varying images. Network: Computation in Neural Systems, 6(3):345–358, August 1995.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[5] Dawei Dong and Joseph Atick. Temporal decorrelation: a theory of lagged and nonlagged responses in
the lateral geniculate nucleus. Network: Computationin Neural Systems, 6(2):159–178, May 1995.
[6] Robert A. Frazor and Wilson S. Geisler. Local luminance and contrast in natural images. Vision Research, 46(10):1585–1598, May 2006.
[7] W. Gerstner and W. K Kistler. Spiking Neuron Models. Cambridge University Press, 2002.
[8] Daniel J. Graham, Damon M. Chandler, and David J.
Field. Can the theory of ”whitening” explain thecenter-surround properties of retinal ganglion cell receptive fields? Vision Research, 46(18):2901–2913,
September 2006.
[9] A. Hyvärinen, M. Gutmann, and P.O. Hoyer. Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2. BMC Neuroscience, 6:12, 2005.
[10] A. Hyvärinen, P. O. Hoyer, J. Hurri, and M. Gutmann. Statistical models of images and early vision.
In Proceedings of the Int. Symposium on Adaptive
Knowledge Representation and Reasoning, 2005.
[11] Jason L. Puchalla, Elad Schneidman, Robert A. Harris, and Michael J. Berry. Redundancy in the population code of the retina. Neuron, 46(3):493–504, May
2005.
[12] Martin Rehn and Friedrich T. Sommer. A networkthat uses few active neurones to code visual input
predicts the diverse shapes of cortical receptive fields.
Journal of Computational Neuroscience, Published
online: 12 October:–, 2006.
[13] F. Rieke, D. Warland, R. de R. van Steveninck, and
W. Bialek. Spikes: Exploring the neural code. MIT
Press, 1997.
[14] Daniel Ruderman. The statistics of natural images.
Network: Computation in Neural Systems, 5(4):517–
548, November 1994.
[15] Eero P Simoncelli and Bruno A Olshausen. Natural
image statistics and neural representation. Annual
Review of Neuroscience, 24(1):1193–1216, 2001.
[16] A. Srivastava, A.B. Lee, E.P. Simoncelli, and S.-C.
Zhu. On advances in statistical modeling of natural
images. Journal of Mathematical Imaging and Vision,18(1):17–33, January 2003.
[17] J.H. van Hateren. A theory of maximizing sensory
information. Biol.Cybernetics, 68:23–29, 1992.
[18] Benjamin Vincent, Roland Baddeley, Tom Troscianko, and Iain Gilchrist. Is the early visual system
optimised to be energy efficient? Network: Computation in Neural Systems, 16(2 - 3):175–190, June 2005.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Mathematical Model of Planning in the Prefrontal Cortex
Makito Oku∗∗

Kazuyuki Aihara†,‡,∗

Department of Mathematical Informatics, Graduate School of Information Science and Technology,
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan†

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan‡

Aihara Complexity Modelling Project, ERATO, Japan Science and Technology Agency (JST),
3-23-5-201 Uehara, Shibuya-ku, Tokyo 151-0064, Japan

Abstract
The prefrontal cortex is involved in a lot of complex
cognitive behaviours, such as problem solving, planning, reasoning, and decision making. However, thebiological mechanisms of these computations are notclear. To understand the mechanisms, we theoreticallyconsider the experimental result of path-planning task
by Mushiake et al., using a mathematical model which
we name the potential network model. The result of
simulations shows that our model is able to take a correct path in most trials regardless of goal positions andblock patterns. Our model also reproduces the characteristics of neurons’ activities both in the prefrontal
cortex and the primary motor cortex. This study indicates that although the potential network model isabstract, it can be useful for modelling higher brain
functions.

1

To answer this question, we made a mathematicalmodel of a path-planning task. The path-planningtask was a task that required multiple stepwise movements of a cursor within a maze to reach a goal. 7–9 Fig.
1 shows the maze used in the task. Players of this task
started from the centre of the maze and tried to reachthe goal avoiding obstacles. If the player of the task
was monkey, it moved its arm to move around in themaze. If the player was human, the player pushed buttons. The rule which assigned muscular-movements tocursor-movements was replaced for every several trials.
By recording of neuron spikes in monkeys’ brains, it
was shown that many PFC neurons selectively fired
when specific cursor-movement was on specific stepduring both the preparatory period and movement execution. 7
Goal 1

Goal 2

Goal 3

Goal 4Introduction

Planning is one of the most complex cognitive functions of human brain. It includes a lot of aspects, suchas selection of future actions, anticipation of future
events that will occur as a result of those actions, temporal maintenance of sequence of those events, evaluation of the sequence, generation of new strategy ifneeded, and memorisation of finally decided plan sothat the planner will take actions according to theplan. Planning is also related to some major problems about brain, such as working memory, 1 cognitive
control, 2 mental imagery, 3 and reward systems. 4
A lot of studies from neuropsychology and brain
imaging show that planning is related to the prefrontal
cortex (PFC). 5,6 The PFC has thought to be involvedin the executive control of behaviour, and planning is
an important aspect of the executive control. Here thequestion arises: what role does the PFC take during
planning of multistep behaviours?

©ISAROB 2007

557

Block 1 Block 2 Block 3

Fig. 1. Path-planning task
On the other hand, Bachmann et al. 10 proposeda mathematically abstract model of neural network.
They considered a firing pattern of Hopfield network
as a point in high dimensional state space, and attractors as points in the space. This model had no
spurious attractors. Furthermore, basins of attractors
were well-defined. In this paper, we connected simplified neural assemblies similar to Bachmann’s model,
considered interactions among them, and named it thepotential network model.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2

Potential network model

Node AThe potential network model is a network model
where each node changes its state continuously according to its potential. The potential is influenced by connected nodes. Suppose a network consists of N nodes.
Each node has a state xi (t) ∈ Rm (i = 1, · · · , N ) anda potential Ui (i = 1, · · · , N ). The state of each nodeis changing on the potential.

A

Node BB

dxi (t)= −α∇Ui (t) (i = 1, · · · , N ).
dtiEach node has several fixed points s1i , · · · , sL∈i
R (i = 1, · · · , N ). These points are called attractors because they could be local minimum potentials.
Fig. 2 shows an example. When node A’s state is nearenough to one of its attractors, then the node affects
next node B’s potential so that one of attractors in
node B gets stable. For simplicity, each node’s potential is formed by linear summation of all attractors ofconnected nodes.

mUj (t) =LiXXkf (xi , ski )Uij

(j = 1, · · · , N ).

i∈ Pj k=1kHere Uijis the potential from k-th attractor of i-th
node to j-th node and Pj is a set of nodes connectedto j-th node.
f is a closeness function. It is a function of distance
between the state of the node and each attractor. In
this paper, we use Gaussian function as the closeness
function.
³ kx − sk k2 ´ii.
f (xi , ski ) = β exp −σ2

Fig. 2. Schematic diagram of the potential network
model

period. When cue signals were displayed, this nodealso reactivated corresponding node to trigger action
execution.
The forms of potentials were mixture of Gaussianfunctions. They biased an attractor or several attractors. There was a potential with its centre at the origin of the state space, which prevented the state fromapproaching any attractors.
In the original experiment, assignment rule between
cursor-movements and arm-movements was changed
for every several trials. Rule, arm, and motion nodes
were expected to perform the translation from cursormovements to arm-movements. In this paper, however, we did not add these three nodes.
High

Goal

Block

Step

Rule
Low

1st

3
3.1

Simulations of path-planning task

2nd

3rd

CursorSetup of simulations

Arm
Motion

We constructed a potential network model for thepath-planning task (Fig. 3). We assumed that cursormovements for three steps were represented separately
in the PFC. 1st, 2nd, and 3rd nodes were corresponding to the three steps and each of them had four attractors corresponding to four directions (up, down, left,
and right). Goal and block information were represented in goal node and block node respectively. These
two nodes biased the three nodes to select appropriate
path. Step node made strong potential in the cursor
node to inhibit action execution during preparatory©ISAROB 2007

558

Fig. 3. Potential network model for path-planning andexamples of potential patterns

3.2

Result of simulations

Fig. 4 shows an example of state transition of 1st,
2nd, 3rd, and cursor nodes in block 3 condition. Tem-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

closeness to
attractors

1
0.8
0.6
0.4
0.2

closeness to
attractors
closeness to
attractors
closeness to
attractors

selection rate

1
0.8
0.6
0.4
0.2
1st

selection rate

3rd

Block 2

2nd

3rd

Block 3

1
0.8
0.6
0.4
0.2
1st

2nd

3rd

time(s)4

2ndtime(s)3rd

0 1 2 3 4 5 6 7 8 9 10

1
0.8
0.6
0.4
0.2

2nd

Fig. 5. Cursor-movement selection rate

0 1 2 3 4 5 6 7 8 9 10

1
0.8
0.6
0.4
0.2

Block 1

1
0.8
0.6
0.4
0.2
1st

1st

0 1 2 3 4 5 6 7 8 9 10

1
0.8
0.6
0.4
0.2

selection rateporal sequence of events during the task was the sameto the original experiment. Two shadowed regions correspond to goal displaying period and block displaying
period. Three black bars indicate movement executions for each step. During preparatory period, statesof 1st, 2nd, and 3rd nodes were biased by goal nodeand block node to approach attractors corresponding
to proper cursor movements. In contrast, state of cursor node was under strong inhibitive potential madeby step node so that it stayed far from any attractors.
After preparatory period, states of the three nodes
fluctuated in the state space because of noise. So theyleft from attractors. When step node reactivated thethree nodes, their states approached attractors again,
which changed the state of cursor node.

time(s)Cursor0 1 2 3 4 5 6 7 8 9 10time(s)Discussion

Medium of modelling of cognitive functions In
general, there are two streams of modelling of human’scomplex cognitive behaviours: production system 11,12
and connectionist model. 13 In addition, there are somehybrid studies. 14,15 The production system is good at
highly complex problems. However, its biological basis
is not clear. On the other hand, connectionist model
has biological background and it has flexible performance. However, this approach has a risk to be redundant and to have complexity irrelevant to the essenceof computation.
The potential network model we proposed here wasintrinsically based on connectionist model. At thesame time, it was easy to implement if-then rules inthis model because each attractor’s basin and effectwere well-defined. Therefore, this model had advantages of both sides.

Fig. 4. An example of time series of state transition

Fig. 5 shows the probability of cursor-movement
selection of each step. In most cases this model chosea correct path. Some errors were seen in the 3rd step inall conditions. The state of nodes seemed to fluctuate
getting away from the basins of attractors. Anothererrors were seen in the 1st step of block 3 condition,
which was failure to overwrite reflective response.

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Role of prefrontal cortex There are many hypotheses about the role of the PFC. 16–19 Among them,the cognitive control hypothesis is broadly accepted.
This study was consistent with the hypothesis. In theframework of cognitive control hypothesis, the PFC
sends bias signal to posterior cortex to overwrite reflective, innate, or prepotent responses. 2 In our model,
goal and block node made potentials to the three nodes
corresponding to each step, biasing appropriate action.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Other issues involved in planning Our model forthe path-planning task concerned only limited aspectsof planning. For example, the players of the pathplanning task were well-trained, so they could accurately find correct path immediately. In fact, neuron spike data from monkeys’ brains suggested that
1st, 2nd, and 3rd cursor-movement-selective neurons
in the PFC started to fire simultaneously during the
preparatory period. 7 Accordingly, stepwise rehearsalof event sequence during the preparatory period was
not performed in our model. In Addition, our model
did not include evaluation system and learning mechanism, which is our future works.

5[5] Unterrainer JM, Owen AM (2006), Planning andproblem solving: from neuropsychology to functional
neuroimaging. J. Physiol. Paris 99(4–6):308–317[6] Goel V, Grafman J (2000), Role of the right prefrontal
cortex in ill-structured planning. Cogn. Neuropsychol.
17(5):415–436[7] Mushiake H, Saito N, Sakamoto K, et al. (2006), Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans. Neuron
50(4):631–641[8] Mushiake H, Saito N, Sakamoto K, et al. (2001), Visually based path-planning by Japanese monkeys. Cogn.
Brain Res. 11(1):165–169[9] Mushiake H, Saito N, Furusawa Y, et al. (2002), Orderly activations of human cortical areas during pathplanning task. Neuroreport 13(4):423–426

Summary

To understand the mechanism of planning in theprefrontal cortex, We theoretically considered the experimental result of path-planning task by Mushiake
et al., using the potential network model. The result of
simulations showed that our model was able to take acorrect path in most trials regardless of goal positions
and block patterns. Our model also reproduced thecharacteristics of neurons’ activities both in the PFC
and the primary motor cortex. This study indicatedthat although our model was abstract and concerned
only limited aspects of planning, it could be useful formodelling higher brain functions.

Acknowledgements
This study is partially supported by Grant-in-Aid
No. 17022012 for Scientific Research on Priority Areas
System study on higher-order brain functions from the
Ministry of Education, Culture, Sports, Science andTechnology of Japan.

References[1] Baddeley A (2003), Working memory: looking backand looking forward. Nat. Rev. Neurosci. 4(10):829–
839[2] Miller EK (2000), The prefrontal cortex and cognitive
control. Nat. Rev. Neurosci. 1(1):59–65[10] Bachmann CM, Cooper LN, Dembo A, et al. (1987),
A relaxation model for memory with high storage density. Proc. Natl. Acad. Sci. U.S.A. 84(21):7529–7531[11] Anderson JR, Bothell D, Byrne MD, et al. (2004),
An integrated theory of the mind. Psychol. Rev.
111(4):1036–1060[12] Kieras DE, Meyer DE (1997), An overview of the
EPIC architecture for cognition and performancewith application to human-computer interaction.
Human-Computer Interaction 12(4):391–438[13] Dehaene S, Changeux JP (1997), A hierarchical neuronal network for planning behaviour. Proc. Natl.
Acad. Sci. U.S.A. 94(24):13293–13298[14] Polk TA, Simen P, Lewis RL, et al. (2002), A computational approach to control in complex cognition.
Cogn. Brain Res. 15(1):71–83[15] Newman SD, Carpenter PA, Varma S, et al. (2003),
Frontal and parietal participation in problem solving
in the Tower of London: fMRI and computationalmodeling of planning and high-level perception. Neuropsychologia 41(12):1668–1682[16] Wood JN, Grafman J (2003), Human prefrontal cortex: processing and representational perspectives.
Nat. Rev. Neurosci. 4(2):139–147[17] Miller EK, Cohen JD (2001), An integrative theoryof prefrontal cortex function. Annu. Rev. Neurosci.
24:167–202[3] Kosslyn SM (2005), Mental images and the brain.
Cogn. Neuropsychol. 22(3–4):333–347[18] Duncan J (2001), An adaptive coding model of neural function in prefrontal cortex. Nat. Rev. Neurosci.
2(1):820–829[4] Schultz W, Dayan P, Montague PR (1997), A
neural substrate of prediction and reward. Science
275(5306):1593–1599[19] Fuster JM (2001), The prefrontal cortex–an update:time is of the essence. Neuron 30(2):319–33©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Pattern Recognition in Chaotic Neural Networks
Guoguang HE1,2,3,*, Luonan CHEN1,2,4 and Kazuyoki AIHARA1,2
Aihara Complexity Modelling Project, ERATO, JST, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8505, Japan
2
Institute of Industrial Science, The University of Tokyo, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8505, Japan3
Department of Physics, College of Science, Zhejiang University, Hangzhou, 310027, P.R. China
4
Osaka Sangyo University, Osaka 574-8530, Japan*Corresponding author, e-mail: gghe@aihara.jst.go.jp
1

Abstract
A chaotic neural networks (CNNs) constructed withchaotic neurons have rich dynamics. The CNNs cangenerate chaotic associative memory dynamics and has
been denoted to be a promising technique in
information processing, such as pattern recognition or
memory search. But the outputs of the CNNs wanderaround all stored patterns and can not be stabilized in
one of its stored patterns or a periodic orbit. It isdifficult to judge when to terminate the chaotic
dynamics, which is imperative in information
processing. In this paper, we discuss pattern recognition
in a CNNs. We propose a chaos control method firstly
for the CNNs. Then we employ the controlled CNNs tocarry out pattern recognition tasks. The simulation
results show that the outputs of the controlled CNNs are
period, not a fixed point. The outputs are dependent on
initial pattern. The controlled network has the ability ofidentifying two initial patterns with small difference.
This is an advantage of CNNs comparing with Hopfieldmodel.
Keywords: Chaotic neural networks; Controlled
dynamics; Pattern recognition; Controlling chaos

1

Introduction

Pattern recognition is one of important fields ofartificial intelligence. One focuses on how a system canobserve the environment, distinguish patterns of interest
from their background and make decisions about theirclassification or categorization in pattern recognition
study. Many methods and techniques have been
proposed for pattern recognition, for example, thetemplate matching, syntactic or structural matching,statistical classification, and artificial neural networks(ANNs) approaches[1]. ANNs have the ability oflearning complex nonlinear input-output relationships
and therefore drawn more and more attentions in recent
years. However, there is an essential difference between
ANNs approaches and the brain in pattern recognition.
Once an output pattern is identified, ANNs remains inthe state until the arrival of next external input, but thebrain does not “stick” to the state and can recall otherassociative memory patterns without additional external
stimulus when a pattern is retrieved from a memory
location. A conventional artificial neuron model is a
simply threshold element transforming a weighted
summation of inputs into the output through a nonlinear©ISAROB 2007

561

output function with threshold. This model isoversimplified so that the actual characteristics of thebiological neurons that have the ability to “jump” fromone memory state to another in the absence of a stimulus
cannot be represented in such neuron model. Biologicalneurons have chaotic behaviors which was observed in asingle neuron by electrophysiological experiments[2],but chaotic behaviors are lack in the conventional
artificial neurons. Therefore various neuron models andneural networks with chaotic dynamics have been
proposed and investigated[3-6]. In this paper, we focus
on a chaotic neural networks (CNNs)[3] composed ofchaotic neurons which were proposed based onelectrophysiological experiments in the squid giant
axons. The chaotic neuron model and CNNs have beenshown to have rich dynamics. Adachi et al.[7] have
proved that the CNNs can generate chaotic associative
memory dynamics in several parameter regions. Due tothe chaotic associative memory dynamics, the CNNs is
expected to be use in information processing, such as
pattern recognition or memory search, etc. However, theoutputs of the CNNs wander around all stored patterns
and can not be stabilized in one of its stored patterns or aperiodic orbit. It is difficult to judge when to terminate
the chaotic dynamics, which is imperative in patternrecognition. The chaos control techniques for the CNNs
were therefore proposed by Nakamura et al.[8], Kushibe
et al. [9], He et al.[10] etc. In Ref. 8 and 9, the chaos in
the CNNs was controlled by making the CNNs to a
Hopfield model[11]. The output of the controlled
networks is a fixed point. The pattern recognition or
memory search therefore achieved. However, the control
target is refereed by comparing the initial pattern withstored patterns in their works. In other words, the initialpattern mapped on a stored pattern had been assigned apriori, which limits the real application in patternrecognition. Besides, a fixed output of the controlled
CNNs makes the CNNs lost advantages of chaotic
dynamics. In Ref. 10, the control target also should been
assigned. Tan and Ali[12] proposed a synchronization
method to achieve pattern recognition in a neuralnetwork with chaos. However, the desired orbit also
should been assigned a priori too.
In order to promote the CNNs to be used in
information processing, such as pattern recognition,
memory search, a chaos control method without
assigning a control target, i.e. a self-adaptive chaos
control method, is necessary. In this paper, we willpropose a chaos control method which need not assign
control target or desired orbit for the CNNs. Then weThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

will perform pattern recognition task by using thecontrolled CNNs.
The article is organized as follows. In section 2, themodels of the CNNs are described briefly. A chaos
control method for CNNs is proposed. In section 3, weapply the controlled CNNs to carry out the patternrecognition task. The discussion and conclusion are
given in the final section.

2 Chaotic Neural Networks and Chaos
Control Method
2.1

in the network. n is the total number of stored memory
patterns.
In the paper, four patterns shown in Fig. 2 are
employed as stored memory patterns, or namely
learning patterns. Each pattern is composed of 10 by 10
binary pixels. Correspondently the network is
constructed with 100 neurons, that is N = 100. A neuron
will be represented by a block “” when its output, xi,
is equal to 1, which means the neuron is “excited”,
while a neuron is denoted by a dot “·” when its output isequal to 0, which means the neuron is “resting”.

Chaotic Neural Networks model

A chaotic neural networks (CNNs) used for patternrecognition is constructed with chaotic neurons byconsidering the spatio-temporal summation of both
external inputs and feedback inputs from other chaotic
neurons[3]. The structure of the CNNs is shown infigure 1. When the external input term is temporally
constant, it can be included in the threshold term. The
dynamics of the ith chaotic neuron in the CNNs at time
t can be described simply as follows:xi (t + 1) = f (ηi (t + 1) + ζ i (t + 1) ) ,N

Figure 1 The structure of the CNNs

(1)

ηi (t + 1) = k f ηi (t ) + ∑ wij x j (t ) ,(2)

ζ i (t + 1) = krζ i (t ) − αxi (t ) + ai ,(3)

Figure 2 Four stored patterns

1.
1 + exp(− x ε )(4)

The dynamics of the chaotic neural networks isdependent on the network parameters. In our numericalsimulations the network parameters are kept as follows:
kr=0.95, kf =0.20, α = 10.0, ai=2.0 (i=1, 2, …, 100), andε = 0.015. It has been shown that the CNNs with above
parameters is chaotic[7, 10]. The outputs of the CNNs
are wandering around all stored and their reversal
patterns, namely associative memory dynamics [7].

j =1

f ( x) =where the xi(t) is the output of the ith chaotic neuron attime step t, the ηi(t) and ζi(t) are the internal statevariables of feedback input from the constituent neurons
in the network and refractoriness of the chaotic neuron
at time t, respectively. N is the number of neurons in the
network. kf and kr are the decay parameters of the
feedback inputs and the refractoriness, respectively. The
parameter ai is the threshold of the ith neuron. The
parameter α is the refractory scaling parameter of aneuron, and the output function of neuron f(·) issigmoidal function with the steepness parameter εdescribed by equation (4). wij are synaptic weights to theith constituent neuron from the jth constituent neuron. A
neuron does not receive the synaptic connection from
itself, i.e. wii =0. The weights are defined according to
the following symmetric auto-associative matrix of nbinary patterns:
wij =

1 n
∑ (2 xip − 1)(2 x jp − 1) ,n p =1(5)where xip is the ith component of the pth binary
pattern with a discrete value of 0 or 1. In this way, thebinary patterns can be stored as basal memory patterns©ISAROB 2007

562

2.2

Chaos control method

The chaos control technique was proposed firstly by
Ott et al. in 1990(OGY method)[13]. Since the pioneer
work of OGY, the chaos control methods have been
greatly developed. Although most of these controlling
methods have been mainly applied to chaotic systemwith small degree of freedoms, several methods havebeen adopted for a system with large degree of freedoms,
such as CNNs [8-10]. Nakamura et al.[8], Kushibe etal.[9], He et al.[10], had proposed chaos control
methods for the CNNs, but in their methods, the control
target should been assigned. Their control methods can
not been applied to real information processing. In orderto make the CNNs to be used in pattern recognition withchaotic dynamics, we purpose a new scheme ofcontrolling chaos for the CNNs by considering
following aims: (I) the output of the controlled CNNs

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

can be stabilized to a periodic orbit or a fixed point; (II)the stable output of the controlled CNNs should be
related with the stored patterns and initial state of the
network. (III) the control target should not been assigned,
i.e. the control method is a self-adaptive method. In ourprevious work[14], we have known that the chaotic
dynamics in the CNNs is dependent on the refractory
scaling parameter of the neurons. When the refractory
scaling parameter is small, the dynamics of the CNNs isa fixed points or periodic. As the refractory scaling
parameter increase, the output of the CNNs becomeschaotic. We therefore assume the chaos in CNNs can becontrolled if the refractory scaling parameter of theneurons is changed by a control signal. This is the ideaof our control method to be proposed here. Considering
self-adaptive method, we take the delay feedback signal
as the control signal. The controlled CNNs is describedin following equations:xi (t + 1) = f (η i (t + 1) + ζ i (t + 1) ),Nlimited space, we only show the periodic output
sequence slice of the controlled CNNs in figure 3. The
left parts of the figure are the initial patterns, the rightparts are the output sequence of one period of thecontrolled network.
From the figure, one can find no matter a initialpattern is a stored pattern (top four initial patterns) or its
noisy pattern (bottom four initial patterns), the most partof patterns appearing in the output periodic sequence ofthe controlled CNNs are the stored pattern and its
reversal pattern while other stored patterns and theirreversal patterns are not appeared in the output
sequence. That is, the controlled CNNs can identify the
initial pattern from the stored patterns. The patternrecognition tasks are therefore achieved.

(6)

ηi (t + 1) = k f ηi (t ) + ∑ wij x j (t ),(7)ζ i (t + 1) = k rζ i (t ) − αβ k u ( t ) xi (t ) + ai ,

(8)

j =1

c

N

u (t ) = ∑ x i ( t ) − x i (t − τ ) .

(9)iwhere u(t) is a control signal determined by thedifference of the output of the chaotic neuron at different
time, β is a control parameter, a positive value smaller
than 1.0, kc is a control strength. When the output of thechaotic neuron is chaos, the control signal u(t) is notzero and αβ kcu (t ) < α . Thus the dynamics of the chaotic
neuron will be changed.

3

Pattern Recognition

Now we investigate the pattern recognition in CNNs.
After the CNNs learning the stored pattern shown infigure 2, the output sequence of the CNNs wanders
around all stored pattern and their reversal patterns, asmentioned in introduction, namely associative memory
dynamics[7]. We perform chaos control in CNNs
according to proposed control method by equations (6) ~(9). The parameters of the control method are taken as β
= 0.945, τ = 3 and kc = 0.6. As pattern recognition tasks,
we take stored patterns shown in figure 2 and noisy
stored patterns, patterns with small noises to stored
patterns, as the initial patterns, and investigate output
sequences of the controlled CNNs. A initial pattern is
injected in the CNNs by taking xi(0) as the initial patternin the internal state ηi and ζi. The initial internal states
are set as 0. We find the dynamics of the network ischanged when the CNNs is controlled by equations(6) ~(9). The output sequences of the controlled chaotic
neural networks are periodic. The period and the output
sequence are dependent on the initial states. Due to the©ISAROB 2007

563

Figure 3 Initial patterns and their response output
sequence slice of the controlled CNNs. The left parts ofthe figure are the initial patterns, the right parts are theoutput sequence of one period of the controlled network.
The control parameters are taken as β = 0.945, τ = 3 and kc= 0.6.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4

Discussion and Conclusion

References

To promote the CNNs to be a technique of
information processing, such as pattern recognition,
memory search, a self-adaptive chaos control method
for the CNNs was proposed based on the fact that the
dynamics of the chaotic neural networks is dependent on
the refractory scaling parameter. The controlled CNNs
was employed to carry out pattern recognition tasks. The
simulation results have shown that the chaotic dynamics
disappear when the refractory scaling parameter ischanged by a control signal which is determined by thedifference of two outputs with time delay. The dynamics
of the controlled CNNs is periodic. The output
sequences of the controlled CNNs are dependent on theinitial pattern. The pattern recognition tasks are
therefore achieved.
We control the CNNs to be a periodic state, not a
fixed point as in Ref. 8 and 9, which make the output
sequences of the controlled CNNs sensitive to the initialstate. From figure 3, we find the period and the output
pattern sequences of the controlled CNNs are different
when the initial pattern are a stored pattern (b) shown in
Fig. 2 and its noisy stored pattern. The same situation
exists when the initial patterns are a stored pattern (d)and its noisy stored pattern, which means the controlled
CNNs is sensitive to the initial state. In fact, whencontrol parameter β take as 0.95, the output sequencesof the controlled CNNs with a stored pattern taken as an
initial pattern are different from those with its noise
pattern as the initial pattern even if the period is same.
We do not show the results of β = 0.95 here because theperiod of the controlled CNNs is long and it is difficult
to show the output sequences in a short space. The
sensitiveness to the initial state in our controlled methodmeans the controlled CNNs can distinguish initialpatterns with small difference. This is the advantage ofour controlled CNNs compared to previous control
methods [8, 9], or the conventional auto-associative
network, such as Hopfield network[11], where theoutput converge on a fixed point, no matter the initialpattern is a stored pattern or a noisy stored pattern.
It should be notice that a rejection mechanism islack in the controlled CNNs. It means that an initialpattern may be attracted to a stored pattern though it
shows low similarity with the stored pattern. Proposing
an improved control method or an improved CNNs
model, with which the CNNs not only recognize an
initial pattern if it has high similarity with a stored
pattern but also reject recognizing as any stored pattern
if it has low similarity with a stored pattern, will be our
future direction.

©ISAROB 2007

564

[1]
[2]

[3]
[4]

[5]
[6]
[7]
[8]
[9]
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[11]

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Aihara K, Takebe T, Toyoda M, Chaotic neuralnetworks, Phys. Lett. A, vol. 144, pp. 333-340,
March 1990.
Freeman WJ, Simulation of chaotic EEG patterns
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Tsuda I, Dynamic link of memory-chaotic
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Nara S, Davis P, Learning feature constraints in achaotic neural memory, Phys. Rev. E, vol. 55, pp.
826-830, Jan. 1997.
Adachi M, Aihara K, Associative dynamics in achaotic neural network, Neural Networks, vol. 10,
pp. 83-98, 1997.
Nakamura K, Nakagawa M, On the associativemodel with parameter controlled chaos neurons, J.
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Kushibe M, Liu Y, Ohtsubo J, Associative
memory with spatiotemporal chaos control, Phys.
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He G, Cao Z, Zhu P, Ogura H, Controlling chaos
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Hopfield JJ, Neural Networks and Physical
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July 2004.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Bistability of Synchronous and Desynchronous Dynamics
in a Network with Gap Junctions
Yuichi Katori1,∗ , Yoshito Hirata1 , Hideyuki Suzuki2 and Kazuyuki Aihara1,2
1
Aihara Complexity Modelling Project,
ERATO, Japan Science and Technology Agency,
4-1-8 Honcho Kawaguchi, Saitama, Japan
2
Institute of Industrial Science, Univ. of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan∗
E-mail: katori@sat.t.u-tokyo.ac.jp

Abstract
Recent physiological studies show a transient synchrony which means an alteration behaviour between
synchronous and desynchronous states. These experimental ﬁndings and related theoretical studies havesuggested that the importance of this kind of dynamics
as bases of cognitive functions. However, an origin of
this characteristic dynamics is still unclear. Here, wereport that the transient activity can be realized in the
network consisted of conductance-based model neurons with a bistability of ﬁring and non-ﬁring states.
Neurons in this network are coupled with gap junctions which is a direct electrical connection with neighbor neurons. The bistability of the neuron play a keyrole to produce the transient dynamics of synchronousand desynchronous states.
Key words: Gap junction, bistability, neural coding, neural dynamics, chaotic itinerancy, synchronization.

1

Introduction

Recent experiments have revealed the transient
dynamics between synchronous and desynchronous
states, e.g., spiking patterns observed in rat inferior
olive neurons show the alteration of rhythmic synchronous states and desynchronous states [1] , local
ﬁeld potential (LFP) data of an animal and electro encephalography (EEG) data of human [3] also exhibit
the transient synchronous activity. Further, functionsof this kind of dynamics have been investigated. The
transient dynamics correlates with an attention anda perceptual binding, facilitates a synaptic plasticity,and coordinates a long-range interaction in the brain©ISAROB 2007

565

[2, 3] . Theoretical study have suggested that neuralcodes switch dynamically along with the state transition [4]. Little is known, however, about how thetransient activity emerges in the neural systems.
Moreover, experiments have revealed the massive
number of gap junctions in various region of the brain.
These gap junctions are specialized areas of the cell
membranes connecting neighbor cells; they induce synchronous ﬁring [5]. In addition to the synchronous activity, theoretical studies suggest that gap junctions
induce chaotic activities including the chaotic itinerancy [6, 7] . The chaotic itinerancy is one of possible
scenarios to realize the transient dynamics between
synchronous and desynchronous states in the networkwith gap junctions.
The purpose of this paper is to present an alternative scenario to produces the transient dynamics in the
gap junction-coupled neural system, base on a characterization of the bistability of neuron. The paper isorganized as following. First, in the next section, weshow the conductance-based model with the bistability and a characteristics of their behaviours. After
that, we construct the network with this neuron byconnecting with gap junctions. In the third section,
we show the result of computer simulation.

2

Model

Here, we describe the model mentioned in above.
First, single neuron model is described in following
subsection. After that, we construct the network consisted of this neuron and gap junctions.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Conductance based model with bistability

1.1

dV
C
dt

dn
τn
dt

chaninel activation, n

0.8

im
el
bl

y
it c

0.6
0.5
0.4

el
bl

t cycleimi

0.3= I(t) − gL (V − EL )0.1

−gN a (V − EN a )0.0−gK (V − EK ),= n∞ (V ) − n,(1)stable fixed point

-0.1
-80

-70

-60

(2)

=

1
[
],
1 + exp V1V−V
2(3)n∞ (V )=

1[].
1 + exp V3V−V
4(4)

The neuron is driven by the external input I(t), theleaky current, the sodium current, and the potassium
current. We chose following parameters to realize thebistable structure. We set C = 31ms, τn = 31ms, themembrane conductances gL = 1, gN a = 4, gK = 4,
and corresponding reversal potentials EL = −78 mV,
EN a = 60 mV, EK = −90 mV. We use steady-state
activation curves m∞ (V ), and n∞ (V ) with the slope
factor V2 = 7mV, V4 = 5mV and parameters V1 = −30
mV and V3 = −45 mV satisfy m∞ (V1 ) = n∞ (V3 ) =0.5.
Figure 1 shows the phase portrait to describes thegeometric view of the model neuron. The model includes two attractors as shown in this ﬁgure. The ﬁrst
is a stable ﬁxed point corresponding to the rest state.
The other is a stable limit cycle with the action potential. The dashed curve in ﬁgure 1 indicates a unstable
limit cycle. If the orbit starts inside of this region, theorbit converges to the stable ﬁxed point. On the other
hand, If the orbit starts outside of this region, the orbit
makes the action potential and converges to the stable
limit cycle. Figure 2 shows a time course of the membrane potential as a typical response of the neuron.
This neuron takes two states of ﬁring and non-ﬁring
even on same intensity inputs. These two states canbe switched by ﬂuctuating inputs (Fig 2 (b)).

566

cle

0.7

0.2

m∞ (V )©ISAROB 2007ne

We used a simple two-variable conductance-basedmodel, which is more plausible than a one-variable
neuron model like the integrate-and-ﬁre model, andis extracting the essential neural dynamics. [6, 8].
This model consists of two variables which are amembrane potential V and a potassium channel activation n [8].

sta

0.9

n-nullcline
v-nullcli

1.0

uns
ta

2.1

-50 -40 -30 -20 -10
membrane potential, V [mv]0

10

Figure 1: Phase portrait of the model neuron. Dottedcurves indicate nullcline of V and n. The bold curveis the stable limit cycle. The point in the crossing ofthese nullclines is the stable ﬁxed point. The dashedcurve is unstable limit cycle. Curves with arrow indicate typical orbits of the model.

2.2

Network with gap junctions

The network with gap junctions and neurons speciﬁed in above is described as follows. We used two
dimensional lattice network with 25 neurons as in ﬁgure 3.
C

dVi
dt

=

Ii (t) − gL (Vi − EL )−gN a (Vi − EN a )−gK (Vi − EK ),(5)n∞ (Vi ) − ni ,(6)m∞ (V ) =(i = 1, · · · , 25),
1],[
1 + exp V1V−V
2(7)n∞ (V ) =1],[
1 + exp V3V−V
4

(8)

τn

dni
dt

=

The i in the subscript of V , n, and I(t) indicatesthe index of neurons. We used 25 neurons in this network, therefore, the i takes an integer of from 1 to 25.

inputmembrane potential [mV]The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

10.0
0.0
-10.0
-20.0
-30.0
-40.0
-50.0
-60.0
-70.0
-80.0
20.0
-5.0

C

D
0

2000
VKOG=OU?

Figure 2: The typical response of the conductancebased model with bistable structure. The time courseof the membrane potential (a) and the ﬂuctuating input (b) are shown. The ﬁring state as in the middle
part of (a) and the rest states as in the both end of(a)are corresponding to the stable limit cycle and thestable ﬁxed point in ﬁgure 1 respectively. These twostates can be switched by the ﬂuctuating input.

Ii (t) is a current induced by external inputs and gap
junctions, and is consisted of three terms:
Ii (t)= I0 + ξi (t)+gEneighbors∑(Vj (t) − Vi (t)).

(9)

jThe ﬁrst term I0 specify a constant input. The second term is a Gaussian noise ξi (t) that is individuallyapplied to each neuron with the strength D. The third
is the current induced by the gap junction with theconductance gE . Each neuron coupled with nearest
neurons as in ﬁgure 3. We assumed the conductancegE are uniform for all gap junction connections.

3

Simulation Results

Figure 4 shows the typical response of this model.
We can observe the transient activity between synchronous and desynchronous states. In the synchronous states, we can also see the rhythmic ﬁring
with a period of about 180 ms. The period is corresponding to the period of the stable limit cycle ofthe neuron. In the desynchronous states, the ﬁring
frequency is low and that spike timing are almost irregular.

©ISAROB 2007

567

Figure 3: The structure of the network. The network
is consisted of 25 neurons described in the body of thispaper. Each neuron is connected with nearest neurons.
The coupling strength of gap junction are uniform andspeciﬁed by gE .

The key mechanism of the alteration of these twostates originate in the bistability of the conductancebased model as you see in the ﬁgure 1. The orbitof each neuron coupled with gap junctions attracts
each other. Consequently, the orbits of these neurons
tend to stay in one side of two attractors. In the synchronous states, the orbits stay on the stable limit cycle. In the desynchronous states, almost of these orbits
trap each other near the stable ﬁxed point. The ﬂuctuating noise allows to escapes from this region and makethe desynchronous spiking. If a certain amount of neurons ﬁre in short time period, the state can change tothe synchronous state.

4

Conclusion

In this paper, we have shown that the transient dynamics of synchronous and desynchronous states canbe realized in the network consisted of bistabe typeneurons and gap junction connections.
The model examined here may play an importantrole for the information coding in the brain , and cognitive roles mentioned in the introduction.

References[1] E. J. Lang, I. Sugihara, and R. Llinas (1996),“GABAergic modulation of complex spike activity by the cerebellar nucleoolivary pathway in rat”
Journal of neurophysiology,76,1,225–275.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

PGWTQPU[8] E. M. Izhikevich (2006), Dynamical Systems in
Neuroscience: The Geometry of Excitability And
Bursting, The MIT press.














PGWTQPUPGWTQPUPGWTQPUPGWTQPU


VKOG=UGE?

Figure 4: The typical response of 25 neurons in thismodel as a raster plot on the time from 0 to 50 s. Vertical axes of each plot show the index of neurons. Eachdot in these plot indicate the timing of the spiking.

[2] G. Buzsaki and A. Draguhn (2004), “Neuronal Oscillations in Cortical Networks” Science
304,5679,1926–1929.
[3] F. Varela, J. P. Lachaux, E. Rodriguez, and J.
Martinerie (2001), “The Brainweb: Phase Synchronization and Large-Scale Integration” Nature
Reviews Neuroscience,2,229–239.
[4] Y. Katori, N. Masuda, and K. Aihara (2006), “Dynamic switching of neural codes in networks with
gap junctions” Neural Networks,19,10,1463–1466.
[5] M.Galarreta and S.Hestrin (1999), “A network of
fast-spiking cells in the neocortex connected byelectrical synapses” Nature,402,72-75.
[6] I.Tsuda, H.Fujii, S.Tadokoro, et al (2004).
“Chaotic itinerancy as a mechanism of irregularchanges between synchronization and desynchronization in a neural network” Journal of Integrative Neuroscience,3,159-182.
[7] H.Fujii and I.Tsuda (2004), “Neocortical gap
junction-coupled interneuron systems may
induce chaotic behavior itinerant among quasiattractors exhibiting transient synchrony”
Neurocomputing,58-60,151-157.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Digital Spiking Silicon Neuron: Concept and Behaviors in GJ-coupled Network
Takashi Kohno∗† and Kazuyuki Aihara∗†
Aihara Complexity Modelling Project, ERATO, JST, Shibuya-ku, Tokyo 151-0064†
Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo 153-8505∗

Abstract
Silicon neuron is electrical circuit that is analogous
to biological neurons. Most spiking silicon neurons
comprise analog circuit technology. We propose a newconcept of spiking silicon neuron that is composed ofonly digital circuit technology. The system equations
were designed by a mathematical-model-based design
method that we proposed for analog silicon neurons
in previous works. This allowed us to design a simple
digital spiking silicon neuron (DSSN) model that produces rich dynamical behaviors comparable to biological neurons. We analyzed an elemental DSSN model
to validate if it possesses fundamental characteristicsof neurons, and its behaviors in gap junction (GJ)coupled networks were studied in order to demonstrate
its ability to exhibit rich dynamical behaviors.

1

Introduction

Silicon neuron study is an attempt to produce neuron analog utilizing electrical circuit technology. One
of its objectives is to construct artificial silicon neuralnetworks that process information similarly to neuralsystems in creatures. Although knowledge on the information processing principles in neural systems isinadequate, silicon neuron and neural networks arebeing studied actively. This is not only because ofsome realistic potential applications such as associative memory and brain-machine interfacing, but also
because analysis by synthesis is one of the most effective tools for brain science. Many silicon neurons
have been constructed using analog electrical circuit
technology, because neural phenomena are produced
by real-valued dynamics. Conventionally, in the analog silicon neuron design trade-off between the circuit
size and richness of neuronal properties has been a disadvantage. In the previous works [1][2], we proposeda mathematical-model-based design policy that allows
us to implement a compact silicon neuron that possesses rich neuronal dynamics.
For silicon neurons, digital circuit technology has
not been applied as extensively as analog technol-©ISAROB 2007569

ogy. However, it has some appealing features suchas insensitivity to the fluctuations in the environment and continuous improvement in the fabrication
technology. Additionally, field programmable gatearrays (FPGAs) allow users to construct their own
ICs. Currently, most digital silicon neurons and neural networks are dedicated processors for classical nonspiking neuron models and network models or simulators for spiking neuron models [3][4]. In this paper,
we propose a new concept for digital silicon neuron
design. By applying a mathematical-model-based design policy to digital circuits, we can design a digital
spiking silicon neuron (DSSN) that possesses properties supported by theoretical models for biologicalneurons with compact circuitry. It is intended to be aconstituent element for digital spiking neural networks
that operate in real time, and has the potential to be
an alternative to analog silicon neurons.
In the next section, we introduce the concept anddesign a model for an elemental DSSN model. In thethird section, we report complex behaviors observedin a GJ-coupled network of the model to demonstratethat it has potential to exhibit rich dynamical behaviors comparable to analog silicon neurons. Finally, we
will briefly refer to the implementation of our model.

2

Concept and Model of DSSN

The basic concept of DSSN is a dedicated system forsolving differential equations of spiking neuron models. The hardware is an ordinary arithmetic circuit
used for numerical integration. The keypoint lies inthe designation of system equations. To implement
a solver for a biological neuron model, massive hardware resources are required because most of the models
are described using complex differential equations. We
can avoid this problem by designing the system equations by a mathematical-model-based design method.
Here, equations that have topological structures intheir phase portrait similar to some theoretical models are designed first, and then, their parameters are
tuned on the basis of bifurcation analysis.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The most fundamental property of biological neurons is the generation of action potentials. Neuralexcitability is another property, which classifies neurons according to the firing frequency at theonsetofPSfragreplacements
nrepetitive firing induced by a sustained stimulus that n-nullclinev
v-nullcline
Istim
is increased gradually. Neurons with Class I excitabil0.8
ity begin to fire repetitively with arbitrarily zero fre(a)(b)
quency, whereas those with Class II excitability begin
with a non-zero frequency. Theoretical studies haveelucidated the mathematical structure behind theseproperties by utilizing the phase portrait and bifurcation analyses [5][6]. These studies not only succeededin explaining the mechanism of various properties ofaction potentials but also showed that saddle-node on(c)(d)
invariant circle and Hopf bifurcations of a stable equiFigure 1: Phase planes for our DSSN model. (a) Nulllibrium corresponding to a resting state produce Class
clines for Class I, II, and I∗ modes. (b), (c), and
I and II excitabilities, respectively.
(d) Closeup around critical structures for Class I, II,
Based on the above mentioned information, we deand I∗ modes, respectively. (S) is a stable equilibsigned a model for elemental DSSN, whose equations
rium (resting state); (T), a saddle; and (U), an unare:stable equilibrium. Stimulus current (Istim ) shifts thedv
φv-nullcline up, resulting in repetitive oscillation. In= (f (v) − n + I0 + Istim ) and(1)dtτ
the Class I∗ mode, v- and n-nullclines are very close1dn
to each other (narrow channel) around v = –0.4.
= (g(v) − n),
(2)
dt
τ
where
f (v) ≡(

g(v) ≡(an (v + bn )2 − cn
−ap (v − bp )2 + cp

when v < 0,when v ≥ 0,kn (v − pn )2 + qn
kp (v − pp )2 + qp

when v < r,when v ≥ r.

(3)
and

(4)

Parameters ax , bx , cx , kx , px , qx , and r determine
the form of the nullclines, and φ and τ are time constant parameters (for x = n and p). These equations
were designed so that they could reproduce topological structures in the phase plane of the Morris-Lecar
model [7]. It is one of the simplest models that show
both Class I and II excitabilities depending on parameter sets. In our equations, multiplication operations between variables are significantly reduced because they consume large hardware resources (cubic
curve is constructed by two quadratic curves). Notethat multiplication between a parameter and a variable can be implemented by the shift operation if weselect the parameter from {2n |n ∈ Z}.
We selected the parameter values so that our model
reproduced the phase plane structure in the MorrisLecar model in the Class I and II modes (see Appendix
for values). In Fig. 1 (a), (b), and (c), the phaseplanes for Class I and II modes of our DSSN model
are shown. These topological structures in the phase©ISAROB 2007

570

(a)

(b)

(c)
(d)
Figure 2: Bifurcations in our DSSN model. (a), (b),and (c) Bifurcation of variable v. Limit cycles are
represented by the maximum and minimum values.
(d) Bifurcation of frequency for limit cycles.
plane were proved to exhibit fundamental propertiesof action potentials by theoretical studies [5]. By selecting the descending limb of the n-nullcline, we canselect the neural excitability classes. Fig. 2 (a), (b),and (d) show the results of the bifurcation analysis.
They confirmed that our parameter sets produced the
expected classes of excitability. Additionally, in theClass II mode, our DSSN model produced chaotic re-The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

sponses against repetitive pulse stimuli similar to thosein biological neurons [8] and an analog silicon neuron[1]. This indicates the potential of our DSSN model
to exhibit rich dynamical behavior.

max. lyapunov exponent

3

Figure 3: One dimensional GJ-coupled network.

GJ-coupled DSSN models

70
60
50

Class I∗Class IClass IIA gap junction (GJ) is a type of physical connection
40
between neuronal cells, which is electrically equivalent
30
PSfrag replacements
to linear resistance. Various regions in the PSfrag
brain are20
replacements
known to contain numerous GJs, whose functions have
10
Rgj
attracted considerable interest from many researchers.
0max. lyapunov exponent
In 2004, Fujii and Tsuda [9] found that some of Class-10Class I∗
0 5 10 15 20 25 30 35 40 45 50
I neuron models exhibited characteristic chaotic beRgj
Class I
haviors when interconnected via GJs. TheyPSfragclassified
replacements
Class II∗(a)these neuron models as Class I and indicated thatRgj = 10Rgj v
the following two conditions in phase plane structures
max. lyapunov
R exponent= 19.5supported this class. The first condition is the exis- gj
Class I∗
tence of a phase plane structure called narrow channel,tClass I
which means that the nullclines for the membrane andClass II(b)ionic conductance variables remain close to each otherRgj = 19.5
Rgj = 10 v
in certain regions. The second one requires the uniquecrosspoint of the above two nullclines to be an unstable spiral equilibrium. These conditions are satisfiedtwhen a weak stimulus current is given to a neuron(c)model with nullclines having specific shapes. In Fig.
Figure 4: Behaviors of a GJ-coupled network of 20
1 (a) and (d), we show the phase planes for our DSSNDSSN models. (a) Maximum lyapunov exponents in∗model in the Class I mode (see Appendix for the paClass I, II, and I∗ modes. Large values are obtained
rameter set). A narrow channel is formed in the region
only in the Class I∗ mode. (b) and (c) Superimposed
shown by the dashed square, when Istim = 0.015. Biwaveforms for v in the Class I∗ mode when Rgj is 10
furcation analysis demonstrated that our DSSN modeland 19.5, respectively. Intermittently chaotic behavbelonged to Class I in this mode (Fig. 2 (c) and (d)).
ior is observed when Rgj in (c), when the maximum
We calculated the maximum lyapunov exponent forlyapunov exponent is relatively small.
the GJ-coupled network of DSSN models to demonstrate that our model with this parameter setting
could operate as a Class I∗ neuron. This network is
the network is synchronous when Rgj is sufficiently
composed of a one-dimensional array of 20 DSSN modsmall, becomes chaotic (Fig. 4 (b)) as Rgj increases,
els interconnected with two nearest neighbors via GJs
and then returns to the synchronous state when Rgj is(see Fig. 3). The current through GJ applied to thesufficiently large. We observed intermittently chaotici) is given as follows:i-th DSSN model (Igj
behaviors for the Rgj values during the transition fromi
Igj
= (vi+1 + vi−1 − 2vi )/Rgj ,(5)where i is the index number for DSSN model (from1 to 20); vi , v for the i-th DSSN model (v0 ≡ v1 andv21 ≡ v20 ); and Rgj , the resistance of the GJ. These
currents are added to Istim for each neuron. In Fig.
4 (a), the maximum lyapunov exponents for the ClassI, II, and I∗ modes are shown. It is large in the ClassI∗ mode when Rgj is approximately between 2.5 and20, whereas it is approximately zero, independent of
Rgj in the Class I and II modes. In the Class I∗ mode,©ISAROB 2007571

the chaotic to synchronous states (Fig. 4 (c)). Thesebehaviors are consistent with that of an analog siliconneuron model we designed in the previous work [10].

4Concluding remark

In the previous sections, we proposed a DSSN
model and indicated that it has potential to reproduce
the fundamental characteristics of neurons. We also
showed that our model could exhibit complex behav-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

ag replacementsv

Rgj = 10

Rgj = 19.5t

ag replacements
Rgj = 10v

(a)

Rgj = 19.5t(b)Figure 5: Behaviors of a GJ-coupled network of 20
DSSNs. (a) Rgj = 10. (b) Rgj = 19.5. Numerical integration was performed by Euler’s method and precision was 28 bit fixed-point. Chaotic and intermittently
chaotic behavior that are consistent with model simulation (Fig. 4 (b) and (c)) were observed.
ior with monotonic stimulus in GJ-coupled networks.
We are planning to implement our DSSN model in
FPGA devices and are performing numerical simulations. Reasonable results are obtained when we selectEuler’s method and fixed-point expressions for numerical integration. For example, we observe complex behaviors in the Class II mode of the DSSN model when
it is stimulated repetitively (not shown), and chaotic
and intermittently chaotic behaviors in a GJ-coupled
network of 20 DSSNs (Fig. 5) that is consistent withthe accurate simulation of the model. These results
are obtained with a time step of 1.0−5 for Eular’s
method and 28 bit fixed-point expression. Numericalprecision is an important factor that affects the circuit
size and operation speed. However, it is quite a difficult problem to give objective criteria for determining
the required precision. This is because interconnected
neurons and even a single neuron are complex systems
and the assessment of their dynamical behavior is acomplicated subject.
For the implementation of a digital spiking siliconneural network, a silicon synapse circuit is required.
We will design this circuit in the near future by using amathematical-model-based method referring to kinetic
models for biological synapses.

Trans. Neural Networks, Vol. 16, No. 3, pp. 754–773,
May 2005.
[2] Takashi
Kohno
and
Kazuyuki
Aihara,“Mathematical-model-based design method ofsilicon burst neurons,” Submitted to Neurocomputing.
[3] Dan Hammerstrom, “Digital VLSI for Neural Networks,” in “The Handbook of Brain Theory and Neural Networks second ed.”, ed. M. A. Arbib, pp. 349–
353, MIT Press, 2003.
[4] “FPGA Implementations of Neural Networks”, ed. A.
R. Omondi and J. C. Rajapakse, Springer, 2006.
[5] J. Rinzel and B. Ermentrout, “Analysis of Neural Excitability and Oscillations,” in “Methods in Neural
Modeling”, ed. C. Koch and I. Segev, pp. 251–291,
MIT Press, 1998.
[6] E. M. Izhikevich, “Neural Excitability, Spiking, andBursting,” Int. J. Bifurcation and Chaos, Vol. 10, pp.
1171–1266, 2000.
[7] C. Morris and H. Lecar, “Voltage Oscillations in the
Barnacle Giant Muscle Fiber,” Biophys. J., Vol. 35,
pp. 193–213, Jul., 1981.
[8] K. Aihara, G. Matsumoto, and Y. Ikegaya, “Periodic
and non-periodic responses of an periodically forced
Hodgkin-Huxley oscillator,” J. Theor. Biol., Vol. 109,
p. 249–269, 1984
[9] H. Fujii and I. Tsuda, “Itinerant dynamics of class I*
neurons coupled by gap junctions.,” Computational
Neuroscience: Cortical Dynamics Lecture Notes In
Computer Science, Vol. 3146, pp. 140–160.
[10] T. Takemoto, T. Kohno, and K. Aihara, “Circuit Implementation and Dynamics of A Two-Dimensional
MOSFET Neuron Model,” Int. J. Bifurcation and
Chaos, will appear in Feb. 2007.

Appendix
A Common parameters:
Par.
an
ap
kp

B

D
References[1] Takashi Kohno and Kazuyuki Aihara, “A MOSFETbased model of a Class 2 Nerve membrane,” IEEE

©ISAROB 2007

572

Value
0.25
0.25
–0.2125

Par.
cn
cp
qp

Value
0.5
0.5
–0.6875

Value
2.0
1.0
–0.205

Par.
pn
τ

Value
–0.3
0.003

Par.
qn
r

Value
–0.705
–0.2

Par.
qn
r

Value
–1.295
–0.1

Par.
qn
r

Value
–0.755–0.25

Class II parameters:
Par.
kn
φ
I0

This study is supported by Aihara Complexity
Modelling Project, ERATO, JST, Japan.

Par.
bn
bp
pp

Class I parameters:
Par.
kn
φ
I0

C

Acknowledgements

Value
8.0
8.0
16.0

Value
4.0
0.6
–0.24

Par.
pn
τ

Value
–0.55
0.003

Class I∗ parameters:
Par.
kn
φ
I0

Value
4.0
0.6
–0.25

Par.
pn
τ

Value
–0.1
0.002

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Hexagon-Based Q-Learning for Object Search with Multiple Robots
Hyun-Chang Yang, Ho-Duck Kim, and Kwee-Bo Sim
School of Electrical and Electronics Engineering, Chung-Ang University
221, Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea
e-mail : hoduck@wm.cau.ac.kr, kbsim@cau.ac.kr

Abstract
This paper presents the hexagon-based Q-leaning for
object search with multiple robots. We organized anexperimental environment with five small mobile robots,
obstacles, and an object. Then we sent the robots to a
hallway, where some obstacles were lying about, tosearch for a hidden object. In experiment, we used threecontrol algorithms: a random search, an area-based action
making (ABAM) process to determine the next action of
the robots, and hexagon-based Q-learning to enhance thearea-based action making process.
Keywords : Hexagon-based Q-Learning, Multiple robots,
Area-Based Action Making, Markovian

1. Introduction
Nowadays, robots are performing human’s work in
dangerous field, such as rescue jobs at fire-destroyedbuilding or at gas contaminated sites; information
retrieval from deep seas or from space; and weather
analysis at extremely cold areas like Antarctica.
Sometimes, multiple robots are especially needed topenetrate into hard-to-access areas, such as undergroundinsect nests, to collect more reliable and solid data.
Multiple robot control has received much attention tooffer a new way of controlling multiple agents more
flexibly and robustly. Ogasawara used distributed
autonomous robotic systems to control multiple robots
that transport a large object[1]. In this paper, we propose
an area-based action making (ABAM) process, which isthe basis of hexagon-based Q-learning, to control
multiple robots against collision and lead individual robot
to search through its own trajectory.
Reinforcement learning allows an agent to actively
decide an action policy based on explorations of its
environment. During exploration of an uncertain state
space with reward, an agent can learn what to do bycontinuous tracking of its state history and appropriately
propagating rewards through the state space[2]. In our©ISAROB 2007573

research, we focused on Q-learning as a reinforcement
learning technique. Because Q-learning is a simple way
to solve Markovian action problems with incomplete
information and on the basis of the action-value function
Q that maps state-action pairs to expected returns [3]. In
addition to this simplicity, Q-learning can adopt to thereal world situation. For example, the state space can bematched with the physical space of the real world. An
action also can be regarded as physical robot movement.
In this paper, we propose the hexagon-based Q-learning
to enhance the area-based action making process so thatthe learning process can better adapt to real world
situations.
The organization of this paper is as follows. In chapter
2, the area-based action making process is introduced. In
chapter 3, hexagon-based Q-learning adaptation ispresented. In chapter 4, experimental results from theapplication of three different searching methods to findthe object are presented. In chapter 5, conclusions arepresented.

2. Area-Based Action Making Process
Area-based action making (ABAM) process is aprocess that determines the next action of a robot. The
reason why this process is referred to ABAM is that a
robot recognizes surrounding not by distances, from itself
to obstacle, but by areas around itself. The key idea of the
ABAM process is to reduce the uncertainty of its
surrounding. It is similar with the behavior-based
direction change, to control the robots [4]. The robots
recognize the shape of its surrounding, then take anaction (turn and move forward) to where the widest space
will be guaranteed. Consequently, each robot can avoid
an obstacle and collision with other robots. Figure 1
depicts the different actions taken by distance-based
action making (DBAM) and by ABAM in the same
situation [5]. Our small mobile robot has the six emitterdetector infrared sensor pairs, which are placed at anangle of 60 degrees with one another to cover 360
degrees. The advantage of ABAM is illustrated by the

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

following example. Figure 2 presents the result of eachaction making process by DBAM and ABAM. In bothcase, the robot is surrounded by 4-obstacles. By DBAM,
the robot will be confused because it perceives that there
is no obstacle in the southeast direction, and then it will
try to keep tracking to the southeast. Finally, it will getstuck between two obstacles. This scenario is shown in
the left picture in Fig. 2. By ABAM, however, the robot
will calculate the areas of its surrounding, and then it willrecognize that an action to the northeast will guarantee
the widest space. Therefore, the robot will change its
direction to the northeast. This scenario is presented in
the right picture in Fig. 2.

Fig. 1. The different actions will be taken by DBAM
and by ABAM in the same situation

Fig. 3. An illustrative example of Q-learning
If the robot takes the action to the right, the value will
be updated for this entry where r = 0, γ = 0.9 are
predetermined values. The formula is presented below.
l( s , a ) ←Ql ( s ', a ')r + γ m ax Q
a '←0 + 0 . 9 m a x { 6 3 , 8 1, 1 0 0 }

←

90

(1)

The Q-learning for our robot system was adapted toenhance the ABAM process. The adaptation can be
performed with a simple and easy modification, named
hexagon-based Q-learning. Figure 4 is an illustrative
example of hexagon-based Q-learning. In Fig. 4,
intuitively, we know that the only thing that was changedis the shape of state space. We changed the shape of thespace, from a square to a hexagon, so that the robot canrecognize its surrounding by 6-areas. According to thisadaptation, the robot takes an action to 6-direction andˆFig. 2. An illustrative example of robot maneuvers by
DBAM(left) and by ABAM(right)
In addition to the obstacle avoidance, ABAM also
make the robots to search their own space [6]. Thisfeature is advantageous when 2 or 3 robots meet at thesame place. When they face each other, each robot will
try to find more wide space. Consequently, the robot willchange its direction to avoid the other robots and start tosearch in its own space again.

has 6-table entry Q value. In the left of Fig. 4, the robot
is in the initial state. Now, if the robot decides that +60
degree guarantee the widest space after calculation of its
6-areas of surrounding, the action of the robot would be
a+60°. After the action is taken, if Area3 is the widest area,ˆthe value of Q (s1, a+60°) will be updated by the formula(2) in the Q-learning algorithm asl ( s , a ) ← r + γ max Ql(s , a )
Q
1
init
2
+ 60 ٛ
a'← 0 + γ max{ Area1, Area 2, " , Area 6}← γ Area 3

3. Hexagon-Based Q-Learning
Q-learning is a well-known algorithm forreinforcement learning. It leads the agent to acquire
optimal control strategies from delayed rewards, evenwhen the agent has no prior knowledge of the effects of
its actions on the environment [7][8]. Figure 3 is an
illustrative example to explain Q-learning algorithm moreclearly. The ‘R’ stands for a robot or agent. The values

where 0 is the predetermined immediate reward. After the
movement from the initial state to the 1st next state,
immediate reward becomes the difference between thesum of total area before action is taken and the sum of
total area after action is taken.

ˆupon the arrows are relevant Q values with the stateˆtransition. For example, the value Q (s1, aright) = 72,
where a right refers to the action that moves R to its right[8].

©ISAROB 2007

(2)

574

Fig. 4. Hexagon-Based Q-learning

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Ultimately, the robot can determine its trajectory bylearning this Qˆ value. In the real world experiment,
however, battery consumption is a problem. If the robot
has to perform infinite iterations to complete task, total
system will fail. Therefore, a system must be set up tocancel the former action and move back to the earlier
state, when the former action causes any bad reward or
result. The hexagon-based Q-learning algorithm ispresented in Table 1.
Table 1. Hexagon-based Q-learning algorithm

Fig. 5. Appearance (left), anatomy (center), andfunctional block diagram (right) of the robot

4.2 Experimentsl ( s , a ) to zeroFor each s , a initialize the table entry Q
Calculate each 6-areas at the current state s
Do until task is completed.
• Take an action a to the widest area
• Receive immediate reward r• Observe the new state s 'l ( s ', a ) is greater or equal than Ql( s , a )If Ql• Update the table entry for Q ( s , a )• s ← s'l ( s ', a ) is too less than Ql( s , a )If Q• M ove back to the previous state• s ← s

4. Experiment Results
We performed experiments by using three different
control methods: random search, ABAM, and enhanced
ABAM by hexagon-based Q-learning. In the first part ofthis chapter, we introduce our self-made small mobile
robot system. Then, we present experimental result withthree different control methods.

The task of the robots is a follows: “Find the hidden
object while tracking through an unknown hallway.” We
set up the color of the object as green and that of 5-robots
as orange. The object was a stationary robot having thesame shape. It was a located at a hidden place near the
obstacle. The 5-robots, which try to search the object,recognize the object by the object’s color and shape. The
5-robots will decide whether they have finished the task
by detecting the object after each action is taken. First, weused the random search control method to find the hidden
object. The main controller generated a random number
and decided the next action corresponding to this number.
Random search is not so strong method to control therobot efficiently. Therefore, random did not perform well.
Moreover, it is very time and power consuming in thereal world situation. In Fig. 6, the white arrow points out
the object (same in Fig. 7 and Fig. 8). During random
search, even though the robots are within a close distanceto the search object, some robots failed to find the object.

4.1 Architecture of Small Mobile Robot
Our small mobile robot system consisted of four subparts and a main micro-controller part. The sub-parts
were camera vision, sensor, motor, and Bluetoothcommunication module. Each sub-part had its own
controller to perform its unique function more efficiently.
Figure 5 shows the appearance, anatomy, andfunctional block diagram of the robot. The main
components of the robot are as follows. For the eye of therobot, Movicam II made by Kyosera is used. It is the
CCD camera and its size is 30×47×29 mm. The robot has
six infrared sensors, emitter and detector pairs, tomeasure the distance around itself. The detector is ST1kla, high sensitivity NPN silicon phototransistors. NMB
PG25L-024 stepping motor is used as the driving part. Itscharacteristics are the following: drive voltage-12V, drive
method 2-2 phase and 0.495° step angle.

©ISAROB 2007

575

Fig. 6. 5-robots are searching the object using random search
Second, we applied ABAM to the robots. With thefeature of ABAM, the robots sense their environment by
6-infrared sensors and calculate 6-area with these values.
When the calculation is done, each robot tries to move towhere the widest area will be guaranteed. In our 2nd
experiment, after the robots started to move, each robot
spread out into the environment. Consequently, theABAM performed better than random search. Figure 7
shows that the two robots, which is located the right sideof the object, succeeded to complete the task. These two
robots are designated by black arrow in Fig. 7.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 7. 5-robots are searching the object using ABAM
Finally, we adopted the hexagon-based Q-learning to
ABAM as a modified control method. This method
allowed the robots to reduce the probability of wrong
judgment and compensated wrong judgment byreinforcement learning. Each robot tried to search its own
area as in the 2nd experiment, however, it canceled thedecided action if the action caused negative (or bad)
immediate reward value. The search with hexagon-based
Q-learning is presented in Fig. 8. The results of ourexperiment are presented in Fig. 9. With random search,
one robot found the object at the 2nd trial and 6th trial,
although these detections can be considered as justcoincidence. Therefore, we can say the random search
has no remarkable meaning. With ABAM, the robots
performed better than with random search, with theaverage performance above 1 during the all trial. Finally,
with the adaptation of hexagon-based Q-learning to
ABAM, the results were remarkable.

same environmental situations were presented. The areabased action making process and hexagon-based Qlearning can be a new way for robot to search an object inunknown space. This algorithm also makes the agents
avoid obstacles during their search. In our research, first,
we need to clarify the problem of accessing to the object.
This means that if multiple robots are to carry out atask such as object transporting or block stacking, therobots need to recognize the object then approach to it.
Therefore, we need to develop the robust accessing
algorithm. Naturally, some grippers need to be attached toboth sides of the robot. Second, our robot systems shouldbe improved so that the main part and the sub-parts
adhere more strongly. In addition, stronger complex
algorithms such as Bayesian learning or TD(λ) methodshould be adapted. Third, a self-organizing Bluetoothcommunication network should be built so that robots cancommunicate with each other robustly even if one ormore robots are lost. Finally, the total system should be
refined.

Acknowledgment:This research was supported by the Development ofSocial Secure Robot using Group Technologies ofGrowth Dynamics Technology Development Project by
Ministry of Commerce, Industry and Energy, Korea.

References

Fig. 8. 5-robots are searching the object using hexagonbased Q-learning

Fig. 9. Experimental result with 3 different control methods

5. Conclusion
In this paper, we presented the area-based action making
process and hexagon-based Q-learning to search theobject, hidden in unknown space, for 5 of our self-madesmall mobile robots. The experimental results from theapplication of the three different control methods in the©ISAROB 2007

576

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Computing,” Prentice-Hall New Jersey, 1997.
[4] W. Ashley, T. Balch, “Value-based observation withrobot teams (VBORT) for dynamic targets,” Proc. ofInt. Conf. on Intelligent Robots and Systems, 2003.
[5] J. B. Park, B. H. Lee, and M. S. Kim, “Remote
Control of a Mobile Robot Using Distance-Based
Reflective Force,” Proc. of IEEE Int. Conf. on
Robotics and Automation 3, pp. 3415-3420, 2003.
[6] P. Ögren, N. E. Leonard, “Obstacle Avoidance inFormation,” Proc. of IEEE Int. Conf. on Robotics
and Automation 2, pp. 2492-2497, 2003.
[7] T. Mitchell, “Machine Learning,” McGraw-Hill
Singapore, 1997.
[8] C. Clausen, H. Wechsler, “Quad-Q-Learning,”
IEEE Trans. on Neural Network 11, pp. 279-294,
2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Motion generation of pneumatic artificial muscle
S.ICHIKAWA, M.HARA, M.SUGISAKA
Department of Electrical and Electric Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan.
E-mail: msugi@cc.oita-u.ac.jp

Abstract
Various commodities that support the senior citizen
physically and psychologically appear by the coming of
the aged society. A lot of supplementary equipment are
installed directly in the user and used. As for such
equipment, physical safety and a psychological sense of
security are requested. Then, recently, artificial muscle isresearched as a wearable actuator that a user can use in
comfort. Therefore, we research the support equipment
by using the pneumatic artificial muscle. The past
research in our laboratory, we made an artificial
muscular robot. And, we measured the characteristic ofeach artificial muscle.
In this research, we generate motion of pneumatic
artificial muscular robot.

1.Introduction
Since the advent of aged society, various support
instruments that support the senior physically andmentally is created. Many of them are equipped directlyby the user.
As for support instruments, physical safety and a mental
sense of security are requested. To secure physical safety
and a mental sense of security, the following are needed.
(1) Even if controlling becomes impossible, support
instruments doesn’t become a trouble.
(2) Support instruments are small and light.
(The senior citizen can easily carry possession.)(3) Support instruments fits flexibly the human body.
(4) No operation sound.
Recently, as a wearable actuator with which a user canequip in comfort, artificial muscles attract attention.
Therefore, we will research the support instrument byusing the artificial muscle.
As the first step, in this research, we measure thecharacteristic of each artificial muscle, and, we makemotion generation of artificial muscular robot.

<Polymer actuator>(Actuator that used polymeric material.)<Shape-memory actuator>(Actuator that used shape-memory material.)<Electrostatic actuator>(Actuator using electrostatic power.)< Pneumatic actuator >(Actuator that used air pressure.)
In this research, we used the Pneumatic actuator(McKibben artificial muscle) as a suitable actuator forthe support instrument that the user equips, because thebalance of the generative force and the contraction
percentage is good, and, the viscoelasticity characteristic
is similar to human’s muscle.
The McKibben artificial muscle has structure thatwrapped the rubber tube by a plastic fiber mesh. Fig.1
and Fig.2 show pattern diagrams of artificial muscle.
When Pneumatic muscle was pressurized, it expands inthe radius direction. Power to contract axially is
generated. If the artificial muscles is set to 100, themaximum pressurization will be set to 70. The artificial
muscle has the expansion and contraction rate of 30%.

Flexible part100

Fig.1 Pattern diagrams before artificial muscle
is pressurizedFlexible part70

2.Artificial muscle
There are various kinds of artificial muscles. Artificial
muscle has following kind.

©ISAROB 2007

577

Fig.2 Pattern diagrams after artificial muscle

is pressurized

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.Artificial muscular robot

5.Expansion and contraction
characteristic measurement
We used the artificial muscle of different four lengths(160,240,300,400mm) in this experiment.First of all, theload is not put on the artificial muscle. And, constant airpressure is injected. Next, the load is not put on two
artificial muscles and constant air pressure is exhausted.
The measurement result is shown in Fig.5-6.
Expansion and contraction rate(%)

Fig.3 shows pattern diagrams of artificial muscularrobot. The frame of the robot is wooden, and it has the
hip joint, the knee joint, and the ankle joint. The hip joint
and the ankle joint are made from the spheroid joint. The
knee joint is made from the hinge joint.
Number of Fig.3 expresses arrangement of artificial
muscles.
1: long peroneal muscle, short peroneal muscle (125mm)
2: anterior tibial muscle, posterior tibial muscle (125mm)
3: tibialis anterior (245mm)
4: musculus triceps surae (400mm)
5: musculus quadriceps femoris (330mm)
6: hamstrings (330mm)
7: gluteus maximus (305mm)
8: musculus iliopsoas (305mm)

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.000

160mm(Injection)
240mm(Injection)
300mm(Injection)
400mm(Injection)
0.100

0.200
Pressure(MPa)

0.300

0.400

(1) elevational view(2) left side viewFig.3 Pattern diagrams of artificial muscular robot

4.Exprerimental apparatus

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.400

160mm(Exhaust)
240mm(Exhaust)
300mm(Exhaust)
400mm(Exhaust)
0.300

0.200
Pressure(MPa)

0.100

0.000

Fig.6 No load characteristic of each length (Exhaust)
These are collectively shown in Fig.7-10.
Expansion and contraction rate(%)

The outline chart of the apparatus used by thisresearch is shown in Fig.4.
The air tube of the artificial muscle and the air tube of aircompressor are connected with the air pressure controller.
The control signal from the computer is given to the airpressure controller, and the artificial muscle can be
expanded and contracted.

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.000

160mm(Injection)
160mm(exhaust)
0.100

0.200 0.300
Pressure(MPa)

0.400

0.500

Fig.7 No load characteristic of 160mm in length(Injection-exhaust)

Fig.4 Experiment device outline chart©ISAROB 2007

Expansion and contraction rate(%)

Fig.5 No load characteristic of each length (Injection)578

240mm(Injection)
240mm(Exhaust)
0.100

0.200 0.300
Pressure(MPa)

0.400

0.500

300mm(Injection)
300mm(Exhaust)

0.100

0.200 0.300
Pressure(MPa)

0.400

0.500

Expansion and contraction rate(%)

Fig.9 No load characteristic of 300mm in length(Injection-exhaust)
30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.000

0.100

0.200

0.300

0.400

Pressure(MPa)

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
-2.50.400-5.0

No load(Exhaust)
1.0kg(Exhaust)
2.0kg(Exhaust)

0.300

0.200

0.100

0.000

Pressure(MPa)Fig.12 Load characteristic of 300mm in length (Exhaust)

400mm(Injection)
400mm(Exhaust)

0.100

0.200 0.300
Pressure(MPa)

0.400

0.500

Fig.10 No load characteristic of 400mm in length(Injection-exhaust)
Injection and exhaust in the same length showed thecharacteristic with a different each curve (Fig.7-10).
Cause of hysteresis was thought elastic deformation ofrubber tube in the artificial muscle.

6.Load characteristics
Next, we add the loads of certain degree to theartificial muscles, and, we measure the characteristics of
expansion and contraction. The measurement result isshown in Fig.11-14. (The measurement result in theartificial muscle of 300mm in length is shown in thispaper.)©ISAROB 2007

No load(Injection)
1.0kg(Injection)
2.0kg(Injection)

Fig.11 Load characteristic of 300mm in length(Injection)

579

Expansion and contraction rate(%)

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.000

Expansion and contraction rate(%)

Expansion and contraction rate(%)

Fig.8 No load characteristic of 240mm in length(Injection-exhaust)

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
-2.50.000
-5.0

27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
-2.50.000-5.0

1.0kg（Injection）
1.0kg（Exhaust）
0.100

0.200

0.300

0.400

0.500

Pressure(MPa)Fig.13 Load characteristic of 300mm in length[Load=1.0kg] (Injection-exhaust)
Expansion and contraction rate(%)

30.0
27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
0.000

Expansion and contraction rate(%)

Expansion and contraction rate(%)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

27.5
25.0
22.5
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
-2.50.000-5.0

2.0kg（Injection）
2.0kg（Exhaust）
0.100

0.200

0.300

0.400

0.500

Pressure(MPa)Fig.14 Load characteristic of 300mm in length[Load=2.0kg] (Injection-exhaust)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
From the above, the expansion and contraction rate
lowers when the load is put regardless of the length of
the artificial muscle.
Next､when no-load, the error of injection and exhaust
was 3.2% at the maximum. When we put the load of
1.0kg, the error of injection and exhaust was 3.0% at the
maximum. When we put the load of 2.0kg, the error of
injection and exhaust was 2.5% at the maximum. As aresult, artificial muscles can perform a certain amount ofhysteresis improvement by applying load.
Moreover, because the error of injection and exhaust isabout 3% at the maximum, the expansion and contraction
characteristic of using
for motion generation of
artificial muscles uses the average value of injection andexhaust.

7. Motion generation of bending andstretching
We generated the bending and stretching motion of therobot by using the above-mentioned measurement result.
The home position of the robot is shown in Fig.15 (a),(c).
The bending position of the robot is shown in Fig.15 (b).

(a) Home position (b) Bending position (c) Home positionof the robotof the robotof the robot
Fig.15 Bending and stretching motion of the robot

8.CONCLUSION
In this research, we made Expansion and contraction
characteristic measurement of artificial muscles, and theimprovement of hysteresis. And, we performed an
artificial muscular robot expansion-and-contraction
operation generation using the result. However, because
the load added to the artificial muscle changes while
operating, we cannot accurately control the robot. In the
future, we will improve the above issue. In addition, we
will generate walk operation of an artificial muscular©ISAROB 2007580

robot.

REFERENCES[1] S.Douno,A.Saitou,R.Kuwata“Knit Structure SMA Actuator for Wearable Artificial
Muscle Systems”[2] Takashi TAKUMA(Osaka Univ.), Koh HOSODA,
MinoruASADA(Osaka
Univ.,
Handai
FRC)“Stabilization of the biped walker with pneumatic muscle
actuator”[3]http://f-kawaraban.cside.com/find/kinoukaibou/
muscles.htm[4]http://www.katto.comm.waseda.ac.jp/~katto/Class/
GazoTokuron/code/thread.html[5]http://www.hlla.is.tsukuba.ac.jp/~yas/sie/pdsoft-2001/
2002-01-17/[6] http://yokohama.cool.ne.jp/chokuto/urawaza/api/

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

An Antibody-Based Computing: An Application to Stable Marriage Problem
Yoshiteru Ishida1, 2
1 Department of Knowledge-Based Information Engineering,
Toyohashi University of Technology,
Tempaku, Toyohashi, 441-8580 Japan
2Intelligent Sensing System Research Center
Toyohashi University of Technology,
Tempaku, Toyohashi, 441-8580 Japan

Abstract
Antibodies, among others, are importantcomponents of the immune system. This paper proposes
using specific recognition capability exhibited by
antibodies for computation, in particular, solving stable
marriage problem which have been studied as
combinatorial computational problems. Antibody-based
computation is proposed by integrating the recognition
capabilities of antibodies. The computation is carried out
on an array form that is suitable not for only expressing
stable marriage problems but for further integration to
antibody microarrays.

1 Introduction
After Adelman pioneered DNA-based computing
in his seminal work [1], many researches establishedthat not only DNA but also other macro molecules
could have computational capability comparable to
DNA (e.g. [2]). Elowitz and Leibler evendemonstrated that a genetic circuit may not be a
dream [3,4]. Even synthetic multicellular systems
have been recently studied [5], leading to synthetic
biology. Possible drug production by engineering
yeast [6] has given a great impact on the area. These
new bio-engineering technologies have provided
bioinformatics with not only new tools but systemic
views.
On the other hand, post-genome era proved thatsequence information of human genome alone is not
sufficient, but higher knowledge of function must be
revealed. Post-genome age naturally proceeds to study
immune systems (as in [7]) focusing not only
components such as antibodies and MHC but its
systemic organization.
After the demonstration of DNA based
computing by Adelman, protein based computing had
been proposed by Hug and Schuler [2] and extended©ISAROB 2007

581

by Balan and Krithivasan [8]. This paper, in a similar
spirit of synthetic biology and system biology, tries toconstruct an information processing devices
incorporating specific recognition capabilities ofantibodies. Stable marriage problem [9, 10] is used asa benchmark problem for demonstrating an array
formatimplementationofantibody-based
computation.

2
Antibody-based
Introduction

Computing:An

One of the major components propelling theinformation processing of the immune system is the
specific recognition of antigens. The immune system
is capable of recognizing even artificially synthesized
substances. Also, it can further classify substances
into the self (those derived from the individual) andnonself. Among those bearing recognition capabilities,
antibody is undoubtedly important. Further, antibodies
have been studied in great detail not only intheoretical biology but in bio-engineering.
Antibody-based computing directly focuses on therecognition capability and integrates it for problem
solving including combinatorial problems.

2.1 Antibody-based computing and DNA-based
computing
Similarly to the DNA-based computing,antibody-based computing utilizes complementary
matching between macro molecules: antibodies. Since
the computational capabilities that DNA-based
computing could be inherited to antibody-based
computing, we rather focuse on the difference
between them. Roughly said in the context of solving
combinatorial problems, the difference may beclarified by the correspondence between a

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

complementary matching (as in Marriage Theorem)
and a stable pairing based on preference (as in Stable
Marriage Problem).
Affinity between antigens and antibodies can bemeasured and their intensities can be ordered (asformatted in an affinity matrix). That is, in contrast to
Matching(DNA1, DNA2)=1 (matched) 0 (notmatched), Affinity(Antigen1, Antibody2) could varyfrom 0 (no agglutination) to 1 (highest agglutination).
This difference would suggest that antibody-based
computing could potentially implement error
tolerance that could not be implemented on the
DNA-based computing. The difference would further
suggest that the antibody-based computing may be
extended to a problem solver, if adaptive mechanismof the immune system realized by antibodies and theirmaintenance is also involved.

problem to antibody-based computing can be done bycomposing
antigens–antibodies
compounds
corresponding to a problem entity. Antibodies andantigens for a compound corresponding to a particular
individual will be determined by considering her (his)preference list over men (women).
Landshteiner’s ABO blood group system [11]may be most familiar and yet simple example. His
blood type system is based on antigens (asagglutinogen) on red blood cells and antibodies (asagglutinin) in the blood serum. Table 1 shows
agglutinogen and agglutinin of each blood type.
Affinity between antibody and antigen is shown in
Table 2. Table 3 indicates the well-known
incompatible transfusion among the blood type A, B,
AB, and O.
Table 1. Landsteiner’s ABO Blood Group System

3 Solving a Combinatorial Problem

Blood Type

A

B

AB

O

3.1 Stable Marriage Problem

Antigen(agglutinogen)

A

B

A, B

None

In a naive form, the problem assumes n men andn women with each member having preference lists tothe members of opposite sex. A pair of a man Mi and awoman Wj is called a blocking pair if they are not pairin the current solution, but Mi prefers Wj to the current
partner and Wj prefers Mi to the current partner as well.
A matching between men and women with no suchblocking pair is called stable.
Having stated the stable marriage problem, it
would be natural to think of the algorithm for
antibody-based computing. That is, the stable
marriage problem (SMP) may be mapped toantigen-antibody reaction so that preference order of
each person in SMP will be reflected in the affinity
intensity between an antibody and an antigen. After
antibodies and antigens are so arranged, the solutionof SMP will emerge by observing concentration of theagglutination. It should be remarked that agglutination
process could be any agglutination (not necessarily
between antibodies and antigens) if their intensities
are measurable and ordered.

Antobody(agglutinin)

β

α

None

α, β

3.2 Mapping a stableantibody-based computing

marriage

problemtoAs stated above, mapping a combinatorial©ISAROB 2007

582

Table 2. Affinity Matrix. Circle indicates that theantibody-antigen reaction would occur if the antibodies in thecolumn meet with antigens in the row.

A

AntigenB

Antibodyα(anti-A)○β(anti-B)○

Table 3. Agglutination when the blood type in the column istransfused with the blood type to the row. Circle indicates thatthe blood type of the column when transfused to that of thecolumn would agglutinate.
Blood Type

A

A
B
AB
O

○
○

B
○
○

AB

O
○
○
◎

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

In this example, we map the relation the woman
Wi (the man Mi) prefers the man Mj (the woman Wj) toother to the relation that if the blood of Wi (Mi) would
be agglutinate when the blood of Mj (Wj) weretransfused. That is, if the woman Wi prefers the man
Mj most the blood type should be so assigned that thetype for Wi comprises of antibody AbWi and antigen
AgWi; and that for Mj of antibody AbMj and antigen
AgMj and the affinity Aff(AbWi, AgMj) are highest.
For the trivial case when the preference lists ofmen and women are as in Table 4, simple assignment
would suffice: a man to type A and another man totype B; for the woman who like a man with type A totype B and for another woman type A (Fig. 1). It
should be noted that assignment to A for two men andto B for two women would not work, since theassignment does not reflect the preference of men andwomen.
In a nontrivial preference list shown in Table 5,
one assignment would be type O to M1 and W1, type A
to M2, and type B to W2 (Fig. 2) For other two
preference lists (with the graph topologically different
from those shown Figs. 1 and 2), it is not possible tomap the blood type with the above correspondence,and other compounds should be synthesized forrealizing the preference lists.
These examples suggest a scheme forsynthesizing antigen-antibody compounds that realize
mapping from given preference lists to thecompounds. If the woman Wi prefers the man Mj toother men, the compound corresponding to Wi
contains antibody AbWi and the compound
corresponding to Mj contains antigen AgMj thatsatisfies Aff(AbWi, AgMj) highest among other AgMj
(j=1...n).
If Mj is the second in the preference list of Wi,
then Aff(AbWi, AgMj) must be second highest and soon. AgMj must realize oders from women Wk otherthan Wi, hence the affinity Aff(AbWk, AgMj) must
realize the order accordingly. (If AgMj alone cannot
realize the order, then new antigen realizing the ordermust be added to the corresponding compound. In fact,
the above example also suggests that compounds canbe composed of a set of antigens and a set of
antibodies.) Constraints for selecting antibodies andantigens for a compond corresponding to a person canbe sumed up as follows:©ISAROB 2007

583

z Aff(AbWi, AgMj) > Aff(AbWi, AgMk) if the woman
Wi prefers Mj to Mk in her preference list for all Wi∊W, and for all distinct pairs Mj, Mk ∊M; andz Aff(AbMi, AgWj) > Aff(AbMi, AgWk) if the man Mi
prefers Wj to Wk in his preference list for all Mi ∊M,
and for all distinct pairs Wj, Wk ∊W.
Table 4. A trivial preference list for two by two stable
marriage problem.

W1
W2

M1
2
2

M2
1
1

M1
M2

W1
2
2

W2
1
1

M1

A

B W1

M2

B

A W2

Fig. 1. A blood type assignment reflecting the preference
Table 5. A non-trivial preference list for two by two stable
marriage problem.

W1
W2

M1
1
2

W1
1
2

W2
2
1

M2
2
1

M1
M2

M1

O

O W1

M2

A

B W2

Fig. 2. A blood type assignment reflecting the preference

3.3 Solving a stable marriage problem with an array
formatAn illustrative example with Landsteiner’s bloodgroup suggests the following algorithm to solve SMP
with an array format. In the array, row i and column jcorrespond to the compound for man i (i.e. AbMi andAgMi) and that for woman j (i.e. AbWi and AgWi). In
other word, at the cross-point ij, two antigen-antibody
reactions between AbMi and AgWj (reflecting man i’spreference) and between AbWj and AgMi (reflecting

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

woman j’s preference) will take place.
Under the assumption that the concentration
observed at each cross-point is proportional to both
Aff(AbMi, AgWj) and Aff(AbWj, AgMi), the array canfind a stable matching by selecting one cross-pointwith highest concentration from each row and column.
This matching is certainly stable one, for suppose
otherwise there must be a blocking pair Mk and Wlsuch that Aff(AbMk, AgWl) > Aff(AbMk, AgWp(Mk))
and Aff(AbWl, AgMk) > Aff(AbWl, AgMp(Wl)) wherep(Mk) denotes a partner of Mk in the current matching.
Then both concentraion at the cross-point kl is higher
than those of kp(Mk) and those of p(Wl)l reflecting theaffinities.
Although obaining a stable mathing shows somecomputational power, it can be solved in O(N2) time
where N is the size of men (and women). Gale andSharpley have invented an wll-known algorithm forgiving stable matchings for man-oriented matching or
woman-sided one [9]. By further assuming that theconcentration observed at a cross-point can reflect theamount of antibodies imposed, the array is capable ofobtaining any stable matching in the array from the
man-oriented (man optimal and woman pessimal)
matching to the woman-oriented (woman optimal andman pessimal) one. By equally increasing all theantibodies AbMi (i=1..n) (or equivalently antigens
AgWj j=1...n), from a unit to α the matching would
become close to the man-oriented one. Similarly,
increase of AbWi (i=1..n) will bias the matching
towards the one of woman-oriented one. Since there
are many variants of the stable marriage problem,
which are NP-hard, devising the array for solving
these problems is a challenging problem. Anotherchallenge is to devise the array as a component ofproblem solver that can deal with not only a particular
problem but with similar problems, as the immune
system has done.

4 Conclusion
We have shown that antibodies, a macro
molecule of the immune system, with a specificrecognition capability can be used for computation asa macromolecule DNA is used for DNA computing.
Focusing on a stable marriage problem, and extending
ABO blood group system, it is shown that theantibody-based computation will be implemented on©ISAROB 2007584

an antibody microarray.
Acknowledgements
This work was supported in part by
Grants-in-Aid for Scientific Research (B) 16300067,
2004. This work was partly supported also by the 21stCentury COE Program “Intelligent Human Sensing”
of the Ministry of Education, Culture, Sports, Scienceand Technology of Japan.

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Bioinformatics 17(4): 364-368 (2001)[3] M.B. Elowitz, and S.A. Leiblerr, “Synthetic
oscillatory network of transcriptional regulators,”
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2006)[7] Z. Guo, L. Hood, et al., “Long-range Multi-locus
Haplotype Phasing of the MHC” the Proceedingsof the National Academy of Sciences, 103-18(May 2, 2006)[8] M.S. Balan and K. Krithivasan, “Parallel
Computation of Simple Arithmetic Using
Peptide-Antibody Interactions,” Biosystems,
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AmericanMathematical Monthly, 69:9–15, (1962)[10] R.W. Irving and P. Leather, and Dan Gusfield,“An efficient algorithm for the optimal stable
marriage,” J. ACM, 34(3):532–543, (1987)[11] K.
Landsteiner,“Zur
Kenntnisderantifermentativen, lytischen und agglutinierenden
Wirkungen des Blutserums und der Lymphe,”Zentralblatt Bakteriologie, 27:357-62 (1900)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Network Route Design of Public Transport System with Network Evolution
Takahiro Majima
Center for Logistics Research
National Maritime Research Institute
Email: majy@nmri.go.jp

Keiki Takadama
Faculty of Electro-Communications
University of Electro-Communications
Email: keiki@hc.uec.ac.jp

Daisuke Watanabe
Center for Logistics Research
National Maritime Research Institute
Email: watanabe@nmri.go.jp

Mitujiro Katuhara
Hokkaido Intellect Tank
Email: fwht4182@nifty.com

Abstract
Characteristics of network in the real world haveattracted a number of scientists and engineers.
Various findings are given from recent studies on
the real world network, sometimes called complex
network. The network evolution to produce
complex network is also investigated actively and anumber of models are proposed. Some model
succeeded to produce public transport networks in
the real world. We make active use of the network
evolution for generating public transport network.
In our model, the network of the bus lines evolves
with a local evaluation value defined by the OD(Origin-Destination) demand multiplied with therequired time including riding time and waitingtime. Since the algorithm with network evolution is
simple, our method is able to provide bus line
network immediately. It is advantage to generate
bus line network especially when a transport
operation is required urgently under disastercircumstances.
Keywords : Complex Network,
Public Transportation, Route
Optimization, Weighted Graph

Network Evolution,
Generation, Local

1. Introduction

Characteristics of network in the real world haveattracted a number of scientists and engineers. Various
findings are given from recent studies on the real world
network, sometimes called complex network. The
complex networks, such as small world network [1] orscale free network [2], lie between two extremes, i.e.,
random network and regularly connected network likelattice. Many complex networks in real world haveinteresting characteristics found in both random andregular networks simultaneously.
The properties on the complex network are revealed
mainly by early studies focused on the un-weighted
relational network [3], in which there is no weight onlinks or vertices, such as distance or traffic amount.
Meanwhile, studies investigating weighted network has©ISAROB 2007

585

begun to appear in recent year. Some of the papers theweight denotes distance between vertices [4][5] or traffic
amount [6].
Further more, the network evolution to produce
complex network is also investigated actively after the
proposal of BA (Barabási and Albert) model [2]. The
model produces the scale free network and the rule of its
network evolution is known as preferential attachment(Rich gets richer). A number of models regarding to the
network evolution were proposed [7][8][9] and some ofthem utilize the distance in the Euclidean space as ameasure determining which node should be connectedfrom the new added node. In the case of transport
network in the Euclidean space, Gastner proposed amodel of network evolution using the distance amongnodes scattering in the Euclidean space and thereby
presented that it can produce the subway network in
Boston with the set of given nodes (stations) [9].
Considering this background, the knowledge ofcomplex network seems to provide us useful information
to design and construct the public transport networks. Our
final purpose is to find an algorithm providing effective
and optimized public transportation system, such as
waterbus or bus expected to reduce traffic congestions
and increase redundancy of transport system underdisaster circumstances. We make active use of theknowledge of the network evolution to apply them to the
problem in public transportation.
The design of public transport system is composedof four components , Network Route Design, Setting Time
Tables, Scheduling of Vehicles and Scheduling of Drivers[10]. Toward the goal, this paper is focused on theprocess of the Network Route Design mainly andproposes a model to generate network using the network
evolution. The network route design is the process todesign the bus lines defined as a sequential order ofstations at which vehicles stop. The bus lines also forms
network over the stations on physical infrastructure
network such as roads or rivers. In our model, thenetwork of the bus lines evolves with a local evaluation
value defined by the OD (Origin-Destination) demand
multiplied with the required time (t raveling time andwaiting time ). Since the algorithm with network evolution

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
is simple, this model is able to provide bus line network
immediately. It is advantage to generate bus line network
especially when a transport operation is required urgently
under disaster circumstances.

addition type. For subsumption type, the line, which one
terminal is connection node, subsumes the new bus line
and target node. The evaluation function is applied ateach growing step and it determines the connection nodeand connection type.

2. ModelIn this paper, the nodes represent bus stops or stations
and one set of links connecting nodes represents one bus
line. Vehicles shuttle along with the set of links
representing the bus line. The model with network
evolution for bus line network is described below. It
separates two processes. One process is to determine
which node should be connected newly added node (i.e.
network evolution). The other process describes how todetermine the number of buses to be engaged into adesigned bus line.
In this paper, the destination is limited to one bus stop
for brevity. However this situation is well observed
around stations of commuter trains. The passengers travelfrom their residential area to the railway station by bus.
Although, there are many bus stops in the residential area,
the number of destination, station of commuter train, islimited.
2.1 Network Evolution
Our model is inspired by Gastner[9]. One node (busstop) is added to the network at each evolution step andconnected one of the existing nodes with one linkcomposing a part of a bus line route. Fig.1 shows theprocess of the growing bus line network. The newly
added node (Node 3 in Fig.1), defined as target node inthis paper, is selected in the order of the distance between
the destination and the target node. Then, one of thealready existing nodes, defined as connection node in thispaper, is selected with an evaluation function described
below and one link connects the target node to theconnection node. After the connection between target
node and connection node, the links are created alongwith the existing links from the connection node to thedestination and this set of links comprises one bus line.
Therefore, at the first step of evolution, the node nearest
to the destination is selected as target node and thedestination node itself becomes connection node. Thus,
the network connected to the destination spread outward
from the destination and it has tree topology (no loop).
Candidate forConnection Node,(Node 0,1,2)
Line2

4
Target
3
Node

2

1

Line1

0

Destination

Fig.1 Schematic of Network Evolution
If selected connection node is terminal of the existing
line, there are two types of connection as illustrated in
Fig.2. One is addition type, the other is subsumption type.
Number of bus lines is simply incremented by one for©ISAROB 2007

586

2

2
Line2

3
Line3
1

Line1

Line2

3
Line1

0

1

(a)

0

(b)

Fig.2 Two Types of Line Connection(a) Addition Type (b) Subsumption TypeThe evaluation function resembling in the following
equation is applied frequently to the designed bus line
network [11].
min Z = Z1 + Z 2 = ∑ Ti Di + w∑ B kik

Where, Di is demand at bus stop i, Ti is required time
for moving from bus stop i to destination composed oftraveling time, Tmi and waiting time, Tw i , Bk is number ofbus engaged into the bus line k, w is parameters
controlling the balance of requirement between user andoperator of vehicle. Users hope to reduce the first term, Z1
and operators hope to reduce the second term, Z2 . Usually,
this evaluation function is applied to the whole bus line
network. However, we apply it for the selection of theconnection node and connection type. Thus, i and k mean
the set of nodes already connecting to the network andalready existing bus lines respectively.
The expected waiting time of a bus stop at where morethan one bus lines stop can be calculated by the following
equation [12].
N 1 N −1 (− 1)r t1 r N − r +1 N −r + 2
1
Twi = t1  + ∑
LL ∑

∑
∑
 2 r =1 (r + 1)( r + 2) j1 =2 j2 = j1+1
jr = jr −1 +1 t j1t j2 Lt j r Where, N is the number of bus lines running throughthe node i, t1 is the smallest headway in the set of tj
representing headway of line j. Further more, theheadway (frequency) is derived from the round trip timeand bus number simply and the shortest path is found by
Dijkstra’s algorithm when considering the topology of thephysical infrastructure network, such as road or rivers.
2.2 Determination of Vehicle Number
It should be satisfied that the transportation capacity islarger than the traffic amount. In the network evolution
process, node is connected one by one and the bus line
generated with addition type in Fig.2 is incremented by
one at each evolution step. Thus, the number of vehicle
assigned for a bus line can be calculated to satisfy thedemand occurred at the target node and needless toconsider the demand at nodes other than the target node.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Bk = Trk Dk Bcapa 

Where, Tr is round trip time of target node k, and Bcapa
is capacity of one vehicle. We should consider anotherway for the case of subsumption type in Fig.2, because,
the round trip time of existing line changes. There areseveral ways to determine the number of vehicles for
subsumption type. In this paper, it is intended todetermine the number of vehicle so that the headway of
already existing line, which one terminal is theconnection node, doesn’t deteriorate. Thus, the following
equation is employed.

)

DestinationA
2.2

Distance
1.6 (km)
B
3.1
1.6

3.5
w=10
Vehicle Speed: 20km/hr
Vehicle Capacity: 50 Persons
C
Demand: A,B,C → Des. 200 Persons/hr
1st Step

2nd Step
B AD = 1 A

DFor Z1
A: 20.4
B:C:
Z1 =20.4, wZ 2= 10x1= Z=30.4

BC

3rd StepA

BBAD =3DFor Z1
B
A: 18.8
B
B: 34.4
CD =2
C: 53.0CZ 1 =106, wZ2= 10x5 => Z=156
A

BBAD =3DFor Z1
B
A: 17.6
B CAD =2
B: 34.4
C: 61.6
Z 1 =114, wZ 2= 10x5 => Z=164

A

C

B BAD =3DFor Z 1
B
A: 17.6
BCBAD = 2
B: 33.2
C: 62.4
Z 1 =113, wZ 2 = 10x5 => Z=163

B AD =1 A
B B D =1
DFor Z1
B
A: 20.4
B: 44.7
C:
Z1 =65.1, wZ 2= 10x2 => Z=85.1

CA
B BAD =2
B A D =1
DFor Z1
B
A: 17.7
B: 38.7
C:C
Z1 =56.4, wZ2 = 10x3 => Z=86.4

SubsumptionA
B BAD = 3
TypeDFor Z 1
B
A: 18.8
B: 34.4
C:C
Z 1 =53.2, wZ2 = 10x3 => Z=83.2

AC

Subsumption
Type

D
For Z1B
B CBAD =5
A: 18.5
B: 34.1
C: 50.0
Z 1 =103, wZ 2= 10x5 => Z=153

C

Fig.3 Example of process of network evolution

3. Application
In this section, the model described previous section isapplied to two situations. One is simple physical©ISAROB 2007

Destination

w =0
B=38
L=5

5km

w = 25
B=19
L=4

Di = 200
persons/hour

Where, subscript k and j represent target node andconnection node respectively.
An example of network evolution is described in Fig.3.
Although the number of nodes is only four, many
possible network of bus line can be considered. It is easy
to imagine that investigation for all patterns leads tocombinatorial explosion when the number of nodes
increases.
1.0

3.1 Simple Network
The arrangement of nodes is illustrated in Fig.4(a).
Twenty nodes are positioned on the circle with diameterof 5 km. The capacity of vehicle, Bcapa and speed, Bs set
to 50 persons and 20km/hr respectively. The demand ofeach node is 200persons/hr and the road network is
assumed to have the topology of complete graph withstraight line. Fig.4(b) and (c) show the bus line network
generated by the model with w=0 and w=25 respectively.

587

(a)

(b)

(c)

Fig.4 (a) Arrangement of nodes and problem to besolved. (b) Result of network evolution with w=0, (c)Result of network evolution with w=25, where L and B
are total number of lines and vehicles respectively
As mentioned above, the model can analyze taking into
account of the balance of requirement between user andoperators with the control parameter, w. Fig.5 shows therelationship between total vehicle number and totalrequired time, TD, normalized with riding time, Tmideal ,calculated assuming that the all nodes are connected tothe destination directly. The result varies by thecontrolling parameter, w. It is found the required time fortraveling to the destination takes about 30% larger thanthat of the idealized bus line network for users. It iscaused from the waiting time and the topology of notdirectly connected to the destination, even if w is set to 0
that is acceptable for users.
Bus Number,B(

B k = max Trk Dk B capa  , B j Trk Trj 

infrastructure network and the other is waterbus line in
the three rivers running through Tokyo metropolitan.

Bus#
TD

40
35
30
25
20
15
10
5
0

1.55
1.50
1.45
1.40
1.35

TD/Tmideal

Thus, the number of vehicle is calculated by thefollowing equation simply.

1.30
1.25
0

1

5

10

50

100

w

Fig.5 Relationship between bus number and totalrequired time for the generated bus line
3.2 Application to waterbus line in the river system
The Tokyo metropolitan and around the region mostpopulated area in Japan have been suffering from heavy
traffic, congestions and commuter rushes on transport
networks. Furthermore considering the fact thatearthquakes occur frequently in Japan, road networks arecollapsed when an earthquake causes massive destruction.
The transport mode using rivers and ships that doesn’t
exist at this time is being expected to contribute to
reducing traffic congestions or redundancy of logistics

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
system under disaster circumstances. Fig.6 shows
physical infrastructure network composed of three rivers
running through Tokyo. There are 24 possible waterbus
stations in the river system.

5. Conclusion
We proposed the model generating rational public
transport network with network evolution. However,there are several subjects to be addressed for the practicaluse. For instance, the number of destination is limited to
one station at this time. Furthermore, in the case of thewaterbus, the congestion at bus stops should beconsidered, because the number of ships taking piers islimited.

Acknowledgement

Fig.6 Rivers running through Tokyo, three solid lines
connecting to ST0 represent generated waterbus lines.
For instance, making the Etchujima station represented
by ST0 in Fig.6 to be destination, the model was applied.
Table 1 shows the demand of six bus stops represented in
Fig.6. The capacity of waterbus, Bcapa and service speed,
Bs set to 100 persons and 8 knot(15km/hr) respectively
according to the ships operating for sight seeing aroundTokyo bay area.
Table 1 Demand of Six Waterbus Stations(Destination: ST0)
Station No.
Demand(Persons/hr)

ST3

ST5

ST 9

ST11

ST16

ST21

34

3

358

607

119

287

Three waterbus lines generated with w=0.1 are drawn
in Fig.6 by solid lines and Fig.7 shows relationshipbetween the number of waterbus and normalized required
time, as is shown in Fig.5. The generated bus lines
network in Fig.6 seems rational and required time
becomes about 30-40 larger than riding times of the
network connected to destination directly.
1.38
1.36

Bus#
TD

25
20
15
10
5
0
0.1

1

1.34
1.32
1.30
1.28
1.26
1.24
10

TD/Tm ideal

Bus Number,B

35
30

100

w

Fig.7 Relationship between waterbus number and totalrequired time for the waterbus line

4. Discussion
Since our model generates bus line network with theevaluation function applied to the local network not to
global network, it doesn’t certify the completely
optimized solution. However, the generated network andrequired time in this paper seem to be rational. Thus,
advantage of our model is to produce the rational solution
immediately. This advantage is considerably useful toengage a transport operation urgently such as disastercircumstance.

©ISAROB 2007

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This study is supported by the Ministry of Education,
Culture, Sports, Science and Technology, Grand-in-Aid
for Scientific Research (B), 17360424, 2005. We would
also like to thank Kazue Honda for her support ofprogramming.

References[1] D.J. Watts, S.H. Strogatz, Collective Dynamics of‘Small-World’ Networks, Nature, Vol.393, 1998,
pp.440-442[2] A.-L Barabási, R. Albert, Emergence of Scaling in
Random Networks, Science, Vol.286, 1999, pp.509512,[3] S.H. Strogatz, Exploring Complex Networks, Nature,
Vol.410, 2001, pp.268-276[4] V. Latora, M. Marchiori, Efficient Behavior of
Small-World Networks, Phys. Rev. lett. Vol.87,
No.19, 2001
[5] T. Majima, M. Katuhara, K. Takadama, Analysis on
Transport Networks of Railway, Subway andWaterbus in Japan, WEIN06 (Workshop on
Emergent Intelligence on Networked Agents)
Proceedings, May 2006, Hakodate, pp.152-166[6] A. D. Montis , M. Barthélemy, A. Chessa, A.
Vespignani, The Structure of Inter-Urban Traffic: A
Weighted Network Analysis , arXiv:physics/0507106
v2, 2005[7] M. Barthélemy, A. Flammini, Optimal Traffic
Networks, arXiv:physics/0601203v2, 2006[8] M. Kaiser, C.C. Hilgetag, Spatial Growth of RealWorld Networks, Phys. Rev. E Vol.69, 036103, 2004
[9] M.T. Gastner, M.E.J. Newman, Shape and Efficiency
in Spatial Distribution Networks, arXiv:condmat/0409702, 2004[10] A. Ceder, Methods for Creating Bus Time Tables,
Transportation Research, Vol.21A, No.1, 1986,
pp.59-83[11] D. Dubois, G. Bel, M. Llibre, A set of Methods inTransportation Network Synthesis and Analysis,
Journal of the Operational Research Society, Vol.30,
No.9, 1979, pp.797-808[12] W. Lampkin, P.D. Saalmans, The Design of Routes,
Service Frequencies and Schedules for a Municipal
Bus Undertaking: A case study, Operation Research
Quarterly, No.18, 1967, pp.375-397

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Robot multiple tasks performance and neural complexity
Genci Capi1 and Koco Bode2
1

Faculty of Information Engineering
Fukuoka Institute of Technology
3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka, 811-0195, Japan
2

Faculty of Mechanical Engineering
Polytechnic University of Tirana
Sheshi Nene Tereza, Tirana, Albania
capi@fit.ac.jp

Abstract
This paper presents a new method for multiple tasks
performance based on multiobjective evolutionary
algorithm. In order to verify the effectiveness, theproposed method is applied to evolve neural controllers
for the Cyber Rodent robot that has to switch properly
between two different tasks. Furthermore, the tasks andneural complexity are analyzed by including the neural
structure as a separate objective function. Results using
Cyber Rodent robot show that multiobjective-based
evolutionary method can be applied effectively for
generating neural networks controlling the robot to
perform multiple tasks, simultaneously.
KEY WORDS
Evolutionary robotics, neural controller, task complexity.

1. Introduction
Traditionally, the research on intelligent agents has
mainly focused on evolution or learning of individual
perceptual-motor and cognitive tasks. Nevertheless,intelligent agents operating in everyday life environments
often are required to perform multiple tasks
simultaneously or in rapid alternation, which is achallenge even for humans and primates.
Several approaches in the literature have been proposed
to address robot multiple tasks performance problem. Up
to now the standard methodology in machine learning
has been to break large problems into small, independent
subproblems, learn the subproblems separately, and thenrecombine the learned pieces [1]. In addition to learning,
evolution of neural controllers is well known forproviding a family of naturally-inspired algorithms which
can successfully address a wide range of robot behavior
learning problems ([2], [3]). In evolutionary robotics,©ISAROB 2007589

different constraints and objectives are handled asweighted components of the fitness function ([4], [5]),applying Single Objective Evolutionary Algorithm(SOEA).
This article presents a novel approach for robot multiple
tasks performancebased on Multiobjective
Evolutionary Algorithm (MOEA) ([6]). Unlike previous
methods, in the experiments presented here, each task is
considered as a separate objective function.
Nondominated Sorting Genetic Algorithm (NSGA) ([7])
is used to generate the Pareto set of neural networks.
In this method, we evolved one single neural controller
for multiple tasks performance, considering relevant
information of each task as sensory inputs. Therefore, as
the number of tasks increases, the neural controllers
become more complex. This makes the evolution process
difficult. In addition, the hardware implementation of
evolved neural controllers may result in poor
performance due to the increased error in sensory data. In
order to further investigate if the MOEA can also
generates efficient neural controllers for multiple tasks
performance; the structure of neural network is added asa separate objective function.
Simulation and experimental results show a good
performance of the proposed method. The nondominated optimal Pareto neural controllers have a good
distribution and CR robot behavior varies fromcompleting each of both considered tasks to flexibly
switching between them. Therefore, as a specificcontribution of proposed method is that in a single run of
MOEA are generated agents with completely different
behaviors, making it possible to select the appropriate
neural controller based on our preferences. Moreover,efficient neural controllers with appropriate sensory
inputs are selected through the course of evolution.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2. NSGAA real number NSGA was employed to evolve the neuralcontroller. In [8], the authors compared the NSGA withfour others multiobjective evolutionary algorithms using
two test problems. The NSGA performed better than theothers did, showing that it can be successfully used tofind multiple Pareto-optimal solutions. In NSGA, before
selection is performed, the population is ranked on thebasis of domination using Pareto ranking. All
nondominated individuals are classified in one category
with a dummy fitness value, which is proportional to the
population size [7]. After this, the selection, crossover,and mutation usual operators are performed.
In the ranking procedure, the nondominated individuals
in the current population are first identified. Then, these
individuals are assumed to constitute the first
nondominated front with a large dummy fitness value [7].
The same fitness value is assigned to all of them. In orderto maintain diversity in the population, a sharing method
is then applied. Afterwards, the individuals of the first
front are ignored temporarily and the rest of population isprocessed in the same way to identify individuals for thesecond nondominated front. A dummy fitness value thatis kept smaller than the minimum shared dummy fitnessof the previous front is assigned to all individuals
belonging to the new front. This process continues until
the whole population is classified into nondominatedfronts. Since the nondominated fronts are defined, thepopulation is then reproduced according to the dummyfitness values. As the individuals in the first front havehigher fitness value, they always get more copies than therest of the population.

3. Multiobjective
Controllers

Evolutionof(Abat), distance (Dbat) and color (Cbat) of the nearest
battery pack, the angle (Arob) and color (Crob) of theprotected robot, the sensor readings of five proximity
sensors (PSi) and the distance sensor (DS) in the front of
CR robot. The five proximity sensors are angled asshown in Fig. 3. The egocentric angle to the protected
robot or nearest battery pack varies from 0 to 1 where 0
corresponds to 45o to the right and 1 to 45o to the left.
The value of these neurons becomes -1 when theprotected robot becomes invisible or there is no battery
pack in the visual field. The proximity sensors canmeasure up to 0.25m, while the distance sensor from0.1m to 0.8m. The proximity and distance sensor reading
varies from 0 to 1, where 0 means no obstacle and 1
touching the obstacle. Random noise, uniformly
distributed in the range of +/- 5% of sensor readings, hasbeen added to the angle of the nearest battery pack, angleof the moving robot, distance sensor and five proximity
sensors. Because the distance to the nearest battery packduring the experiments is determined based on thenumber of pixels, the random noise in simulations is
considered in the range of +/- 10%. Based on thecharacteristics of CR visual sensor, in simulations, thevisual distance to the nearest battery pack is limited up to
1.2m.

NeuralFig. 1. Environment.

3.1 Tasks and Environment
Gen 1
Gen 30Gen 100

1.2
1

Box5

0.8
0.6
0.4

Box1 Box2

We implemented a feed-forward neural controller with
11, 4 and 2 units in input, hidden and output layers,respectively. The inputs of neural controller are the angle

590

Box3

Box4

0.2
0
0

3.2 Neural Architecture©ISAROB 2007

1.4

f1

The CR robot has to learn to perform two different tasks:
protecting another moving robot by following it closely;
and keeping a high level of energy by capturing thebattery packs distributed in the environment (Fig. 1). The
entire environment is a rectangular of 4m x 3.5m
surrounded by walls. There are 15 battery packs in green
color distributed in the environment. The CR robot starts
in the same initial position and orientation. The
individual life time of each agent is 700 time steps,
where each time step lasted 0.1s. During this time the redcolor protected robot follows a rectangular trajectorywith a constant velocity of 0.1m/s.

5

10

15

f2

Fig. 2. Pareto optimal solutions of different generations.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Initial CR
robot position
(a)
Abat
Dbat
Cbat
Arob
Crob
PS1
PS2
PS3
PS4
PS5
DS
H1
H2
H3
H4
RmotorLmotortime step(b)

Rmotor Lmotor

Abat Dbat Cbat Arob Crob PS1 PS2 PS3 PS4 PS5 DS(c)Fig. 3. CR multiple task performance (Box3). (a) CR trajectory. (b) Unit activation. (c) Hinton diagram ofconnection weights.

3.3 Evolution
For any evolutionary computation technique, achromosome representation is needed to describe eachindividual in the population. The genome of every
individual of the population encodes the weight
connections of neural controller. The genome length is
52 and the connection weights ranged from -10 to 10. For
the protecting task, the target distance dt between the CR
robot and the protected robot is considered 0.3m. In orderto minimize the difference between the target and real
distance dr the fitness, f1, is considered as follows:
max_ stf1 =∑ d −d
i
t

i
r

(1)

i =1

where max_st is the maximum number of steps.

©ISAROB 2007

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The fitness of battery capturing task, f2, is simply the
number of battery packs captured during the individual
lifetime. If an individual happens to hit the protectedagent or the wall, the trial is terminated and a low fitness
is attached. Therefore, such individuals will have a low
probability to survive. The set of genetic parameters used
are: Nger=100, Npop=50, σshared=0.4.

4. Results
In this section, we first discuss the best solutions
obtained from the MOEA in terms of multiple task
performance. All the simulations were performed in a
Pentium 4 3.2GHz computer.
Fig. 2 shows the Pareto optimal front for generations 1,
30 and 100, averaged for five different runs of MOEA.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

During the first 30 generations there is a great
improvement on the quality and distribution of Pareto
optimal solutions. From this figure, it can be deduced
that MOEA is equally capable of finding the best solution
for each objective when two conflicting objectives are
considered, simultaneously.
The behavior of CR controlled by the neural controllerof Box 3 solution is shown in Fig. 3(a). The CR robot,
while follows the protected agent, captured eight of thebattery packs distributed in the environment. Fig. 3(b)shows that all sensory units are activated during the CR
motion. The proximity and distance sensors helped the
CR robot not to hit the protected robot while it moves
very close and perpendicular to the moving direction of
the protected robot (around 150 steps and 575 steps). The
Hinton diagram of Box 3 neural controller (Fig. 3(c))
shows the Dbat has strong weight connections with hidden
units. This leads us to the conclusion that CR robot
switches between two tasks based on the activation of
Dbat unit.

4.1 Neural and Task Complexity
In the following, the results of applying MOEA to evolve
efficient neural controllers are presented. In difference
from previous approaches, where the fitness function of
obstacle avoidance task and the structure of neuralcontroller are included in a single fitness function, we
considered the structure of the neural controller as aseparate objective function. The complexity of evolved
neural structure generated by MOEA could be also used
as an index to measure empirically the task complexity.
In addition of 52 genes encoding the weight
connections of the neural network, the genome encodes
15 binary genes (11 for sensory input neurons and 4 forthe hidden neurons), which indicate if an input or hidden
unit exists in the network or not.
The objective function f3 is considered as follows:f 3 = nri + nrh(2)
where nri ,nrh are the number of input and hidden units.
Fig. 4 shows that as the neural controller complexity
increases, the solutions move to the upper-left corner,
which means a better performance. Not surprisingly, themost complex neural networks control the CR robot to
perform both tasks by switching between them based on
the environment conditions. The neural network has
seven units in the input and hidden layers and the CR
robot captured fourteen battery packs while trying tokeep a short distance from the protected robot. In
addition, the number of units to complete only theprotecting task is larger than that of battery capturingtask, five and four units, respectively.

©ISAROB 2007

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Fig. 4. Performance of neural controllers with different number of
sensory and hidden units.

5. Conclusion
This paper has experimentally investigated theeffectiveness of applying MOEA to address the robot
multiple tasks performance problem. We considered two
different tasks for the CR robot that has to protect
another robot and capture the distributed battery packs.
In particular, we demonstrated that in a single run of
MOEA are generated robust neural controllers withcompletely different characteristics ranging fromperforming each of all considered tasks to simultaneously
performing different tasks by flexibly switching between
them. In addition, the MOEA generated efficient neuralcontrollers with minimum number of sensory and hidden
units for multiple tasks performance.

References:[1] A. Waibel, H. Sawai, and K. Shikano, “Modularity andscaling in large phonemic neural networks,” IEEE
Transactions on Acoustics, Speech and Signal Processing,vol. 37, no. 12, pp.1888-98, 1989.
[2] S. Nolfi, “Evolving robots able to self-localize in theenvironment: The importance of viewing cognition as theresult of processes occurring at different time scales,”Connection Science, vol. 14, no. 3, pp. 231-244, 2002.

[3] G. Capi, and K. Doya, "Evolution of neuralarchitecture fitting environmental dynamics,"
Adaptive Behavior, vol. 13, no. 1, pp.53-66, 2005.
[4] D. Floreano, and F. Mondada, “Evolution of homing
navigation in a real mobile robot,” IEEE Transactions on
Systems, Man, and Cybernetics-Part B, vol. 26, pp. 396-407,
1996.
[5] D. Cliff, and G. F. Miller, “Co-evolution of pursuit andevasion II: Simulation methods and results”, From animals
to animats 4, pp. 506-515, 1996.
[6] K. Deb, Multi-Objective Optimization using Evolutionary
Algorithms, John Wiley & Sons, Chichester, UK, 2001.
[7] N. Srivinas, and K. Deb, “Multiobjective optimization using
non-dominated sorting in genetic algorithms”, Evolutionary
Computation, vol. 2, no. 3, pp. 279-285, 1995.
[8] A. H. F. Dias and J. A. de Vasconcelos, "Multiobjective
genetic algorithms applied to solve optimization problems,"
IEEE Transactions on Magnetic, vol. 38, no. 2, pp. 11331136, 2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Motion control of biped robot by using simulation
K.KABATA, M.HARA, M.SUGISAKA
Department of Electrical and Electric Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan.
E-mail: msugi@cc.oita-u.ac.jp

Abstract

2. Theory

Recent years, researches about biped robot areextensively studied at the enterprise and the university.
The purpose is human assistance, but it aims
entertainment now.
Modern society is created for human to live easily.
Therefore, to assist human, we need robot that is suitable
for life space.
The vertical projection area of biped robot on the floorside is small, therefore, biped robot can move crookedroad and narrow road. So we think it is best system onlife space that has many irregular ground.
In this paper, we calculate Center of Gravity and Zero
Moment Point by simulation.

2.1 ZMP (Zero Moment Point)
ZMP is a point to replace the floor reaction force ofthe normal element that joined on the backside of the
foot.
Support polygon is shown in Fig.1. Left square shows
the footprint of the left leg, and Right square shows the
footprint of the right leg.
Operation that ZMP is not in support polygon cannot
be achieved.

1. Introduction
Recent years, researches about biped robot areextensively studied at the enterprise and the university.
The purpose is human assistance, but it aims
entertainment now.
Modern society is created for human to live easily.
Therefore, to assist human, we need robot that is suitable
for life space.
In the past research, wheel type robot was amainstream. As a reason, the major factor is easy andsteady to control. But, the fault is not able to move at
irregular ground.
The vertical projection area of biped robot on the floorside is small, therefore, biped robot can move crookedroad and narrow road. So we think it is best system onlife space that has many irregular ground. But, it isdifficult to control.
In this paper, we made the biped robot with
servomotor to generate walking motion by application
software, and calculate Center of Gravity and Zero
Moment Point by simulation.

©ISAROB 2007

593

Fig.1 Support polygon

2.2 Rate of stance phase
Human's walking consists of state of one leg and stateof both legs. State of one leg calls single stance phase,and state of both legs calls double stance phase.
Single stance phase is a period standing with one leg,and goes ahead. Double stance phase is a period standing
with both legs, and switches axopodium.
Static walking cycle is always to keep balance.
Dynamic walking cycle is presupposes to keep motion.
Human’s walking cycle (static and dynamic) are
shown in Table 1 and Table 2.
Table 1 Human’s walking cycle (static)
Double stance phase

Single stance phase

40%

60%

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4. Experiment

Table 2 Human’s walking cycle (dynamic)
Double stance phase

Single stance phase

20%

80%

4.1 Experiment of biped robot

3. Specific of biped robot and simulation
Specific of biped robot is shown in Table 3. Setting ofsimulation is shown in Table 4.
Table 3 Specific of biped robot
Actuator

Servomotor

ComputerIntel Pentium Ⅲ 487MHz

Mother board

HSWB-01

Application software

HSWB-01C

We use HSWB-01C to control the biped robot. Thisapplication software is to control servomotor equipped in
the biped robot.
First of all, we set home position to create motion of
the biped robot. Next, we create the position of
servomotor by the hour.
We can create various motions by the position of theservomotor. We set position by the trial and error.
In this paper, we use this method shown in Fig.2 tomove biped robot.

STEP 1. Set home position

Height 449[mm]

×

Size

STEP 2. Create the position of
servomotor by the hour

Width 200[mm]×Length 120[mm]DegreeHip joint 3×2

ofKnee joint 1×2

Freedom

Ankle joint 2×2

STEP 3. Transmit it to the robot

STEP 4. Motion
Fig.2 Method of motion creation

Table 4 Setting of simulation
Language

Borland C++ Builder5

Length of thigh0.145[m]Length of shin

0.155[m]DegreeHip joint 3×2

ofKnee joint 1×2

Freedom

Ankle joint 2×2

Figure of biped robot's joints are shown in Fig.3.
Hip joint
Hip joint 2

Knee joint
Ankle joint
Ankle joint 2

We made Setting of simulation same as Specific ofbiped robot.

Fig.3 Figure of biped robot's joints©ISAROB 2007

594

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5. Result

4.2 Experiment of simulation
Size and Degree of Freedom are done as well as bipedrobot, and perform the advancement operation for 5
seconds. We gain the result of ZMP and Center ofGravity, in that time.
Screen of simulation is shown in Fig.4.

5.1 Result of biped robot
Angle change graph of Right-and-Left (Hip joint,
Ankle joint 2) and Angle change graph of
Backward-and-Forward (Hip joint 2, Knee joint, Ankle
joint) are shown in Fig.5.
Position of servomotor (Fig.3) and the color of graph(Fig. 5) are corresponding.
Fig.5 (a) shows the change in Right-and-Left. Fig.5 (b)shows the change in Backward-and-Forward.
Hip joint (Right)

Ankle joint 2 (Right)

Hip joint (Left)

Ankle joint 2 (Left)
20(a) Top view

15

Angle[°]

10
5
0
-5

0

2

4

6

8

10

12

-10
-15
-20

Time[sec](a) Right-and-Left
Hip joint 2 (Right)
Knee joint (Right)
Ankle joint (Right)
Hip joint 2 (Left)
Knee joint (Left)
Ankle joint (Left)(b) 3D viewFig.4 Screen of simulation

Angle[°]

100
80
60
40
20
0
-20 0
-40
-60
-80
-100

2

4

6

8

Time[sec](b) Backward-and-Forward
Fig.5 Angle change graph©ISAROB 2007

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10

12

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Table 5 Robot’s walking cycle result

Reference

Double stance phase

Single stance phase

41%

59%

The result of this experiment is similar to the ratio of
double stance phase and single stance phase in human's
static walking.

5.2 Result of simulation
Result of simulation is shown in Fig.6.

Center of Gravity

Zero Moment Point
0.04

Right-and-Left

0.03
0.02
0.01
0

-0.01 0

0.1

0.2

0.3

0.4

0.5

0.6

-0.02
-0.03
-0.04

Backward-and-Forward

Fig.6 Result of simulation

6. Conclusions
In this paper, we researched about walking motion.
We transmit instruction value to the biped robot, andgenerate walking motion by application software. And,
we perform walking simulation.
Comparing with human walking motion and bipedrobot’s walking motion, the ratio of this result is similar
to the ratio of double stance phase and single stancephase in human's static walking.
It is necessary to research about Center of Gravity andZero Moment Point more.
In the future, we complete motion of biped robot byusing these results.

©ISAROB 2007

Nikkei mechanical ･ Nikkei design joint
compilation, ” Robolution ” , Nikkei BP Co. ,P.40~41, 2001
[2] Ryuichi Nakamura, Hiroshi Saito, “Basic
Kinematics”,
Ishiyaku
publication
Co.Ltd,P.334~342, 2002
[3] T.Shinohara, "Real-time orbit generation of bipedrobot", 2001
[4] http://www.ops.dti.ne.jp/~manva/robot/zmp.htm

[1]

596

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A New Technique for Adjusting the Learning Rate of RPEM Algorithm
Automatically
Xing-Ming Zhao1,2 , Yiu-ming Cheung3 , Luonan Chen1,2,4 , Kazuyuki Aihara1,2
1. Aihara Complexity Modelling Project, ERATO, JST, Japan
2. Institute of Industrial Science, The University of Tokyo, Japan
3. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
4. Department of Electrical Engineering and Electronics, Osaka Sangyo University, Japan
2

Abstract
Recently, a new Rival Penalized Expectation Maximization (RPEM) algorithm has been proposed for estimating the parameters of the normal mixture model,
meanwhile determining the number of classes automatically. The RPEM is an adaptive algorithm utilizing a small constant learning rate. To speed up its
convergence speed, this paper proposes a new method
to dynamically adjust the learning rate of the RPEM
algorithm on line. The numerical results have shown
the promising results of the proposed algorithm.

The RPEM-DLR Algorithm

The mixture model assumes that each group of datais generated by an underlying probability distribution.
Suppose the number of classes is k, and the numberof samples is N . In the RPEM algorithm, the likelihood function for a mixture model can be defined ina weighted form, i.e.,l(Θ; x) =Z X
kg(j|x, Θ)lnp(x|Θ)dF (x),

(1)

j=1

1

with

Introduction

Mixture models have been widely used in data mining [1], image processing [2], gene expression analysis[3], and so forth. In the literature, the ExpectationMaximization (EM) algorithm [4] has been widelyused to estimate the parameters of the normal mixture model, which, however, needs to pre-assign classnumber. Generally, the EM algorithm almost always
leads to a poor result if the class number is not appropriately pre-assigned. Recently, a new Rival Penalized Expectation-Maximization (RPEM) algorithm
was proposed by Cheung [2] [5]. The RPEM algorithm can determine the number of classes automatically by gradually fading out the redundant components from the mixture during the parameter learning
process. In [2] [5], the RPEM utilizes a small constantlearning rate to ensure the algorithm’s convergence,
which, however, needs more iteration steps. Indeed,
we can dynamically adjust the learning rates to speed
up the performance convergence of the RPEM. In thispaper, we present a new method accordingly for sucha task. Hereinafter, we denote the RPEM algorithmwith dynamic adjustment of learning rate as RPEMDLR algorithm.

©ISAROB 2007597

p(x|Θ) =kX

αj p(x|θj ) ,

(2)

j=1
k
X

αj = 1, ∀ 1 ≤ j ≤ k, αj > 0 ,

(3)

j=1

and
k
X

g(j|x, Θ) = 1,

(4)

j=1

where Θ = {αj , θj }kj=1 is the set of model parameters, F (x) is the cumulative probability function of x,p(x|θj ) is a multivariate probability density function(pdf) of x, αj is the proportion that x comes fromClass j, and gs are designable weights. The details
can be found in [5].
The RPEM algorithm in normal mixture models
can be summarized as follows:• Initialization: Given a specific k (k ≥ k ∗ , k ∗ isthe true class number), we initialize Θ. Then, at
each time step t, we implement the following two
steps:

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

• Step 1: Fixing Θ(old) , calculate h(j|xt , Θ(old) ),(old)g(j|xt , Θ(old) ) and αj , where(old)exp(βj )
(old)
.
αj
= Pk
(old)
)
i=1 exp(βi(5)• Step 2: Fixing h(j|xt , Θ(old) )s, update Θ
(new)

βj

(new)

µj

(old)

= βj
(old)

(old)

+ η1 [g(j|xt , Θ(old) ) − αj−1(old)= µj+η2 g(j|xt , Θ(old) )Σj−1(new)

=

Σj

],

(6)

3

Experimental Results and Discussions

In this section, two sets of data were used to investigate the performance of the RPEM-DLR algorithm.
First, we generated 1,000 synthetic data points froma mixture of three bivariate Gaussian densities:µp1(x|Θ) =(old)(xt −µj),(7)−1(old)[1 + η2 g(j|xt , Θ(old) )]Σj−η2 g(j|xt , Θ(old) )Ut,j ,

where
(old)

Ut,j

=

−1(old)

[Σj

(old)

(xt − µj)(xt −−1(old)µj )T Σj], η1 and η2 are small positive
learning rates with η1 ¿ η2 ≤ 1. The procedure
repeat until Θ converges.
In RPEM algorithm, it can be seen that the learning
rate is generally a fixed small positive constant. Actually, the choice of the learning rate can affect the convergence performance of the RPEM. In general, there
is a tradeoff between the residue deviation and rate ofconvergence [6]. When using a fixed learning rate, it
should be small enough for the algorithm to converge.
The smaller the learning rate, the smaller the residue
deviation, but the slower the convergence speed. It is
usually difficult to determine an optimal learning rate
in advance because it is problem dependent.
According to the condition for the asymptotic convergence provided by a standard theorem [7] fromstochastic approximation theory, the learning rateshould satisfies:lim η(it) = 0, and

it→∞

∞
X

η(it) = ∞,

(8)

it=1

where it is the it-th epoch. Under the circumstances,
we propose a new method here for adjusting the learning rate dynamically. The learning rate is defined as:

where η(j, it) is the learning rate for the j-th component in the it-th epoch, and αj is the proportion thatx comes from Class j. The initial learning rate η0 is
set at a fixed small positive constant. Therefore, thelearning rate will be adjusted dynamically according
to αj in each epoch.

©ISAROB 2007

598

1
1
µ

Suppose the number of seed points was set k = 10
and k = 20, respectively. We initialized each of Σj s
to be an identity matrix, and all βj s to be zero. The
initial learning rate η0 was set at 0.01. With the same
initial parameters, the performance of the RPEMDLR algorithm was compared to that of the RPEM
algorithm. The results obtained by RPEM-DLR and
RPEM are shown in Figure 1, where the points markedby ‘+’ are the learned cluster centers via RPEM-DLR
and EM, respectively. As shown in Figure 1, both
the RPEM algorithm and RPEM-DLR can locate thecluster centers correctly by pushing away the redundant seed points when the pre-assigned class number
is larger than the true mixture number, i.e., k = 3.
Positions of the 10 seed points learned by RPEM

Positions of the 20 seed points learned by RPEM

10

10

5

5

0

0

0

2

4

6

8

0

2

4

6

8

Positions of the 10 seed points learned by RPEM−DLR

Positions of the 20 seed points learned by RPEM−DLR

10

10

5

5

0
0

1 − α(j),η(j, it) = η(j, it − 1) ∗1 + α(j)1 ≤ j ≤ k, and 1 ≤ it < ∞,¶ µ¶0.10 0.05,]
0.05 0.20¶ µ¶1.0
0.10 0.0+0.4G[x|,]5.0
0.0 0.10
µ
¶ µ
¶
5.0
0.10 −0.05+0.3G[x|,].
5.0−0.05 0.10

0.3G[x|

0
2

4

6

8

0

2

4

6

8

Figure 1: Convergent positions of the seed points
learned via RPEM and RPEM-DLR for the data generated by p1.
Furthermore, we investigated the computation time
taken by RPEM and RPEM-DLR algorithm during
the learning procedure. The number of the seed points
was set at 5, 10, 20 and 30, respectively. Table 1

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 1: The comparison of computation time of
RPEM and RPEM-DLR (seconds)
number of
methods
seed points
RPEM
RPEM-DLR5
101.5961
11.186110239.0037
33.988920402.2885
112.722130
1.0397e+003
225.6845

0.01

0.01

0.005

0.005

0

5

10
15
20
5 seed points

25

0

0.01

0.01

0.005

0.005

0

20

40 60 80
20 seed points

100

0

10

20 30 40
10 seed points

50

50
100
30 seed points

150

Figure 2: Learning curves of ηj s via RPEM-DLR for
the data generated by p1µ

p2(x|Θ)¶ µ¶10.15 0.05= 0.3G[x|,]10.05 0.20
µ
¶ µ
¶
1.0
0.15 0.0+0.4G[x|,]2.5
0.0 0.15
µ
¶ µ
¶
2.5
0.15 −0.1+0.3G[x|,]2.5−0.1 0.15

The number of seed points was set at 10 and 20,
respectively. We initialized each of Σj s to be an identity matrix, and all βj s to be zero. The initial learning rate η0 was set at 0.01. Again, The performanceof the RPEM-DLR algorithm was compared to that of
the RPEM algorithm. The results obtained by RPEMDLR and RPEM are shown in Figure 3. It can be seenfrom Figure 3 that both the RPEM and RPEM-DLR
algorithm can stabilize at the corresponding cluster
centers when the number of seed points is larger than
the true mixture number.
Positions of the 10 seed points learned by RPEM

Positions of the 20 seed points learned by RPEM

6

6

4

4

2

2

0

0

−2
−2

−2
−2

0

2

4

6

Positions of the 10 seed points learned by RPEM−DLR

shows the comparison of the computation time by
RPEM and RPEM-DLR under the same conditions.
As shown in Table 1, the computation time taken
by RPEM-DLR is much less than that of the RPEM
algorithm, i.e. our proposed RPEM-DLR algorithm
largely reduces the calculation time. In a word, ourproposed RPEM-DLR algorithm is really efficient, andspeeds up the learning of the RPEM technique.
We further investigated the corresponding values ofηj s learned via RPEM-DLR when the number of seedpoints was set at 5, 10, 20 and 30, respectively. As
shown in Figure 2, the values of ηj s corresponding to
the extra seed points approached to zero slower thanthose of the true ones, which was reasonable because
the learning rate was a monotonously dropping function of αj s. It can seen from Figure 2 that the learning rate was adjusted dynamically during the learning
procedure, which speed up the learning of the RPEM.
Upon the data clusters well-separated above, we
further investigated the performance of the RPEMDLR algorithm on the data clusters that were considerably overlapped. We generated 1,000 synthetic data
points from a mixture of three bivariate Gaussian densities:©ISAROB 2007

599

6

6

4

4

2

2

0

0

−2
−2

0

2

4

6

0

2

4

6

Positions of the 20 seed points learned by RPEM−DLR

−2
−2

0

2

4

6

Figure 3: Convergent positions of the seed points
learned via RPEM and RPEM-DLR for the data generated by p2.
The computation time taken by RPEM was compared to that of the RPEM-DLR algorithm during thelearning procedure. The number of the seed points was
set at 5, 10, 20 and 30, respectively. Table 2 shows thecomparison of the computation time by RPEM and
RPEM-DLR under the same conditions. As shown in
Table 2, the computation time taken by RPEM-DLR
is much less than that of the RPEM algorithm, whichshows again that our proposed RPEM-DLR algorithm
can adjust the learning rate dynamically and speed up
the learning of RPEM.
Furthermore, Figure 4 shows the learning curves ofηj s when the number of seed points was set at 5, 10, 20

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 2: The comparison of computation time of
RPEM and RPEM-DLR (seconds)
number of
methods
seed points
RPEM
RPEM-DLR5
124.5791
9.373510
152.4893
25.997420
486.4795
111.019630
1.0870e+003
266.6434

Acknowledgment
This work was partially supported by the Research
Grant Council of Hong Kong SAR under Projects
HKBU 2156/04E, HKBU 210306, and by Faculty Research Grant of Hong Kong Baptist University with
the Project Code: FRG/05-06/II-42.

References
0.01

0.01

0.005

0.005

0
0

10
20
5 seed points

30

0
0

0.01

0.01

0.005

0.005

0
0

50
20 seed points

100

0
0

[1] X. F. Zhang, C. M. Lam, and W. K. Cheung,“Mining Local Data Sources for Learning Global
Cluster Models via Local Model Exchange,” TheIEEE Intelligent Informatics Bulletin, vol. 4, pp.
16–22, 2004.
20
40
10 seed points

60

50
100
30 seed points

150

Figure 4: Learning curves of ηj s via RPEM-DLR for
the data generated by p2

and 30, respectively. As shown in Figure 4, the learning rates changed as we expected, and the RPEMDLR technique can adjust the learning rate dynamically in the learning procedure. It can be concluded
from the above experiments that the RPEM-DLR algorithm outperforms RPEM in terms of computation
time, and adjusts its learning rate dynamically in thelearning procedure.

4

Conclusions

This paper proposed a new method to dynamicallyadjust the learning rate of RPEM algorithm. Compared to the constant learning rate as used in the
RPEM algorithm, our proposed method can efficiently
speed up the performance convergence of RPEM algorithm. The numerical results have demonstrated its
efficacy.

©ISAROB 2007

600

[2] Y. M. Cheung, “A rival penalized em algorithm towards maximizing weighted likelihoodfor density mixture clustering with automatic
model selection,” in Proceedings of the 17th
International Conference on Pattern Recognition(ICPR’04). United Kingdom: Cambridge, 2004,
pp. 633–636.
[3] X. F. Zhang, C. M. Lam, and W. K. Cheung,“Mining Local Data Sources for Learning Global
Cluster Models via Local Model Exchange,” TheIEEE Intelligent Informatics Bulletin, vol. 4, pp.
16–22, 2004.
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adaptive k-means algorithm with dynamic adjustment of learning rate,” IEEE Transaction on
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[7] A. Dvoretzky, “On stochastic approximation,”in Proc. 3rd Berkeley Sym. on Math. Stat. andProb., J. Neyman, Ed.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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8 M9t S - )56{M| 7b%rÆ)Ñ¹Â°»¶¯-ºsÂsÆþºÂ© °»¶ÆPà-Ø-Ø ÈThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Eﬀect of facility closure in the SEIR epidemic model
Hiroshi Maeda1
1

Yasushi Ohkusa2

Graduate School of Information Science and Technology, The University of Tokyo
2
Infectious Disease Surveillance Center, National Institute of Infectious Diseases
3
Institute of Industrial Science, The University of Tokyo
4
Aihara Complexity Modelling Project, ERATO, JST
Abstract

Avian inﬂuenza H5N1 has caused large outbreaks
in birds in Southeast Asia. This virus is highly virulent in humans who have been infected directly frombirds. But, the virus has not achieved human-tohuman transmission. If a new virus of bird ﬂu capable of human-to-human transmission appears, thischange could cause pandemic inﬂuenza. We propose
the SEIR epidemic model of inﬂuenza transmission toassess the inﬂuence of facility closure as a containment
strategy. Mathematical models are important tools inanalyzing the spread and control of infectious diseases.
If the fraction of infected individuals exceeds the set
threshold, we execute the facility closure for the set
period and there are assumed to be no transmission
among a population. If the basic reproduction number R0 was assumed to be 2.0, our model suggested
that it was not necessarily the case that long period offacility closure reduced the prevalence. The ﬁnal sizeof an epidemic depended on the number of infectedindividuals when the susceptible fraction was equal to
1/R0 .

1

Kazuyuki Aihara3,4of pandemic [2].
Inﬂuenza prevention and containment strategies
can be considered under the broad categories of antiviral, vaccine, and nonpharmaceutical measures. In thisstudy, we focus on nonpharmaceutical measures, especially in the facility closure. For example, schools are
known to be the primary context of inﬂuenza transmission [3]. However, no data or analyses exist for recommending illness thresholds or rates of change that leadto considering closing or reopening schools [4].
The purpose of this study is to clarify the impact offacility closure by changing the threshold or the duration of closure. Here we construct a simple epidemic
model of inﬂuenza transmission with deterministic differential equations. Mathematical models and computer simulations are useful tools for building and testing theories, assessing quantitative conjectures, answering speciﬁc questions, and determining sensitivities to changes in parameter values [5]. The model formulation process clariﬁes assumptions, variables, andparameters. We can use mathematical models in comparing, planning, implementing, and evaluating various detection, prevention, and control programs.

Introduction

The threat of pandemic inﬂuenza has increased for
decades [1]. H5N1 highly pathogenic avian inﬂuenza
is causing outbreaks among poultry in Southeast Asia.
The transmission from birds to human and other mammalian species has been sporadic. The virus has not
required the ability to be transmitted from humanto human. But, if mutation of the virus occurs, thenovel variant could be capable of sustaining human-tohuman transmission. Besides, pandemic inﬂuenza cancause a public health crisis because many people are
immunologically naive to the new virus. Although antiviral drugs oﬀer protection against infection, production delays would limit availability in the ﬁrst months©ISAROB 2007605

2

Model and general theory

We use a mathematical model called the SEIR epidemic model, which is represented as follows:
dS
dt
dE
dt
dI
dt
dR
dt

= −βSI,(1)= βSI − σE,(2)= σE − γI,

(3)

= γI.

(4)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

S(t), E(t), I(t), and R(t) are the number of susceptible, exposed, infective, and recovered individuals,
respectively. This model is based on the KermackMcKendrick model [6].
The susceptible class S contains individuals who
have a risk of becoming infected. When there is acontact of a susceptible with an infective so that transmission occurs, the individual enters the exposed class
E in the latent period. An exposed individual is infected but non-contagious. After the latent period, theindividual enters the infective class I in the infection
period. An infective individual is contagious, that is
capable of transmitting the infection. After the infection period, the individual enters the recovered class
R. A recovered individual is permanently immunity
to further infection.
Movements out of the class E and I are governedby σE and γI, respectively. It is shown that theseterms correspond to exponentially distributed waitingtimes. For example, the transfer rate γI corresponds
to P (t) = e−γt as the fraction that is still in the infective class t units after entering this class and to 1/γ
as the mean waiting time. We deﬁne the duration oflatent period and infection period as 1/σ and 1/γ.
The key value governing the time evolution of theseequations is the basic reproduction number R0 , which
is deﬁned as the mean number of secondary infections
generated by a primary infection in a susceptible population [7]. R0 for the SEIR model is given by
R0 =

βN
,
γ

(5)

where N is the total number of individuals so that
N = S(t) + E(t) + I(t) + R(t). If R0 < 1, one infected individual will infect fewer than one susceptible individual before recovering. The infection will
die out certainly. If R0 > 1, one infected individual
will infect more than one susceptible individual before
recovering. There is some possibility of a major epidemic. Therefore, R0 is considered as the thresholdthat determines whether an infection can persist in a
population or not.
We propose the new epidemic model with facility
closure. If the proportion of infective individuals exceeds the threshold of closure θ, we assume that facilities are closed for d days and that there is no transmission among the people. The dynamics with closure
is represented as follows:
dS
dt
dE
dt

©ISAROB 2007

= 0,

(6)

= −σE,

(7)

606

dI
= σE − γI,
(8)
dt
dR
= γI.
(9)
dt
If the proportion of infective individual is less than thethreshold after the facility closure of d days, facilities
reopen and the transmission occurs again. The dynamics with or without closure follows equations (1)(4). Figure 1 shows the dynamics with or without
closure schematically.

Not Closed

S

Closed

S

βSI
0

E
E

σE
σE

I
I

γI
γI

R
R

Figure 1: The dynamics of the SEIR epidemic modelwith or without closure

3

Results

We performed a numerical simulation to investigate
the dynamics of our epidemic model. As an initialstate, we set {S(0), E(0), I(0), R(0)} = {99, 1, 0, 0}.
Since recent estimates of the basic reproduction number of the 1918 pandemic strain were in the range 2-3[8], we assumed that R0 = 2.0. We also assumed distributions of infectiousness consistent with previous
studies [9], giving mean latent and infection periodsof 1.9 days and 4.1 days, respectively. These assumed
parameters are shown in Table 1. We estimated thevalue of β, σ, and γ from equation (5) and Table 1.
Table 1: Parameters for transmission
Parameters Description
Value
R0
Basic reproduction number 2.0
1/σ
Mean latent period
1.9
1/γ
Mean infection period
4.1

Figure 2 shows the transition of the susceptible fraction, exposed fraction, infective fraction, and the sumof exposed and infective fraction without facility closure. The horizontal auxiliary line shows that the susceptible fraction is 0.5 and the vertical auxiliary one
shows the day when it is 0.5. Both the susceptible fraction and infective fraction has the maximum on about
30th day. About 80% of a population is infected on
the 180th day.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1

0.9

S(t)

d=3
d=5
d=7
0.8
PrevalenceSusceptible Fraction

0.8

0.6

0.4

0.7

0.6

0.2

0

0.5
0

20

40

60

80 100 120 140 160 180
Day

0

0.02

0.04 0.06 0.08
0.1
Criterion of Closure

0.12

0.14

Exposed and/or Infective Fraction

0.2
E(t)
I(t)
E(t) + I(t)0.15

Figure 3: The relation between the threshold of closureand the prevalence

0.1

4Discussion

Figure 2 represents that the number of exposed andinfective individuals reaches a peak on the 30th day.
After this time, the following formula is formed:

0.05

0
0

20

40

60

80 100 120 140 160 180
Day

Figure 2: The transition of susceptible fraction, exposed fraction, infective fraction, and the sum of exposed and infective fraction without facility closure

Figure 3 shows the relation between the threshold ofclosure and the prevalence, which is the proportion ofrecovered individuals. We examined three parameters
for the duration of closure d, i.e. three, ﬁve, and sevendays. The prevalence doesn’t reduce monotonicallywith reducing the threshold of closure regardless of theclosed period. Many of the long term closure reduce
the prevalence broadly if the threshold of closure is
ﬁxed. But, there is a little possibility that the longterm closure produce somewhat high prevalence thanshort term one.
Figure 4 shows the transition of susceptible fraction
and the sum of exposed and infective fraction withfacility closure for ﬁve days. Thresholds of closure θ
were assumed to be 0.06, 0.07, and 0.09. When thethreshold is 0.06, the facility closure is implemented
twice on the 20th and 40th day.

©ISAROB 2007

607

dI
dE
+
< 0.
dt
dt

(10)

From equations (2), (3), and (5), we can obtain thefollowing formula:
1
S
<
.
N
R0

(11)

This formula indicates that the number of exposed andinfective individuals start to reduce when the susceptible fraction is less than 1/R0 . This threshold of reduction is 0.5 because we assumed that R0 = 2.0. This
analytical solution gives good agreement with experimental results in Figure 2.
From equations (10) and (11), at least a half of a
population is infected in this model. Therefore, thenumber of exposed and infected individuals when thesusceptible fraction is equal to 1/R0 is important value
to reduce the prevalence.
Figure 4 is an example of why zigzag lines are drawn
in Figure 3. First, the transition of the sum of exposed
and infective fraction achieves a peak twice when thethreshold of closure θ is 0.09 or 0.07. If the threshold changes from 0.09 into 0.07, the sum of exposed
and infective fraction in the ﬁrst peak is reduced but
in the second peak is increased. In the second peak,
both susceptible fraction is the same by 0.5, and moreprevalence is produced by more exposed and infective
individuals with θ = 0.07 than with θ = 0.09. Second, if we reduce θ from 0.07 much further more, theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1

0.8
Susceptible Fraction

closure. Long period closure has the high possibility
to reduce the prevalence than short period closure.
However, there are some situations it is better to close
facilities for long period.

θ=0.06
θ=0.07
θ=0.090.6

0.4

References

0.2[1] Anthony S. Fauci (2006) Pandemic inﬂuenza
threat and preparedness. Emerging Infectious Diseases 12(1):73-77

0

Sum of Exposed and Infective Fraction

0

20

40

60

0.2

80 100 120 140 160 180
Dayθ=0.06
θ=0.07
θ=0.090.15[3] Anthony Heymann, Gabriel Chodick, Brian Reichman, et al. (2004) Inﬂuence of school closure on theincidence of viral respiratory diseases among children and on helath care utilization. The Pediatric
Infectious Diseases Journal 23(7):675-677

0.1

0.05

0
0

20

40

60

80 100 120 140 160 180
Day

Figure 4: The transition of susceptible fraction andthe sum of exposed and infective fraction with facility
closure for ﬁve days
transition of the sum of exposed and infective fraction achieves a peak three times as θ = 0.06 in Figure 4. The sum of exposed and infective fraction with
θ = 0.06 is less in the third peak when the susceptible
fraction is equal to 0.5 than with θ = 0.07. These two
mechanism generate the zigzag line in Figure 3.
In this study, we reveal that the low prevalence isgenerated by the small number of exposed and infective individuals when the susceptible fraction is equal
to 1/R0 . However, since R0 of a future newly emergent inﬂuenza strain is unknown, we can not change
the number of exposed and infective individuals purposely. It seems to be the desirable measure to close
facilities at the low threshold and for a long term if at
all possible.

5[2] Klaus Stöhr, Marja Esveld (2004) Will vaccines beavailable for the next inﬂuenza pandemic? Nature
306:2195-2196

Conclusions

We propose the simple epidemic model based on the
SEIR epidemic model to explore the eﬀect of facility©ISAROB 2007

608

[4] World Health Organization Writing Group (2006)
Nonpharmaceutical interventions for pandemic
inﬂuenza, national and community measures.
Emerging Infectious Diseases 12(1):88-94[5] Herbert W. Hethcote (2000) The mathematics ofinfectious diseases. SIAM Review 42(4):599-653[6] W. O. Kermack, A. G. McKendrick (1927) Contributions to the mathematical theory of epidemics-I.
Proceedings of the Royal Society 115A:700-721[7] Roy M. Anderson, Robert M. May (1991) Infectious diseases of humans: dynamics and control.
Oxford University Press, Oxford[8] Christina E. Mills, James M. Robines, Marc Lipsitch (2004) Transmissibility of 1918 pandemic inﬂuenza. Nature 432:904-906[9] Lila R. Elveback, John P. Fox, Eugene Ackerman,
et al. (1976) An inﬂuenza simulation model for immunization studies. American Journal of Epidemiology 103(2):152-165

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Systematic Approach to Analysis of Robustness in Oscillatory Networks
Ruiqi Wanga,c , Luonan Chena,b,c
aAihara Complexity Modelling
Project, ERATO, JSTb
Osaka Sangyo University

Kazuyuki Aiharaa,ccInstitute of Industrial Science
The University of Tokyo

2

Abstract
Robustness as a system-level property is mainly determined by structural characteristics rather than finetuning parameter values. The relative contribution of
network components or interactions to robustness remains little studied. By decomposing an overall network into smaller subnetworks and then analyzing effects of the interactions between them, relative importance of the network components for robustness property of oscillations can be derived. Moreover, the improvement of robustness against perturbations can bealso made through modification of the structural characteristics or regulatory interactions of the network.
Our analysis focuses on a molecular network that produces spontaneous oscillations in Dictyostelium discoideum cells.

1

introduction

A linear analysis approach to robustness
We model a biochemical network asẋ = f (x, p),

where x is the state vector containing the concentration or activity of all components in the network andp are the parameters. Since our goal is to consider the
relative importance of interactions for robustness, we
decompose the overall network (1) into smaller subnetworks Γi consisting of single components modelledbyẋi = fi (xi , ui , p).
(2)
Each subnetwork Γi has components xi as its onlyinternal state and output, while all other components
are treated as inputs ui .
The linearization of system (1) around an equilibrium x0 is given by∆ẋ(t) = A∆x(t)Robustness is a property that allows a system maintains its functions despite external or internal perturbations and uncertainty [1]. It is a key to understanding cellular complexity and elucidating designprinciples. Owing to intimate links to cellular functions, robustness properties of many oscillatory networks through negative or interlocked feedback loops
have been extensively investigated [2]. Investigationof robustness for general oscillators may focus on thepersistence of regular oscillations, which does not prelude quantitative changes in period or amplitude tooccur. While for circadian oscillators, it may focus
on the period and amplitude sensitivities to evaluate
their precise time-keeping ability with respect to noises
or parameter variations. Most works mainly assessrobustness as a system-level property. The relative
contribution of network components or interactions torobustness remains little studied.

©ISAROB 2007

609

(1)

with

A=

∂f (x(t), p)|x0 , (3)∂xwhere ∆x(t) = x(t) − x0 denotes deviation of the concentrations or activities from the equilibrium x0 . The
decomposition of the linearized system (3) into onecomponent subnetworks Γi is as follows∆ẋ(t) = Ã∆x(t) + (A − Ã)∆u,(4)

where Ã is a diagonal matrix containing the diagonal
entries of A. In this way, the linear biochemical network (3) can be seen as an open-loop interaction free
network (4). In other words, we analyze the wholenetwork by breaking the feedback loop at each step,
viewing the effects of all other components on each xi
as input signals, and after analyzing the relative importance of each component on the robustness properties of the oscillations, we do re-close the loop byletting ∆u = ∆x.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
The key to our approach is standard feedback conk 12
trol theory. Following Laplace transformation, the link 13
k 11
ear open-loop system (4) then transforms to an algebraic equation in s
k 14
k1
external
cAMP

CAR1

∆x(s) = L(s)∆u(s),(5)

where L(s) = (sI − Ã)−1 (A − Ã). The element Lij (s)corresponds to the transform function from the component j to component i in the absence of any feedback
effects, i.e. the ratio of the output xi to the input xj .
According to the generalized Nyquist criteria, for astable open-loop network L(s), a sufficient condition
for instability under positive feedback is that one characteristic locus λi (L(jω)) crosses the real axis to theright of the point 1 at a single frequency ω = ωcrit . A
perturbation applied to element Lij (iωcrit ) such that
this λi (L(jω)) moves to the point 1 on the real axis
corresponds to a stabilizing perturbation.
A perturbation that moves one characteristic locus
λi (L(jω)) at ω = ωcrit to the point on the real axis
corresponds to making the return difference I − L(jω)
singular at the ω = ωcrit , that is,det(I − Lp (jωcrit )) = 0,

(6)where Lp is the perturbed open-loop system.
The required perturbation ∆ij is given by∆ij (ωcrit ) = −1,[RGA(I − L(jωcrit ))]ij(7)

where RGA(M ) = M × (M −1 )T and the × symbol
denotes element by element multiplication (Hadamard
or Schur product). Thus, elements with relative smallvalues of stabilizing perturbations |∆ij | correspond to
pairwise interactions that have a large influence on
stability and play an important role in destabilizing
the equilibrium. In other words, elements with relative small values of stabilizing perturbations |∆ij | aresensitive to perturbations, while elements with relativelarge values of stabilizing perturbations are more robust to perturbations. If all stabilizing perturbations
are large, the oscillations of the network will be morerobust.

ACA

k5
ERK2

k9

k8

k 10
REG A

k7

k6

k2

internal
cAMP

k3
PKA

k4

Figure 1: Molecular interactions generating cAMP oscillations in Dictyotelium discoideum cells. Components are connected by regulatory interactions that are
direct or indirect. Arrows and bar heads indicate positive and negative regulation, respectively. Pulses ofcAMP are produced when adenlylate cyclase (ACA)
is activated after the binding of extracellular cAMPto the surface receptor CAR1. When cAMP accumulates internally, it activates the protein kinase PKA.
Ligand-bound CAR1 also activates the MAP kinase
ERK2. ERK2 is then inactivated by PKA and nolonger inhibits the cAMP phosphodiesterase REG A.
A protein phosphatase activates REG A such that
REG A can hydrolyse internal cAMP. When REG A
hydrolyses the internal cAMP, PKA activity is inhibited by its regulatory subunit, and the activities ofboth ACA and ERK2 go up.
cAMP observed during the early development of D.
discoideum [3]. The deterministic dynamics is governed by the following equations:
d[ACA]/dt = k1 [CAR1] − k2 [ACA][PKA],
d[PKA]/dt = k3 [internal cAMP] − k4 [PKA],
d[ERK2]/dt = k5 [CAR1] − k6 [PKA][ERK2],
d[REG A]/dt = k7 − k8 [ERK2][REG A],
d[internal cAMP]/dt = k9 [ACA] − k10 [REG A][internal cAMP],d[external cAMP]/dt = k11 [ACA] − k12 [external cAMP],
d[CAR1]/dt = k13 [external cAMP] − k14 [CAR1],(8)

3

Results

Adenosine 30 , 50 -cyclic monophosphate (cAMP) oscillations in Dictyostelium discoideum cells are necessary for chemotaxis and further development of Dictyostelium cells. The model, based on the network
depicted in Fig.1, induces spontaneous oscillations in©ISAROB 2007

610

where ki (i = 1, · · · , 14) are kinetic constants. The
model is based on the common positive and negative
control elements.
For the oscillatory network shown in Fig.1, from thelinearization around the underlying equilibrium we obtain ω = ωcrit = 0.8560 rad/min, at which the criticalcharacteristic locus λcrit (L(jω)) crosses the real axis to|∆pq||∆pq|

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
the right of the critical point (1, 0). The rank-ordered(a) 0.04
stabilizing perturbations are shown in Fig.2(a). Even
∆3,7 ∆3,2∆ ∆the largest amplitude of stabilizing perturbations is
0.03
4,3 5,4∆5,1
small (< 0.035), and it shows the poor robustness∆∆
0.02 ∆ ∆ ∆ 1,7 1,2
properties of the model. Such result is also supported
6,1 7,6 2,5in [4].
0.01
0 1 2 3 4 5 6 7 8 9 10 11
Different from the parameter sensitivity analysis(b)used in [2], by which the clues on the importance
0.20
∆3,2
of individual regulatory processes on the oscillations
∆
3,7
∆ ∆
4,3 5,4
and relative importance of individual regulatory pro0.12∆
cesses can be directly derived by the linear analysis∆ ∆1,2 5,1∆6,1 ∆7,6 ∆2,5 1,7approach. The parameter sensitivity analysis needs
0.04
a large amount of computing, as shown in [2], while
0 1 2 3 4 5 6 7 8 9 10 11
the relative importance can be easily obtained by the
Numberlinear analysis approach, as shown in Fig.2(a). For instance, the network shows a higher sensitivity toward
perturbations affecting external cAMP. It has shown
Figure 2: The magnitude of the relative perturbathat constant high levels of external cAMP lead to attion |∆pq | required in element Lpq of the open-loop
tenuation, whereas a brief pulse of cAMP can advancemodel, i.e. the effect of component q on component
or delay the phase such that interaction cells become
p in absence of feedback interactions, so stabilize theentrained [3]. The regulation of PKA inhibiting ERK2
closed-loop system, where x = [x1 , · · · , x7 ] represents
enhances the robustness properties, although its role
the concentrations of the seven proteins: x1 =[ACA],in enhancing robustness is poor. The results show thatx2 =[PKA], x3 =[ERK2], x4 =[REG A], x5 =[internal
different regulatory mechanisms are of different imporcAMP], x7 =[external cAMP], and x7 =[CAR1]. (a)tance for the robustness of the network.
At the nominal parameter values. (b) At k2 = 1.5
From the Lyapunov’s indirect method, it follows
Mol−1 min−1 and k3 = 1.6 min−1 , all other paramethat local stability of an equilibrium can be deterters at their nominal values.
mined from the linearization of the system aroundthe equilibrium. The system is locally unstable at theof catalytic subunit present. The modified form of theequilibrium if its Jacobian has some eigenvalues in the
dynamics for PKA thus reads
open right-half plane. Since oscillations can be traced
2
to destabilization of an underlying equilibrium, lineard[PKA]/dt = k3 [internal cAMP] − k4 [PKA]. (9)stability analysis can be used to identify mechanisms
Note that the changes in the mathematical description
causing the oscillations by analyzing the destabilizcapture the specific interactions between the internal
ing mechanisms of the underlying equilibrium. Linear
cAMP and PKA. Equation (9) and other equations inanalysis, therefore, can also be used to determine theEquations (8) except the second one define the strucmechanistic basis of the robustness property due to theture of the modified model.
direct connection between robustness and functionalStill using the monotone control theory, we obtainity.
that the modification of the structural characteristics
does not change the number of equilibria. We still use
3.1 Improvement of robustness
the two sets of parameter values, because the modified
model can produce similar oscillations at these values,
The PKA holoenzyme is composed of two tightly
and obtain ωcrit = 1.0545 rad/min at the nominal pabound regulatory subunits R and two catalytic subrameter values. The perturbations required to stabiunits C. Different from the linear kinetics used in the
lize the underlying equilibrium for the modified model
original model [3], we use second-order kinetics as anat the same two sets of parameter values are shown inapproximation of the interactions of two molecules ofFig.3. We can see that the relative perturbations reinternal cAMP on each of the two regulatory subunits.
quired are largely increased due to the modification ofThus, the rate of accumulation of the disassociatedthe structural characteristics. A direct comparison of
catalytic subunit is proportional to the square of theFig.2 and Fig.3 indicates that the slight modification
amount of internal cAMP. The catalytic subunits reof the structural characteristics has a large impact onbind with PKA independently, and hence their rate of
the robustness property. Even the smallest perturbaremoval is assumed to be proportional to the amount©ISAROB 2007

611

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
tion required for the modified model is greater than
Table 1: Comparisons of DOR for the two models
the largest one for the original model.
For the original model, although the magnitude of
the perturbations at the second set of parameter values
is relatively larger than at the nominal ones, the orderkeeps unchanged. Although the order changes due tothe parameter variations, the order of the most important pairwise interactions corresponding to L2,5 , L6,1 ,L7,6 , L5,1 , and L1,2 , which involve the components
ACA, PKA, REK2, internal cAMP, external cAMP,and CAR1, does not change despite the parametervariations. These components are instrumental to generate oscillations and relatively sensitive to perturbations. The large difference between the magnitude of
the relative perturbations for the original and modified
models further confirms that the structural characteristics is the major determining factor for robustness
properties, although sometimes parameter variations
can also have some contribution to them.

|∆pq|(a) 1.2
∆

0.7
∆

∆

∆

∆

1,7

4,3 5,4 3,2∆6,1 ∆7,6 ∆5,1 ∆1,2

|∆pq|

(b)

1

2

3

6

7

8

∆

∆

∆

∆

∆

4

5 6 7
Number

8

4

5

1.2
4,3 5,4

0.7
∆

2,5

0.2
0

1

∆

9 10 11

3

∆

∆
3,7
1,7 3,2

9 10 11

modified model
kil
kiu
DOR
0.64 28.22 0.68
0.08 20.18 0.91
k3,min 38.96 0.94
k4,min 4.64 0.68
k5,min k5,max 1.00
0.00 3.24 0.75
k7,min 9.64 0.90
0.34 k8,max 0.74
k9,min 1.18 0.75
k10,min 7.72 0.90
k11,min k11,max 1.00
1.52 13.38 0.63
9.32 k13,max 0.59
1.46 12.42 0.64

Conclusion

References

Figure 3: The magnitude of the relative perturbations|∆pq | required for the modified model. (a) At the nominal parameter values. (b) At k2 = 1.5 Mol−1 min−1
and k3 = 1.6 min−1 , all other parameters at theirnominal values.
The improvement of robustness properties by themodification of structural characteristics can be also
supported by single parameter robustness analysis.
The bifurcation points and degrees of robustness forthe modified model are also shown in Table 1. We
can see that all of them are largely increased due tothe the modification of structural characteristics. it
is clear that the single parameter intervals in whichstable oscillations occur are largely increased.

©ISAROB 2007

2.0
0.9
2.5
1.5
0.6
1.0
1.0
1.3
0.3
0.8
0.7
4.9
23.0
4.5

original model
kil
kiu
DOR
1.92 k1,max 0.04
0.72 1.60 0.20
k3,min 2.76 0.09
k4,min 1.58 0.05
0.54 k5,max 0.10
0.10 0.86 0.07
k7,min 1.10 0.09
1.18 k8,max 0.09
0.60 0.32 0.06
0.00 0.88 0.09
0.68 k11,max 0.03
2.64 5.18 0.05
22.22 k13,max 0.03
2.58 4.78 0.06

A linear analysis approach was proposed to study
the relative importance of components for robustness.
A modification scheme which captures the specific interactions between the internal cAMP and PKA wasdeveloped to enhance robustness.

∆
6,1 7,6 5,1 1,2

2

k1
k2
k3
k4
k5
k6
k7
k8
k9
k10
k11
k12
k13
k14

4

2,5

0.2
0

NV

Remarks. (1): ki,min = 0 and ki,max = 90, i =1, · · · , 14. (2): Abbreviations: P, parameters;NV,nominal values. (3) DORi = 1 − max pli /pi , pi /pui ,stable limit cycles occur parameter range (pli , pui ).

∆

3,7

P

612

[1] J. Stelling, U. Sauer, Z. Szallasi, F. J. Doyle III,
J. Doyle, “Robustness of cellular functions,” Cell,
Vol. 118, pp. 675-685, 2004.
[2] J. Stelling, E. D. Gilles, F. J. Doyle III, “Robustness properties of circadian clock architectures,”Proc. Natl. Acad. Sci., Vol. 101, pp. 13210-13215,
2004.
[3] M. T. Laub, W. F. Loomis, “A molecular networkthat produces spontaneous oscillations in excitable
cells of Dictyostelium,” Molecular Biology of theCell, Vol. 9, pp. 3521-3532, 1998.
[4] L. Ma, P. A. Iglesias, “Quantifying robustness of
biological network model,” BMC Bioinformatics,
Vol. 3, 38, pp. 1-13, 2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Phase Synchronization of Limit Cycle Oscillators in a Fluctuating Environment
Koh Hashimoto∗1,∗ and Kazuyuki Aihara∗1,∗2,∗3
Aihara Complex Modelling Project, ERATO, JST
Room M203, Komaba Open Laboratory, The University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPAN∗2
Institute of Industrial Science, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPAN∗3
Graduate School of Information Science and Technology, University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, JAPAN∗koh@aihara.jst.go.jp∗1

Abstract
We study the synchronization of limit cycle oscillators in a ﬂuctuating environment. When environmental conditions ﬂuctuate due to various kinds of noise,the dynamics of elements in the environment are inevitably perturbed and this may cause some synchronization between them. We analyze this phenomenonusing models in which system parameters are subject
to external noise and ﬂuctuate within a certain range.
Using the phase reduction method, we discuss the synchrony of limit cycle oscillators and show that the Lyapunov exponent is negative when amplitude of noise
is suﬃciently small, namely, synchronization of limit
cycle oscillators in a ﬂuctuating environment is stable.

1

introduction

Phase synchronization of limit cycle oscillators is aubiquitous phenomenon, found in a variety of biological, chemical and physical ﬁelds, and has attracted
much attention for several decades since Winfree’s pioneering work [1] in 1960s. Populations of limit cycle
oscillators that are subject to a strong periodic force
may be entrained to oscillate at the same frequency
as the periodic driving force. Alternatively, limit cycle oscillators interacting with each other directly or
indirectly can synchronize precisely due to mutual interactions among oscillators. In either case, external
or internal noise sources may disturb the phase synchronization of the oscillators because the main eﬀect
of noise on oscillation is phase diﬀusion. And therefore noise has long been considered to exert a negative
inﬂuence on synchronization of limit cycle oscillators.
However noise can play also an active role in syn-

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613

chronization of non-interacting oscillators. Challenging works by Teramae and Tanaka [2] and Goldobin
and Pikovsky [3] have shown that oscillators under
the inﬂuence of common weak additive noise can synchronize regardless of their intrinsic properties andthe initial conditions. Since this noise-driven synchronization does not depend on the natural frequency ofoscillators, it obviously diﬀers from entrainment toan external periodic force. Using the phase reduction method, they proved in general that the maximal Lyapunov exponent of an orbit is always negativewith little constraints when they are subject to weak
Gaussian-white noise and this means the phase synchronization of the oscillators.
In their works, noise perturbs states of elementsin the system directly and amplitude of ﬂuctuations
are identical for all elements. However, in some natural systems, the inﬂuence of noise on elements should
be construed in a diﬀerent way. In a variety of natural systems, environmental conditions such as temperature and pressure, which determine the dynamics
of elements in the systems, are perturbed by various
kinds of noise sources. In such systems, noise does
not perturb system states directly but dynamics ofsystems. Under such an environment, dynamical elements, which exhibit oscillation, are perturbed indirectly by those kinds of noise sources and amplitude
of ﬂuctuations are depend on the states of elements.
Thus we investigate behaviors of non-coupling limit
cycle oscillators in a ﬂuctuating environment and show
another scenario to reach synchronization. In a mathematical model, we can treat this situation by adding
noise on system parameters instead of system states.
Using the phase reduction method which is applicable
to an arbitrary oscillator [4, 5], we analytically calcu-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

late the Lyapunov exponent of the synchronizing stateand prove that the exponent is negative with some reasonable restrictions. With system parameters ﬂuctuating within a structurally stable region under inﬂuence of external weak noise, phase synchronization oflimit cycle oscillators can occur.

2

Model Description

Population of N identical nonlinear oscillators with
ﬂuctuating parameters is described asẋ(i) = F (µ; x(i) )µ̇ = η(µ, t) = −∇U (µ) + ξ(t)

(1)
(2)

where i = 1, . . . , N and x(i) is a state vector of the i-th
element in this system. F is common dynamics of theelements and µ is a parameter vector of the function
F . µ has its energy function U and is perturbed bynoise ξ. ξ is a vector of Gaussian white noise. The
elements of the vector are normalized as hξk (t)i = 0
and hξk (s)ξl (t)i = 2Dkl δ(s − t), where D = (Dkl ) is avariance matrix of the noise components.
We assume that:(a) µ is bounded within a bounded domain by the energy function U with probability 1,(b) F has no bifurcation in the domain,(c) F has a limit cycle attractor C(µ) in the domain,(d) F is continuously diﬀerentiable by µ and x,(e) Dkl is suﬃciently small.
For these assumptions, we can assume that an attractor of F is always a limit cycle, which varies continuously with changing of the parameter vector µ, andthat a state of an element is always suﬃciently close
to the limit cycle C(µ).

3Reduction To Phase Dynamics

Just as in the previous works, we use the phasereduction method to analyze this system. However,in this system, the limit cycle, on which the elements
are, varies constantly according to ﬂuctuations of theparameters. And this makes it diﬃcult to deﬁne aphase for an element. Thus, we should make some
preparations for phase reduction.
At ﬁrst, with constant parameter vector µ, we candeﬁne a phase for a point on C(µ) following standard procedure [4, 5]. In this article, we normalize
phase by the period of the limit cycle C(µ) so thatits range is [0,1], where 0 and 1 represents the same

©ISAROB 2007

614

phase. We represent a phase θ for a point x ∈ C(µ)
with constant parameter vector µ by θ = Θµ (x) and
its reverse function by x = Xµ (θ). If the parameter
vector µ is constant, phase dynamics are simply written as θ̇(i) = ω(µ). Note that the zero phase pointXµ (0)(= Xµ (1)) can be chosen arbitrarily.
Secondly, a phase for a point in neighborhood ofC(µ) can be deﬁned using an isochrone of a pointon C(µ), i.e., identify a point x0 ∈/ C(µ) to a pointx ∈ C(µ) in a way that the orbits from the twopoints asymptotically coincide with the parameter vector ﬁxed to µ (see Fig.1), we represent this map from apoint x0 ∈/ C(µ) to a point x ∈ C(µ) by x = Ψµ (x0 ),and let the phase of x0 be the phase of x = Ψµ (x0 )with constant parameter µ.

Figure 1: When the parameter vector is ﬁxed to µ, aphase of a point x0 in a neighborhood of C(µ) can bedeﬁned by identifying its phase to a phase of a point
x which satisﬁes that the orbit from x asymptoticallycoincide with the one from x0 .
When the parameter vector varies from µ to µ+∆µat time t, a phase of an element varies according tochanging of the attractor C(µ) → C(µ + ∆µ). A
map from a phase with µ to a phase with µ + ∆µthat describes phase slipping caused by varying theparameters at time t can be deﬁned as
θ0 = Φµ,∆µ (θ) = Θµ+∆µ (Ψµ+∆µ (Xµ (θ))).

(3)

Fig.2 is an example of Φ. Note that, because the zero
phase point can be chosen arbitrarily as mentioned
above, we can always align the phase for µ + ∆µ tosatisfy Φµ,∆µ (0) = 0 (Φµ,∆µ (1) = 1) as we see in
Fig.2. With this alignment, a value ∆s = Φµ,∆µ (θ)−θ
means a phase shift caused by changing of the parameter vector µ → µ + ∆µ.
Imagine that the variation of the parameter vector µ → µ + ∆µ is occurred continuously during ∆tThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

With following additional assumptions:(f ) φ is second-times continuously diﬀerentiable by θ,(g) φ is continuously diﬀerentiable by µ and η,
we can obtain the following formula:
Z 1 Z
X∂φ0 (µ, 0, θ) ∂φ0 (µ, 0, θ)Dkldθ dP (µ)
λ=−
∂ηk
∂ηl
0
k,l

(6)

Figure 2: If the parameter vector varies from µ to
µ + ∆µ at time t, a phase of each point is redeﬁned
by Eq.(3). Origin of phase for µ + ∆µ is aligned sothat Φµ,∆µ (0) = 0 (Φµ,∆µ (1) = 1) is satisﬁed. ∆s =
θ0 − θ means a phase shift caused by changing of theparameters.

without moving an element x by F in order to extract
only the eﬀect of phase shift from dynamics. Now weshould deﬁne a phase shift function not for µ and ∆µ
but for µ and µ̇. And this is derived by taking a limitof ∆t to 0 as
∆s(µ, ∆µ, θ)φ(µ, µ̇, θ) = lim
.
∆t→0
∆t

(4)

Using Eq.(4), we can reduce Eq.(1) as following:θ̇ = ω(µ) + φ(µ, µ̇, θ) = ω(µ) + φ(µ, η, θ)(5)where ω(µ) is a rotating velocity term determined byµ and F , and φ(µ, η, θ) is a phase shift term determined by µ, µ̇ and F . In fact, this reduction is valid
only when ω(µ) is suﬃciently larger than φ(µ, η, θ)and the assumption (e) that we have in the previous
section ensures this.

4

Phase Synchronization Induced By
Fluctuating Environment

Suppose that the two phases have an inﬁnitesimallysmall diﬀerence ∆θ = θ2 − θ1 where θi obeys Eq.(5).
Then the Lyapunov exponent is deﬁned as the longdtime average oflog ∆θ. By replacing the long timedt
average with the ensemble average with respect to ξ,
we can represent the Lyapunov exponent as¿Àdλ=log ∆θ .
dt
ξ©ISAROB 2007615

∂φwhere φ0 means φ0 =and P (µ) is a steady distri∂θbution function of µ.
We have the last assumption here:(i) φ(µ, η, θ) 6= 0 for almost every (µ, η, θ).
This assumption means that ﬂuctuation of parameters
almost always causes phase shift. Since this assump0(µ,0,θ)tion ensures ∂φ ∂η
6= 0 for almost every (µ, η, θ)kand the variance matrix Dkl is always positive deﬁnite,
λ is negative. This means that the phase synchronization induced by perturbation of system parameters is
stable in an arbitrary oscillator system with the assumptions we have.

5

Simulation

In this section, we demonstrate that phase synchronization of limit cycle oscillators can occur when noise
strength is suﬃciently small by numerical simulation
using van der Pol oscillator and measure the Lyapunov
exponents numerically.
Dynamics of van der Pol oscillator is described asẍ = γ(1 − x2 )ẋ − x − bx3
where γ and b are system parameters. Within a certain
region of (γ, b), this system has a structurally stable
limit cycle attractor. This diﬀerential equation can be
rewritten in following form.
‰ẋ1 = x2(7)ẋ2 = γ(1 − x21 )x2 − x1 − bx31
In order to implement a ﬂuctuating environment, weregard all coeﬃcients in the terms in these diﬀerential
equations as parameters (µk ) and attach some additional terms (µ1 , µ3 x22 , µ8 x21 ) as follows:‰ẋ1 = µ1 + µ2 x2 + µ3 x22ẋ2 = µ4 (µ5 − µ6 x21 )x2 − µ7 x1 − µ8 x21 − µ9 x31
And we adopt a “U-shape” function U (µ) =P
k Uk (µ) with(0(|µk (t) − µk (0)| < 0.05)
Uk (µ) =2.5(µk (t) − µk (0))4 (otherwise)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

for the energy function of µ. Initially, the parameters
are set as: µ1 = µ3 = µ8 = 0, µ2 = µ5 = µ6 = µ7 =1, µ4 = γ, µ9 = b so that dynamics at the initial time isequivalent to the original equations Eq.(7). The simulation results are shown in Fig.3. At the initial time,the elements in the system are not synchronized at all.
However, after long transient, they reach synchronization almost completely.

0-0.002-0.004-0.006-0.008-0.01-0.012
1.2

1.2

1.0

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0

10

20

30

40

50

-0.2
0

10

(a)
3

2

2

1

1

0

0

-1

-1

20

30

40

20

30

(c)

40

50

-3
1000

1010

1020

1030

1040

1050

(d)

Figure 3: (a) and (b) show temporal evolution of parameters µ1 , . . . , µ9 for [0, 50] and [1000, 1050] respectively. They ﬂuctuate all along the time by noise although bounded by U . Temporal evolution of x2 of 16
orbits which start from points randomly chosen is plotted in (c) and (d). The variance matrix of the noise is
set as Dkk = 0.01, Dkl = 0(k 6= l). The parameters γ
and b are γ = 0.2 and b = 1.
Fig.4 shows the Lyapunov exponents that are numerically calculated for various noise strengths. Whennoise strength is smaller than a certain value, λ decreases linearly with the increase of the noise strength
as indicated by Eq.(6).
Meanwhile, when noise
strength is strong, λ increases with the increase of thenoise strength and too strong noise eventually destabilize the synchronization of oscillators and the Lyapunov exponent is no longer negative.

6

Summary

0.025

0.03

0.035

0.04

This result is achieved regardless of details of dynamics and initial distributions of elements.
In this article, we only treated the case in whichparameters ﬂuctuate continuously under inﬂuence ofnoise. Nagai and Nakao [6] discussed phase synchronization induced by a ﬂuctuating input which jumps
between two values at random moments and provedthat when intervals of the jumps are suﬃciently large
and phase shift map is monotonic, the Lyapunov exponent of the system becomes negative. Using theirideas, our model is also applicable to the case in whichparameters are perturbed discontinuously by noise.
Studies for this case will be reported in the future.

References[1] A. T. Winfree, “Biological Rhythms and the Behavior of Populations of Coupled Oscillators”, J.
Theor. Biol., Vol. 16 pp. 15-42, 1967.
[2] J. Teramae, D, Tanaka, “Robustness of the NoiseInduced Phase Synchronization in a General Classof Limit Cycle Oscillators”, Phys. Rev. Let., Vol.
93, pp. 204103, 2004.
[3] D. S. Goldobin, A. S. Pikovsky, “Synchronization of self-sustained oscillators by common white
noise”, Physica A, Vol. 351, pp. 126-132, 2005.
[4] Y. Kuramoto, Chemical Oscillation, Waves, andTurbulence, Springer-Verlag, Tokyo, 1984.

We analyzed phase synchronization induced by perturbation of system parameters by reducing the dynamics to phase dynamics. And we proved that whennoise that perturb parameters are suﬃciently weak
and perturbation of parameters almost always causes
phase shift, the Lyapunov exponent becomes negative.

©ISAROB 2007

0.02

50

-2

-2

10

0.015

(b)

3

-3
0

0.01

Figure 4: The horizontal axis is Dkk (Dkl = 0) andthe vertical axis is the Lyapunov exponent λ. Eachpoint is obtained by taking an average of 25 trials.

0.0

0.0
-0.2
0

0.005

616

[5] A. T. Winfree, The Geometry of Biological Time,
Springer-Verlag, New York, 2001.
[6] K. Nagai, H. Nakao, “Synchrony of neural oscillators induced by random telegraphic currents”,
Phys. Rev. E, Vol. 71, pp. 036217, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Mathematical Modeling of Frogs’ Calling Behaviors and its Possible Application to
Artificial Life and Robotics
Ikkyu Aihara1 , Hiroyuki Kitahata2 , Kenichi Yoshikawa2 , and Kazuyuki Aihara3,4
1
Department of Applied Analysis and Complex Dynamical Systems,
Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan2
Department of Physics, Graduate School of Science,
Kyoto University, Kyoto 606-8502, Japan
3
Institute of Industrial Science, The University of Tokyo, Tokyo,
4
ERATO Aihara Complexity Modelling Project, JST
Abstract
This paper theoretically describes calling behaviors of Japanese tree frogs Hyla japonica with a simple model of phase oscillators. Experimental analysis
showed that while isolated single frogs called nearly
periodically, a pair of interacting frogs called alternately. We model these phenomena as a system ofcoupled phase oscillators, where each isolated oscillator behaves periodically as a model of the calling ofa single frog and two coupled oscillators shows antiphase synchronization, reflecting the alternately calling behavior of two interacting frogs. Then, we extend
the model to a system of three oscillators corresponding to three interacting frogs and analyse the dynamics. We also discuss a biological meaning of the calling
behaviors and its possible application to Artificial Lifeand Robotics.

1

Introduction

Nonlinear dynamics like synchronization has beenboth experimentally and theoretically analyzed inmany biological systems [1–7] with respect to possiblefunctions. In this paper, we consider calling behaviors of frogs from the viewpoint of nonlinear dynamics. There have been some experimental studies on
synchronization of calls of frogs. Loftus-Hills studied
the synchronization in calling behaviors of frogs Pseudacris streckeri [8], where tape-recorded calls wereused to evoke response of frogs. Lemon and Struger
studied acoustic entrainment to randomly generated
calls in frogs Hyla crucifer [9]. Here, we theoretically study spontaneous calling behaviors [10, 11] ofJapanese tree frogs Hyla japonica shown in Fig. 1
and discuss a possible application to artificial life androbotics.

©ISAROB 2007

617

Figure 1: Japanese tree frog Hyla japonica.

2

Experimental Results

Male Japanese tree frogs Hyla japonica which werecollected from breeding assemblages in paddy fields inKyoto, Japan were used for the experiment. Spontaneous mating calls were recorded and analyzed.
Figure 2 shows an example of the waveforms ofthe calls recorded from (a) a single frog calling alone
and (b) two interacting frogs calling together. Whilea single frog called nearly periodically as shown in
Fig. 2(a), two frogs called alternately as shown in
Fig. 2(b). The detail of the experiment was reported
elsewhere [10, 11].

3
3.1

Mathematical Modeling of Frogs’
calling behaviors
Phase oscillator model

We model the calling behaviors of frogs as phaseoscillators. The calling of a single frog is regarded

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

+1

Amplitude

Amplitude

+1

0

0

-1

-1

1.0

0.5

0

0

1.5

1.0

0.5

Time(s)Time(s)

(a)

(b)

1.5

Figure 2: The waveforms of the calls of (a) a single frog and (b) two interacting frogs. A single frog called nearly
periodically, and a pair of frogs called alternately.

as a periodic phase oscillator, and the calling of two
interacting frogs as two coupled phase oscillators.
First, we consider the situation that each frog calls
alone. The calling behavior of respective frogs is described as a phase oscillator with the phase variable θwith θ ∈ S1 = (R mod 2π) = [−π, π]/{−π ≡ π} [6]as follows:
dθ
dt

= ω,

(1)

where ω is an intrinsic natural frequency. It is assumed
that θ = 0 correspond to each call. This model represents the property that single frog calls periodically.
Then, we model the situation that two frogs call together through interaction. The system is described as
two coupled phase oscillators with two phase variables
θA and θB as follows (see also [10, 11]):
dθA
dt
dθB
dt

= ω + gAB (θB − θA ),(2)= ω + gBA (θA − θB ),(3)

where ω is the intrinsic frequency that is assumed tobe the same between two frogs, and gAB and gBA are
2π-periodic functions that represent the mutual interaction. To examine whether two oscillators synchronize, we analyze the dynamics of the phase differenceφ ≡ θA − θB with φ ∈ S1 . Subtracting Eq.(3) from
Eq. (2), we obtain the following equation on φ:dφ= gAB (−φ) − gBA (φ).
dt(4)

Here, we assume gAB and gBA to be a sinusoidal
function for the sake of simplicity, according to the©ISAROB 2007

618

former studies [2, 3], then, Eq.(4) is calculated as follows:dφ= 2K sin φ,(5)dt
where K is a positive coupling coefficient as schematically shown in Fig. 3(a). The stable equilibrium pointd dφ
φ∗ which satisfies dφ= 0 and dφ<( dt )
dt
φ=φ∗

φ=φ∗

0 is given by φ∗ = π. This stable equilibrium point reproduces the experimental result qualitatively, namely
these two oscillators synchronize in anti-phase.

3.2

Extension to a system of three frogs

Next, we extend this model to a system of three
coupled oscillators as follows:dθA= ω − K1 sin(θB − θA ) − K3 sin(θC − θA ),(6)dtdθB= ω − K1 sin(θA − θB ) − K2 sin(θC − θB ),(7)dtdθC= ω − K3 sin(θA − θC ) − K2 sin(θB − θC ),(8)dt
where Ki ’s (i = 1, 2, 3) are symmetrical coupling coefficients between two frogs as schematically shown in
Fig. 3(b). Here, for the simplicity, we assume thatω = 1.0 and K1 = K3 = 1.0. In order to examine dynamical properties in this system, we define the phasedifferences φ1 ≡ θA − θB and φ2 ≡ θB − θC . Then,
we change the value of K2 from 0 to 1.0 as the bifurcation parameter and numerically examine the stableequilibrium points φ∗1 and φ∗2 .
The bifurcation diagram is shown in Fig. 4. In theregion 0 < K2 < 0.5, oscillators A and B synchronize in anti-phase and oscillators B and C synchronize

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

(a)

(b)

Figure 3: Schematic diagrams in modeling of a system of (a) two frogs and (b) three frogs.

π

φ*1,2

2π/3

0

0

0.5
K2

1

Figure 4: The bifurcation diagram in the system ofthree coupled oscillators, where the red line represents
the phase difference φ∗1 , and the blue line φ∗2 .

in-phase. With increasing the value of K2 in the region 0.5 < K2 < 1.0, on the other hand, the property
of synchronization in this system gradually changes,
and finally three oscillators synchronize in tri-phaseat K2 = 1.0.

4

A Possible Application to Artificial
Life and Robotics

We discuss a biological meaning of the calling behaviors and a possible application to artificial life androbotics. Generally speaking, it is said that the main
purpose of calling by male frogs is to attract females
and tell their own positions to other males [12].
If one male frog mates with one female in a oneto-one manner, it is important for two males to makefemales distinguish them each other. In fact, manykinds of frogs are known to mate in such a one-to-one
manner [13], including the mating in Japanese treefrogs [14]. Thus, it is probable that two male Japanesetree frogs call alternately to be distinguished by a fe-©ISAROB 2007

619

male. On the other hand, male Japanese tree frogs are
known to inhabit with a low density in breeding assemblages [12]. Then, we suppose that the anti-phasesynchronization of two male frogs can play a role oftelling their own positions to other males, resulting insparse distribution. In that meaning, anti-phase synchronization would be applicable for multiple artificial
agents and robots to prevent collisions each other insome real or abstract spaces.
In the actual system of male frogs, coupling coefficients would depend on the distance between malefrogs, because they interact by calling and hearing.
It was numerically confirmed by varying the coupling
coefficient K2 that the system of three coupled oscillators shows more complicated properties than that oftwo frogs does similarly to coupled chemical oscillators [15]. Therefore, the calling behaviors in a system
of many frogs should be much more complicated. For
the purpose of understanding such a system, it is important to extend the model to a system of many oscillators. A simple extension of our model to a larger
system composed of N frogs is given as follows:
N
dθi
1 X
= ωi −
Kij sin(θj − θi ),dt
N − 1 i=1

(9)where for the ith frog (i = 1, 2, . . . , N ), θi is the phasevariable, ωi is an intrinsic natural frequency, and Kij
represents interaction with the jth frog. It is an important future problem to analyze such a system ofmany frogs both experimentally and theoretically.
Moreover, such a study would provide useful mechanisms of controlling distributed systems composed ofmany artificial agents and robots. For example, inphase synchronization and anti-phase one may represent cooperation and competition between agents androbots. Moreover, frogs that call in phase together
can be interpreted as a cooperative cluster, which may

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

produce emergence of a kind of communication.

5[4] A. Pikovsky, M. Rosenblum, and J. Kurth, “Synchronization: A Universal Concept in Nonlinear Sciences,” Cambridge University Press, Cambridge (2001).

Conclusion

We have theoretically modeled the calling behaviors of Japanese tree frogs Hyla japonica as a system ofcoupled phase oscillators where two coupled phase oscillators synchronize in anti-phase like the real calling
behaviors of two frogs. Biologically speaking, the antiphase synchronization would be important for a frog totell his own position to the other frog. In this meaning,anti-phase synchronization would be applicable to prevent collisions of multiple agents and robots. Then, we
have extended the model to a system of three coupledoscillators and confirmed that such a system shows
more complicated properties than that of two oscillators does. For the purpose of application to a system
of many agents and robots, it would be an important
future problem to analyze a system of many frogs bothexperimentally and theoretically. It is also our future
problem to modify our models more realistic, for example, by considering phase shift parameters and distribution of intrinsic frequencies of frogs.

Acknowledgements
This study was partially supported by a Grantin-aid for the Scientific Research (No.18GS0421) on“Construction of functional de novo cell” and the 21stCentury COE Program of “Center of Excellence for
Research and Education on Complex Functional Mechanical Systems” at Kyoto University from the Ministry of Education, Culture, Sports, Science and Technology of Japan. We would like to thank K. Tsumoto,K. Sogo, Y. Kuramoto, I. Tsuda, H. Daido, H. Kori,
and A. Nagano for their stimulating discussions andvaluable comments.

[6] F.C. Hoppensteadt and E.M. Izhikevich, “Weakly
Connected Neural Networks,” Springer-Verlag,
N.Y., 73 (1997).
[7] R.E. Mirollo and S.H. Strogatz, “Synchronizationof Pulse-Coupled Biological Oscillators,” Siam J.
Appl. Math., 50(6), 1645 (1990).
[8] J.J. Loftus-Hills, “Analysis of an Acoustic Pacemaker in Strecker’s Chorus Frog, Pseudacris
streckeri (Anura:Hyli-dae),” J. Comp. Physiol.,90, 75 (1974).
[9] R.E. Lemon and J. Struger, “Acoustic Entrainment to Randomly Generated Calls by the Frog,
Hyla crucifer,” J. Acoust. Soc. Am., 67(6), 2090
(1980).
[10] I. Aihara, H. Kitahata, S. Horai, K. Aihara,
and K. Yoshikawa, “Synchronization Experimentally Observed in Calling Behaviors of JapaneseRain Frogs (Hyla-japonica),” Proc. of 2006 Inter.
Symp. on Nonlinear Theory and its Applications,
767 (2006).
[11] I. Aihara, S. Horai, H. Kitahata, K. Yoshikawa,
and K. Aihara, “Dynamical Calling Behaviors
Experimentally Observed in Japanese Tree Frogs(Hyla japonica),” submitted to IEICE. Trans. on
Fundamentals.
[12] M. Matsui, “Natural History of the Amphibia,”
University of Tokyo Press, Tokyo (1996) inJapanese.
[13] H. Iwasawa and M. Kuramoto, “Systematic Zoology,” Nakayama-Shoten., 9, 364 (1996) inJapanese.

References[1] A.T. Winfree, “Biological Rhythms and the Behavior of Population of Coupled Oscillators,” J.
Theoret. Biol, 16, 15 (1967).
[2] Y. Kuramoto, “Chemical Oscillations, Waves,and Turbulence,” Springer-Verlag., Berlin (1984).
[3] S.H. Strogatz, “Nonlinear and Chaos,” Perseus
Publishing, Cambridge, (1994).

©ISAROB 2007

[5] G.B. Ermentrout and J. Rinzel, “Beyond a
Pacemaker’s Entrainment Limit: Phase Walkthrough,” Am. J. Physiol., 246, 102 (1984).

620[14] T. Matsubashi and F. Okuyama, “Frogs andToads of Japan +Salamander,” Yama-Kei Publishers, 53 (2002) in Japanese.
[15] M. Yoshimoto, K. Yoshikawa and Y. Mori,“Coupling among Three Chemical Oscillators:
Synchronization, Phase-Death and Frustration,”
Phys. Rev. E, 47, 864 (1993).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Local cause of coherence in Boolean networks
Chikoo Oosawa1,∗,†, Kazuhiro Takemoto2 , Shogo Matsumoto3 , Michael A. Savageau41

Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
Iizuka, Fukuoka , 820-8502, Japan
2
Bioinformatics Center, Institute for Chemical Research, Kyoto University,
Gokasho, Uji, Kyoto, 611-0011, Japan
3
Graduate School of Computer Science and Systems Engineering,
Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan
4
Department of Biomedical Engineering, College of Engineering,
University of California, Davis, 95616, U.S.A.
Nov. 15 2006
Abstract

1

We have performed numerical study on random
Boolean networks with power-law rank outdegree distributions to ﬁnd local structural cause for emergenceof high or low degree of coherence in binary state variables of the entire networks. The degree of randomness and coherence of the binary sequence, are measured by entropy and mutual information, depend on
local structure that consists of a node with highly connected, called hub, and its upstream nodes, and typesof Boolean functions for the nodes. With the larger
number of output connections from a hub, the eﬀects
of Boolean function on the hub are more prominent.
The local structures that give larger entropy tends togive rise to larger mutual information. On the basis of both numerical results and structural condition
we derived time-independnt transmission characteristic function of state variables for local structures. We
show good relations between the numerical and theanalytical results, reveals that dynamical properties
from the whole networks can be inferred from the differences in the local structures.
keywords : Boolean networks; power-law; coherence;
mutual infomration; entropy; transcriptional regulatory networks∗ Correspoding author: chikoo@bio.kyutech.ac.jp† Also at :Bioalgorithm Project, Faculty of Computer Scienceand Systems Engineering, Kyushu Institute of Technology©ISAROB 2007

621

Introduction

Biological system is consist of complex adaptive systems, for example neural and transcriptional regulatory system whose structure often can be abstracted
to graph or network. In general the systems perform their functions correctly when certain appropriate communications among nodes are established because such systems need to add, delete own nodes orchange a strength of connectivity to optimally adapt
to exogenous inputs. Transcriptional regulatory network is one of the complex adaptive systems where thenode mainly corresponds to transcriptional unit, andresponds to environmental changes to survive and proliferate. The Boolean network[1] is one of the discrete
dynamical models for the transcriptional regulatory
network and exhibits binary sequences of state variables that represents expression pattern of transcriptional regulatory network. Since the state variables
in Boolean network are sensitive to inputs from othernodes via directed edges, and aﬀect on other nodes
vise versa, a quality of communication is characterized as a size of mutual information. The mutual information indicates degree of coherence, synchronization,amount of information content in state variables or potential for computational capability of the network[2].
In this study, we show local structural cause for
emergence of coherence in Boolean network. Since weembeded power-law rank output connectivity distributions in Boolean networks whose input connectivity
Kin =2, the model networks have some hubs that integrate many output connections. Because the hubs synchronously transmit their state to downstream nodes,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

We constructed 104 Boolean networks in eachpower-law rank distribution [See Fig. 1] with ﬁxed
network size. 2x103 initial states are applied to each
network. Four diﬀerent Boolean functions [See Table 1] are used in almost equalprobably [See Table 2].
Please note that all generated networks use the sameamount of resources since the size of network is ﬁxed,
256 nodes and 512 directed edges.

Please notice that Table 2–3, Fig. 4–6 can be foundin Sec.6, Appendix.

2

Model

Dynamics of the Boolean networks [1, 5] is determined by theXi (t + 1) = Bi [X(t)](i = 1, 2, ..., N ),Table 1: 4 of 16 Boolean functions with indegree
Kin =2. In this paper we used only 1,2,4, and 8 areused because of feasibility and biological meaning of
the functions[3, 4].
Inputs
0
0
0
1
1
0
1
1
Decimal index©ISAROB 2007

0
0
0
1
1

Output
0
0
0
1
1
0
0
0
2
4

1
0
0
0
8

622

100

N=256

type Iγ ~ 0.810

type IIγ ~ 0.511(1)

where X(t) is the binary state, either 0 or 1, of node i
at time t, Bi (·) is Boolean functions [See Table 1] used
to simultaneously update the state of node i, and X(t)is a binary vector that gives the states of the N nodes
in the network. After assigning initial states X(0)to the nodes, the successive states of the nodes are
update by input states and its Boolean function. The
dynamical behavior of these networks is represented
by the time series of binary states. The time course
follows a transient phase from an initial state until
a periodic pattern, called an attractor, is eventually
established.

Numerical condition

Number of output connections, Kout

the downstream nodes are aﬀected by single or multiple hubs simultaneously. The structural condition
seems to automatically provide global coherence instate variables, however the structural aspects give
only possible inﬂuence from hubs, in fact we need toconsider a type of Boolean function at hubs and their
upstream nodes. We show both eﬀect of Boolean functions and the number of output connections on size ofentropy and mutual information. In addition some results can be explained by transmission characteristic
functions that can be derived from numerical results
and local structures.

10

100

256

1000

Rank

Figure 1: Power-law rank connectivity distributionsin the model. Power exponent, γ for type I and II isabout 0.8 and 0.5, respectively, where Kout (rank) ∼rank−γ . We performed only sigle network size that is
256 in the paper.

We have measured entropy(randomness) and mutual information(coherence) of state variables to characterize the dynamics of Boolean networks[2, 5] [SeeFig. 2].

4
4.1

Results
Numerical results

In total we obtained 70622 and 177098 attractors
from type I and type II distribution, respectively. Sizeof entropy and mutual information are measured fromthe attractors. Together with the network structural
condition we show the dependence of distribution’srank on entropy and mutual information in Fig. 3.
The dependence of Boolean function on entropy andmutual information are priominent in higher rankedhubs on both style of output distributions. The
Boolean functions that give larger entropy tends toThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

0.8
0.75
0.7

50
40

give rise to larger mutual information. These results suggest that the collective(global) coherence instate variables of networks may be due to style of upstream(local) conditions, including connections among
upstream nodes and assignment of Boolean functions.

mutual information

Proportion of positive

2

623

4

5

6

7

8

type I

9

b)

0.8
0.7
0.6

4

2
: H=8

: H=1

: H=2|4

0
1

2

3

4

5

6

7

8

9

0.90

c)

type II
0.875

0.85

45

0.825

40
35
30
: H=8

: H=1

: H=2|425

Proportion of positive
mutual information

1©ISAROB 2007

3

0.9

Transmission characteristic function

In order to elucidate dependence of rank on randomness and coherence we ﬁrst focus on local structre
around hubs as shown in Fig. 4. Because each node
has two inputs by deﬁnition, outputs from hubs are
subject to 4 inputs and 3 Boolean functions. Fromnumerical results we obtain statistical properties todetermine the relationships between 4 inputs. Fig.
5 shows two statistical properties, 1) Range of input
probability is almost limited from 0.0 to 0.5. 2) Degree of correlations between inputs are very low in therange of input probability.
The two statistical properties allow us to take afollowing analytical approach. We deﬁne the output property as a function of input probability, called
transmission characteristic function [See ﬁg. 6]. For
the simplicity we assume that 4 inputs(p1 ∼ p4
in Fig. 4) receive binary sequence with the sameprobability[6]. The transmission characteristic functions can be described as at most 4th order polynomial and obtained with combination of Boolean functions[Table 3] on the local structure[Fig. 4].
When we ignore the speciﬁcity of Boolean functions
in upstream nodes in a local structure, we can get av-

30
20

1

Proportion of positive
entropy

Figure 2: Flow of state variables from upstreams todownstream. Since input connectivity for all nodes
equls to 2(=Kin ), there are two pathways for mutial
information in each node. Input sequence Ini1 for thenode i is the same as the output sequence of an upstream node Outj1 and that the output sequence ofOuti for the node i is the same as the input sequenceof a downstream node Ink . When node i has multiple output connections, they have the same binary
sequece because state variables in networks is subject
to Eq. (1).

: H=1

: H=2|4

Entropy / bit

60

: H=8

4.2

a)

Mutual information
between in and out / bit

In i2
Out j2 j2

type I

0.85

Entropy / bit

MI2

In i1 j1
Out j1

2

3

4

5

0.8

type II

d)

0.75

0.7
1.6

0.65

1.4
1.2
: H=8
1

2

: H=2|4
3

: H=1

4

1.0

Mutual information
between in and out / bit

k

Ink
i
Out i

0.9

Proportion of
positive entropy

MI1

5

Hub’s rank
High

Output connectivity

Low

Figure 3: Dependence of proportion of positive entropy a) and c) and mutual information b) and d)on type I and II distribution in Fig. 1. Three diﬀerent symbols denote diﬀerent styles of Boolean function
on hubs, square:type 8, triangle:type 2 or 4, and circle:type 1 [Table 1]. The vertical bar ”|” in the legend
denotes one of the either Boolean function on bothside of the bar. H denotes hub’s Boolean function.
Dashed horizontal lines indicate the proportions fromall realizations [See Table 2].

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

erage transmission characteristic function, means thatconsidering only hub’s Boolean function, also be obtained from weighted average of the indiviaual transmission characteristic functions. The average transmission characteristic function can be written in :
(
) (
)
1
3
(1,∗,∗)(2|4,∗,∗)
Pout (p) =< Pout
(p) =
16
16
) (
)
(
9
1
(8,∗,∗)(∗,∗,∗)< Pout (p) =,< Pout (p) =
4
16

(2)

where an asterisk ”*” denotes one of any Booleanfunctions in Table 1. The triple asterisks in the braces
represent that the average transmission characteristic
function from over all 18 [See Table 3 and Fig. 6]
Boolean function combinations. The order of theirentropies is described as:()()(1,∗,∗)(2|4,∗,∗)
H Pout (p) < H Pout
(p)
(
)
(
)
(∗,∗,∗)(8,∗,∗)< H Pout (p) < H Pout (p) .

(3)

The same order of entropy size is already shownin Fig. 3 a) and c). In particular the tendency canbe seen in higher-ranked hubs in both style I and II,
demonstrating that transmission characteristic functions are useful tool to infer network dynamics.

5

Acknowledgements
This work was partially supported by Grant-in-Aid
No.18740237 from MEXT(JAPAN) and US Department of Defence Grant N00014-97-1-0364 from the Ofﬁce of Naval Research.

References[1] Kauﬀman SA (1992),The Origins ofder,Oxford University Press,New York.

Or-[2] Langton CG (1990), Computation at the edge ofchaos: phase transition and emergent computation.Physica D, 42:pp.12-37.
[3] Raeymakers L (2002), Dynamics of Boolean
Networks Controlled by Biologically Meaningful
Functions.J. Theor. Biol., 218:pp.331-341.
[4] Setty Y, Mayo AE, Surette MG, Alon U (2003),
Detailed map of a cis-regulatory input function.Proc. Natl. Acad. Sci., 100:pp.7702-7707.
[5] Oosawa C, Savageau MA (2002), Eﬀects ofalternative connectivity on behavior of randomly constructed Boolean networks.Physica D,
170:pp.143-161.

Summary and Discussion

After making comparison avearage taransmission
characteristic functions and numerical results wefound that good relations between them, suggestingthat local structure at hubs eﬀects on global dynamical properties in networks. These results may as wellprovide a blueprint of design principle for an artiﬁcial
gene regulatory network and help to elucidate the roleof hubs in dynamical system.
In recent years many large-scale complex networks
such as social, metabolic and neural networks havebeen paid great attention from many ﬁelds. It has
been revealed that the biological networks containprominent local structures, called motif, subgraph or
clique that consist of small number of nodes and edges[7, 8]. In the artile we demonstrate just one of local
structures in simpliﬁed dynamical model for transcriptional regulatory networks. In fact size of biologicalcomplex adaptive systems varies in very long time due
to many various events. Since clique models[9, 10]seem to promising approach for explaining growing©ISAROB 2007

complex networks , an exchange of information among
a local structures may contribute to the mechanisim
for maintenance and growing of complex networks.

624[6] Oosawa C, Takemoto K (2005), Transmission
characteristics of 3-node Boolean subnetworks
with random binary sequence inputs.Technical
Report of IEICE, NLP2005-73,NC2005-65(200511):pp.13-18.
[7] Milo R, Shen-Orr S, Itzkovitz D, Kashtan N,
Chklovskii D, Alon U (2002), Network Motifs:
Simple Building Blocks of Complex Networks,
Science, 298: pp.824-827.
[8] Balázsi G, Barabási A-L, Oltvai ZN (2005),
Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc. Natl. Acad. Sci.,
102:pp.7841-7846.
[9] Takemoto K, Oosawa C (2005), Evolving networks by merging cliques.Physical Review E,
72:046116-1∼6.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007[10] Takemoto K, Oosawa C, Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity.Mathematical Biosciences, to be appeared.

Appendix(H,U,U)

Pout (p)

H=2
2508
2517
2452
2471
2556
2467
2609
2494
2519

H=4
2540
2459
2564
2518
2529
2476
2486
2491
2445

H=8
2456
2460
2521
2436
2461
2541
2380
2513
2507

U

p1
p2

U

p3
p4

H

0.6
Average = 0.244
0.2
0.0

0.2

0.4

0.6

0.8

1.0

1.0
0.6

Average = 0.0285 / bit

0.2
0.0

0.2

0.4

0.6

0.8

1.0

Mutual information between inputs / bit

1.0

c)

0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00

0.8
0.6
0.4

Existing probability

H=1
2496
2564
2463
2575
2454
2516
2525
2502
2529

Mutual information between inputs / bit

Hub’s rank
1
2
3
4
5
6
7
8
9

1.0

Input probability
Cumulative
probability

Table 2: The numbers of realizations out of 104 networks are indicated. Diﬀerent 4 Boolean functions [SeeTable 1] at hubs are used equalprobably. ”H” in thetable denotes hubs Boolean function [See Fig.4].

Cumulative
probability

6

0.2

0.0
0.0

0.2

0.4
0.6
0.8
Input probability

1.0(H, U, U)
Figure 4: Typical local structure around hub: Squares
and directed edges (arrows) correspond to nodes andconnections that are pathways of binary sequence, respectively. Dashed arrows indicate inputs from upstreams. H and U in the squares correspond to
Boolean function on hub and its upstream nodes. Eachnode has one of Boolean functions, 1,2,4, and 8 asshown in Table 1. A set of three characters in abrace at the bottom of each ﬁgure denotes a set ofBoolean function combination on nodes in the local(H,U,U )(p) represents transmission charstructure. Pout
acteristic function of input probability p [See Fig.6]
and the combination of Boolean functions [See Table
3].

©ISAROB 2007

625

Figure 5: Statistical properties of inputs to local structure in Fig.4: a) Cumulative distribution of input
probability(p1 ∼ p4 ). b) Cumulative mutual information between 4 inputs. c) Correlation between input
probability a) and mutual information b). These distribution are obtained from 1st-ranked hubs on thebasis of 70622 attractors from 104 networks with style
I distribution and 2x107 initial states.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20071a)

(1,1,1)b)(1,8,8)pout(p)

0.8
0.6
0.4
0.2

©ISAROB 2007

626

e)

(1,1,1)(1,8,8)

0.8
0.6
0.4

f)

0.2

0.2

0.4

0.6

0.8

1

0.2

0.4

p

pout(p)

1

c)

0.6

0.8

1

p

(8,8,8)d)0.8(8,*,*)
0.6
0.4(*,*,*)(2|4,*,*)(1,*,*)

0.2

H (pout(p)) / bit(1,U,U)(2,U,U) or (4,U,U)(1,1,1)(1,1,1)(2,1,1)† (4,1,1)† (2|4,1,1)0(1,1,2)† (1,1,2|4)0 (2,1,2) (4,1,2) (2|4,1,2|4)(1,1,4)† (1,1,8)(2,1,4) (4,1,4)(2|4,1,8)(1,1,8) (1,2|4,2|4) (2,1,8) (4,1,8) (2|4,2|4,2|4)(1,2,1)† (1,2|4,8) (2,2,1) (4,2,1) (2|4,2|4,8)(1,2,2)(1,8,8)(2,2,2) (4,2,2)(2|4,8,8)(1,2,4)(2,2,4) (4,2,4)(1,2,8)(2,2,8) (4,2,8)(1,4,1)†(2,4,1) (4,4,1)(1,4,2)(2,4,2) (4,4,2)(1,4,4)(2,4,4) (4,4,4)(1,4,8)(2,4,8) (4,4,8)(1,8,1)(2,8,1) (4,8,1)(1,8,2)(2,8,2) (4,8,2)(1,8,4)(2,8,4) (4,8,4)(1,8,8)(2,8,8) (4,8,8)
16 =⇒ 632 =⇒ 6H (pout(p)) / bit

1

Table 3: The combination number of Boolean functions on the local structure shown in Fig. 4. 64(= 43 )combinations can be reduced into 18 because of symmetry in local structure [See Fig.4] and in-out relations in Boolean functions [See Table1]. There are
two major columns and each major column consist 2
subcolumns. The left subcolumns contain all possiblecombinations of Boolean functions in the case that hub
has one of Boolean funtions. The right subcolumns
indicate integrated combinations. The combinations
with dagger can be consolidated into the combinations
with prime. A set of numbers at both ends of arrowat the bottom line in each major column denotes theoriginal and reduced number of combinations. When
Boolean function of 8 is located in hub, the similar
procedure as shown in left major column can be taken.

1(8,*,*)(8,8,8)g)(*,*,*)

0.8(2|4,*,*)

0.6(1,*,*)

0.4
0.2

h)
0.2

0.4

0.6

p

0.8

1

0.2

0.6

0.4

0.8

1.0

p

Figure 6: Example of transmission characteristic functions a)–d) and their output entropy e)–h). Eachset of 3 characters in a brace shows a Boolean function combinations [See Table 1 and Fig.4]. A vertical bar ”|” denotes one of the either Boolean function on both sides of the bar. An asterisk ”*” denotes one of any Boolean functions in Table 1. The
triple asterisks, (*,*,*) means that all 18 possible combinations. a) Transmission characteristic function for(1,1,1)(1,1,1), Pout (p) = p4 . Other transmission characteristic functions are also described as at most 4th order polynomial. c) Entropy of the transmission characteristic function in a). d) Average transmission characteristic functions as indicated in braces. h) Entropyof the transimission characteristic function in d). As
a guide for a)–d), thick region of these curves correspond to p ∈ [0, 0.5]. Dashed lines show Pout (p) = p
or H(Pout (p)) = p relationship for e)–h).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Continuous Modeling of Biomolecular Systems Based on Process Calculus
Yang Pu, Xiaoju Dong
BASICS Lab
Department of Computer Science and Engineering
Shanghai Jiaotong University, Shanghai 200240, China
Abstract
Process calculus is a kind of models for concurrent systems. Recent researches in Systems Biology have applied
process calculus as the computational model to capture thedynamic behaviors of biomolecular systems. In this paper,
we integrate a continuous framework with stochastic πcalculus to model the biomolecular systems. To verify thecorrectness of this approach, a modified stochastic Pi machine(SPiM) which was previously developed to simulate
the systems described by stochastic π-calculus is proposed.
From the consistency of the data obtained from the simulation of biomolecular system and the experimental data, it
shows that the continuous framework introduced into Stochastic π-calculus is effective in simulating biomolecularsystems.

1 introduction
Concurrency theory [1], especially the process calculus [2], has been used as a suitable tool to study Systems Biology [3, 4]. The main characters of biomolecular systems
are interaction and the concurrence. The systems modeledby process calculus is the same as biomolecular systems tosome extent. The main idea of using process calculus is tomodel molecular processes as interaction/communicating
systems. That is to see biological components as concurrent processes and their interaction as process communication or process movement.
Using stochastic π-calculus [5] to formal model biological systems was first introduced by C.Priami [6]. The stochastic π-calculus enables its application to a wide varietyof biomolecular systems in which quantitative aspects are
key. In stochastic π-calculus, kinetic constant in biologicalreactions are abstracted as channels with base rates, and theactual reaction rate is calculated as an actual channel ratefrom the base rate and the number of processes offeringcommunications. The selection of a time step and actual
communication is based on these actual rates and follows
Gillespie algorithm. Using the Gillespie algorithm [7], we
can obtain the probability distribution for rules and times.

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The simulation of biomolecular processes which modeledby stochastic π-calculus is executed based on the Gillespie
algorithm that calculates explicitly which reaction occurs
next and how long it takes. But there are two problems inthis simulation method. First, as we know, the biomolecular system is a concurrent system, all the reactions react
independently. They are not sequential but parallel. But
by using Gillespie algorithm, all the reactions occur one
by one. It cannot describe the real biological system. Second, the most important benefit of process calculus is used
to exhibit the concurrence of systems. By using Gillespie
algorithm in the simulation, we cannot see the benefit ofprocess calculus. Gillespie algorithm cannot simulate thesystems described by process calculus accurately.
To address the above problems, we integrate continuous
framework to simulate the biomolecular systems. This is
inspired by the fact that, in vivo, biomolecular reactions
evolve in a continuous way following a rate that depends
on the concentration of the reactants. Therefore, we should
deal with a non-integer number of processes in process calculus. Furthermore, we need to develop approximations inorder to simulate the continuous reactions. Here we suppose time step ti+1 − ti is small enough to assume thatthe reaction rate and the concentrations of reactants remain
constant. To address the concurrent problem, we use Law
of Mass Action which states that the rate of a reaction is
proportional to the product of the concentration of the reactants. And in every time step, the communications between
processes are applied in parallel way according to the rate.
In order to validate the efficiency of this continuous framework, we modified the stochastic Pi machine(SPiM) [8] tointegrate the continuous framework, so called continuous
SPiM. The original SPiM is a system used to simulate stochastic π-calculus based on Gillespie Algorithm. From theconsistency of the data obtained from the simulation of Circadian Clock [9] by the continuous SPiM and the experimental data, it shows that the integration of this continuous
framework with stochastic π-calculus is effective in modeling biomolecular systems.
The paper is organized as follows. The stochastic πcalculus used in this paper is introduced in the next section.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
In section 3 the continuous simulation is described. We
According to Definition 2.1, the basic component is aapply our approach to model Circadian Clock in Section 4.
summation Σ, which is a choice between zero or more outFinally, conclusions are given in section 5.
put xhnior input x(n) actions that the component can perform. P |Q is parallel composition, and a given component
P can contain a restricted reaction channel νxP . Replication !π.P represents multiple copies of a given component
2 The Stochastic π-Calculus
π.P . π is either output or input actions. Two components
in a biological system can react by performing complemenIn a biomolecular system, the molecules are abstracted
tary input and output actions on a common reaction chanas computational processes and the network of interactnel. As show in Definition 2.2, summation containing an
ing molecules are abstracted as a mobile concurrent system
output xhni.P can react with a parallel summation conin stochastic π-calculus. A complicated chemical process
tainingan input x(m).Q. The reaction occur with rate(x),can always be decomposed into a set of many elementary
after
which
the name n is bound to m in process Q andbimolecular reactions, such as (A + B → · · · ) or uniprocesses
P
and Q{n/m} execute in parallel.
molecular reactions, such as (A → · · · ). Two unimolecular reactions can be regarded as one bimolecular reactions, such as (A + A → · · · ). Trimolecular reactions
3 Continuous Simulationsuch as (A + B + C → · · · ) are very rare. Therefore, thebiochemical reactions between reactants can be abstracted
A configuration of the biomolecular system described
as a communication between two channels with the samebystochastic π-calculus is a matrix of Mn×2 (R+ ) wherename. This kind of systems is composed by a commumi,1 represents the concentrations of interacting molecules
nity of co-existing computational process that communiwhich is described by input channel xi (m), while mi,2 repcate with each other and that change their interconnection
resents the concentrations of output channel xi hni. The elstructure at execution time. The stochastic π-calculus used
ements in the matrix are real numbers. An instantaneousin this paper is introduced in [8]. Here, we briefly introduce
configuration E(t) = (mi,j (t))1≤i≤n,j=1,2 with each inthe syntax and the reduction of the stochastic π-calculus.
stant t ∈ R+ .
Definition 2.1 The syntax of stochastic π-calculus is as
To model the reactions we use the Law of Mass Action
follows
which states that the rate of a reaction is proportional to the
product of the concentrations of the reactants. That is, if we
P, Q ::= νxP Restriction
have a reaction of the form A + B → · · · , then the rate of|P |Q Parallel
this reaction is r = k × |A| × |B|, for unimolecular reactions, such as (A → · · · ), the rate is r = 14 × k × |A|2 ,Σ
Summation
where k is called kinetic constant. In the stochastic ππ.P Replication
calculus, we are using the rate of channel to represents kinetic constant, and the concentrations of the reactants canwhere:
be obtained from the configuration Mn×2 (R+ ). In orderΣ ::= 0Null
to simulate evolution of biomolecular systems in computer,|π.P + Σ Action
we need to develop approximations. Here for simplicity weuse the rectangle rule; that is, we suppose tl+1 − tl = p issmall enough to assume that the rate and the configuration
π ::= xhni
Outputremain constant. With this assumption we can approximatex(m)Input,x 6= m
the effect of a communication during an interval of time oflength p by p × r. The implementation of continuous simuDefinition 2.2 The reduction of stochastic π-calculus is as
lation based on stochastic π-calculus executes five steps asfollows, each channel x is associated with a corresponding
follows:
reaction rate given by rate(x):1. Initialize the configuration of the biomolecular systemrr
described by stochastic π-calculus
Q ≡ P → P ′ ≡ Q′ ⇒ Q → Q′r

2. Initialize the absolute simulation time t = 0

r

3. Select out the communications which can be applied.

P → P′r⇒

νxP → νxP ′r⇒

P |Q → P ′ |Q

P → P′

4. According to the rates of the communications, configuration and the small enough time interval p, calculate

rate(x)xhni.P + Σ|x(m).Q + Σ′ −→ P |Q{n/m}©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
the quantities of reactants and productions changed in
A-related processeach communication, and update the configuration.
DN A A ::= tA().(DN A A|RN A A)|pA(u).DN A A2(u)DN A A2(u) ::= tA′ ().(RN A A|DN A A2(u))|uhi.DN A A
RN A A ::= trA().(RN A A|A)|drA()
A ::= pAhuAi.uA().A|pRhuRi.uR().A|νu(bindhui.A Bound(u))|dA()
A Bound(u) ::= dA().ReleaseR|u().A
ReleaseR ::= uhi

5. Set the time t = t + p, and go to step 3.

4 Simulation of Circadian ClockR-related process

Circadian Clock phenomena are found in a large variety of organisms from cyanobacteria to mammals, andhave probably evolved more than once. Recent works [9]suggest that the biomolecular mechanism of clocks sharescommon features over a wide range of organisms.The interaction consists of two interleaved feedback loops. In thepositive loop, the activator element enhances its own expression. In the negative loop, the activator element enhances the expression of the negative element, which inturn sequesters the activator, as shown in Figure 1. The biological process involves two genes, an activator, A, anda repressor R, they are transcribed into mRNA and subsequently translated into protein. The activator A binds to the
A and R promoters, and increases their basal transcription
rates. Thus, A acts as the positive element in the system,
whereas R acts as the negative element by sequestering theactivator. This simple model is not intended to abstract any
particular biomolecular system, but to capture the basic design principles shared by many systems, and believed to
produce its basic functionality.

DN A R ::= tR().(DN A R|RN A R)|pR(u).DN A R2(u)DN A R2(u) ::= tR′ ().(RN A R|DN A R2(u))|uhi.DN A R
RN A R ::= trR().(RN A R|R)|drR()
A ::= bindhui.R Bound(u)|dR()
R Bound(u) ::= dR().ReleaseA|u().R
ReleaseA ::= uhi

In order to simulate our model abstracted by stochastic π-calculus we chosen the following basal channel
rate: rate(tA) = 4, rate(tR) = 0.001, rate(trA) =1, rate(trR) = 0.1, rate(drA) = 1, rate(drR) =0.02, rate(bind) = 100, rate(pA) = 10, rate(pR) =10, rate(tA′ ) = 40, rate(tR′ ) = 2, rate(dA) =0.1, rate(dR) = 0.01. And we take the stochastic πcalculus abstraction of Circadian Clock as the input of continuous SPiM. The numbers of channels and processes obtained from continuous SPiM were plotted as a function oftime and illustrate the oscillatory behavior.

Figure2: The oscillatory behavior of A P rotein

Figure 1: The network for a core Circadian ClockWe start by building a stochastic π-calculus abstractionof the Circadian Clock process. The reactions in theprocess are abstracted as communications on channels,
and reaction rates as channel rates.
Figure3: The oscillatory behavior of R P rotein©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
From Figure2, we can see the oscillatory behavior of A
[2] Milner R., Parrow J., Walker D., A Calculus of Moprotein, and Figure3 exhibits us the oscillatory behavior ofbile Processes. Information and Computation, 1992,
R protein. At first, the kinetic constant of A promoter’s100(1): 1-40(Part I), 41-77 (Part II).
transcription is larger than R promoter’s. With the accu[3] Ciobanu G., Ciubotariu V., Tanasa B., A pi-Calculus
mulation of A proteins, it will activate A and R promoters,
Model of the Na Pump. Genome Informatics, 2002, 13:
increase their transcription rates. It will result in more A
469-471.
and R proteins, meanwhile, because R − A binding has
the largest kinetic constant, more R proteins will bind to[4] A. Regev, W. Silverman, and E. Shapiro. RepresentaA proteins to repress the binding between A protein andtion and simulation of biochemical processes using theA, R promoters, and decrease the number of A proteins.
pi- calculus process algebra. In Pacific Symposium on
Small number of A proteins may lead to low transcription
Biocomputing, volume 6, pages 459-470, Singapore,rates of A and R promoters because there will be few ac2001. World Scientific Press.
tivated A and R promoters. The small kinetic constant of
R promoters then leads to oscillations, which can be de[5] C.Priami, Stochasitc π-Calculus, The Computer Jourscribed as successive transitions between induced and renal, 38:578-589, 1995.
pressed states.
[6] Priami C., Regev A., Shapiro E., Silverman W., AppliAs shown in Figure2 and Figure3, the results of thecation of a Stochastic Name-passing Calculus to Repmodel yield the required oscillatory behaviors. In this we
resentation and Simulation of Molecular Processes. Inhave reproduced the known result of [9], providing support
formation Processing Letters, 2001, 80(1): 25-31.
for the correctness of the continuous abstraction framework
based on stochastic π-calculus.
[7] D.T.Gillespie. Exact stochastic simulation of coupledchemical reactions. Journal of Physical Chemistry,
81(25):2340−2361, 1977.

5 Conclusion
In this paper we integrate a continuous framework withthe stochastic π-calculus. The numbers of channels andprocesses in the stochastic π-calculus are regarded as thereal number to show continuous quantities of the substances. The communications in the stochastic π-calculus
occurred in a parallel way in each time unit. This approachhas been used to formal model the Circadian Clock. In order to validate our approach, we modified the Stochasitc
Pi Machine(SPiM) to approximate the continuous simulation of biomolecular systems. From the continuous simulation of Circadian Clock, it can show us that the continuous
framework of stochastic π-calculus is a reliable approachfor simulating quantitive aspect of biomolecular systems.

Acknowledgements
This work is supported by The National Distinguished
Young Scientist Fund of NNSFC (60225012), BDCC
(03DZ14025), The National Nature Science Foundation ofChina (60473006), MSRA, and The BoShiDian Research
Fund (20010248033).

References[1] Milner R., Communication and Concurrency. New
York: Prentice Hall, 1989.

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[8] Andrew Phillips and Luca Cardelli. A correct abstract
machine for the stochastic pi-calculus. In Bioconcur04.
ENTCS, August 2004.
[9] N.Barkai, Leibler S. Circadian clocks limited by noise.
Nature, 403(6767):267−268, 2000.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Modeling Chaos Neural Networks for Classification of EEG Signals

M. Nakagawa, IEICE member
Department of Electrical Engineering
Nagaoka University of Technology
Nagaoka, Niigata 940-2188, JAPANmasanaka@vos.nagaokaut.ac.jp

T. Q. D. Khoa, IEEE member
Department of Electrical Engineering
Nagaoka University of Technology
Nagaoka, Niigata 940-2188, JAPANkhoa@ieee.org

Abstract
This work proposes essential improvements based on
Chaos theory to enhance adaptive ability of Feed-forward
Neural Network (FFNN). A novel structure of a single
neuron is proposed with a feedback connection and a
periodic active function. The proposal model obtained thebest results on least mean square error as well as dramatic
decrease of training time. Results are also illustrated andcompared through XOR problem, 7-point problem andapplication for classification of EEG data.

Furthermore, the backpropagation algorithm can converge
in a local minimum or oscillate between two (or more)different solutions shown in Fig. 1.

Keywords: Chaos Neural Networks, backpropagation,
electroencephalogram (EEG)

1

Fig. 1. Error surface of FFNN.

Introduction

Artificial Intelligent (A.I) Computation always reaches
higher ability to be adaptive with changes of realistic
environments. Neural Networks is one of the mostinteresting areas of A.I. of which the well-known
characteristic is the learning ability. However the trainingof networks is being argued to improve it. Freeman(1991) decided that chaos may be the chief property thatmakes the brain different from an artificial-intelligencemachine. K. Aihara [2] said that a usual neuron model is a
simple threshold element transforming a weighted
summation of the inputs into the output through a nonlinear. However from the viewpoint of neurophysiology,
there is a firm criticism that real neurons are far more
complicated than simple threshold elements.

The rule for weight-modifications shows some similar
structures with a well known chaotic equation, so a
possible explanation for the hyper-sensitive andsometimes problematic behavior of the backpropagation
algorithm may be found in chaos theory shown by K.
Bertels and et. al in[5].

One of the problems associated with the
backpropagation algorithm is its parameterization.
Beforehand, the value of a number of parameters needs tobe specified. It has been found that very small variationsin these values can make the difference between good,average or bad performance. This also implies that one
can never be sure to have found the optimal solution.

EEG signals classification have been carried out withmany different methodologies as Autocorrelation in [7],Neural Networks in [8], Fuzzy logic in [9]. This work
proposes a novel neural network with some improvements
based on Chaos theory to speed up the learning time andto escape the local optimum. The present learning modelis properly characterized in terms of periodic chaos©ISAROB 2007

631

This work follows the approach considering neuralnetworks as a Chaos dynamic system which has been
proposed by K. Aihara [2], and M. Nakagawa [1]. Manyof applications were examined in [1] such a chaotic
memoryretrievalmodel,
image
processing,telecommunication signal synchronous.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

neuron to involve a chaotic dynamics as well as externalor autonomous control of the periodicity. The obtained
results are examined by XOR problem, 7-point problem
andapplicationforclassification
ofElectroencephalogram (EEG) signals. In addition
illustrations are compared to some conventional neuralnetworks.

The standard FFNN is usually 3 layers following: Input
layer: X= {x1, x2,…,xN}, hidden layer and output layer.
All neurons are connected by weights and their output svalue are calculated through net value and transfer
function.
Net value:N

net = ∑ wij .xi + θ

(1)

i =1

2

Electroencephalogram (EEG)Electroencephalogram (EEG) signals provide one
possible means of human–computer interaction, which
requires very little in terms of physical abilities. By
training the computer to recognize and classify EEG
signals, users could manipulate the machine by merely
thinking about what they want it to do within a limited setof choices.

Transfer function is usually used a saturation function
such sigmoid function.

3.2 A Periodic Chaos Neural Network Model
This work mentions a novel FFNN with periodic activefunctions and a feedback factor, k, from the output to the
inputs of a neuron. It is shown that a neuron with anability of dynamic memory feedback is better theconventional model with such a monotonous mapping asa sigmoid function. The utility of periodic chaos is used toescape from local minimum values and speed up thelearning time. In addition the global minimum value
could be reached efficiently since chaos region inbifurcation diagram.

Fig. 2. EEG data acquisition.

k

The reliable operation of brain–computer interfaces(BCIs) based on spontaneous electroencephalogram(EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations andclassifiers for BCI are open research questions whose
difficulty stems from the need to extract complex spatial
and temporal patterns from noisy multidimensional timeseries obtained from EEG measurements.

Σ

Periodic
CHAOS

Fig. 4. Chaos Neural Network Model.
Modified net value:

3

Models1)

net (t + 1) = k .net (t ) + ( ∑ w.x + θ )3.1 The conventional Neuron(2)Periodic chaos transfer function:xΣY (t ) = sin(
TRANSFERynet (t ))τ (t )

Where:

τ (t + 1) = τ (t ) + ke.τ (t ).(1 − τ (t ))Fig. 3. Conventional NN model.

©ISAROB 2007

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632

(4)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The use of chaos functional help networks independent
on the initial conditions and can not trap in the saturationof transfer function. In addition, this speeds up thetraining time of network and can avoid the local minimum.

4

Results

4.2 7-point problem
This problem has 7 patterns with 2 inputs and 1 output.
X1=[0 0 1 1 0.5 0.25 0.75], X2=[ 0 1 0 1 0.5 0.75 0.25],
and Y=[0 1 1 0 1 0 0]. Conventional Model: 7 hidden
neurons, logsig transfer function, learning rate=0.8.The
conventional model result is Epochs =10.000, Tolerated
error = 0.3884 shown in Fig. 7.

The proposed model is compared to the conventionalmodel within the same conditionals such as number ofneurons, number of layers, initial values of weights, 10-3
in tolerance square error, and upper limit of learning
epochs as 104 times.

4.1 XOR problem
Conventional Model: with 2 hidden neurons, logsig
transfer function, learning rate=0.8. The conventionalmodel result is Epochs =10.000, Tolerated error =0.0619shown in Fig. 5.

Fig. 7. Tolerance square error of conventional FFNN.

Result of Chaos Neural Networks Model is shown in
Fig. 8. with Epochs=22, Tolerated error = 0.001

Fig. 5. Tolerance square error of conventional FFNN.

Result of Chaos Neural Networks Model is shown in
Fig. 6. with Epochs=48, Tolerated error = 0.001

Fig. 8. Tolerance square error of Chaos Neural Networks.

4.3 Classification of 2 EEG types

Two types of EEG data used to examine are footmoving and left-hand moving. These data are obtained
from 16 channels and they are equivalent to 16 inputs of
Neural Models. Result of Chaos Neural Networkclassifies EEG data into 2 groups with Tolerance error=
0.1. The output errors are shown in Fig. 11.
Fig. 6. Tolerance square error of Chaos Neural Networks.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Therefore, other applications of the proposals will be
investigated in future works.

Acknowledgements
This study was supported by The 21st Century COE(Center of Excellence) Program “Global Renaissance by
Green Energy Revolution” from Ministry of Education,
Culture, Sports, Science and Technology of Japan.

Fig. 9. EEG data of foot moving.

References

Fig. 10. EEG data of left-hand moving.

Fig. 11. Output error of classification 2types EEG data.
This results is just limited in 2 types of EEG signals,
however the proposed neural network model is clearly
adaptive with the kinds of EEG data. With more than 2signals, the binary output will be extended with more than
1 neuron.

5

Conclusion

From conventional neural networks with the backpropagation learning algorithm, this work proposes some
improvements based Chaos theory such as modifying net
value with a feedback factor and changing the transfer
function to avoid the saturation of outputs. However, allintelligent computing algorithms need to examine
adaptive characteristic through realistic applications.

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[1] Masahiro Nakagawa, Chaos and Fractals in Engineering, World
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[2] K. Aihara, T. Takabe and M. Toyoda, “Chaotic Neural Networks”,
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Truong Quang Dang Khoa was born in the South of Vietnam in 1979.
He graduated from the Hochiminh University of Technology in 2002,
and received the Master Degree in 2004. Now he is working as a
research assistant in Nagaoka University of Technology under the
support of COE program. His research interests are Artificial Intelligent
Computation, Chaos, Fractals and Power System Analysis. He is amember of IEEE.
Masahiro Nakagawa was born in Kyoto, Japan in 1958. He received Ph.
degree in the Department of Applied Physics from Nagoya University in
1988. He has been a faculty member of engineering at the Nagaoka
University of Technology since 1982, and became a Professor in 2001.
He is a member of the Japanese Neural Network Society, the PhysicalSociety of Japan, Japanese Society of Applied Physics.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Fundamental Research on Polymer Material as Artificial Muscle

Takayuki Hanamoto, Yasuhiro Neba, Yutaka Fujihara, Junichi HayashidaMatsue National College of Technology

Abstract

which is usually regarded as the artificial muscle. In this paper,

Presently, breakthrough industrial technology and scientificapplication about the dielectric elastomer EAP (Electro Active

technology produce various instruments of downscale andweight saving. But the metal parts are widely used for

we describe the results of the basic experiment andPolymer).

magnetic motors. It is difficult to lighten the weight and to

2. Dielectric elastomer EAP artificial muscle[1][2][5][6]actuators that can be built into motor. We focus attention on

 Dielectric elastomer EAP artificial muscle has the similarelectrical stimulation with a significant shape or size change.

soft electrode as the Fig.1. A dielectric is film-shaped andminiaturize the motor. Therefore, we need small, lightthe polymer material artificial muscle that responds toIn this research, the artificial muscle was produced byusing the conductive grease and the polymer material. We
used several kinds of polymer material films in our

experiments to compare the performance of them. The
displacement when the voltage is applied to electrode wasmeasured. At last the result and conclusion are given.

structure to the condenser that a dielectric is put with in thevery thin. The high voltage of about 4000V is impressed onthat film. The big electric field occurs between the electrodes

and electrodes attract each other. A dielectric film produceselastic deformation and expands in the horizontal direction byusing elastomer of the acrylic form and the urethane gel.

Keyword: EAP, Artificial muscle, Acrylic form elastomer,
Urethane gel

1. Introduction
 The needs of miniaturization and weight saving of themachine are being increased more due to the development of
the technology in recent years (For example, the small
actuator for the miniature camera is mounted in the portable

telephone). As we know, the biped robot which imitates aperson's movement appears. But, it still seems that the
movement of the robot is awkward. The reason is that

person's muscle moves in a straight line, but the motion of therobot is by the rotation movement of servomotor, and thismotion is thought as the main reason.

 From these reasons, we paid attention to the soft actuator©ISAROB 2007

635

Fig.1

Structure of experiment

The displacement of compression between electrodes isvery small. Therefore the dielectric elastomer EAP can beused as the actuator by applying the expanding force.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3. Fundamental experiment [1][2][3][5][6] The stress that occurs between the electrodes is expressedby the equation (1).

P = ε 0ε r E 2       ,where P: stress,(1)

ε 0 : permittivity in vacuum, ε r : relative

permittivity, E : electric field strength.

Fig.2. Photograph of experimental sample

By the experiment, we confirm that the relationshipbetween the displacement and the electric field strength
corresponds to the equation(1). And we also examine thatwhich materials are suitable for the soft actuator

The urethane gel and the acrylic form elastomer are used

for the experiments. Each sample is fixed on the frame of anacrylic, which is shown in Fig.2. Fig.3 shows the structureof Fig.2. The reason to fix the film is to prevent the film fromslackening. And it is also to prevent the electric discharge
which happens easily for the experiment by high voltage.

Conductive grease was used for the electrode. And thediameter of the electrode is 15mm. The voltage of 4000V wasapplied to the electrode. The displacement was measured by

Fig.3. Structure of experimental sample

the displacement measure instrument which uses the image
processing .

And also the characteristics of the multi-layer of acrylic

form elastomer are examined. Fig.4 shows the structure ofmulti-layer experiment. Since acrylic form elastomer is easy
to be tested, it is selected to be used as the sample.

Fig.4. Structure of multi-layer experimental sample©ISAROB 2007

636

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

From Fig.5, it is clarified that even a small voltage can

4. Experimental result and consideration

4.1 Comparison urethane gel between acrylic form

affect the displacement of urethane gel when compared with

between urethane gel between acrylic form. It shows that thecan be obtained by the 20 MV/m of electric field strength, Fig.5 shows the result of the comparative experiment

displacement describe a parabola according to the increasingof the electric filed strength. The relationship between themcoincides with the equation (1).

acrylic form. And the maximum displacement of urethane gelthat is half of the voltage for the same displacement of acrylic

form. But the response rate of urethane gel is slower thanthat of acrylic form.

4.2 Effect of the multi-layer of acrylic form




Next, for confirming the effect of the multi-layered acrylic

 



form, the acrylic forms of from one layer to five layers are tested and the results of the multi-layer of acrylic form are




shown in Fig.6. Each multi-layer of acrylic forms results in

the similar curve.













   

        High voltage is necessary to change dielectric elastomer   

EAP, and the thinner the film of EAP becomes, the lower thevoltage to apply is. However, the thinner the film becomes,

Fig.5 Relationship between electric field strength anddisplacement (One sheet)increase the strength by the same voltage of the single layer.
displacement in the vertical direction to the electrodes can beused for the application of the multi-layer EAP.



 

On the other hand, the multi-layer of acrylic form canFurthermore, it has the possibility that the compression

The compression displacement in the vertical direction will

be large when we increase the number of the multi-layer and 

the more the strength of the film reduces.

the multi-layer EAP will be able to apply as an actuator.







5. An application example as an actuator










 

     

 
 
 
 
 



 







We propose an actuator for the pump to circulate the air as

an application example. The main parts of the actuator can                    

consist of the dielectric elastomer EAP and the spring,
Therefore the actuator can be miniaturized and lightened theFig6. Relationship between electric field strength anddisplacement (multi-layer)©ISAROB 2007

637

weight. The structure of the proposed application example isshown in the Fig.7.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Big evolution was also made for the biped walking robot inrecent years by easily imitating a person's movement. But, thevibration that occurs by the rotation movement of motors

causes a big trouble when controlling many motors of thebiped robot.

The soft actuator which can make a straight line-likemovement by only given voltage without vibration attract theFig.7 Structure of application example as actuator

attention of the researchers of robotics. Because it is possible

At the initial condition, the springs press down theelastomer film to the central axis. And a film stretches by

increasing a high voltage and at the same time the springs

to be used to the various fields as mentioned above, a soft

actuator can be thought as the new technology for the
research and application of various filed.

extend to the direction of the central axis. When the applied

References

By repeating this cycle, the gas inside the pump is circulated.

[1]Yoshihito Nagata(2004), The front of development of awhich is the weak point of present pulse pattern pump that is[2]Haruo

voltage becomes low, the spring regains the initial position.
This simple mechanism can break down the

problemdifficult to miniaturize. And it also may be able to be applied
to the artificial heart if the fluid circulate is strengthened byusing the strong spring. However, because the high voltage
power supply is used, it is necessary to take care about theelectrical discharge.

dielectric elastomar EAP artificial muscle in the introduction.
The fundamental experiment of artificial muscle was done,

and the application of artificial muscle was proposed. The

various other applications are suggested by a lot of. Therefore, the artificial muscle is improved[3][4]

If the artificial muscle which respond instantly to the very

weak electric stimulus from the human body can be
developed, a big revolution will be brought to the artificial
limb technology.

©ISAROB 2007

MuscleKODANSHA(in Japanese)ismystery, p.p16-64,[3]Scott Stanford, Neville Bonwit, Ron Pelrine, Roy

Kornbluh, Oibing Pey, Seiki Chiba, (2004) Electroactive

Polymer Artificial Muscle (EPAM) for Biomimetic Robotics,

Second conference on artificial muscle, National Institute of[4]Yoseph Bar-Cohen(2004) Electroactive Polymer [EAP]In this paper, we described the soft actuator that is called

every day.

sugi(2003)

Advanced Industrial Science and Technology

6. Conclusions

researchers

soft actuator.,NTS(in Japanese)

638

Actuators as Artificial muscles, p.p529-581, SPIE.

[5]J.Hayashida, F.Dai, Y.Fujihara, (2006) Fundamental Studyof Dielectric Elastomer as Artificial Muscle, Proceedings ofthe Eleventh International Symposium on Artificial Life andRobotics 2006(AROB 11th, 06), CDversion, p.p611-614[6]Y.Fujihara,

J.Hayashida,

F.Dai,(2006)

Dielecrtric

Elastomer Actuators as Artificial Muscles, Proceedings of theEighth International Conference on Industrial Management,
Qingdao(China), p.p891-894

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fundamental Research on Face Recognition by Genetic Algorithm
Fengzhi Dai1, Touru Adachi1, Yutaka Fujihara1, Huailin Zhao2
1.

Department of Control Engineering,

2.

Matsue National College of Technology, Japan

College of Physics Science & Technology,
Zhengzhou University, China

daifz (fujihara)@matsue-ct.jp
Abstract

zhao_huailin@yahoo.com

Since GA has been considered to be a robust andObject recognition is playing an increasingly important

global searching method (although it is sometimes said

object can be recognized by the template matching

optimization) [3], it is used as the recognizer. In thisrole in modern intelligent control. Traditionally, special

method, but the recognition speed has always been aproblem. In this paper, the genetic algorithm (GA) -based face recognition method is proposed. The
chromosomes generated by GA contain the informationof the facial model, by which can we recognize it in animage. The purpose of the paper is to propose athat GA cannot be used for finding the global
paper, the chromosomes generated by GA contain

information about the image data, and the genetic andevolution operations are used to obtain the best match

to the template. Searching for the best match is the goalof the paper.

In this paper, section 2 gives the encoding method ofpractical method of face recognition. Finally, thethe GA and the experimental settings that is used. In

traditional template matching method, and some otheraddressed. Some conclusions are given in section 4.

experimental results, and a comparison with theconsiderations, are also given.

Key Words: Face recognition, Genetic algorithm,

Template matching

section 3, the experiment and the analysis are

2. Theory and experimental settings

For an image recognition system, the interested partthat has special features has to be detected and1. Introduction

We know that the higher the degree of intelligence ina control system, the more important the image

recognized from the original image. The wholeprocedure is shown in Figure 1.

Standard
patterntemplate

recognition technology. For an intelligent control

system (autonomous mobile vehicle, robot, etc), it isnecessary to acquire information about the external

world automatically by sensors, in order to recognize
its position and the surrounding situation. A camera is

one of the most important sensors for computer vision.

Image

Preprocessing

That is to say, the system endeavors to find out what isThereliability

andtime-responseofobjectrecognition have a major influence on the performanceand usability of the whole object recognition system[1]. The template matching method is a practicable andreasonable way used to the object detection [2]. This
paper gives an improvement in the general templatematching method.

©ISAROB 2007

639

Patternmatching

ResultFigure 1 Object recognition system

in an image (the environment) taken by the camera:
traffic signs, obstacles or guidelines, etc.

Featureextraction

One of the oldest techniques of pattern recognition

is matching filtering [4], which allows the computationof a measure of the similarity between the original
image f ( x , y ) and a template h( x , y ) . Define themean-squared distanced fh = ∫∫ { f ( x , y ) − h( x , y )} dxdy
2

2

(1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
R fh = ∫∫ f ( x , y ) h( x , y ) dxdy , if the image and templatefrom the template [7].

are normalized bymethod is also presented [8]. The executive time shows∫∫ f ( x , y ) dxdy = ∫∫ h ( x , y ) dxdy
2and then
2
d fh

(2)

2

For comparison, the general template matching

the effectiveness of the GA-based recognition method.

Figures 2 and 3 are the template and original images

for the experiment. The values are the width × height in= ∫∫ { f ( x , y ) − h( x , y )} dxdy

pixels of the image.

2= ∫∫ { f ( x , y ) − 2 f ( x , y ) h ( x , y ) + h ( x , y )}dxdy
2= 2 ∫∫ f ( x , y ) dxdy − 2 R fh

2

Table 1 Some GA parameters

2(3)

For the right hand side of eqn.(3), the first term isconstant, and thus Rfh can measure the least-squared

similarity between the image and template [5]. If Rfh
has a large value (which means thatd 2fh is smallenough), then the image is judged to match theSourceFieldsGenerations
Population
Size

Reproduction(selection)

template. If Rfh is less than a preselected threshold, therecognition process will either reject the match or
create a new pattern, which means that the similarity

between the object in the image and the template is notsatisfied.

Crossover

Mutation

2.1 Genetic encoding

As introduced above, the chromosomes generated by

200

Pr of the best individuals will beselected to survive.
The remainder (1-Pr) will be treatedby the genetic operators (crossoverand mutation)
Offspring are produced from parents
by exchanging their genes at thecrossover point, the ratio is Pc
Producespontaneous
randomchanges in various chromosomes.
General random changes occur at therate of Pm

Table 2 Settings for the experiment

the GA contain information about the image data, so
the first step is to encode the image data into a binary

The original and template images
x, y, rate, ēMax = 300 (the stopping criterion)

string. The parameters of the center of a face (x, y) in

Parameters

Lengthand the rotating angle ē are encoded into the elementsy

8 bitsthe original image, the rate of scale to satisfy eqn.(2),of a gene [6]. Some important parameters of the GA
used here are given in Table 1, and the search field andregion are given in Table 2.

As shown in Table 2, one chromosome contains 4

bytes: the coordinate (x, y) in the original image, the

x

rate

ē
Pr

Pc

Pm

Region8 bits(0, max_x)

8 bits(1.0, 3.0)

8 bits0.6(0, max_y)(-35o, 35o)

0.2

0.2

rate of scale and the rotation angle ē .
2.2 Experimental setting

The experiment is done by first loading the original

and the template images. The GA is used to findwhether or not there is the object of the template in theoriginal image. If the answer is YES, then in theoriginal image the result gives the coordinates of thecenter of the object, the scale, and the rotating angle©ISAROB 2007

640

32 × 32

Figure 2 Template for matching(size: temp_x × temp_y)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
The maximum number of generation is limited to300, and the threshold of the matching rate is set to 0.9.
Thus if within 300 generations the matching rate canreach 0.9, then it is said that the template is found in
the original image (the template matched the original

image by the threshold). The result of the GA-based

face recognition is given in Figure 4 and Table 3. The
result shows that it reaches the matching rate 0.944 at
the 112th generation.

Table 3 Results of searching by GA

320×240
Figure 3 The original image (size: max_x, max_y)
3. Experiment and comparison

The genetic operations and GA parameters arepresented in Table 1 and Table 2. The fitness is defined
as

0.944
112
5
(112, 107)
1.02
2.24

For the purpose of comparing the effect of the∑ ∑ | f ( x, y, rate,θ ) − temp(i, j) |GA-based algorithm, the result of the general matching(temp _ x) × (temp _ y) × 255

that although both the original image (the top-lefttemp _ y temp _ x

fitness = 1.0 −Fitness
Generation
Time [sec](x, y)
Rate
Angle [deg]

j =0

i =0

(4)

In eqn.(4), temp(i,j) is the gray level of thecoordinate (i, j) in the template image, the width andheight of which are temp_x and temp_y.

f(x, y, rate,ē ) gives the gray level in the original image, themethod [8] is also presented. From Figure 5, we see

image in Figure 5) and the template (the top-rightimage) are simplified by binarization, the matchingtime is 3 min 18s. The recognized result is thebottom-left image in Figure 5.

coordinate of which is calculated by translation from (x,y), and by changing the scale and the rotation angle ēfrom the template [6, 7]. Since the images are 256
gray-level images, in eqn.(4), division by 255 ensuresthat the resulting fitness is between 0 and 1.

Figure 5 Result for the general matching method
4. Conclusions

In this paper, the GA-based image recognition

method is tested, and a comparison with the general

Figure 4 Result of the GA-based recognition©ISAROB 2007

641

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
matching method is presented.

The GA starts with an initial set of random solutions

the 7th Int. Symp. on Artificial Life and Robotics,
vol. 2, 538-541

called the population. Each individual in the population[2] K. R. Castleman (1998), Digital image processing,problem. By stochastic search techniques based on the

Simon & Schuster Company, Press of Tsinghua

is called chromosome, and represents a solution to themechanism of nature selection and natural genetics,

Original edition published by Prentice Hall, Inc., a
University

genetic operations (crossover and mutation) and[3] H. Iba (1994), Foundation of genetic algorithm –search the best solution [9].

[4] K. Deguchi, I. Takahashi (1999), Image-based

evolution operation (selecting or rejecting) are used to
In this paper, the chromosomes generated by the GA

contain information about the image, and we use thegenetic operators to obtain the best match between theoriginal image and the template. The parameters are thecoordinate (x, y) of the center of the object in theoriginal image, the rate of scale, and the rotation angle
ē .

In fact, translation, scale and rotation are three main

invariant moments in the field of pattern recognition.

However, for face recognition, the facial features are

solution of mystic GA, Omu Press (in Japanese)

simultaneous control of robot and target object
motion by direct-image-interpretation, Proc. the1999 IEEE/RSJ International Conf. of Intelligent
Robot and System, vol. 1, 375-380[5] B. Jaehne (1995), Digital image processing -concepts, algorithms, and scientific applications,
The Third Edition, Springer-Verlag[6] T. Agui, T. Nagao (2000), Introduction to image
processing

using

programming

Shoko-do press (in Japanese)

languageC,difficult to extract, and are calculated by the general[7] F. Dai, T. Kodani, Y. Fujihara (2006), Research on

three main invariant moments will not be invariant

Proc. of the 11th Int. Symp. on Artificial Life andpattern recognition theory and method [10]. Even thesebecause the facial expression is changed in different

Face Recognition System by Genetic Algorithm,
Robotics, CD version, 63-66

images.

[8] Ishibashi’s Studying Room of C++ (in Japanese).

result within an upper predetermined threshold. The[9] M. Gen, R. Cheng (1997), Genetic algorithms andThus the recognition only gives the best matching

result in the paper shows that the recognition issatisfied.

http://homepage3.nifty.com/ishidate/vcpp.htm

engineering
Publication

design,A

wiley-Interscience

By using GA-based recognition method, the settings[10] H. Takimoto, Tasue Mitsukura, M. Fukumi, N.

selected), the determination of the genetic operations,system based on the feature extraction method,of the search field (in this paper, (x, y, rate, ē ) isand the selection and the optimization of the fitness

function all have a strong effect on the level ofrecognition of the resultant image.

Based on the results of experiments described here,

further work will emphasize (i) optimizing the fields ofchromosomes, and (ii) improving the fitness function
by adding some terms to it [7]. This work is importantand necessary in order to improve the GA-based facerecognition system.
References[1] M. Sugisaka, X. Fan (2002), Development of aface recognition system for the liferobot, Proc. of©ISAROB 2007

642

Akamatsu (2002), A design of face detection
Proc.

of

12th

Symp.

on

Fuzzy,

Artificial

Intelligence, Neural Networks and Computational
Intelligence, 409-410

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Characteristics of Mckibben Muscle Based on The Pneumatic Experiment System
Huailin Zhao*,** (zhao_huailin@yahoo.com)

Masanori Sugisaka**Fengzhi Dai****College of Physics Science and Technology, Zhengzhou University, China**

Department of Electrical and Electronic Engineering, Oita University, Japan***

Department of Control Engineering, Matsue National College of Technology, Japan

Abstract: In the pneumatic experiment system in which

not

only

Mckibbenmuscles

but

also

the

Mckibben muscles are applied, the electropneumaticelectro-pneumatic regulator. But the former literatures

regulator provides gas pressure to Mckibben muscles.

usually didn’t mention the electro-pneumatic regulator.

The structure of the electropneumatic regulator is

Here the writer thinks that the integrated pneumatic

analyzed, and that the electropneumatic regulator cansystem should include both Mckibben muscles and thestabilize its output gas pressure is pointed out. The

electro-pneumatic regulator which will effect the

stiffness expression of Mckibben Muscles is formulatedcharacteristics of Mckibben muscles. This paper is just tofrom both its force model and the characteristic of theinvestigate the characteristics of Mckibben muscles

electropneumatic regulator. The process of gas pressure

based on the integrated pneumatic experiment system.

transmission with the whole pneumatic experiment
system is investigated. The model of the gas pressuretransmission

based

the

Pneumatic experiment system
Fig.1 is an ordinary pneumatic experiment system.

experiment system is established. The experiment is

The computer-controlled electro-pneumatic regulator candone to prove its validity. The result shows, that the inner

output determined gas pressure. Mckibben muscle input

pressure of Mckibben muscle doesn’t vary when its

by the pressed gas contracts and output pull force andlength is changed by outer force, and there are bath athen works on the load. The gas source is usually from apure time delay and a raise course of gas pressure whencompressor. Obviously the experiment should mainly

Mckibben muscles are input by pressed air. The pureinclude both the electro-pneumatic regulator andtime delay comes from the turning on of the valve of the

Mckibben muscles.

regulator,

integrated

2.

pneumatic

electropneumatic

on

and

the

raise

coursecorresponds with the filling process of the inner gas
pressure of Mckibben muscle.
Keywords: Mckibben muscle; pneumatic experiment
system;electropneumatic

regulator;

stiffness;gas

pressure transmission

1.

Introduction
Mckibben pneumatic artificial muscle (it will be called

as Mckibben muscle later) is a kind of motion engine
which is stimulated and can contract. It constructs with

Fig.1 Pneumatic experiment system

two layers-the inner layer rubber tube and the outer layer
fiber wave. When the inner layer closed tube is input by

Firstly let’s discuss the electro-pneumatic regulator. It

pressed gas, the outer layer wave expands and shortens

is a computer-controlled system which can output[1-3]

its two ends and then outputs pulling force. Therefore,

determined gas pressure. Its input is the electric current

the electro-pneumatic regulator is necessary, which isor electric voltage which’s quantity can be controlled bycapable of providing the required gas pressure. In thisthe computer, and the output gas pressure is proportionalcase, the whole pneumatic experiment system includes

to the current or the voltage within definite field. It has©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

multiple output channels for the practical need. Everyon the concept, th stiffness of Mckibben muscle is theoutput channel can output a gas pressure proportional todifferential of its output force to its length. To Mckibben

the input. Fig.2 is just the principle block diagram related

muscle, there has been clear conclusions about the force

one channel. Thereinto, the control circuit can control all

characteristic especially its static force characteristic.

the channels. From Fig.2, every channel consists of the

Different literatures gave the same ideal force output

control circuit, two electromagnetism proportion valves,

expressions. Some of them gave revisory ones forand one pressure sensor. The pressure sensor measuresidiographic products of Mckibben muscles[7-8]. In thisthe output gas pressure and feedback it to the control

paper the ideal force output characteristic will be used tocircuit. Accordingly, a closed feedback loop is made up.

discuss the stiffness.

It is not difficult to understand that the closed loop can
automatically regulate and restrain the out gas pressure

The force characteristic expression iswhen it changes because of the later links. This is why
the electro-pneumatic regulator can output stable gas

F=Pb2(3L2/b2-1)/(4πn2)(1)

pressure. The compressor provides the pressed gas input
to the electro-pneumatic regulator. The magnitude of the

Thereinto, F is the force output, P is the input gas

gas pressure should be larger than the maximal output

pressure, and L is the current length of Mckibben muscle.

gas pressure.

Both b and n are structure parameters and they keep
constant. Formula (1) expresses that the force output of
Mckibben muscle is proportional to gas pressure, and isnonlinear to the length.
Based on both the stiffness and its force characteristicof Mckibben muscle, the stiffness k is
k=dF/dL=d[Pb2(3L2/b2-1)/(4πn2)]/dL=Pb2(3L2/b2-1)/(4πn2)dP/dL + 3PL/(2πn2)
There are two items in the above formula. Generally,
dP/dL in the first item is very difficult to calculate,because the inner pressure has to change with its volume

Fig.2 Principle figure of the electropneumatic regulator

varying to enclosed gas. To Mckibben muscle, if therelated valve is shut up after some pressed gas is input,

3.

The stiffness of Mckibben muscle

its inner gas becomes enclosed, and its length change
themust make its inner volume change, consequently its

characteristics of Mckibben muscle itself, especially its

inner pressure P will change. Based on the above

There have been
[4-6]

many

papers discussed. But few paper mentioned its

analysis to the electro-pneumatic regulator, the inner

stiffness. Mckibben muscle changes its shape whenpressure actually as the controlled object, together withacting, a little like the spring. To the spring, its stiffness

the related channel of the electro-pneumatic regulator,

is constant within definite field. To Mckibben muscle,

constitute a enclosed loop control system. In practice, thehow about its stiffness? Now let’s discuss this problem.

inner pressed gas can exchange its mass with theforce characteristic

The basic concept of the stiffness is the needed force

regulator. It is not an enclosed gas. The sensor in themaking a shape change in unit length. It is just thechannel of the regulator will send feedback signal about

differential of force to length in actual calculation. Basedthe change immediately to the control circuit when the©ISAROB 2007

644

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

inner pressure varies because of some outer factors. Thentransmission of the gas pressure will greatly effect thetherelatedforming of the contraction force of Mckibben muscle. To

electromagnetism proportion valve act. Then definite gas

a factual pneumatic experiment system, the opening ofwill be input to or output from Mckibben muscle, so as

the gas valve of the electropneumatic regulator has

to stabilize the inner pressure. Some experiments havedefinite time delay, and different valves have different

clearly proved that. The inner pressure changes instantly

delays. To the familiar valve products, the time delay is

and recover to the former immediately when the length

usually less than 50ms. For simple calculation, it can beof Mckibben muscle is enforced to change after

considered as a pure delay less than 50ms. The actual

determined gas pressure is input. Therefore it can get

delay in the used electropneumatic regulator is about

control

circuit

will

make

the

30ms. In addition, the diameter of Mckibben muscle isusually much larger than the one of the gas transmission

dP/dL=0tube. So the gas pressure inputting to Mckibben muscle
can’t be stepped. It should have a very complicated riseSo, the stiffness of Mckibben muscle iscourse. For simple dealing with, the course is considered2

k=3PL/(2πn )(2)

as one-order inertia link. Its rise time will be much larger
than the delay of the electropneumatic regulator.

Based on the formula (2), there is conclusion that theProbably it can be a few hundred milliseconds[7,11] andstiffness of Mckibben muscle is not constant. It is

the ideographic quantity is related with the inner volume

proportional to gas pressure, and is proportional to theof Mckibben muscle. Based on the above analysis, thelength too. Larger the pressure, larger the stiffness. The

opening of the gas valve considered as the pure delay,

stiffness at the beginning of contraction is larger than theand the gas pressure transmission from the output port ofone after contraction under the condition of the constant

the gas valve to the inerior of Mckibben muscle as

inner pressure. This is quite different from the spring

one-order inertia course, the input pressure P can bewith constant stiffness. Just like he spring, there is a

expressed as

length field within which the expression (2) is correct.
Because there is a length field in the force expression (1),P(t) = P0 (1-e-(t-Td)/Tc) u(t-Td)(3)and the length field is just the normal operation field of
Mckibben muscle.

Thereinto, P0 is the static volume, Td is the time delay of

Formula (2) express that the stiffness of Mckibben
muscle has finite volumes within its normal operation

gas valve, and Tc is the time constant of the one-orderinertia course.

field. In another word, the force needed to change its

A experiment is conducted to validate the above

length is not very large. Just because there is so called

conclusion. Because of that it is different to directly“compliance” which means that the shape is easy to bemeasure the inner pressure of Mckibben muscle, achanged by the outer force, the robot actuated bymeasurement of its pull force is done to validate the

Mckibben muscles is related safe to human when contact

formula (3) indirectly. Fig.3 shows the comparison[9-10]

with itbetween the pull force model and the experiment data in.

which the used Mckibben muscle has a nominal length4.

The model of gas pressure transmissionin Mckibben muscle

300mm and nominal diameter 20mm, under the

Mckibben muscle is stimulated. In another word, it

300mm. Besides, the time delay of the electropneumatic

contracts after gas pressure is input and then output force.

regulator is 30ms and the time constant of the one-order

So the gas pressure is greatly related with the output

inertia course is 260ms. From the formula (1), The pullforce. Formula (1) has proved that the output force isforce F and the gas pressure P will change in the same

proportional to the input gas pressure. Therefore, theway if L keeps constant. From fig.3, the raise course of©ISAROB 2007645

condition of that P0 equals to 0.5MPa and L keeps

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the gas pressure is consistent with the experiment on

actuators for robotics and automationp. European

whole. Then it infers that the gas pressure transmission

Journalmodel expressed by formula (3) is basically close to theEngineering, 2002, 47(1): 10

factual system. This model will greatly consult thedesign of the control algorithm of Mckibben muscle.
From

fig.3,

theopening

time

delay

of

of

MechanicalandEnvironmental[2] Richard Q. Design, analysis, and control of a low
power joint for walking robots by phasic activation

theelectropneumatic is much less than the gas pressure
filling time to mckibben muscle. Scanning he whole gaspressure transmission, according to the pull force model,
the gas pressure gets to 90% of the stable volume atabout 600ms.

of Mckibben muscles, IEEE Transactions on Robots
and Automation, 1998,15(4): 599[3] John I. Artificial pneumatic muscle. Poptronics,
2000(11): 49[4] Chou C P. Study of human motion control with aphysiology based robotic arm and spinal levelneural controller. Washington University, 1996
[5] Huailin Zhao, M.Sugisaka, Datai Yu, Modeling ofpneumatic artificial muscle actuator, Proceedings ofthe 10th international symposium on artificial lifeand robotics, Feb.2005, Japan[6] Glenn K. Klute, Blake Hannaford. Accounting for
Elastic Energy Storage in McKibben Artificial
Muscle Actuators, ASME Journal of Dynamic
Systems, Measurement, and Control, 2000,122(2):386~388.
[7] Bertrand T, Pierre L. Modeling and control ofMckibben artificial muscle robot actuators, IEEE
Control System Magazine, 2000,20(2): 15[8] Manuello Bertetto, M.Ruggiu. Characterization and

Fig.3 Comparison between

modeling of air muscles, Mechanics Research

the model and the experiment

Communications, 2004(31): 185~194.
[9] Kazuhiko K, R.Alan P, D.Mitchell W, et al. ISAC:

5.

Conclusion

Foundations

In factual applications of Mckibben muscles, to a typical
pneumatic

experiment

system,usuallyin

Human-HumanoidInteraction,

IEEE Intelligent Systems, 2000(4): 38

the[10] Gabrielle J M, Tuijthof H, Just L H. Design,

electropneumatic regulator is used to control the gas

actuation and control of an anthropomorphic robotpressure. Based on the whole system, the stiffness ofarm, Mechanism and Machine Theory, 2000, 35(7):Mckibben muscle is proportional to both its gas pressure945

and its length. Enforcing to change its length, its inner[11] A.I.T Kunihiko, ITO.Kazuhiko KUNIEDA, Takasi

gas pressure doesn’t change. When gas pressure input,

KNODO, Yukihide HARADA, The bilinear model

there is a time delay of several hundred milliseconds

of muscle-skeletomotor system and the impendance

because of both the delay of the gas valve of thecharacteristics of rubber artificial muscle[A], theelectropneumatic regulator and the filling course of thefifth Japan robot conference, 1987

inner gas pressure of Mckibben muscle. And the fillingcourse is much larger than the delay of the gas valve.

Reference[1] Frank D, Dirk L. Pneumatic Artificial Muscles:©ISAROB 2007

646

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Eﬀect of Using Partial Solutions in Creating New Candidate Solutions with EDA
and ACO Schemes
Shigeyoshi Tsutsui
Department of Management Information
Hannan University
Matsubara, Osaka 580-8502 Japan
Abstract
In previous studies, we have proposed an algorithm
named the edge histogram based sampling algorithm(EHBSA) with EDA scheme for permutation domains.
In EHBSA, new solutions are obtained by combining
partial solutions which exist in the current population,and partial solutions newly generated according to anedge histogram model of the current population. In
this paper, we show using partial solutions can maintain the diversity of the population, resulting in a successful search. We also show that using partial solutions in ACO can greately enhance its performance.

1

Introduction

Genetic Algorithms (GAs) are widely used as
robust black-box optimization techniques applicable
across a broad range of real-world problems. GAs
should work well for problems that can be decomposed
into sub-problems of bounded diﬃculty [1]. However,ﬁxed, problem-independent variation operators are often incapable of eﬀective exploitation of the selected
population of high-quality solutions [1, 2]. One of themost promising research directions is to look at thegeneration of new candidate solutions as a learning
problem, and use a probabilistic model of selected solutions to generate the new ones [3, 4]. The algorithms
based on learning and sampling a probabilistic modelof promising solutions to generate new candidate solutions are called estimation of distribution algorithms(EDAs) [5] or probabilistic model-building genetic algorithms (PMBGAs) [3].
Most work on EDAs focuses on optimization problems where candidate solutions are represented byﬁxed-length vectors of discrete or continuous variables.
However, for many combinatorial problems permutations provide a much more natural representation forcandidate solutions. Despite the great success of EDAs
in the domain of ﬁxed-length discrete and continuous©ISAROB 2007

647

vectors, only few studies can be found on EDAs for
permutation problems [6, 7].
In previous studies [8, 9], we have introduced apromising approach to learning and sampling probabilistic models for permutation problems using edge
histogram models. This algorithm is called the edge
histogram based sampling algorithm (EHBSA). In
EHBSA, new solutions are created by combining partial solutions which exist in the current population,and partial solutions newly generated based on theedge histogram model of the current population. The
EHBSA worked well on several benchmark instancesof the traveling salesman problem (TSP). Nonetheless, the methods proposed are not limited to TSP, likemost other TSP solvers and specialized variation operators. As a result, this approach provides a promising
direction for solutions of other problems that can beformulated within the domain of ﬁxed-length permutations; ﬂow shop scheduling is an example of such aproblem as described in [9].
In this paper, we focus our attention on the eﬀects
of using partial solutions in EHBSA. The basic sampling algorithms in EHBSAs are very similar to thesampling algorithms that are used in ant colony optimization (ACO) [10, 11] and thus this method can beapplied to ACO [12]. We also discuss these results.
In the remainder of this paper, Section 2 gives ageneral scheme for using partial solutions in EDAs. In
Section 3, we discuss the eﬀect of using partial solutions in EHBSA and show how using partial solutions
can maintain the diversity of the population, resulting
in a successful search. The eﬀectiveness of using partial solution in ACO is discussed in Section 4. Finally,
Section 5 concludes this paper.

2

Using Partial Solutions in EDAs

Figure 1 shows a typical scheme of EDAs. EDAs
evolve a population of candidate solutions to theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Selection

Existing solutions

FitnessP

Estimate of distribution

M= p(x1, x2,…, xn)
Generate solutions according
to partial solutions and M

Figure 2: Scheme of EDAs with Partial Solutions

the whole population in generating new strings. The
algorithm starts by generating a random permutation
string for each individual population of candidate solutions. Promising solutions are then selected using
any popular selection scheme. An edge histogram matrix (EHM) for the selected solutions is constructed
and new solutions are generated by sampling, basedon the edge histogram matrix. New solutions replacesome of the old ones and the process is repeated until the termination criteria are met. An example of
EHM at t EHM t = (eti,j ) is shown in Fig 3. The integer value of each (eti,j ) represents number of edges
from node i to node j in the population. The fractional value represents minimum value to give a bias
to control pressure in sampling nodes.
node j
st1 = ( 0, 1, 2, 3, 4 )st2 = ( 1, 3, 4, 2, 0 )st3 = ( 3, 4, 2, 1, 0 )st4 = ( 4, 0, 3, 1, 2 )st5 = ( 2, 1, 3, 4, 0 )Selection(a) P(t)Psel

Fitness

Psel

Partial solutions

node igiven problem by building and sampling a probabilistic model of promising solutions. EDAs start with arandom population of candidate solutions (individuals). Each iteration of EDAs starts by selecting better
individuals from the current population. Next, theprobability distribution M of the selected populationof individuals (P sel ) is estimated. New individuals arethen generated according to this estimate, forming the
population of candidate solutions for the next generation. The process is repeated until the termination
conditions are satisﬁed.
The scheme of EDAs with partial solutions is shownin Figure 2. In the ﬁgure, new solutions are obtainedby combining partial solutions which exist in the current population, and partial solutions newly generated
according to the model M of P sel . What kind of eﬀect
can we be expected with this scheme in Figure 2? Ina typical EDA scheme in Figure 1, new solutions are
directly reﬂected from the distribution of P sel . If theselection operator is not designed properly, the repetition of iterations within the scheme may cause strong,but incomplete positive feedback to model M , resulting in a premature convergence of the population anda failed search. On the other hand, with the scheme
described in Figure 2, new solutions are generated not
only according to model M but also using partial solutions from the existing solutions in P . This reduces
the rapid change of the population. Thus, we can expect that using partial solutions as shown in Figure 2
has the eﬀect of maintaining diversity and preventing
premature convergence of the population.

0 3.1 2.1 2.1 3.1
3.1 0 4.1 3.1 0.1
2.1 4.1 0 1.1 3.1
2.1 3.1 1.1 0 4.1
3.1 0.1 3.1 4.1 0(b) EHM t

Figure 3: An Example of EHM

P

Estimate of distribution

M= p(x1, x2,…, xn)
Generate solutions according
to M

3.2

Using Partial Solutions in EHBSA

Figure 1: General Scheme of EDAs

3
3.1

EHBSA
Edge Histogram Model

An edge is a link or connection between two nodes.
The basic idea of the edge histogram based samplingalgorithm (EHBSA) is to use the edge distribution of©ISAROB 2007

648

In EHBSA, a template individual, from which apartial solution is obtained, is chosen from P (t) (normally, randomly). A crucial question when we create a
new solution c[] is how to determine which part of thepartial solution the c[] will borrow from the template.
To ensure robustness across a wide spectrum of problems, it is advantageous to introduce variation bothin the portion and the number of nodes of the partial
solution that is borrowed from the template. First wechoose the starting node position of the partial solution randomly. Thereafter, the number of nodes of theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

partial solution must be determined. Let us represent
the number of nodes that are constructed based on
EHM by ls . Then, lc , the number of nodes of the partial solution, which c[] borrows from the template, is
lc = L–ls . Here, let us introduce a control parameterγ which can deﬁne E(ls ) (the average of ls ) by E(ls ) =L×γ. To determine the sampling portion in a string,
we used the n cut-point approach in previous study[8]. However with the approach, E(ls ) is L/2, L/3, .
. . for n= 2, 3, and so on, and, γ corresponds to 1/n,i.e., γ can take only the values of 0.5, 0.333, and 0.25,
corresponding to n= 2, 3, 4 and so on. In the current
research, we extend this elementary method to a more
ﬂexible technique which allows for γ taking values in
the rage [0.0, 1.0]. The probability density functions
for ls in this research are:1−γ
fs (l) =Lγ() 1−2γγl1−, for 0 < γ ≤ 0.5.
nγ
fs (l) =L(1 − γ)( ) 2γ−1l 1−γ, for 0 < γ ≤ 0.5.
n(1)

Table 1: Results of EHBSA on gr48 and pr76

L
template T[]
ctop

partial solution
LuJ

new solution c[]sampling
EHMlc

L  lsFigure 4: Using partial solutions in EHBSA

3.3

Results

In this section, we show the results using the scheme
described in Section 3.2 on TSP instances of gr48 andpr76. The γ values were tested from 0.1 to 1 with step
size 0.1. Here note that γ = 1 corresponds to the case

©ISAROB 2007

J

(2)

Using partial solutions in EHBSA is summarized in
Figure 4. For a given γ value, ls is generated according
to Eqs. 1 or 2, and ctop , the ﬁrst position of partial
solution for c[] to borrow from the template, is sampled randomly. Then, the partial solution of lengthlc = L–ls , and which starts from ctop is copied to c[]from the template. Then the remaining sequence ofnodes of length ls in c[] is sampled according to EHM
probabilistically.

lswhere no partial solutions are used. The following
control parameter values were used: population size
N = L×2 (L is the number of cities), maximum number of tour constructions Emax = L×20, 000. Results
are summarized in Table 1 where #OP T indicates thenumber of runs which found the best-known solution,
M N E indicates the mean number of tour constructions to ﬁnd the best-known solution in those runs
where it did ﬁnd the solution, and Error indicatesthe average excess value from the best-known solution
over 25 independent runs.
From these results, we can see that good results of#OP T , M N E, and Error are found with γ values
in [0.3, 0.4] for both gr48 and pr96. In the case inwhich we do not use partial solutions (γ = 1), the second worst results were obtained on both instances, i.e.,#OP T = 0 for both instances, and a Error = 1.159%
for gr48 and Error = 9.747% for pr76, respectively.

649

0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1

#OPT
0
25
25
24
23
16
4
0
0
0

gr48
MNE
185458.3
115680.5113575.6135540.6
182385.0256192.0-

pr76
Error #OPT
MNE
Error
1.753%
014.895%0.000%
10 1276794.0 0.175%
0.000%
24735272.6 0.005%
0.022%
23
595011.3 0.010%
0.029%
21
622464.3 0.045%
0.099%
17
687602.9 0.092%
0.278%
13
960202.4 0.115%
0.682%
2 1400801.5 1.076%
0.912%
05.336%
1.159%
09.747%

Figure 5 shows the change of the diversity of the
population. Here, the diversity was measured by thestandard deviation (ST D) of tour length of individuals
in the population and was averaged over 25 runs. From
this ﬁgure, we can see that for γ = 0.1 the change of
the ST D is very slow. This is because only 10% of
new edges are sampled in generating a new string on
average. Thus, the population did not converge in thedeﬁned maximum number of tour constructions Emax .
With γ values of 0.3, and 0.4, each ST D gradually
becomes smaller as the number of tour constructions
increases, resulting in successful searches. However, inthe case in which we do not use partial solutions (γ =1), the population loses diversity rapidly, resulting ina failed search. Thus, we can see the eﬀectiveness ofusing partial solutions with appropriate γ values.

4

Using Partial Solutions in ACO

As a bio-inspired computational paradigm, ACO
has been applied with great success to a large number

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

10000

STD

1000

process is very slow. Thus we can see that using appropriate small values of γ can prevent over exploitation.

0.1
0.2

100

0.3
10

0.4

1

0.8

1

Error

㪈

0.1
1000

J

0.1

10000
100000 1000000
No. tour constructions

0.1
0.3

0.01

0.5
0.001

0.7
0.9

0.0001Figure 5: Change of population diversity on gr48
0.00001
10000©ISAROB 2007

650

Figure 6: Convergence process on kroA100
Here we show cAS with a local search on TSP. Oneof the best performing local searches for TSP is thewell-known Lin-Kernighan algorithm (LK) [13]. We
used a Chained LK which applies the basic LK repeatedly. For γ value, γ =0.4 was used.

MMAS

Chained LK

Error
T avg
(%)

#OPT

c AS
c AS (J =0.4)
non-c AS (J =1)
Error
Error
T avg
T avg
(%)
(%)
#OPT

TSP

#OPT

Table 2: Results of cAS with LK on TSP. Tavg is average time in second to ﬁnd optimal in successful runs.
The machine we used had two Opteron 280 (2.4GHz)
processors with Java code.
#OPTof hard problems in permutation domains [11]. The
ﬁrst ACO algorithm was called the Ant System (AS),and was applied to the TSP. Since then, many variant
ACO algorithms have been proposed as extensions of
AS [11]. On a TSP, AS works as follows: Let m be thetotal number of ants. Initially, each ant is put on arandomly chosen city k(k=1, ..., n). Let τ ij (t) be thetrail density on edge (i, j) at iteration t. Each of m
ants at iteration t makes a tour by choosing a sequenceof cities. When all ants complete their tours, the trail
density of each edge on their tours is updated. We
can note here that the τij (t) corresponds to EHM t =(eti,j ) of EHBSA and thus ACO has a tight relation
with EDAs.
In this section, we present the results of a new ACO
which uses partial solutions in generating new solutions. Please see [12] for more detail. The algorithm
is called the cAS (cunning AS). In traditional ACO
algorithms, each ant generates a solution probabilistically or pseudo-probabilistically based on the current
pheromone trail τij (t). In cAS, an agent called cunning ant (c-ant) is introduced. The c-ant diﬀers fromtraditional ants in the manner of solution construction. It constructs a solution by borrowing a part ofexisting solutions. The remainder of the solution isconstructed based on τij (t) probabilistically as usual.
An agent which has a solution borrowed by a c-ant iscalled a donor ant (d-ant). Using c-ant in this way,
we can prevent premature stagnation the of search, because only a partial solutions are newly generated, andthis can prevent over exploitation caused by strong
positive feedback to τ ij (t) as discussed in Section 2.
Figure 6 illustrate the convergence process by thechange of Error on kroA100 TSP instance for γ values of 0.1, 0.3, 0.5, 0.7, and 0.9. Early stagnationsof search can be observed with γ vales of 0.7 and 0.9.
With γ values of 0.3 and 0.5, stagnations of search occur much later in the search. With a γ value of 0.1,
no stagnation can be observed. But the convergence

1000001000000
No. tour constructions

pr2392 25 0.00 104.9 24 0.00 137.2 12 0.00 211.3 4

Error
T avg
(%)

T max

0.17 122.4 240

fl3795 25 0.00 435.1 15 0.00 615.9 17 0.00 770.7 0

0.57

-

1400

rl5934 25 0.00

0.27

-

3300

1336.1

1 0.001854.6

10 0.00

2533.60

To conﬁrm the eﬀectiveness of combining cAS with
LK, we also tested the following three algorithms:
non-cAS with LK (i.e., γ=1; no partial solutions areused), MMAS with LK, and Chained LK alone. The
results are shown in Table 2. We can see all algorithms
of cAS, non-cAS, and MMAS showed very small values of Error by combining LK and thus the advantage
of combining these algorithms with LK is very clear.
However, when we focus our attention on the results of#OPT, all algorithms except for cAS could not attain#OPT = 25. In contrast to this, cAS could attain#OPT = 25 within the allowed run time Tmax showing the smallest Tavg (average time in seconds to ﬁnd
optimal in successful runs) among algorithms tested.
Thus, we can see that using partial solutions is useful when the approach is combined with local search.
Figure 7 shows the variations of Tavg and #OP T forvarious γ values.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

O

O

O

25

O

Tavg

600
Tavg

O

O

O

O

15

500
10

O

O

O

25

O

Tavg
2000

#OPT

O

2500

20

O

20

#OPT

15
10

#OPT

O

Tavg

O
O

#OPT

700

1500
400

5
0

300
0.2

0.3

0.4

0.5

0.6

0.7

0.8

1

5

O

1000
0.2

0.3

0.4

0.5

0.6

J

J

(1) fl3795(2) rl5934

0.7

0.8

0

[4] P. Larrañaga and J. A. Lozano, Estimation of distribution algorithms: A new tool for evolutionary
computation, Kluwer, Boston, MA, 2002.

1

Figure 7: Results of cAS for variable γ values with LKon (1) ﬂ3795 and (2) rl5934

5

Summary

In previous studies, we have proposed an algorithm
referred to as the edge histogram based sampling algorithm (EHBSA) with EDA scheme for permutation
domains. In EHBSA, new solutions are obtained by
combining partial solutions which exist in the current
population, and partial solutions newly generated according to an edge histogram model of the current
population. In this paper, we showed that using partial solutions can maintain diversity of a population
resulting in a successful search. We have also shownthat using partial solutions in ACO can greatly enhance its performance. Thus, we can expect that ascheme using partial solutions in generating new solutions can be used in a wide range of EDAs. Appling
this scheme to EDAs in other domains, such as realcoding and binary coding remains for for future work.

Acknowledgements
This research is partially supported by the Ministryof Education, Culture, Sports, Science and Technologyof Japan under Grant-in-Aid for Scientiﬁc Researchnumber 16500143.

References[1] D. E. Goldberg, The design of innovation:
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[2] M. Pelikan, Bayesian optimization algorithm:
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Urbana, IL, 2002. (Also IlliGAL Report No.
2002023).

©ISAROB 2007

[3] M. Pelikan, D. E. Goldberg, and F. Lobo, “A survey of optimization by building and using probabilistic models,” Computational Optimization andApplications, Vol. 20, No. 1, pp. 5-20, 2002.

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on Parallel Problem Solving from Nature (PPSN
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[9] S. Tsutsui and M. Miki, “Solving ﬂow shop
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516, 1973.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Stochastic Analysis of Schema Distribution in OneMax Problem
Hiroshi FURUTANIFaculty of Engineering
University of Miyazaki
Miyazaki City, 889-2192

Susumu KATAYAMA
Faculty of Engineering
University of Miyazaki
Miyazaki City, 889-2192

Abstract

We have studied e ects of stochastic uctuationin the process of GA evolution of OneMax problem.
We applied the Wright-Fisher model and the di usion model for the analysis of GA with mutation andcrossover. By using the di usion model, we derived
the stationary distribution of the rst order schema
frequency. In the comparison of numerical experiments and the theoretical calculations, we found thatcrossover is a very important factor determining thework of stochastic uctuation.

1 Introduction
It is a dicult problem to determine the population size N in the application of GAs. Too small N
may cause a poor performance in nding optimal solution(s), while too large N costs unnecessary computational power. The study of population sizing requires
a stochastic treatment, which is in general a diculttask for the mathematical analysis.
In genetics, there are theories of Markov processes
and di usion equations. Population genetics uses the
Wright-Fisher model of Markov processes 1], and diffusion models 2]. In the eld of GAs, researchers
apply Markov theory and its di usion approximation. Nix and Vose proposed a stochastic modelof GA evolution within the framework of Markov
processes 3]. There are also analyses which use diffusion theories 4, 5].
Theoretical analyses in Genetics usually focus on
one locus, and study the changes in frequencies of alleles within the locus. On the other hand, GA researchers treat multi-locus systems. This causes problems of dimensionality in Markov and di usion approaches. To make the analysis tractable, Asoh andMuhlenbein restricted their study within the rst order schemata 4]. In this study, we also used a mathematical framework of the rst order schema theory,©ISAROB 2007

652

Makoto SAKAMOTO
Faculty of Engineering
University of Miyazaki
Miyazaki City, 889-2192

and investigated their evolution by using the WrightFisher model and di usion equations. We analyzed
the evolution of GA with mutation and crossover in
OneMax problem.

2 Mathematical Model
A population is consisted of N individuals, and wex N throughout the evolution process. We represent
individuals by binary strings of length `, and there aren = 2` genotypes. We identify integers and binary
strings byi < i(`)     i(1) >
where i(k) 2 f0 1g is the kth component of the binary
string.
Denoting a genotype < i(`)     i(1) > by i, we
represent the number of individuals with genotype i
at generation t by Ni (t). We also use the relative
frequency xi (t) for describing the evolution( ) = Ni (t)=N:xi t

The process of tness proportionate selection is
given byxi (t + 1) = fi xi (t)=f(t)
(1)
where f(t) is the average tness of the populationn;1

( ) = X fi xi (t):

f t

i=0

(2)

3 Deterministic Model
We derive here the evolution equation for the rst
order schema within the framework of the deterministic theory.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.1 Linkage Equilibrium

4 Markov Model

Linkage means the correlation between the di erent loci in a population, and if there is some correlation we call this linkage disequilibrium 6]. In GA
applications, we usually generate an initial populationby producing 0 and 1 randomly and independently ateach bit position. The initial population of this type isobviously in linkage equilibrium. However, as the GA
calculation proceeds, the population frequently goes
into linkage disequilibrium state. The causes of thischange are the functional form of the tness and genetic drift in the selection process 6]. It should be
noted that crossover and mutation have the e ect ofrecovering linkage equilibrium, and if crossover works
suciently, the population is in linkage equilibrium
throughout the evolution process.
The distribution of a population in linkage equilibrium is given by using relative frequencies

The stochastic model like Markov model explicitly
treats the number of schemata. We consider the frequencies of two alleles at some locus. Two alleles aredenoted by A and a, and the number of individuals
having allele A and a are N0 and N1 , respectively.
Since N = N0 + N1 is constant, we consider N1 in thisanalysis.
We rst consider the case in which both alleles havethe same tness. The probability Q(j ji) that N1 = i
at generation t becomes N1 = j at generation t + 1 isgiven by the binomial distribution

()=

xi t

Ỳ

k=1

()

hi(k) t 

(3)

( j)=

Q j i

N



j

i

j

N

i

1; N

N ;j

:

(7)

The probability qi (t) of N1 = i at generation t obeys
the evolution equation( + 1) =

qj t

N
X
i=0

( j) ()(8)

Q j i qi t :where hi(k) (t) is a frequency of the rst order schemaat position k, and i =< i(`) : : :  i(1) >. We also use
the notation of h(0k) and h(1k) for the rst order schema
frequencies of bit 0 and bit 1, respectively.

This equation is represented by using (N + 1)dimensional vector q(t)3.2 OneMax Function

where T is transversion, and the matrix Q = Q(j ji)]fiq(t) = (q0 (t) : : :  qN (t))T 

In this study, we use the OneMax tness function

q(t + 1) = Q q(t) :X̀

This model is called as the Wright-Fisher model 1]．
We can express the evolution process analytically if
we obtain the eigenvalues and eigenvectors of Q. We
know the eigenvalues of the matrix 1]

fi

=

k=1

()

i k :

(4)

Since the string of all ones < 1 1 : : :  1 > is the optimum solution for this landscape, bit 1 is the favorable
allele at all positions.
In the deterministic schema theory, the evolution ofthe rst order schema in linkage equilibrium is givenby 8]. The relative frequency of the rst order schemaat position k is determined by(k)(k )h1 (t + 1) = ah1 (t) + b
(5)where constants a and b are
11
a = (1 ; ) (1 ; 2) b = (1 ; 2) + :``
The solution is given in terms of a,b0 = 1 ;2 + (1 ; 2)=` and the initial value h1 (0)h1 (t) = at fh1 (0) ; b0 g + b0 :©ISAROB 2007

(6)

653

1 1 1 ; N1 

:::

NY
;1
i=1

(9)

1 ; Ni

Unfortunately, we do not have the analytical expression of all eigenvectors. We only have two eigenvectors
corresponding to the eigenvalue 1
qA = (1 0 : : :  0)T 

qa = (0 0 : : :  1)T 

These eigenvectors represent two absorbing states inMarkov process. All processes converge to one of theseabsorbing states. The eigenvector qA stands for theextinction state where there is no favorable allele in
the population, and qa for the xation state of thefavorable allele.
Next, we consider the case where two rst orderschemata have di erent tness values. We use allele

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A and a to denote binary values 0 and 1, respectively,
and their tness values are
fA

= 1

fa

Noting that i, therefore y(t), is xed, we calculate its
expectation value= 1 + s (s  0):

Efy(t)g =The transition matrix Q(j ji) is given by( j) =Q ji

N( )j f1 ; u(s)gN ;j  (10)u sj(1 + s)i :u(s) =
N + siThis matrix also has a maximum eigenvalue 1 withmultiplicity 2, and their corresponding eigenvectors
areqA = (1 0 : : :  0)T 

qa = (0 0 : : :  1)T :( ) ! i=N

h1 t

in the evolution equation of the rst order schema (5),
we have the transition matrix( j) =

Q j i

() =

u y

N
j
ay



( ) f1 ; u(y)gN ;j  (11)u y jwhere y = i=N . This transition matrix has eigenvalues

0  : : :  N
0

= 1 1 = a : : :  k =

kY
;1
i=0

j=0

Using
N j
X
N

 qj (t + 1) =

N j
X

N

N

j

j=0



( )j f1 ; u(y)gN ;j u ywe haveEfy(t)g = u(y) ; y = (a ; 1)y + b:

(13)

In the same way, we haveVfy(t)g = u(y)f1 ; u(y)g=N:(14)

From equations (13) and (14), we dene two functions of y( ) = (a ; 1)y + b( ) = u(y)f1 ; u(y)g=N:

V y

The di usion approximation of Markov process in
OneMax problem is given in terms of M (y) and V (y)(y t) = 1 @ 2 fV (y) (y t)g ; @ fM (y) (y t)g:
@t
2 @y2
@y
(15)
This equation is called Kolmogorov forward equation,and (y t) stands for the probability density functionof the relative frequency y and time t.
We derive the solution of Kolmogorov forward equation (15). At t ! 1, we try to obtain the stationary
solution (y). Since @ (y t)=@t = 0, (y) satises@(1 ; i=N ) : : :

a

(12)

These eigenvalues are independent of b, and the second
largest eigenvalue 1 = a does not depend on N .

5 Diusion Model
Though it is believed that Markov model can reproduce many evolution processes in biology, its mathematical analysis is very dicult. Therefore, the approximation of Markov model by the di usion equation is used in genetics 1]. In the following, we derive
the di usion equation for OneMax problem.
We deney(t) = y(t + 1) ; y(t)

©ISAROB 2007

 qj (t + 1) ; y(t)

NX
Vfy(t)g = N12 j 2  qj (t + 1) ; Efy(t)g2:

M y+ b = a Ni + b

j=0 N

and variance

j=0

It is also true that qA and qa are absorbing states.
Finally, we derive the transition matrix on the OneMax tness. Replacing

N j
X

654

d2 fV (y) (y)g ; 2 d fM (y) (y)g = 0:dy2dyBy integrating the di erential equation, we have(y) = C (ay + b)2c1 ;1 (1 ; ay ; b)2c2 ;1(16)where C is a normalization constant, and c1 and c2are
c1 = N b=a2  c2 = N (1 ; a ; b)=a2 :The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
WITH CROSSOVERWITHOUT CROSSOVER

88

N=60,180

THEORY

N=180

THEORY

N=20
7

AVERAGE FITNESSAVERAGE FITNESS

7

6

5

4

N=60

N=20

6

5

0

10

20

30

40

4

50

0

10

20

GENERATION

30

40

50

GENERATION

Figure 1: N dependence of f(t), with crossover. Figure 2: N dependence of f(t), without crossover.

6 Numerical Experiment

WITHOUT CROSSOVER©ISAROB 2007

655

800

FIXATIONPROBABILITY

In this section, we compare results of the theoretical analysis with numerical experiments. We use thetness proportionate selection and uniform crossover.
The length of string is ` = 8. We performed 1000 runs
for each parameter set, and averaged over them.
Figure 1 shows the average tness f(t) with theuse of three population sizes, N = 20 60 180. We
used the crossover rate = 1 and mutation rate
 = 0:001. The solid line with the label THEORY is
obtained by the deterministic evolution equation （ 5 ）and f(t) = `h1 (t). Figure 2 shows the same results of
GA calculations without crossover.
In the calculations with crossover, we observe small
N dependence except for N = 20, and these results almost coincide with the deterministic theory. This fact
can be explained by the fact that the population is inlinkage equilibrium due to the action of crossover. In
linkage equilibrium, Wright-Fisher model can describe
the distribution of alleles in good approximation, in
which the second largest eigenvalue does not have N
dependence. Since the population size is small in thecase of N = 20, there is large e ect of genetic driftthat makes the population in linkage disequilibrium.
On the contrary, we observe large di erences in the
results of di erent N in Fig.2. Especially, if N is small,the performance of the calculation is very poor. The
strong N dependence suggests large e ects of genetic
drift in calculations without crossover.
Figure 3 explains the result of N = 20 withcrossover. In 1000 repeated calculations, the numbersof xations and extinctions of the favorable rst orderschema are shown as functions of generation. From

1000

600

400

EXTINCTION

200

0

0

10

20

30

40

50

GENERATION

Figure 3: Fixation and extinction probabilities ofthe favorable rst order schema. Without crossover.
N = 20，` = 8,  = 0:001.
this gure, we see that about 15% of favorable schema
disappear from the population.
Linkage equilibrium due to crossover does not meansmall N dependence. Figures 4 and 5 show distributions of the favorable rst order schema at the stationary states (t = 100) with  = 0:02 and = 1. The
abscissa represents the relative frequency of bit one.
The solid lines are the results of numerical calculations
and dotted lines are those of di usion approximation（ 16 ）. The large di erence between two distributions
comes from the di erence in N .

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
0.06

0.25

N=180

N=20

0.05
0.2

DISTRIBUTION

DISTRIBUTION

0.04

0.03

0.15

0.1

0.02

0.05
0.01

0

0

0.2

0.4

0.6

0.8

0

1

i/N

Figure 4: Distribution of the favorable rst
order schema,  = 0:02.

0.2

0.4

0.6

0.8

1

i/N

Figure 5: Distribution of the favorable rst
order schema,  = 0:02.

7 SummaryThis study treats the schema evolution in OneMax
by the use of the Wright-Fisher model and di usion
equations. If we can dene favorable and unfavorable
genes in one locus model, the xation and extinction
states are absorbing states in usual Markov processes,
and this brings the N -dependence in GA calculations,for example, on the multiplicative landscape. While inthe case of OneMax problem, the extinction state acts
as a reecting wall, and the probability of extinction
state is zero. The reason is that the probability of
the genotype < 0 0 : : :  0 > is zero. The assumptionof linkage equilibrium means the probability of h0 is
also zero. Therefore the state N1 = 0 cannot work
as an absorbing wall. This partly explains the weak
N -dependence in OneMax problem.

References
1] Ewens, W.J.: Mathematical Population Genetics. I. Theoretical Introduction, Second Edition.
Springer-Verlag, New York (2004)
2] Crow, J. F., Kimura M.: An Introduction to Population Genetics Theory. Harper and Row, NewYork (1970)
3] Nix, A. E., Vose, M. D.: Modelling Genetic Algorithm with Markov Chains. Annals of Mathematical and Articial Intelligence. 5 (1992) 79{884] Asoh, H., Muhlenbein, H.: On the Mean Convergence Time of Evolutionary Algorithms without©ISAROB 2007

0656

Selection and Mutation. Parallel Problem Solvingfrom Nature, Lecture Notes in Computer Science,
866, Springer-Verlag, New York, (1994) 88-97
5] Suzuki, H., Iwasa, Y.: Crossover Accelerates
Evolution in GAs with a Babel-like Fitness
Landscape: Mathematical Analyses. Evolutionary Computation, 7 (1999) 275-310 Errata: 8(2000) 121-122
6] Maynard Smith, J.: Evolutionary Genetics. 2ndedition, (Oxford University Press, Oxford, 1998).
7] Furutani, H.: Schema Analysis of Genetic Algorithms on Multiplicative Landscape. in Recent
Advances in Simulated Evolution and Learning,(World Scientic, Singapore, 2004) Chapter 6
8] Furutani, H.: Schema Analysis of OneMax
Problem {Evolution Equation for First Order
Schemata{. in Foundations of Genetic Algorithms 7, (Morgan Kaufmann, San Francisco,2003) 9{269] Whittaker, E.T. and Watson, G.N.: A Course ofModern Analysis, 4th Edition, (Cambridge University Press, Cambridge, 1927) 281{296The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Identiﬁcation of Exon-intron Boundaries by Integration ofBase-oriented Genetic Programming and Statistical Heuristics
Kunihito YAMAMORI
Univ. Miyazaki
Miyazaki 889-2192

Yuji FUJITA
Mitsubishi Space Software
Ibaraki 305-0032

Masaru AIKAWA
Univ. Miyazaki
Miyazaki 889-2192

Abstract

Junk

Ikuo YOSHIHARA
Univ. Miyazaki
Miyazaki 889-2192

Junk

Junk

non-transript region

Genetic Programming (GP) is one of the promising
methods to detect exon-intron boundaries from DNA sequences. In conventional method, two- or four-bit binary
codes represent four bases, and each bit of codes are used
as input to the identiﬁcation model. These bit-oriented GP
is simple, but it is diﬃcult to know which bases are key
to identify the boundary. Here we develop a novel baseoriented GP which can directly use bases as inputs. Moreover, we integrate the model generated by base-oriented
GP with heuristics that is deﬁned as bias of appearance
frequencies on particular loci around “GT” and “AG” extracted from about 8,000,000 DNA sequences. Simulation
results show that our method can improve the accuracy
about 10% on the AG boundary identiﬁcation.

the bases as inputs to the model, and it also achieves accurate exon extraction from DNA sequences. So we modify
Genetic Programming (GP) to be able to handle the basecharacters directly. And we combine the GP and statistical
heuristics extracted from 8 million DNA sequences.

1 Introduction

2

DNA sequences and heuristics

A gene in DNA consists two parts; one is exon and theother is intron. Exon encodes information how to synthesize proteins. Intron exists between exons, and functionof intron is not clear still yet. Now it is a very important
subject to automatically extract exon regions and intron regions from DNA to diagnose some kinds of diseases or tocreate new medicine. However, it is very diﬃcult to analyze gene data because it is too many and still increasing
day after day.
To automatically extract exons and introns from DNA
sequences by computers, Some methods have been investigated until now. Kamimai[1] had proposed multi-modal
neural network and succeeded to improve accuracy of identiﬁcation. Ohta[2] had developed a combination of GA(Genetic Algorithm) and GMDH (Group Method of Data
Handling), and showed good performance of identiﬁcation. In these method all bases in a region including focusing bases are encoded to two- or four-bits binary codes,
and each bit is used as inputs to the identiﬁcation model.
These binary encoding lead indistinctness which bases are
the keys to identify boundaries.
Our aim is to develop a method that can directly use

2.1

Decision of input region©ISAROB 2007

657

transcript region
Gene

Boundaries
exon

intronexon

Figure 1: DNA and gene.

Figure 1 illustrates that most 5’-end and 3’-end of introns has the bases “GT” and “AG”[3]. Since the sequences“GT” and “AG” also appear in exons and introns, it is impossible to identify boundaries by this rule. On the other
hand some bases often appear in speciﬁc locus around theboundary. We utilize this bias of appearance to identify
boundaries between exons and introns.
Human DNA sequences was obtained from NCBI1
database, then ±200 bases around “GT” or “AG” are extracted. The number of extracted sequences is as follows;• Sequences with “GT”
– Boundary data: 26,046– Non-boundary data: 7,610,718• Sequences with “AG”
– Boundary data: 22,633– Non-boundary data: 8,481,368

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.3

In this method a couple of bases are used as heuristics.
These heuristics are called as 2-tuple heuristics. The 2tuple heuristics are found from the ±200 bases around thefocusing “GT” and “AG”, then 30 2-tuple heuristics will be
combined with the model by the following steps.

Locus
Address

Figure 2: Addressing for loci.
Here P(b, X) denotes the appearance probability of thebase ‘X’ at the locus b, and the address of locus b is deﬁned
as shown in Figure 2. The P(b, X) only from sequences including boundary is described as PB (b, X), and that withoutboundary is also described as PN (b, X). Then the diﬀerence Pd (b, X) between PB (b, X) and PN (b, X) is calculated
as Equation (1).
Pd (b, X) = PB (b, X) − PN (b, X),

Embedding method(1)

here b = {−200, −199, · · · , +200} and X ∈ {A, G, C, T }.
From the bias of Pd (b, X), we decide to use 20 bases to
identify “GT” boundary (10 bases for 5’-side and 3’-side),and 37 bases to identify “AG” boundary (30 bases for 5’side, 7 bases for 3’-side)[4].
Fujita[4] also said that the appearance frequencies of
speciﬁc bases at some loci are diﬀerent between boundaries and non-boundaries. So we deﬁne heuristics as the
combination of the base and the locus with high bias in appearance frequency. These combination of speciﬁc loci andbases is integrated with GP by the following two method.

Step.1 The appearance frequency P2B (b1 , b2 , X1 , X2 ) andP2N (b1 , b2 , X1 , X2 ) are calculated from boundary dataand non-boundary data, respectively. Here b1 and b2
is address of locus, and X1 and X2 denote bases.
Step.2 CalculationofP2d (b1 , b2 , X1 , X2 )2
2
|PB (b1 , b2 , X1 , X2 ) − PN (b1 , b2 , X1 , X2 )|.

=Step.3 30 combinations of (b1 , X1 ) and (b2 , X2 ) with thelargest P2d (b1 , b2 , X1 , X2 ) are selected as the 2-tuple
heuristics.
Step.4 Two sub-trees are constructed; a terminal node b1
and a comparing node Eq(b1 , X1 ), and a terminal nodeb2 and a comparing node Eq(b2 , X2 ).
Step.5 The comparing node with “AND” operator integrates two sub-trees if P2d (b1 , b2 , X1 , X2 ) > 0. Otherwise they are integrated by a node with “NAND”
operator.

2.2 Majority method

These 2-tuple heuristics are inserted into individuals at
the following processes.

In this way Pd (b, X) is used as heuristics and combined
through the majority operator into the model generated by
GP. Following steps shows how to combine the model andthe heuristics.

Insertion into initial individuals: 30 individuals are randomly selected and a part of individual is exchanged
to a 2-tuple heuristics at the generation of initial population.

Step.1 Calculation of Pd (b, X).
Step.2 Four combinations of b and X with the highest|Pd (b, X)| are selected as heuristics.
Step.3 Four sub-trees consisting of a terminal node anda comparing node explained in Section 3 are constructed. The comparing node has Eq(X, I) if the biasof appearance frequency at the locus b is positive.
Otherwise it has Eq(X, I).
Step.4 Four sub-trees and an individual generated by
GP is combined through the tree-ﬁfth majority nodeM(3/5).
All individuals combine with the same heuristics, and theﬁtness is evaluated by the output of the ﬁnal M(3/5). The
same sub-trees as the heuristics never appear in individuals.
1 National Center for Biotechnology Information©ISAROB 2007

658

Insertion at mutation process: A part of individual is exchanged to 2-tuple heuristics at the mutation process.

3

Genetic operations

3.1

Operators

In this research we directly use the base charactersthemselves as inputs to GP. Figure 3 shows an example ofindividual to identify boundaries generated by GP. Fig.3
shows that terminal nodes keep the address of locus, and a
non-terminal node is either a logic operator or a comparing
operator. A node with a logic operator calls as a logic node,
and a node with a comparing operator also calls a comparing node. Equation (2) says that the comparing operator

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Logic Operator

Table 2: Available combination at crossover operation.
L. node C. node I. node
L. nodeOKOKNG
C. nodeOKOKNG
I. nodeNGNG
OKComparingOperator
Locus
Address

where Yk is 1 when the identiﬁcation result by model
says true (boundary) and Yk0 is also 1 when the k-th∑
sequence is actually boundary. So Tk=1 Zk means thesum of the correctly identiﬁed sequences.

Figure 3: An example of individual.
Table 1: Logic operators in the model.
#s of arguments logic operator
1
NOT
2
AND OR XOR NAND NOR XNOR
M(2/3) Two-third majority3
NM(2/3) Negative of M(2/3)
returns 1 if the input I from the terminal node is as same as
the target base character X.
{
1
X = I,(2)
Eq(X, I) =0
X , I, X, I ∈ {A, G, C, T }Table 1 shows available logic operators in logic nodes.
Since an output of a terminal node is a character and inputs
to a logic operator have to be boolean, a comparing node iscoupled with a terminal node. These nodes are arranges asa tree for a boundary identiﬁcation model.

3.2 Genetic operations
To make models, GP performs the following genetic operations.
Initialization: It randomly generates terminal nodes, logic
nodes and comparing nodes, then arranges them as individuals.
Crossover: It randomly selects two individuals then exchange a part of individual (sub-tree) each other. So
two children are generated by this operation. This
operation does not allow some kinds of sub-tree exchanging because a terminal node must combine witha comparing node. The combinations of exchangeablenodes are completed in Table 2. In Table 2, L. node,
C. node and I. node denote a logic node, a comparing
node and an input node, respectively.

=

T
∑

Zk ,

Selection: roulette strategy and elite preserving are employed.

4

Experiments and discussions

4.1

Parameters

We make eleven data sets from the sequences mentioned
in Section 2, then a data set is used for model generation,and the others are used for model evaluation. No duplication exists among the data sets. The parameters of GP for
simulations are as follows;• Numbers of individuals: 100• Maximum nodes in a model: 200• Crossover ratio: 100%
• Mutation ratio: 1%• Maximum generations: 4,000
The number of simulations are 10 on each data set formodel evaluation.

4.2

Measures for evaluation

Sensitivity S n and Speciﬁcity S p deﬁned by Equation (4)and Equation (5) evaluate accuracy.

Fitness evaluation: Equation (3) deﬁnes the ﬁtness.
f itness

Mutation: This operation randomly exchange the operator in a node to the other one with the same numberof arguments if the selected node is a logic node. If aterminal node is selected, the other locus is employed.
When a comparing node is selected, the other comparing operation take place of original one.

(3)

k=1

{
Zk

©ISAROB 2007=

1, Yk0 = Yk ,0, Yk0 , Yk ,

Sp =

659

Nc
× 100 (%),
B

(4)

Nc
× 100 (%),
Nc + Nm(5)

Sn =The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5

5VCPFCTF/CLQTKV[

Automatic extraction of exon regions from DNA sequences is very diﬃcult because the sequences have toomuch variety, and the number of sequences is also too huge.
In particular, the identiﬁcation of “AG” boundaries was
not accurate by previous works because features to identify “AG” boundaries dispersedly exist in sequences.
In this research we develop a novel method to identify
boundaries. At ﬁrst, we decide the appropriate region forboundary identiﬁcation from the bias of appearance frequencies on bases at each locus. The analysis reveals that±10 bases are enough for “GT” boundaries, and 37 bases
for “AG” boundaries. Then we think out a novel baseoriented identiﬁcation model for GP that can directly handle the character of bases. Finally we compare the two
method to integrate the model by GP and statistical heuristics. The embedding method achieved about 10% accuracy
improvement to detect “AG” boundaries.
To achieve more accurate identiﬁcation, the model considering solid structure of protein will be remaining as future works. Furthermore faster processing is also needed.

'ODGFFKPI

Figure 4: S n by each method on “AG” boundaries.

5VCPFCTF/CLQTKV[

Acknowledgments'ODGFFKPI

A part of this research is supported by Grant-in-Aid for
Scientiﬁc Research #17700239.

Figure 5: S p by each method on “AG” boundaries.

where B denotes the number of boundary data, Nc and Nm
denote the number of correctly identiﬁed boundaries andmiss identiﬁed non-boundaries, respectively.

4.3 Results and discussions
Figure 4 and Figure 5 show the average S n and S p for
veriﬁcation sequences. Here we only show the result on“AG” boundaries because relatively poor results was reported in previous works[2]. In Fig.4 and Fig.5, “Standard”
means the result by GP without heuristics.
In Figure 4, S n by majority method falls about 10% thanthat of standard GP, it means the model combined withthe majority operator often ignore the boundaries. It isbecause the answer from the model generated by GP andanswers from the heuristics sometimes compete and cancel each other. Figure 5 illustrates that the GP with 2-tuple
heuristics improves S p about 10% than that by standard
GP. It means that the embedding method can detect moreboundaries than those by the other methods, and less nonboundaries are detected as boundaries.
Experimental results show that embedding method with
2-tuple heuristics improves accuracy of boundary identiﬁcation with keeping boundary detection.

©ISAROB 2007

Conclusions

660

References[1] Y. Kamimai, I. Yoshihara, K. Yamamori and M. Yasunaga, “Methods to Extract Exon Regions from DNA
Sequence – Development of Multi-modal Neural Network –”, Fuzzy, Artiﬁcial Intelligence, Neural Networks and Computational Intelligence (FAN’02), pp.
385–390 (2002).
[2] T. Ohta, I. Yoshihara, K. Yamamori and M. Yasunaga,“GP-based Method for Identifying Exon Region in
DNA Sequences”, Proc. 4th Asia-paciﬁc Conferenceon Simulated Evolution and Learning (SEAL’02), pp.
cr1333(CD–ROM) (2002).
[3] M. B.Shapiro and P. Senapathy, “RNA splice junctionsof diﬀerent classes of eularyotes: sequence statistics
and functional implications in gene expressions”, Neucleic Acids Research, Vol. 15, pp. 7155–7175 (1987).
[4] Y. Fujita, K. Yamamori, I. Yoshihara and M. Aikawa,“Generation of Character-base Model by Genetic
Programming to Identify Exon-Intron Boundaries”,
Tech.Report IEICE (CAS2005-75), Vol. 105, No. 503,
pp. 1–6 (2006) (In Japanese).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Variable-Length-Segmental-Transmission-Line and its Design Guidelines
KOIZUMI N.
Univ. of Miyazaki,
Miyazaki, 889-2192 Japan

HAYASHI K.
Univ. of Tsukuba,
Ibaraki, 305-8583 JapanYAMAMORI K.
Univ. of Miyazaki,
Miyazaki, 889-2192 Japan
Abstract
Waveform distortion is a serious problem in higherfrequency signals on printed circuit boards (PCB). To
overcome this problem, we have already proposed segmental transmission line (STL) method. That method
divides transmission lines into several segments withdi erent line width. Each line width is adjusted tomake the re ection noises cancel out each other forminimizing the signal distortion.
In this research, we analyze the waveform reshaping mechanism of STL. Next, we apply STL to a
DIMM (Dual In-line Memory Module) clock-line forhigh-speed computers. As a result, we obtained helpful STL design guidelines and we showed e ective example of STL.

1

introduction

While VLSIs (e.g. Pentium CPU) perform in GHz
clock-speed nowadays, signals propagating on motherboards in PCs are still in hundreds of MHz frequencyrange. This great di erence between two operation
speeds is one of serious bottlenecks for high speed processing. In addition, higher signal integrity on the
PCB is required to overcome this problem.
On a PCB, many ICs and other electronics
components are connected with transmission lines.
These connections cause characteristic-impedancemismatches in the transmission lines, and they bringre ection noises that distort signal waveforms considerably. Re ection noises increase as signal frequency
increases, hence the higher speed electronics systems
will be a ected more terribly by the noises.
In order to overcome this problem, we propose thesegmental transmission line (STL) formerly. STL is
the method in which a transmission line is divided into
multiple segments of di erent line-widths. We make

©ISAROB 2007

661

YASUNAGA M.
Univ. of Tsukuba,
Ibaraki, 305-8583 JapanYOSHIHARA I.
Univ. of Miyazaki,
Miyazaki, 889-2192 Japan
impedance mismatches intentionaly so as to generate
re ection waves. Each line width is adjusted to makethe re ection noises cancel out one another to minimize the signal distortion.
We have shown the following three approaches:・ Single objective, xed-length segment [1]:Widths in xed length segments are adjusted toreshape a waveform in a single point.
・ Single objective, variable-length segment:
Both widths and lengths in segments are adjusted
to reshape a waveform in a single point.
・ Multi objective, variable-length segment [2]:Both widths and lengths in segments are adjusted
to reshape a waveform in a two points in a line.
In this research, we analyze the waveform reshaping
mechanism of STL and the purpose is to obtain helpful
design guidelines. Next, we show e ective examples of
DIMM clock-line using STL.

2
2.1

Segmental Transmission Line
Fixed-length STL

Fig.1-(a) shows a structure of the xed-length STL.
A transmission line is divided into 10 same-length segments of individual line widths W1 to W10 , in thiscase. An Ron and a pulse source in the gure represent a source VLSI, and RD and RT are a dumping
and termination resistances, respectively. Electronics
components such as memory modules or VLSI chips
connected to the transmission line are represented as
capacitances Cl s that cause waveform distortion.
・ In the transmission line, because of thecharacteristic-impedance mismatch between twosegments a re ection noise occurs at the segmentinterface.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Clock line
Clock inRD

Clock
DriverClock in

RT

Start

Evaluation & Selection

Initialization

net-list generation

CrossoverTransmission Line analysis

Mutation

Output data

Evaluation & Selection

RD

Fitness calculation
W1 W2 W3 W4 W5

W6 W7 W8 W9

Cl

Cl

Ron

W10

Termination
RT

Selection

End

(a)
L

L

W1

Figure 3: Parameter decision ow using GA
L

L

W2

W4

W3

L

variable length segments (see Fig.2) instead of xed
ones with a reduced parameter set of line widths. Although the number of parameters increases due to thevariable-length, we can decrease the line-width range
drastically instead and reduce the parameter set as awhole. Given m segments and n line width, the total
search space enlarges to nm . Additionally given thelength of segment by real number value, it is impossible to search the optimized parameter-set by analyticaltechnique. Thus, we use GA (Genetic Algorithm) todecide parameters in the variable-length STL.

W5(b)
Figure 1: The structure of STL

L1

L2

L3

L4

L5

W1

W2

W3

W4

W5

3
3.1

Figure 2: An example of variable-length STL・ Re ection noises are superimposed with the distorted signal at signal output point where an electronics component is connected.
・ In the STL, multiple characteristic-impedances inthe segments are adjusted together so that thetheir re ection noises superimposed at the output
reshape the distorted signal.
・ In the xed-length STL, each segment is designed
to give the desiged characteristic-impedance bychanging its line width as shown in Fig.1-(b).
Yasunaga et al. have designed transmission lines fortwo memory modules in PCs by using the xed-STL.
They used 10 to 20 segments in the designed under 99
width-candidates to reshape waveforms.

2.2

Variable-length STL

Next, we propose the variable-length STL to reduce
the parameter set. In the variable-length STL, we use©ISAROB 2007

662

Optimization using GA
STL design flow

Fig.3 shows the design ow for the STL. This generation alternation model is combined with MGG (Minimal Generation Gap) [3] and POSS (Pareto Optimal
Selection Strategy). POSS is just same idea as elitist
strategy at single-objective optimization.
We use SPICE for tness calculation in the GA.
SPICE [4] is the analog circuit simulator which is mostwidely used.

3.2

Genetic Coding

We use two chromosomes separately to one
transmission-line (see Fig.4):
・ One chromosome is composed of pairs of widthsof adjoining segments or resisters.
(We call the chromosome is width-chromosome)・ The other chromosome is composed of site of thesegment boundaries.
(We call the chromosome is length-chromosome)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Constant

x1

Constant

x3

x2

1.5cm

Constant

x4

x5

x6

P1

RD
W1

W3

W2

W4

W5

W6

W7 W8 W9
Clock

RT

Ron

RD

P2
3.0cm

3.0cm

3.0cm
5.0cm
7.0cm

DriverRT
encodeZi = f (Wi )Capacitance（10pF）
RD , Z1

Z1 , Z2

Z2 , Z3

・・・ Z7 , Z8

Z8 , Z9

Z9 , RT

individual

Figure 6: Model of clock line with a memory modulex1 x2 x3 x4 x5 x6
length-chromosome

width-chromosome

Figure 4: Composition of an individual with chromosomes

I(t) :
R(t) :ε:4R(t )Voltage

∆S

τ1

τ2

time

Figure 5: Fitness de ned by the di erence between
observed waveform and ideal waveform

3.3

Genetic Operation

Crossover:
One point crossover is used for the widthchromosome. On the other hand, BLX is used forthe length-chromosome. BLX is typically crossover ofreal-coded GA.
Fitness evaluation:
We de ne tness function as follows. S is the difference area between the observed waveform on thedesigned transmission line and the ideal waveform, as
shown in Fig.5. As S decreases getting close to theideal waveform, tness value increases.
S=

Z τ2

|I(t)

R(t)|dt(1)

τ1

f itness =©ISAROB 2007

1
S+ε

Experiments

4.1

I (t )(2)

663

Ideal waveform
Observed waveform
Small constant

Design target

We designed an actual clock line with a memory
module (DIMM: Dual In-line Memory Module) on a
typical PCB in a personal computer by using our proposed method. The phase of the re ection depends
on the length of the segment because the re ection is
generated by the boundaries of the segments. Thus,
we examine a correlate of line length and waveform
distortion.
Fig.6 shows experimental target. Because thelength between modules is given, we experimentchanging with length of P2 RT like a g.6.

4.2

Conditions

Experimental conditions are as follows:
・ Propagating signals
Signal amplitude 3.3V
Switching time
20ps
Signal cycle time
2ns
・ GA Parameters
Population size
100
BLX-α
0.366
Crossover/generation
10
End condition
over 1000 generations
・ Variable length STL parameters
Impedances
20, 30, 100, 120Ω
RD & RT
20 120Ω- Number of segments
R D P1 3P1 P23P2 R T 3

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

411

observed waveform
ideal waveform

1.5cm
3.0cm
5.0cm
7.0cm

10

3.5

3

9
2.5

8

2

1.5

7

1

6
0.5

5

0

-0.5

4
2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

3

7

4

5

6

7

8

9

10

Figure 8: Waveform about normal transmission line
4

Figure 7: Pareto solutions

observed waveform
ideal waveform
3.5

3

4.3

Results

2.5

2

Fig.7 shows Pareto group of each length at nal
generation. The horizontal axis shows area-di erence
at P1 and the vertical axis shows area-di erence at P2 .
As a result, the longer distance between P2 RT is,
the smaller area-di erence is.
Fig.8 shows observed waveform at P1 on normaltransmission line. The normal transmission line isall-same-width (76Ω) transmission line. Fig.9 shows
waveform in one of paretos of 7.0cm which was designed by our method. In those gures, the dashedlines show the ideal waveform without distortion. In
Fig.8, the waveform is distorted like sin curve. On theother hand, the waveform in STL are reshaped like
ideal waveform.

5

Conclusion

In this paper, we showed variable length STL withchanging with transmission line length. We applied
the variable length STL to a DIMM clock line andsucceeded in reshaping waveform.
We will speed up the GA calculation and apply thevariable length STL to other high-speed clock line inmore practical-scale circuits on PCBs in the future
works.

Acknowledgements
This work was supported in part by grant of theJapan Science and Technology Agency.

©ISAROB 2007

664

1.5

1

0.5

0

-0.5
3

4

5

6

7

8

9

10

Figure 9: Waveform about STL

References[1] M. Yasunaga, I. Yoshihara and Jung H.Kimm,“The design of segmental-transmission-line forhigh-speed digital signals using genetic algorithms” proc. of CEC2003, pp. 1748-1755.
[2] N. Koizumi, I. Yoshihara, K. Yamamori, M. Yasunaga, “Enhancement of the Variable-LengthTransmission-Line design method for multi-point
optimization” proc. of CEC2006, CD-ROM.
[3] H. Sato, I. Ono, S. Kobayoshi, “A New Generation
Alternation Model of Genetic Algorithms and its
Assessment” Trans. of JSAI, Vol.12, No.5, pp.734744, 1997. (In Japanese)[4] http://bwrc.eecs.berkely.edu/Classes/IcBook/SPICEThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Reconfigurable-VLSI-based Double-lens Tracking-camera
Moritoshi Yasunaga,
Noriyuki Aibe,
Yoshiki Yamaguchi

Yorihisa Yamamoto

Takaaki Awano

Ikuo Yoshihara,

Graduate School of Systems

Yamamoto System Design Inc.,

Bethel Inc.,

Faculty of Engineering,and Information Engineering,3481 Oozone, Tsukuba

Aragane 3-11, Ishioka,

University of Miyazaki,

University of Tsukuba,

Ibaraki, 300-3253, Japan

Ibaraki, 315-0021, Japan

1-1 Gakuen Kibanadai Nishi,

Ten-no-dai 1-1-1, Tsukuba,

yori@edonagasaki.ne.jp

t-awano@bethel.co.jp

Miyazaki, Miyazaki,

Ibaraki, 305-8573 Japan.

889-2192, Japanyasunaga@cs.tsukuba.ac.jp

yoshiha@cs.miyazaki-u.ac.jp

susu@susutawari.org
yoshiki@cs.tsukuba.ac.jp

Abstract
Watching and tracking an object as seeing awide whole view is one of advantages in theeye-system. We propose and develop a tracking
camera system that mimics the eyes using
double-lens modules. In the system, a wide wholeview is captured through the wide-lens module
while the target in it is tracked and magnified
through the telescope-lens module. Electronic
circuits for the tracking control are implemented
onto the reconfigurable VLSI, or FPGA in order toembed the parallelism in the tracking algorithm
into the hardware. An FPGA-based prototype
developed successfully performs high speed
tracking along with the video-rate.

1. Introduction
We human beings can watch and track an object
without losing wide whole view. Thanks to thissimple concurrent function, but with complex
mechanism, of the eye we can recognize the target
correctly under huge amount of information rushes
into the brain via the eyes. Our goal in this paper is to
propose and develop a novel tracking-camera
composed of double-lens modules for wide andtelescope views concurrently, which is the samefunction as the human eye has.
Initially, we designed a camera with a single
lens-module system that resembled the eye inappearance. It has been, however, turned out that afine-resolution sensor-device with high-speed random
access capability is indispensable for the single
lens-module system and its cost is much more
expensive than for the consumer product.

©ISAROB 2007

665

On the basis of the initial examination mentioned
above, in this paper, we propose a camera system
consists of two lens-modules: one module is tocapture wide views while the other one is to track andmagnify the target with its pan-tilt mechanism. Thiscamera system has been designed and developed
based on the Bio-inspired camera [1] and the PNN(Probabilistic Neural Network) pattern recognition
system [2] that was developed by us using FPGAs(Field Programmable Gate Array). On these days, theFPGA, in which we can reconfigure its logic circuits
anytime in contrast to the VLSI processor or DSP, isgetting well known and is widely used for
rapid-prototyping-design in hardware developments.
Novel architectures for the new tracking camera havebeen efficiently and easily implemented onto theprevious systems and we could achieve a high-speed
tracking process to keep up with the video-frame rateof 33ms through the reconfigurabilty of the FPGA.

2. Overview of the Tracking Camera System
Figures 1 and 2 show a photograph and a designfor the double-lens tracking-camera prototype,respectively (an electrical-circuit board mounting an
FPGA to control the camera is not included in it).
Whole views are captured through the wide-view lensmodule (the small-lens module in the figure) in the
beginning, and the captured images are processed in
an FPGA. In the FPGA, the target position is detected
immediately and it is fed back to the otherlens-module for the telescope view with the pan-tilt
mechanism (the large-lens module in the figure). The
double-lens tracking camera does not closely

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 1 Photograph of the Double-lens
tracking-camera.
resemble the eye in appearance, but it gives us thesame function as the eye has inexpensively.
Furthermore, we also newly developed amagnet-based pan-tilt drive in place of the traditional
motor-based drive, which enables the lens-module tomove faster than the traditional one by tree times ormore. The motor-based drive consists not only of themagnetic parts but also of the other mechanical parts
that impede the camera from high-speed rotation andsmall size, while the troublesome mechanical parts
are removed in the magnet-based pan-tilt drive.
With the newly developed magnet-based drive(see Fig. 2), the lens-module is directly driven to
move in the pan-tilt directions. The lens-module has
four magnets in the right-angled direction each other.
An electromagnet is placed opposite to the four
magnets and driving forces to move the lens module
are generated and controlled by the amplitude and the
directions of the current applied to the electromagnet.
We could achieve high-speed pan-tilt movement of
about 3-time faster and reduce its size to less than 1/2of the conventional pan-tilt camera by the
direct-drive mechanism.
Figure 3 shows a photograph of theelectrical-circuit board to control the camera at the
tracking-speed-rate of 33ms/frame. A novel
tracking-algorithm, in which the image-template forthe tracking process is updated according to thecaptured target-image in every frame rate of 33 ms
interval (details are described later), is implemented
into the FPGA. The camera shown in Fig. 1
controlled by the board demonstrates high-speed
performance enough for the fish-tracking applicationin which the target fish is detected among a group offish and is tracked with its magnified picture in an
aquarium (details are described later).
The tracking algorithm described in the next
section can be also implemented onto thegeneral-purpose
processors
or
DSPs.
The
software-based processing, however, does not fit to©ISAROB 2007

666

Fig. 2

Design for the Double-lens
tracking-camera.

.
the sequence of the tracking processes because theintrinsic parallelism in the processes cannot beembedded into the programs. On the contrary, we canimplement the parallelism as it is onto the FPGA, sothat the tracking-procedure can be executed at thevideo-frame rate or more. Furthermore, by using
FPGA, some parameters in the algorithm can beembedded into the logic circuits directly and they canbe changed to meet each application. This hardware
tuning improves the system performance well to meetdesired conditions depending on each application.

3.Tracking Architecture for the Double-lensCameraFigure 4 shows the block diagram accordingwith the signal flow form the wide-lens camera
module to the telescope lens camera module.
A whole view including the target is captured
with the wide-lens module. During the A/D

Fig. 3

Photograph of the FPGA-board.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 4Block-diagram of the tracking-process.

conversion process, the image is preprocessed before
the target detection. The entire image is sampled
digitally and its image size is reduced to 45 x 73
pixels. Each pixel is also digitalized or, quantized to
8-bit.
The
size
reduction-rateandthequantization-rate are not fixed to 45 x 73 and 8-bit(we use binary quantization in the application in thispaper). Those values can be changed andimplemented, that is, reconfigured easily onto the
FPGA directly.
In the ordinary algorithms for the target
detection, the pre-sampled target-image-templates arescanned through the whole image and the object
matched to the target is searched in it. In the ordinary
approach, however, it is getting difficult to track theobject correctly as its moving-speed increases. In oursystem, the target-image-template is updated during
the tracking, contrastively to the fixed set of the
pre-sampled target-image-templates. The target
image that was tracked in one previous frame tn-1 isused as the template for the present frame of tn.
According to this updating procedure, the template is
updated in real-time, or on-line manner. The target
image in the present frame tn is almost the same as in
the previous one in the frame tn-1, so that the
template-image can correctly matches the target evenunder its high-speed motion because of the very shortinterval between frames. Under the real-time
updating procedure, the target thus can be precisely
rocked in the tracking.
The real-time updating mentioned above works
well principally if the updating interval is much
shorter than the target motion. The video-frame-rate©ISAROB 2007667

of 33ms, however, is not so short to the target thatmoves fast close to the camera, and it sometimes
happens that the center of the target gradually moves
from that of the template and the tracking fails
finally.
In order to overcome this problem, we propose
the following center-feedback algorithm andimplement it in the system:
1) The weighed center of the template is
calculated.
2) The difference, or distance between theweighted center and template center iscalculated.
3) The template the center of which is adjusted isre-captured according to the difference
calculated in 2).
The position of the target is searched in every frame
using the template under the center-feedback
algorithm. The most matched area to the template is
detected and its position is output through thecomparator and the max-detector. The detected
position is converted to analog signal finally and is
input to the pan-tilt drive of the telescope camera.
The real-time updating in the template has beenreported in the other system already [3]. In [3],however, real-time updating was applied to the
convolution images. We have overcome the problemof the real-time updating applied to the original
images.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 5

A screen-shot of the fish-tracking.

4. Application to the Fish Tracking

5.Conclusions

We apply the system to the fish-tracking inwhich a target fish is detected among multiple fish in
an aquarium under the wide-lens view and the target
is tracked being magnified through the telescope-lensmodule. We also developed the network interface toconnect the camera system with the Internet tobroadcast the live images widely.
Figure 5 shows a screen-shot of the broadcasted
image via the Internet. The upper-right picture shows
the magnified image of the target fish tracked withthe pan-tilt telescope lens module while the upper-left
picture shows the wide-lens view of the entire
aquarium. The target fish in the entire aquarium
image is magnified by 3-time, so that its face and fins
can be clearly observed. The lower-left andlower-right images are taken with another camera forthe demonstration purpose. The lower-left picture
shows the entire demonstration system in which thecamera is set in front of the aquarium (the shield of
the camera is removed). The lower-right picture
shows the telescope-lens module tracking the target.
In the application, the target fish could betracked successfully even under the multiple fish
circumstances. And observers could watch the target
fish clearly seeing the entire aquarium. We are now
planning to apply this double-lens tracking-camera to
the real surveillance applications.

A tracking-camera mimicking the eye wasproposed and developed. The camera consists ofdouble-lens modules: one for the wide-view and theother for the telescope view of the tracked target. The
center-feedback algorithm was proposed and it was
implemented onto a FPGA to track objects at
high-speed motion. A magnet-based pan-tilt drive
was also developed for high-speed tracking. A
prototype of the camera was developed and it showeddesired performances in a fish-tracking application.

©ISAROB 2007

668

Acknowledgements
The research and development in this paper is
supported in part by the MEXT 2006 Cooperation of
Innovative Technology and Advance Research in
Evolutional Area and the 2006 grant of Japan Society
for Promotion Science (No. 16360197).

References[1] N. Aibe, et al., “Performance Evaluation System for
Probabilistic
NeuralNetwork
Hardware,”
Proc.
International Symposium on Artificial Life and Robotics(AROB 8th '03), pp.471-474, January 2003.
[2] K. Hayashi, et al.,“A Bio-inspired Tracking-camera
System”, International Symposium on Artificial Life andRobotics (AROB 11th '06), pp.755-758, Japan, 2006.
[3] S. C. Wong, M. Jasiunas, and D. Kearney, “TOWARD A
RECONFIGURABLE TRACKING SYSTEM,” Proc. 2005
International Conference on Field Programmable Logic andApplications (FPL), pp. 456-462, August 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fault Detection by Evolution Strategies Based Particle Filters
K. Uosaki
Dept. of Management and Information Sci.
Fukui University of Technology
Fukui 910-8505

2 Fault detection

Abstract
Fault detection in dynamic systems has attracted considerable attention in designing systems with safety and reliability. Though a large number of methods have been proposed for solving the fault detection problem, it is hardly
apply to nonlinear stochastic state space models. A novel
filter called the Evolution Strategies based particle filter(ESP) proposed by recognizing the similarities and the difference of the processes between the particle filters andEvolution Strategies is applied here to fault detection of
nonlinear stochastic state space models. Results of numerical simulation studies exemplify the applicability of thisapproach.

1 Introduction
The problem of fault detection in dynamic systems has
attracted considerable attention in designing systems withsafety and reliability. In the past two decades, a large number of methods have been proposed for solving the fault
detection problem [1], [2], [3]. Among these, the modelbased approaches using the quantitative analytical model ofthe system to be monitored are by nature the most powerful ones. For all model-based approaches, the decision of a
fault is based on the innovations based on the state estimate
obtained from the observed input-output data and a mathematical model of the system. Though the fault detection
method can be easily constructed in linear/Gaussian state
space models where the well-known Kalman filters [4],[5] can employ to evaluate the state estimate, the idea
is generally difficult to apply to nonlinear systems with
non-Gaussian noises. In this paper, a new fault detection
method is proposed for nonlinear/non-Gaussian state spacemodels using the idea of the backward sequential probability ratio test (BSPRT) [6] and the evolution strategies based
particle filter (ESP).
 Supported in part by FUT Research Promotion Fund.

©ISAROB 2007

T. Hatanaka
Dept. of Information and Physical Sci.
Osaka University
Osaka 565-0871

669

Consider here the following set of nonlinear state spacemodels indexed by .

        (1)     (2)where    are the state variable, input and observation, respectively,      and      are known possibly nonlinear functions, and  andare independently








identically distributed (i.i.d.) system noise and observationnoise sequences, respectively. The system works normallyand its behavior is governed by the given normal mode
, andmodel described as in (1) and (2) indexed bythen the model may change to the given fault mode model
indexed by
 at unknown time
. The problem
here is to develop a detection procedure to find a modelchange as quickly as possible.

3 Evolution Strategies Based Particle Filters
Recent massive increase of the computational power
leads to much attentions to particle filters, a simulationbased filter based on Bayesian sequential analysis, and a
lot of particle filters have been developed. Recognizingthat the importance sampling and resampling processes in
Sampling importance resampling (SIR) particle filter [7]are corresponding to mutation and selection processes in
Evolution Strategies (ES) [8], which is one of Evolutionary Computation approaches [9], we can develop a novel
particle filter called Evolution strategies based particle filter (ESP) as in [10].
Resampling process in SIR filter is introduced to avoid thedegeneracy phenomenon in Sequential importance sampling (SIS) particle filter [11], [12], where all but one of
the normalized importance weights are very close to zeroafter a few iterations and a large computational effort iswasted to updating trajectories whose contribution to thefinal estimate is almost zero. It selects offspring with prob , and thisability proportional to the importance weightcorresponds to selection process in ES with the importanceThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
weight as fitness function. On the other hand, the imporwe can introduce a backward LLR (BLLR), where LLR iscomputed in reverse (backward ) from the current observaaccordtance sampling process in SIR filter samples
tion to the past observations: 
ing to the importance density      , and this  

corresponds to mutation process in ES from the viewpoint  (3)

 from the extrapolated parents  
of generating offspring

 





    with perturbation by  . The main difference
is resampling in SIR is carried out probabilistically and theweights are reset as  , while the selection in ES is deterministic and the fitness function is never reset. Hence, by
replacing the resampling process in SIR by the deterministic selection process in ES, we can derive a new particle
filter called Evolution strategies based particle filter comma(ESP(,)) and Evolution strategies based particle filter plus
(ESP(+)) [10]. As shown in [10], the ESP filters behave
more stable than SIR both in squared estimation errors andprocessing time by their deterministic selection process, wewill develop fault detection methods using the ESP.

4 Fault Detection by Evolution Strategies
Based Particle Filters
Then the fault detection problem to be considered here
can be reduced to perform a hypothesis testing for the hypotheses: (Normal mode) : System models are indexed by  (Fault mode) : System models are indexed by 
Wald’s sequential probability ratio test (SPRT) [13] is acommon testing procedure, where the logarithm of likelihood ratio function (LLR)          
is evaluated and compared with two threshold values   until it exceeds these thresholds. It is known thatthe fault detection system based on the above mentioned
Wald’s SPRT formulation minimizes, on the average, thetime to reach a decision for specified error probabilities if
the system is either in the normal mode or the fault mode
from the beginning of the test. However, the characteristicsof the fault process differs from it; the system is initiallyoperated in normal mode and then transition occurs to thefault mode at time instant during observations. To fit thissituation, the idea of the backward SPRT (BSPRT) [6] isintroduced.
Rewriting the hypotheses representing normal and fault
modes in fault detection process as     (Normal mode) :System models at time (Fault mode) :System models at time







   ©ISAROB 2007

   are indexed by   are indexed by







      








We can express the BLLR approximately with the conventional LLR as     
(4)
with 
 by assuming           ( and   are indepen
























dent), and the decision rule for acceptance of the hypothesis that the system is in the fault mode can be restated as    for some   






or,

(5)







 

 

 

(6)



Introducing the statistics called the maximum BLLR,       



(7)

with


    (8) 
where      is the one step output prediction
density of  under the hypothesis    , thenthe decision rule based on the BLLR can be expressed as“If    , where  is a suitable constant, one ter















minates observation with acceptance of the hypothesis thatthe system is in the fault mode. Otherwise, one continue
observations as the system is likely not in the fault mode.”

To compute
approximation    in the statistics (7), we can use the grid Æ   



 



(9)
is corresponding to thewhere the second superscript
models. Then the pdf         canbe approximated as    

















      
  
 








On the other hand, we can approximate the pdf
      in (8) by

 



          















670






The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 200715
density      .
10,
Sample behaviors of state estimates by ESP(,) with5
based on the model   , and BLLR
0andare given in Fig. 2 with corresponding results by-5
EKF as well for comparison.
-10
takes positive value and is
The test statistics BLLR
-15
growing up rapidly after the change point both in ESP
0
50
100
150
200
and EKF, we can detect the model change when the BLLRt
exceeds the suitable threshold . Moreover, it should be(a) State
noted that, as shown in Fig. 2, the state estimate by EKF
15shows poor behavior and hence the behavior of test statistics sometimes provides poor detection result. Eventually,
12.5
the rate of false alarm 1 and miss alarm2 are higher by the10detection procedure using EKF than by the procedure us7.5
ing ESP as shown in Table 1 that summarizes 10 simulation5
. Theseresults of fault detection with the threshold
2.5



x





 











y





0

0

50

100
t

150

200

Table 1: Fault detection result(b) Observation

Figure 1: Sample behavior of state and observation processes     are samples from the pdf
    . Thus, we can conduct the fault
detection by evaluating the BLLR  with the pdf estimates obtained by two ESP filters under the system model     and compare  with suitable threshold
.


where












5 Numerical Examples              (10)                with  for normal mode and     for fault
are i.i.d. zero-mean Gaussian random
mode, and  and














variates with variance 10 and 1, respectively. We assume
that the fault occurs at
. A sample behaviorof the true state and corresponding observation processes isshown in Fig.1. Here the Gaussian distribution with mean    and variance 10 is chosen as the importance©ISAROB 2007

False alarm rate
1/20
5/20

Miss alarm rate
0/20
1/20

results illustrate the applicability of the proposed approachfor fault detection of nonlinear stochastic state space models. By introducing the other choice of evolution processes
such as crossover and suitable choice of evolution parameters it is expected the improvement of the performance, andtheir better choice will be pursued.

6 Conclusions

To exemplify the applicability of the proposed ESP filters, we carried out a numerical simulation. We consider
the following nonlinear state space model with known parameters.



Fault detection by ESP
Fault detection by EKF

671

Fault detection in dynamic systems has attracted considerable attention in designing systems with safety and reliability. Though a large number of methods have been proposed for solving the fault detection problem, it is hardly
apply to nonlinear stochastic state space models. A novel
filter called the Evolution Strategies based particle filter(ESP) proposed by recognizing the similarities and the difference of the processes between the particle filters andEvolution Strategies is applied here to fault detection of
nonlinear stochastic state space models. Numerical simulation studies have been conducted to exemplify the applicability of this approach.
1 The test statistics exceeds the threshold, i.e., the decision that systemmodel has changed is made even when the system model does not change.
2 The test statistics never exceeds the threshold, i.e., decision that thesystem model does not change is made even when model changes.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
40

20

20

10

3000

0

lambda

x

x

2000

0

-20

-10

-40

-20

1000

0

-1000

-2000

-3000

0

50

100
t

150

State estimate with model

200

0

50

100
t

150

State estimate with model½(solid line : estimate; dotted line : true state)

0

200

BLLR

¾

50

100
t

150

 and conventional LLR (solid line :  ; dotted line :  )(solid line : estimate; dotted line : true state)

200


15

10

10

5

5

0

0

-5

1000
lambda15xx(a) EKF

-5

-10

-10

-15

-15

0

50

100
t

150

State estimate with model200½(solid line : estimate; dotted line : true state)

500

0

-500

0

50

100
t

150

State estimate with model200¾(solid line : estimate; dotted line : true state)

0

50

BLLR

100
t

150

200

 and conventional LLR (solid line :  ; dotted line :  )(b) ESP

Figure 2: Sample behaviors of state estimates and test statistics by ESP and EKF

References[1] M. Basseville, “Statistical approaches to industrial
monitoring problems — Fault detection and isolation,” Proc. 11th IFAC System Identification, pp. 413–
432, 1997.
[2] P. M. Frank, “Fault diagnosis in dynamic systems using analytical knowledge based redundancy — A survey and new results,” Automatica, vol. 26, pp. 459–
474, 1990.
[3] A. S. Willsky, “A survey of design methods for failure
detection in dynamic systems,” Automatica, vol. 12,
pp. 601–611, 1976.
[4] R. Kalman and R. Bucy, “New results in linear filtering and prediction theory,” J. of Basic Engineering,Trans. ASME Series D, vol. 83, pp. 95–108, 1961.
[5] B. D. O. Anderson and J. B. Moore, Optimal Filtering, Englewood Cliffs: Prentice-Hall, 1979.
[6] K. Uosaki, “Failure detection using backwatrd
SPRT,” Proc. 7th IFAC Symp. System Identification
and Parameter Estimation, pp. 1619–1624, 1985.
[7] D. B. Rubin, “Using the SIR algorithm to simulate
posterior distribution,” in Bayesian Statistics, Edited©ISAROB 2007

672

by J.M. Bernardo, M.H. DeGroot, D.V. Lindley and
A.F.M. Smith, vol. 3, pp. 395–402, London: Oxford
University Press, 1988.
[8] H.-P. Schwefel, Evolution and Optimum Seeking,
New York: J. Wiley, 1995.
[9] T. Bäck, Evolutionary Computation, London: Oxford
University Press, 1996.
[10] K. Uosaki and T. Hatanaka, “Nonlinear state estimation by evolution strategies based particle filters,”
Proc. 16th IFAC World Congress on Automatic Control, 2005.
[11] A. Doucet, N. de Freitas and N. Gordon (Eds.), Sequential Monte Carlo Methods in Practice, Berlin:
Springer-Verlag, 2001.
[12] S. Arulampalam, S. Maskell, N. Gordon and T.
Clapp, “A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans.
on Signal Processing, vol. SP-50, no.2, pp. 174–188,
2002.
[13] A. Wald, Sequential Analysis, New York: J. Wiley,
1947.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A synergetic particle swarm optimization algorithm-DHPSO
Jinchao.Guoskyzzuli@126.com
Department of control science & engineering
huazhong University of science & technology
Wuhan, HuBei,430074
Abstract
To improve the design-quality of robotics, more andmore artificial intelligence and evolutionary algorithm
were introduced into the design procedure, such as
routing-programming, data-fusion, vision-processing,and so on. These new methods prompt the developmentof robotics greatly. An improved Particle Swarm Optimization (PSO) method, Dual-hierarchy Particle Swarm
Optimization algorithm (DHPSO) was proposed in thispaper. Compared with the regular particle swarm optimizer(RPSO), DHPSO adopts dual hierarchy structure. In thebottom layer, several particle groups try to find out current
optimal solutions in the multi dimension searching space
respectively. In the top layer, one group particles chase
the global optima. The proposed method is tested on threebenchmark functions. All simulation results show the
proposed method is better than the regular PSO in speed
and precision performance.
Keywords: Robotics, PSO, DHPSO, Hierarchy structure

1 introduction
Nowadays swarm algorithms and other intelligent algorithms are used widely in many fields. We have used Particle Swarm Optimization (PSO) algorithm in route programming of robot design successfully and made some improvement of this algorithm on its structure. More will
be discussed about Particle Swarm Optimization algorithm
below. Just like a wealth of heuristic algorithms such as genetic, evolution, and simulated annealing. Particle Swarm
Optimization algorithm is a new entrant to the family of
evolutionary algorithms originally introduced by Kennedy
and Eberhart in 1995[1][2]. As parameters in PSO algorithm is fewer contrast to other evolution algorithm, moreover PSO algorithm is easy to realize and has a good performance in searching for the optima in real value searching
space. Researchers and experts pay a great deal of attention
to it in the last decades. Though PSO has so many merits,©ISAROB 2007

673

Guangzhao.Cui
cgzh2008@163.com
Key lab of informationed apparatus
Zhengzhou university of Light industry
Zhengzhou, HeNan. 450002
it’s easy to be trapped in local optima, and other drawbacks
such as premature, low precision, and non-convergence.
In order to overcome these demerits of PSO algorithm,
many researchers make lots of contributions, and give out
many modified algorithms based on the regular PSO. Junjun Li and Xinhua Wang propose a refined PSO algorithm
based on simulated annealing[6]; Qianli Zhang, Xing Li
and Quang-AHN TRAN introduce mutation operator to
the regular PSO algorithm[7]; Chnming Yang and Dan Simon made Each particle learn from its previous worst position and its group’s previous worst position, and give out anovel method to program PSO algorithm[8]; Jang-Ho Seo,
Chang-Hwan Im, Chang-Geum Heo and Jae-Kwang use N
groups of particle swarm to do the optimization, and geta new algorithm MGPSO[9]. Most of these algorithms putemphasis on parameter selection or combination with other
evolution algorithms to get new derivations. This is difficult to solve the innate flaw of PSO algorithm-Local minima trapping, furthermore, enhance the complexity. Under these premises, we proposed dual hierarchy PSO algorithm, it holds the concepts of the regular PSO and tries
to keep balance between ”exploration” and ”exploitation”
between different hierarchies. Compared with the regular
PSO algorithm, DHPSO improved searching speed and enhanced the global optima searching ability. The paper isorganized as follows. In Section II, we introduce the concept of regular PSO and the method used in the study DHPSO, Section III outlines the experimental setup, parametersettings, and benchmark functions used. The experimental
results are presented in Section IV. Finally, Section V contains a discussion of the experimental results.

2 The regular PSO and Proposed algorithm
2.1 The regular PSO
PSO algorithm was developed by Kenney and Eberhart
to model birds flocking and fish schooling for food, inwhich particles representing the candidate solutions toThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the problem in a multidimentiona1 search space. Every
particle has a position vector ~x encoding a candidate
solution to the problem and a velocity vector ~v . Moreover,each particle contains a small memory that stores its own
best position seen so far pid and a global best position
pgd obtained through communication with its neighborparticles. The position and velocity vector of each particle
are updated every iteration, and this is done respectively
according to equation 1 and 2 as below, where ω is known
as the inertia weight as described in[3][4]. The parameters
c1 and c2 are set to constant values, which are normallygiven as 2 , and rand is randomly generated value between
0 and 1. i ∈ (1, 2 · · · N ) and N ∈ (1, 2 · · · D). N andD denote particle number and searching space dimension
respectively. Inertia weight ω is adjusted with interaction
according to equation 3.
vi+1,d = ω · vid + c1 · rand (pid − xid ) ++ c2 · rand (pgd − xid )
xi+1,d = xid + vi+1,dωmax − ωmin
ω = ωmax −·iGenmax

(1)
(2)
(3)

3 Preparation of experiment and benchmark function
c1 and c2 are set to 2, the total population N of each algorithm will be set to equal, in experiment it is 1000. Moreseeds will be required when problem is complex. and ωmaxand ωmin are set to 0.9 and 0.4, vector ~x belongs to a scopeof[-50 50] so that the search process will not last too long,vector ~v is set to be [-20 20]. For DHPSO, At different hierarchy the particle number in each group can be different,
but the total number N–sum of particle number of different
hierarchy should be the same with that of regular PSO algorithm, just to keep same complexity. In experiment, we
set two groups in the bottom layer of DHPSO, and eachgroup has 150 particles, so the top layer number is 700
obviously. The dimension number D is set to 2 and 10 respectively. Comparison functions adopted here are threebenchmark functions used by many researchers. They are
the Griewank, Rastrigrin and Rosebrock functions. The
definitions of these functions are presented below:
µ ¶
n
n
X
Y
1
xi
2
f1 (x) =·xi −cos √ + 1
4000 i=1i
i=1f2 (x) =2.2 Proposed algorithm
Some researchers give out the concepts of ”swarm fitness covariance” and ”degree of convergence” and try to
make criteria on which to investigate the convergence ofalgorithm. All these make contribution to meliorate regular PSO in a way, However, these methods can’t improve
PSO algorithm in essence, because they just try to enhance
the global optimization searching rate by repeat of program
running in a sense. Some existing methods adopt operators of genetic algorithm or evolutionary strategy, suchas crossover, mutation, and sharing. These methods canmodify regular PSO algorithm in some ways, but improve
complexity considerably at the same time. All these makemodified PSO algorithm not easy to realize. If groups areused for DHPSO, the computational cost increases only inthe order of O (N ) compared with one-group PSO because
each group follows the basic concepts¡ ¢ of conventional PSO
algorithm, whereas roughly O N 2 increments is required
for GA crossover computations if an larger number of populations is used in GA. In DHPSO algorithm, The velocityof the bottom layer updating adopts equation 1 to guarantee a good local ”exploration” performance, and in the top
one, velocity updates according to equation 4–the local updating formula, to gain a better convergence speed.
vi+1©ISAROB 2007

= ω · vi + c2 · rand (pgd − xid )

(4)

674

f3 (x) =nX¡¢
x2i − 10 cos 2πxi + 10

i=1
n ³
X

(5)
(6)

´
¡
¢2
2
100 xi+1 − x2i + (xi − 1)
(7)

i=1

4 Simulation result comparison
Each test repeats 10 times and each iteration runs 100
generations. We list out the different minima of the benchmark functions that we got using RPSO and DHPSO
method. The preliminary conditions are specified in thetable list.
Fig.1 to Fig.6 show the convergence result of DHPSO
and RPSO. The benchmark function used and the particular conditions of each figure are specified in the figure caption.
Table 1: the mean optimization result of RPSO and DHPSO with D =2 and N=1000
funcion
f1
f2
f3

RPSO
0.00000.00000.0000

DHPSO
0
0
0

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.5
DHPSO
RPSO

Table 2: The mean optimization result of RPSO and DHPSO with D =10 and N=1000

3

2.5

RPSO
0.107622.20800.0000

DHPSO
0.103818.6500
0.00002
fitness

funcion
f1
f2
f3

1.5

1

0.5

0

0

10

20

30

40

50
i

60

70

80

90

100

Fig. 3: Rastrigrin, N=1000, D=2

0.03
DHPSO
RPSO

0.025

160
DHPSO
RPSO

0.02

fitness

140
0.015
120

100
fitness

0.01

0.005

80

60
0

0

10

20

30

40

50
i

60

70

80

90

100

40

Fig. 1: Griewank, N=1000, D=2

20

0

0

10

20

30

40

50
i

60

70

80

90

100

Fig. 4: Rastrigrin, N=1000, D=10

0.9
DHPSO
RPSO
4.5
0.8

DHPSO
RPSO
4

0.7
3.5
0.6

fitness

fitness

3
0.5

0.4

2.5

2

0.3

1.5

0.2

1

0.1

0.5
0

10

20

30

40

50
i

60

70

80

90

100
0

Fig. 2: Griewank, N=1000, D=10

0

10

20

30

40

50
i

60

70

80

Fig. 5: Rosebrock, N=1000, D=2©ISAROB 2007

675

90

100

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[3] Shi, Y.H., Eherhart, R.C., (1998). A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation., Anchorage, Alaska, May 4-9, 1998.

4

6

x 10

DHPSO
RPSO

5

fitness

4[4] Shi, Y.H, Eberhart, R.C., (1999). Empirical study ofparticle swarm optimization, 1999 Congress on Evolutionary Computation, Washington DC, USA, July 69,1999.

3

2[5] S.L.HO, Shiyou Yang, Guangzheng Ni ang H.C.Wang,”A particle swarm optimization method with enhanced
global search ability for design optimizations of electromagnetic devices,” IEEE, Tran. Magn, Vol.42, No.4,
2006

1

0

0

10

20

30

40

50
i

60

70

80

90

100

Fig. 6: Rosebrock, N=1000, D=10
Because vector ~x and vector ~v are generated randomly,
the initial value may be different for the two algorithms
in the beginning. However, the two algorithms still canbe compared, for they have the same variation iterationand same complexity degree. From Fig.1 to Fig.6 above,
we can get that DHPSO method has a good precision than
RPSO method, and as well as the convergence speed.

5 Conclusion
From the simulation results, we can see DHPSO has abetter performance than the RPSO algorithm, but these experiments are executed under a particular cases and narrowsettings, so the conclusion is not comprehensive and definite. For further research, more work can be put into thefollowing aspects:
1. The relation ship between inertia weight and convergence of algorithm.
2. How to improve global searching ability of algorithm.
3. Seeking the theory support of PSO algorithm.
4. Combining other heuristic algorithm and applying
PSO to more engineer use.

References[1] R. C. Eberhart and J. Kennedy, ”A New Optimizer Using Particles Swarm Theory,” in Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39-43.
[2] R. C. Eberhart and J. Kennedy, ”Particles Swarm Optimization,” in Proc. IEEE International Conference on
Neural Network, Perth, Australia, 1995.

©ISAROB 2007

676

[6] Junjun Li and Xihuai Wang, ”A modified particle swarm optimization algorithm,” in Proc. IEEE
world congress on intelligent control and Automation,
Hangzhou, China., 2004 ,pp.354-356.
[7] Qianli Zhang, Xing Li and Guang-AHN.TRAN,”A modified particle swarm optimization algorithm,”in Proc, IEEE International conference on Machine learning and cybernetics, Guangzhou, China,
2005.pp.2993-2995.
[8] Chuming Yang and Dan Simon, ”A new particle swarm optimization technique,” in Proc. IEEE,
International conference on systems engineering,
August,2005,pp.164-169.
[9] Jang-Ho Seo,Chang-Hwan Im, Chang-Geum Heoand
Jae-Kwang Kim, ”multimodal function optimization
based on particle swarm optimization,” IEEE, Trans.
Magn, vol. 42, No. 4, April 2006,pp. 1095 - 1098.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Inevitability of bio-molecules
Ken NAITOH
Waseda University, 3-4-1, Ookubo, Tokyo 169-8555 Japan
Email: k-naito@waseda.jp
Key Words: Origin, Inevitability, Asymmetry, Bases, Amino acids

Abstract
Inevitability of the bio-molecules which are related tonitrogenous-bases, amino-acids, DNA, RNA, and heavier
molecules is clarified by using the molecular
fluid-dynamics, i.e. the cyto-fluid dynamic theory. We try
to solve the problem of why living beings use only five
types of nitrogenous bases and twenty types ofamino-acids. It should be stressed that the keyword is themysterious number, the n-th root of n, which lies between
1.0 and the golden ratio. The present report may give themilestone for understanding the origin of life.
1.

Introduction

There are some approaches in order to know the
principle for designing life system.
Researchers on the origin of life have synthesized
artificially amino-acids and nucleic-acids. [1] However,the chain reaction of the fundamental molecules is still infog.
Chirality [2] is thought to be an important concept forunderstanding self-organizing process of life. However,this concept can not clarify all of the dynamical processes
underlying life.
By using informatics and structure mechanics, Aristid
Lindenmayer [3], D'Arcy Wentworth Thompson [4], andDonald E. Ingber [5] have showed the inevitability oflarge structures such as tree, shell, and cell. However, theinevitability of biological monomers and polymers has
not been revealed so well, although the number offundamental blocks is limited to only five bases andtwenty types of amino acids.
Parameter or index is necessary to clarify theinevitability. DNA is composed by four bases of Adenine(A), Guanine(G), Cytosine(C), and Thymine(T).
Frequency of usage of Guanine and Cytosine among thesefour bases (GC rate) is used world-widely as the important
indexical property for controlling bio-chemical reaction
processes. The hydrogen bond connection between
Guanine and Cytosine is stronger than that between
Adenine and Thymine. This index is related to theinstability of molecules while increasing temperatures.
Thus, the GC rate is important for optimizing thereplication quality in the Polymerase chain reaction©ISAROB 2007

677

(PCR). [6]On the other hand, we can classify four bases such as A,
T, G, and C into two groups, purine and pyrimidine.
Purines, i.e., A and G, have relatively large size, while
pyrimidines, i.e., C and T, are small. The present index,
the size ratio of purine and pyrimidine (YU rate) is related
to the molecular size, i.e., weight. However, the YU rate is
not employed for analyzing biological processes so well.
2. Analysis based on YU rate
2.1 Size of bases [7, 8, 9, 10, 11]Asymmetric size ratios of purines and pyrimidines arearound 1.50 in their hydrogen bonds within DNA andRNA, although the size ratio of 1.0, for hydrogen bondconnections of identical sizes of bases, is often observed
in RNA (Figs. 1, 2, and 3). Size ratios of 1.0 andapproximately 1.5 can be described by the mysterious
number of the n-th root of n (Fig. 3). This key number
proposed here for solving inevitability of the symbiosis isunrelated to chirality.
About 1.5

Purine（2 types）

Pyrimidine（3 types）

1.0

Fig. 1 Five types of nitrogenous bases for DNA and RNA.

Here, we will consider two nitrogenous bases, a purine
and a pyrimidine, connected by hydrogen bonds in a large
quantity of water. Owing to the influence of thenitrogenous bases, water molecules around the bases havedifferent densities and arrays in comparison with those far
away. (It is well-known that weight of hydrated watermolecules per a base-pair inside DNA is of the order ofthat of the bases-pair.) Thus, we divide the water into two
regions. A base and water molecules surrounding the baseare defined as a continuum particle, parcel. Then, weThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

assume that the parcel size is proportional to the size ofbase. Further, we assume that the parcel acts as a flexible
continuum particle.

Pyrimidine

PurinePyrimidine

2:3

Pyrimidine

1:1

Fig. 2 Hydrogen-bonded bases.

the G-C pair and 1.2 for the A-U pair. Second reason isrelated to the fact that base pairs are joined to sugars andphosphoric acids by covalent bonds. Total molecularweight, including those of the nitrogenous base, sugar,
and phosphoric acids, decides the frequency ratio of
purines and pyrimidines in RNA (Fig. 4).
Third reason is that, because the frequency ratio of
purines and pyrimidines in the stems of tRNA is 1.0,
RNAs having longer stems bring the mean value closer to
1.0. Ribosomal RNA shows frequency ratio of around
1.30, which is larger than that of tRNA, because rRNA
has a shorter stem than tRNA. (An extremely longstem-length results in DNA without leaves.)
2

＋

3

=5

Pyrimidine

Sugar+ Phosphoric acids

PurineSugar+ Phosphoric acids

3

＋

3

=6

Fig. 3 Mysterious number, the n-th root of n, whichappears in the size ratio of purines and pyrimidines.

Fig. 4 Schematic diagram of nitrogenous bases connectedby covalent bonds illustrating that sugars and phosphoric
acids reduce the frequency ratio.

Based on these assumptions, we derived the deterministic
momentum equation describing particle deformation. The
momentum equation shows that life prefers the size ratioof hydrogen-bonded nitrogenous bases taking the n-th
root of n, which has the values of 1.0, 1.41, and 1.44 for
n=1, 2 and 3, respectively (Fig. 3).

3.Clover structure [11, 12, 13]

2.2 Two purines and three pyrimidines [9, 10, 11]Asymmetric size ratio of purines and pyrimidines ofaround 1.50 results in different molecular weights.
Molecular weight differences lead to “two” types ofpurines and “three” types of pyrimidines, because thelarger purines produce fewer types. Mass balance leads toa frequency ratio of 1.5, i.e., two purines and threepyrimidines (Fig. 1).
2.3 Frequency ratios of purine and pyrimidine differ
between tRNA and rRNA [11, 12, 13]
Let us examine hundreds of transfer RNA (tRNA).
Averaged frequency ratio of purines and pyrimidines intRNAs is about 1.10. The asymmetry can be confirmed,
but the value is less than 1.50.
What is the frequency ratio of purines and pyrimidinesin ribosomal RNA (rRNA)? The databases on rRNA show
a value of around 1.30, which is relatively larger than thatfor tRNA.
This value less than 1.50 comes from three reasons.
One is that the precise molecular weight is about 1.4 for

The next question is why RNA has a complex clover
structure. This stems from the third reason mentioned
above. Density asymmetry of purines and pyrimidines
results in a leaf structure.
We examined the stochastic computer model simplified
based on molecular dynamics. [11, 12, 13] First, we
prepared seventy-six individual nitrogenous bases(purines and pyrimidines) in an initial pool. These bases
were connected randomly by hydrogen bonds. If weemploy a lot of pools, an enormous variety of sequencearrays can be generated. Computational experiment
revealed that, when the frequency ratio of purines andpyrimidines in the initial pool was between 1.0 and 1.5,
clover structures were produced with a relatively highprobability.
The reason why a frequency ratio between 1.0 and 1.5
promotes clover structures is simple. An extremely large
frequency ratio, say, far above 1.5, can not produce thestem in tRNA, because the presence of only one type ofbase cannot form the pairing of purine and pyrimidine.
More complex structures such as ribosomal RNA(rRNA) can also be explained by the above-mentioned
dynamical mechanism, which brings fractal structure.
4. Inevitability of non-coding RNA
Recently, the research group in RIKEN [16] has reported©ISAROB 2007

678

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

that there are lots of non-coding RNA in introns and junk,
although functions of the non-coding RNA are in fog.
Here, we will show a hypothesis related to inevitability ofthis non-coding RNA. Stochastic computer model in
Section 3 generates small amount of clover structures.
Sequences having clover structures close to tRNA are
about 1.0 % in all of the possible sequences, which
generated randomly. What are the substantial sequencesof unstable structures of 99 %? These sequences of
unstable structures may correspond to those generated inancient pre-biotic processes. Then, these sequences of
unstable structures may become the non-coding RNA.
Stochastic model [11, 12, 13] may tell us that we know
only 1 % of molecules existing inside living cell.

correspond to whether the center locus in the anti-codon isa purine or a pyrimidine. [11, 12, 13]Asymmetric frequency ratio of purine and pyrimidine
in codons may lead to the frequency asymmetry ofhydrophilic and hydrophobic amino-acids. Thus, thefrequency ratio of purine and pyrimidine (YU rate) is also
important for optimizing proteins and enzymes.

5. Inevitability of five bases
Density ratio of purine and pyrimidine in tRNA having
smaller leaves is around 1.10, while that in rRNA having
larger leaves is about 1.30. Why will two different sizes ofleaves exist? Mass conservation can not clarify theinevitability of two different sizes of leaves. Energy
conservation is necessary for explaining this point.
In many cases, GC pairs are richer in tRNA than in rRNA.
Amount of GC pairs is related to the difference of stem
length. Actually, tRNA, which has relatively small leaves
and long stems, has richer G-C pairs than rRNA.
Hydrogen bond connection in G-C pair is stronger thanthat in A-U. Stronger hydrogen bond will stabilize longer
stems.
Two types of base-pairs, G-C and A-U, are necessary to
produce two types of RNAs, i.e., tRNA and rRNA.
Inevitability of four bases among five can be understoodby the present discussion.
6. Twenty types of amino acids
Triplet inside the leaf of tRNA, anti-codon, permits
sixty-four patterns at the maximum, because A, U, G, andC are possible for each locus among the three ones. Then,because the third locus inside anti-codon has relatively
weak connection with codon, the minimum number ofpatterns of triplet is sixteen. This implies that the numberof amino acids are between sixteen and sixty-four. As theresult, the weak connection at third locus permits twenty
types of amino acids, a little more than sixteen.
It should be emphasized that clarification of leaf size
shown in the above sections leads us to the outline ofinevitability of twenty amino acids.
7. Hydrophobic and hydrophilic amino acids
As is well-known, three nitrogenous bases correspond to
one type of amino acid. Array of these three nitrogenous
bases is codon. The database shows that the frequency
ratio of purines and pyrimidines in codons is also about
1.30. [11, 12, 13] The frequency ratio of purines andpyrimidines for the center locus of anti-codon is also
asymmetric. (See Fig. 5.)
Most of hydrophobic and hydrophilic amino acids©ISAROB 2007

679

Fig. 5 Twenty types of amino-acids and anti-codon.
8. Structure of proteinsAs the center locus inside codon mainly decides whether
the amino acid corresponding is hydrophilic orhydrophobic, both of hydrophilic and hydrophobic aminoacids exist in proteins. Symbiosis of hydrophilic andhydrophobic molecules brings the complex shapes ofproteins, because hydrophobic parts are often folded
inside proteins.
9. Causual chain of bio-molecules
Several types of molecules such as nitrogenous bases,amino-acids, RNA, DNA, and proteins are related to eachother, through the principle of the n-th root of n and theconservation laws.
Although the inevitability of size ratio of purine andpyrimidine is explained by momentum conservation in theformer section, mass conservation law explains that manysizes and frequencies of bio-molecules are inevitable inthe causual chain of molecules. Will the explanations
shown with mass conservation law be correct? This
question is related to whether cell or earth is considered. If
the system is earth, mass conservation law will bedominant, because there are less materials coming fromthe space, although energy comes from outside of theearth, the sun. Here, we think the earth as control volume,
not cell. This relates to the food-chain.
Unbalance of purine and pyrimidine in living beings willlead to instability of life without doubt, because the poolof only purine can not generate clover structures such astRNA and rRNA.
10. Origin
In foregoing sections, we examined the inevitability ofseveral lengths of biological molecules, from base toprotein. However, we do not explain how the molecular

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

length increases during 4600 million years.
Let us see the DNA length of representative species
plotted against time. (See Fig. 6.)
The Earth might be born about 4600 million years ago
and first life might emerge 3600 million years ago. T.
Oshima thought that the first primitive cell had DNA of
the order of 1.0 x 10 5 base pairs. [15] (The first cell can begenerated with the DNA length of 1.0 x 105 base pairs,
about 300 types of enzymes. Each enzyme can be codedwith 300 base pairs of DNA.) Then, about 3600 million
years after the first cell bring human beings of 3.0 x 10 9bp.
Let us examine further on this. First, we assume that the
DNA length increase with the rate of ten times per 1000
million years. As the DNA length was 1.0 x 10 5 base pairs
3600 million year ago, about 10 9 base pairs, the order ofhuman being, can be possible now, because about 4000
million years pass from the first cell. This increasing rateof DNA length implies that DNA should have the order of
104 base pairs 4600 million years ago. (See Fig. 6.)
The problem is whether the synthesis of DNA with 104
base pairs could be possible or not, 4600 million years ago.
The possibility is shown by Yanagawa et al. [1] thatpolymer macromolecules such as protein or cell
membrane can be produced by synthesizing amino acids
under high pressure and high density of metal during a
few weeks. The other data which supports 104 bp of DNA
4600 million years ago is that too many cycles of PCR
during hours produces longer DNA sequences. [23]Discussion shown above does not give the clear answer
to the question of whether life was born on the earth or not.
DNA length plotted against time should be analyzed
further.
11. Conclusion
Inevitability of the bio-molecules such as
nitrogenous-bases, amino-acids, DNA, RNA, and heavier
molecules is clarified, although there is also eventuality
around the inevitable molecules.

[9] Naitoh, K. (2001). Cyto-fluid Dynamic Theory, Japan
Journal of Industrial and Applied Mathematics, Vol.18,
No.1.
[10] Naitoh, K. (2000). Cyto-fluid Dynamic Theory of theOrigin of Base, Information, and Function. Proc. of 6th Int.
symp. on Artificial life and Robotics 6th.
[11] Naitoh, K. (2002). Cyto-fluid Dynamic Theory of theOrigin of Base, Information, and Function. Artificial Lifeand Robotics, 6-1 & 2, 82.
[12] Naitoh, K. (2005) Self-organising mechanism of
biosystems, Artificial life and robotics, 9, 96-98.
[13] Naitoh, K. (2005) Seimei no Kihon Bunshi wo
Tsuranuku Patan (Patterns underlying bio-molecules),
NIKKEI SCIENCE, 58-65.
[14] Naitoh, K. (2006) Gene Engine and machine Engine,
Springer Japan.
[15] Oshima, T. (1995) Seimeiwa Nessuikara Hajimatta,
Tokyo Kagaku Doujin.
[16] RIKEN Genome Exploration Research Group andGenome Science Group. (2005) Antisense Transcription
in the Mammalian Transcriptome, SCIENCE, 309.
[17] Lowe, T.M., Eddy, S.R., (1997). Nucleic Acids Res.,25, 955. (available at http://rna.wustl.edu/tRNAdb/.)[18] Benson, D.A., Boguski,M.S., Lipman, D.J., andOstell, J., Ouellette, B. F. F., (1998). Nucl. Acids Res., 26,
1.
[19] Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J.,
Ostell, J., Wheeler, D. L., (2003). Nucleic Acids Res., 31,
23.
[20] Nakamura, Y., Gojobori, T., Ikemura, T., (2000).
Nucl.
Acids
Res.,
28,
292.
(availableat
http://www.kazusa.or.jp/codon/.)
[21]
DNA
Data
Bank
of
Japan,
http://www.ddbj.nig.ac.jp/.
[22] JCM On-line catalogue, Japan Collection of
Microorganisms, RIKEN (The Institute of Physical andChemical Research), http://www.jcm.riken.go.jp/[23] Naitoh, K. (2005) DNA computing based on actual
biological sequences and accurate reaction control, Proc.
of Sixth Int. Nobeyama Workshop on the new century ofcomputational fluid dynamics, 2003, 60-66.

References©ISAROB 2007

680

1.E+10
1.E+09
1.E+081.E+07DNA length[1] Yanagawa, H. (1991), Seimeiwa Ikani Tsukuraretaka,
TBS Britaniaca.
[2] Kuroda, R. (1992) Seimei Sekai no Hitaishousei,
Chuukou Shinsho.
[3] Prusinkiewics P. And Lindenmayer A. (1990) The
algorithmic beauty of plants, Springer-Verlag.
[4] Thomson, D. (1961) On Growth and Form, Cambridge
University Press.
[5] Ingber, D.E. (1998) The Architecture of Life. 52.
Scientific American.
[6] Mullis K.B. et al (1994) PCR, BIRKHAUSER.
[7] Naitoh, K. (1999). Cyto-fluid Dynamic Theory for
Atomization Processes, Oil&Gas Science and Technology,
Vol. 54, No.2 205-210.
[8] Naitoh, K. (2000). Mesoscopic Kinetic Theory for
Fluid Phenomena and Biochemical Reaction Processes. J.
of Advanced Science, 12-3, 223 (2000).

1.E+06
1.E+05
1.E+04
1.E+03
1.E+02
1.E+01
1.E+00
0

0.5

1

1.5

2

2.5

3

Year [x 1000 million]

Fig. 6 DNA length plotted against year.

3.5

4

4.5

5

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Calculation of 3-D nonnegative outer product expansion by the power method
and its application to digital signal processing
Chiharu OKUMA*, Jun MURAKAMI* and Naoki YAMAMOTO**
Dept. of Information and Computer Sciences*,
Dept. of Information and Communication Engineering**,
Kumamoto National College of Technology,
2659-2, Suya, Koshi, Kumamoto pref., 861-1102, JAPAN E-mail:chiharu@cs.knct.ac.jp

Abstract
The power method is known as a convenient
way to calculate eigenvectors of a matrix. We used
this method for calculating 3-D outer productexpansion previously.
In this paper, we try
calculating 3-D nonnegative outer productexpansion by using the power method. In order toperform this calculation, we add nonnegative
constraint conditions to the repetition process of thepower method. Our method shows a significant
reduction of computation time than the nonlinear
optimization method.

key words:
3-D Outer Product Expansion, Power Method, 3-D
array, Nonnegative Outer Product Expansion.

1. Introduction
In the field of image processing and digital
signal processing, multi-dimensional digital filters
are usually used.
In order to design amulti-dimensional digital filter, multi-dimensional
design specification is generally reduced to a set of
1-dimensional (1-D) specification array. Then thedesired multi-dimensional filter can be obtained bydesigning a set of 1-D digital filters and combining
them each other [1].
3-dimensional (3-D) outer product expansion[2] is usually used to decompose 3-D data arrays
into products of 1-D vectors. This expansion is anextension of the singular value decomposition(SVD) of a matrix to a 3-D array. To simplify the
structure of resultant 3-D digital filter, the numberof terms of the expansion is desired to be reduced asmuch as possible. Therefore, such terms have togive least square approximation to the original 3-D
array under some constraint conditions. In practice,although a large amount of computation time isrequired to calculate that expansion, the nonlinear
optimization method is exploited ordinarily. We
previously proposed the method, which uses thepower method, for the purpose of calculating that
3-D outer product expansion and showed theefficiency of our method in comparison with thenonlinear optimization method [3]. The power
method is known as a basic numerical technique to©ISAROB 2007

681

calculate eigenvalues of a matrix [4].
Since the 1-D vector obtained by 3-D outer
product expansion represents magnitude response in
the practical digital filter design problem, every
elements of the vector should be physicallynonnegative.
We call this expansion 3-D
nonnegative outer product expansion. Though thenonlinear optimization method can be used to
calculate this expansion likewise as the case ofordinary 3-D outer product expansion, thecomputation time is the weakness of this method
similarly as above.
In this paper, we try calculating 3-D
nonnegative outer product expansion by using thepower method. In order to perform this calculation,
we add nonnegative constraint conditions to therepetition process of the power method.

2. Definition of 3-D Outer Product
Expansion
A L × M × N 3-D array A3 can be
decomposed by the 3-D outer product expansion as

A3 = ∑ σ i (ui ⊗ v i ⊗ w i ) ,
r

i =1

(1)

σ1 ≥ σ 2 ≥ L ≥ σ r ,where the expansion vectors ui , v i , w icorrespond to the singular vectors of the SVD of amatrix, the expansion coefficients σ i and thenumber of expansion terms r correspond to the
singular values and the rank of a matrix similarly,and ⊗ denotes the outer product operation. The
expansion vectors are normalized asLui = ∑ ui ( j ) 2 = 1,j =1Mv i = ∑ v i ( j ) 2 = 1,

(2)

j =1
N

w i = ∑ w i ( j ) 2 = 1,
j =1

where
u i ( j ) , v i ( j ) , w i ( j ) show the j-thelement of the vector ui , v i , w i respectively.

3. Calculation Algorithm for 3-D Outer
Product Expansion by the Power
MethodThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The algorithm for calculating 3-D outer
product expansion by the power method is describedas follows [3].
( p)

( p)

Step 1. Choose the initial vectors u n , v n ,( p)wn
arbitrarily, where these vectors must be
normalized, and the subscript p and n are set tozero and one respectively at the beginning of thisrepetitious procedure.
Step 2. The residual 3-D array B3 is obtained bysubtracting sum of products of the expansion
vectors ui , v i , w i , which has been calculatedby this time, from original 3-D array A3 as
follows:n −1

B3 = A3 − ∑ σ i (ui ⊗ v i ⊗ w i ).

(3)

i =1

Step 3. Calculate the L × M matrix( p)asmultiplying B 3 by vector w n

Fby

F = B3 ⋅ w n .
(4)
The (i, j ) element of the matrix F can be
represented as(5)
F (i, j ) = ∑ B 3 (i, j , k )w n ( p ) (k ) .

Step 5. After increase n and set p to zero, repeat
this procedure from Step 1.

4.

Calculation of 3-D Nonnegative Outer
Product Expansion by the Power
MethodThe method which we described in Section 3
can be applied to calculation of a 3-D nonnegativeouter product expansion by adding nonnegative
constraint conditions to the repetition process of thepower method. Actually, the following steps areinserted into Step 3.
Step A1. The repetition vector u n

Next, apply the power method to thematrix F as follows:( p +1)( p)
un= Fv n ,(6)( p +1)( p +1)vn= F T un.
Like wise the M × N matrix G and the
N × L matrix H are obtained by

G = B3 ⋅ v n( p +1)= Gu n( p +1)= GT wn,( p +1)(7),

H = B3 ⋅ u n ( p +1) ,( p +1)= H T wn

( p +1)unun,( p +1), vn( p +1),

must be normalized.
Repeat Step 3 until the following aresatisfied for sufficiently small value ε :⎧ u ( p +1) − u ( p ) < ε ,n⎪ n⎪ ( p +1)
( p)
(8)− vn < ε ,⎨ vn⎪ ( p +1)
− w n( p ) < ε .
⎪⎩ w n( p +1)

The n-th expansion vectors un( p +1)( p +1) −= max(u n( p +1)= − min(u n,0),( p +1)

(11)

,0).

Step A2. Calculate the norm of u n
un
un
un

( p +1) −( p +1)( p +1).

( p +1) +andChoose the nonnegative vector

from these vectors by following way.
⎧ u ( p +1) +⎪ n=⎨( p +1) −⎪⎩− u nThe vector v nchosen as follows:⎧ v ( p +1) +⎪ n( p +1)=⎨vn( p +1) −⎪⎩− v n⎧ w
⎪ n=⎨
( p +1) −⎪⎩− w n
( p +1) +( p +1)

Step 4.

( p +1) +( p +1) +≥ un( p +1) −( p +1) +< un( p +1) −

( un
( un

and

).

(12)
are also( p +1)( p +1) +≥ vn( p +1) +

< vn

( vn
( vn

wn

)

( p +1) −( p +1) −

)
),
(13)

where the obtained vectors u n
wn

and

un
, where the former is composed of
positive number or zero and the latter iscomposed of negative number or zero as( p +1)( p +1) +( p +1) −
un
= un
− un
,

v n ( p +1) = Hu n ( p +1) ,wn( p +1) +( p +1) −( p +1),( p +1)wn
un( p +1)in equationun(6) is divided into the vector of

( p)

k

( p +1)( p +1),vn, wn
are obtained from Step 3. Here,rename these vectors as u n , v n , w n .
The n-th coefficient σ n is obtained frominner product operation as as©ISAROB 2007 σ n = B3 (u n ⊗ v n ⊗ w n ).
682
(10)

wn

( p +1)( wn( wn( p +1) +≥ wn( p +1) −( p +1) +< wn( p +1) −

)
).

(14)

5. Calculation of 3-D Orthogonal Outer
Product Expansion
Since the resultant expansion terms of 3-D
outer product expansion do not satisfy orthogonality,
the 3-D orthogonal outer product expansion [3] isdefined by(15)
A 3 = σ ijk u i ⊗ v j ⊗ w k ,∑ ()

i, j,kwhere σ ijk are the expansion coefficients.

This

expansion can be calculated by introducing theGram-Schmidt orthogonalization process [5] into
the Step 3 of the algorithm described in Section 3 asfollowing procedure.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Step B1. Along with the Gram-Schmidt process,( p +1)( p +1), v'n,calculate the vectors u' nhd ( x i , y j , z k )of a 3-D digital filter design

problem [2] is used to consider the validity ofcalculation algorithm described above.
w'n
by subtracting the previously
1, (0 ≤ r ≤ 0.4)⎧
( p +1),
obtained quantities from vectors un⎪ (0.6 − r ), (0.4 ≤ r ≤ 0.6) (22)hd ( xi , y j , z k ) = ⎨( p +1)( p +1)vn, wn
respectively as⎪ 0.2
0, (r ≥ 0.6),( p +1)( p +1)( p +1)
T
⎩
u' n
= un
− u1 u n
u1 −where( p +1)( p +1)TT
u2 un
u 2 − L − u n −1 u n
u n −1 ,1r=
x i2 + y 2j + z k2 ,(16)π
( p +1)( p +1)( p +1)T
v'n= vn− v1 v n
v1 −
iπ
⎫
xi =
, (0 ≤ i ≤ L'−1), ⎪( p +1)( p +1)
T
T
L
'
1
−
v2 vn
v 2 − L − v n −1 v n
v n −1 ,
⎪
(23)
jπ
⎪
(17)
yj =
, (0 ≤ j ≤ M '−1),⎬( p +1)( p +1)( p +1)
T
M '−1
w'n
= wn
− w1 w n
w1 −
⎪
kπ
⎪
( p +1)( p +1)TT, (0 ≤ k ≤ N '−1). ⎪
zk =
w2 wn
w 2 − L − w n −1 w n
w n −1 .
N '−1
⎭
(18)
is
constituted
by
The
3-D
array
A
3
Normalize the vectors in above equations( p +1)( p +1)( p +1)() = hd ( xi , y j , z k ).
A
i
,
j
,
k
(24)
3
, vn
, wn
.
to obtain un
Since the magnitude specification hd ( x i , y j , z k )Step B2. By the procedure in Section 3 and the Step
is zero when r ≥ 0.6 , the size of the 3-D array is
B1
in
this
section,vectors
L = L′ × 0.6,
L×M×N,
where
reducedtou1 , u 2 , L , u m of the equation (15) are obtained
M = M ′ × 0.6, N = N ′ × 0.6.
inorder, where m = min(L,M,N). In casethat L>m,the remaining L − m vectors[3-D Outer Product Expansion]
terms can be calculated by using
Table 1 shows that the calculated expansion
Gram-Schmidt orthogonalization process as'TTcoefficientsby the power method give goodu n = u n − (u1 a n )u1 − (u 2 u n )u 2 L
approximation to those by the nonlinear(19)− (u n −1T u n )u n −1 ,optimization method.
n = m + 1, L , L
Table 1. Resultant coefficients of 3-D outerwhere u n are the initial vectors and the
product expansion by the nonlinear'
vectors un are to renamed as u n after they
optimization method and the power method.
arenormalized.
Likewise
vectors(L’=M’=N’=20)
Nonlinearv m +1 , L , v M and w m +1 , L , w N are calculated.
σ i Optimization Power Method Relative
Error[%]
MethodStep B3. For every combination of p, q and r,
1 2.275862E+01 2.275862E+01 0
calculate the expansion coefficients σ pqr as( p +1)

(

(

(
)

)
(

)

(

(
)

)
(

)

)

(

(

)

)

σ pqr = A3 (u p ⊗ v q ⊗ w r ) ,2

4.283573E+00

4.283573E+00

0

(20)

3

3.025678E+00

3.025678E+00

0( p = 1,2, L L , q = 1,2, L M , r = 1,2, L N ).

41.400982E+00

1.400982E+00

051.129514E+00

1.129514E+00

06

6.526013E-01

6.526013E-01

073.698252E-01

3.698252E-01083.454422E-01

3.454422E-01

0

To improve in calculation time of these steps,
a part of the Step B1 is modified.
The
modification is described below.
After the calculation of the expansion
vectors u1 , u2 , L , um −1 , the remaining vector um
can be calculated byum ' = um − (u1T an )u1 − (u2T un )u2 L− (um −1T um )um −1,(21)'

where u m is the initial vector. The vector um
is normalized immediately, then the vector renamed
as u m . This slight modification leads to an
improvement in calculation time.

6. Experimental Results
The following magnitude©ISAROB 2007

specification
683

9

3.403220E-01

3.403220E-01

0102.840046E-01

2.840046E-010112.598434E-01

2.598434E-010122.108770E-01

2.108770E-01

0132.008905E-01

2.008905E-010

141.767691E-01

1.767691E-01

0151.535205E-01

1.535205E-01

0161.494289E-01

1.494289E-010171.064934E-01

1.064934E-01

018

1.006265E-01

1.006265E-01

0191.002772E-01

1.002772E-010209.568394E-02

9.568395E-02-6.27E-06

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

7.

Figure 1 illustrates the convergence propertyof the power method described in Section 4. The
relative error of the method, which calculates 3-D
nonnegative outer product expansion, is about 10%
at n=10, while the error less than 10% at n=3 in caseof 3-D outer product expansion.

In this paper, we showed the calculation
results of the 3-D outer product expansion, 3-D
nonnegative outer product expansion, and 3-D
orthogonal outer product expansion in the following
development environment.

Relative error[%][3-D Nonnegative Outer Product Expansion]

100
90
80
70
60
50
40
30
20
10
0

3-D Outer Product
Expansion
3-D Nonnegative Outer
Product Expansion

0

1

2

3

4

5

6

7

8

9 10

Number of terms n

Figure 1. Convergence property of the power
method.

[3-D Orthogonal Outer Product Expansion]
Figure 2 shows the calculation time ofmodified method in Section 5 compared with theusual method. In this experiments, the 3-D array isconstructed by random integer in the range of[1,1000]. From the figure, the calculation time canbe reduced slightly by using the proposed method.

Calculation time[Sec]1.60001.4000Usual 3-D Orthogonal
Outer Product Expansion

1.2000Modified 3-D Orthogonal
Outer Product Expansion

1.00000.80000.60000.40000.20000.00003 4 5 6 7 8 9 10 11 12 13 14 15Size of 3-D array (L=M=N)
Figure 2 Calculation time of 3-D orthogonalouter product expansion.

©ISAROB 2007

684

Conclusions

System: FreeBSD 3.4-RELEASE
Development language:C (GNU C compiler v2.7)
Accuracy:
floating point of double precision typeThe results lead to the following conclusions:(1) Using the previously proposed method to
calculate the 3-D outer product expansion andthe 3-D nonnegative outer product expansion, we
obtained the expansion coefficients and vectors
precisely.
(2) By modifying the usual method for calculationof the 3-D orthogonal outer product expansion,
the calculation time could be reduced slightly incomparison with the previously proposed
method.

References[1]Deng, T. B. and Kawamata, M., ”Design of
Two-Dimensional Recursive Digital Filters
Based on the Iterative Singular Value
Decomposition”, Trans. IEICE, Vol.E73, No.6,
pp.882-892,1990.
[2]Ohki, M. and Kawamata, M., ”Design ofThree-Dimensional Digital Filters Based on theOuter Product Expansion”, IEEE Trans. Circuits
Syst., Vol.CAS-37, No.9, pp.1164-1167, 1990.
[3]Murakami, J., Yamamoto, N. and Tadokoro,
Y., ”High-Speed Computation of 3D Tensor
Product Expansion by the Power Method”,
Electronics and Communications in Japan, Part 3,
Vol.85, pp.63-72,2002.
[4]Wilkinson, J. H., “The Algebraic Eigenvalue
Problem”, Oxford University Press, 1965.
[5]Press, W. H., Flannery, B. P., Teukolsky, S. A.
and Vetterling, W. T., “Numerical Recipes in C”,
Cambridge University Press, 1988.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Adaptive Knowledge Base for Japanese-to-Braille translation
Satoshi Ono
Takashi Yamasaki
Shigeru Nakayama
Department of Information and Computer Science,
Faculty of Engineering, Kagoshima University
1-21-40 Korimoto, Kagoshima, 890-0065 Japan{ono, sc101056, shignaka}@ics.kagoshima-u.ac.jp
Abstract
This paper proposes a method for generating adaptive knowledge base involving two knowledge representations – rule and case. Combining rules andcases makes it possible to solve problems accurately
and quickly, and to acquire new cases from problemsolving results. The proposed method does not require
manually adjustment of the thresholds and provides
highly qualiﬁed solutions. This paper also proposes a
Japanese-to-Braille translation system which uses theadaptive knowledge base as mentioned above. Experimental results have showed that the case acquisition
can reduce errors, and that the threshold adjustment
signiﬁcantly reduces segmentation errors.
keywords:
knowledge base, rule-based reasoning,case-based reasoning, threshold adjustment, Japaneseto-Braille translation

1

Introduction

Rules and cases are valuable knowledge representations that mutually supplement drawbacks of each.
While rules embody understanding that has been codiﬁed over the years by experts, cases contain the knowledge of a domain in a relatively unprocessed form.
Rules are appropriate for representing general domain
knowledge and cases are appropriate for representing
the exceptions of that knowledge[1, 2, 3]. By combining rules and cases, it is possible to solve problems
accurately and quickly, and to acquire new cases fromproblem-solving results[4, 5].
In previous methods using both rules and cases[4,5, 6, 7], rule-based reasoning (RBR)[8] is performed
ﬁrst, and then case-based reasoning (CBR)[1, 2, 3] isperformed. RBR solves a problem tentatively, but alsochooses an exceptional case set. Retrieving the chosencases, instead of all cases, reduces the processing timeof case application, consequently a drawback of CBR.
In the previous methods, only a common threshold©ISAROB 2007685

of similarity is used to judge which exceptional cases
should be applied. Although the threshold is manually
adjusted, such adjusting is diﬃcult because the appropriate threshold diﬀers with each exceptional case.
In this paper, we propose an adaptive knowledge
base (AKB), which is composed of a rule base, an
indexed case base and a method for adjusting cases’
thresholds. The proposed method eliminates the manual adjustment of the threshold and provides higher
qualiﬁed solutions than the existing methods with thesame knowledge base. In AKB, each case has a threshold in order to reuse exceptional cases that the existing
methods cannot reuse because of the uniﬁed threshold.
The thresholds are automatically adjusted after caseacquisition. Providing a threshold for each case makes
it easier to automatically adjust thresholds. Moreover,adjusting the threshold for each case increases the opportunity of reuse and decreases the risk of incorrectuse.
This paper also proposes a Japanese-to-Brailletranslation system AJ2B which uses AKB as mentioned above. Japanese-to-Braille translation is a task
involving two procedures – sentence segmentation andkanji-to-kana conversion. Accurate automatic translation is diﬃcult due to the ambiguous, complicatedrules peculiar to Japanese-to-Braille translation andJapanese language itself. A Japanese sentence is astring of characters concatenated without blanks, so
spaces must be inserted between words to get a properinterpretation. Kanji must be converted to kana, because Braille characters expressing Japanese correspond only to kana.
Experimental results shows that the case acquisition can reduce errors, that the threshold adjustment
signiﬁcantly reduces segmentation errors, and that the
proposed system reaches almost the same translation
quality as the most popular software on the market
called “Extra Ver.4.0.”

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

TC: mid
TC : large
TC: small(a) Boundary with large Tc .

(b) Boundary with small Tc .

(c) Boundary with adjusted Tc .

Figure 1: Boundaries depending on TC .

2
2.1

Step 3: If a compelling case was found, apply the operator it suggests, else proceed to apply theoperator suggested by the rules.

Proposed adaptive knowledge base
Overview

In an attribute space, rules are represented as
hyperrectangles and cases are represented as points
whose coverage areas become Voronoi diagram. Although RBR quickly solves problems by applying a
few rules, irregular or exceptional problems are diﬃcult for RBR to solve. CBR can accurately solve these
problems by applying many cases. Unfortunately, processing time increases proportionally to the number ofcases used.
AKB utilizes rules to represent general knowledge,
and cases to represent exceptional knowledge. This
causes the knowledge base to maintain only negative
cases, which have diﬀerent operators from rules thatthey belong to. Positive cases, which have the same
operator as rules that they belong to, are used only
for learning process, not for solving problems. Consequently, the proposed knowledge base solves problems
more accurately than RBR and more quickly than
CBR because of less amount of knowledge than CBR.

2.2

Problem solving process

The fundamental idea of combining rules and cases
is to apply the rules to a target problem to produce adraft solution; but if the target problem is judged tobe compellingly similar to a known exceptional case of
the rules, then the exceptional case is applied rather
than the rules[4, 5, 6, 7]. The idea above is therefore
realized through the following procedure:
Step 1: Use the rules to select an operator to apply.
Step 2: Search for exceptional cases that would contradict this choice of operator, stopping if
and when a compelling case is found.

©ISAROB 2007

686

2.3

Threshold adjustment

In the existing method using both rules and cases,
the case coverage areas become hypersphere whose radiuses are decided by the uniﬁed threshold TC in theattribute space. Large TC prevents incorrect case application, but it also decreases the chances to reusecases correctly are also decreased. In contrast, small
TC makes case coverage areas large and increases thechanges to apply cases, but it increases the possibilityof inadequate case application.
The AKB involves a threshold adjustment method
for the hybridization of rules and cases. First, in the
proposed knowledge base, each exceptional case hasa threshold for deciding whether the case should beapplied. This enables control of inﬂuence sphere at
every exceptional case. Second, each threshold are
adjusted automatically at a learning stage, while theexisting method requires a developer to adjust the uniﬁed threshold manually. The AKB, therefore, reuses
cases which are not reused eﬃciently in the existing
method and prevents reusing cases which are reused
incorrectly in the existing method.
The thresholds are adjusted one by one by the following procedure:
Step 1: Initialize a threshold TCx of an exceptionalcase Cx by 1.0 − δT .
(L)

Step 2: If TCx ≤ TCx , terminate.
Step 3: For all learning data PL in the root rule ofCx :Step 3a: Apply Cx if Sim(P, Cx ) > TCx .
Step 3b: If no error occurs, TCx ← TCx − ΔT and
go to Step 2.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
operator, and a priority score to resolve rule conﬂicts. A condition is stated by checking the value of)an attribute. Attributes are obtained by morphological analysis, e.g., parts of speech, character type, amora, and reading which is a pronunciation written in
kana. All of those attributes are deﬁned as symbolic
attributes.
A case consists of an operator, a set of attributes ofKanji-Kana Conversion
morphemes to which the operator of this case makes
revision, and an identiﬁcation number of a root rule,i.e., the rule for which the case is an exception. We
deﬁne an object to which an operator of a rule or a casemakes revision, i.e., a string or an interval between
characters, as a spot.

4
Figure 2: Japanese-to-Braille translation process.
Step 3c: If errors occur, TCx ← TCx + ΔT andterminate.

3

Application to Japanese-to-Brailletranslation

The task for translating Japanese into Braille is deﬁned as translation of mixed Japanese into Braille, andis done in two steps – sentence segmentation and kanjito-kana conversion. A Japanese sentence is a stringof characters concatenated without blanks, so spaces
must be inserted between words to get a proper interpretation. Kanji must be converted to kana, because Braille characters expressing Japanese correspond only to kana.
The many translation rules that must be obeyed,
are themselves ambiguous and full of exceptions[7, 9].
In sentence segmentation, for example, rules requirethat semantic and phonetic information be considered.
In kanji-to-kana conversion, rules for distinguishing ordinary vowels are unclear, i.e., ‘ ’, ‘ ’, ‘ ’, ‘ ’,and ‘ ’, and the symbol ‘ ’ , denoting a long vowel,
are used in writing, whereas a long vowel in regularJapanese is only used to express words of foreign origin or imitation sounds. It is thus very diﬃcult to represent rules so that they are followed automatically by
a computer.
Cases and rules segment sentences and revise
strings in a draft. A rule consists of an operator (example: insert a space), conditions for applying the©ISAROB 2007

687

Experimental results

We compared AJ2B and the most popular Japaneseto-Braille translation software on the market — Extra
Ver.4.0. A document “The Copyright Act” is used forevaluation. In the document, all sentences are divided
into two groups — even sentences for training andodd ones for evaluation. The opposite experiments
in which odd sentences are used for training and evensentences for evaluation are also performed. J2B[6, 7]
is also tested for evaluating the eﬀectiveness of threshold adjustment and case acquisition.
J2B and AJ2B utilizes a Japanese morphological analyzer called ChaSen with modiﬁed dictionary which
does not involve extra foreign compound words. AJ2B
parameters are decided as follows: TCLR = 0.9, TCLS =0.9, ΔT = 0.01.
Figure 3 shows the number of errors and translation
time of the proposed method and Extra in each experiment. Errors are categorized into pronounce andsegmentation errors.
As shown Figure 3), case acquisition in J2B is significantly eﬀective in decreasing errors. This is because“The Copyright Act” is a document that deﬁnes laws
and that involves a lot of the same wording and quite
similar wording. The important point to note is thatthe case acquisition in J2B can eliminate almost halfof segmentation errors . This indicates that the ﬂexibilty of cases is eﬀective in representing exceptional
segmentation knowledge of Japanese-to-Braille translation. In addition, the threshold adjustment can also
reduce segmentation errors to almost 80% of J2B withcases. This indicates that the threshold adjustment
can expand the ﬂexibility of each case. In contrast,
the threshold adjustment has no eﬀectiveness on pronounce errors because cases for revising the pronounce

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Acknowledgements

5.0

400
218
4.0

This research was partially supported by the Ministry of Education, Science, Sports and Culture,
Grant-in-Aid for Young Scientist (B), No.16700412,
2004–2006.

4.0
3.6

3.8
3.0

3.2
114

200

90

96

2.0

164
100

109

99

108

0

Translation time [s]Number of errors

300

1.0

Segmentation errors
Pronounce errors
Translation time

0.0
Extra ver.4
w/o user dic.

J2B
w/o cases

J2B
w/t cases

AJ2B(a) The Copyright Act: Even senteneces are used forlearning and odd sentences are used for test.
5.0

400
4.0

4.0

References[1] C. K. Riesbeck and R. C. Schank (1989), Inside
Case-Based Reasoning.
Hillsdale, New Jersey:
Lawrence Erlbaum[2] R. C. Schank, and A. Kassand, and C. K. Riesbeck(1994), Inside Case-Based Explanation. Hillsdale,
New Jersey: Lawrence Erlbaum

300
3.5

3.5
3.0

3.2
200

2.0

90
144

73

100

59
1.0

87

[3] B. Bartsch-Spörl, M. Lenz, and A. Hubner (1999),
Case-based reasoning - survey and future directions, Proceedings XPS-99, Springer Verlag, LNAI

Translation time [s]Number of errors

148

71

71

J2B
w/t cases

AJ2B

0

Segmentation errors
Pronounce errors
Translation time

0.0
Extra ver.4
w/o user dic.

J2B
w/o cases(b) The Copyright Act: Odd senteneces are used forlearning and even sentences are used for test.
Figure 3: Experimental results[4] A. R. Golding and P. S. Rosenbloom (1991),
Improving rule-based systems through case-based
reasoning, in In Proceedings of the Ninth National
Conference on Artiﬁcial Intelligence, pp. 22–27[5] —— (1996), Improving accuracy by combining
rule-based and case-based reasoning, Artiﬁcial Intelligence, vol. 87, pp. 215–254

errors should be applied only to the same or quite similar wordings.

[6] S. Ono, Y. Hamada, Y. Takagi, et al.(2000), Interactive japanese-to-braille translation using casebased knowledge on the web, in Proceedings of the
Sixth Paciﬁc Rim International Conference on Artiﬁcial Intelligence, pp. 638–646

A glance at the graphs in Figure 3 also reveals thatthe threshold adjustment does not aﬀect the translation time, although translation time increased by usingcases.

[7] S. Ono, Y. Takagi, Y. Hamada, et al.(2003),
Japanese-to-braille trnslation and error correction
support using case-based knowledge, (in Japanese)
Human Interface, vol. 5, no. 4, pp. 491–498

5[8] F. Hayes-Roth, D. Waterman, and D. Lenat(1983), Building Expert Systems. Addison-Wesley
Publishing Co., Reading

Conclusions

In this paper, we proposed an adaptive knowledge
base composed of rules and cases. The proposed
knowledge base acquires exceptional cases from existing Braille documents and adjusts their threshold for
judging whether the case should be applied or not.
Experiments in Japanese-to-Braille translation problem showed that acquiring cases and adjusting theirthresholds reduce errors.

©ISAROB 2007

688

[9] E. Suzuki, S. Ono, T. Hiraoka, et al. (1997) Interactive japanese sentence segmentation system fortranslating japanese into braille, in Proceedings of
the Natural Language Processing Paciﬁc Rim Symposium, vol. 1, 1997, pp. 621–624

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Language Evolution and the Baldwin Eﬀect
Yusuke WATANABE
Reiji SUZUKI
Takaya ARITA
Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
E-mail: watanabe@alife.cs.is.nagoya-u.ac.jp, reiji@is.nagoya-u.ac.jp, arita@nagoya-u.jp
Abstract
Recently, a new constructive approach characterized
by the use of computational models for simulating theevolution of language has emerged. This paper investigates the interaction between the two adaptation processes in diﬀerent time scales, evolution and learning of
language, by using a computational model. Simulation
results show that the ﬁtness increases rapidly and remains at a high level, while the phenotypic plasticity increases together with the ﬁtness but then decreases andgradually converges to a medium value. This is regarded
as the two-step transition of the so-called Baldwin eﬀect.
We investigate the evolutionary dynamics governing theeﬀect.
Keywords: language evolution, Baldwin eﬀect, genetic algorithm, recurrent neural network, artiﬁcial life.

1

Introduction

Humans are the only species that has evolved sophisticated language. For hundreds of years, many researchers
have investigated why and how it could be possible. Recently, a new constructive approach characterized by theuse of computational models for simulating the evolution of language has emerged. Language is an emergent
system that has been created and maintained throughlanguage faculty evolution in a long time scale and cultural change in a short time scale, and thus these models
treat either biological evolution or cultural evolution of
language. The most obvious purpose of language is tocommunicate information. If we use natural selection to
explain the evolution of language faculty, an individual
carrying a “beneﬁcial” grammatical mutation must havea higher ﬁtness. However, how could the mutation be
beneﬁcial, if all the other less-evolved individuals in the
population could not have understood her [1]. Therefore,
it is a very plausible idea that learning combined withevolution played a crucial role in the evolution of language. We focus on the interaction between these two
adaptation processes driving the evolution of language
in diﬀerent time scales by using a computational model
based on the constructive approach.
The Baldwin eﬀect, which is the focus of this paper,
explains the interaction between evolution and learning
in general by paying attention to balances between beneﬁt and cost of learning through the two steps [2]. In
the ﬁrst step, life time learning gives individual agents
chances to change their phenotypes. If the learned traits
are useful for agents and make their ﬁtness increase, they

©ISAROB 2007

689

will spread in the next population. The learning behavior
acts as a beneﬁt in this step. In the second step, if theenvironment is suﬃciently stable, the evolutionary pathﬁnds innate traits that can replace learned traits (genetic
assimilation), because of the cost of learning. Throughthese steps, learning can accelerate the genetic acquisition of learned traits without the Lamarckian mechanism,
which has been clearly demonstrated with a variety ofmodels [3]. When analyzing the interaction between evolution and learning, one of the most important aspects
is the cost of learning, because the second step of the
Baldwin eﬀect can not occur, if learning is ideal, in otherwords, there is no cost at all arising from the learning
process.
We adopt a speaker-hearer model proposed by Batali[4], in which each agent used a simple recurrent neuralnetwork and structured utterance, in other words, partially compositional communication could be obtained bylearning from each other. We use the model in a combined framework of cultural learning and genetic evolution. Adopted cultural learning is an extended versionof Iterated Learning proposed by Kirby and Hurford [5],which is based on vertical (oblique) communication fromadults to children and horizontal communication between
adults. Evolution of the weights in the neural network
is achieved by a genetic algorithm. In order to examine
whether and how the Baldwin eﬀect might occur, we use
a mechanism for the evolution of the plasticity (learnability) of each weight in the neural network as we did in
[6].

2

Model

A conceptual overview of the model is shown in Figure 1. There are two types of communication: vertical(oblique) communication which is unidirectional transmission from adults to children and horizontal communication which is bidirectional transmission between adults(Figure 2). In the ﬁrst stage, each child agent learns tointerpret the characters produced by a biological parent
and randomly-selected cultural parents in each communicative episode. In the second stage, a communicative
episode is repeated between a pair of grown-up agents of
their generation in which each agent alternates between
learning to interpret sequences of the characters producedby other agents and producing sequences of characters.
Then, the next generation is produced based on the ﬁtness of agents based on their communicative accuracy
in the ﬁrst and second stages. In the third stage, each

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

VerticalHorizontal
communication
communicationcnth generation.79 .88 .02 .33 .56 .97

lifetime
Verticalcommunication①②

Recurrent
network

Genetic
inheritance

Culturalinheritance

1

1

0

0

1

1

BP Learning

d

①

③

Recurrent
network

②

c

③

d

Character aprocessinga.77 .81 .01 .93 .11 .21
1

1

0

1

0

0

Character selection(n+1)th generation

cda(uttered sequence)
HearerFigure 1: Overview of our model.
1

Cultural parents

1

1

0

1

0

0

1

0

(Sun)

1

0

0

(bright)

Figure 3: A communicative episode.

Parents
BiologicalparentSpeaker
Structural pattern

AdultsSpeaker
Children
HearerSpeaker or hearer
Horizontal communication

Vertical communication

Figure 2: Two types of communication.

agent as a parent just produces sequences of charactersfor their biological and cultural children as their parents
do for them in the ﬁrst stage.
There are two forms of linguistic representation inthis model: 1) I-language: Internal language as patterns of connection weights in the neural network, 2)
E-language: External language as sequences of utteredcharacters. Linguistic information in I-language can be
inherited from a generation to the next generation via
the following two ways: 1) genetic inheritance: initialconnection weights of the agents are transmitted to theirchildren through evolutionary operations (Lamarckian
inheritance is not adopted), 2) cultural inheritance: Elanguage is produced from I-language through use andis transmitted to the I-language of the next generation
through learning (vertical communication).
Each agent uses a simple recurrent neural network (Elman network) consisting of three layers of neurons (4character input units each of which corresponds to eachof 4 character (a, b, c, or d) and 30 context input units,
30 hidden units and 10 output units). A communicative
episode is illustrated in Figure 3. Agents produce sequences of characters to encode structural patterns (vectors) each of which stores 10 real values between 0 and
1. The values in the patterns are partitioned into twogroups: the left four of the values are taken as encoding a subject and the right six of the values are taken
as encoding a predicate. There are 5 patterns each forthe subjects and predicates, and therefore 25 diﬀerent
patterns.
In the beginning of each communicative episode, a subject and a predicate are randomly selected. In order tochoose which character to send at each point in a sequence, the speaker agent determines which of the fourcharacters would bring its own output pattern closest to
the structural pattern being conveyed. She stops sending
if all the speaker’s output units are correct for the structural pattern. If the sequence of characters which the©ISAROB 2007

690

agent produces does not reach a limit length of ten characters, the agent succeeds in producing the sequence ofcharacters. The hearer agent then processes the sequenceof characters sent by the speaker, and produces an output
pattern. The back-propagation algorithm is conducted tomodify the weights of the network using the diﬀerence between the speaker’s and the hearer’s structural patterns.
The network is trained until it converges.
Biological evolution is achieved by a genetic algorithm
as follows. Each agent has a pair of chromosomes containing the same number of genes initially assigned to random values. Each gene in the chromosome GW encodes
the initial connection weight in the neural network, andeach gene in the chromosome GP represents whether thecorresponding connection weight in the neural network is
plastic (“1”) or not (“0”). If a gene of GP is 0, the corresponding connection weight is invariable in the lifetime.
GW consists of a real value within the range [−1.0; 1.0].
Agents obtain a reward when they correctly interpret asequence of characters or when they successfully produce
a sequence of characters in a communicative episode regardless of the hearer’s success. Total rewards when thesecond stage is completed are used as their ﬁtness values. A new population is generated by the tournament
selection, and then a mutation is applied with a prespeciﬁed probability. A mutation in GW changes the current
value into a randomly generated value within the range[−1.0; 1.0] and a mutation in GP ﬂips the current binary
value.

3

Experiments

We conducted an experiment for 140 generations. The
following parameters were used: N (number of agents) =100, N p (number of parents) = 5, r (reward) = 1, m
(mutation probability) = 0.01, s (tournament size) = 2,
Lv (number of learning iterations for vertical communication) = 990000, Lh (number of learning iterations forhorizontal communication) = 1485000. The initial population was generated on condition that initial values in
GW were taken at random within the range [−1.0; 1.0]
and the proportion of “1” in GP for each agent was uniformly distributed within the range [0.05; 0.95] at intervals of 0.05.
Figure 4 shows the transitions of the ﬁtness that is theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1

0.6

Plasticity of population
Fitness

an agent

0.5

0.8

0.4

Fitness

0.6

0.3

0.4

0.2
0.2

0.1

0
0

20

40

60

80

100

120

140

Generation

0
0

Figure 4: Fitness and plasticity of population.

0.2

0.4
0.6
Plasticity of population

0.8

1

Figure 7: Correlation diagram of ﬁtness and plasticity of
population.

1
The beginning of vertical communication
The end of vertical communication
The end of horizontal communication
0.8

0.6

0.4

0.2

0
0

20

40

60

80

100

120

140

Generation

Figure 5: Linguistic coherence among the same generation.

average reward of a agent per communicative episode andthe “plasticity of population” which is the ratio of “1” inall GPs of the population. We see that the ﬁtness increased rapidly during the ﬁrst several generations andkept high afterward, which means the agents have developed an accurate communication system through evolution and learning. Plasticity increased together with theﬁtness, but then decreased and gradually converged tosome medium value (genetic assimilation). This is a typical two-step evolution caused by the Baldwin eﬀect, a
key concept clarifying the interaction between evolution
and learning.
Figure 5 shows the transitions of the coherence at the
beginning or end of each stage. Coherence is the average
1
Linguistic coherence among two successive generations

0.8

0.6

0.4

0.2

0
0

20

40

60

80

100

120

140

Generation

Figure 6: Linguistic coherence among two successive generations.

©ISAROB 2007691

ratio of agents uttering the majority sequence for every
possible structural pattern. The coherence both after theﬁrst and second stages increased rapidly and remained at
a high level around 0.85. Also, the coherence before theﬁrst stage (innate coherence) moved from 0.1 to 0.2 in theﬁrst step. The diﬀerence in coherence between before thesecond stage and the third stage is supposed to mean thediversity ampliﬁed by the selected cultural parents in theﬁrst stage. Figure 6 shows that the coherence among two
successive generations tends to increase while it shows achaotic oscillation.
Table 1: A part of the characters shared most in the140th generation. In most of the sequences, we observedthat subjects correspond to suﬃxes (bold font) and predicates correspond to preﬁxes (underline).
subjects and predicates 1000 1011 0101
011100bbd
bbc
bd
101001aaacad100011ca
cccd

Table 1 shows a part of the sequences used by a majority of the population in the 140th generation. The
agents in the 140th last generation tended to share a little
shorter sequences than previous generations. Also, syntactic regularities in the order of token sequence tended
to be observed more clearly.
Here, we investigate the evolutionary dynamics whichgoverns the Baldwin eﬀect. The agents in the ﬁrst generation varies greatly both in the amount of the plastic
phenotypes and the connection weights of the network.
Agents with more plastic connections could communicate
with others successfully in this situation and therefore
could occupy the population within several generations.
Figure 7 shows the correlation between plasticity and ﬁtness in the 1st generation. This also supports the scenariothat the plasticity drives the evolution in this step.
In the second step, plasticity gradually decreased toabout 0.55 around the 140th generation while keeping
the ﬁtness high. This shows a dramatic change between
the both steps in the necessity of the plasticity caused by
the change in genetic organization. We conducted several additional experiments in order to clarify the factors

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1.00 0.95 0.05 0.00

by making the relationship between genotype and phenotype less correlated [8].
Another aspect of implicit cost, which is purely speciﬁc to linguistic evolution, is related with the variationin parents. In the ﬁrst stage, each child learns sequences
uttered from the biological and randomly selected cultural parents. Overlearning to the parents with new local dialect owing to mutation could cause a decrease in
rewards both in vertical communication and succeeding
horizontal communication.

Figure 8: The distribution of the proportion of the plastic
connection weights in each locus.

driving the evolution in the second step. As a result, thefollowing factors drastically decreased the selection pressure for the evolution of plasticity compared with theevolution of the connection weights, and thus it gradually decreased to 0.55 by a random drift in the second
step.
The ﬁrst factor is the decrease in the necessity of learning caused by a linguistic shift towards easier language.
The variation in the initial connection weights decreased
rapidly in the ﬁrst stage, which made the learning in thesecond stage easier because the language that should belearned and shared among agents became nearer to theinnate language of each agent. The fact that the variation in the plasticity in the population decreased rapidly
in the ﬁrst stage also decreases the selection pressure forplasticity. We see that the coherence was about 0.1 inthe ﬁrst generation while it was 0.2 in the generations of
the second step, which supports this explanation.
There is another language-speciﬁc factor. Figure 8
shows the distribution of the proportion of the plastic
connection weights in each locus. It is shown that thearchitecture of the network (the location of the plastic
connection weights) evolved to be identical. Also, there
is a possibility that regularization and compactiﬁcationin the uttered sequences played a role to become easier
for agents to acquire. The fact that the coherence among
two successive generations tended to keep high afterward
is supposed to show this possibility. These factors are
speciﬁc to language evolution and seem parallel with theidea by Deacon that language evolves to be adaptive tohuman cognitive capacity [7].
The second factor is implicit cost associated withlearning. In our experiments, there is no explicit learning
cost in learning such as ﬁtness tax which is proportional
to the learning period. Also, the length of each communicative episode seems enough for learning to converge,
which is supported by the result of the experiment (notshown) in which the length of the episode was doubled.
However, the interactions among weights or genetic epistasis based on abundant plasticity could cause the bad
eﬀect in the back-propagation learning, leading to thedecrease in the reward. In this sense, phenotypic plasticity evolves under such selection pressure [6]. This type of
cost becomes larger, as the necessity of learning decreases
through the phase of evolution. Note that epistasis could
have an opposite function to repress genetic assimilation©ISAROB 2007

692

4

Conclusion

This paper investigates the interaction between evolution and learning of language by using a computationalmodel which we believe to be a minimal model to capture
the essence of it. We have found that the factors speciﬁc to language evolution or linguistic behavior might
have a crucial role in shaping its evolutionary pathways.
Speciﬁcally, it has been shown that the second step inthe Baldwin eﬀect (genetic assimilation) could be driven
by the random drift caused indirectly by the adaptive
shift in language or overlearning to a variety of parents.
It should be noted that genetic assimilation in this evolutionary scenario does not necessarily need unchanged
linguistic environment.

References[1] Geschwind, N.: Some Comments on the Neurology
of Language in Biological Studies of Mental Processes(ed. D. Caplan), MIT P, 1980.
[2] Turney, P., Whitley, D. and Anderson, R. W.: Evolution, Learning, and Instinct: 100 Years of the Baldwin
Eﬀect, Evolutionary Computation, 4(3), 4-8, 1996.
[3] Hinton, G. E. and Nowlan, S. J.: How Learning Can
Guide Evolution, Complex Systems, 1, 495-502, 1987.
[4] Batali, J.: Computational Simulations of the Emergence of Grammar in Approach to the Evolution of
Language, 405-426, Cambridge UP, 1998.
[5] Kirby, S. and Hurford, J. R.: The Emergence of Linguistic Structure: An Overview of the Iterated Learning Model in Simulating the Evolution of Language(ed. A. Cangelosi and D. Parisi), Springer Verlag,
2002.
[6] Suzuki, R. and Arita, T.: The Baldwin Eﬀect Revisited: Three Steps Characterized by the Quantitative
Evolution of Phenotypic Plasticity, Proc. of 7th Eur.
Conf. on Artiﬁcial Life, 395-404, 2003.
[7] Deacon, T. W.: The Symbolic Species: The Coevolution of Language and the Brain, Norton, 1997.
[8] Yamauchi, H.: The Diﬃculty of the Baldwinian Account of Linguistic Innateness, Proc. of the 6th Eur.
Conf. on Advances in Artiﬁcial Life, 391-400, 2001.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A distributed algorithm of group robots applied to maze searching
T. Onitsuka
O. ArakiInstitute of Pure and Applied Physics
Tokyo University of Science
Tokyo, Japan
Abstract

leader who controls the group.

In this study, we proposed a simple algorithm ofgroup robots, which assumes to work independently
works on each of the robots. The algorithm uses local distance information without speciﬁc centralized
control. Usually they move around randomly and individually. Whether the distance between the robots
increases beyond a constant value or decreases below
another constant switches their behavior into shrink
mode or expansion (random search), respectively. In
the shrink mode, a robot which is farthest from theothers is selected as a leader, and the others go straight
toward the leader until the distance decreases below
the value. Applying this algorithm to maze searching on computer simulations, we observe interesting
properties as follows: (1) The parameter values whichswitch shrink and expansion modes aﬀect the performance of solving the maze problem, suggesting theimportant relation between the values and the structural scale of the maze. (2) Proceeding and exploring
behaviors with dividing and merging subgroups are
self-organized. This leads to better performance anaverage compared with the results by a set of robots
composed of a random searching robot.

1

Introduction

Recently, robotic technologies have been developed
such as humanoid robots who can walk using two
legs, micromachines with electromagnetic oscillation,and so on. Generally, to make them more intelligent,
more complicated algorithms and control systems areneeded. However, even an insect can show complicatedand intelligent behaviors, which suggests that a groupcomposed of simple and distributed agents may havethose functions. In this study, we focus on the algorithm of each simple component, which shows intelligent behaviors as a system. In other words, intelligent
behaviors emerge, even though each robot follows thesame and simple algorithm. Thus, there is no speciﬁc©ISAROB 2007

T. Tamura
Personal Computer & Network Company
TOSHIBA Corporation
Tokyo, Japan

693

Research approaches inspired by emergent intelligent behaviors of swarms is called Swarm Intelligence (SI)[1][2][3]. SI systems are typically made up of
a population of simple agents interacting locally with
one another and with their environment. Although
there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Representative examples of
SI systems are Ant Colony Optimization (ACO)[4] andParticle Swarm Optimization (PSO)[5][6]. In a swarm
of insects or a school of ﬁsh, when one ﬁnds a desirable path, the rest of the members will follow it in
PSO. Assuming an evaluation function over the search
space, the vector of velocity of each agent is repeatedly modiﬁed depending on the agent’s position with
global minimum and on the local information. As thiscalculation proceeds, every agent tends to move toward the position where the objective function has an
optimized value. While every agent follows a simple
and the same algorithm, the group quickly reaches tooptimized position as a group. Our study assumes thesituation in which the objective function is not clear.
We focus on clustered and searching behaviors such as
swarm intelligence without using the objective function over the ﬁeld.
The purpose of this study is to propose a simple algorithm in each agent distributed when the objectivefunction cannot be used. Each agent can utilize limited local information and communicate each other.
The key of the algorithm is the alternative modes of
expansion and shrink. Each robot always measuresthe distance from each other, and notices a leader ofthe group when the distance increase beyond a constant. Every robot except the leader follows the leader
until the distance decreases below another constantvalue (shrink mode). Then, each robot goes random
direction again (expansion mode). While the simulation robots are searching in the maze, we can observe
not only the performance of how soon they reach theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

goal but also emergent behaviors of robustness andadaptability of the system. Our algorithm does notassume centralized control structure. Thus, the system will not stop, even if some of them are lost or
broken. And our algorithm is so simple that we canimplement it on the hardware robots easily. The mostimportant feature of our algorithm is random searching and a clustered behavior like swarms or a school ofﬁsh at the same time. In this study, we did computer
simulations in a maze using software robots to show
the properties of the algorithm. The parameter values
which decide mode switching are shown to be crucial
to the performance of the maze searching. Depending on the values, the system was able to reach a goal
earlier than random searching.

2from the rest may ﬁnd a new way, which will promote
exploration as a group.

Algorithm

As a ﬁrst step of our study, we constructed an agent
model such as a swarm as simple as possible, whosealgorithm is shown as follows(Fig.1): First, each robot
moves around randomly without a leader (“expantionmode”(Fig.1-(1)). Each one assumes to go straight in
one direction until it reaches a wall, then it reﬂects
to another direction. The reﬂection angle is randomly
selected in order to avoid inﬁnite bouncing in a deadend. When a robot whose distance from the otherrobots exceeded a constant value of a parameter (d1),the robot becomes a leader (Fig.1-(2)). At this time,at least two robots or more become leader’s candidates. The position where the previous “expansion”
mode starts in each robot is memorized, and the distances between the position and the current position
are compared. Then the robot whose value is largest
is selected as a leader. If the distance is also the same,
it is randomly selected. This is because we think one
which was advanced larger from the previous expansion should be a leader. Once the leader decided, theother robots go straight toward the leader (“shrinkmode”(Fig.1-(3))). Only the leader keeps going before
as it does, while the other robots changes each direction toward the position of the leader and keep following it at an equal speed until the longest distance
between any two robots decreases below another constant parameter (d2). Then, the leader is dismissed
and the group’s movement returns back to the ﬁrst
free searching (Fig.1-(4)). The velocity of all motions
is assumed to be constant except when the direction ischanged, when the mode has changed, and while one
is following a leader. These procedures are repeated
and the leader is selected every time the distance exceeds d1. This algorithm expects that some robots far©ISAROB 2007

694

Figure 1: Schematic behaviors of the algorithm
Although an obstacle or walls in the maze may also
split the group into some subgroups during searching,
there is no problem because the leader is selected ineach subgroup. As a result, two or more groups might
coexist and continue searching, which will lead to aneﬃcient search. When the leader is decided, the robots
those follow the leader are limited in the range thatthe distance from the leader is less than the value ofthe third parameter (d3). This limitation is needed
so that the eﬀect of the robot whose position is too
far away on the selection of a next leader becomes
weaker. This may partially avoid back-and-forth motion of each group.

3
3.1

Computer simulations
MethodTo examine properties of the above-mentioned algorithm, we did computer simulations using softwarerobots in a simple maze. We observed behaviors ofrobots (N =20) in the maze (Fig.2), which start at theposition in the corner and search around until theyﬁnd the goal in another corner. In addition, we measured the time (number of procedural steps) spent by
all of the robots from the start to the goal. The result
is averaged over 100 trials with diﬀerent random seeds
for each combination of parameter values. When arobot reaches the goal, it assumes to stay there without becoming a leader.
The size and the width of walls of the maze is shownas follows:map size = 500x500 dot, and wall width =The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 2: map
20 dot. Parameter values are as follows unless otherwise indicated:d1 = 60 - 140 (10 interval), d2 = 50,
d3 = d1+25, Number of robots = 20, and Speed ofrobots = 6 dot/step.

3.2

Time to the goal

Figure 3 shows the average number of steps until all
robots reach the goal as the value of d1 changes from60 to 140 with ﬁxed value of d2 and d3. The result
suggests that there exists an optimal value around 110
for d1 (When d1=110, the mean value indicates 4299
steps). When d2 changes with ﬁxed d1 and d3, theaverage steps are shown in Fig. 4 (d1=100,d3=150).
The eﬀect of d2 on the attainment is not so clear as
d1.

Figure 4: Average steps to the goal (d1 = 100, d3 =150)

For comparison, we tried another set of simulations
using a swarm of robots, which do not follow our algorithm but follow just individual random searching.
They do not expand or shrink together. Each robot
goes straight, reﬂects at the wall to a random direction, and stops when it reaches the goal. In this case,it takes 5425 steps on the average for all of them toreach the goal. Thus our algorithm shows better performance an average.
These results show that the value of the parameters, especially d1, aﬀects the performance of solving
the maze problem, which suggests the relationship between the values and the scale of the maze. Whend1 is smaller than the width of a dead end, it will bediﬃcult to escape from there. To escape there andexplore other ﬁelds, d1 should be as large as the size.
When d1 is much larger than the size of the maze, aleader is not be selected and emergent behaviors are
random like a Brownian motion. Thus, matching of
the parameter values are needed for the scales of themaze (width of the roads, for example) to improve theperformance. Adaptation or dynamical ﬁtting of theparameter values are one of our future problems.

3.3

Figure 3: Average steps to the goal (d2 = 50, d3 = d1
+ 25)

©ISAROB 2007

695

Decentralized search

We observed interesting behaviors during the mazesimulations. Two of them are shown in these subsections.
One mass of robots at ﬁrst divides a few times at thewalls as the time proceeds. Then each group explores
here and there independently(Fig 5). This is one of themost interesting features of SI, decentralized search.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Acquired information during searching about position
or evaluation, which is not communicated in current
version, would be useful to make the behavior more
intelligent.

The behaviors such as proceeding and exploring in
the maze emerge in spite of the simple algorithm using
only local information i.e. distance from each other.
This behavior can be regarded as emergent SI. Application to another problem, making the algorithm moregeneral and distributed, and hardware implementation
are also our future works.

4

Figure 5: Decentralized search

3.4

Self-organized adaptation

Self-organized adaptational behaviors to circumstances are also observed. On the straight way they
pass through there together, each explores independently in a closed way, and they return backward together. This is due to the shrink and expansion mechanism, which regularly corresponds to the searching
behavior in the maze.

Conclusion

In this study, we proposed a simple algorithm ofgroup robots, which independently works on each of
the robots. The algorithm uses local distance information without speciﬁc centralized control. Applying
this algorithm to maze searching on computer simulations, we observe interesting properties as follows:(1) The values of the parameters, especially d1, aﬀect
the performance of solving the maze problem, suggesting the imporatn relation between the values and thestructural scale of the maze.
(2) Proceeding and exploring behaviors with deviding
and merging subgroups are self-organized. This maylead to better performance compared with the results
by a set of robots composed of a random searchingrobot.

References[1] E. Bonabeau & M. Dorigo & G. Theraulaz, SwarmIntelligence: From Natural to Artiﬁcial Systems,
Oxford Univ Press, 1999.
[2] J. F. Kennedy & R. Eberhart & Y. Shi, SwarmIntelligence, Morgan Kaufmann Pub., 2001.
[3] A. P. Engelbrecht, Fundamentals of Computational
Swarm Intelligence, John Wiley & Sons, 2006.
[4] M. Dorigo & T. Stutzle, Ant Colony Optimization,
MIT Press, 2004.

Figure 6: Escape from blind alley
Decision policy of choosing a remote leader promotes getting out from closed ﬁelds. Another important parameter d2 tends to limit the closest distance between the robots. While following the leader,each distance rarely decreases less than d2 because thespeed of the leader and the others are the same. Whenthe leader encounters a wall, which may be in a deadlock, the robot will be dismissed from the leader withthe distance condition satisﬁed.

©ISAROB 2007

696

[5] J. Kennedy & R. Eberhart, “Particle swarm optimization,” Proc. of the IEEE Int. Conf. on Neural
Networks, Piscataway, NJ, pp. 1942-1948, 1995.
[6] M. Clerc & J. Kennedy, “The Particle SwarmExplosion, Stability, and Convergence in a Multidimensional Complex Space,” IEEE Transactions on
Evolutionary Computation,Vol. 6, pp. 58-73, 2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Interaction of agents in financial markets and informational method to quantify it
Aki-Hiro Sato
Department of Applied Mathematics and Physics
Graduate School of Informatics
Kyoto University, Kyoto, JAPAN 606-8501
Abstract
In this article the informational method to quantify behavioral similarities of the market participants
is proposed regarding the financial market as a manybody system. An agent-based model of a financial
market in which N market participants deal with M
financial commodities is considered. In order to measure the agents’ interaction the spectral distance defined by the Kullback-Leibler divergence between two
normalized spectra of behavioral frequencies is introduced. The validity of the method is evaluated byusing the behavioral frequencies obtained from theagent-based model. It is concluded that the perception and decision parameters of agents who treat two
commodities tend to be similar when those behavioral
frequencies are similar.

1

introduction

A concept of complex systems [1] is expected to provide new insights for the intelligence since the knowledge and wisdom which are composed of the intelligence are widely distributed into the whole humanbeings. Recently economically motivated problems
seem to become considerable issues in the literatureof the artificial intelligence. When one regards economical processes as phenomena which occur in the
many-body systems one realize that enormous amountof agents who interact with one another on the fieldform a complicated dynamical network and that they
emerge through such agents’ dynamical network [2].
Therefore one recognizes that the agent-based modeling is a promising methodology in order to deepen
an understanding of such economical processes at a
microscopic level.
In recent years processing performance of computers and storage capacity almost allow us to gather together large amount of data of economical processes [3]and to directedly simulate economical processes forthe order of population of the human-beings. Specif-©ISAROB 2007

697

ically there exist some studies on a method to simulate large size systems and an application of simulation techniques to social/economical phenomena.
Moreover an large amount of data about economical
processes are available due to spreading of information and communication technology. When we treat
with a phenomenon having large degree of freedom orlarge amount of data it is important to find a rule toreduce the information. When one numerically simulates such agent-based models one faces to the problem for inferring the whole parameters of agents fromobservable quantities of the objective. And one immediately senses that the number of parameters which
one has to infer in order to make the agent-based simulation are explosive. Generally speaking it is hardto infer the whole parameters of a multi-dimensional
nonlinear dynamical system from the less observable
quantities than the number of parameters. Two kindsof possibilities to cope with this matter are addressed:(1) to employ the GA algorithm for the agent model,
and to infer the whole parameters asymptotically.
(2) to compare the behaviors of agents, to quantify
relative differences among agents, and to infer distributions of parameters.
In this article according to the second possibility
the method to quantify behavioral similarities amonginteracting agents is considered [4]. An agent-basedmodel of the financial market is formalized [5] and usefulness of the proposed method is evaluated throughthe agent-based simulation.
The remainder of this article is organized as follows.
In Section 2 the agent-based model of the financial
market where N market participants exchange M financial commodities is formalized. In Section 3 the informational method to quantify the behavioral similarity of market participants is considered. As the resultof applying the proposal method to the behavioral frequency it is confirmed that the behavioral frequencies
have a tendency to be similar when both perception

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

and action parameters of the market participants are
similar. Section 4 is devoted into concluding remarks.

2

Model

A model of the financial market where N market
participants deal with M financial commodities is considered (see Figure 1). Each market participant perceives information from environment and makes a decision of his/her investment attitude based on the information and make an action. The actions for themarket participants to be permitted are buying, selling and waiting.
exogenous informationy ij (t)commoditiess i (t)endogenous information

Financial Market

Furthermore it is assumed that the information is
described as the endogenous factor, moving average oflog return over T , plus the exogenous factor, si (t):Market participants

Figure 1: A conceptual illustration of the agent-basedmodel of the financial market where N market participants exchange M financial commodities.
For simplicity the i-th market participant perceives
information xi (t), which is evaluated as a scalar value.
This information builds a momentum in which eachmarket participant decides his/her investment attitude. The market participant interprets the information and determines his/her attitude on the basis of
the interpretation. Since the possibility of interpretation is very high and dependent on time and market
participants uncertainty for the i-th agent to interpret the information xi (t) at time t is assumed to bemodeled by random variables »i (t). And the interpretation xi (t) + »i (t) drives feeling to determine his/her
investment attitude. Furthermore the feeling about
the feeling valid the feeling and drives his/her actions.
In order to model the feeling about the feeling we introduce a multiplicative factor aij (t) which represents
the feeling about the feeling of the i-th market participant for the j-th financial commodity. If aij (t) is
positive then the feeling about the feeling supports the
feeling. If aij (t) is negative then the feeling about the
feeling refutes the feeling. The absolute value of aij (t)represents intensity of the feeling about the feeling.
Since the determination depends on both the feelingand the feeling about the feeling the investment at-©ISAROB 2007

titude is assumed to be determined from the valueΦij (t) = aij (t)(xi (t) + »i (t)). If it is large then themarket participant tends to make a buying decision.
Contrarily if it is small then he/she tends to make aselling decision. For simplicity it is assumed that a
trading volume can be ignored.
The action is determined on the basis of his/herfeeling about the feeling. Since the decision and action have strong nonlinearity the action is determinedwith Granovetter type threshold dynamics [6]. In order to separate three actions at least two thresholds areneeded. Defining the threshold for the i-th market participant to determine buying the j-th financial comSBS
B(t) (θij(t) > θij(t)),
modity as θij(t) and selling it as θij
three investment attitudes (buying: 1, selling: -1, andwaiting: 0) are determined byB(Φij (t) ‚ θij
(t))
 1
B
S
0
(θij (t) < Φij (t) < θij(t)) .
yij (t) =(1)S¡1 (Φij (t) • θij (t))

698

xi (t) =M
XS
B
Cik (|θik(t)|, |θik(t)|) £

k=1

1 X
Rj (t ¡ τ ∆t) + si (t),T ¿ =1T(2)

S
B
where Cij (|θij(t)|, |θij(t)|) represents focal points of
the i-th market participant for the j-th financial commodity. It seems reasonable to assume that Cij (x, y)
is a monotonically decreasing function of x and y.
The excess
PN demand for the j-th financial commodity, N ¡1 i=1 yij (t), drives the market price of thej-th financial commodity [7]. To guarantee positivemarket prices, we choose a log return,

Rj (t) = log Sj (t + ∆t) ¡ log Sj (t),(3)

and define the log returns as the excess demand,
Rj (t) = °N ¡1NX

yij (t),(4)i=1where ° is a positive constant to show a response of the
return to the excess demand. Furthermore the total
behavioral frequency to submit their quote request of
the j-th financial commodity is defined as
1 X|yij (t)|.
∆t i=1N

Aj (t) =(5)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

In order to simulate the agent model we assume
Cij (x, y) = CAj (k)/(x2 + y 2 ) (C > 0), and aij (t) =a(t) + wij (t), where a(t) and wij (t) is sampled fromthe Gaussian distribution. a(t) is assume to be a more
slowly varying random variable than wij (t). As shownin Figure 2 market price changes Rj (t) show fat-tailed
fluctuations. The probability density function of pricechanges is leptokurtic and have fat-tails.
0.08
0.06
0.04

R(t)

0.02
0

similar then one may thinks that perception and decision parameters of these agents have a tendency to be
similar. In order to quantify the similarity of two behavioral frequencies the informational method with anasymmetric spectral distance defined by the KullbackLeibler divergence between two normalized spectra of
behavioral frequencies is employed. The reason employing this method is because the spectral distancecan measure similarity of the underlying dynamics aswell as that of time series.
The idea to measure the similarity of the behavioral
frequencies Ai (t) of the i-th financial commodity is toevaluate the power normalized spectra. By using thediscrete Fourier transform of Ai (t),

-0.02
-0.04

Ãi (n) =

-0.06

‡
·
xi (t) exp ¡2πint/N(0 • n • N ¡ 1),

t=0

-0.08

0

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
t

(6)
(a)

the power spectrum is estimated by103

probability distribution function

N¡1
X

Pi (n) = Ãi (n)Ãi (n)⁄210where ⁄ represents the complex conjugate. Because
the Kullback-Leibler divergence (KL) is a functionalof two normalized functions [8, 9] one needs to normalize the power spectrum in order to apply it to power
spectra,Pi (n).
(8)pi (n) = PN ¡1
n=1 Pi (n)

1

10

1

-1

10

-0.06

-0.04

-0.02

0
R

0.02

0.04

0.06

(7)

(b)

The similarity of two spectra is defined by
Figure 2: Time series of market price changes (a) and aprobability density functions for them (b) for N = 500,
M = 10, T = 50, Λ = 0.23, σ = 0.1, a„ = 0.1, σa = 2.0,
C = 100.

3

MethodMarket participants operate terminal computersand submit their quotation when they want to buy
or sell several amount of financial commodities. Since
their intention (internal states of the market participants) drives their behavior, observing their behaviors seems to be equivalent to inferring their internal
states. Hence comparing their behavior may lead tocomparing their perception and decision parameters.
In order to measure similarity of the agents’ parameters from their behavior it is necessary to specify arepresentative quantity. One of candidates for specifying such a representative quantity is the behavioral
frequency. If the behavioral frequencies of agents are©ISAROB 2007

699

Kij =

N
¡1
X
n=1

pi (n) log

pi (n).
pj (n)(9)which is a non-negative Kij ‚ 0, and asymmetricKij 6= Kji . Kij = 0 if and only if pi (n) = pj (n). What
the direct current component is ignored is equivalent
to eliminating the mean value of the behavioral frequency.
In general an asymmetric matrix can be described
as Kij = Jij + Iij by using the transformation Jij =(Kij + Kji )/2 and Iij = (Kij ¡ Kji )/2. Specifically Jij is called a symmetric Kullback-Leibler divergence (SKL). The SKL possesses a symmetric andnon-negative properties: Jij = Jji and Jij ‚ 0 if andonly if pi (n) = pj (n). Of course an alternative symmetrical divergence, for example, Jensen-Shannon divergence [9], is also applicable for measuring the difference between power spectra.
Under the assumption that their whole actions areobservable one can calculate the spectral distance ofthe sum of actions for the ith commodity and theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

jth commodity. If the spectral distance is small thenthe agents who treat the ith commodity and thejth commodity seem to have similar focusing points
and strategies. Namely we may estimate the rate of
the market participants who have the same focusing
points and the same strategies through the behaviors
of agents.
This method to quantify the behavioral similarities of the market participants is demonstrated by using the agent-based model of financial market. From
the numerical simulation of the agent-model of thefinancial market in which 10 financial commodities
are dealt relationships between the similarities of theagents’ parameters and the behavioral similarities arecalculated. Figure 3 shows the SKL matrix as a fully
connected network in which the thin/thick lines between two nodes (financial commodities) exhibit similar/dissimilar behavior between these financial commodities. It is found that the total behavioral frequencies of two financial commodities fluctuate similarly if
the agents’ parameters are similar.

fined by the Kullback-Leibler divergence between two
normalized power spectra of the behavioral frequency
was calculated. It is conclude that the parametersof the agents who treat two commodities tend to be
similar when the behavioral frequencies for two commodities are similar.

Acknowledgements
This study was partially supported by a Grantin-Aid for Scientific Research (# 17760067) from the
Ministry of Education, Culture, Sports, Science andTechnology.

References[1] H. Haken, “Information and self-organization:a macroscopic approach to complex systems”,
Springer-Verlag, Berlin (1988).
[2] M. Aoki, “New Approaches to Macroeconomic
Modeling: Evolutionary Stochastic Dynamics,
Multiple Equilibria, and Externalities as Field
Effect”, Cambridge University Press, New York
(1996).
[3] M.M. Dacorogna, R. Gençay, U. Müller, R.B.
Olsen, and O.V. Pictet, “An introduction to highfrequency finance”, Academic Press, San Diego(2000).
[4] A.-H. Sato, “Quantifying similarity between markets with application to high-frequency financial
data”, the Physical Society of Japan, 75, 084005(2006).

Figure 3: The SKL matrix among 10 financial commodities calculated from the behavioral frequencies
obtained from the agent-based model for N = 500,
M = 10, T = 50, Λ = 0.23, σ = 0.1, a„ = 0.1, σa = 2.0,
C = 100 is shown as a fully connected network inwhich the thin/thick lines exhibit similar/dissimilar
nodes (financial commodities).

4[6] M. Granovetter, “Threshold models of collective
behavior”, The American Journal of Sociology,
Vol. 83, pp. 1420-1443 (1978).
[7] J.L. CcCauley, “Dynamics of Markets”, Cambridge University Press, Cambridge (2004).
[8] R. Veldhuis, and E. Klabbers, “On the computation of the Kullback-Leibler measure for spectral
distance”, IEEE transactions on speech and audio
processing, Vol. 11, pp. 100-103 (2003).

Summary

The agent-based model where N market participants exchange M financial commodities was formalized. In order to measure the similarity of two total
behavioral frequencies the spectral distance matrix de-©ISAROB 2007

[5] A.-H. Sato, “Frequency analysis of tick quotes on
foreign currency markets and the double-thresholdagent model”, Physica A, 369, pp. 753-764 (2006).

700[9] J. Lin, “Divergence measure based on the Shannon entropy”, IEEE Transaction on information
theory, Vol. 37, pp. 145-150 (1991).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Trend Predictions of Tick-wise Stock Prices by Means of Technical Indices
Selected by Genetic Algorithm
Seiji Tokuoka and Mieko Tanaka-Yamawaki
Department of Information and Knowledge Engineering, Tottori University, Tottori 680-8550
Japan
Abstract
We propose a systematic method for predicting thetrend of the price time series at several ticks ahead of
the current price by means of genetic algorithm, used
to optimize the combination of the frequently usedtechnical indices such as various moving averages,
deviation index from the moving averages, and so on.
We show that the proposed method gives goodpredictions on the directions of motion with the rate ashigh as 80% for multiple stocks of NYSE selectedfrom four different .business types. We also show thatthe performance improves if we combine two or three
indices, compared to the case of using a single index.
However, the performance seems going down as we
increase the number of the indices from the optimum
value.

1. Introduction
Forecasts of financial time series is a fascinating
game to most of us. This game became more attractive
than ever with the recent progress of Internet.
Tick-wise price motions are by themselves thrillingnews for day-traders. Many of them use the pricecharts as well as other information to help theirdecisions. Still no comprehensive enough way is
known to compile those elements of knowledge into asystematic procedure. In this paper, we attempt toestablish such a systematic procedure to make us win
the game by using evolutional computations to identify
a combination of useful technical indices applied on
the real-world tick data. Since the tick-wise prices
have strong trends and features [1], unlike the dailyclose values of stock prices known to be the random
walk, we expect such predictions quite possible.
Indeed we have observed a long-term stability of thetick-wise price fluctuations in terms of the direction ofmove of the next tick [2]. For example, the conditional
probability of the up motion after the down motion isalmost like a universal constant for several years [3].
P(up|down) = 1 – P(down|down)=0.7(1)
Guided by this fact, we have attempted to predict©ISAROB 2007701

the immediate future price by means of evolutional
computations [4].
However, it is hard to use the information of thenext tick in a practical situation. It would be more
useful to know the trend of the price time series a few
minutes, or few hours ahead of us, to win the forecast
game, for example. We therefore talk about predicting,say, the price level at 10-tick ahead by using several
technical indices.

2. Tick data of stock prices
We have selected 8 stocks from NYSE, bychoosing two from each of the four different groups ofbusiness, including retail stores, computer business, oil,automobile. We use bid-prices of those stocks for ayear of 1993 (January 1 to December 31). We show thesymbols, business types, total ticks in the year of 1993,
and the average interval between ticks in Table 1.
Table 1 : Tick-wise stock prices in 1993
symbol
BBY
SMRT
APC
BP
CA
IBM
F
GM

businessretailretailoiloilcomputercomputerautomobile
automobile

ticks
54821
12525
23685
73562
65051
455233
194561277241interval (s/tick)
109
473
253
83
92
14
32
23

3. Elements of Technical Analysis
There are two kinds of approaches for financial
forecasts: technical analyses and fundamental analyses.
In the technical analyses, investors use the pricemotions such as chart patterns to predict the future
price, while in the fundamental analyses they use
global information on the company such as financial
statements and health, its management and competitive
advantages, and its competitors and markets

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

comprehensively, to judge the essential value of thestock under consideration.
In this paper, we use the technical analyses topredict the intra-day price motions. Although there arenumbers of indices found in the literatures, we selectthe ones that are supposed to be effective for the
intra-day analysis. For example, candlesticks are notincluded in our analysis. We focus on the following
three types of indices:(1) Price trends (MA, EMA, MAD, SMA-LMA)(2) Price oscillations (MACD, RCI, RSI, MO,PHL)(3) Volume oriented indices
The investors use their favorite combinations of
those indices. Our purpose is to study the way ofchoosing the most suitable combination of the indicesfor each stock and their conditions by employing theevolutional computation, and establish the automatic
prediction generator on the trend of the pricemovement at several ticks ahead.

4. Our Method
4.1 Quantization of indices
Each index is quantized into a number of finite
states. For example, the moving average (MA) cantake two different states in comparison to the price,
PRICE > MA or PRICE < MA.. On the other hand, therate correlation index (RCI) ranges between -100 and
100, which are quantized into five states of interval 40,
such as [-100, -60], [-60, -20], [-20, 20], [20, 60], and[60, 100]. Table 2 shows the number of states we havechosen for each index.

4.2 Combination of indices determined by GA
The total number of the possible combinations of
10 indices is 1023. By using the genetic algorithm, we
attempt to search for the best combination for eachstock under various conditions.

4.2.1

Gene representation

A chromosome is a set of indices determined bymeans of genetic algorithm. The best indices aresupposed to be chosen after learning is completed.

Fig. 1: Two examples of gene coding

4.2.2

Genetic evolution based on adaptability

We have used the following parameters in theprocess of learning in genetic algorithm:
Population=100, Number of generations repeated= 500,
Crossover rate= 0.9, Mutation rate= 0.01. We use theelite preservation selection as well as the rouletteselection.

4.2.3

Resulting combination of indices

The optimum combinations of indices obtained byapplying the above method on the eight stocks areshown in Table 3.
Table 3: Best combination of indices for eight stocks

Table 2 : Number of state for each index
Label

Index

0

MO1(Momentum)3

1

MO2(Two-step-Momentum)

92

MA1(Moving Average)2

3

MA2(LongMA+ShortMA)

6

4

RSI(Relative Strength Index)

65

MACD(MA Converg/Diverg)

2

6

MAD(MA Deviation rate)

4

7

RCI(Rank Correlation Index)

5

8

PHL(Psychological Line)

69

EMA(Exponential MA)

6©ISAROB 2007

Symbol
APC
BBY
BP
CA
F
GM
IBM
SMRT

# of States

Combination of indicesMO1,MA1,MACD,RCI
MO2,RSI
MO2,MA2,MAD
MO1,MA2
MO2,MA1,MA2
MO2,MAD,PHL
MO2,RSI,MAD
MO2,MA2,MACD

4.3 Pattern classifications
Once the indices are selected, the prediction
strategies are determined for all the possible states ofthe set of multiple indices by using data. For the choiceof MO1, MA, and RCI, there exist 30(=3×2×5)
combination of the states as shown in Fig.2.

702

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 5: Parameter used for each index
Index

MO1
MO2
MA1

MA2

short
long

RSI

MACD

Fig. 2: A pattern represented in the tree structure

4.4 Method of Learning
The prediction strategy is identified as the majorityof the direction of motion at X-ticks after the pattern ofeach combination of states appears. For example, theprediction strategy of X-ticks after the state (MO=0,
MA=0, RCI=0) appears is learned to be “DOWN”,
since there are more DOWN events than UP events
after this pattern in the learning data as illustrated inTable 4.
Table 4: Strategy table(MO1,MA,RCI)UP
DOWN
Strategy
0,0,0
0,0,1
0,0,2

55
23
123

…

…

…

…

120
6
100

DOWN
UP
UP

2,1,3
2,1,4

23
8

123
4

DOWN
UP

5. Testing new data
5.1.1

Setting

We test those prediction strategies on new data. The
last half of the data set is served for this purpose. The
prediction referred here is the UP/DOWN motion of
the price at 10 ticks ahead compared to the current
price.

5.1.2

Index used for the test

We examine the following three cases.
1. One for each index (total 9)
2. Combination of indices obtained in Section3.4
3. Combination of all the indices©ISAROB 2007

703

short
long
signal

MAD
RCI
PHL

EMA

short
long

Period
1
1
10
5
15
10
5
15
5
10
10
25
5
15

6. Result
Figure 3 shows the result of IBM and SMRT. The
combination of indices selected by GA is the highest,though index 2, index 6, and index 9 are almost at thesame level. This fact tells us that the combination ofindices improves the result.
Indices related to moving averages, for example,
MA, EMA, and MAD are proved effective forprediction. While MACD and RSI are not good alone,they improve the prediction rate if combined withother indices such as moving averages.
The combination of all the ten indices also
performed well on the busy stocks of dense quot rates,
although the result is poor for slow stocks like APC,
BBY, and SMRT, which implies that the combinationof all the indices requires heavy sized data for learning.
Figure 4 shows the prediction rate according to thenumber of indices. The best rate is shown for every
size of the combined indices. For the case of APC, thecombination of 3 indices performs best, and the rate
decreases as the larger number of indices are combined.
For GM, the rate of correct prediction increases as thenumber of indices increases up to 5, then starts
decreasing after that.
The prediction rate improves when two or moreindices are used. However, the combination of too
many indices lowers the hitting rate.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 6 : Prediction rate according to stock symbols
Brand

Coming true rate (%)

1staverage

APC

57.35

55.23

BBY

58.79

57.34

BP

65.36

63.44

CA

63.03

61.77

F

68.7

67.75

GM

78.54

77.37

IBM

82.25

81.38

SMRT

67.15

64.02

7. Conclusion

Fig.3: Prediction rate for IBM (above) and SMRT
(below) according to each index

In this paper, we have extracted prediction
strategies from the tick data of eight stocks of NYSE,
by using the optimum combinations of technicalindices chosen for each stock symbol based on genetic
algorithm. As a result, we have obtained correct
prediction rate as high as 81% for frequently
exchanged stocks like IBM, or GM. The average
prediction rate of eight stocks ended up to be about
65%.
We suppose that the reason of such a high rate ofcorrect prediction lies in the characteristics of thetick-wise motion of stock prices. Especially, the strong
correlation between the prices within the order of afew minutes (i.e., order of 10 ticks) resulted in thegood performance of our method.
We have seen in this study that the moving
averages are powerful tools for predicting the pricerange of the immediate future in the intraday timeseries of stock prices. We have also seen that the
combination of multiple technical indices improves theprediction power compared to the case of using single
index.

References

Figure 4:Comparison of prediction rate according to
number of indices for APC(above) and GA (below).

©ISAROB 2007704

[1] Takayasu H(Ed.) (2001), Empirical Science of Financial
Fluctuantions, Springer[2] Tanaka-Yamawaki M (2003), Stability of Markovian
Structure Observed in High Frequency Foreign Exchange
Data, Ann. Inst. Statist. Math, Vol. 55, pp.437-446[3] Tanaka-Yamawaki M, Komaki S (2003), Characteristic
Features of High Frequency Financial Time Series, Proc.
AROB8th, pp.74-77[4] Tanaka-Yamawaki M, Motoyama T (2004), Predicting the
Tick-wise Price Fluctuations by Means of Evolutionary
Computation, Proc. IEEE-CEC, pp.955-958

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Effective Indices to Characterize
Short Sequences of Human Random Generations
Masashi Mishima and Mieko Tanaka-Yamawaki
Department of Information and Knowledge Engineering, Tottori University, Tottori
680-8550 Japan

Abstract
The brain impediment such as dementia is a serious
problem today. It would be very useful if software forprivate diagnosis were available. In this paper, we show
the effectiveness of the human random generation test
(HRG) for such software and propose a set of four
indices to be used for classifying the HRG data.
Human generated random numbers have strong
characteristics compared to the computer-generated
random numbers [1,2], and it is known to be correlated
to the individual characters of subjects [3-6]. However,
the analysis using the correlation dimension or HMM[4-6] requires a long data sequence thus not suitable fordiagnoses.
We therefore focus on short sequences of HRG andsearch for effective indices to detect the sign of brain
disability hidden in the HRG data. We study data takenfrom subjects of different age groups and successfully
differentiated data from the different age groups.

The longer the gathered number becomes, the more thesubject’s load increases.
Here we report our recent study on short HRG oflength 50. Our purpose is to examine whether the shorthuman random numbers can be used for early detectingof the brain impediment such as dementia, and identify
effective indices to diagnose the symptom.
We examine various indices found in the literature[7] and select effective ones. We also propose a newindex, RP, to classify the features of the short data
sequences effectively. By using a set of four efficientindices, we have successfully classified data to theproper age groups. Assuming that the dementia is anextreme case of the shortage of memory capacity due toaging, we propose this method for diagnosis of theearly symptom of brain deterioration. We further
present our result in terms of self-organized map(SOM) [8,9].

1. Introduction

2. Method of Data Acquisition

The human random generation test (HRG) is ahandy way to measure the brain condition. It checks theflexibility of thinking in a simple manner without using
any apparatus. Earlier it was used in the filed of clinicalpsychology to test the degree of advanced stage ofschizophrenia [1]. Around 1970 in Japan, a number of
researchers in the field of developmental psychology
conducted a statistical study of HRG by collecting datafrom subjects of various developing stages, including
the elementary school pupils, high school students andgrownup adults [2]. Later we employed computer-based
analysis to detect subtle difference in the data takenfrom normal adults [3], by using the correlation
dimension as well as the technique of HMM [4-6].
However, those methods implicitly assume large-sizeddata thus not suitable for practical need of diagnosis.

Our method of data acquisition is as follows. The
subjects are asked to generate 50 numbers orally byrandomly choosing one letter from {0, .., 9}. Data areorally generated by the subjects and are immediately
recorded by the researchers into computers. The oral
test is more suitable than the writing test for detecting
the level of concentration of the subjects who tries togenerate the numbers evenly by memorizing thesequence in the past. During the test, the subjects are
directed to close their eyes or to see the ceiling in order
to avoid external disturbance. We do not specify the
speed of generations in order not to give extra pressure
to the subjects.
We have taken 50 data sets from each subject of age
20s (male and female) and 10 data sets from eachsubject of age 30s, 40s, and 50s (male and female). In©ISAROB 2007

705

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

addition, we have obtained data of three patients ofschizophrenia.

100 letters. For this reason, we consider only H definedin Eq. (1)3. Human random numbers

3.3 Coupon Score (CS)

3.1 Basic Properties
We assume that the degree of complexity of thehuman-generated random numbers reflect the capacityof the brain. If the subjects attempt to generate every
figure of 0 to 9 as well as every arrays of figures evenly,
they would employ the maximum brain capacity inorder to memorize as many figures as possible they
have generated so far. As the subjects get tired, theconcentration level would go lower Thus we use thedeviation from the randomness as the symptom of thedeterioration. We attempt to quantify the memory
capacity by using human random numbers. We
introduce the used indices as follows. In this chapter,normal person’s value is average of the 20s subject’s
data.

3.2 Entropy (H)The Shannon-entropy H is the first index to
examine the degree of randomness. H measures theaverage information obtained from a sequence of letters,
and it measures how random the letters are in asequence of letters. It can be quantified as in Eq.(1) byusing the probability pi of appearance of the i-th figure.

H = −∑ pi log pi(1)i

H takes its maximum value when every letter
appears with the same probability. Dividing thisquantity by its maximum value Hmax, we use therelative entropy for convenience. In this definition, H
ranges from 0 to 1. We assume that the higher value of
H means the more active memory capacity of the brainof the subject, in order to use the letters evenly.
It would be necessary to consider the evenness of
the arrays of letters, because, e.g., a sequence such as“0123456789” cannot be considered as random,although the nine letters appear evenly. We need tomaximize entropies of arrays of various lengths in order
to measure the randomness. However, those entropies
of arrays are not suitable to measures the randomness ofthe sequences shorter than 100, because it is too short
to have all the patterns of arrays within the capacity of©ISAROB 2007

706

The coupon score (CS) [3] is defined as the lengthof sequence before all the letters (0 to 9) appear. The
CS is approximately 30 on the average for machine
generated random numbers. However, the average of
normal person is 17, which means that human strongly
wish to use every letter compared to computer
programs. The CS occasionally takes very large values
for those who have very strong preference in choosing
specific numbers.

3.4 Turning Point Index (TPI)
The turning point index (TPI) [3] measures how
frequently the switch from ascending pattern todescending pattern, and vice versa, occurs in the data
sequence. The turning point (TP) is defined as the letter
after which the pattern changes. For example, “135426”
has two turning points, 5 and 2. The turning point index(TPI) is defined as the ratio of TP and its expectedvalue, where m (=50) denotes a maximum data size.
TPI = 100 ×TPobserved
TPexp ected

TPexp ected =2(m − 2)
3(2)

The TPI is highly vulnerable to the human brain
condition. When the subjects is active, it tends to belarger than one, while for inactive subjects or patientsin advanced stage of mental disease it tends to besmaller than one.

3.5 Adjacency (ADJ)
A remarkable feature of human generated random
numbers is the lack of repeats of the same figures
successively. Guided by this fact, we utilize theadjacency (ADJ) in order to characterize the data.
Focusing on the difference between two adjacentfigures, we classify the data by the absolute values ofthe differences (d) between adjacent figures into fourtypes, d=0, d=1, d=2, d>2. All the data indicate that therate of d=0 is extremely lower in human generated datacompared to computer generated random numbers.
Also the rate of d=1 is a good measure of mental
condition. For example, the data taken from theschizophrenia patients are characterized by an excess

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

amount of d=1 compared to the data from normal
subjects.

unrepeated pairs.
Note that RP ranges from 0 to 100, irrelevant to thesize of the data sequence, unlike NSQ.

3.6 Null Score Quotient (NSQ)
The null score quotient (NSQ) [3] measures thedegree of deviation from the even generation of pairs(array of length 2). It is defined as

NS
(3)
a2 −1
where NS denotes the numbers of pairs notappearing in the sequence and a denotes the size ofletters used. In the case of using decimal figures{0,…,9}, a=10.
This is a useful index for long sequences of HRG,
since the differences between subjects/conditions in
HRG reflect nicely in this index. However, NSQ is notsuitable for the data of length 50, which is too short tocontain all the patterns of pairs for a=10.
NSQ = 100 ×4. Study on the different age groups
We apply those indices introduced in the previous
chapter on various data including schizophrenic
patients, normal subjects in different age groups(20s,
20s, 40s, 50s and over), and computer-generated
random numbers.
Figure 1 shows the mean values of indices, RP,
NSQ, TPI, ADJ, CS, H. for 20 subjects, in the order ofascending age groups. The subject number from 1 to 10
belong to the age group of 20s, the number from 11 to
13 belongs to the age group of 30s, and the number
from 14 and 15 belongs to the age group of 40s, and thenumber from 16 to 20 belongs to the age group of 50s
and over. We do not distinguish the actual ages within
an age group.

3.7 Repeat Pattern (RP)120

where NRS denotes the number of unrepeated pairs, mdenotes the length of the sequence(m=50), and ndenotes the length of array(n=2 for pair). The more therepeated pairs, the larger the value of RP, indicate thedeterioration of the memory capacity of the subject. We
have also studied the case of n=3. The result was notvery different from the case of n=2(pair). Thus we stickto the case n=2 and consider only the case of©ISAROB 2007

707

100

in dic e s value

80

RP
NSQ
TPI
ADJ
CS
H

60
40
20
0
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20
subject number

Fig. 1. Values indices vs. individual subjects
120
100
80

indices value

We propose a new index to be used in place of NSG
for the case of short HRG. Since the subjects of HRG
try to generate the next letter based on their memory of
the last generated letter, NSQ is a good measure for the
memory capacity of the subjects. However, the problem
is that the value of NSQ ranges from 51.5 to 100 for thecase of data sequence of length 50. We need a better
index for short data.
Consider the case when the generated data is“1358763” so far, and 5 is about to come out next, one
would make an effort to avoid 5, by considering thepreviously generated 35. Human would pay all theeffort to improve the randomness (complexity, in fact).
Guided by this thought, we define a new index as thefrequency of repeated pairs, by Eq.(4).
NRS(4)
RP = 1 −m − (n − 1)

RP
NSQ
TPI
ADJ
CS
H

60
40
20
0
rand

20s

30s
40s
age groups

50s

sampleFig. 2. The value of indices vs. age groups

Fig.2 shows the mean values of the same indices as
in Fig.1 calculated for each different age group. In both
figures, the entropy H show the relative value to its
maximum, and are expressed in terms of percentage.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

In both figures, the values of indices show strong
correlation to the age groups. This fact suggests thepossibility of using HRG for dementia diagnosis. It also
indicates the effectiveness of the indices used in ourwork.

means pf those four indices. A denoting the younggroup of age 20s aggregate to the left, while C denoting
the older group of age 50 and higher aggregate to theright, and B denoting the middle age group are locatedin between.

5. SOM for multi-dimensional dataA

The indices used so far are mutually dependent. We
compute the Pearson product-moment correlation
coefficient between indices and show the result inTable.1.
Table1. correlation coefficients
RP

RP

NSQ

TPI

ADJ

CS

NSQ

TPI

ADJ

CS

H

0.97

-0.25

0.34

0.13

-0.3

-0.29

0.39

0.15

-0.33

-0.6

-0.07

0.1

0.02

0.04

-0.66

According to the result shown in Table.1, there is astrong positive correlation between NSQ and RP and
the information from those two indices are overlapping.
We prefer RP to NSQ because the range is larger in RP
for short data of length 50 thus more suitable to observe
the difference.
A negative correlation exists between H and CS. We
prefer H to CS based on the following observation. The
value of CS can easily become large if the subject
forgets to say one letter no matter how random theother parts of the data are. It is rather hard to
distinguish between a completely regular sequence but
missing one letter, and a highly complex sequence but
missing only one letter.
Thus, we are left with four indices, RP, TPI, ADJ andH. However, they are still mutually dependent. One
good way of presenting multi-dimensional data is givenby the self-organizing map (SOM), which works wellfor the case of mutually dependent multiple variables.
The result is shown in Fig.3, where A, B, C denote theage group of 20s, 30s+40s, and 50s+up, respectively.
All the indices are normalized to be in the range of [0,1]. The parameters of SOM are chosen as follows. The
Map size is 15×10, learning coefficient α is 0.5, theinitial radius is 9, and 10000 cycles of learning.
Fig. 3 shows the data are properly classified by©ISAROB 2007

708

A A

A

A

Ｂ

Ｃ

A
A
A

Ｃ

Ｂ

Ｂ

Ｃ

A
Ｂ

A

Ｃ

Ｃ

Fig. 3. RP, TPI, ADJ and H for data from three agegroups, A(20s),B(30s,40s),C(50+) represented by SOM

6. Conclusion
We have examined indices applicable for short
HRG in order to diagnose dementia, and selected a setof four effective indices including RP, which we haveproposed in this paper. We have also presented theresult by SOM. We have successfully classified the dataof different age groups, conditions, etc. according totheir characteristics by using the indices of our choice.

References[1] Early works are critically reviewed in: Wagenaar WA,(1972), Psychological Bulletin 77, pp.65-72.
[2] Ransuu Test Kenkyuukai (Society of Human Random
Generation Tests) (1973) Shizen 1973-8. pp.49-57[3] Iba Y, Tanaka-Yamawaki M (1996), Proc. 4th Int. Conf.
Soft Computing IIZUKA’96, pp.467-472.
[4] Tanaka-Yamawaki M, Masuda H, Kawagoe M (1998),
Proc.3rd AROB, pp. 610-613[5]Tanaka-Yamawaki

M(l998),Proc.

ICONIP’98,

pp.215-218;[6] Tanaka-Yamawaki M (1999), Human Generated Random
Numbers and a Model of the Human Brain Functions;
Proc.1999 IEEE SMC, pp. 223-228[7] Towse JN, Nell D (1998) Analyzing human random
generation

behavior,

Behavior

Research

Methods,

Instruments, & Computers,30(A), pp.583-59[8] Kohonen T (1995), Self-Organization maps (in Japanese)[9]SOM_PACK http://www.cis.hut.fi/research/som-research/

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Intelligent Management of Distributed Dynamic Sensor Networks
Peter Sapaty
Institute of Mathematical Machines and Systems, National Academy of Sciences
Glushkova Ave 42, 03187 Kiev Ukraine
Tel: +380-44-5265023, Fax: +380-44-5266457, sapaty@immsp.kiev.ua

Masanori Sugisaka
Department of Electrical and Electronic Engineering, Oita University
700 Oaza Dannoharu 870-1192 Japan
Tel: 097-554-7831, Fax: 097-554-7841, msugi@cc.oita-u.ac.jp

Jose Delgado-Frias
School of Electrical Engineering & Computer Science,
Washington State University, Pullman, WA 99164-2752, USA
Tel: (509)335-1156, Fax: (509)335-3818, jdelgado@eecs.wsu.edu

Joaquim Filipe
Departamento Sistemas e Informática,
Escola Superior de Tecnologia de Setúbal, 2910-761 Setúbal, Portugal+351 265 790 040, +351 265 721 869 (fax), j.filipe@est.ips.pt

Nikolay Mirenkov
University of Aizu, Aizu-Wakamatsu, Fukushima-ken 965-8580, Japan+81-242-37-2500, +81-242-37-2528 (fax), nikmir@u-aizu.ac.jp

Abstract
A universal solution for management of dynamic sensor
networks will be presented, covering both networking andapplication layers. A network of intelligent modules,
overlaying the sensor network, collectively interprets
mission scenarios in a special high-level language, which
can start from any nodes and cover the network at runtime.
The spreading scenarios are extremely compact, which may
be useful for energy saving communications. The code will
be exhibited for distributed collection and fusion of sensor
data, also for tracking mobile targets by scattered andcommunicating sensors.
Keywords: sensor networks, intelligent management,
distributed scenario language, distributed interpreter,
tracking objects, hierarchical data fusion.

1 Introduction
Sensor networks are a sensing, computing andcommunication infrastructure that allows us to instrument,
observe, and respond to phenomena in the naturalenvironment, and in our physical and cyber infrastructure [1,2]. The sensors themselves can range from small passive
microsensors to larger scale, controllable platforms. Theircomputation and communication infrastructure will beradically different from that found in today's Internet-based
systems, reflecting the device- and application-driven natureof these systems.
Of particular interest are wireless sensor networks(WSN) [3,4], consisting of spatially distributed autonomous
devices using sensors to cooperatively monitor physical or©ISAROB 2007709

environmental conditions, such as temperature, sound,vibration, pressure, motion or pollutants, at different
locations. WSN, however, have many additional
problems in comparison to the wired ones. The
individual devices in WSN are inherently resource
constrained--they have limited processing speed, storage
capacity, and communication bandwidth. These devices
have substantial processing capability in the aggregate,
but not individually, so we must combine their many
vantage points on the physical phenomena within the
network itself.
In addition to one or more sensors, each node in asensor network is typically equipped with a radiotransceiver or other wireless communications device, asmall microcontroller, and an energy source, usually abattery. The size a single sensor node can vary fromshoebox-sized nodes down to devices the size of grain of
dust.
Typical applications of WSNs include monitoring,
tracking, and controlling. Some of the specificapplications are habitat monitoring, object tracking,nuclear reactor controlling, fire detection, trafficmonitoring, etc. In a typical application, a WSN isscattered in a region where it is meant to collect data
through its sensor nodes. They could be deployed inwilderness areas, where they would remain for many
years (monitoring some environmental variable) without
the need to recharge/replace their power supplies. They
could form a perimeter about a property and monitor the
progression of intruders (passing information from one
node to the next). At present, there are many uses for
WSNs throughout the world.
In a wired network like the Internet, each router

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
connects to a specific set of other routers, forming a routing
graph. In WSNs, each node has a radio that provides a set ofcommunication links to nearby nodes. By exchanging
information, nodes can discover their neighbors and perform
a distributed algorithm to determine how to route dataaccording to the application’s needs. Although physicalplacement primarily determines connectivity, variables suchas obstructions, interference, environmental factors, antennaorientation, and mobility make determining connectivity apriori difficult. Instead, the network discovers and adapts towhatever connectivity is present.
Fig. 1 shows what we will mean as a sensor network forthe rest of this paper. It will hypothetically consist of (many)
usual sensors with local communication capabilities, and (alimited number of) those that can additionally transmit
collected information outside the area (say, via satellite
channels). Individual sensors can be on a move, some may
be destroyed while others added at runtime (say, dropped
from the air) to join the existing ones in solving
cooperatively distributed problems.
The aim of this paper is to show how any imaginabledistributed problems can be solved by dynamic selforganized sensor networks, if to increase their intelligencewith a novel distributed processing and control ideology
and technology effectively operating in computer networks.
Transmitter

Transmitter

S
S

Sensor

S

S
S

S

S

Local communication
capabilities

SensorS Sensor

S

Figure 1. Distributed sensors and their emergent network.

2 The Distributed Management Model
The distributed information technology we are using
here is based on a special Distributed Scenario Language(DSL) describing parallel solutions in computer networks asa seamless spatial process rather than traditional collection
and interaction of parts (agents).
Hierarchicalechoing andcontrolSpreadingactivities

Start
Spreadingactivities

Advances in space

Figure 2. Runtime coverage of space.

©ISAROB 2007

710

Parallel scenarios can start from any interpreter of thelanguage, and then spread and cover the distributed spaceat runtime, as shown in Fig. 2. The overall managementof the evolving scenarios is accomplished via the
distributed track system providing hierarchical commandand control for the execution of scenarios, with a varietyof special echo messages. We will mention here only key
features of DSL, as the language details can be foundelsewhere in the current proceedings [5] and from its
previous versions described in [6-8].
A DSL program, or wave, is represented as one ormore constructs called moves (separated by a comma),
embraced by a rule, as follows:wave→

rule ({ move , })Rules may serve as various supervisory, regulatory,
coordinating, integrating, navigating, and data processing
functions, operations or constraints over moves. A movecan be a constant or variable, or (recursively) an
arbitrary wave itself:
move→ constant | variable | wave

Variables classify as nodal, associated with spacepositions and shared by different waves, frontal, moving
in space with program control, and environmental,
accessing the environment navigated. Constants may
reflect both information and physical matter.
Wave, being applied in a certain position of the
distributed world, can perform certain actions in a
distributed space, terminating in the same or in otherpositions. It provides final result that unites local resultsin the positions (nodes) reached, and also produces
resultant control state. The (distributed) result and thestate can be subsequently used for further dataprocessing and decision making on higher programlevels. Parallel waves can start from different nodes inparallel, possibly intersecting in the common distributed
space when evolving in it independently.
If moves are ordered to advance in space one after theother (which is defined by a proper rule), each new moveis applied in parallel in all the nodes reached by theprevious move. Different moves (by other rules) can also
apply independently from the same node, reaching newnodes in parallel.
The functional style syntax shown above can express
any program in DSL, but if useful, other notations can beused, like infix one. For example, an advancement inspace can use period as operator (separator) between
successive steps, whereas parallel actions starting fromsame node can be separated by semicolon. For improving
readability, spaces can be inserted in any places of the
programs--they will be automatically removed before
execution (except when embraced by quotes).
The interpreter may have its own physical body (say,in the form of mobile or humanoid robot), or can bemounted on humans (mobile phones). A network of theinterpreters can be mobile and open, changing its volume
and structure, as robots or humans can move at runtime.
We will be assuming for the rest of this paper that every
sensor has the DSL interpreter installed, which may havea software implementation or can be a special hardwarechip.
In the following sections we will show and explain
the DSL code for a number of important problems to be

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
solved by advanced sensor networks, which confirms theefficiency of the proposed distributed computational andcontrol model.

3 Collecting Events throughout the Region
Starting from all transmitter nodes, the following
program regularly (with interval of 60 sec.) covers stepwise,
through local communications between sensors, the wholesensor network with a spanning forest, lifting information
about observable events in each node reached. Through thisforest, by the internal interpretation infrastructure, the lifteddata in nodes is moved and fused upwards the spanning
trees, with final results collected in transmitter nodes andsent in parallel outside the system using rule Transmit(See Fig.3).
Hop (all transmitters).
Loop (
Sleep (20).
IDENTITY = TIME.
Transmit (Fuse (
Repeat (Free (observe (events));
Hop (directly reachable, first come)))))Global
loop
Start
S

Repeated
parallelnavigation
S

S

S

Looping
updatesS

Looping
updates
S

infra
S

S

S

FailedS

S

Persistent
links

Start

HierarchicalHop (all transmitters).
Loop (
Sleep (120).
IDENTITY = TIME.
Repeat (Hop (directly reachable, first come).
Remove links (all).
Stay (create link (-infra, BACK))
)
)

S

S

Fused
data

of

In the previous program, we created the whole
spanning forest for each global data collection loop,
which may be costly. To optimize this process, we may
first create a persistent forest infrastructure, remembering
which nodes were linked to which, and then use it for afrequent regular collection and fusion of the scattereddata.
As the sensor neighborhood network may changeover time, we can make this persistent infrastructurechangeable too, updating it with some time interval(much larger, however, than the data collection one),after removing the previous infrastructure version. Thiscan be done by the following program, which regularly
creates top-down oriented links named infra startingfrom the transmitter nodes (as shown in Fig. 4).

Globalloop

SS

4 Regular Creation
Infrastructures

infra
infra

S

Activecode

S

S

Figure 4. Runtime creation of hierarchical infrastructure.
Figure 3. Parallel navigation and data collection.
Globally looping in each transmitter node (rule Loop),
the program repeatedly navigates (rule Repeat) the sensor
set (possibly, in competition with navigation started fromother transmitters), activating local space observation
facilities in parallel with the further navigation. The
resultant forest-like coverage is guaranteed by allowing
sensor nodes to be visited only once, on the first arrival inthem. The hierarchical fusion rule Fuse, collecting thescattered results, also removes record duplicates, as thesame event can be detected by different sensors, leaving
only most credible in the final result.
To distinguish each new global navigation process fromthe previous one, it always spreads with a new identity forwhich, for example, current system time may be used (using
environmental variables IDENTITY and TIME of thelanguage).

©ISAROB 2007

711

This infrastructure creation program provides
competitive asynchronous spatial processes, so each time,even if the sensors did not change their positions, theresultant infrastructure may differ, as shown in Fig. 5.

S
S

infra

S

infra

S

S

S

infra

infra
S

S

S

Figure 5. Another possible infrastructure.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Having created the persistent infrastructure, we can use
it frequently by the event collection program which can be
simplified and updated now as follows:
Hop (all transmitters).
Loop (
Sleep (20).
Transmit (Fuse (
Repeat (Free (observe (events));
Hop (+infra)))))

The global infrastructure creation program (looping
slowly) and the event collection and fusion one (loopingfast) can operate simultaneously, with the first one guiding
the latter on the data collection routes, which may changeover time.

5 Routing Local Events to Transmitters
We have considered above the collection of distributed
events in the top-down and bottom-up mode, always with
the initiative stemming from root nodes of the hierarchy, thelatter serving as parallel and distributed tree-structured
computer.
In this section, we will show quite an opposite, fully
distributed solution, where each sensor node, being an
initiator itself, is regularly observing the vicinity for the casean event of interest might occur. Having discovered an
event, each node independently from others launches aspatial cyclic self-routing process, via the infrastructure
links built before, which eventually comes to the transmitter
node, bringing with it the event information. The databrought to transmitters should be fused with the alreadyexisting there. The corresponding program will be as
follows.
Hop (all nodes).
Frontal (Transfer). Nodal (Result).
Loop unconditional (Sleep (5).
Nonempty(Transfer = observe (events)).
Repeat (hop (-infra)).
Fuse and assign (Result, Transfer))

S
S

Observing
infra

S

Observing

Observing
S

S

S

Event
discovered

S

infra

S

S
Event
discovered

Figure 6. Routing scattered events to transmitters.

The transmitter nodes, accumulating and fusing local
events, arriving from sensor nodes independently, cansend them outside the system. Different strategies can beused here. For example, one could be waiting until there
are enough event records collected in the transmitter
before sending them, and the other one waiting for somethreshold time and only then sending what was
accumulated (if any at all). The following program
combines these two cases within one solution, where
arriving data from sensors is accumulated in nodal
variable Result.
Hop (all transmitters).
Loop unconditional (Or (Quantity (nodal ((Result)) >= 100,(sleep 60. Result != nil)).
Transmit and clear (Result).
)

This program in every transmitter can work inparallel with the previous program collecting events andlooping in every sensor (in transmitters as well, as they
are assumed to be sensors too), and also with the earlier
program, starting in transmitters, for the regularinfrastructure updates.

6 Tracking Mobile Objects
Let us consider some basics of using DSL for
tracking mobile (ground or aerial) objects moving
through a region controlled by scattered but
communicating sensors. Each sensor can handle only
limited part of space, so to keep the whole observation
continuous, the object seen should be handed overbetween the neighboring sensors during its movement,along with the data accumulated during its tracking andanalysis.
The space-navigating power of the model discussed
here can catch each object and accompany it individually,
while moving between the interpreters in different
sensors, just accompanying the movement in physicalspace by an active mobile intelligence spreading in acomputer network [6]. This allows us to have an
extremely compact and integral solution unattainable byother approaches based on communicating agents. The
following program, starting in all sensors, catches anobject it sees, and follows it wherever it should go if notseen at this point any more (more correctly: if its
visibility becomes lower that the given threshold).
Hop (all nodes).
Frontal (Threshold) = 0.1.
Frontal (Object) = search (aerial).
Visibility (Object) > Threshold.
Repeat (Loop (
visibility (Object) > Threshold).
Maximum destination (Hop (all directly reachable).
Visibility (Object) > Threshold))

The program investigates the object’s visibility in all©ISAROB 2007

712

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007),
Sleep (120)).
Level == Max.
Transfer = Max & WHERE.
Repeat (hop (- infra)).
Transmit (Transfer)neighboring sensors in parallel, and moves control alongwith the program code and accumulated data (the latter notshown in this simplified program) to the neighboring sensor
seeing it best -- again, if its visibility exceeds the thresholdgiven (see Fig. 7).

S

Loopingin nodesS

S

Tracking mobile
intelligence

S

S

S

S

S

)

S

S

S

S

MovingobjectS

Region of interest

Figure 7. Active tracking of a mobile object.
This was only a skeleton program in DSL, showing thespace tracing techniques for controlling single physicalobjects. It can be extended to follow collectively behavinggroups of physical objects as a whole (say, flocks of animals,
mobile robots, or troops). The spreading individual objecttracing intelligences can cooperate in the distributed sensor
space, and can be optimized jointly for the pursuit of global
mission goals.

7 Averaging Parameters from a Region
Let us consider how it can be possible to asses thegeneralized situation in a distributed region given, say, by aset of its border coordinates, in a fully distributed way,where sensors located in the region can communicate withdirect neighbors only. Assume, for example, that theparameter of interest is maximum pollution level throughout
the whole region (it may also be temperature, pressure,radiation level, etc.) together with coordinates of thelocation showing this maximum.
The following program, starting in all sensors located in
the region, regularly measures the pollution level in its
vicinity, updates local maximum, and by communication
with direct neighbors attempts to increase maximum there
too, if this is possible. Eventually, after some expected timeof such local communication activity, all sensors will havethe same maximum value registered in them, which will be
the one of whole region too (see the overall organization in
Fig. 8).
Nodal (Level, Max, Region).
Frontal (Transfer).
Region = region definition.
Hop (all nodes, Region).
Loop unconditional (Or parallel (Loop unconditional (Sleep (5).
Level = measure (pollution).
Stay (Level > Max. Max = Level).
Transfer = Max.
Hop (directly reachable, Region).
Transfer > Max. Max = Transfer©ISAROB 2007

S
Generalized
result S
Emergent
leadernode
S Self-routing713

to transmitter

S

S

Local communications

Figure 8. Distributed averaging with active routing.
As there may be many sensors located in the region ofinterest, we will need forwarding only a single copy ofthis resultant maximum value to a transmitter for an
output. This can be achieved by delegating this only tothe sensor whose measured local value is equal to theaccumulated maximum in it, which will correspond tothe overall region’s maximum after some time. Having
understood that it is the leader (after a proper time delay),such a sensor organizes repeated movement to thenearest transmitter via the earlier created infrastructure,carrying the resultant maximum value in frontal variable
Transfer, outputting it outside the system in thetransmitter reached, as shown in Fig. 8.
Similar organization may be introduced for finding
averaged values, or even for assembling the global
picture of the whole region with any details collected byindividual sensors (the latter may be costly, however,with more practical solution shown in the next section).

8 Assembling Full Picture of a Region
To collect details from some region via local sensors
and merge them into the whole picture could, in principle,
be possible via local single-level exchanges only, as in
the previous section, but the amount of communications
and data transfer, as well as the time needed, may beunacceptably high. We were finding only a single value(maximum) via frequent internode communications, withminimal length exchanges. But for obtaining the detailed
global picture of the region or some distributed
phenomenon, we may have to gradually grow this picture
in every sensor node (or in many of them)simultaneously, with high communication intensity
between nodes. Also, there may be difficult to determine
the completeness of this picture staying in local sensors
only.
It is clear that much higher integrity and process
structuring, especially with the use of hierarchies, may
be required in order to see the whole distributed picture

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
via dispersed sensors with limited capabilities in nodes andemergent
communications.
Differenthigher-levelapproaches can be proposed in DSL for solving such classes
of problems.
We will show only a skeleton here of how to makespanning tree coverage of the distributed phenomenon, andthen hierarchically collect, merge, and fuse partial resultsfrom sensors into the global picture. The latter to be
forwarded to the nearest transmitter via the previously
created infrastructure using links infra, as shown in Fig. 9.

S
S

Initiator
S

S

Full
picture S

Self-routing
to transmitter

S
S

Echoedpartial results

S

S

Active spacecoverage

9 Conclusions
We have presented a universal and flexible approachof how to convert distributed sensor networks withlimited resources in nodes and casual communications
into a universal spatial machine capable of not only
collecting and forwarding data but also solving complex
computational and logical problems as well as making
autonomous decisions in distributed environments.
The approach is based on quite a different type of ahigh-level language allowing us to represent systemsolutions in the form of integral seamless spatial
processes navigating and covering distributed worlds atruntime. This makes parallel and distributed application
programs extremely short, which may be especially
useful for the energy saving communications between
sensors.
The code compactness and simplicity are achieved
because most of traditional synchronization and data oragent exchanges (which are also on a high level, withminimum code sent) are shifted to efficient automatic
implementation, allowing us concentrate on global
strategies and solutions instead.

References
Figure 9. Space coverage with global picture fusion.
[1] D. Culler, D. Estrin, and M. Srivastava,“Overview of Sensor Networks” , Computer,
August 2004, pp.41-49. Published by the IEEE
Computer Society.
[2] C-Y Chong,and S. P. Kumar, “Sensor
Networks: Evolution, Opportunities, andChallenges”, Proc. of the IEEE, Vol. 91, No. 8,
August 2003, pp.1247-1256.
[3] Wireless sensor network--Wikipedia, the freeHop (random, all nodes,
detected (phenomenon)).
Loop (
Frontal (Full) = fuse (Repeat (Free (collect (phenomenon));Hop (directly reachable, first come,detected (phenomenon)))).
Repeat (hop (-infra)).
Transmit (Full))encyclopedia, www.wikipedia.org.

In more complex situations, which can be effectively
programmed in DSL too, we may have a number ofsimultaneously existing phenomena, which can intersect in a
distributed space; we may also face combined phenomena
integrating features of different ones. The phenomena (likeflocks of birds, manned or unmanned groups or armies,
spreading fire or flooding) covering certain regions maychange in size and shape, they may also move as a whole
having internal organization, etc.
In the previous versions of this language [6, 7], a varietyof complex topological problems in computer networks had
been investigated and successfully programmed in a fully
distributed and parallel manner, which included connectivity,
matching with graph patterns, weak and strong components
like articulation points and cliques, also diameter and radius,
optimum routing tables, and the like, as well as selfrecovery after indiscriminate damages (see [6] especially).

©ISAROB 2007

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[4] F. Zhao and L. Guibas, Wireless Sensor
Networks: An Information Processing Approach(The Morgan Kaufmann Series in Networking),
Morgan Kaufmann, 2004, 376p.
[5] P. Sapaty, A. Morozov, R. Finkelstein, M.
Sugisaka, D. Lambert, “A New Concept of
Flexible Organization for Distributed Robotized
Systems”,
Proc.
TwelfthInternational
Symposium on Artificial Life and Robotics(AROB 12th’07), Beppu, Japan, Jan 25-27,
2007. 8p.
[6] P. S. Sapaty, Mobile Processing in Distributedand Open Environments, John Wiley & Sons,
ISBN: 0471195723, New York, February 1999,
436p. (www.amazon.com).
[7] P. S. Sapaty, Ruling Distributed Dynamic
Worlds, John Wiley & Sons, New York, May
2005,
256p,
ISBN
0-471-65575-9(www.amazon.com).
[8] P. Sapaty, M. Sugisaka, R. Finkelstein, J.
Delgado-Frias, N. Mirenkov, “Advanced IT
Support of Crisis Relief Missions”, Journal of
Emergency Management, Vol.4, No.4, ISSN
1543-5865, July/August 2006, pp.29-36(www.emergencyjournal.com).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Brain Enhancement Attempt Based on Visual Recognition
Yinlai Jiang
Department of Intelligent Mechanical Systems
Kochi University of Technology
Kami, Kochi 782-8502, Japan
096404j@gs.kochi-tech.ac.jp

Abstract
Cortical plasticity makes it possible to enhance thehealth and ability of the human brain through proper
training task. In the present study, a visual interpolation
task was proposed and two experiments were designed toevaluate it. In experiment 1, six subjects were asked to
identify English letters, whose black pixels were partiallyerased to an erasure ratio by three kinds of erasurerectangle, displayed for short time periods. Its resultsshows the correct rate’s descending trend with theincrease of erasure ratio, the enlargement of erasurerectangle or the reduction of display time. In experiment 2,
five subjects were asked to recognize the letters whichwere erased and displayed with the same parameters
during three weeks, three times per week. The
improvement in the correct rates approved theeffectiveness of the task to brain enhancement.
Keywords: letter recognition, visual interpolation,
cortical plasticity, brain enhancement.

1. Introduction
Brain, which interferes with nearly all the function ofhuman body, is considered as the most complex andimportant part of the human body. It is desirable todevelop and enhance the brain function so as to improve
the human ability for life. Furthermore, it is reported thatwith the progress of aging, the incidence of brain diseasesuch as senile dementia increase which reduces the qualityof life in aged society [1]. Therefore, improvement ofbrain ability, maintenance of brain health and preventionof brain disease are becoming more and more necessary.
The human brain memorizes, reasons and concludes on
the basis of the information from senses of vision,
hearing, touch, smell and taste. Of all the senses, vision
provides the most information which is about 83% [2]. In
order to process and respond to visual information, mostparts of the cerebral cortex such as visual cortex, parietal
association area and frontal association area are engaged[3]. Therefore, it is possible to measure the brain’s visual©ISAROB 2007

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Shuoyu Wang
Department of Intelligent Mechanical Systems
Kochi University of Technology
Kami, Kochi 782-8502, Japanwang.shuoyu@kochi-tech.ac.jp
information process ability and check the health of brain
parts associated with vision through proper visual stimuli.
Moreover, it is also possible to activate the visionassociated parts by presenting proper stimuli to the eyes inorder to improve the health and ability of these parts.
[4~7]．
In this study, a method to enhance brain function
through the human visual interpolation ability is proposedand validated. Visual interpolation ability is the ability
that humans can recognize an object based on object parts.
In order to study the human visual interpolation ability, anerased letter recognition task, in which an algorithm wasdesigned to create incomplete letters, was presented. And
on the basis of the task two experiments were designed tovalidate the effectiveness of the method.

2. Letter recognition task
A Hewlett-Packard Compaq nx9000 notebook
computer was used for letter recognition task. LCD
resolution was 1024 × 768 pixels, and color was 32 bits.
Letters were extracted from the Microsoft Paint program
installed in Windows 2000 environment. The font was
MSP Gothic, and font size was 72. Letter color was blackand the background was white. Letters were presented in
the middle of 26 bitmap images of 128-pixels length andwidth. A program was developed to partially erase theletter images, display them, and record subjects’ answers.
The program interface and a representative letter image
are shown in Fig.1.

Fig.1 Program interface and letter image
The program used rectangles to partially erase theletters. Firstly, rectangle length, width, position over theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

letters, and gradient were randomly determined. Then
black letter pixels covered by the rectangles were thenerased. This procedure was continued until the ratio of thenumber of erased pixels to the number of the black pixels
in the original image reached a predefined ratio.
2.1 Experiment 1
In experiment 1 erasure was categorized into threelevels according to rectangle size: In the first level, therectangle size was 1 pixel × 1 pixel. In the second level
the rectangle length was 4 ~ 8 pixels and width was 2 ~ 4
pixels. In the third level the rectangle length was 8 ~ 16
pixels and width was 4 ~ 8 pixels. Taking R as example,
these three levels of erasure are shown in Fig.2 in which L
denotes the length, and W denotes the width of therectangle. As can be observed, the higher the ratio, themore difficult the erased letter is to recognize. On theother hand, the bigger the rectangle, the wider the blocksof left black pixels are while the farther the remaining
parts of the letter become.
0.7

0.8

0.86

0.9

0.92

Erasure
Ratio

L: 1
W: 1

erasure ratio and display duration. In each section, 26
letters were selected and recognized at a random order.
2.2 Experiment 2
In experiment 2, the rectangle length was set to 1 ~ 16
pixels and width was set to 1 ~ 8 pixels. Erasure ratios
were the same to those of experiment. Display duration
was set to 200ms. Erased letters used in experiment 2 was
illustrated in Fig. 3. The procedure was the same to that ofexperiment 1.
0.7

0.8

0.86

0.9

0.92

L: 1~16
W: 1~8

Fig.3 Erased letters in experiment 2 (R)Five students at Kochi University of Technology served
as volunteer subjects. Subject age was 20-30 years. Vision
was normal or corrected to normal. Experiment 2 wasfinished in three weeks in order to examine changes of thesubject’s correct rates. In each week, the subjects canchoose 3 days between Tuesday and Thursday optionally.

3. Results
3.1 Experiment 1

L: 8~16
W: 4~8
Erasure Rectangle

Fig.2 Erased letters in experiment 1 (R).
Six students at Kochi University of Technology served
as volunteer subjects. Subject age was 20-30 years. Vision
was normal or corrected to normal. In the experiment,
firstly, a letter is chosen. Secondly, it was erased anddisplayed in the center of the screen for a short period.
And then a dialog was presented for the subject to selectan answer and next letter will be erased and displayed. If
the subject was not sure whether the letter was correctly
identified, he or she was instructed to skip to the next
letter. Experiments were completed in 3 days, one erasurelevel per day. Five levels of erasure ratios (0.7, 0.8, 0.86,
0.9 or 0.92) were set at each erasure level, and the display
duration was set to 300 ms, 200 ms and 100 ms at eacherasure ratio. Thus the experiment was divided into 45
sections according to three parameters: rectangle size,©ISAROB 2007

716

The results of Experiment 1 are shown in Fig.4. Six
subjects’ average correct rates of each section are plotted
as a function of the experimental parameters in Fig.4a.
And the average correct rates of different rectangle sizes,
erasure ratios and display durations are plotted in Fig.4b,
Fig.4c and Fig.4d respectively. In Fig.4b, the correct rate
decreased with the increase of rectangle size. In Fig.4c thecorrect rate almost linearly decreased as the erasure ratio
increased. The correct rate also decreased with the
reducing of display duration as is shown in Fig.4d.
1
0.9
0.8
0.7

L： 1 W ： 1

0.6

L ： 4 〜8
W:2 〜4
L ： 8 〜16
W:4 〜8

0.5
0.4

300
200
100
300
200
100
300
200
100
300
200
100
300
200
100

W: 2 ~4

Correct Rate

L: 4~8

0.7

0.8

0.86

0.9

0.92

Experiment Parameters(a) Correct rates of different experiment

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

every week while it decreased after 3 or 4 days break.

Correct Rate

1

Table.1 1st week

0.8

0.6
L： 1 W ： 1

L ： 4 〜8
W:2 〜4

L： 8 〜16
W:4 〜8

0.7

0.8

0.86

0.9

0.92

Avg

1

0.83

0.75

0.67

0.57

0.53

0.67

2

0.87

0.87

0.73

0.67

0.55

0.74

3

0.94

0.85

0.79

0.68

0.67

0.79

Table.2 2nd week

Rectangle Size

0.7

0.8

0.86

0.9

0.92

Avg

1

0.96

0.89

0.79

0.62

0.55

0.76

2

0.95

0.88

0.85

0.68

0.61

0.79

3

0.95

0.88

0.85

0.73

0.65

0.81

(b) Correct rates of different erasure levels

Correct Rate1

0.9

Table.3 3rd week

0.8

0.7
0.70

0.80

0.86

0.90

0.92

Erasure Ratio(c)Correct rates of different erasure ratios

Correct Rate

0.8

0.86

0.9

0.92

Avg

1

0.91

0.88

0.78

0.75

0.61

0.78

2

0.97

0.89

0.85

0.78

0.7

0.83

3

0.97

0.9

0.85

0.68

0.62

0.80

4. Discussion

0.9

In the present study, the human visual interpolation
ability and the training of the ability was studied through avisual interpolation task using erased letter.

0.85

0.8
300

200

100

Display Duration(d)Correct rates of different display durations
Fig.4 Results of experiment 1
3.2 Experiment 2
Five subjects’ average correct rates of each day in the
1st, 2nd, and 3rd weeks are listed in Table.1, Table.2, andTable.3 respectively. The total average correct rates of the
1st, 2nd, and 3rd weeks were 0.73，0.79，0.81 respectively.
The increment of correct rate during each week gradually
decreased. And that the ninth day’s correct rate was lower
than that of the eighth day. Compared with correct rate of
the third day in the previous week，the correct rate of thefirst day in the current week decreased. The change in thecorrect rate shows that the correct rate increased during©ISAROB 2007

0.7

717

Humans recognize and remember objects according totheir features, which are usually correlated [8, 9]. Whenbeing erased, a letter’s features are reduced as well as thecorrelations between the features. When recognizing anerased letter, the features of the image are sampled fromthe remnant black pixels and compared with thememorized features, and a decision is made based on
similarity [10]. Therefore, as the erasure ratio increases,
more features are erased (Fig.2), leading to a decrease incorrect identification rate (Fig.4a, Fig.4c). On the other
hand, when the rectangle size was 1 pixel × 1 pixel, theremnant black pixels symmetrically distributed in theerased letter images (Fig.2), it is easy for the human visualsystem to process the information, such as edge detection
and shape recognition, so that nearly all the features canbe sampled, which resulted in high correct rates close to 1.
As the rectangle size increases, the distribution of remnant
black pixels becomes asymmetrical in bigger blocks,
which makes it difficult to sample the features from theremnant black pixels [11]. At the same time, features that

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

can not be sampled also increases. So the correct rate
decreased in the experiment (Fig.4a, Fig.4b). The bigger
the rectangle, the farther the parts of the erased letter
become, which reduced the correlations between theremaining features (Fig 2). And the smaller the rectangle,
the more easily edge and shape detection can beaccomplished, which may also contribute to the increaseof correct identification rate [12]. It is suggested that
although the visual category of the stimulus shortly after
visual processing has begun (e.g., 75 - 80 ms), decision
making does not complete until 150 ms after stimulus ispresented [13]. It takes time to transmit and process visual
information in the brain. Therefore, when the display
duration was prolonged, the features that can be sampledduring the duration increased and the correct rate also
improved (Fig.4a, Fig.4d).
Cortical plasticity endows the brain with the ability toadapt to stimulus it receives. And it makes it possible toenhance the brain ability through proper training. Thechanges in brain activity that occur as humans learn new
perceptual skill through perceptual learning has beenreported by several studies [14]. In this study,enhancement of the subjects’ recognition ability was
indicated by increase of correct rate in experiment 2. The
results also suggested that the training should be done
successively n order to keep the correct rate at a high level
because the average correct rate declined after 3 or 4 days
break in the experiment.

5. Summary
In this study, a brain enhancement method through thehuman visual interpolation ability was proposed. We
analyzed the human visual interpolation ability using anerased letter recognition task. The results showed that asfeatures and correlations between features in letter images
were destroyed by erasing and as the display duration was
shortened, the correct rate decreased. And we examinedthe changes in correct rate during a three-week task. The
results indicated the possibility to improve humancognition ability through the human visual interpolation
ability.
It has been reported that perceptual skill learning has
been associated with increased activity in the inferior
temporal and fusiform gyri as skill is acquired and activityof the caudate nucleus during skill learning [14]. In future
work, we plan to measure and image the hemoglobin
levels dynamically in the brain during the visualinterpolation task in order to find out the regions thatengage in the human visual interpolation.

©ISAROB 2007

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References[1]. Hatakenaka K, Ikegami S，Arimatsu Y(1988), Brain
aging--considering the life and death of neuron (inJapanese). Kyoritsu Shuppan, Tokyo.
[2]. Treichler, D. G. (1967). Are you missing the boat intraining
aids?.
Film
andAudio-Visual
Communication, 1: 14- 16.
[3]. Fukuda J, Sato H(2002), Brain and vision ― what
and how to see?(in Japanese). Kyoritsu Shuppan,
Tokyo.
[4]. Karni A and Sagi D(1991), Where practice makes
perfect in texture discrimination: evidence forprimary visual cortex plasticity. Proceedings of the
National Academy of Sciences, 88, pp. 4966 – 4970.
[5]. Poldrack. RA, Desmond JE, Glover GH and Gabrieli
JDE (1998), The neural basis of visual skill learning:
an fMRI study of mirror reading. Cerebral Cortex, 8,
pp. 1047- 3211.
[6]. Liu Z (1999), Perceptual learning in motion
discrimination that generalizes across motion
directions. Proceedings of the National Academy of
Sciences, 96(24): 14085- 14087.
[7]. Gilbert CD (1998), Adult Cortical Dynamics. Physiol.
Rev, 78(2): 467- 485.
[8]. Bjork EL, Bjork RA (1998), Memory. Academic
Press.
[9]. Singer, HW, Gray CM (1995), Visual feature
integration and the temporal correlation hypothesis,
Annual Review of Neuroscience, 18, pp. 555- 586.
[10]. Townsend JT, Ashby FG (1982), Experimental testof contemporary mathematical models of visual letter
recognition. Journal of experimental psychology:
Human perception and performance, 8(6): 834- 864.
[11]. Pelli DG, Farell B, Moore DC (2003), The
remarkable inefficiency of word recognition, nature.
423(12): 752- 756.
[12]. Murray SO, Kersten D, Olshausen BA, PSchrater,and Woodsi DL (2002), Shape perception reduces
activity in human primary visual cortex. Proceedingsof the National Academy of Sciences, 99(23): 1516415169.
[13]. Rufin V, Thorpe SJ (2001), The time course ofvisual processing: from early perception to decisionmaking. Journal of Cognitive Neuroscience, 13(4):454- 461.
[14]. Poldrack RA (2002), Neural systems for perceptual
skill learning. Behavioral and cognitive neuroscience
reviews, 1(1):76- 83.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Designing Brain Machine Interfaces for Rehabilitation: A Study
Hema C.R. Sazali Yaacob

Abd. Hamid Adom
R. Nagarajan Paulraj M.P.
School of Mechatronic Engineering
Northern Malaysia University College of Engineering,02600 Jejawi, Arau, Perlis, MALAYSIA
E-mail: hema@kukum.edu.my

Abstract
Brain Machines Interfaces provide a digital channel forcommunication in the absence of the biological channels.
Brain machine interfaces are considered as potential
devices to rehabilitate patients with motor nerve
disorders namely those who have lost their motor
functions as well as communication functions. In thispaper a study is conducted to understand the functions of
the motor cortex region of the brain and its role in thedesigning of brain machine interfaces for rehabilitation.
A survey on the current brain interfaces is alsopresented.

Keywords
Brain Machine Interfaces, Rehabilitation Robotics, EEG

1. Introduction
Locked-in syndrome is now becoming a popularterm. Patients with this disease loose total control overtheir motor functions and communication channels. The
patients are however aware of their surroundings due to astill active brain. Modern life support and medication
help these patients to live longer lives prolonging theirpersonal and social dependencies on the society. Current
research on brain signals has shown the feasibility ofusing brain signals to directly control devices likecomputers. An extended approach is to use this signal tocontrol devices like prosthetic arms and wheel chairs.
This paper aims at highlighting some of the disease
which disrupt the biological communication channels
and the possibilities of brain signals to control external
devices through a digital link.
Many disorders can disrupt the neuromuscularchannels used by the brain to communicate with andcontrol its external environment. Amyotrophic lateralsclerosis (ALS), brainstem stroke, brain or spinal cord
injury, cerebral palsy, muscular dystrophies, multiple
sclerosis, and numerous other diseases impair the neuralpathways that control muscles or impair the muscles
themselves. They affect nearly two million people in the
United States alone, and far more around theworld .Those most severely affected may lose allvoluntary muscle control, including eye movements andrespiration, and may be completely locked in to theirbodies, unable to communicate in any way. In theabsence of methods for repairing the damage done bythese disorders, there are 3 options for restoring function.

©ISAROB 2007

719

M. Rizon

The first is to increase the capabilities of remaining
pathways. Muscles that remain under voluntary control
can substitute for paralyzed muscles. People largely
paralyzed by massive brainstem lesions can often use eye
movements to answer questions, give simple commands,
or even operate a word-processing program; and severely
dysarthric patients can use hand movements to produce
synthetic speech[1]. The second option is to restore
function by detouring around breaks in the neuralpathways that control muscles. In patients with spinal
cord injury, Electromyography (EMG) activity frommuscles above the level of the lesion can control directelectrical stimulation of paralyzed muscles, and thereby
restore useful movement .The final option for restoring
function to those with motor impairments is to provide
the brain with a new, non-muscular communication andcontrol channel, a direct brain–computer interface (BCI)
for conveying messages and commands to the external
world.
At present, only EEG and related methods,
which have relatively short time constants, can function
in most environments, and require relatively simple andinexpensive equipment, offer the possibility of a new
non-muscular communication and control channel, apractical BCI. EEG based communication attracted little
serious scientific attention until recently, for at least 3
reasons. First, while the EEG reflects brain activity, sothat a person’s intent could in theory be detected in it, theresolution and reliability of the information detectable inthe spontaneous EEG is limited by the vast number ofelectrically active neuronal elements, the complex
electrical and spatial geometry of the brain and head, andthe disconcerting trial to-trial variability of brain function.
The possibility of recognizing a single message orcommand amidst this complexity, distortion, andvariability appeared to be extremely remote. Second,
EEG-based communication requires the capacity toanalyze the EEG in real-time, and until recently therequisite technology either did not exist or was extremely
expensive. Third, there was in the past little interest in thelimited communication capacity that a first generation
EEG-based BCI was likely to offer. Recent scientific,
technological, and societal events have changed thissituation.

2. Motor Functions and the Brain
The anatomical region of the brain known asprimary motor cortex is the focal region for muscle
contractions. Stimulations in this region elicited highly
localized muscle contractions at various locations in theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

body. This mapping is represented somatotopically on
the motor cortex, where the surface area devoted tocontrolling the movements of each body part varies in
direct proportion to the precision of the movements thatcan be made by that part
The motor cortex is divided into the premotor area(or premotor cortex) and the supplementary motor area.
The premotor cortex is believed to help regulate postureby dictating an optimal position to the motor cortex forany given movement. The supplementary motor area, for
its part, seems to influence the planning and initiation of
movements on the basis of past experience. The mere
anticipation of a movement triggers neural transmissions
in the supplementary motor area. Besides the frontal
cortex, the posterior parietal cortex clearly plays a role involuntary movements, by assessing the context in which
they are being made. The parietal cortex receives
somatosensory, proprioreceptive, and visual inputs andthen uses them to determine such things as the positionsof the body and the target in space. It thereby produces
internal models of the movement to be made, prior to theinvolvement of the premotor and motor cortices.
The parietal lobes are themselves closely interconnectedwith the prefrontal areas, and together these two regions
represent the highest level of integration in the motor
control hierarchy. It is here that the decisions are madeabout what action to take. In brain imaging, whensubjects are asked to move their thumbs, activity isobserved in the posterior parietal and somatosensory
areas [2].

(8-13 Hz); theta (4-8Hz); delta (0.5 -4 Hz) [3]. Until early
1990s, most of the researches on EEG were focused on
analyzing brain related disease and sleep patterns. The
early 1990s witnessed a rapidly growing body ofresearch involving detection of human brain responses
and putting these techniques to appropriate uses to help
disabled people. Most of the research during this period
involved surgically implanting electrodes to acquire thesignals. With the introduction of external electrodes
during the turn of the century [2000] EEG has initiatedthe development of BCI to control the cursor ofcomputers. Currently this research has been directed
towards producing BMI which can control a prostheticarm or a wheelchair [1].
3.2 International 10-20 System
To perform consistent testing of EEG recordings asystem called the International 10-20 electrodeplacement system was developed [4]. This systemcreated a method of labeling electrode locations to be
used worldwide. The EEG electrodes are placed on thescalp at 10 and 20 percent of a measured distance. Figure
1 shows the international 10-20 Electrode placement
positions [4].

3. Electroencephalography
EEG is a technique that reads scalp electrical activity
generated by brain structures. The EEG is measured
directly from the cortical surface. When brain cells or
neurons are activated, the local current flows areproduced. EEG measures mostly the currents that flow
during synaptic excitations of the dendrites of many
pyramidal neurons in the cerebral cortex. Only large
populations of active neurons can generate electricalactivity recordable on the head surface; weak electricalsignals detected by the scalp electrodes are to bemassively amplified. The cortex is a dominant part of thecentral nervous system. The highest influence of EEG
comes from electric activity of cerebral cortex due to its
surface position [3].
3.1 EEG Research History
The existence of electrical currents in the brain was
discovered in 1875 by an English Physician Richard
Caton. In 1924 Hans Berger a German neurologist
amplified these electrical signals using ordinary radio
equipment and coined the term electroencephalogram todescribe brain electric potentials in humans. In 1934
Adrain and Mathews published a paper verifying theconcept of ‘human brain waves’ [or EEG] and identified
regular oscillations around 10 to 12 Hz which theytermed as alpha rhythm. Brain waves have been
categorized into four basic groups: beta (>13 Hz); alpha©ISAROB 2007

720

Figure 1 International 10-20 Electrode Placement
System

4. Brain Machine Interface: Techniques
BMI can be broadly classified into two types,
Sensory and Motor. Sensory BMI are designed to replacea damaged organ such as retinal prosthesis to help theblind and cochlear implants for the deaf. The Motor BMI,
on the other hand, seeks to translate electrical brain
activity that represents intent to move into usefulcommands to external devices. Sensory BMI requiresvery accurate placement of a few tiny electrodes that
stimulate the appropriate site in the brain, and thedevice’s job is to simulate the role of the appropriate
sensory organ as accurately as possible. In motor BMI,
the electrodes are placed “anywhere” in the appropriate
cortex area and their number is much higher. The

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

decoding problem for motor BMI is much harder, since
there is little knowledge of how the motor cortex encodes
information, and also due to only a small fraction of thecells is being probed [1]. There are two basic types ofmotor BMI: non-invasive and invasive. Research on
non-invasive BMI started in the 1980s by measuring
brain electrical activity over the scalp.
Brain computer interfaces [BCI] have beendeveloped to move computer cursors. Through training,
subjects can learn to control their brain activity in apredetermined fashion that is classified by a patternrecognition algorithm, and converted into one of several
discrete commands usually cursor actions (up/down,
left/right) on a computer display. The computer presents
a set of possibilities to the users, and they choose one ofthem through these cursor actions, until a task iscompleted. This approach, requiring only signal
amplification and classification is known as a brain
computer interface. BCI classification algorithms
combine machine learning techniques with biomedical
domain knowledge [1].
Five main techniques are adopted in designing brain
computer interface [5].
a. P300 Detection
The P300 component is a positive going evoked
response potential (ERP) in the EEG with a latency of
about 300ms following the onset of a rarely occurring
stimulus the subject has been instructed to detect.
Detecting the P300 response reliably requires averaging
the EEG response over many presentations of the stimuli.
b. EEG mu-rhythm Conditioning
Subjects’ mu-rhythm [Appears at 9-11 Hz thisactivity appears to be associated with motor cortex [6].
Amplitudes are detected while training them to move a
computer cursor up and down on the screen. Results also
implied that frequency bands other than mu and beta
ranges may contain useful information.
c. Visual Evoked Potential Detection
Electrodes are placed over the visual cortex to detectchanges in evoke potentials when the subject
concentrates on a particular block out of the 64 blocks on
a computer screen.
d. EEG Pattern Mapping
In this technique the EEG patterns are detected andclassified for a particular action. Readiness potentials or
EEG patterns are studied during experiments such as
moving joystick in four directions.
e. Detecting lateral hemisphere differences
Induced lateral differences in relative brain
hemisphere activation are studied during experiments
where subjects hear arguments through left, right or both
headphones.
4.1 Need for Brain Machine Interfaces
EEG signals are being studied to rehabilitate people©ISAROB 2007

721

with motor disorders. About 10 million people all over
the world suffer from neurodegenerative diseases such ascerebral palsy or amyotrophic lateral sclerosis [locked-in
people], stroke and paralysis [7]. These diseases impair
their ability to control their muscles and are unable tograsp objects, work with appliances or communicate inany way except through their brains. In Malaysia theincrease of stroke and paralytic patients is of major
concern, modern life support technology allows these
individuals even those who are locked–in, to live long
lives, so that the personal, social and economic burdensof their disabilities are prolonged and severe.
There is a growing concern in the community today
to help these disabled people and improve their living
conditions. The Malaysian government has implemented
many social welfare schemes such as rehabilitation andindependent living policies to improve the conditions ofthese patients through rehabilitation service and byprovision of assistive and rehabilitative devices.
4.2 Motor Disabilities and Restoring Options
Many disorders can disrupt the neuromuscularchannels through which the brain communicates withand controls its external environment. Amyotrophic
lateral sclerosis, brain stem stroke, brain or spinal cord
injury, cerebral palsy, muscular dystrophies, multiple
sclerosis and numerous other diseases impair the neuralpathway that controls the muscles themselves.
Peripheral nerve disorders like Guillain-Barré Strohl
Syndrome, Chronic Inflammatory Demyelinating
Polyneuropathy,
Polyneuropathies,
Diabetic
Neuropathies, Mononeuropathies - including carpal
tunnelsyndrome
andulnar
neuropathies
Peripheral Nerve Injuries,Amyotrophic Lateral Sclerosis(ALS) , Radiculopathies , Small Fiber Neuropathies, andOccupational
Neuropathies
also
effectthecommunication channels and these patients can beprovided rehabilitation through BMI .
Restoring these motor functions can be done
depending on the severity of the impairment. In case ofpartial impairments, restoration can be done byincreasing capabilities of remaining pathways, such as
substituting muscles under control for paralyzed muscles,
namely eye movements in the case of brainstem
impairment and hand movements in the case of severely
dysarthric patients. One other option is by rerouting
around the neural breakaways that control muscles, forexample using EMG signals from areas above the levelof nervous break to restore useful movements. When theabove two options are not possible the only solution to
restore motor function is to provide a non muscular
control channel directly from the brain to devices such as
wheelchairs. Patients with diseases like locked-in
syndrome and partial paralysis are not able to produce
any type of movement. Independent to this the sensory
and cognitive functions of the brain are not or partiallyaffected. These patients are very much aware of theirenvironment but are not able to communicate throughThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

speech or eye movements. Though many physiologicalsignals such as EMG, fMRI, MEG and PET are available,
research has proved that only EEG signals and related
methods which have short time constants, can function
in most environments and require relatively simple andinexpensive equipment that offer the possibility of a new
non-muscular channel for a practical BMI [9].
The development of BMI to control wheel chairs isstill under proof of concept stage. The next section
analyses some of the research efforts and studies towards
developing a BMI for motor movement and subsequent
control of a powered wheel chair.
4.3 Brain Machine Interfaces and Rehabilitation
EEG based BCI/BMI has been under study since theearly nineties. BCI only provide interface between brain
and computer, so far BCI have been developed to control
computer cursors on the other hand BMI are more
focused towards developing interfaces between brain anddevices like prosthetic arms , wheelchairs etc.. BMI areused to replace impaired motor nerves and to provide analternative communication channel to control devices
like a wheelchair. Research studies have been conductedto study the EEG signals evoked by motor movements
and recognition of these signals towards developing
interfaces. Most research studies on EEG are currently
focused on developing algorithms for classification of
EEG signals related to movement. A review of theliterature shows that three methods have been adopted in
extracting the EEG feature data, namely Autoregression,
Independent Component Analysis and Neural Networks.
This section reviews some of these research studies.
A DSLVQ classifier for feature selection of EEG
signals was proposed by Pregenzer et al [10]. Two
different types of experiments are used to show that
DSLVQ is an appropriate feature selector for a BCI. The
first experiment employs DSLVQ to select the mostdistinct electrode positions from a large number ofpossible positions. The second experiment uses DSLVQ
to analyze the importance of 1-Hz bands of EEG power
spectra for the prediction of three different types of
movement. The conclusions of this study show that themost important electrode position and frequency bands
are not identical for all subjects.
Guger and et al [10] use Common Spatial Pattern(CSP) filters to analyze real-time EEG signals.
Experiments involved three subjects. Twenty seven EEGelectrodes overlaying the whole primary and sensory
motor cortex are used. The method proposed uses
covariance to design common spatial patterns and isbased on simultaneous diagonalization of two covariancematrices. The decomposition of the EEG leads to newtime series which is optimal for discrimination of two
populations. The patterns are designed such that signals
resulting from filtering with CSP have maximum
variance for left trials and minimum variance for righttrials and vice versa. The research demonstrates that CSP
can be used to anise EEG signals in real time in order togive feedback to subjects as classification accuracy©ISAROB 2007

722

improved with few days of trials.
Pfurtscheller and et al [11,12] have studied theseparability of left and right motor imagery using
autoregressive parameters. Four subjects were used in
the experimental process and EEG, EMG and EOG
signals are recorded from electrodes overlapping sensory
motor area. Subject specific frequency components are
selected using the DSLVQ classifier. Due to the lateralityof the EEG patterns, the side [left or right] of theimagined movement can be determined with an online
error between 10 to 31.8 %. The online classification ofsubject specific frequency bands were analyzed by aneural network. An overall improvement of classification
was achieved using the off-line adaptive autoregressivemodel [ARR]. However the ARR method is found to besensitive to artifacts, therefore artifacts must be
controlled.
Haselsteiner and Pfurtscheller [13] have compared
two different neural network topologies to classify asingle trial EEG data from a BCI. The classifiers are the
MLP and the FIR MLP. The static weight of the standard
MLP is replaced with finite impulse response filters in
the FIR MLP. The study shows that FIR MLPs performed
better can standard MLP with lesser error rates.
Mahalanobis distance-based classifiers are analyzed
by Babiloni et al [14], to classify the diagonal and fullcovariance matrix features of the EEG signals. EEG data
are recorded from four electrodes placed in the C3, P3,
C4 and Imagined hand movement recognition using Low
Resolution Surface Laplacian and Linear P4 position ofInternational 10-20 system. These classifiers were able todetect imagination of hand movements with aclassification accuracy of 98%.
Another imagined hand movement recognition
using Low Resolution Surface Laplacian and Linear
Classifier is proposed by Concotti et al [15] which use
nine electrodes; the classifiers have an accuracy of 90%.
Neural network based classifiers of EEG features
have been investigated by some researchers [16, 17,18].
Back propagation neural classifiers have also been used
to analyze the EEG signals related to mental tasks.
Research on BMI is being extended to the next stageof translating them to control signals to operate devices.

Conclusions
BMI is still at the proof-of-concept stage, currently
this work is undertaken by bio and neuroscience
researchers. The contributions from computer engineers,
psychologists and mathematician are essential to takethis to the next stage. The developments of more accurate
data models that carry more spatio-temporal informationfrom the spikes in the motor cortex are required. The
signals are non-gaussian and no stationary, so they arevery difficult to model well with present algorithms [1].
So far, control BMI has focused on cursor
movements, applying this concept to a mechanical handor a device such as a wheelchair will prove to be more

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

challenging. Although the theoretical and technicalproblems are difficult, BMI research is at a very exciting
phase, thanks to the tight integration of research incomputer science, engineering, and neuroscience. Thereis optimism about impacting the daily lives ofparaplegics in the same way that sensory BMIs benefited
hearing impaired patients [1].
The non-invasive BMI has potential applicability
beyond the restoration of lost movement andrehabilitation in paraplegics and would enable normal
individuals to have direct brain control of external
devices in their daily lives. Therefore, the impact of BMI
on our society promises to surpass that of any earlier
digital technology.
References[1] JOSE PRINCIPE,(2005). Brain Machine Interfaces:
Mind over Matter.
http://www.ece.ufl.edu/publications/Archives/inthe
news/2005/brainmachine.html[2] The Brain from top to Bottom
http://www.thebrain.mcgill.ca/flash/a/a_06/a_06_cr/a_06_cr_mou/a_06_cr_mou.html[3] MICHAL TEPLAN.(2002). Fundamentals of EEG
Measurement. Measurement Science Review,
Volume 2, Section 2.
[4] D.G. DOMENICK. (1998). International 10-20
Electrode
Placement
Systemfor
Sleep.
http://members.aol.com/aduial/1020sys.html[5] RICHARD H.C. SEABROOK . (1994 ). The Brain
Computer Interfaces : techniques for Controlling
Machine.
http://www.cs.man.ac.uk/aig/staff/toby/research/bci/richard.seabrook.brain.computer.interface[6] BEN & HUGH,(2004) EEG Biocontrol Technology
and Applications’ ,http://www/biocontrol.com/eeg.html[7] NATIONAL INSTITUTE OF BIOMEDICALIMAGING AND BIOENGINEERING, (2004)Catch
a
Brain
Wave,
onlinearticle,http://www.nibib.nih.gov/eAdvances/102104.htm[8] CNI Neuromuscular & Peripheral Nerve Disorders
Centerwebsitearticlehttp://www.thecni.org/neuromuscular/[9] JONATHAN R WOLPAW, NILES BIRBAUMER,
DENNISJMCFARLAND,GERTPFURTSCHELLER, THERESA M. VAUGHAN.
(2002).
Brain-computerinterfacesforcommunication and control. Elsevier Clinical
Neurophysiology Transactions. 113(2002), 767-791.
[10] M. PREGENZER AND G. PFURTSCHELLER .
(1995) . Distinction Sensitive Learning Vector
Quantization ( DSLVQ) – Application as a Classifier
based Feature Selection Method for a Brain
Computer Interface. In the Proceedings of the
Fourth International Conference on Atrificial Neural©ISAROB 2007

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Networks, 433 -436.
[11] GERT PFURTSCHELLER, CHRISTA NEUPER,ALOIS SCHLOGL AND KLAUS LUGGER.
(1998). Separability of EEG Signals Recorded
During Right and Left Motor Imagery Using
Adaptive Autoregressive Parameters. IEEE
Transactions on Rehabilitation Engineering Vol.6
No. 3. 316-325.
[12] C.GUGER,
H.RAMOSERAND
G..
PFURTSCHELLAR. (2000). Real-Time EEG
Analysis with Subject-Specific Spatial Patterns for a
Brain-Computer Interface (BCI). IEEE Transactions
on Rehabilitation Engineering, Vol 8, No.4. 447 –
456.
[13] ERNSTHASELSTEINERANDGERTPFURTSCHELLER. (2000). Using Time Dependent
Neural Networks for EEG Classification. IEEE
Transactions on Rehabilitation Engineering
Vol.8.No.4. 457-463.
[14] FABIOBABILONI,LUIGIBIANCHI,FRANCESCO SEMERARO, JOSE del R MILLAN,JOSEPMOURINO,ANGELACATTINI,
SERENELLA SALINARI, MARIA GRAZIAMARCIANI AND FEBO CINCOTTI, (2001)‘Mahalanobis Distance-Based Classifiers are able to
Recognize EEG Patterns by using Few EEG
Electrodes., Proceedings of the 23rd Annual EMBSInternational Conference ,Istanbul Turkey.
[15] FEBO CONCOTTI, LUGI BIANCHI, JOSE DEL
R MILAN, JOSEPH MOURINO, SERNELLASALINARI, MARIA GRAZIA MARCIANI AND
FABIO BABILONI, (2001), Brain Computer
Interface : the use of Low Resolution Surface
Laplacian and Linear Classifiers for the Recognitionof imagined Hand Movements, Proceedings of 23rd
Annual EMBS International Conference , Turkey ,pp 655-658.
[16] TORSTEN FELZER AND BERND FREISLEBEN(2003), Analyzing EEG Signals Using theProbability Estimating Guarded Neural Classifier,IEEE Transactions on Neural Systems andRehabilitation Engineering Vol. 11, No.4 2003. pp361-371.
[17] DAMIEN COYLE, GIRESH PRASAD AND
THOMAS M MC.GINNITY (2004) , Extracting
Features for a Brain Computer Interface by Self
–Organizing Fuzzy neural Network based Time
Series Prediction, 26th Annual International
Conference of IEEE EMBS , USA , pp 4371- 4374.
[18] C.W. ANDESON, S.V DEVULAPALLI AND
E.A.STOLZ,(1994) EEG as a means ofCommunication: Preliminary experiments in EEG
analysis using Neural Networks, Proc. 1st Annual
ACM Conference on Assistive Technologies ,pp141-147.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Soft-Hard Memory for Cognitive Systems
Liang Xiong
Dept. of Automation
Tsinghua Univ.
Beijing, China. 100084

Nanyuan Zhao
Dept. of Automation
Tsinghua Univ.
Beijing, China. 100084

Abstract
We propose a novel memory infrastructure, called
Soft-Hard Memory (SHM), and its corresponding
learning scheme for cognitive learning systems.
SHM is designed according to cognitive principles
and based on the Soft-Hard Structure Theory. In
SHM, memory is divided into soft-structure andhard-structure, which enables bootstrap and adaptive
learning processes. It is intuitive, high-performanceand easy to implement. Experiments in our robot
project demonstrate its effectiveness.
Keywords: Soft-Hard Structure, Memory, Learning,
Cognitive System, Robot

1

Introduction

People have been pursuing the object of ”intelligent
machine” for a very long time. Among all the intelligent features, learning is one of the most desirable.
The most distinct feature of a learning system is thatit can continuously learn from the environment, including its own performance and status, to improve
itself. This feature is essential for life.
In recent years, machine learning (ML), a broad
subfield of artificial intelligence, has developed rapidly.
ML is concerned with techniques that enable computers to learn automatically. Researchers are making great efforts in developing such methods. Now,
machine learning is receiving more and more attentions. And in the field of robot and computer vision,
ML approaches are also widely applied. For example,
Kazuhiko [1] uses Isomap [2] , which is a non-linear dimensionality reduction (NLDR) technique in machinelearning to discover low dimensional manifold, to learn
the spatial-temporal structure of motion sequences intheir robot system. In computer vision and graphics, clustering techniques have become a mainstream
approach for graph-cut and image segmentation tasks[3].

©ISAROB 2007

724

Changshui Zhang
Dept. of Automation
Tsinghua Univ.
Beijing, China. 100084

Although machine learning has achieved a lot in developing practical intelligent methods to solve specificproblems, its current learning paradigms are not suitable for human-like learning. Typical machine learning algorithms are based on strong hypotheses or models. What the machine learns is in fact parameters,
while the underlying models can seldom change. Consequently, the data storage structure (memory) is alsohierarchical. This framework is clearly not eligible foradaptive learning tasks. It is very hard to make righthypotheses. Even if the hypotheses are proper now,
it may become unsuitable when the environment haschanged. Another question is that, where did the models come from? There is little predefined knowledge, ifany, in a human baby’s brain, but he/she can still learn
and grow to be an intelligent being. In fact, there havelong been arguments that mainstream AI researches
has deviated from the nature of intelligence. Specifically, in the field of ML, adaptiveness and the abilityof bootstrap are the two missing features in most algorithms. By contrast, we call a learning method withthese two properties cognitive learning.
Memory is thought to be the core of a cognitive
system, and learning relies heavily on it. Before wecan develop an evolving learning machine, a competent memory structure has to be built. Extensive investigations have been taken on human memory, but
its actual mechanism is still unclear. Here we only
focus on a satisfying engineering realization. In cognition and psychology, human memory is divided into
short-term memory, working memory and long-term
memory. Existing memory structures for cognitive
systems are mainly based on this notion. For instance,
Kazuhiko [1] uses the same memory configuration as
described above, and has each part constructed specifically.
In this article, we design a memory infrastructure,
called Soft-Hard Memory (SHM, pronounced as shim),
to facilitate cognitive learning processes. In SHM,
memory is divided into soft memory and its counterpart, hard memory. Basically, this memory scheme

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007is also similar to the natural classification of humanmemory. However, it is designed from the perspectiveof evolution. Through the interaction between thesetwo parts, the system can hopefully realize bootstrap
and adaptiveness.
The rest of this article is organized as follows: In
section 2 we analyze the human learning process andindicate necessary features of a cognitive learning system. Section 3 introduces the theory of Soft-Hard
Structure and describes the Soft-Hard Memory in detail. In section 4, we introduce some preliminary experiments of this memory structure in our cognitive
robot project. Conclusion is in section 5.

2

Soft Structure

Hard Structure

Figure 1: Soft-Hard Structure
until the evaluation module finds a good result. Thusa creation is realized. Yet, this system is not feasible
for complex tasks since it is computationally impossible for a large amount of variables.
In order to avoid the difficulty of computing, thesystem should not run in a totally random manner. To
solve this problem, we can learn from evolution, whichis the most spectacular process of creation in nature.
Aberrance and selection is the core of Darwin’s evolution theory. Zhao [4] proposed that soft-hard structure(SHS) is also a key for evolution to success.
According to SHS theory, an evolutionary system
can be divided into two parts: soft structure and hard
structure. Soft structure is the active part where creations and reconstructions occur. Hard structure is
the static part which supports soft structure and subsistence of the system (Figure 1). The key idea of SHS
is: soft structure provided the possibility of creation(by randomness and evaluation), while hard structure
can maintain creations and keep evolution in a certain
direction.

Cognitive Learning

For any intelligent beings, either natural or artificial, adaptive learning is crucial to survive in changing
environment. Current algorithms usually simplify the
problems to estimation of parameters. However, unalterable models are obviously not suitable for adaptive
learning, which is not just a process of accumulatingand enhancing, it also involves the creation, reconstruction and oblivion of knowledge.
Moreover, human learning is a bootstrap process.
That means we can learn infinite knowledge basedon very few priors. From a micro-perspective, an infant can learn and grow to be an expert with little
initial knowledge. And from a macro point of view,
the accumulating of human knowledge is a phenomenal example. Neural science shows that this ability ismainly due to the structure of neural networks in human brains. From a engineering view, there must be
some “grow point” in the system, where new models
and concepts can be generated.
Adaptiveness and bootstrap ability are the twomost importance features for cognitive learning.
Clearly these two features are not feasible on a hierarchical memory structure since the number of layers isnecessarily infinite. There must be some kind of feedback mechanism. Soft-Hard Structure [4] in generalized evolution theory provides such a solution.

3
3.1

3.2

Soft-Hard Structure Theory

Creation of knowledge is the most difficult task inlearning. Ashby [5] proposed Ultra Stable System as
the simplest model for creative machines. In this system, status of the machine keeps changing randomly
2725

Learning Scheme

Cognitive learning is a process of evolution. The
machine has to adjust itself according to the changeof environment constantly. From this view point, softhard structure theory can shed light on the problem
of bootstrap and adaptive learning.
Given some basic evaluation criterion, such as the
chance to feed and the time to complete a task, thebootstrap process can be done by “soft learning”.
When having to finish a task without any prior knowledge, a clear choice is to try randomly. Once a good result is found, this experience is recorded, “hard learned(modeled)” into hard structure. After enough accumulation of trials, the machine will have enough experience to do more complex jobs. And further knowledge

Soft-Hard Memory©ISAROB 2007

Modify&Grow

SupportThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

can be derived based on these experiences.
Adaptive learning can also be achieved by interactions between soft and hard structures. As a prior,the system has to be able to detect the inconsistency
between new experiences and prior models, or the contradiction between existing models. Usually, the system uses existing knowledge (models) to go throughordinary jobs. When new contradicting experiences
are gained, soft structure will decide how to deal with
them: modify models, or build a new one. And when
the inconsistency between models in the hard structure grows to a certain level, those models will be extracted back to the soft structure for further operation. Thus the system’s knowledge can be updated asneeded.

3.3

Soft-hard memory possesses many merits besides its
bootstrap and adapt abilities. First of all, SHM is intuitive and compatible with common cognitive knowledge. It is similar to the classification of memory inpsychology, and the working process is very clear. Secondly, this structure is flexible and can be readily extended. Actually there is no limitation on the number
or the form of concepts, and all information stored canbe changed as necessary. Thirdly, the conflict of vast
information and processing speed can be well balanced
under this structure. SM can work as a buffer of information. Data in it can be processed when the system
is not busy, so the load of the machine will not fluctuate dramatically. Finally, it can be easily implemented
as a parallel running system. This framework enables
physical separation of memory system and other modules (such as reasoning). So the speed and scalability
is guaranteed.

Soft-Hard Memory Infrastructure

In order to facilitate the learning scheme proposed
above, we designed a soft-hard memory (SHM) infrastructure. In SHM, memory is divided into two parts.
Soft memory (SM) provides an active data storage forthe soft learners. It primarily stores direct, concretematerials that is perceived or generated by the system. Information in soft memory will be forgotten
soon. Hard memory (HM) is a static and persistent
database for the system. Information in hard memory
is more important and highly condensed, such as basic
value functions, learned models and concepts. These
information is the core of hard structure and will notchange unless obsolete.
Interactions between SM and HM are realized
through proper learning algorithms. In principle, thesoft learner uses functions and models from HM to examine the information in SM. When experiences arerecognized as new knowledge, the hard learner willbuild new models. When inconsistency in HM is detected (e.g. when results from models contradicts witheach other), those inconsistent parts will be extracted
back to SM, either to be modified, fused or discarded.
Actual forms of SM and HM are not specified
here. People can, and should, design particular storage scheme with regard to requirements. For example, SES [1] can be used for SM, and a ordinary relational database is already competent for simple learning tasks.
The configuration of SHM is quite similar to traditional short-term/long-term memory. Their functions
do overlap a lot. However, SHM is designed from theperspective of evolution, while most other architectures are from the point of psychology or information
processing.

4
4.1

726

Cognitive Robot Project Overview

In the cognitive robot project, we are planning tobuild a intelligent robot system to accomplish the following tasks:
Identify people: The robot will first identify the
person appears in the camera as stranger or an
acquaintance. Face images are used for recognition.
Learn acquaintance: If the robot sees a stranger for
many times, it will turn him/her to an acquaintance.
Move to the target site: When a task is confirmed, the robot have to choose a route and moveto the site. Possible obstacles must be avoided efficiently.
Fetch an object from the person: The robot willuse its robot arm to fetch a specified object fromthe person’s hand. Optimum motion sequence
should be adopted.
During the process, this robot will use past experiences to solve current problems. This involves intensive learning. Furthermore, all these tasks have to befinished in a real-time situation. To do this, we will
use the learning scheme in section 3.2 and SHM as its
primary data storage.

4.2

Learning of acquaintanceIn this task, the robot has to learn to know people.
At first, all the people are strangers. As the robot sees
3

©ISAROB 2007

Application

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20075a person for certain times, it will classify him/her as
an acquaintance. This is a basic cognitive behavior.
The identification of people is accomplished by facerecognition. We use Bayesian Face Models [6] (BFM)
for this task. In BFM, face images are used as training
samples (prototype). When a new face is perceived,
the system will calculate the probabilities that thisnew face is the same as each prototype. If no probability is high enough, it is rejected as belongs to noone. There are two key components in BFM: prototypes and the probability model, which are both stored
in HM.
This module runs as follows: all perceived faces
are initially stored in SM, and then soft learner will
identify them using prototypes and probability modelfrom HM. If a face is rejected, hard learner will turn it
into a new prototype in HM. By continuously adding
prototypes, the robot’s knowledge about people keeps
growing. We call this process “absorbing”.
When the prototype set in HM changes, the probability model, which is calculated from prototypes,
should be updated too. However, since the updatingprocess is very time consuming, we can not do this on
every change. Instead, when the system is idle, a monitoring thread will extract prototypes from HM to SM.
Then a new model is learned and stored back to HM.
Besides, because BFM is not perfect, there must be
some redundancy or even mistakes in the prototype
set. The system will refine it as necessary. We callthese activities “reflection”, which is in fact processes
that reconstruct the knowledge. By using “absorbing”
and “reflection”, this system can learn acquaintances
adaptively.

4.3

Based on soft-hard structure theory, we propose a
cognitive learning scheme and its corresponding memory structure. They are designed from a evolutionary
view point. Using them we can hopefully realize a
cognitive learning machine, which is a machine withbootstrap ability and can learn adaptively. However,this proposal is still a conceptual guideline for the design of practical systems. Details have to be specified
and implemented according to actual requirements. In
our project, this guideline can be well suited into concept and action learning tasks.
Soft-Hard structure theory is a powerful foundation
for developing evolutionary machines. In the future,
more functional parts of the robot project will be designed based on it.

Acknowledgments
This study is carried out as a part of “R&D promotion scheme funding international joint research”
promoted by NICT (National Institute of Information
and Communications Technology) of Japan.

References[1] Kazuhiko Kawamura, “Cognitive Approach to a
Human Adaptive Robot Development”, Robot andHuman Interactive Communication, 2005. ROMAN 2005. IEEE International Workshop on,
pp.629-634.
[2] J. B. Tenenbaum, V. de Silva, and J. C. Langford.
“A global geometric framework for nonlinear dimensionality reduction”, Science, 290(5500):2319–
2323, 2000.

Learning of action

In order to fetch objects from peoples hand effectively, optimized motion sequence of the robot arm has
to be learned. Compared with learning of people, thisis a more complex job, and we are still improving it.
At the beginning, the robot will try randomly (basic heuristic guide may be incorporated for efficiency).
Current environment and all the action/result pairs
are stored in SM. Then the data are used to learn amodel that can make decisions according to current
environment configuration. On the low level, motion
sequences to finish a basic action will be learned; on
the high level, the robot has to make general decisions
according to environment. In general, this is still anabsorb/reflect process. Now, actual learning method
for motion sequences is under consideration.

[3] Jianbo Shi, Malik, J. “Normalized cuts and image segmentation”, Pattern Analysis and MachineIntelligence, IEEE Transactions on, Volume: 22 ,Issue: 8, pp.888 - 905, Aug. 2000
[4] Nanyuan Zhao, Cognitive Science and Generalized
Evolution, Beijing, China, Tsinghua Press, 1994.
[5] W. R. Ashby. Design for a Brain. Chapman Hall
Ltd, 1960.
[6] B. Moghaddam, C. Nastar, A. Pentland. “A
Bayesian Similarity Measure for Direct Image
Matching”, Proc. of Inernational Conference on
Pattern Recognition (ICPR’96), vol.6, pp. 350-358.

4©ISAROB 2007

Conclusion

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Artificial Realization of Decision Making for Sleep Stage of EEG Contaminated with Artifacts:
Conditional Probability of Knowledge-Base of Expert Visual Inspection
Bei Wang1,2, Xingyu Wang2, Junzhong Zou2, Fusae Kawana3, Masatoshi Nakamura1
1

2

Department of Advanced Systems Control Engineering,

Department of Automation, East China University of Science andSaga University,

Technology, Meilong Road, Shanghai 200237 (China)
3

Honjomachi, Saga 840-8502 (Japan)

Department of Clinical Physiology, Toranomon Hospital,
Tokyo 105-8470 (Japan)

wangbei@cntl.ee.saga-u.ac.jp

it had limitation to detect the characteristic waveforms

Abstractfrom artifacts.

An artificial realization of decision making for

In this study, we investigated on the artificial re-sleep stages of EEG (electroencephalograph) contami-

alization of decision making for sleep stages of EEG

nated with artifacts was developed in order to construct a

contaminated with artifacts in order to construct a reli-reliable sleep stage recognition system for clinics. The

able sleep stage recognition system for clinics. The

methodology was based on the conditional probability ofmethodology was in the field of statistics by using con-knowledge-base of expert visual inspection. Expert vis-

ditional probability of knowledge-base of expert visual

ual inspection was the manual scoring result of sleep

inspection, based on our previous study (M. Nakamura

stages by a qualified EEGer (F.K.). Knowledge-base wasand T. Sugi [4]). Expert visual inspection was the manual

constructed in terms of probability density functions ofscoring result of sleep stages by a qualified EEGer (F.K.).

characteristic parameters with each sleep stage, accord-

Knowledge base was constructed in terms of probability

ing to the expert visual inspection. Artificial realization

density functions of characteristic parameters, accordingof decision making for sleep stage was based on theto the expert visual inspection. The EEGer made visualvalue of conditional probability. Totally, the overnightinspection of sleep stages and artifact contamination forsleep EEG recordings of four subjects were analyzed.

one’s overnight sleep recordings. The artificial realiza-

The results showed a close agreement with the expert

tion of decision making for sleep stage or artifact was

visual inspection by EEGer.

based on the value of conditional probability.

Key words: sleep EEG, artifacts, decision making forsleep stage, conditional probability, knowledge-base ofexpert visual inspection

2. Method
2.1 Subjects and Data Acquisition

1. Introduction

The overnight sleep of four subjects after the

In human sleep, there are several types of sleep

treatment of Continuous Positive Airway Pressure werestages. Sleep stage determination has clinical importancerecorded in the Department of Clinical Physiology, To-

for the inspection of sleep related disorders. As one’s

ranomon Hospital, Japan. Sleep EEGs were recorded atovernight sleep EEG is long-term recording, it is inevi-

four sites C3/A2, C4/A1, O1/A2 and O2/A1, according

tably contaminated by variance artifacts under usual re-to the International 10-20 System (H. H. Jasper [5]).

cording conditions in most hospitals and sleep laborato-

EOGs (electrooculogram) were derived near the eyes,ries (P. Anderer et al [1], D. P. Brunner et al [2]).

LOC/A1 and ROC/A1. Chin-EMG (electromyogram)

In many other studies, waveform detection tech-

was obtained from muscle areas on and beneath chin.

nique (first applied by Smith et al. [3]) could be foundSleep EEGs and EOGs were recorded under a sampling

for the computerized sleep stage scoring. When sleep

rate of 100 Hz, Chin-EMG under a sampling rate of

EEG was recorded under the usual recording condition,

200Hz.

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B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Table 1: Classification of sleep stages and artifact contaminated stage A.

2.2 Expert Visual Inspection

2.3 Knowledge-base

A qualified EEGer (F.K.), who is one of theKnowledge-base is in terms of probability density

co-author, made visual inspection on the sleep EEG re-functions of characteristic parameters corresponding tocordings of four subjects. Their overnight sleep re-each stage. Based on the manual scoring result of expert

cordings were separated into consecutive 30-second

visual inspection, the epochs of ones overnight sleep

segments, which is called epoch. Each epoch was as-recording can be classified into different stages. For eachsigned with a single stage or artifact contamination by

stage, a set of characteristic parameters (Table 2) are

the EEGer based on the well-known Rechtschaffen andcalculated to make the probability density functions in

Kales criteria [6] and her clinical experience.

Cauchy distribution.

Table 1 showed the expert visual inspection of sleep

Based on the definition of Cauchy distribution, thestages and the corresponding characteristics. In Recht-

probability density function of parameter y in stage ζ canschaffen and Kales criteria, sleep is consisted of stage

be mathematically expressed by Eq. 1,

awake, rapid eye movement (REM) and non-REM(NREM) sleep which is further divided into stage 1,

f (y |ζ ) =bπ (( y − a ) 2 + b 2(1)stage 2, stage 3 and stage 4. The EEGer inspected thewhere a is the location parameter and b is the scale pa-

sleep stages based on the corresponding characteristics

rameter. The values of a and b are determined by apply-of a certain frequency bands as in Table 1. In addition,

ing least square method on the histograms.

stage awake was separated into open and close eyesawake. Because alpha activity is predominant when subjects closed eyes and was relaxed, significantly attenuates when subjects opened eyes or became tension. The
EEGer also inspected the artifacts accompanied withtonic activity in sleep EEG. Stage A was defined as an
extra stage of artifacts besides the other sleep stages.

©ISAROB 2007

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Table 2: Parameter Definition

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

had the largest location value in the distribution of the

2.4 Artificial Realization of Decision Making

duration of alpha activity (8-13Hz). In the amplitude ofSleep stage determination can be considered as de-high frequency (25-50Hz), stage A of artifact contamina-

cision making problem. Artificial realization of decision

tion had the largest location value, discriminated frommaking is proposed to solve the multiple decision mak-other sleep stages. Stage REM was separated from othering problem (M. Nakamura and T. Sugi [4]). It is carried

stages with lowest location value in the amount ofout based on the value of conditional probability,chin-EMG (25-100Hz). Stage 3 and 4 shared the largestf ( y k | ζ ) Pk | k − 1 ( ζ )i

Pk | k ( ζ i ) =in∑ f (y |ζ
j =1

k

j

,

(2)

location value in the duration of s1 (0.5-2Hz), whichcorresponding to the slow wave activity.

) Pk | k − 1 ( ζ j )3.2 Decision Making of Sleep Stages
where Pk |k −1 (ζ ) is the predicted probability of previi

Another two subjects were analyzed as test data.

ous segment. The decision of sleep stage or artifact con-

The calculation processing of conditional probability andtamination is made by choosing the stage which has thepredicted probability was illustrated in Fig.2 with time

maximum value of conditional probability, asseries of an epoch. The later part of this epoch was con-

ζ * : max( Pk |k (ζ i )) .

(3)

Then predicted probability is calculated by
Pk + 1| k ( ζ i ) = ∑ t ij Pk | k ( ζ j ) ,nsion making method. The stage determination for thisepoch was stage awake because half of sleep EEG was(4)contaminated by tonic artifacts. The stage scoring resultof expert visual inspection was also stage awake.

j =1

where

taminated by artifacts and detected by the artificial deci-

tij corresponds to the transitional probability

between every two stages.

The evaluation of sleep stage determination for
two test subjects were given in Table 3. Stage 1 and 2
were combined as light sleep of NREM, stage 3 and 4
were deep sleep of NREM. The number in Table 3

3. Resultshowed the agreement between artificial decision mak-3.1 Knowledge-base of probability density functions
The overnight sleep recordings of two subjects
were utilized to obtain knowledge-base. The probability

ing of sleep stages and expert visual inspection for eachsubject respectively. Average accuracy was also evaluated in Table 3.
Table 3: Evaluation of sleep stage determination

density functions of each characteristic parameter andstage were shown in Figure 1. In Fig.1, black dots werethe location parameter of Cauchy distribution and white
dots the scale parameter. Stage awake with eyes closed

Figure 1: Probability density functions of characteristic parameters corresponding to each stage.

©ISAROB 2007

730

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

50μ V

5s

LOC/A1

ROC/A1Chin-EMG

C4/A1

C3/A2

O2/A1

O1/A2
K

K+1

K+2

K+3

K+4

K+5

Stage

Pk|k-1

Pk|k

Pk+1|k

Pk+1|k+1

Pk+2|k+1

Pk+2|k+2

Pk+3|k+2

Pk+3|k+3

Pk+4|k+3

Pk+4|k+4

Pk+5|k+4A

0.61

3.50e-3

9.51e-2

6.08e-7

8.61e-2

0.02

0.02

0.94

0.86

0.99

0.91

0.99

O(W)

0.32

0.27

0.27

0.02

0.02

7.60e-2

7.99e-2

0.01

0.03

1.06e-20.02

2.68e-3

C(W)

0.01

4.86e-55.30e-2

9.24e-7

6.47e-2

5.04e-5

6.88e-2

6.05e-3

0.02

1.54e-3

0.02

4.04e-5R

8.94e-2

1.97e-5

1.46e-22.34e-3

2.17e-2

1.90e-3

2.12e-2

6.95e-5

1.68e-3

9.55e-6

1.26e-5

1.13e-6

1

0.04

0.72

0.68

0.96

0.92

0.97

0.92

0.04

0.07

1.53e-20.03

2.99e-62

6.88e-2

1.03e-3

0.03

0.01

0.05

3.20e-2

0.04

3.35e-6

0.01

3.81e-50.01

7.06e-935.65e-92.4e-127

2.98e-7

2.11e-11

3.51e-5

7.31e-99.27e-6

3.60e-10

1.00e-82.24e-81.31e-7

8.90e-1245.18e-14

1.37e-20

2.88e-14

2.31e-22

2.22e-13

2.30e-18

7.67e-11

6.59e-16

3.78e-12

1.38e-13

2.35e-10

1.56e-15

ResultStage 1

Stage 1

Stage 1

Stage A

Automatic determination:

Stage awake

Visual inspection:

Stage awake

Stage A

Pk+5|k+5

Stage A

Figure 2: Artificial realization of decision making for sleep stage of EEG contaminated with artifact.

all-night EEG power spectra”, Sleep Research,

4. Conclusion

Vol.5, pp. 155-164, 1996

In this study, method of conditional probability of[3] J. R. Smith, I. Karakan and M. Yang, "Automatedknowledge-base of expert visual inspection was utilized

Analysis of the Human Sleep EEG, Waking andfor sleep stage determination. The results showed a close

Sleeping",

agreement with expert visual inspection by EEGer. Arti-

Vol.2, pp. 75-82, 1978

Electroenceph.

clin.

Neurophysiol.,fact contamination can also be detected in sleep EEG.

[4] M. Nakamura and T. Sugi., "Multi-Valued Decision

Compare with other sleep stages, REM was in low accu-

Making for Transitional stochastic Event: Determi-

racy and need to be improved. With the knowledge-basenation of Sleep Stages through EEG Record",of expert visual inspection, the proposed artificial reali-

Transactions on Control, Automation and Systems

zation system of decision making for sleep stage hasEngineering, Vol. 4, pp.239-243, 2002

[5] H. H. Jasper, "Ten-twenty electrode system of theInternational Federation", Electroenceph. clin.
Neurophysiol., Vol.10, pp. 371-375, 1958

strong performance in the clinical practice.
References[1] P. Anderer, S. Roberts, et al, “Artifact Processing in
Computerized Analysis of Sleep EEG - A Review”,
Neuropsychobiology, Vol.40, pp.150-157, 1999
[2] D. P. Brunner, R. C. Vasko, et al , “Muscle artifacts
in the sleep EEG: automated detection and effect on©ISAROB 2007

731

[6] A. Rechtschaffen and A. Kales, A Manual ofStandardized Terminology, Techniques andScoring System for Sleep Stages of Human Subject, California Brain Information Service, USA,
1986

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Study on Quantum-Inspired Evolutionary Algorithm based on Pair Swap
Takahiro Imabeppu Shigeru Nakayama Satoshi Ono
Department of Information and Computer Science
Faculty of Engineering, Kagoshima University
Kagoshima-shi, 890-0065 Japan{sc102005, shignaka, ono}@ics.kagoshima-u.ac.jp
Abstract

evolutionary computation, quantum computing,quantum bit, pair swap, knapsack problem

tion and prevents premature convergence, and the migration procedures can control search diversiﬁcationand intensiﬁcation. However, the adjustment of anumber of parameters is required for the number ofgroup and migration intervals for each problem.
Therefore, we propose Quantum-inspired Evolutionary Algorithm based on Pair-Swap method(QEAPS). We have showed that the performance of
QEAPS is better than that of QEA in 0-1 Knapsack
Problem(KP)[10]. In this paper, we keen on evaluating
the performance of QEAPS against that of QEA in 0-1
KP, and we show that the eﬀectiveness of QEAPS. To
be concrete, the search performance and the robustness of QEAPS are veriﬁed by performance comparison of constraint handling methods ( when the sum
total of the item exceeds the capacity of the knapsackin 0-1KP).

1

2

Quantum-Inspired Evolutionary Algorithm based On Pair Swap

2.1

Quantum Bit Representation of Gene

Quantum-Inspired Evolutionary Algorithm (QEA)
is proposed as one of approximate algorithms to solve
combinatorial optimization. QEA is evolutionary
computation that uses quantum bits and suerposition
states in quantum computing. Although conventional
QEA is a coarse-grained parallel algorithm, it involves
many parameters that must be adjusted manually.
This paper proposes a new method of Pair Swap which
exchanges each best solution information between two
individuals. Experimental result shows that the proposed method is a simpler algorithm and can ﬁnd highquality solution in binary Knapsack Problem.

Keywords

Introduction

Quantum computer[1, 2] is a computation modelusing quantum mechanical principles such as superposition state, interference eﬀect, and entanglement
state. Recently, stochastic combinatorial search algorithms combined with evolutionary algorithm havebeen recently proposed by incorporating quantum mechanical principles or quantum bits[3, 4, 5, 6].
Han et al.[5, 6] have proposed Quantum-inspired
Evolutionary Algorithm (QEA) in which each gene isrepresented by a quantum bit. QEA can do singlepoint search and automatically shift from global search
to local search like Simulated Annealing (SA)[7]. QEA
can also perform multi-point search like Classical Genetic Algorithm (CGA) in order to solve large-scale
optimization problems.
In QEA, there are more than one subpopulations(groups) like Island GA (IGA)[8, 9], and inter- andintra-group migration procedures are performed. Evolution in each group enables coarse-grained paralleliza-©ISAROB 2007

732

QEA and QEAPS uses a quantum bit (qubit) as agene, while, in conventional genetic algorithm (CGA),
a gene is usually a deﬁnite value of binary, integer,
real number, or character. The individual i in thegeneration t is composed of the chromosome represented as a tensor product of the qubits, qi = qi1 ⊗qi2 ⊗ · · · ⊗ qim and the best solution information thatis binary string discovered in search process (PersonalBest) bi = [bi1 , bi2 , . . . , bim ] . Here, m is the number of genes or qubits included in an chromosome.
The qubit qij (j = 1, ..., m) has stochastic superposition state (vector sum) of the two vectors |0⟩ and |1⟩with each complex probability amplitude.
[]
αij
qij = αij |0⟩ + βij |1⟩ =
,
(1)
βij

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

te
da
Up

i2

i3

…

bi
Ma

ke
rot
a

tio

Repeat

qi

 
i1

Make initial population0f ( bi )
0.46
0.46

im

1

0

…

1

Update bi
if f ( pi) > f ( bi)pi 1 1

na
ngl
el

ist
u

Until evaluation
time reaches thelimit…

Observeuiqi

For each
Individual i

RepeatMake pi byobserving qi
Evaluate pi
Update qif ( pi)
0.32

Update personal
best bi

1 … 0
Evaluate pi

For each pair i and j
generated randomlyiRepeatSwap bi and bj

Figure 1: Evaluation of an individual.
Figure 2: The algorithm of proposed QEAPS.

Table 1: Lookup table of the rotation angle θik
θik
αik βik
αik
<0
=0
-θC
—
θC
±θC
0
0

βik
=0
±θC
—
0

transformation can be used to change the ratio of theprobability amplitudes αik and βik of the superposition state.
][[ ′ ] []
cos(θik ) −sin(θik )
αik
αik
=
.
(2)
′
βik
sin(θik ) cos(θik )βik|αij |2 is the probability that the state of |0⟩ is observed, and |βij |2 is the probability that the state of|1⟩ is observed. The binary string pi is obtained byobserving qi . The ﬁtness value f (pi ) of the individual
i can be calculated from pi like CGA.

qi is upgrade following the rotation angle list ui andthe rotation matrix.
If f (pi ) > f (bi ), then the best solution of the newindividual is replaced by the currently observed binary
information.

2.2

2.3

f (pi ) ≥ f (bi )

pik

bik

0
1

1
false
0
false
Otherwise

αik βik
>0
θC
-θC
0

Procedures in QEA and QEAPSThe algorithm of QEAPS that we propose is shownin Figure 2. We ﬁrst describe the common process of
QEA and QEAPS (white part of Figure 2), and thenwe describe the diﬀerent process of QEA and QEAPS(black part of Figure 2).
To begin with, the initialization
is carried out by√
setting αik and βik to 1/ 2 in order to equally observe the states of |0⟩ and |1⟩ in the individual i(= 1).
Next, the evolution of an individual with qubits andthe exchange of the best solution information in theindividual are repeated according to the following procedure, until a given termination condition is satisﬁed.
The procedure of the individual update is shown
Figure 1 and as follows. First of all, pi is obtained byobserving qi . And, the ﬁtness f (pi ) is calculated frompi , and the ﬁtness of the individual is decided.
Then, the rotation angle list ui = [θi1 , θi2 , . . . , θim ]is made from each value of pij and bij and the magnitude correlation of f (pi ) and f (bi ). This list is
used to increase and decrease the observation probability of |1⟩ and |0⟩. How to decide the rotation angle
θik (k = 1, . . . , m) is shown in Table 1[5, 6]. Unitary©ISAROB 2007

733

Migration Method of QEA

Migration strategy of QEA involves local migrationand global migration. The local migration is the process of distributing the best solution information of anindividual with the highest ﬁtness in each group, toall other individuals in each group, and repeated inevery generations. The global migration is the processof distributing the best solution information of an individual with the highest ﬁtness in all groups, to allother individuals in all groups, and repeated in every ﬁxed generations. QEA shows the centralizationof the search, but must determine two parameters of
the number of groups and the timing of global migration by considering problem characteristics and scale,convergence speed in a group, rotation angle as QEA
fundamental parameter[5, 6].

2.4

Pair Swap Method of QEAPSQEAPS utilizes pair swap operation instead ofglobal and local migration of QEA. To begin with,
two individuals are randomly selected as a pair fromall individuals in the whole group. Then, n/2 pairs are

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

generated by selecting two individuals from n (evennumber) individuals with no overlaps in the group.
Only each best solution information is exchanged ineach pair with out carrying out any operation on thequbits in the individual.

3

Conputational Experiments

3.1

Experiment Preparation

The 0-1 KP is used for the evaluation experiments
in order to prove the eﬀectiveness of the proposed
QEAPS. The KP in the paper [9] is used as a benchmark problem. The number of items N is 100(the ﬁrst
100 items are used in the benchmark problem). The
weight limit in the KP is set to be 50% of the total
weight of all items. Parameters such as the population and the number g of groups in QEA are followedby the previous researches[6], respectively. Parameters used in QEAPS are followed by QEA as shown in
Table 2.
When the evaluation times, the number of ﬁtness
calculation time, reach the preset value, the searchstops. We perform the same experiments 30 times
using each technique for each problem.

3.2

Constraint Handling Methods

We compare the performances of QEAPS and that
QEA in the following three methods to handle constraint violation.
1. Fitness = 0 (Zero)If the condition is not satisﬁed, then f = 0.
2. Penalty Function (Penalty)
The penalty function shown by the equation 3 is
used.
{ n
}
n
∑
∑
aj pij − α max

f (pi ) =j=1

wj pij − C

0,

, (3)

j=1

where, pij is the value of the j-th gene in thechromosome of the i-th individual, aj is the
proﬁt of item j, wj is the weight of item j,
and C is the capacity of the knapsack, and α =maxj=1...n {aj /wj }.
3. Random Repair (Repair)Random Repair [5] that consists of the following procedures in applied. Step 2 is applied evenwhen weight limits are satisﬁed.

©ISAROB 2007

734

Table 2: Parameter conﬁgurations.
Parameter names
Number of individuals
Number of subpopulations (groups) (g)Number of individuals
in a subpopulation
Rotation angle (θC )Number of observations
Interval of global migration

Values used
QEA
QEAPS
10, 20, 30, . . . , 100
5–2, 4,. . . , 20

–

0.01π

0.01π

1

1

100

–

Step 1: One item is randomly selected and removed
until the knapsack capacity is ﬁlled.
Step 2: One item selected randomly is put in the
knapsack until capacity are exceeded. Whencapacity are exceeded, the item put at theend is removed.

3.3

Experimental Result

Regarding evaluation criteria, we focus on the optimal solution discovery rate per trial number Opt[%],the mean ﬁtness mf . The upper limit of evaluation
times is set to N × 103 as a termination condition of
the search.
As a function of the individual total numbers, Opt,
mf are shown in Figure 3 and 4. First, when paying attention to Opt, The performance of QEAPS isbetter than that of QEA in the same number of individuals in all constraint handling methods. In Opt
of QEA, Repair is the highest, and Zero and Penaly
are lower. The diﬀerence is seen for all number of individuals between the methods. On the other hand,such diﬀerence is not so seen at QEAPS. Moreover,Opt is almost 100% in any method when more than
40 individuals are used.
The result of mf in Figure 3 and 4 also indicatethat QEAPS has higher-performance than QEA. In
mf of QEA, there is obvious diﬀerence between thatof Repair and these of Zero and Penalty. In QEAPS,
the highly qualiﬁed solutions are obtained in all constraint handling methods. It should be noted that mf
of Repair, Penalty and Zero of QEAPS is higher thanthat of Repair in QEA.
In QEAPS, the diﬀerence between Repair, Zero,and Penalty is quite small. Even though Zero andPenalty are simple and not specialized in the knapsackproblem, they can search for optimal solutions, consequently QEAPS is more robust against constraint

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Opt (Penalty)

mf (Penalty)

Opt (Repair)

mf (Repair)

21,500100

21,40080

21,50021,400mf (Zero)mf (Penalty)

60

60

21,30040

21,20020

21,10020

021,000021,300mf (Repair)

21,20040

Opt (Zero)
Opt (Penalty)

Mean fitness of best sol.: mf

mf (Zero)

Discovery rate: Opt [%]Discovery rate: Opt [%]80

Opt (Zero)

Mean fitness of best sol.: mf

100

21,100Opt (Repair)
10

20

30

40

50

60

70

80

90

Figure 3: Discovery rate and evaluation time (QEA)
methods.

4

20

30

40

50

60

70

80

90

100

Figure 4: Discovery rate and evaluation time (QEAPS)[4] S. Nakayama, I. Iimura, and T. Ito, ”Consideration on quantum inteference crossover method in
immune algorithm (in Japanese),” IEICE Trans.
Information and Systems, PT.1 (Japanese edition), J-88-D-I(12):1795–1799, 2005.

Conclusion

In this paper, we focused on evaluating the performance and robustness of QEAPS to improve QEA.
We compared the performane between the constraint
handling method in 0-1 KP. Experimental result showthat QEAPS is able to discover the optimal solution at
the higher probability compared with QEA, and thatthe solutions found by QEAPS are of even quality,
and that QEAPS is robust against constrain handling
methods.
We plan to verify the search performance in alarger-scale problem, improve the algorithm, examine
application to other combination optimization problems, and clarify the characteristic of the problem towhich QEAPS is eﬀective.

[5] K.-H. Han and J.-H. Kim, ”Quantum-inspired
evolutionary algorithm for a class of combinatorial optimization,” IEEE Trans. Evolutionary
Computation, 6(6):580–593, 2002.
[6] K.-H. Han and J.-H. Kim, ”On setting the parameters of qea for practical applications: Someguidelines based on empirical evidence,” In Genetic and evolutionary computation – GECCO
2003, pages 427–428, 2003.
[7] S. Kirkpatric, C. D. Gelatt, and M. P. Vecchi,”Optimization by simulated annealing” Science,
vol.220, no.4598, pp.45-54, 1983
[8] T. C. Belding, ”The distributed genetic algorithm
revisited,” In Proc. the Sixth Int’l Conf. on Genetic Algorithms, pages 114–121, 1995.

References[1] M. A. Nielsen and I. L. Chuang. Quantum Computation and Quantum Information. Cambridge
Univ. press, New York, 2000.
[2] S. Nakayama, ”Consideration on the squareroot gates of quantum logic gates (in Japanese),”Trans. the Japan Society for Computational Engineering and Science, 7:77–82, 2005.
[3] A. Narayanan and M. Moore, ”Quantum-inspired
genetic algorithms,” In Proc. IEEE Int’l Conf. on
Evolutionary Computation, pages 61–66, 1996.

©ISAROB 2007

21,000
10

100

735

[9] I. Iimura, S. Ikehata, and S. Nakayama, ”Consideration on noah’s ark strategy in parallel
genetic algorithm with object-shared space (inJapanese),” J. Japan Society of Information andKnowledge, 13(2):1–7, 2003.
[10] S. Nakayama, T. Imabeppu, S. Ono, and I.
Iimura, ”Consideration on Pair Swap Strategy
in Quantum-Inspired Evolutionary Algorithm (inJapanese),” IEICE Trans. Information and Systems, PT.1 (Japanese edition), J89-D-I(9):2134–
2139, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Epidemic Models and a Self-Repairing Network with a Simple Lattice
Yuta Aoki1 and Yoshiteru Ishida1, 2
1 Department of Knowledge-Based Information Engineering,

Toyohashi University of Technology,
Tempaku, Toyohashi, 441-8580 Japan
2Intelligent Sensing System Research Center
Toyohashi University of Technology,
Tempaku, Toyohashi, 441-8580 Japan

Abstract
We proposed a self-repairing network where
nodes are capable of repairing neighboring nodes bymutually copying. A critical point where faulty nodes
can be eliminated has been investigated. This paper
further studies dynamics of eradicating faulty nodes
by comparing the self-repairing network withmathematical epidemic models such as SIS models. It
is shown that the self-repairing network, which is a
probabilistic cellular automaton, can be regarded as an
epidemic model in some restricted situations.

1. Introduction
Information networks are complex systems withmultiple and interacting processes work in parallel on
various network structure. Self-recovery of suchnetworks have been studied with a focus on the
network structure [1] as well as interacting processes[2]. We have been studying on a self-repairing
network with a focus on state propagation andregulation keeping the network model simple enoughfor analysis [3, 4]. In the model, the two state
propagations have been involved: the abnormal state
propagation by unsuccessful repair as well as thenormal state propagation by successful repair.
Epidemic models have been studied for a longtime, and nonlinear properties of the models haveinvestigated in great detail. The models include not
only those described by differential equations [5] but
by probabilistic cellular automata [6] or even
including moving agents [7]. On the other hand, phasetransitions have been studied on models extended
from an Ising model in the field of statistical physics
but involving probabilistic cellular automata [8].
Our model has been already related with a modelin statistical physics [3]. This paper specificallyfocuses on the relation between the self-repairing
network and an epidemic model called SIS model.

©ISAROB 2007

736

2. A Self-Repairing Network Model
2.1. A model by a probabilistic CA [3]The self-repairing network is a network whose
nodes can be normal (0) and abnormal (1); and arecapable of repairing the neighbor nodes by copying its
content. Each node repairs the neighbor nodes with aprobability Pr. The repair will be successful with aprobability Prn when it is done by a normal node, butwith a probability Pra when done by an abnormal
node. Further, all the repair must be successful for thetarget node to be normal when repairing is done bymultiple nodes (Fig. 1).

Fig. 1. Repair success rate when repairing is done bynormal node (above, left) and by abnormal node (above
right); all the repairs by neighbor nodes must be
successful for the target node to be normal (below).
Solid arcs indicate repairing and dotted arcs indicate
state change.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

a = − Pr ( Prn − Pra ) ,22

b = −2 Pr (1 − Prn ) ( Pr ( Prn − Pra ) + 1) + Pr ( Pr − 2 Pra ) ,c = Pr (1 − Prn ) ( 2 − Pr (1 − Prn ) )When Prn > Pra
Fig. 2. 1-dimensional structure with two adjacent neighbor
nodes

When repairing is done by copying, the marked
difference from conventional repairing is that the
repairing rather could have contaminated other nodes
rather than cleaning: the double edged sword in
repairing. Thus, it is important to investigate on
conditions under which the network may be cleaned.
To clarify the conditions, we used a probabilistic
cellular automaton [3, 4] which turned out to be
Domany-Kinzel model [8] is some particular cases.
In a 1-dimensional structure with two adjacentneighbor nodes (Fig. 2), the probabilities for each ruleof state change are listed in Table 1.

Table 1. Transition probabilities of the one dimensional
probabilistic cellular automaton [4]StatechangeTransition Probability

000→1

Pr (1 − Prn )( Pr Prn − Pr + 2 )001→1

Pr (1 − Prn Pra ) + Pr (1 − Pr ) ( (1 − Prn ) + (1 − Pra ) )
2

101→1

Pr (1 − Pra ) ( 2 − Pr (1 − Pra ) )010→1

1 − Pr Prn ( 2(1 − Pr ) + Pr Prn )011→1

1 − Pr ( ( Prn + Pra )(1 − Pr ) + Pr Pra Prn )111→1

1 − Pr Pra ( Pr Pra + 2(1 − Pr ) )2.2

Steadystate

analysis

with

mean

y∞ can be calculated as follows.

)(1− b − b 2 − 4ac2aIn order for abnormal nodes eradicated, c must be 0i.e. Prn = 1 , for suppose otherwise normal nodes could
have spread abnormal states. When c = 0 , the following
condition (2) must be satisfied for abnormal statesy∞ =eradication, since the time derivativedy
dt

negative in the equation (1).
Pra 1
≥
Pr
2

must be(2)

2.3 Simulation Results
Although the mean field approximation above
suggested parameter conditions for abnormal node
extinction, it must be verified by computer simulation
for the self-repairing model by a probabilistic cellular
automaton. To investigate the dynamics of normal
node population when Prn varies, a computer
simulation is conducted with parameters listed inTable 2. Fig. 4 plots the time evolution of normal
nodes when Prn varies as well as numericalcalculation of the mean field approximation.
Table 2. Simulation Parameters

field

approximation [4]Let y denote a fraction of abnormal nodes, andlet the probability of being abnormal (1) beapproximated by y at any nodes. Then the dynamics
of y can be described by the equation (1) where a, b,
and c are constants determined by the threeparameters of the self-repairing network.
dy= ay 2 + by + c ,(1)
dt©ISAROB 2007

hence a < 0 , the steady state

737

Parameters
Time steps for each trial
Number of trials
Number of nodes
Initial number of normal nodes
Pr
Prn
Pra

Value
1500
10
400
200
1.0
0.6, 0.9, 1.0
0～1(0.02)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.2 Parameter correspondence between an SIS
model and a self-repairing network
In a random graph with a mean degree k , thedynamics of the fraction of infected (abnormal) nodes
y can be described as follows.
dy= β k (1 − y ) y − γydt
= −β k y 2 + ( β k − γ ) yFig. 4. Time evolution of the number of normal nodes when Prnvaries (legends)

3. Self-Repairing Network and SIS Model
3.1 An SIS modelThus, when Prn =1 and hence c=0 in the equation (1),parameters a, b of a self-repairing network will be
related to the parameters of the above SIS model asfollows.
a = −β kb = β k −γ
Since

Epidemic models assume the states such as S
(susceptible), I (infected), and R (recovered).
Combining such states allowed, epidemic models
varies from SI, SIS, and SIR. Among them, in an SIS
model assumes that susceptible nodes will fall into theinfected state with an infection rate β when theneighbor nodes are infected. The infected nodes will
be recovered with a recovery rate γ and become
susceptible state again. The SIS model can describe,for example, a sexually transmitted diseases; venereal
disease gonorrhea [5].
Susceptible (S) and infected (I) state respectively
correspond to normal (0) and abnormal (1) state in the
self-repairing network. Fig. 5 shows the statetransition between S(0) and I(1).

Fig. 5. State Transition in an SIS Model. Solid arcs indicate
repairing and dotted arcs indicate state change. Solid arcs
indicate repairing and dotted arcs indicate state change.

©ISAROB 2007

738

we

considerone-dimensional

cellularautomaton for the self-repairing network, k

could

be evaluated as 2, and hence;2
2
P (1 − Pra )β= r
2
γ = Pr Pra ( Pr Pra + 2(1 − Pr ) )

(3)
(4)

3.3 Simulation Results
To examine the correspondence between the
SIS Model and the self-repairing network, simulations
are conducted with parameters listed in Table 3. Fig.
6 plots the number of normal nodes varying the repair
success rate Pra when repaired by abnormal nodes.
The SIS model and the self-repairing network mostly
matches, however, both models do not match with thenumerical solution obtained from the mean filed
approximation (1), particularly when Pra is greaterthan 0.3.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 3. Simulation Parameters

Parameters
Time steps for each trial
Number of trials
Number of nodes
Initial number of normal nodes
Pr
Prn
Pra
β
γ

Acknowledgements

Value
500
10
400
200
1.0
1.0
0～1(0.02)

This work was supported in part by Grants-inAid for Scientific Research (B) 16300067, 2004. This
work was partly supported also by the 21st Century
COE Program “Intelligent Human Sensing” of the
Ministry of Education, Culture, Sports, Science andTechnology of Japan.

Pr (1 − Pra )2
Pr Pra ( Pr Pra + 2(1 − Pr ) )2

2

References[1] Dezso, Z. and Barabasi, A.-L.: Halting viruses in

[2]

[3]

[4]

[5]
Fig. 6. Relation between SIS Model and Self-Repairing Model.
[6]

4. Conclusion
The self-repairing network involves repairing by
mutual copying in a network. Hence, it models not
only abnormal state propagation (unsuccessful repair)but normal state propagation (successful repair). Thus,
it differs from the epidemic models which models
only abnormal state propagation (infection), andrecovery is modeled as an independent event (not as
propagation). This paper shows, in spite of thedifference, self-repairing network can be reduced toan SIS model under certain conditions.

©ISAROB 2007

739

[7]

[8]

scale-free networks, Phys. Rev. E 65, 055103,(2002)
Brown, A. and Patterson, D.: Embracing Failure:
A Case for Recovery-Oriented Computing(ROC), High Performance Transaction Systems
Workshop (TTPS '01) (2001)
Ishida, Y.: A Critical Phenomenon in a SelfRepair Network by Mutual Copying ， Lecture
Notes in Artificial Intelligence (LNAI 3682),(2005), pp.86-92
Ishida, Y.: Complex Systems Paradigms for
Integrating Intelligent Systems, in Computational
Intelligence Handbook, Berlin & Heidelberg,
Springer, to appear 2007.
Lajmanovich, A.and Yorke, J.A.: A deterministic
model for gonorrhea in a nonhomogeneous
population, Mathematical Biosciences, vol.28,(1976) pp.221-236
Rhodes, C.J. and Anderson, R.M.: Dynamics in a
Lattice Epidemic Model, Phys. Rev. Lett. A, 210(1996) pp. 183-188
Boccara, N. and Cheong, K.: Critical behaviour ofa probabilistic automata network SIS model forthe spread of an infectious disease in a populationof moving individuals, J. of Physics A: Math.
Gen. 26 (1993) pp. 3707-3717
Domany, E. and Kinzel, W.: Equivalence of
cellular automata to Ising models and directed
percolation，Phys. Rev. Lett. 53 (1984) pp. 311

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The Novel Feature Selection Algorithm for Emotion Recognition
Ho-Duck Kim*, Young-Im Cho**, and Kwee-Bo Sim*
* School of Electrical and Electronics Engineering, Chung-Ang University
221, Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea
e-mail : kbsim@cau.ac.kr** Department of Computer Science, The University of Suwon
San2-2, Wauri, Bongdam-eup, Hwaseoung, Gyeonggi-do, 445-743, Korea
e-mail : ycho@suwon.ac.kr

Abstract
This paper presents an original feature selection
method for Emotion Recognition which includes many
original elements. Feature selection has some merit
regarding pattern recognition performance. Thus, wedeveloped a method called an ‘Interactive Feature
Selection’ and the results (selected features) of the IFS
were applied to an emotion recognition system (ERS),
which was also implemented in this research. Ourinnovative feature selection method was based on a
Reinforcement Learning Algorithm and since it required
responses from human users, it was denoted an‘Interactive Feature Selection (IFS)’. By performing the
IFS, we were able to obtain three top features and apply
them to the ERS. Comparing those results from arandom selection and Sequential Forward Selection(SFS) and Genetic Algorithm Feature Selection (GAFS),
we verified that the top three features were better than
the randomly selected feature set.
Keywords: Reinforcement Learning, Feature selection,
Emotion Recognition, SFS, GAFS, IFS

1. Introduction
Emotion recognition research has been typicallyattempted using four kinds of medium. They are speech,
image, physiological signal, and gesture. EEG, ECG,
and SC sensors are used to obtain a physiological signal
but the signal from those sensors may be obstructed byelectrical signals from fluorescent lamps or electric
home appliances. This problem is the one obstacle inemotion recognition using a physiological signal. For animage, this means facial expression recognition and themain problem in this case is usually lighting conditions,which often change, or personal accessories like glasses
which affect recognition performance. A problem of©ISAROB 2007

740

gesture recognition is similar to that of image
recognition and the bigger problem is that it may notinclude much information regarding emotion. Apartfrom above the problems which these three media
present, speech signal can send much more information
regarding emotion. For example, talking over the
telephone, one can recognize emotions and this shows
the validity of speech signal for emotion recognition.
The commonly used feature set for emotion recognition
from speech consists of pitch, energy, formant, andspeech rate. Some researchers select all four of thefeature sets, others select only one, and the features are
generally extracted statistically from the four featuresets. In [1], 17 features were extracted from pitch,energy, speech rate and so on with sex also beingclassified. In addition, In [2], 11, 40, and 13 features
were extracted. The fact that feature set selection is not
fixed suggests that features may or may not be relevant
to emotion recognition. This problem will plague
researchers in this field until exceptional results are
obtained. For this case, there is a GA based selection
method, Floating search method and so on which cansomewhat reduce difficulties for researchers [3]. In [1],a Forward Selection (FS) method was used. In [4], aSequential Forward Selection algorithm was also used
and the best feature subset was selected out of 39
candidate feature sets. These feature selection methods
provided a good solution for “The curse ofdimensionality” and contributed to the performance ofpattern recognitions. In addition, feature selection
methods included supervised and unsupervised cases.
Generally, a supervised case is employed more often
than an unsupervised case. This is due to unsupervised
feature selection methods having a high probability ofincorrect results for corresponding patterns regarding
perceived speech [5]. We propose a method using
reinforcement learning taking advantage of both thesupervised and unsupervised method, which canalleviate the shortcomings of both methods. Researches

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007of the reinforcement learning have been proceededusing many methods, i.e. Dynamic programming,
Monte Carlo method, TD method, Q learning etc.
proposed by Sutton and Barto. Since there is such avariety of methods and the main elements such as“state”, “action” and “reward” may be freely definedand implemented by a developer, this method is thought
to be a very important one for machine learningtechniques[7]. In this study, we propose a method which
selects feature sets by calculating rewards received
when an action is performed in a state. In particular, thismethod does not only calculate the frequency of
emotion transit but also the sum of the rewards for theevaluation of a feature selection. Therefore, this method
has the advantage that the more frequently it contacts auser, the better its performance becomes.
The outline of the paper is as follows, In Section 2, it
explains the emotion recognition method and Section 3
explains the proposed algorithm. The Section 4 shows asimulation and result of using the proposed algorithm.
The Section 5 concludes and shows future works.

2. Emotion Recognition Method
This paper addresses emotion recognition by
extracting features of speech. The emotion recognition
with speech is largely divided into cases using acoustic
information and language or discourse information. The
former is a method that uses some feature sets such as
pitch, formant, speech rate, timbre, etc. and the latter
uses the meaning of a word. That is, whether the word ispositive or negative to whether it represents a happy or
sad state. The process of emotion recognition consists ofcollecting emotional speech, the acoustic analysis,
implementing DB, feature set extraction and suchfeatures are trained and classified with emotions using apattern classification method.
We used an artificial neural network for patternclassification, which commonly performs well and isrobust to a signal with noise. It has been the mostpopular method to use in the pattern recognition field.
This method commonly uses a Back Propagation
Algorithm for tuning network parameters. In this study,
we fixed the setting to ANN as follows, The number of
Input Units and Hidden Units and Output Units andLearning rate and Tolerance and Sigmoid function are
3~5 and 11 and 2 and 0.003 and 0.25 and 1 / (1 + e − 3 x ) ,respectively.

©ISAROB 2007

741

3. The Interactive Feature Selection Algorithm
Typically, researchers in the emotion recognition use
various feature sets. Some researchers looked into therelation between acoustic analysis and emotion and used
the feature sets based on that relation. However, because
this method is subjective, it may easily lead to local
minima. For this reason, recent studies consider afeature selection method for finding small superiorfeatures (4~10) out of as many as 30 to 90 features.
Most researchers do not use all features because theycannot confirm whether they are valid or not and noises
with every features may deteriorate. Therefore, feature
selection methods are popular in the patternclassification field [5]. In the former, a feature subset isselected independently of the learning method that willuse it. In the latter, a feature subset is selected using anevaluation function based on the same learningalgorithm that will consider this subset of features.
Although wrapper approaches have been shown toperform better, they can be rather time-consuming and it
is sometimes infeasible to use them [5]. For this reason,
the proposed algorithm tries to combine thecharacteristics of both the wrapper and filtering.

3.1 Reinforcement Learning Algorithm.
Reinforcement learning consists of an agent andenvironment and is a learning method that leads theagent to perform a target action for a user. The processof learning is as follows, given an environment, anagent firstly performs an action and the agent receives areward for the action from the environment. At that time,each time step is denoted as t, an environment state
which the agent may be include is denoted as st ∈ S(S is a set of possible environments) and an action isdenoted as at ∈ A( st ) ( A( st ) is a set of possible actions ina state). A reward for an action is denoted as rt andwhen an episode has been completed, the rt

isexpressed as the following equation.
T

Rt = ∑ γ k rt + k + 1

(1)

k =0

Where γ is a discount coefficient in the above equationand does not make the sum of rewards an infinity in thecase of being defined as t = ∞ . In addition, if the
discount coefficient is zero, it means that only thecurrent reward value is admitted. That is, we can give
the weight to a future value differently according to thediscount coefficient. Finally, reinforcement learning is amethod that determines a policy to maximize the eq. 1.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.2 Sequential Forward Selection Algorithm
Sequential Forward Selection (SFS) is the simplest
greedy search algorithm. In this paper, we will briefly
explain this algorithm. Fig. 1(a) shows the algorithm.
Starting from the empty set, sequentially add the featureemotion recognition system that includes this algorithm
will be applied to a home robot or appliance. Due to thischaracteristic, this algorithm is a user adaptive systemthat can efficiently solve a problem and the more a user
is in contact with this system, the better it will perform.

x + that results in the highest objective function
J (Yk + x + ) when combined with the feature Yk that has

already been selected.

3.3 Genetic Algorithm Feature Selection Algorithm
The Genetic Algorithm is popular method for finding
an optimized solution. This algorithm has also good
performance to the problems like nonlinear problems,
which are hard to be solved by using the classic
optimization techniques.
The problem we are treating is also a nonlinear
problem, and thus we think this problem may be solved
by Genetic Algorithm (Fig. 1(b)). So, we tried to searchgood feature set using the Simple Genetic Algorithm.

Fig. 2. IFS Algorithm(a) SFS Algorithm(b) GAFS algorithm

Fig. 1. SFS and GAFS algorithm

3.4 Interactive Feature Selection Algorithm
The Interactive Feature Selection (IFS) algorithm weare proposing is an algorithm based on reinforcement
learning. Specially, popular algorithms such as SFS,SBS and so on, are deductive algorithms but our
proposed algorithm is inductive. Also, these feature
selection algorithms are based on the rationale ofcorrelation and information-theoretic measures.
Correlation is based on the rationale that good feature
subsets contain features highly correlated with a class,
yet are uncorrelated with each other. The IFS is also
based on the correlation concept. Moreover, the feature
selection algorithms consist of a search strategy and anevaluation by objective function but the conventional
methods are incompetent in the search strategy part.
Fig. 2 shows an IFS process. We assume that an©ISAROB 2007

742

Fig. 3. IFS example
Fig. 3 shows an example of the IFS algorithm and isbased on the fig. 2. First, this algorithm starts with a fullfeature set and when a new feature set and an emotion
identifier is inputted, it assigns a +1 or -1 to the “returnsign”(if an old emotion ID equals a new emotion ID
then +1, Otherwise -1). Thereafter, the product of“return sign” and the difference of each feature is stored
in an array “Point storage”. This iteration is repeated for
one episode (user can arbitrarily define an episode).
After the episode, the feature set that was selected first

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

is applied to an objective function(Pattern Classification
System) and the evaluation result is stored. If the next
evaluation result is worse than the previous, the worst
feature of the selected feature set will be replaced withthe best feature among those that were not selected (step
6 in fig. 3.

4. Experimental Results
We applied 11 original features to the IFS simulator;
Pitch features (max, min, mean, median, crossing rate,increasing rate), Loudness; Intensity (max, min, mean),Sect. No and Speech rate. This program was made only
for an IFS and the results from this program wereapplied to the Emotion Recognition System using ANN.
That is, the feature set applied to ERS was the features
previously outputted by IFS and then the emotion
recognition experiment was performed. Classification
was attempted using four methods.
We can see that the IFS system searched for better
results and improved gradually. In the algorithm,
because the searching work was performed again when
the new evaluation result was worse than the previous
one, there was some range in the steady state.
Fig. 4 is comparison graph of four methods (IFS, SFS,GAFS and random selection) with the changed feature
no. As expected, random selection performed poorly but
IFS and SFS and GAFS similarly performed better. In
the IFS and SFS and GAFS case, the results show asubtle distinction but the IFS with features 1, 2 and 3
was better. However, with features 4 and 5, SFS showed
better results.

Fig. 4. Emotion Classification Rate Comparison

5. Conclusions
This paper presents a solution to feature selection
when there is an emotion recognition problem. The
solution called the IFS performed as well as an SFS. In
particular, it is reinforcement based learning and©ISAROB 2007743

supplements the role of search strategy in the feature
selection process. Using the IFS simulator, we foundsome of the best features and used them in the emotion
recognition experiment and results were compared tothose of SFS and Random selection. Performance wasslightly better than SFS. However, IFS has somedisadvantages. If the amount of training data is too
small, selection results may be not good. SFS does not
require much training data. It is also sufficient thattraining data be only one set. If an objective function isclear, SFS will be adequate. However, in the case ofemotion recognition, SFS may not perform as well as it
had. In this case, the correlation-based method like the
IFS will be better.

Acknowledgment:This research was supported by Seoul R&BD
Program (No. : 106876) from Seoul city of Korea.

References[1] C. M. Lee and S. S. Narayanan “Toward detecting
emotions in spoken dialogs,” IEEE Transactions on
Speech and Audio Processing, vol.13, pp. 293-303,
2005.
[2] J. Wagner, J. H. Kim, and E. Andre, “FromPhysiological Signals to Emotions: Implementing
and Comparing Selected Methods for Feature
Extraction and Classification,” Proceedings of
ICME, pp. 940-943, 2005.
[3] P. Pudil and J. Novovicova, “Novel Methods for
Subset Selection with Respect to Problem
knowledge,” IEEE Intelligent Systems, March, pp.
66-74, 1998.
[4] Y. L. Lin and W. Gang, “Speech Emotion
Recognition based on HMM and SVM,”
Proceedings of Machine Learning and Cybernetics,
Vol.8, pp. 4898-4901, 2005.
[5] E. F. Combarro, E. Montanes, I. Diaz, J. Ranilla, andR.Mones, “Introducing a Family of Linear
Measures for Feature Selection in Text
Categorization,” IEEE transactions on Knowledge
and Data Engineeringl, Vol.17, No.9, pp. 12231232, 2005.
[6] G. H. John, R. Kohavi, and K.Pfleger, “Irrelevant
Features and the Subset Selection Problem,”Proceedings of Machine Learning, pp. 121-129,
1998.
[7] R. S. Sutton and A. G. Barto, “Reinforcement
Learning,” An Introduction, A Bradford book,London, 1998.
[8] C. H. Park and K. B. Sim “The Implementation of
the Emotion Recognition from Speech and Facial
Expression System,” Lecture Notes in Computer
Science(LNCS), vol. 3611, pp. 85-88, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A trial to improve the intelligibility of spontaneous
concatenative speech synthesis
Kei Fujii
Department of Information and Computer Sciences
Kumamoto National College of Technology
2659-2, Suya, Kohshi-city, Kumamoto 861-1115
Email: fujii @ cs.knct.ac.jp

ABSTRACT
Realization of spontaneous speech synthesis is a one ofrecently focused research purposes. In previous research,authors reported about a trial to apply concatenative
speech synthesis to spontaneous speech, especially, someevaluations of unit selection performance inconcatenative speech synthesis. From these results, we
obtained the limit of the conventional method. In thispaper, TD-PSOLA is introduced to obtain the synthetic
speech improving about spectral and prosodic
discontinuity which causes degradation of intelligibility.
As the result, intelligibility score of synthetic speech has
been improved by 4%.

Processing flow of this method for reading speechsynthesis is shown in Fig. 1. The target feature generation
part makes a series of appropriate target prosodic featuresfrom parsed input text information. According to thesetarget feature series, the unit selection part chooses themost preferable wave segment series from the corpus.
Output synthetic speech is made by connecting thesewave segments, thus, is recycling of a speaker’s natural
voices.
Input Text
Text Analysis
Target Feature Generation

Keywords: Spontaneous speech synthesis, Concatenative
speech synthesis, Unit selection, Intelligibility, TDPSOLA1INTRODUCTION

Speech Corpus

Wave Concatenation
Synthetic SpeechTechniques about spontaneous speech processing are aone of recently focused research areas, and are expected
to apply to machines that used for our life such as robots.
Corpora of Japanese spontaneous speech haveconstructed in Japan these days [1][2] . The realization of
spontaneous speech synthesis is the one of these purposes.
Akagawa et al have investigated about the possibility torealize HMM-based spontaneous speech synthesis [3]. As
same purpose, authors have tried to apply concatenative
speech synthesis to spontaneous speech, and haveobtained 77.4% of sentence intelligibility (94.2% in caseof natural speech) from the evaluation experiment about
spontaneous synthetic speech by concatenative speechsynthesis with about 100-minutes speech corpus [4].
Discontinuity between adjacent speech wave segments isa one of the reasons of this degradation about
intelligibility. In this paper, author introduces TDPSOLA as post processing of concatenative speechsynthesis for intelligibility improvement.

1.1 Concatenative speech synthesis

Fig.1: Processing flow of concatenative Text-to-Speechsynthesis
In unit selection, quality degradation of a synthetic
speech is expressed as two kinds of costs shown in Fig.2.
ti-1

ti+1

ti

744

Targets

Target cost (equ.(1))
ui-1

ui

ui+1

Units

Concatenative cost (equ.(2))
Fig.2: Cost calculation in unit selection
The target cost which expresses the quality
degradation caused by the difference between i-th
candidate unit ui and i-th target ti is defined as the normof vector of sub-costs as follows
N tgt

Concatenative speech synthesis is the method thatmakes speech sound well-keeping naturalness and voice
quality of a speaker by introducing large speech corpus[5]. Speech corpus for this method accumulates various
speech wave segments of a speaker and their features.

©ISAROB 2007

Unit Selection

Ctgt (ui , t i )SC tgt ( j, ui , ti )

2

,

(1)

j 1

where Ntgt is the number of sub-costs (Ntgt = 3 in thispaper: difference of F0, difference of duration andThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

difference of power). SCtgt means the j-th sub-costfunction that is calculated according to the weighted
distance between a feature of candidate unit and a featureof target.
The concatenative cost that expresses the difference
between two candidate units which are stuck mutually isdefined as

C con (u i , ui 1 )N con

SC con ( j , u i , ui 1 )

2

, (2)

j 1

where Ncon is the number of sub-costs (Ncon = 3 in thispaper: F0, power and MFCC). SCcon is the j-th sub-costfunction that is calculated as the weighted distance about
prosodic features (F0 and power) and phonetic features(MFCC) on the boundary.
These costs are integrated by follows

C (u i , u i 1 , t i )C tgt (ui ,t i ) 2

Ccon (ui , ui 1 ) 2 . (3)When a target series are given as (t1 L tM), the series of uwhich have the minimum value of the sum of equ. (3) are
the most preferable. This series of u is searched throughthe corpus by using Viterbi algorithm.
Authors applied concatenative speech synthesis tospontaneous speech in the previous research, andobtained 77.4% of sentence intelligibility (94.2% in caseof natural speech). One of the reasons of this degradationis the discontinuity of adjacent speech wave segments. In
this paper, TD-PSOLA (described later) is introduced as
post processing to connect each segment smoothly. Fig. 3
shows the processing flow for performance evaluation inthis paper. The target features are extracted from input
natural speech (it is assumed that they are ideal features
obtained by perfect target feature generation in Fig. 1).
Prosody of each selected wave segment is modified by
TD-PSOLA to the same as target prosody.
Input Natural Speech

1. Each glottal closure position is marked on source
speech waveform (giving the pitch mark to source
speech waveform).
2. Each pitch waveform is cut out from source
speech waveform with the window function
corresponding pitch interval. The window functionused in this paper is the hanning window whose
length of the left side and the right side of thewindow are set to the same as the preceding andthe following pitch mark intervals.
3. Each pitch waveform is placed to the nearest
target pitch mark position.
4. Target speech waveform is obtained by adding
each pitch waveform.
Fig. 4 and Fig. 5 depict the procedure which modifies F0of speech. If F0 is lowered, the target pitch mark interval
is wider than the source pitch mark interval, hence some
pitch waves are deleted (Fig. 4). Oppositely, if F0 israised, the target pitch mark interval is narrower than thesource pitch mark interval, and some pitch waves areused two or more times (Fig. 5).
Source speech
waveform

Pitch waveformthat havetaken hanningwindow
Target speech
waveform(F0 is lowered)Fig.4: Pitch modification (F0 is lowered)
Source speech
waveform

Target Feature Extraction
Unit Selection

prosody [6]. Prosodic modification by TD-PSOLA isdone according to the following procedures:

Speech Corpus

Pitch waveformthat havetaken hanningwindow

Prosodic Modification
Wave Concatenation
Synthetic Speech

Fig.3: Processing flow of synthetic speech generation for
performance evaluation of unit selection and postprocessing

Fig.5: Pitch modification (F0 is raised)

1.2 TD-PSOLATime Domain Pitch Synchronous Overlap Add (TDPSOLA) is a one of the techniques that modifies speech©ISAROB 2007

Target speech
waveform(F0 is raised)745

TD-PSOLA is simple processing and can modify
speech prosody directly in time domain. If source pitchinterval and target pitch interval are same, source

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

waveform can be kept exactly. By introducing TDPSOLA as post processing of unit selection, prosody ofeach selected wave segment can be corrected to the target
prosody. This also means that the prosodic sub-costs
become comparatively not important if TD-PSOLA isused. Thus, it is expected that the spectral discontinuity
between adjacent speech wave segments also can be
decreased by increasing the weight of sub-costs about
spectral continuity, or by decreasing the weight of subcosts about prosodic features.

2

CORPUS SPECIFICATIONThis section is described about the spontaneous speech
corpus which we constructed to use for concatenative
spontaneous speech synthesis.

2.1 Recording conditions
We employed two female professional narrators to collect
their conversation. To record each conversation voice
without mixing of the other’s voice, the soundproof
chamber and recording equipments settings are used
(depicted in Fig. 6). The chamber has divided into two
sub-rooms by soundproof wall with window. Eachnarrator put on the headphone and the lavaliermicrophone while spontaneous speech recording. Their
voices were recorded by the digital recorder separately
and sent to the other’s headphone through the mixer. The
recording equipments are shown in Table 1.
window

recorderSoundproof wallmixer

Fig.6: Soundproof chamber for conversation recording
Table 1: Recording equipments and conditions
Microphones
Recorder
Mixer
Sampling frequency
Quantizing bit

SONY ECM-77B
marantz PMD670
MACKIE 1202-VLZ Pro
48 kHz
16 bitWe recorded for three days and collected thespontaneous speech wave of about 140 minutes. Fromthese data set, in this time, one speaker’s voice waspicked up and made into the corpus of about 100 minutes(including pause time) describing in the following
subsection.

©ISAROB 2007

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2.2 Corpus construction
The procedure that the recorded data is made into the
corpus is the following.
2.2.1 Speech wave division
The recorded data was divided by sentences (it is difficult
to define the sentence. One segment placed between
silences is considered to be the sentence in this paper. ).
As the result of this operation, 3004 speech files wereobtained.
2.2.2 Utterance text dictation
The pronunciation of utterance of each speech wave fileis dictated to text file with “hiragana (Japanesesyllabary)”. Conversation speech contains not only
accurate pronunciation but also imprecise articulation,laugh, filler, disfluency and so on. Although these
phenomena have an important role for composing naturalcommunication, it is difficult to control them by acomputer. A method having the possibility to solve thisproblem is to introduce some label set (e.g. speech act
labels) [7] and develop a new unit selection techniquewhich uses them. However in this paper, these label set
were not used and all of utterances were described with“hiragana” phonologically as much as possible toexamine the performance of the conventional speech unit
selection.
2.2.3 Phoneme segmentation
Each speech wave is furthermore divided into phoneme
segments. This operation was done by the Juliansegmentation-kit included in the Julius: an open-source
continuous speech recognition software based on Hidden
Markov Model [8]. Speech wave file, phoneme utterance
text and acoustic model are necessary for this kit
performing. Speech file must be down-sampled to 16 kHz.
Phoneme utterance text is made from above-mentioned“hiragana” utterance text by the Perl script. Finally, theacoustic model for female speakers is used.
2.2.4 Feature extraction
Unit selection of concatenative speech synthesis needssome features (F0, Power and MFCC) to estimate thedistortion degree between speech wave units. F0 and
power are extracted by the free software snack soundtoolkit [9]. This toolkit has two methods of F0 extraction.
We selected the one that is equal to the get_f0 commandincluded in the toolkit ESPS/waves+ by Entropic Inc.
Speech waves were pre-emphasized (0.97 ) for
power extraction. MFCC is extracted by the tool (called
wav2mfcc) provided by the Julius.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

EVALUATION

To examine the quality of spontaneous synthetic speech,
author has a subjective evaluation experiment. Ten
sentences were prepared at random. Four subjects
listened synthetic speech with a headphone in an office(not in a soundproof chamber), and told what they said.
Two types of answers were requested to subjects: (a) theanswer when they listened to a synthetic speech one timesand (b) the answer when they listened to it again several
times (the number of listening times are unrestricted).
Sentence intelligibility is calculated as follows:SI

100 N s CPi[%] ,N s i 1 Pi

(4)

where Ns, Pi and CPi are the number of subjects (Ns = 4 inthis paper), the number of phonemes of synthetic
sentences for i-th subject, and the number of correct
phonemes included in the answer sentences of i-th subject
respectively.
The result is shown in Table 2. The result ofsynthetic speech by only the unit selection (postprocessing has not used) and the result of natural speech
are also shown for comparison. Although the result ofsynthetic speech with using TD-PSOLA is lower than the
result of natural speech, it is higher than the conventional.
That is, TD-PSOLA improved the intelligibility of theconventional synthetic speech (+4% for type (a) and+5.8% for type (b)).
Table 2: Sentence intelligibility (number of listening
times are (a) once and (b) unrestricted)
Kind of speech
Unit selection
Unit selection+ TD-PSOLA
Natural speech

Sentence intelligibility [%]
(a)
(b)
62.9
77.4
66.9

83.2

92.6

94.2

On the other hand, deteriorated synthetic speech by
the proposal method was observed. It is thought that big
modification to the prosody is a one of causes of thequality degradation. There are future works as follows:(a) employment of the sub-cost function corresponding todegree of prosodic modification, (b) comparison withother speech waveform modification method.

4

CONCLUSION

In this paper, TD-PSOLA is introduced as postprocessing of spontaneous concatenative speech synthesis
to obtain the synthetic speech decreasing spectral andprosodic discontinuity which causes degradation of
intelligibility. From the subjective evaluation experiment,
intelligibility of synthetic speech has been improved +4%
when once listening and +5.8% when listening time is©ISAROB 2007

747

unrestricted. As future work, (a) employment of the subcost function considering about degradation caused byprosodic modification, (b) comparison with other speech
waveform modification method.

Acknowledgements
The author is grateful to the Applied Linguistics
Laboratory in Nara Institute of Science and Technology
for the conversation recording cooperation.

References[1] The CREST/ESP Project, http://feast.his.atr.jp/[2] K. Maekawa, “Corpus of Spontaneous Japanese: Its
Design and Evaluation,” Proceeding of ISCA & IEEE
Workshop on Spontaneous Speech Processing andRecognition (SSPR 2003), 2003.
[3] T. Akagawa, K. Iwano and S. Furui, “Towardrealization of HMM-based spontaneous speechsynthesis,” (in Japanese) Technical Report of theInstituteofElectronics,
Information
andCommunication Engineers (SP2005-16), pp.25-30,
2005.
[4] K. Fujii, R. Ueda, H. Kashioka and N. Campbell, “A
trial to apply concatenative speech synthesis tospontaneous speech,” Proceeding of International
Technical
Conferenceon
Circuits/Systems,
Computers and Communications, Vol.2, pp.653-656,
2006.
[5] N. Campbell and A. W. Black, “Prosody and theselection of source units for concatenative synthesis,”in Progress in Speech Synthesis, Springer Verlag, Inc.,New York, 1995, ch. 22.
[6] E. Moulines and F. Charpentier, “Pitch-synchronous
Waveform Processing Techniques for Text-to-speech
Synthesis Using Diphones,” Speech Communication,
No.9, pp.453-467, 1990.
[7] N. Campbell, “What Type of Inputs Will We Need for
Expressive Speech Synthesis?,” Proceeding of IEEE
2002 Workshop on Speech Synthesis, 2002.
[8] http://julius.sourceforge.jp/[9] http://www.speech.kth.se/snack/The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Feature Based Estimation for Mapping Robot Environments using Fuzzy Kalman
FilterChandima Dedduwa Pathiranage
Dept. of Advanced Systems Control Eng.
Graduate School of Science and Eng.
Saga University
1-Honjomachi, Saga 840-8502, Japan

Keigo Watanabe, Kiyotaka Izumi
Dept. of Advanced Systems Control Eng.
Graduate School of Science and Eng.
Saga University
1-Honjomachi, Saga 840-8502, Japan

Abstract

Estimation of an unknown variable distorted by noise
can be challenged by probabilistic approaches to give aresonable estimation. In SLAM problem, Kalman ﬁlteting of the state estimates is widely used due to its
popularity as it directly provides both a recursive solution to the navigation problem and a means of computing consistent estimates for the uncertainity in thevehicle and landmark locations on the basis of statistical models for vehicle motion and relative landmark
locations. Extended Kalman ﬁlter (EKF) has been
identiﬁed as a very good state estimator for the SLAM
problem because it gives very accurate solution to the
SLAM. A good EKF algorithm for the SLAM problem
has been demostrated by Dissanayake et al. [1].
In the history of nonlinear control systems, fuzzy
logic control has played a major role in controlling
nonlinear systems. Fuzzy logic has been a promising
control tool for the nonlinear systems. Fuzzy state
estimation is a topic that has receieved very little attention. Fuzzy Kalman ﬁltering [2] is a recently proposed method to extend Kalman ﬁlter to the casewhere the linear system parameters are fuzzy variables
withing intervals. As a solution to fuzzy state estimation, Takagi-Sugeno (T-S) fuzzy model based on
observation for nonlinear systems has been illustrated
in [3]. As the ﬁrst step of ﬁnding full fuzzy Kalmanﬁlter algorithm for the SLAM problem, we here introduce feature based mapping of the robot environment
using the fuzzy Kalman ﬁlter algorithm presented in[3]. Simulation results show that the new approach to
mapping with fuzzy logic gives accuarate state estimation with less computational complexity compared to
the EKF approach.
Section 2 presents the T-S fuzzy model for nonlinear
system and the state estimation. Section 3 presents
the state estimator for each local system in T-S model.
Section 4 illustrates feature based mapping for vehiclelandmarks system and oﬀers some simulation results
and Section 5 mentions some concluding remarks.

This paper introduces a fuzzy Kalman ﬁlter based
approach for mapping robot environments. TakagiSugeno fuzzy models for nonlinear system are adopted
to represent the vehicle and landmarks state equations. The complete system of the vehicle and landmarks model is decomposed into several linear models.
Using the Kalman ﬁlter theory, each local model is ﬁltered to ﬁnd the local estimates. The linear combination of these local estimates gives the global estimate
for the complete system. This estimator is simulatedusing Matlab for the vehicle-landmark system and results prove that the new approach can accurately map
the environment.

1

Introduction

Simulataneous localization and map building(SLAM) has been a long term study in the autonomousvehicle research community. The ability to place anautonomous vehicle at an unknown location in an unknown environment and then have building a map,using only relative observations of the environment
and moreover to use this map simultaneously to navigate would indeed make such a robot “autonomous.”
Thus the main advantage of SLAM is that it eliminates the need for artiﬁcial infrastructure or a priori topological knowledge of the environment. A solution to the SLAM problem would be inestimablevalue in a range of application where absolute position or precise map information is unobtainable, including amongst others, autonomous planetary exploration, subsea autonomous vehicles, autonomous airborne vehicles, and autonomous all-terrain vehicles intasks such as a mining and construction.
The solution to the SLAM problem is the estimation of vehicle and landmarks states. How accurately
these states are estimated depends on the estimator.

©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20072

Kalman Filtering for Nonlinear Systems Presented by Takagi-Sugeno
Fuzzy Model

Nonlinear systems can be approximated as locallylinear systems in much the same way that nonlinear
functions can be approximated as piecewise linear
functions. Nonlinear systems can be represented byfuzzy linear models of the following form+h2 [k]K[k]Sv K T [k]We can ﬁnd the optimal value of K[k] by taking thepartial derivative of the trace of P + [k] with respect toK[k] and setting it equal to zero, which gives:(K[k]C − I)P − [k]C T + h2 [k]K[k]Sv = 03

if z1 [k] is Fi1 and...and zg [k] is Fig thenx[k + 1] = Ai x[k] + Bi u[k] + Gi w[k]y[k] = Ci x[k] + v[k](i = 1, ..., L)(1)

This is referred to as a Takagi-Sugeno fuzzy model.
The zj are premise variables, k is the time index, Fij
are fuzzy sets, x[k] ∈ Rn is the state vector, u[k] ∈ Rm
is the deterministic input, w[k] is the process noise,y[k] ∈ Rr is the measured output, and v[k] is themeasurement noise. The dynamic behavior of the xi [k]and y i [k] signals is presented as follows:xi [k + 1] = Ai xi [k] + hi (z[k])Bi u[k] + hi (z[k])Gi w[k]y i [k] = Ci xi [k] + hi (z[k])v[k](i = 1, ..., L)(2)Complete proof of Eq. (2) can be found in [3]. Suppose we are given an n-dimentional linear discrete timesystem of the form:x[k + 1] = Ax[k] + h[k]Bu[k] + h[k]Gw[k]y[k] = Cx[k] + h[k]v[k](3)where the scalar h[k] ∈ [0, 1], the process noise w[k]is white with PSD Sw , the measurment noise v[k] iswhite with PSD Sv , and the process noise and measurement noise are uncorrelated. Although the A,
B and C matrices are constant, the system is timevarying because of the time-varying scalar h[k]. If thepremise variables are functions of the state or control,then the system is also nonlinear because h[k] is a function of the state or control. The state x of the system
can be estimated by the Kalman ﬁlter, which can bederived by assuming a recursive estimator of the form:x̂+ [k] = M [k]x̂− [k] + K[k]y[k]x̂− [k + 1] = Ax̂+ [k] + h[k]Bu[k](4)

M [k] and K[k] are related by M [k] = I − K[k]C.
If h[k] is independent of x, it can be shown that thecovariance is propagated as follows:−

P [k] = (I − K[k]C)P [k](I − K[k]C)+©ISAROB 2007

T

749

(5)

(6)

A State Estimator for the T-S Fuzzy
Model

The steady state Kalman ﬁlter presented in the preceding section can be used to estimate the states ofeach of the L dynamic systems given in Eq. (2). This
will give us L local steady state estimated as follows:
TPi− [k + 1] = Ai (Pi− [k] − Ki [k]Ci Pi− [k])ATi + Gi Sw Gi

Ki [k] = Pi− [k]CiT (Ci Pi− [k]CiT + Sv )−1−x̂+
i [k] = (I − Ki [k]Ci )x̂i [k] + Ki [k]y i [k]+x̂−
i [k+1] = Ai x̂i [k]+hi [k]Bi u[k](i = 1, ..., L) (7)Note that Sw and Sv in the above quations canbe repleced with (1/3)Sv and (1/3)Sw respectively
for E(h2 [k]) = 1/3. Since we know that x[k] =∑L
i=1 xi [k], we can combine the local state estimatesin Eq. (7) to estimate the state of the T-S fuzzy model(Eq. (1)) as:L∑x̂i [k](8)x̂[k] =
i=1

4

Illustration of Feature Based Mapping using Fuzzy Kalman Filter

In the following, the vehicle state is deﬁned by xv =[x, y]T where x and y are the coordinates of the centerof the rear axel of the vehicle with respect to someglobal coordinate frame. The landmarks are modeled
as point landmarks and represented by a cartesian pair
xf = [xi , yi ]T , i = 1, ..., N . Both vehicle and landmark
states are registered in the same frame of reference.
1) The Process Model: Figure 1 shows a schematic
diagram of the vehicle in the process of observing alandmark. The following kinematic equations can beused to predict the vehicle state from the orientationof the vehicle φ and velocity input V :ẋ = V cos(φ)
ẏ = V sin(φ)(9)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Pi(xi, yi)

1

h2

h1

0.8
Membership grade hi

ri

θi

φ
(x, y)

0.6

0.4

0.2

0
-2

-1.5

-1

-0.5

Global Reference Frame

Eq. (9) can be used to obtain a discrete-time vehicle
process model in the form]
] [[x(k) + ∆T V (k)cos(φ(k))
x(k + 1)+ w[k]=
y(k) + ∆T V (k)sin(φ(k))
y(k + 1)
(10)
The landmarks in the environment are assumed to bestationary point targets. The landmark process modelis thus]] [[xi (k)xi (k + 1)
(11)=
yi (k)yi (k + 1)
for all landmarks i = 1, ..., N . Eq. (10) together with
Eq. (11) deﬁnes the state transition matrix for thevehicle-landmarks system.
2) The Observation Model: In general, the range ri (k)and the bearing θi (k) to a landmark i are recordedby the range and bearing sensors. In this illustration, it is assumed that sensor data are processed
to give the horizontal xvf (k) and vertical yvf (k) distances between the vehicle position and a landmark
position in the same reference frame as the observations. The range measurements and bearing measurments are taken from the center of rear vehicle axis
where the vehicle position (x, y) is taken. Refering
to Fig. 1 and the above description, the observation
model for a speciﬁc landmark can be formulated as(12)

where vx and vy are the noise sequences associatedwith the xvf and yvf respectively and assumed to be©ISAROB 2007

0.5

1

1.5

2

Figure 2: Vehicle-landmarks membership functions

Figure 1: Vehicle and observation kinematics

xvf (k) = xi (k) − x(k) + vx (k)yvf (k) = yi (k) − y(k) + vy (k)0

φ (rad)750

equal.
3) Estimation Process: Fuzzy Kalman ﬁlter describedin Sections 3 and 4 is employed to generate the estimates for the fuzzy dynamic models given in Eq. (2).

4.1

Simulation Results

In this section, we are going to show the simulation
results for the feature based mapping for the systemcomposite of Eqs. (10), (11) and (12) while assuming
the inital estimate and covariance to start estimation
process. The process model given by Eqs. (10) and(11), and the observation model given by Eq. (12) canbe used to formulate the dynamic system as follows:∆T cos(φ(k))
 ∆T sin(φ(k))  V (k) + w[k]x[k + 1] = x[k] + 


0
0
]
[
−1 0 1 0x[k] + v[k] (13)y[k] =
0 −1 0 1
where ∆T is the sample time. w[k] and v[k] are theprocess and observation noise respectively. Now consider the following two subsystems.
The ﬁrst system is as follows:∆T
x1 [k + 1] = x1 [k] + h1 [
y 1 [k] =cos(φ(k))

000−1 0 1 0
0 0 0 0
 V (k) + h1 w[k]]
x1 [k] + h1 v[k] (14)The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Y

15000

120
100

80
10000

Error in landmark state

Decreasing feature uncertainty

60
40
20
0
-20

Vehicle trajectory

5000

-40
-60

Landmark locations

-80
-100

-50

0

0
0

50

100

100

X

200

300

400

500

600

Time steps

Figure 3: Evolution of map over time

Figure 4: Landmark state estimation errors

where h1 = cos2 (φ(k)) is the membership function forthe ﬁrst subsystem. The second subsystem system isgiven as:


0
∆T


sin(φ(k))  V (k) + h w[k]x2 [k + 1] = x2 [k] + h2 
2


0
0
]
[
0 0 0 0x2 [k] + h2 v[k] (15)y 2 [k] =0 −1 0 1

map building performs well and provides state estimates that converge to zero.

where h2 = sin2 (φ(k)) is the membership function forthe second subsystem. Membership grade functions
are shown in Fig. 2. It can be seen that h1 + h2 = 1
and the combination of these two subsystems results inthe dynamic system model shown in Eq. (13). The two
local state vectors of each subsystem are in the formof the two local state vectors given by Eq. (2) and are
estimated according to the Eq. (7) and are combined
according to the Eq. (8) to obtain the global state estimate. The system and Kalman ﬁlter equations were
simulated using Matlab. An enivornment with 6 arbitrarily placed landmarks was simulated with a given
vehicle trajectory. Landmark location states were updated using Kalman ﬁlter equations for 600 times.
Sumulation results are depicted in Figs. 3 and 4. Figure 3 shows the evolution of the map over the time. It
can be seen that error ellipses are getting converged tothe acutal landmark locations as the map of the landmark locations is being build when the vehicle navigates through the enviroment. Figure 4 shows thatthe errors in each landmark state decrease over timeand reach the minimum value 0. The above mentioned
results indicate that the newly presented method for©ISAROB 2007

751

5

Conclusion

We have proposed a new approach to state estimation based on Takagi-Sugeno nonlinear fuzzy model.
Kalman ﬁlter state estimator was modiﬁed to givea fuzzy Kalman ﬁlter. Kalman ﬁlter state estimator equations were designed for each of the local systems of the T-S model and local ﬁlters were combined
to obtain the global estimator. We showed that the
proposed estimator minimizes the expected value ofthe estimation error and converges to zero over time.
Simulation results have been presented for a nonlinear
vehicle-landmark system, showing the eﬀectiveness of
this scheme of state estimation.

References[1] M. W. M. G. Dissanayake, P. Newman, S. Clark,
H. F. D. Whyte and M. Csorba,“A solution to
the simultaneous localization and map building(SLAM) problem,” IEEE Trans. on Robotics andAutomation, vol. 17, no. 3, pp. 229–241, June 2001.
[2] G. Chen, Q. Xie and L. S. Shieh,“Fuzzy Kalman ﬁltering,” Journal of Information Sciences, no. 109,
pp. 197–209, 1998.
[3] D. Simon, “Kalman ﬁltering for fuzzy discrete time
dynamic systems,” Applied Soft Computing, vol. 3,
pp. 191–207, 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Kansei with Behavioral Patterns for Human-Robot Interaction in Ubiquitous
Environments
Janaka Chaminda Balasuriya*, Kouhei Kamohara*, Chandrajith Ashuboda Marasinghe**,Keigo Watanabe*, and Kiyotaka Izumi**Department of Advanced Systems Control Engineering
Saga University, 1-Honjomachi, Saga 840-8502, Japan**Software Engineering Lab, Department of Computer Software
University of Aizu, Aizu-Wakamatsu, Fukushima, Japan*jcbala@lycos.com, k.kouhey.8–5017@docomo.ne.jp,{watanabe,izumi}@me.saga-u.ac.jp, **ashu@u-aizu.ac.jp
Abstract

aesthetic qualities of the robots behavior patterns [2].

Humans have two distinct features with compared
to any other living being; unique physical nature andemotions / feelings. Anybody who studies on humans
or trying to construct human like machines should consider these two vital facts. When robots are interactingwith humans and other objects, they certainly have asafe distance between them and the object. But how
can this distance be optimized when interacting withhumans? Will there be any advantages over achieving this? Will it help to improve the condition ofrobots? Can it be a mere constant distance? How
will the humans react? In order to ‘humanize’ robots,
they (robots) should also have certain understating ofsuch emotions that we, humans have. In this research
project, authors are trying to ‘teach’ one such humanunderstanding, commonly known as ‘personal space’to autonomous mobile robots.

1

Introduction

As Simmons et al. [1] describe, recent research inmobile robot navigation has utilized autonomous mobile robots in service ﬁelds. To operate them in anenvironment with people, it requires more than just
localization and navigation. The robots should recognize and act according to human social behavior toshare the resources without conﬂict [2].
The comfort level of the humans for which the robot
is working will be very important if the robot is to do
its job eﬀectively. But very little work has been performed in trying to understand how people would interact with a robot, how to make them comfortable, ways
for robots to indicate their feelings, etc. to analyze the©ISAROB 2007

752

1.1

Distance from the robot to the vicinity

Usually there is a ‘space gap’ between the robot
to the near by. As Stentz [3] and many others had
mentioned, this was just a constant of space. Thismechanism was quite acceptable for the systems suchas transporting, surveillance and monitoring, etc. In
other words, such kind of safe distance was good for
non-human interacting purposes. Can the same be applied for human interaction? Although it will give someresults, it will not enhance or optimize the real requirement in need, i.e. to build up harmonious relationshipwith humans.

1.2

Nakauchi model

When Nakauchi and Simmons [2] studied about personal space and applied it to moving robots, it wassome improvement over the ‘blind’ safe distant. In
their mechanism, Nakauchi and Simmons had experimented using human subjects for ‘correct distance’ or‘personal space’ in order to have pleasant feeling towards the two interacting parties.

1.3

Walters experiments

Another set of experiments were conducted by Walters et al. [4] to ﬁnd the personal space zones, initial
distances between robot and humans, etc. They also
tried to compare human-robot interpersonal distanceswith that of the human-human interpersonal distances
as described by Hall [5]. According to Hall, the generally recognized personal space zones between humans

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

fuzzy if-then rules with appropriate membership functions to generate the desired input-output combination. It is especially useful when needed to apply afuzzy inference to already collected input-output data
pairs for model building, model following, etc. where
there are no predetermined model structures based on
characteristics of variables in the system.

Table 1: Personal space zones.
Zone
Range (m)Interaction
Intimate0 – 0.15
Loved ones
Close
0.15 – 0.45 Close friends
Personal 0.45 – 1.20
Friends
Social
1.2 – 3.60
Strangers
Public3.60 +
Public

3.1

2

Variation of Personal Space

Although it is possible to ﬁnd a personal space fora speciﬁc instance of environment, it is highly volatiledepending on the two interaction parties and not definitely a constant. As Walters et al. [4] suggested,
diﬀerent robot social models, perhaps with very different initial personalities, may be more acceptable todiﬀerent users. For example, adjustments of social distances according to a user’s personality trait will be a
promising direction.

Considering the procedure as Nakauchi and Simmons [2] or the Walters et al. [4] to obtain a sense of
personal space for robot / human interaction, a similar
experimental condition was constructed. Here a robot(or a model) is kept at the end of a scaled line in aroom and a human is asked to move closer to it.
160
Train data
Check data

140

Output (cm)are well known and are summarized (for Northern Europeans) in Table 1.

Gathering data

Test data

120

100

80

60

40
0

10

20

30

40

50

60

70

80

Data set index

Appearance
Appearance(A)(A)
Familiarity
Familiarity(F)
(F)

ANFIS
ANFIS

Personal
Personal
Space
Space(PS)(PS)

Figure 2: Input data sets (for training, checking andtesting) to the APS ANFIS.

HeightHeight(A)(A)

3.2
Figure 1: APS ANFIS block diagram.
The personal space at any given instance varies depending on cultural norms and on the task being performed. Appearance, familiarity, gender, age, heightof the bodies, etc. were considered to be important.
Height, appearance and familiarity were considered (asthe initial stage for simplicity) to generate an active
personal space (APS) determination system and theblock diagram is shown in Figure 1.

3

ANFIS for Personal Space Determination

Adaptive Neural Fuzzy Inference System or simply
ANFIS can be used as a basis for constructing a set of©ISAROB 2007

753

Experimental procedure

As the experiment proceeds, one human subject is
instructed to move towards the robots as if he needs totalk with it. The human subject is asked to be along
the scaled line and look at the robot face and movecloser to it until he feels safe enough to make conversation with it. In the mean time the robotic model was
positioned so as to make its face towards the humansubject. During the whole time of the experiment, therobot did not do anything and the human subject did
all the active tasks of walking, thinking, etc. The distance between the two parties was obtained by usinga camera or by direct human observer (who reached
the two parties once they got stabilized). The humansubject had no previous experience with the robot andthe authors wanted the human subjects to be curious
as well as cautioned about the robot that they are going to meet. In other wards human subjects had no

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

idea what kind of robotic system that they are goingto face with or any capabilities that it possesses until
they meet the robot.
The robots and the robotic models used in theseexperiments are ‘KooruKun’ (PA10 robotic manipulator), ‘BoxChyan’ (robotic model), and ‘BasinKun’(previously known as ‘CHRCarry Hospital Robot’ reused with several modiﬁcations). The ﬁrst one was astationary robot with 200 cm in height and 20 cm average diameter, next was a movable robot model with100 cm height, 50 cm diameter and around 3 Kg, andthe last is also a movable robot with 170 cm height,
generalized circular diameter of 60 cm and weight ofabout 25 Kg. The data gathered are grouped for training, checking and testing for the ANFIS and are shownin Figure 2.

(H)

µH1

Π

µH2

Π

h1
h2

Π
Π
ω1

µH3

ω2

µA1
Appearance

µA2

(A)

Σ

µA3

Familiarity

(PS)

µF2

(F)
Layer :

Personal
Space

ωN

µF1

I

µF3

Π

II

III

hN
IV

VI

Figure 3: APS ANFIS architecture.

4

OII, p = µHi (Hi )(2)

OII, q = µAq (Aj )(3)

OII, r = µFr (Fk )(4)

OIII, t = ht = µHi (Hi ) ∗ µAq (Aj ) ∗ µFr (Fk )ANFIS Architecture

The architecture of the APS determination network
is illustrated in Figure 3. Layer (I) to Layer (III) represent the antecedent part of the fuzzy neural network,whereas Layer (V) and Layer (VI) represent the consequence part. As shown in Figure 3, the domain of
discourse of height (H) is described by fuzzy variable H
with p number of linguistic values (p = 3), the domainof discourse of appearance (A) is described by fuzzy
variable A with q number of linguistic values (q = 3),
and the domain of discourse of familiarity (F) is described by fuzzy variable F with r number of linguistic
values (r = 3). Hence each input variable is unique inthe sense of domain of discourse. It is assumed that

©ISAROB 2007(1)

for height, appearance and familiarity respectively.
Layer (III): In this layer, the nodes labeled as Π compute the T-norm of the antecedent part. Thus the rule
evaluates the conditions of the inputs and they are continued to the layer (V) for normalization. The output
of any node t, where t = 1, · · · , N , where N = p ∗ q ∗ r,in this layer is described by the following equation:Π
V

OII, i = µXi (x)where x is the input to node i, and Xi is the linguisticlabel (big, medium, small, etc.) associated with thisnode function. In other words, OII,i is the membershipfunction of Xi and it speciﬁes the degree to which thegiven x satisﬁed the quantiﬁer Xi . Hence the output
from the 2nd layer will be:Σ gHeighteach node of the same layer has a similar function, as
described below. Here we denote the output of the ith
node in layer (X) as O(X),i .
Layer (I): Layer (I) consists of three types of nodes;
height (H), appearance (A) and familiarity (F). The
current value of height (H), i.e. the crisp input to theheight node is Hi , appearance node is Aj and familiarity node is Fk . No computation is carried out at thislayer.
Layer (II): This layer acts as the fuzziﬁcation layer ofthe fuzzy neural network. At this layer, the output ofa node connected to the current value of input variableacquires the fuzzy membership value of the universe of
discourse. Every node i, where i = 1, · · · , p (or q or r),in this layer is an adaptive node with a node function

754

(5)

where ht represents the ﬁring strength of the tth ruleand there are N such rules as total.
Layer (IV): The ﬁrst node of layer (IV) at fuzzy neuralnetwork, which has symbols
and g, generates theoutput through the following function:g(x) =

1x(6)

with a linear summed input. Then the output of theﬁrst node of layer IV is given by
OIV, 1 =

1
N

t=1

(7)
ht

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

160
Trained ANFIS data
Check data

Personal space (cm)Other nodes just carry forward the outputs of previous
nodes to the next layer.
Layer (V): This layer normalizes the ﬁred rule values.
Each node labeled as Π in this layer multiplies thevalue carried forward by previous node with the outputof the ﬁrst node at Layer (IV). Then the output of any
mth node of this layer can be given by the following
equation:hm(8)
OV, m = N

ht

140

120

100

80

60

40
0

10

20

30

40

50

60

70

80

Data set index

t=1

Layer (VI): Layer (VI) is the defuzziﬁcation layer
 ofthe fuzzy neural network. The node labeled asinthis layer calculates the overall output as the summation of all incoming signals. Then the personal distancevalue for certain input variables is given by:
N


wm hm

OV I = P ersonal Diatance = m=1N


(9)
hn

n=1

where wm denotes a constant value in the consequencepart of the mth rule. The overall output is theweighted mean of wm with respect to the weight hm .
The connection weights are trained by applying the
hybrid algorithm. The error tolerance was set to zero.
The error is calculated by comparing the output of
the expert knowledge with that of fuzzy neural network
for the same input data, x. The adaptation of themth weight, wm , at the lth time step is given by thefollowing equation:hmwm (l + 1) = wm (l) + γ [yd − ya ] N

hn

(10)

n=1

where γ represents a small positive learning rate, andyd and ya represent the desired output and actual output respectively for the personal space value selected
for the training.
The trained ANFIS with check data is shown in Figure 4.

5

Summary

In this research project, a determination system ofan active personal space has been analyzed. This can©ISAROB 2007

755

Figure 4: Trained FIS with check data.
be considered as the ﬁrst step for constructing an active personal space system for any robot in any environment. But for a target as such, many experiments in vast environmental situations should have to
be involved. It is a must to obtain similar data withthe so–called humanoids to make this experiment complete. This system gave encouraging results in an ofﬂine mode with limited facilities. Authors are planningto make the current system more realistic and get thefunctioning in a real time mode, and are continuously
working on it.

References[1] R. Simmons, R. Goodwin, K. Z. Haigh, S. Koenig,
and J. O’Sullivan, “A layered architechture foroﬃce dilivery robots,” in Proc. of Autonomous
Agents, 1997, pp. 245–252.
[2] Y. Nakauchi and R. Simmons, “A social behavioral robot that stands in line,” Autonomous
Robots, vol. 12, 2002, pp. 313–324.
[3] A. Stentz, “Map-based strategies for robot navigation in unknown environments,” in Proc. of AAAI,
1996, pp. 110–116.
[4] M. L. Walters, K. Dautenhahn, K. L. Koay, etal. “Close encounters: spatial distances between
people and a robot of mechanistic appearance,” inProc. of 5th IEEE RAS Int. Conf. on Humanoid
Robots, Dec. 2005, pp. 450–455.
[5] E.T. Hall, The Hidden Dimension: Man’s Use ofSpace in Public and Private, The Bodley Head
Ltd, London, UK. 1966.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Improvement of Group Performance of Job Distributed Mobile Robots by
Emotionally Biased Control System
Sajal Chandra Banik, Keigo Watanabe and Kiyotaka Izumi
Department of Advanced Systems Control Engineering
Graduate School of Science and Engineering
Saga University, 1-Honjomachi, Saga 840-8502, Japan
baniksajal@yahoo.com, {watanabe, izumi}@me.saga-u.ac.jp
Abstract
This paper deals with the implementation of emotions in mobile robots performing a speciﬁed task
in a group to develop intelligent behavior and easier form of communication. The overall group performance depends on the individual performance, group
communication and synchronization of cooperation.
With the emotional capability, each robot can distinguish the changed environment, can understand colleague robot’s state, enables adaptation and reacts
with changed world. The adaptive behavior of a robot
is derived from the dominating emotion in an intelligent manner. In our control architecture, emotion
plays a role to select the control precedence among thealternatives like behavior modes, cooperation plans
and goals. Emotional interaction is happened among
the robots and a robot is biased by the emotional stateof the colleague robot in performing task. Here emotional control is used for better understanding to readthe colleague’s internal state, for faster communication
and better performance eliminating dead time.
Keywords: Emotion, Multi-agent system, Markov
modeling theory, Colleague robot, Distributed job,
Emotional embodied intelligence.

1

Introduction

Emotions have important role in intelligence, decision making process, learning, social interaction, perception, memory, creativity and more [1]. At present
emotional robotics is not an oxymoron, moreover researchers are working to incorporate emotional embodied intelligence in robotics. Damasio [2] suggested
that emotions can provide a selection mechanism foreliminating bad alternatives and then decision making
process is simpliﬁed. For a human team, a lot of emphasis is given on the emotional state of the members©ISAROB 2007756

of a team and on the understanding each others’ emotions and thus keeping all motivated to the generalgoals [3], [4]. Emotions act like a value system, allowing each individual to take a decision rapidly throughthe perception of situation and then act quickly. By
understanding the colleague’s emotional state, others
can understand the situation that they are in and also
can be motivated to do the task. For example, if one
is in happy state, others can ﬁnd out the reason ofhappiness, then he is motivated to follow the happycolleague in doing job. On the other hand, if one isin fear state, then others can get alert and understandthe situation and thus plan to escape the situation bycollaborating with the feared colleague or by compensating for his action. In [5], Nair et al. showed that inmixed agent teams (agent-human teams) as well as inpure agent teams, the introduction of emotions could
help in bringing the same advantages that emotions
bring to human teams.
In this paper, we describe a method to increase
the group performance of a team of robots with theimplementation of emotionally biased control system
by taking the advantages from emotional understanding, communication and synchronization. Here, thechoice of behavior depends on the current emotional
state of each robot as well as emotional state of colleague robot. The emotions can be considered mainly
as a particular type of information that is exchanged
among the robots. Section 2 describes the related
works where some researchers have applied emotions
to control robots and multiagent system. Section 3
clearly describes the purpose of the research work, thesystem where emotion is applying to control the robot
team and the control strategy. Description of simulation software and its application to simulate emotional
based behavior is given in Section 4. Finally, Section 5 concludes with advantages of the emotionallybiased control method as well as discusses the limitations with some open issues.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20072

Related workWorld

The idea of artiﬁcial emotion is increasingly usedin designing autonomous robotics agents, by making
robots to experience emotionally with the changed environment or to make interaction with other agents[6], [7]. In our research work, the topics are related
to cooperation between job distributed robot teams,
computational architecture for modeling emotions, useof emotion for control and avoiding stagnation.
Mataric et al. [8] and Parker’s [9] research works
have relation with distributed coordination of robots,
but have signiﬁcant diﬀerences in backgrounds andapproaches, and also have diﬀerent architecture with
diﬀerent implementation. In [8], control architecture
is based on subsumption architecture and cooperation emerged from the structure of behavior. Later,
Schneider-Fontan and Mataric included an emotionalmodel for using in communication for the minimization of interference [10]. In [11], Murphy et al. developed a multiagent control approach for interdependent tasks which imbues the agents with emotions andallows a satisfactory behavior to emerge. In this approach, a formal multilevel hierarchy of emotions isused where emotions modify active behaviors at thesensory-motor level and also change the set of active
behaviors at the schematic level. It mainly focuseson interdependent tasks, not purely cooperative andone robot cannot perform the other one’s task. Our
work in this article is diﬀerent in task mode (job is distributed to each robot, but each one is capable to doother one’s job if necessary) and emotion is generated
based on Markov modeling theory [12].

3ApproachThis section describes distributed job among theagents, control architecture and emotional model thathas been applied for dominating emotion generator.

3.1

Distributed job

For the simplicity, we have considered two robots
working in a group. One robot cleans the center partof a ﬂoor by pushing laid objects to the wall side andanother (colleague robot) picks up the objects from thewall side simultaneously. But in case of any inabilityof the robot (due to over work-load with respect totime limit or if battery operated and power shortage
occurred or any other causes), colleague can also help
in cleaning ﬂoor and thus continuing the work process©ISAROB 2007

757

Perception
Feedbackfromexternal
agent

EmotionGenerator

Motivation

Action Selection
Command selection

Communication
with other agent

Figure 1: Architectural view of the control mechanism
and communication
targeting to ﬁnish the assigned task in time maintaining a constant level of performance.

3.2

Control and communication strategy

The architecture is based on four basic subsystems:
perception, motivation, emotion generator and actioncommand selection subsystems as shown in Fig. 1. The
world (or environment) is perceived by the perception
subsystem through some parameters. The motivation
subsystem selects the present need (or goal) to be satisﬁed through the subsequent analysis of the perceived
parameters and emotion. The behavior of the motivation subsystem can be expressed by the following
equation:f (p1 , p2 , . . . , pn , e) = M odif ication input
where p1 , p2 ,. . . , pn are the perceived parameters rendered by the perception subsystem and e is the generated emotional state of the agent. The selection subsystem selects two things at a time: one is the action
to be performed by itself which is best suited with thepresent need and the other is a communication command to express its emotional state and situation to
the external agent (colleague). In response to the command, colleague robot sends a feedback to its perception subsystem. Behaviors of each robot are adapted
according to two emotional signals: global one, generating for all robots and local one, generating speciﬁcally for itself [18].

3.3

Emotional modelWith the emotionally biased control system, wewant to exploit the roles that emotion plays in biological systems which enhance adaptation in dynamic,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

uncertain and cooperative environment. The emotional model consists of four basic emotions [13]: joy,anger, fear and sad. The eliciting conditions and functional responses of each emotion are summarized inTable 1. An application of Markov modeling theory
for our purposes is described by Markovian emotion
model as shown in Fig. 2. Although this model isjustiﬁed by previous work in psychology of modelinghuman emotion [12], [14], [15], we have applied it forpure agents emotion due to its memoryless property
as behaviors and commands are highly dependent onemotional present state than the history of arriving
the state.
In the Markovian emotion model, the nodes represent the emotional states and the arcs/arrows indicate
the probability of getting out of states to the directedstate. The arc/arrow values are set to initial values(e.g. q1 , q2 ,. . . , q16 ) which give the initial state transition matrix of Markov model. These values can be
modiﬁed later on with the inﬂuence of emotion inducing factors: α, β, γ and δ for joy, anger, fear andsad respectively. For example, the probability of statetransition (arc/arrow values) from joy to other states
can be expressed by following equations:

Anger

Fear

Figure 2: Markovian emotion model
Table 1: Emotion eliciting status and eﬀects on colleague
EmotionJoy
Anger

Panger/joy = q2 + (β − α)q2
Pf ear/joy = q3 + (γ − α)q3

Fear

Psad/joy = q4 + (δ − α)q4
Pjoy/joy = 1 − (Panger/joy + Pf ear/joy + Psad/joy )where q2 , q3 , p4 are the initial arrow values forPanger/joy , Pf ear/joy and Psad/joy respectively. Thesenew values are used to get the updated state transition matrix. In [12], [16], more details of the modeland computational procedures are given.

4

Joy

Sad

Sad

Eliciting status
Success inachieving goal.
Goal either fails
or is about to faildue to colleague.
Believes a goal islikely to fail.
Unable to do
usual job orgoal fails.

Eﬀects on colleague
Motivated
to work.
Commanded toincrease
performance.
To be care aboutother.
Evoked to help
or alternative.

Simulation Results

The simulation is performed using KiKS which is a
Matlab based Khepera simulator and can simulate ina very realistic way [17]. For simulation purpose two
robots are considered: the Cleaner and the Collector.
The Cleaner robot is able to push the balls (as laidobjects on the ﬂoor) to wall side and the Collector
has the wall following character to collect the balls
from wall side shown in Fig. 3. But it is also able
to help in cleaning ﬂoor and vice versa, if necessary.
The emotional state of each robot is a function of its
present working condition, work load and colleague’s
emotional state. The emotion eliciting conditions andeﬀects on colleague are created according to Table 1.

©ISAROB 2007

758

Figure 3: Floor cleaning and collecting behavior of arobot

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5

Conclusion

The aim of this work has been to explore emotions
as one of the possible methods to control multi-robot
systems. This illustrated the way to achieve desired
cooperation among the job distributed robots using
emotions. Although emotion generation is very complex, here a Markovian emotion model is applied emphasizing the present state, which seems to work well.
With the emotional causes, the collector robot can dynamically adjust its speed which leads to better collecting process in a synchronized way. On the other
hand, the cleaner robot is also able to modify its behavior as well as environment by its internal emotions
which lead it to actions.
To bring the same advantages for pure agent teamthat emotions bring in human teams, it needs more exploration of emotion generation and implementation.
The behavior is also a function of emotional intensity,
which is ignored for simplicity of the emotional model.
Also a burning question arises about the co-occurrenceof emotions and their ﬁltration process. These drawbacks are the pending issues for future work.

[9] L. Parker, “Alliance: An architecture for fault
tolerant multi-robot cooperation,” IEEE Trans.
on Robotics and Automation, vol. 14, pp. 220–
240, 1998.
[10] M. Schneider-Fontan and M. Mataric, “Territorial multi-robot task division,” IEEE Trans. on
Robotics and Automation, vol. 14, pp. 815–822,
1998.
[11] R. R. Murphy , C. L. Lisetti, R. Tardif , L. Irish
and A. Gage, “Emotion-based control of cooperating heterogeneous mobile robots,” IEEE Trans.
on Robotics and Automation, vol. 18, no. 5, pp.
744–757, 2002.
[12] C. Arun, “A computational architecture to model
human emotions,” in Proc. of International Conference on Intelligent Information System, pp.
86–89, 1997.
[13] K. Oatley, Best Laid Schemes: The Psychology ofEmotions, Cambridge University Press, 1992.

References[1] R. W. Picard, Aﬀective Computing, First MIT
Press paperback edition, 2000.
[2] A. R. Damasio, Descartes’ Error: Emotion, Reason, and the Human Brain, New York: G.P. Putnam, 1994.
[3] J. Katzenbach and D. K. Smith, The Wisdom ofTeams, Harper Business, 1994.
[4] J. Jennings, The Teamwork: United in Victory,
Silver Burdett Press, Englewood Cliﬀs, NJ, 1990.
[5] R. Nair, M. Tambe, and S. Marsella, “The role ofemotions in multiagent teamwork: a preliminary
investigation,” in Jean-Marc Fellous and Michael
Arbib(Eds.), Who Needs Emotions: The Brain
Meets the Robot, Oxford University Press, 2005.
[6] C. Breazeal and B. Scassellati , “Infant-like social interactions between a robot and a humancaretaker,” Adaptive Behavior, vol. 8, no. 1, pp.
47–72, 2000.
[7] J. D. Velásquez, “A computational framework foremotion-based control,” in Workshop on Grounding Emotion in Adaptive System, Conference on
Simulation of Adaptive Behavior, 1998.

©ISAROB 2007[8] M. J. Mataric, M. Nilson, and Simsarian, “Cooperative multi-robot box-pushing,” in Proc. of
IROS, Pittsburgh, PA, pp. 556–561, 1995.

759[14] J. Bates, “The role of emotion in believable
Agents,” Communication of the ACM, vol. 31, no.
7, pp. 122–125, 1994.
[15] A. Margulies, “Empathy, virtuality, and the birthof complex emotional states: Do we ﬁnd or do wecreate feelings in the other,” in S. L. Albon, D.
Brown, E. J. Khantzian, and J. E. Mack(Eds.),
Human Feelings: Explorations in Aﬀect Development and Meaning, The Analytic Press, 1993.
[16] K. S. Trivedi, Probability and Statistics with Reliability, Queuing, and Computer Science Application, Prentice-Hall, 1982.
[17] S. Theodor, KiKS is a Khepera Simulator, 2001.
[18] R. Daneshvar, C. Lucas, “Improving reinforcement learning algorithm using emotions in multiagent system,” Lecture Notes in Artiﬁcial Intelligence, no. 2792, pp. 361–362, 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Feed Rate Control Using Fuzzy Reasoning for NC Machine Tools
F. Nagata, K. Kuribayashi

Y. Kusumoto

K. Watanabe

Tokyo University of Science, Yamaguchi

Fukuoka Ind. tech. Center

Saga University

Sanyo-Onoda, Japan

Ohkawa, Japan

Saga, Japan

Abstract

In this paper, a 3D design and machining system
based on an NC machine tool with a rotary unit is introduced to e ectively produce attractive paint rollers.
A post-processor is rst proposed to transform a base
tool path called cutter location data (CL data) to NC
data, mapping the y-directional pick feed to the rotational angle of the rotary unit. The 3D machining system with the post-processor allows us to easilytranscribe a relief design on a at model surface to ona cylindrical model surface. The post-processor has
another function that systematically adjusts the feed
rate according to the curvature of each design to prevent the machined surface from being chipped. The
post-processor generates suitable feed rate codes byusing a simple fuzzy reasoning method while checking
edges in relief designs. Experimental results show thatwooden paint rollers with attractive relief designs canbe successfully machined without any chipping.
1

Introduction

In home making industry, handy paint rollers
with a simple pattern are generally used to transcribe
its design to a wall just after painting. Interior planners and decorators want to use more attractive paintrollers, however, the types of the patterns are limited
to several common ones. In order to eÆciently provide
user-oriented roller designs, a new 3D design and machining system should be considered for limited production of a wide variety of paint rollers. Up to now,
although advanced 3D machining systems have beendeveloped in various manufacturing industries [1-3],
roller models with a relief design don't seem to be
successfully machined at the present stage.
In this paper, a 3D design and machining system
based on an NC machine tool with a rotary unit is introduced to e ectively produce attractive paint rollers.
The paint rollers used in general have little or no attractive design, and their designs are also limited toat or several simple patterns. This paper addresses
how to easily make attractive paint rollers with highyield rate. The most important point is that proper©ISAROB 2007

760

NC data are straightforwardly generated for the control of the rotary unit. To meet the need, a postprocessor is proposed for the NC machine tool with arotary unit. The proposed post-processor transforms
a base tool path called cutter location data (CL data)
to NC data, mapping the y-directional pick feed to therotational angle of the rotary unit. The 3D machining system with the post-processor allows us to easilytranscribe a relief design on a at model surface to ona cylindrical model surface. The post-processor has
another function that systematically adjusts the feed
rate according to the curvature of each design to prevent the machined surface from being chipped and to
shorten the cycle time for machining. The feed ratemeans the tangential velocity. The post-processor generates suitable feed rate codes by using a simple fuzzy
reasoning method while checking edges in relief designs. Experimental results show that wooden paintrollers with attractive relief designs can be successfully
carved without any chipping.
2

Post-Processor for NC Machine Tool
with a Rotary Unit

In this section, an NC machine tool with a rotary
unit and its post-processor are introduced to eÆciently
machine cylindrical models. The machined models canbe used as elaborately designed paint rollers. The
paint roller is very useful and convenient to directlytranscribe a relief design to a wall just after painting.
However, the paint rollers used in general have little
or no attractive designs and also their designs are limited to at or simple patterns. Unfortunately, eventhe 5-axis NC machine tools are not good at carving a
relief design on a cylindrical model. To solve this problem, a post-processor is proposed for the NC machine
tool with a rotary unit. The post-processor allows
the NC machine tool to produce elaborately designed
paint rollers. An attractive 3D design drawn on a atmodel surface can be easily transcribed to a cylindricalmodel surface.
First of all, we introduce an NC machine tool
MDX-650A provided by Roland D.G. as shown inThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
y_end

k fe
P ic
ed
dire
n
ctio
(y)

y_startFigure 1: NC machine tool with a rotary unit.
Fig. 1. The NC machine tool equips with an auto
tool changer ZAT-650 and a rotary unit ZCL-650A.
The mechanical resolution of the rotary unit is about
0.0027 degrees. The NC machine tool has four degrees
of freedom, i.e., three translations and one rotation. In
order to provide many kinds of paint rollers with wide
variety and low volume manufacturing, such a machining system that can directly carve a relief design on acylindrical workpiece must be realized.
Next, we discuss the problem concerning the 3D
machining of cylindrical shape with a relief design.
When the modeling of a roller is conducted by using a 3D CAD, a base cylindrical shape is modeledin advance. Then a favorite relief design is drawn on
the cylindrical model. However, the modeling of relief
design on the cylindrical shape is so diÆcult and complicated task even if using any 3D CAD. Furthermore,its 3D machining is also more diÆcult even if using the
5 axis NC machine tool, in which NC data generated
from the CAM are composed of x-, y-, z-, b- and cdirectional components.
This paper addresses how to easily make a paintroller with an attractive relief design. The most important point is that proper NC data for the NC machine
tool with a rotary unit can be generated straightforwardly. To meet the need, a post-processor is proposed to successfully transcribe the design on a atmodel to on a cylindrical model. The post processor
allows us to directly carve a relief design on a cylindrical workpiece. We here describe the feature of the
post-processor. A desired relief design is rst modeled
on a at base model. CL data are secondly generated
with a zigzag path as shown in Fig. 2. In this case,the coordinate system should be set so that the pickfeed direction is parallel to the table slide directionof the NC machine tool, i.e., y-direction. The proposed post-processor transforms the CL data into NC
data, mapping the y-directional position to the rota-©ISAROB 2007

761

tio
direc
Feed

n (x)

Figure 2: Example of zigzag path on a at model.
tional angle of the rotary unit. As can be seen fromthe components of the NC data, when the rotary unit
is active, the table slide motion in y-direction is inactive. The post-processor rst checks all steps in CL
data, and extracts the minimum value y min and the
maximum value y max in y-direction. The angle a(i)for the rotary unit is obtained from360  fy(i) y minga(i) =(1)y lengthwhere y length is the length in y-direction and is easily
obtained by y max T y min. The CL data in the i-th
step [x(i) y(i) z(i)] is transformed
into the NC data
composed of [x(i) a(i) z(i)]T by using Eq. (1). The
length in y-direction is translated into the circumference of the roller model. It is expected that the relief
design shown in Fig. 2 is desirably sculptured on thesurface of a cylindrical workpiece. The proposed system provides a function that easily transcribes an attractive design from on a at model to on a cylindrical
workpiece xed to the rotary unit.
3

Feed Rate Control Using Fuzzy Reasoning

It is known that an F-code such as F3000.0 (i.e.,
3000 mm/min) is generally used to set the tool's feed
rate to an NC machine tool. The feed rate is one of
the most important parameters to smoothly control
NC machine tools and to reduce the total machining
time. Although the feed rate should be set as fast as
possible, if the cutter path has a large curvature or
small edge then undesirable vibrations and material
chipping would occur. This means that the machining
accuracy tends to go down and we can't obtain the
precise shape as the model designed by a 3D CAD.
Especially, when a wooden paint roller with a relief
design is machined, the problem of edge chipping can't
be avoided. The feed rate should be suitably down so

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

cA1
Fmin + 0:1Fbase
cB1
0:5Fmin

Table 1: Consequent constants of fuzzy reasoning for d(i) and d(i).
cA2Fmin + 0:2Fbase
cB2
0:2Fmin

cA3
Fmin + 0:4Fbase
cB3
0:1Fminthat the model surface can't be damaged by the edge
chipping. However, conventional post-processors don't
possess the function to systematically adjust the feed
rate so as to suppress the undesirable edge chipping.
The proposed post-processor has a function that
automatically adjusts the feed rate according to thecurvature of each model not only to shorten the total time for machining but also to keep out the edge
chipping. Generally, the main-processor of CAM calculates the cutter path p(i) = [x(i) y(i) z(i)]T with alinear approximation so that the workpiece can be machined within a tolerance to a designed model. Therefore, the larger the curvature is, the higher its point
density is. Accordingly, considering the curvature results in acquiring the distance d(i) = kp(i +1) p(i)kbetween two adjacent steps of the CL data and its increment d(i) = d(i + 1) d(i).
In this section, we propose a fuzzy feed rate generator that generates suitable feed rate codes accordingto d(i) and d(i). The fuzzy feed rate generator consists of two simple fuzzy reasoning parts whose consequent parts are constant. When the current position
X (k) = [X (k) Y (k) Z (k)]T of the end-mill at the discrete time k is X (k) 2 [p(i); p(i + 1)], the fuzzy rules
are described byRule 1 IF d(i) is A~1 THEN F (i) = cA1
Rule 2 IF d(i) is A~2 THEN F (i) = cA2...
Rule L IF d(i) is A~L THEN F (i) = cAL
andRule 1 IF d(i) is B~1 THEN F (i) = cBB1
Rule 2 IF d(i) is B~2 THEN F (i) = c2...
Rule L IF d(i) is B~L THEN F (i) = cBL
where A~j (j = 1; :::; L) and B~j are the j-th antecedent
fuzzy sets for two fuzzy inputs d(i) and d(i); cAj andcBj are respectively the consequent constants at thej-th rule for the feed rate F (i) and its compensation
F (i); L is the fuzzy rule number. The con dence ofeach antecedent part at the i-th rule is obtained by(2)!jA = Aj fd(i)g©ISAROB 2007

762

cA4
Fmin + 0:6Fbase
cB4
0:1Fmin
~
A1

1

cA5
Fmin + 0:8Fbase
cB5
0:2Fmin

~
A2

~
A3

10

20

~
B2

~
B3

-30

-10

cA6

Fmin + Fbase
cB6
0:5Fmin

~
A4

~
A5

30

40

~
B4

~
B5

10

30

~
A6

µ Aj
0.5

0

0

1

~
B1

(a)

50

d i =OO?
~
B6

µB j
0.5

0
-50

(b)

50

∆d i =OO?

Figure 3: Antecedent membership functions.
!jB = Bj fd(i)g(3)
where X () denotes the con dence of a fuzzy set labeled by X . Therefore, the fuzzy reasoning results
for the feed rate and its compensation are respectively
calculated by
PLj=1 !jAcAj(4)
F (i) = PL A
k=1 !kPLj !jB cBj
F (i) = PL B
!
=1

k=1 k

(5)

The resultant fuzzy feed rate F~ (i) is realized in theform
F~ (i) = F (i) + F (i)(6)
Note that the fuzzy set used is the following Gaussian
membership function
X (x) = expflog(0:5)(x p )2 q 2 g(7)
where p is the center of membership function and q is
the reciprocal value of standard deviation. Figures 3(a) and (b) show the antecedent membership functions

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

[mm]

100

50

0

(a)Distance

between two adjacent steps in CL data

[mm]

100

0

4

Experiments

Experiments using the proposed post-processor
were conducted through actual machining of cylindrical workpieces. Figure 4 shows the carving scene ofa paint roller without undesirable chipping. Figure 5
shows one of the results, where the feed rate codes are
generated from the fuzzy feed rate generator describedin previous section. It is observed from the result thatthe feed rate F~ (i) is suitably varied according to thecurvature of the surface. Note that the periodicallyappeared feed rate 600 mm/min is forcibly given every pick feed motion, where the rotary unit is rotatedwith a small angle, e.g., 0.56 degrees. The quantity ofthe small angle depends on the ratio of y length to thepick feed as shown in Fig. 2. In the case of Fig. 5,
the total machining time was reduced about 20% compared with the case of using a constant feed rate 600
mm/min. As can be seen, the proposed fuzzy feed rate
generator provides a more intuitive and nely tunable
feed rate function for post-processor.
5

Conclusions

In this paper, a post-processor using a fuzzy feed
rate generator has been proposed for an NC machine
tool with a rotary unit. The post-processor allowed©ISAROB 2007

763(b)The increment[mm/min]Figure 4: Carving scene of a paint roller.
designed for d(i) and d(i), respectively. The reciprocal values of the standard deviations are 0.2 and
0.1, respectively. The corresponding constant values
in consequent parts are tabulated in Table 1, in which
Fmax and Fmin are the maximum and minimum values
for the feed rate estimated in advance; Fbase denotes
Fmax Fmin. These fuzzy rules are tuned based on theexperience of a skilled operator. Note that the fuzzy
reasoning part yields not only larger values than Fmax
but also smaller values than Fmin with the combination of d(i) and d(i).

-100
2400
2200
2000
1800
1600
1400
1200
1000
800
600
400

0

100

of the distance200
step i300400(c) Variable feed rate

Figure 5: An example of the variable feed rate.
us to easily transcribe a relief design from on a atmodel to on a cylindrical workpiece. The fuzzy feed
rate generator also generated suitable feed rate codes
according to the curvature of the relief design, so thatthe total machining time could be drastically reduced
without any chipping on the carved surface. Experimental results showed that attractively designed relief
paint rollers could be successfully carved by using the
proposed 3D machining system.
References[1] Y. Takeuchi, K. Wada et al.,\Study on postprocessor for 5-axis control machining centers {In case of spindle-tilting type and table/spindletilting type{," Journal of the Japan Society for
Precision Engineering, Vol. 60, No. 1, pp. 75{79,1994 (in Japanese).
[2] X. J. Xu, C. Bradley et al.,\Tool-path generationfor ve-axis machining of free-form surfaces basedon accessibility analysys," International Journal of
Production Research, Vol. 40, No. 14, pp. 3253{
3274, 2002.
[3] S. L. Chen, T. H. Chang et al.,\Post-processor development of a hybrid TRR-XY parallel kinematic
machine tool," The International Journal of Advanced Manufacturing Technology, Vol. 20, No. 4,
pp. 259{269, 2002.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Control of Three-Link Underactuated Manipulators Using a Switching
Method of Fuzzy Energy Regions
Keisuke Ichida, Kiyotaka Izumi, Keigo Watanabe, and Nobuhiro Uchida
Department of Advanced Systems Control Engineering,
Graduate School of Science and Engineering, Saga University,
1-Honjomachi, Saga 840-8502, Japan
E-mail: 05633001@edu.cc.saga-u.ac.jp; {izumi, watanabe}@me.saga-u.ac.jp
Abstract
In general, manipulators used for industry and academic laboratory have actuators to drive each joint.
On the other hand, underactuated manipulators handled by our research have some passive or free joints
without actuators and brakes. We recently developed
a switching method of fuzzy energy regions to control
such manipulators. In such a method, it needs to design parameters related to energy regions and gains ofsome partly stable controllers based on the computed
torque method. The switching method is here applied
for a three-link underactuated manipulator. We optimize such design parameters related to fuzzy energy
regions by a genetic algorithm. The eﬀectiveness of thepresent method is illustrated with some simulations.

1

Introduction

Underactuated manipulators have some passive or
free joints in general, where the number of inputs is lessthan the degree of freedom. These passive or free joints
cannot generate dynamic torques at all. To control
underactuated manipulators, a number of researches
have been studied[1]. These systems have complex
structural properties, and they have to control a number of generalized coordinates by few inputs. However, reducing the number of actuators brings someadvantages such as lightweighting, compactiﬁcationand cost reduction. The present authors have alreadyproposed a switching control mehtod, in which somepartly stable controllers were designed by computed
torque method and the related switching lows in fuzzy
reasoning or genetic algorithm (GA)[2] were obtained
to select one controller among them. However, sucha method does not necessarily give a robust result
against the change of parameters, such as initial conﬁgurations of the manipulator. We discussed the application of a logic based switching method, which has©ISAROB 2007

764

Table 1: Deﬁnition of input torques, generalized coordinates and physical parameters
Symbolsτ1 , τ2
θ1 , θ2 , θ3m1 , m2 , m3
l1 , l 2 , l 3
lg1 , lg2 , lg3
I1 , I2 , I3
µ1 , µ2 , µ3

Physical meaning
Input torque [Nm]Link angle [rad]Mass of link [kg]Link length [m]Distance between joint
and center of gravity [m]Moment of inertia [kgm2 ]Viscous coeﬃcient [Ns/m2 ]been proposed by Hespanha et al.[3], to systems likeunderactuated manipulators with drift term. We also
recently developed a fuzzy energy region based switching method[4]. Note however that in such a method, itneeds to design parameters related to energy regions
and gains of some partly stable controllers based on
the computed torque method. The present paper isconcerned with a three-link underactuated manipulator by applying the similar switching method. We here
optimize design parameters related to fuzzy energy regions by a GA. The eﬀectiveness of the present method
is illustrated with some simulations.

2

Underactuated Manipulator

Figure 1 shows a three-link underactuated manipulator where the ﬁrst and second joints are active andthe third joint is passive. In Table 1, τ1 and τ2 denote the torques applied to each joint; θ1 , θ2 and θ3
denote the angle of each link; and other physical parameters are shown in it. The dynamical model of theunderactuated manipulator is given as follows:
M (θ) θ̈ + h(θ, θ̇) = τ(1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where


M (θ)


M11 (θ) M12 (θ) M13 (θ)=  M12 (θ) M22 (θ) M23 (θ) 
M13 (θ) M23 (θ) M33 (θ)

M11 (θ)=2(m1 lg1+ I1 + m2 l12 + m3 l12 )2
2+(m2 lg2+ I2 + m3 l22 ) + (m3 lg3+ I3 )+2l1 (m2 lg2 + m3 l2 ) cos θ2+2m3 l1 lg3 cos(θ2 + θ3 )+2m3 l2 lg3 cos θ3
M12 (θ)M13 (θ)M22 (θ)=2
2+ I3 )+ I2 + m3 l22 ) + (m3 lg3(m2 lg2+l1 (m2 lg2 + m3 l2 )cosθ2=+m3 l1 lg3 cos(θ2 + θ3 )+2m3 l2 lg3 cos θ32(m3 lg3+ I3 ) + m3 l1 lg3 cos(θ2 + θ3 )=+m3 l2 lg3 cos θ322(m2 lg2+ I2 + m3 l22 ) + (m3 lg3+ I3 )

M23 (θ) =Figure 1: Model of three-link underactuated manipulator+2m3 l2 lg3 cos θ32(m3 lg3+ I3 ) + m3 l2 lg3 cos θ32
M33 (θ) = m3 lg3+ I3h(θ, θ̇) = [h1 (θ, θ̇) h2 (θ, θ̇) h3 (θ, θ̇)]T
h1 (θ, θ̇) = −l1 (m2 lg2 + m3 l2 )(2θ̇1 θ̇2 + θ̇22 ) sin θ2−m3 l1 lg3 (2θ̇1 + θ̇2 + θ˙3 )(θ˙2 + θ˙3 )sin(θ2 + θ3 ) − m3 l2 lg3 (2θ̇1 θ̇3 + 2θ˙2 θ˙3
2+θ˙3 ) sin θ3 + µ1 θ̇1h2 (θ, θ̇) = l1 (m2 lg2 + m3 l2 )θ̇12 sin θ2 + m3 l1 lg3 θ̇12
sin(θ2 + θ3 ) − m3 l2 lg3 (2θ̇1 θ̇3 + 2θ˙2 θ˙3
2+θ˙3 ) sin θ3 + µ2 θ̇2h3 (θ, θ̇) = m3 l1 lg3 θ̇12 sin(θ2 + θ3 ) + m3 l2 lg3 (θ̇1+θ̇2 )2 sin θ3 + µ3 θ̇33Deﬁnition of Energy

Energy is deﬁned by using generalized coordinates.
The desired joint angle of each link is θdi and the errorof joint angle is denoted by4

ei = θdi − θi .

(2)

Then, the energy of each link is deﬁned by
4

Ei = e2i + ė2i .

©ISAROB 2007

(3)

765

Figure 2: Ideal energy response

4

Fuzzy Energy Region Based Switching

We design controller 1 (C 1 ), controller 2 (C 2 ) andcontroller 3 (C 3 ) for a three link manipulator. They
are used as partial stabilizing controllers to stabilize each link. We can deﬁne an energy region related to each controller. Assuming that we use thesame fuzzy energy region method as used for two-link
manipulator[4], we can express the energy plain in thispaper as ideal responses of energy illustrated in Figure 2.
If an exponential function is used, the design parameters of boundary curve to divide energy region
are denoted by the amplitude and the time constant.
Control responses of manipulators depend on these parameters. Such parameters need to be set in an ideal
way. But it is diﬃcult to set at once, so that theboundary curve is denoted by fuzzy expression in thisresearch. If a boundary curve has any fuzziness denoted by the present fuzzy reasoning, then there appears an advantage of present method in setting thedesign parameters roughly. We ﬁrst consider a straightline approximation shown in Figure 3. After obtain-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 5: Membership functions for E1 + E2 > E12a
Figure 3: Region approximations for a logical switching







Figure 6: Block diagram of the proposed control system

Figure 4: Membership functions for E1 + E2 ≤ E12a

ing such an approximation, fuzzy sets for E3 can bedeﬁned for E1 + E2 ≤ E12a and E1 + E2 > E12a cases
respectively, as shown in Figure 4 and Figure 5, where
E12a , E3a , E3b , and E3c are the design parameters of
fuzzy sets. In order to realize ideal energy responses,
fuzzy rules are given as follows:

Rule 1:
Rule 2:Rule 3:Rule 4:Rule 5:Rule 6:
Rule 7:Rule 8:If E3 = S then s1 = 1
If E3 = M S and It¡1 = 1 then s2 = 1
If E3 = M S and It¡1 = 2 then s3 = 2
If E3 = M S and It¡1 = 3 then s4 = 2
If E3 = M B and It¡1 = 1 then s5 = 2
If E3 = M B and It¡1 = 2 then s6 = 2
If E3 = M B and It¡1 = 3 then s7 = 3
If E3 = B then s8 = 3

Here, a parameter It¡1 which means the index of controller for one-step delay, is introduced, because onestep delayed controller must be retained in the overlapped energy region according to the ideal energy response. si is the index of controller that must be usedin the fuzzy rule i. The corresponding control system
is shown in Figure 6.

©ISAROB 2007

766

5

Optimization by GA

We discuss about the design parameters of fuzzy
rules using GA. These parameters related to the energy regions are E12a , E3a , E3b , and E3c . Each parameter is assumed to be encoded by 32 [bit]. A costfunction is deﬁned by using the error energy of time
responses for diﬀerent two initial state vectors:T1x(0) = [1 θ T (0) 1 θ̇ (0)]T= [ π/4 π/4 π/4 0 0 0 ]T2x(0) = [2 θ T (0) 2 θ̇ (0)]T= [ π/6 π/6 π/6 0 0 0 ]TT

Then, the sampling interval is 0.01 [s], the simulation
time is 30 [s], and the desired state vector is to beconverged to zero.
The size of a population is 100 and the maximum
number of generations is 500. Simulation condition
used here are shown in Table 2.
The cost function is given by
fc =

2
∑

fi

i=1

 ∑3000 ∑3
j=2501k=1 Ek (j)fi =if E1 (j) + E2 (j) ≤ 40 and E3 (j) ≤ 40
120(3000 − jd ) otherwise(4)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Table 2: Setting parameters of simurations
Conditions
Mass of each link

Setting value
m1 =0.346 [kg]m2 =0.236 [kg]m3 =0.079 [kg]l1 =0.20, l2 =0.20 [m]l3 =0.22 [m]lg1 =0.1, lg2 =0.1 [m]lg3 =0.11 [m]µ1 =0.00 [Ns/m2 ]µ2 =0.00 [Ns/m2 ]µ3 =0.02 [Ns/m2 ]Kp1 =25.0, Kp2 =25.0
Kp3 =25.0
Kv1 =10.0, Kv2 =10.0
Kv3 =10.0

Length of each link
Distance between centerof gravity and each joint
Coeﬃcient of viscous
friction of each joint
proportional gain
derivative gain

Figure 8: Simulation results with initial state vector[π/2 π/3 0 0 0 0]

a satisﬁed condition.

6

In this paper, we have applied a switching control
method using fuzzy energy regions to a three-link underactuated manipulator. Since it was assumed to use
the same fuzzy energy region method as used in twolink manipulator, we naturally expressed the energy
plain for three-link. Therefore, several design parameters related to the fuzzy energy region were able tobe trained by genetic algorithm, introducing a costfunction to be used in the optimization process. In
the furure, we want to check the projection form ofenergies for three-link underactuated manipulators.

Figure 7: Generation history of GA

where i is the index of simulation trains, j is the index of discrete time, k is the index of energy of eachlink, and jd is the index of discrete time when a certain link energy diverges to inﬁnite. As a result, a
training history in cost function is shown in Figure 7.
At this stage, the parameters related to the energy region are converged. The parameters of fuzzy energy
region are then set to E12a =7.463264, E3a =1.329838,
E3b =12.482597, and E3c =13.929500.
Now, the other initial state vector is set toTx(0) = [θ T (0) θ̇ (0)]T= [ π/2 π/3 0 0 0 0 ]T
We evaluated the total error energy of each link, where
the evaluation function, fe was applied only for last 15
seconds when the control converged to zero roughly.
Then, the evaluation function was fe =0.113969. The
response of each link angle is shown in Figure 8. Thus,
we conﬁrmed that the present method converged with©ISAROB 2007

Conclusions

767

References[1] Y. Nakamura, T. Suzuki, and M. Koinuma, “Nonlinear behavior and control of a nonholonomic freejoint manipulator,” IEEE Trans. on Robotics andAutomation, vol. 13, no.6, pp. 853–862, 1997.
[2] L. Udawatta, K. Watanabe, K. Izumi, and K.
Kiguchi, “Control of underactuated manipulators
using fuzzy logic based switching controller,” J. ofIntelligent and Robotic Systems, vol. 38, pp. 155–
173, 2003.
[3] J. P. Hespanha and A. S. Morse, “Stabilization ofnonholonomic integrators via logic-based switching,” Automatica, vol. 35, pp. 385–393, 1999.
[4] K. Ichida, K. Izumi, K. Watanabe, and N. Uchida,“Switching rules based on fuzzy energy regions fora switching control of underactuated robot systems,” Proc. of the Int. Conf. on Control, Automation and systems (ICCAS), pp. 1949–1964, 2005.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Central Pattern Generators Based on Matsuoka Oscillators for the Locomotion of
Biped Robots
Guang Lei Liu , Maki.K.Habib , Keigo Watanabe , Kiyotaka Izumi
Department of Advanced Systems Control Engineering,
Graduate School of Science and Engineering, Saga University,
1 Honjomachi, Saga 840-8502, Japan
E-mail: liuguanglei@hotmail.com, {habib, watanabe, izumi}@me.saga-u.ac.jp
Abstract
Biologically inspired control approaches based on
central pattern generators (CPGs) with neural oscillators have been drawing much attention to generate rhythmic motion for biped robots that resemble
human-like locomotion. This paper describes the design of a neural oscillator based gait rhythm generator
using a network of Matsuoka oscillators to generate
a walk pattern for biped robots. This includes properconsideration of oscillator’s parameters, such as a time
constant adaptation rate, coupling factors for mutual
inhibitory connections, etc., to obtain a stable anddesirable response from the network. The paper examines the characteristics of a CPG network with six
oscillators and the eﬀect of assigning symmetrical andasymmetrical coupling coeﬃcients among oscillators
within the network structure under diﬀerent possibilities of inhibitions and excitations. The kinematics
and dynamic of a ﬁve-link biped robot has been modeled and its joints are actuated through simulation by
the torques output from the neural rhythm generator
to generate the trajectories for hip, knee, and ankle
joints. The parameters of the neural oscillators are
tuned to achieve ﬂexible trajectories. The CPG based
control strategy are implemented and tested throughsimulation.

1

Introduction

Recent studies on the biped locomotion enabled humanoid robots to navigate real environments [1]. ZMP(Zero Moment Point) based control approaches areused to describe the stability and control of bipedrobot systems by following a targeted trajectory [2].
These approaches have focused on the ability of executing planned movements at any instance by ensuring
surface contact between the sole and the ground. In©ISAROB 2007768

general, the developed ZMP based control algorithms
for bipedal locomotion have been shown to be eﬀective to achieve bipedal locomotion in legged robots
with ﬂat feet. However, they require precise modelingand precise joint actuation with high control gains.
From the biological point of view, locomotion of human and animals do not require such precision, and
it is quite diﬀerent from that of current biped robots.
There are evidences showing the existence of various
oscillatory or rhythmic pattern generation activities
within the neural circuitry in almost every animal,
and most of them are produced without receiving any
particular extrinsic oscillatory stimulus [3]. The fundamental mechanisms of animals, rhythmic biologicalmovement, such as locomotive motion of quadrupeds,
ﬂapping of bird wings, swimming of ﬁsh, crabs, etc.
are typical examples of oscillatory activities that havebeen studied both in biological science and in engineering.
Neurobiological studies revealed that rhythmic motor patterns are controlled by neural oscillators referred to as CPGs which generate oscillatory signals[4]. It has been also suggested that sensory feedback plays an important role in stabilizing rhythmic
movements by coordinating the physical system andthe CPGs [5]. In contrast to oﬀ-line trajectory planning, biologically inspired control approaches basedon CPGs with neural oscillators have been drawing
much attention to generate rhythmic motion for bipedrobots that featured with self-adaptive properties tocope with change in their environment [6-8]. The neural oscillator proposed by Matsuoka [3] is widely used
to model the ﬁring rate of two mutually inhibiting neurons described in a set of diﬀerential equations. Thismodel is used in robotic applications to achieve designated tasks involving rhythmic motion which requires
interactions between the system and the environment.
However, it is very diﬃcult to determine the CPG parameter values for various robots and environments,

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

since there is no design principle to determine the parameter values.
This paper describes the design of an oscillatorbased gait rhythm generator using a network of Matsuoka oscillators to generate a walk pattern for bipedrobots. This includes proper consideration of oscillator’s parameters such as a time constant, coupling
factors for mutual inhibitory connection, etc., to obtain a stable and desirable response from the CPG
network. The paper examines the characteristics of
CPG network with six oscillators and the eﬀect of assigning symmetrical and asymmetrical coupling coeﬃcients among oscillators within the network structure
under diﬀerent possibilities of inhibitions and excitations.

2

The CPGs and Neural Oscillator
Model

Almost all species developed completely diﬀerent
form of locomotion perfectly suited to its morphology
and environment to ensure its survival. To achieve
locomotion, the neural system generates rhythmic signals that are sent to the musculo-skeletal system inorder to produce torques on the diﬀerent joints of theanimal. There are some evidences showing that thelocomotion patterns in human are generated at the
spinal level, and as such, it has been considered thathumans use a system that is comparable to a CPG for
their locomotion. CPGs are neural networks that can
produce rhythmic motor patterned outputs without
rhythmic sensory or central input.
Bipedal locomotion seems to be more complicatedthan the mentioned process as the balance is much important and critical with only two legs, while it makes
the control extremely crucial. CPG based approachis directly inspired from biological considerations andcan be represented by diﬀerent mathematical models
such as oscillators, artiﬁcial neurons, vector ﬁelds, etc.
Each CPG usually represents one degree of freedom(DOF). Oscillator based CPGs use the concept of limit
cycles which are very convenient in the case of locomotion as they can return to their stable state after asmall perturbation and they are almost not inﬂuenced
by a change in the initial conditions. Diﬀerent models could be used to represent the interaction between
the CPG and the reﬂex system that represents thetype of the feedback mechanism from internal sensory
information and the interaction with robot environment. In order to represent the CPG and generate therequired signals several nonlinear oscillators that are©ISAROB 2007769

coupled together have been developed, such as, Hopf,
Rayleigh, Van del Pol, Matsuoka, etc. oscillators.
Due to its simplicity and eﬀectiveness, Matsuoka
oscillator is widely used in many researches on robotics
and CPGs [3, 9]. It is based on the mutual inhibitionof two artiﬁcial neurons that generate a periodic signal
as output. The model of each neuron has represented
by two equations with two state variables as below,

τri

dui
dt

dfi
dt
yi (ui )τai= ¡ui +n∑wij yj + wsi s0 ¡ bfi

j=1+ f eedi(1)= ¡fi + yi

(2)=

max{0, ui }(3)

The ﬁrst state variable is ui that corresponds tothe membrane potential of the neuron body, and thesecond state variable is fi that represents the degreeof fatigue or adaptation (self-inhibition) in the neuron,
while yi is the output of the neuron. The subscripts i, jdenotes the neuron number, τri , is the time constants
that speciﬁes the rise time when given step input.
The frequency of the output is roughly proportional
to 1/τri . In addition, τai , is the time constant thatspeciﬁes the time lag of the adaptation eﬀect. wij , denotes inhibitory synaptic connection weight from thejth neuron to the ith∑ neuron; wij • 0 for i 6= j, andwij = 0 for i = j.
(wij yj ), represents the total input from the neurons inside a neural network. s0 is
a driving input and ws0 denotes a connection weightof the driving input, and f eedi , is an input feedback
sensor signal to the neuron and represents the internal sensory information and interaction between the
robot and its environment (f eedi has been added to
the neuron model of Matsuoka to represent the feedback sensory information [8]), and it is used mainlywith a closed-loop CPG model.
Figure 1(a) shows the general Matsuoka neuronmodel described by equations 1, 2 and 3. Matsuoka oscillator consists of two neurons that are linked reciprocally while inhibit and excite each other alternatively
to produce an oscillation as output. Such activity isused to account for the alternating activities of ﬂexor
and extensor muscle at each joint during walking. The
self-inhibition is governed by the bfi connections while
the mutual inhibition is done through the wij yj andwji yi connections. The output torque will equal toτr = yj ¡ yi . Figure 1(b) shows two coupled neuronsof the Matsuoka oscillator.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

τ1
s0
y1
y2

yn

wsi

wi1
wi 2

...

win

Σ

−
feedi
+

+
−

−

1
τ ri

∫ dt

ui

max{0, ⋅}

y1

yi

feed1

b

+

Σ

y2

fi

1
τ ai

∫ dt

+

yi

1

−

ws 2
w12

w21

y2

(a)

feed 2

E

F
ws1

1

y1

(b)

Figure 1: (a) General Matsuoka neuron model; (b) One oscillator consisting of an extensor and a ﬂexor

3
3.1

Biped Robot andRhythm Generator

NeuralHigh level activity coordinatorto activate the relevant
neural rhythm generators

The general control strategy for gait
generation

Figure 2 introduces the adopted general control
strategy for the bipedal robot aiming to generate ﬂexible rhythmic walking gait, and it includes three parts.
The ﬁrst part consists of two major elements. The
ﬁrst element represents the high level activity coordinator that can set and activate the relevant neuralrhythmic motion based on external and internal sensory information. The second element within the ﬁrst
part represents the network of coupled neural oscillators aiming to generate synchronized rhythmic signals.
The locomotor movement results from torques generated by the neural rhythm generator and acting at
each joint of the robot. The second part of the control
strategy includes the model of the musculo-skeletal
system along with the mathematical formulation of
the dynamic equations of motion using Newton-Euler
method. The last part represents the feedback signalsthat aim to establish a closed-loop to enable real time
adaptation for the walking gait.

3.2

External information

Gait

The bipedal musculo-skeletal model

Figure 3 shows the simple model for simulating abipedal musculo-skeletal system that has been considered in this paper. It has ﬁve joints and two identicallegs each with three DOFs corresponding to hip, knee
and ankle joints. Each leg is composed of a thigh (links
2 and 3), and a shank (links 4 and 5). In the dynamic
model of the bipedal musculo-skeletal system, the links
are considered to be of uniform rectangular shape with©ISAROB 2007

770

CPGs parameter

Feedback•reactive forces•joint positions and velocities•the internal sensory information

Network of coupledneural oscillators togenerate proper rhythmic
signals

Torque generation
Musculo-skeletal system
Dynamic equation of motion
using Newton-Euler method

Actual trajectories

Figure 2: The general control strategy for the bipedal
robotmass at its center. A point mass is used to represent
the remaining part of the body and it is described bylink 1 at the hip. Both legs are integrated at link
1 while assuring suitable detachment. The joints arenumbered as Joints 1, 2, 3, 4, 5 from the side of thebody, where Joint 1 is the hip joint, Joints 2 and 3 areknee joints, and Joints 4 and 5 are ankle joints.
Due to their low inertia, a point foot has been considered into the dynamics during the support phase,and contact with the ground has been represented by
two dimensional spring and damper. Vertical and horizontal ground reaction forces are modeled and calculated each time the ankle ﬁrst makes contact with theground respectively. A slippage model is establishedby using a condition that manages the relation between both reactive forces and the static friction coefﬁcient of the ground. In addition, the described model

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Link 1

musculo-skeletal is,−1

Hip jointτ2ẍ = P (x)[C(x)P (x)] [D(x, ẋ)¡ C(x)Q(x, ẋ, Tr (y), Fg (x, ẋ))]τ1+ Q(x, ẋ, Tr (y), Fg (x, ẋ))

Link 2

Link 3
τ4

To solve the motion equations, the y values are provided as an output from the neural rhythm generator
that is proportional to the torque. In addition, thefeedback signal from the bipedal robot to the neuralrhythm generator is represented by the joint positionsand velocity of diﬀerent moving parts of the body, andthe contact forces with the environment.

τ3Knee joint

Link 5

Link 4yx

Ankle joint

τ6
Left Legτ5
Right Leg

3.4

Figure 3: Five link model for a biped locomotion
is bounded to move within the sagittal plane, and thetorques acting at the joints to realize the walking gait
are assumed to be generated by the neural gait rhythm
generator.

3.3

The mathematical motion formulation of the model

By using the Newton-Euler dynamic formulation,
the general form of equations of motion for the bipedal
musculo-skeletal are derived as below [8],ẍ

= P (x)F + Q(x, ẋ, Tr (y), Fg (x, ẋ))(4)

where x is a (14 £ 1) vector of the inertial positions of
5 links and the initial angles of 4 links; P is a (14 £ 8)
matrix; F is a (8 £ 1) vector of constraint forces; Q isa (14 £ 1) vector; Tr is a (6 £ 1) vector of torques; Fg
is a (4 £ 1) vector of forces on the ankle which depends
on the state of the terrain; and y is a (12 £ 1) vectorof the output of the neural rhythm generator.
From the model, the equations of the kinematic constraints are formulated, and hence the acceleration canbe obtained by diﬀerentiating these equations twicewith respect to time. The yielded equations can be
written in the following compact form,
C(x)ẍ = D(x, ẋ)(5)The constraint forces can be obtained by substituting
equation (4) into equation (5), and to get the required
accelerations without the use of the constraint forces,
the yielded equation of forces is substituted into equation (4). Hence, the compact form of the acceleration
equations that represents the motion of the bipedal©ISAROB 2007

(6)

771

The model for the neural rhythm generator

The neural oscillators are the main elements thatcompose the model of the neural rhythm generator.
The simplest model of the neural oscillator consists of
two mutually inhibited neurons with the self adaptation in each of them. Each neural oscillator has fourstate variables. Two variables represent the inner stateof each neuron (ui and uj ), and the other two statevariables represent the degree of adaptation for eachneuron (fi and fj ), respectively. Six of the neural oscillators have been used to model the neural rhythm
generator for the bipedal robot. Two oscillators havebeen used at the left and right side of the hip and one
oscillator has been used at each of the knee and ankle joints. Figure 4 illustrates the arrangement of theneural oscillators in relation to the adopted bipedal
musculo-skeletal system. The odd number oscillator
represents ﬂexor (F) and the even number oscillator
represents the extensor (E), while τ1 to τ6 represent
the output torques from the oscillators. The conﬁgured neural oscillators have inhibitory connections.
The two neurons of each oscillator generate torques
in opposite direction, i.e., the direction of contractionof ﬂexor and extensor muscle. The algebraic sum of
the torques at each neural oscillator is proportional to
the torque at the relevant joint during bipedal walk.
The inhibitory connection between the hip oscillators
produces alternate excitations to give the alternation
between the movements of the two legs.
The parameters for each neural oscillator and theinterconnection between the oscillators are tuned experimentally to achieve the generation of a consistentpattern that assembles human biped motion. The
feedback signal from the bipedal robot to the neuralrhythm generator is represented by the joint positionsand velocity of diﬀerent moving parts of the body, andthe contact forces with the environment. In case of a

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

physical biped robot, the feedback sensory information are sensed through diﬀerent internal and externalsensors.

[2] Vukobratovic, M., Borovac, B., Surla, D., andStokic, D., Biped Locomotion: Dynamics, Stability, Control and Application, Springer,1990.

4[3] Matsuoka, K., Sustained oscillations generated bymutually inhibiting neurons with adaptation, J.
of Biological Cybernetics, vol. 52, pp. 367–376,
1985.

Simulation and Results

In order to produce a suitable relative phase at
the joints of each side of the bipedal, the interconnection between the neural oscillators at each side of thebipedal has been chosen in the way that the ﬂexor andextensor of the hip oscillators can inhibit the extensor
neurons of the knee and ankle oscillators as illustrated
in Figure 4 and we can call this model as one-rank
model. The simulation result of the walking gait for
bipedal robot is shown in Figure 5. Based on thismodel, both of the knees’ oscillators have been chosen
to inhibit the ankles’ ﬂexor neurons as shown in Figure 6 and we can call this model as two-rank model.
The simulation result of this case is shown in Figure
7. The total time is 2 seconds and the ground is level.

5

Conclusions

This paper has presented a CPG based control approach composed of a network of coupled neural oscillators to generate proper rhythmic motion for bipedal
robot. This approach avoids the need to have a perfect knowledge of the robot’s dynamics as comparedto the trajectory based methods. In addition, it isa more general and adaptive to design controllers forbided robots. Moreover, reﬂexes that are produced by
the robot’s feedback sensors are used to manage external eﬀects within the robot environment and balance
control. However, currently there is no systematic design principle that can determine the parameter values
for the oscillators and assigning eﬃcient coupling between oscillators. Hence, the next step of this work
will focus on adapting the parameters of each neural oscillator with the possibility of reconﬁguring thecoupling mechanism between oscillators in real timethrough learning paradigm.

References[1] Hirai, K., Hirise, M., Haikawa, Y., and Takenaka, T., The development of Honda humanoid
robot, in Proc. of the International Conferenceon Robotics and Automation (ICRAf98), vol. 2,
pp. 1321–1326. 1998.

©ISAROB 2007

772

[4] Grillner, S., Wallen, P., and Brodin, L., Neuronal
network generating locomotor behavior in lamprey — circuitry, transmitters, membrane properties, and simulation, Annual Review of Neuroscience, vol. 14, pp. 169–199, 1991.
[5] Nakamura, Y., Sato, M., and Ishii, S., Reinforcement learning for biped robot, in Proc. of the 2nd
International Symposium on Adaptive Motion ofAnimals and Machines (AMAM2003), ThP-II-5,
Kyoto, March 4–8, 2003.
[6] Nakanishi, J., Morimoto, J., Endo, G., Cheng,G., Schaal, S., and Kawato, K., Learning fromdemonstration and adaptation of biped locomotion, Robotics and Autonomous Systems, vol. 47,
pp. 79–91, 2004.
[7] Williamson, M. M., Robot arm control exploiting natural dynamics, Ph.D. Thesis, MIT, Cambridge, Massachusetts, June 1999.
[8] Taga, G., Yamaguchi, Y., and Shimizu, H., Selforganized control of bipedal locomotion by neuraloscillators in unpredictable environment, Biological Cybernetics, vol. 65, pp. 147–159, 1991.
[9] Matsuoka, K., Mechanisms of frequency and pattern control in the neural rhythm generators, Biological Cybernetics, vol. 56, pp. 345–353, 1987.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

F

F
τ2

O3

O1

O4

O12

τ2

O2 E

O4

O3

O7

O5

O11

O1

τ4
O8

O6

O12

O10

O9

τ1
O2 E

τ3

τ4
O8

τ1

τ3
O7

O5

O11

τ6

τ5

τ6

Left leg

Right leg

Left leg

O6

O9

O10
τ5

Right legFigure 6: Two-rank CPG network for a biped locomotion

Figure 4: One-rank CPG network of biped model

motion history
1.2

motion history
1.2

Actual trajectory

Actual trajectory
1

1
0.8

y-coordinate [m]y-coordinate [m]

0.8

0.6

0.4

0.6

0.4

0.2

0.2

0

0

-0.2
-0.5

0

0.5

1
1.5
x-coordinate [m]

2

2.5

3

Figure 5: The simulated walking gait of bipedal using
one-rank model in Fig. 4©ISAROB 2007

773

-0.2
-0.5

0

0.5

1
1.5
x-coordinate [m]22.5

Figure 7: The simulated walking gait of bipedal using
two-rank model in Fig. 6

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Networks on Earth from the climate data
Kazuko Yamasaki
Masanori Ohshiro
Eiji Nunohiro

Kenneth J. Mackin
Kotaro Matusita

Abstract
We extract scale free networks from air temperaturerecords covering whole earth of NCEP-NCAR reanalysis data. To do this, we use the phase synchronization which analyzes nonlinear nonstationary phase of
the time series of the temperature at some locations
on the earth and calculate the phase diﬀerences withtime delay between every pare of them Networks showsscale free nature which is thought to contribute to thestability of the climate. These studies will help environmental conservation of our earth.

1

Introduction

Nowadays a lot of attention is paid to various networks in the world , especially which show scale freenature. But only a few studies have been done aboutclimate networks on the earth. [4],[5] The earth is investigated in various methods like whole earth simulator. Empirical Orthogonal Function (EOF) decomposition is popular method to extract Elnino, Southern
Oscillation etc. But networks may give us other kindsof understandings about climate on the earth. To extract meaningful networks from climate data of theearth, we use phase synchronization which describes
synchronization of time dependent phase of nonlinear oscillations instead of linear method like crosscorrelation. We use temperature of the atmosphere
covering the whole surface of the earth. We draw
networks by connecting two locations which synchronizes each other. We show some seasonal networks
and study their scale free nature.

1.1

Data

The global National Centers for Environmental Prediction - National Center for Atmospheric Research(
NCEP-NCAR ) provides reanalysis dataset of various
climate data in every 100-hPa atmospheric pressurelevels and every grid with a resolution of 2◦ latitude
× 2◦ longitude. We choose 47 locations from the grids©ISAROB 2007774

so that density of the locations is almost equal on
the earth and use 400-hPa, 500-hPa and 600-hPa andstudy the daily temperature data from 1979 to 2005.

1.2

Phase synchronization

Recently phase synchronization is applied to a lotof complex dynamical systems which shows nonlinear nature and traditional cross-correlation technique
may not work well, for example chaotic system , brain
activity[1], the binocular ﬁxation eye movements [3]and temperature and precipitation in diﬀerent regions
etc [2]. In this method, we use nonlinear phase which
is deﬁned by a Hilbert transform and the generalized
phase-diﬀerence is ϕn,m (t) = nφ1 (t) − mφ2 (t) between
two oscillators. After taking mod (π), if the generalized phase-diﬀerence is constant, we can say two oscillators are synchronized and shifted by the constantin phase. But generally has time dependence, so wehave to see the histogram of ϕn,m to judge whether
two oscillators are systematically shifted or not. Then
we use the Shannon entropy. If the entropy equals 0,
they are shifted constantly and if the entropy is large,
each cycle of two oscillators are independent so they
are not synchronized. Here we have time series xj (t)of the temperature at the jth location on the earth.
• We construct the complex signals
zj (t) = xj (t) + iyj (t) = Aj eiφj (t)by Hilbert transform of xj (t). Now we have thenonlinear phase φj (t) at the jth location.
• We
deﬁne
the
generalized
phasediﬀerence ϕn,m,i,j (t) for various m and n values
and every pare of the locations i and j.
ϕn,m,i,j (t) = (nφi (t) − mφj (t)) mod 2π• We create a histogram of ϕn,m,i,j with M binsof size 2π/M and we get the frequency distribution pk of each bin k, that show how the phasediﬀerence of the pare of two locations occurs.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

• To quantify the systematic occurrence of thephase-diﬀerence, we use the Shannon entropy S
and deﬁne an index ρn,m,i,j .

1.00
0.20

0.50

By deﬁnition, the maximum entropy and therange of the index are Smax = lnM and 0 ≤ρn,m,i,j ≤ 1. ρn,m,i,j = 1 means complete phasesynchronization, in this case the distribution of
the frequencies pk shows sharp peak at a value of
k.

0.10

pk ln pk

k=1

0.05

M


probability distribution

S=−

0.02

Smax − S,Smaxsummerfall
winterspring0.01

ρn,m,i,j =Degree Distribution

2

3

4

5

6

7

number of links

1.3

1.00
0.50
0.20
0.10
0.05

probability distribution

0.02

The phase synchronization method is beyond thecross-correlation method in the two points. It analyzes the nonlinear relationship and considers the timedelay directly. But for now we do not distinguish thenegative and the positive correlation in the phase synchronization.

Degree Distribution

2

We extract climate networks as follows. Here we
consider only n = 1 and m = 1 phase-diﬀerenceρ1,1,i,j . We standardize the max value of the index
ρ1,1,i,j by dividing the average value of them amongdeferent values of time delays τ .
ρmax,i,j = max(ρ1,1,i,j (τ ))/mean(ρ1,1,i,j (τ ))So every ith-jth location pare has the set values of( ρmax,i,j , τmax,i,j ) . To decide the links of the networks, we use the thresholds (ρ̃, τ̃ ) in both values of (ρmax,i,j , τmax,i,j ) and deﬁne the network matrix Ni,j1

if (ρmax,i,j > ρ̃) ∧ (τmax,i,j < τ̃ )0other wise

To test the consistency of the networks, we check thecommon links of data in the consecutive pressure levels data and in the consecutive 6 years terms in 25
years data. Changing the thresholds (ρ̃, τ̃ ), we calculate the correlation of two network matrixes in thetwo consecutive data Fig.1 shows the correlation of
the two networks and the fraction of existing common
links. We see the correlation of the two networks and©ISAROB 2007

3

4

5

6

number of links

Climate Networks

Ni,j =

summerfall
winterspring0.01• Next we shift the time t → t + τ in one of the twotime series xi (t) , xj (t) and calculate the phasediﬀerence ρn,m,i,j (τ ) between one time series withshift τ and the other time series without shift.
Finally we have max value of the index ρn,m,i,j (τ )among deferent values of τ .

775

Figure 3: Degree distribution about outgoing links (left ) and incoming links ( right)
the fraction of existing common links are trade-oﬀ.
When we need high consistency of the network anda lot of existing common links, this is multi-objective
optimization problem. So we pay attention to Paretooptimal frontier. We see τ̃ ∼ 10 days calculations staynear the Pareto-optimal frontier and when we changes
another threshold ρ̃, it almost reaches peak at ρ̃ ∼ 4.
So we choose the two threshold (τ̃ , ρ̃) = (10, 4). Now
we can draw directed networks with the direction oftime delays. Fig.2 show the networks in each seasons.
We study the distribution of number of links and it
shows scale free like Fig.3.

References[1] P. Tass, M.G. Rosenblum, J. Weule, J. Kurths, A.
Pikovsky, J. Volkmann, A. Schnitzler, H.-J. Freund, Phys. Rev. Lett. 81 (3291) (1998).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

1.0
0.8

synchronization 6 years 1−1 500mb−600mb

0.6

max delay

0.4

5
10
15
20
30
50
200

0.0

0.0

0.2

0.4

0.6

0.8

5
10
15
20
30
50
200

correlation coefficient and fraction of links

synchronization 6 years 1−1 500mb−600mb
max delay

correlation coefficient

Correlation and Fraction of Links vs Threshold

0.2

1.0

Trade−off between Correlation and fraction of Links

0.01

0.02

0.05

0.10

0.20

0.50

1.00

fraction of existing common links

2

3

4

5

6

7

threshold

Figure 1: To test the consistency of the networks, we investigate the common links of data in the consecutive
pressure levels data and in the consecutive 6 years terms in 25 years data. Changing the thresholds (ρ̃, τ̃ ), we
calculate the correlation of two network matrixes in the two consecutive data Fig.1 shows the correlation of thetwo networks and the fraction of existing common links.
[2] D. Rybsky, S. Havlin, A. Bunde, Physica A 320(2003) 601.
[3] S. Moshel, J. Liang, A. Caspi, R. Engbert, R.
Kliegl, S. Havlin, A.Z. Zivotofsky, Ann. NY Acad.
Sci. 1039 (2005) 484.
[4] A.A. Tsonis, P.J. Roebber The architecture of theclimate network Physica A 333 (2004) 497 - 504[5] A.A. Tsonis, K.L. Swanson, and P. J. Roebber What do networks have to do with climate?
:American meteorological society (2006) pp.585595©ISAROB 2007

776

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 2: Seasonal networks by temperature of pressure level 500-hPa ( left ) and 600-hPa (right) with threshold(τ̃ , ρ̃) = (10, 4)©ISAROB 2007

777

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

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                                                                                    !                                           "   #                                                         #                      $ %                 &                                            '( )* #                  %              
 +,         - +,            +,                "#                                      '.*!                                              &                   ©ISAROB 2007778

  /     "                  '.* #                              &           $ %                                
  x   f =         #                            "   f = f (x) = x = 1   x #                      wT f = 0                wT f + b = 0   T          0                                    w̃  := (w , b) f̃ := (f  , 1)   :=   %       
 1            "    
b '2*&
    N      i 
 
  
  f i
  M         S M        yi ∈ {±1}      yi = sgn(w∗ T f i )  w∗                          (f i , yi )         (yi f i , 1)
                  #     f   
MS+= {f|f T w∗ > 0}          
3     w ∈ S M           S M           3   (f i , yi )         w∗       {w|yi w T f i > 0}    &

f (x)  The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
  αi    9                3  αi =0 f i      #    ŵ             4  (!                        
 

M

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Input Space

Mw*

Weight Space4  (5 6      #         

3  N   

     w ∗AN = {w|yi wT f i > 0, i = 1, . . . , N },

^
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            '7* 4  )  S M  #        AN                     1  0                                    w      "   1   SMw*

       4   :  :            f i  
  ŵT f i = 1                  ŵ       
 ';* ŵ                          S M #         ŵ S M    ŵ/ŵ                S M       ŵ                AN 4  . '<*

4  .5        ŵ                     ^w

                             1                    (            
5

^
w*w

(  
4  )5 &         

8                "           4      "         1
min w2
w,ξi 2 w f i ≥ 1.

#           ŵ     
ŵ =N


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
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   1    n   ) 8    (n + 1)   1  

. !     1    
(n + 1)                                  1    '.*           
     1         1   %    &                       

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
     #     N → ∞    5         

                     -           )     5(            n   
) #   (n + 1)                    . !                     (n + 1)    )        1     

         )              ( -         
)= 3                

3                          
'7*              '.*            5MS+•                     Mn •       
Mn 



Mn+1 = [1 − aMn ]Mn + aMn [λMn ]= Mn − a[1 − λ]Mn2 ,

1
css =
.
a(1 − λ)⎞1/M

sinrdrdω ⎟⎜
⎜ M −1 0⎟
aMn = ⎜ S
⎟
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⎝
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M−1
sin
rdrdω
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 
IM =

.

>

√
πΓ[M/2].
rdrdw =
2Γ[(M + 1)/2]

?

(M IM )1/M

M−1

sin
0

<

0

Mn

 π/2

M−1

                           3                                                                                          An      Mn      ALn+1 AR
n+1   x = θ ∈ (−Mn , Mn )  4  2               ALn+1
 Mn + θ    ARMn − θ @    n+1
              
(A
((
()

|ALn+1 | = |DM |(Mn + θ)M ,M
M|ARn+1 | = |D |(Mn − θ) ,|An | = |DM |MnM ,

2
7

;

|An+1 |
1
E
=
|An |
2Mn

3                          a λ7 #                        aMn                                       ©ISAROB 2007

 Mn

  |DM |           M                               ALn+1    
|ALn+1 |/|An |                                        
css
MN =
N

⎛

                                                                                            M  &        aMn     

780

 Mn
−Mn

2

|ALn+1 |
An

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2
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2

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(.

2
.
=
2M + 1  λ 

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n+1 |
An

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.

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007            #   css   (M IM )1/M
css =
1/M

2
1 − 2M+1; <(; (7



new example

θ
-M n

x

Mn

R

An+1

          C  &     % D  (7<AA(.A (>.AAA<>   
E   C  

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  

        '.*                      B                                "    
4  25                    x = θ ∈ (−Mn , Mn )#    %        (;               N = 5000       S+M          )          
4  7                                             M = 4  M = 20 #               %         
100MarginMargin

10

−1

10

−2

10

−1

10

−3

−4
0

10

1

2

10

10

10

10

0

10

1

10

2

10

# of Examples# of Examples

M =4

M = 204  75 9 

!

3

3

10

     )

 #     "   )                     ©ISAROB 2007

'7* : #   # &    IC         J !   K<?&  ?.>L?2.

'<* : # 0    IC              1  J     (<  (( 
)7A>L)7)? )AA7

−2

10
−3

10

10

'2* : #  IC    ,   9  8  0     J          K>?&  >2<L>7) )AA;

';* D -       "      H   &5 # ,
)AA)

0

10

'.* : #  F   I#     
         J   

(??;

1

1

 & E           
 H   8:5 H   8  ,

')* @  G  H @
(??>    

10'(*  0            0 F 0F5    (??7

781

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

           

                       ! "                                                                                                                    !"            #      $ !"%                                                     &'()* #   !"                                   +                +    +      $,-%                      "     &.*    !"              !"    !"    &/*    &0('1* #         !"    &.*            !"              $234%            &''* 5            
      !"      +           
 234                  234            234 #   &.*      234©ISAROB 2007782

             
        &.*                !"                  234 #                                                6                                                    $ν  !"%       !"               β           6        !" &/*    N     f (n)        y(n)   ν  !"     minw,b,ξn ,βN1w2 + C
ξn − β2n=1  y(n) (w f (n) + b) ≥ β − ξn , ξn ≥ 0, $'%  ξn             +
5   w̃ = (w; b) ∈ F̃  f̃ = (f ; 1) ∈ F̃  h         F̃        F ×R       w f +b =0           w̃  f̃ = 0                ν  !"    
    +    6      ν  !" (1)        
6       w̃2 = w2 + b2 .
$7%

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

C=1

C=1/2

C=1/3

8  '9          :C                         

8  ;9 #            

H+
wI
8  )9      +       

H−
8  79 #            ν !" &'1*       6   
 :     (1)     1
min w2αn 2
0 ≤ αn ≤ C,  w =N

αn y (n) f (n) ,

n=1
N


αn = 1,n=1Ny (n) αn = 0,

$;%

n=1

  αn    <     < α̃n = 2αn C̃ = 2C   (3)     1
min w2
α̃n 2

1 
  w = 21α̃n f (n) −α̃n f (n) , $)%2y (n) =1
y (n) =−10 ≤ α̃n ≤ C̃,α̃n =α̃n = 1. $.%y (n) =1

y (n) =−1

4  (5)        (4)               y(n) = 1  ˜    y(n) = −1      alphan ≥ 0   #        α̃   0
 C               $8 '%                 (4)  ν  !"   ©ISAROB 2007783

      wI                H+  H−        $8 7%           6          &'1('7*              ν  !"        
!"              
 C = 1/M   M                              +                 M     $8 ;%            +    C                                $8 )% &0*                   C → 1/N                
                  234 #                                      α̃n       
0  C = 1/M  :       !"    

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

10

-1

x3
Generalization Error

x1
w

x2

10

-2

10

2

10

3

K=2
10

-1

8  .9 #        ν  !"      !"     6      ν  !"                  '               C   
8            6   9 5    =   ν !"                       &> ';*           M  $8 .% :         6           :                    !"    ν  !"    !"    N        x(n) ∈ RK   n    (x(n) , y(n))
       N((10y(n) , 0K−1 ) , IK )
 y(n) = 1  0 < n ≤ N/2  y(n) = 1 
N/2 < n ≤ N 
5     ν  !"              1/N &')'.* 4                   !"                         !"           8 /   K
 2  3  N  100  1000 #                              N          !

 :       !"                  234

©ISAROB 2007

784

10

-2

10

2

10

3

K=3

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007&)* # @            "5 -  7111
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©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Towards integrated multimedia understanding
for intuitive human-system interaction
Masao Yokota
Fukuoka Institute of Technology
yokota@fit.ac.jp
Abstract
The Mental Image Directed Semantic Theory (MIDST) has
proposed an omnisensory mental image model and its
description language Lmd intended to facilitate intuitive humansystem interaction such that happens between non-expert people
and GISs (Geographic Information Systems). This paper
presents a systematic method for formulating and computing
natural concepts (i.e., mental images) of physical reality in Lmd
and its application to spatial language understanding in view ofcross-media operation on text and picture.

1. Introduction
In the field of ontology, special attention has been paid
to spatial language covering geography because its
constituent concepts stand in highly complex relationships
to underlying physical reality, accompanied with
fundamental issues in terms of human cognition (forexample, ambiguity, vagueness, temporality, identity, ...)
appearing in varied subtle expressions [1]. Most of the
traditional approaches to spatial language understanding
have focused on computing purely objective geometric
relations (i.e., topological, directional and metric
relations) conceptualized as spatial prepositions or so,
considering properties and functions of the objects
involved [e.g., 2]. Such verb-centered expressions as S1and S2, however, are assumed to reflect not much thepurely objective geometrical relations but very muchcertain dynamism at human perception of the objects
involved because they can refer to the same scene in theexternal world. This is also the case for S3 and S4 and we
often encounter such intuitive spatial expressions in ourdaily life.
(S1) The path sinks to the brook.
(S2) The path rises from the brook.
(S3) The roads meet there.
(S4) The roads separate there.
Anyway, this fact may lead to a certain barrier
preventing non-expert or ordinary people and computer
systems from comprehensible communication in natural
language in such a way as shown in Fig.1. Therefore, theirsemantic descriptions should be grounded in humanperceptual representations, possibly, cognitively inspired
and coping with all kinds of spatial expressions including
such verb-centered ones as S1-S4 as well as prepositioncentered ones. The Mental Image Directed Semantic©ISAROB 2007

786

Theory (MIDST) [3] has proposed a dynamic model ofhuman perception yielding omnisensory image of theworld and classified natural event concepts (i.e., event
concepts in natural language) into two types of categories,‘Temporal Events’ and ‘Spatial Events’. These are definedas temporal and spatial changes (or constancies) in certain
attributes of physical objects, respectively, with S1-S4
included in the latter. Both the types of events are
uniformly analyzable as temporally parameterized loci inattribute spaces and describable in a formal language Lmd.
This paper presents a brief sketch of Lmd and asystematic method to formulate and compute naturalconcepts of physical reality comprising spatial language
semantics in order to facilitate intuitive huamn-systeminteraction, that is, interaction between non-expert people
and computer systems such as GISs (Geographical
Information Systems). This work is intended to model amore intuitive ontology of space and time by generalizing
our concerned findings [e.g., 3-5] and to apply it to
intuitive cross-media operation on text and picture.

Fig.1. Miscommunication due to different perceptual
groundings.

2. Mental Image Description Language Lmd
2.1 Omnisensory Image Model
The MIDST treats word meanings in association withmental images, not limited to visual but omnisensory,modeled as “Loci in Attribute Spaces”. An attribute spacecorresponds with a certain measuring instrument just like abarometer, thermometer or so and the loci represent the
movements of its indicator. Such a locus is to bearticulated by “Atomic Locus” with an absolute timeinterval [ti, tf] (ti< tf) as depicted in Fig.2 (left) andformulated as (1).
L(x,y,p,q,a,g,k)(1)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
This formula is called ‘Atomic Locus Formula’ whose
first two arguments are often referred to as ‘Event Causer(EC)’ and ‘Attribute Carrier (AC)’, respectively. A logicalcombination of atomic locus formulas defined as a wellformed formula (i.e., wff) in predicate logic is called
simply ‘Locus Formula’.
The intuitive interpretation of (1) is given as follows.
“Matter ‘x’ causes Attribute ‘a’ of Matter ‘y’ to keep(p=q) or change (p ≠ q) its values temporally (g=Gt) or
spatially (g=Gs) over a time-interval, where the values‘p’ and ‘q’ are relative to the standard ‘k’.”
When g=Gt and g=Gs, the locus indicates monotonic
change or constancy of the attribute in time domain andthat in space domain, respectively. The former is called‘temporal event’ and the latter, ‘spatial event’. For
example, the motion of the ‘bus’ represented by S5 is atemporal event and the ranging or extension of the ‘road’by S6 is a spatial event whose meanings or concepts areformulated as (2) and (3), respectively, where ‘A12’
denotes ‘Physical Location’. These two formulas aredifferent only at the term ‘Event Type (i.e., g)’.
(S5) The bus runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gt,k)∧bus(y)(2)(S6) The road runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gs,k)∧road(y) (3)Fig.2. Atomic Locus (left) and Locus of ‘fetch’ (right).

Fig.3. FAO movements and Event types.
The author has hypothesized that the difference between
temporal and spatial event concepts can be attributed to
the relationship between the Attribute Carrier (AC) andthe Focus of the Attention of the Observer (FAO) [4]. To
be brief, it is assumed that the FAO is fixed on the whole
AC in a temporal event but runs about on the AC in a
spatial event. According to this assumption, as shown in
Fig.3, the bus and the FAO move together in the case of
S5 while the FAO solely moves along the road in the caseof S6.

©ISAROB 2007

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2.2 Tempo-logical connectives
The definition of a tempo-logical connective Κi is given
by D1, where τi, χ and Κ refer to one of purely temporal
relations indexed by an integer ‘i’, a locus, and an
ordinary binary logical connective such as the conjunction‘∧’, respectively. The definition of each τi is provided with
Table 1 implying the trivial theorem T1, where thedurations of χ1 and χ2 are [t11, t12] and [t21, t22],
respectively. This table shows the complete list of
temporal relations between two intervals, where 13 typesof relations are discriminated by the suffix ‘i’ (-6≤ i ≤6).
This is in accordance with Allen’s notation [6] which, tobe strict, is for ‘temporal conjunctions (=∧i)’ but not forpure ‘temporal relations (=τi)’.
D1.
χ1 Κi χ2 ↔ (χ1 Κ χ2) ∧ τi(χ1, χ2)
T1. τ-i(χ2, χ1) ≡ τi(χ1, χ2) (∀i∈{0,±1,±2,±3,±4,±5, ±6})
The temporal connectives used most frequently are thetemporal conjunctions ‘SAND (∧0)’ and ‘CAND (∧1)’,standing for ‘Simultaneous AND’ and ‘Consecutive
AND’, conventionally denoted by ‘Π’ and ‘•’,respectively. Employing these connectives, for example,
the English verb concept ‘fetch’ can be defined as (4)and depicted as Fig.2 (right). Furthermore, the underlined
part of (4) stands for the concept of ‘carry’ and thisrelation can be formulated as (5) employing the temporal
implication ‘⊃-4’, reading that an event ‘fetch(x,y)’ is
necessarily finished by an event ‘carry(x,y)’. This kind of
formula is not an axiom but a theorem deducible from thedefinitions of event concepts in the formal system.
(λx,y)fetch(x,y)↔(λx,y)(∃p1,p2,k)L(x,x,p1,p2,A12,Gt,k)•((L(x,x,p2,p1,A12,Gt,k)ΠL(x,y,p2,p1,A12,Gt,k))(4)∧x≠y∧p1≠p2(5)fetch(x,y) ⊃-4 carry(x,y)
Table 1. List of temporal relations
Definition of τi
Allen’s notation
τ0(χ1, χ2)equals(χ1,χ2)
t11=t21∧t12=t22
equals(χ2, χ1)
τ0(χ2, χ1)
τ1(χ1, χ2)meets(χ1, χ2)
t12=t21τ-1(χ2, χ1)
met-by(χ2, χ1)
τ2(χ1, χ2)starts(χ1, χ2)
t11=t21∧t12<t22
τ-2(χ2, χ1)started-by(χ2, χ1)
τ3(χ1, χ2)during(χ1, χ2)
t11>t21∧t12<t22
τ-3(χ2, χ1)
contains(χ2, χ1)
τ4(χ1, χ2)
finishes(χ1, χ2)
t11>t21τ-4(χ2, χ1)
finished-by(χ2, χ1)∧t12=t22
τ5(χ1, χ2)
before(χ1, χ2)
t12<t21τ-5(χ2, χ1)
after(χ2, χ1)
t11<t21∧t21<t12τ6(χ1, χ2)overlaps(χ1, χ2)∧t12<t22
overlapped-by(χ , χ )τ (χ , χ )
-6

2

1

2

1

2.3 Empty event
An ‘Empty Event (EE)’, denoted by ‘ε’, stands fornothing but for absolute time elapsing and is explicitlyThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
defined as D2 with the attribute ‘Time Point (A34)’ and theStandard of absolute time ‘Ta’. Usually people can know
only a certain relative time point by a clock that is seldom
exact and that is to be denoted by another Standard in the
Lmd. Hereafter, ∆ denotes the total set of absolute timeintervals. According to this scheme, the suppressed
absolute time-interval [ta, tb] of a locus χ can be indicatedas (6).
D2. ε([ti,tj])↔(∃x,y,g)L(x,y,ti,tj,A34,g,Ta),
where [ti, tj]∈∆ (={[t1, t2] | t1<t2 (t1, t2∈R)}).
χΠε([ta,tb])(6)

3. Semantic description of physical reality
3.1 Event concepts
The semantic description of an event is compared to amovie film recorded through a floating camera because itis necessarily grounded in FAO’s movement over the
event. Therefore, as already pointed out, S1 and S2 canrefer to the same scene in spite of their appearances, where
what ‘sinks’ or ‘rises’ is FAO and whose conceptual
descriptions are given as (7) and (8), respectively, where
the special symbol ‘_’ is defined by (9), standing for an
anonymous variable bound by an existential quantifier,and ‘A13’, ‘↑’ and ‘↓’ refer to the attribute ‘Direction’, andits values ‘upward’ and ‘downward’, respectively. Such afact is generalized as ‘Postulate of Reversibility of a
Spatial Event (PRS)’ that can be one of the principal
inference rules belonging to people’s intuitive knowledge
about geography. This postulation is also valid for such apair of S7 and S8 as interpreted approximately into (10)and (11), respectively. These pairs of conceptual
descriptions are called equivalent in the PRS, and thepaired sentences are treated as paraphrases each other.
(∃y,p,z)L(_,y,p,z,A12,Gs,_)ΠL(_,y,↓,↓,A13,Gs,_)∧path(y) ∧brook(z) ∧p≠z
(7)(∃y,p,z)L(_,y,z,p,A12,Gs,_)ΠL(_,y,↑,↑,A13,Gs,_) ∧path(y)∧brook(z) ∧p≠z
(8)L(…,_,…) ↔ (∃ω)L(…,ω,…)(9)(S7) Route A and Route B meet at the city.
(∃p,y,q)L(_,Route_A,p,y,A12,Gs,_)Π
(10)
L(_,Route_B,q,y,A12,Gs,_) ∧city(y) ∧p≠q(S8) Route A and Route B separate at the city.
(∃p,y,q)L(_,Route_A,y,p,A12,Gs,_)Π
(11)
L(_,Route_B,y,q,A12,Gs,_) ∧city(y)∧p≠q
For another example of spatial event, Fig.4 (up)concerns the perception of the formation of multiple
objects, where FAO runs along an imaginary object socalled ‘Imaginary Space Region (ISR)’. This spatial event
can be verbalized as S9 using the preposition ‘between’and formulated as (12), corresponding also to suchconcepts as ‘row’, ‘line-up’, etc.
(S9) □ is between ∆ and ○.

©ISAROB 2007

788(∃y,p)(L(_,y,∆,□,A12,Gs,_)•L(_,y,□,○,A12,Gs,_))Π
(12)
L(_,y,p,p,A13,Gs,_) ∧ISR(y)For more complicated examples, consider S10 and S11.
The underlined parts are deemed to refer to some events
neglected in time and in space, respectively. These events
correspond with skipping of FAOs and are called‘Temporal Empty Event’ and ‘Spatial Empty Event’,denoted by ‘εt ’ and ‘εs ’ as Empty Events with g=Gt and
g=Gs at (6), respectively. Their concepts are described as(13) and (14), where ‘A15’ and ‘A17’ represent the attribute‘Trajectory’ and ‘Mileage’, respectively. From theviewpoint of cross-media reference, the formula (14) canrefer to such a spatial event depicted as the still picture in
Fig.4 (down) while (13) is to be interpreted into a motion
picture.
(S10) The bus runs 10km straight east from A to B, andafter a while, at C it meets the street with the sidewalk.
(∃x,y,z,p,q)(L(_,x,A,B,A12,Gt,_)Π
L(_,x,0,10km,A17,Gt,_)ΠL(_,x,Point,Line,A15,Gt,_)Π
L(_,x,East,East,A13,Gt,_))•εt•(L(_,x,p,C,A12,Gt,_)
ΠL(_,y,q,C,A12,Gs,_)ΠL(_,z,y,y,A12,Gs,_))∧bus(x)∧street(y)∧sidewalk(z)∧p≠q(13)(S11) The road runs 10km straight east from A to B,
and after a while, at C it meets the street with the sidewalk.
(∃x,y,z,p,q)(L(_,x,A,B,A12,Gs,_)Π
L(_,x,0,10km,A17,Gs,_)ΠL(_,x,Point,Line,A15,Gs,_)Π
•(L(_,x,p,C,A12,Gs,_)
L(_,x,East,East,A13,Gs,_))•εsΠL(_,y,q,C,A12,Gs,_)ΠL(_,z,y,y,A12,Gs,_))∧road(x)∧street(y)∧sidewalk(z)∧p≠q(14)
There are a considerable number of postulates of space
and time to facilitate intuitive interaction between humans
and IMAGES-M [4], and the PRS (Postulate ofReversibility of a Spatial Event) is one of the mostimportant. This postulate can be formulated as (15) using‘≡0’, where χ and χR is a locus formula and its ‘reversal’
for a certain spatial event, respectively. The recursive
operations to transform χ into χR are defined by (16)-(18),where the reversed values pR and qR depend on theproperties of p and q. For example, (14) is transformed
into (19) to be verbalized as S12, where pR =p and qR =q
for A12; pR =-p and qR =-q for A13.
(15)
χR.≡0.χ(χ1•χ2)R ↔ χ2R •χ1R
(16)
(χ1Πχ2)R ↔ χ1R Πχ2R
(17)
(L(x,y,p,q,a,Gs,k))R↔ L(x,y,qR,pR,a,Gs,k)(18)(∃x,y,z,p,q)(L(_,x,C,p,A12,Gs,_)Π
L(_,y,C,q,A12,Gs,_)ΠL(_,z,y,y,A12,Gs,_))•εs•(L(_,x,B,A,A12,Gs,_)ΠL(_,x,0,10km,A17,Gs,_)Π
L(_,x,Point,Line,A15,Gs,_)ΠL(_,x,West,West,A13,Gs,_))∧road(x)∧street(y)∧sidewalk(z)∧p≠q(19)(S12) The road separates at C from the street with the
sidewalk and, after a while, runs 10km straight west
from B to A.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Our future work will include establishment of learningfacilities for automatic acquisition of word concepts fromsensory data and human-robot communication by natural
language under real environments.
H: What is between the buildings A and B?
S: The railway D.
H: Where do the street A and the road B meet?
S: At the crossing C.
H: Where do the street A and the road B
separate?
S: At the crossing C.

Fig.5. Q-A on a map between a human (H) and
IMAGES-M (S).
Fig.4. Complicated spatial events: ‘row’ (left) and‘example of road map’ (right).

3.2 Object concepts
A physical object can be semantically defined as a
combination of its properties and its relations with others.
For example, the semantic descriptions of ‘rain’, ‘wind’and ‘air’ can be given as (20)-(22), reading ‘Rain is water
attracted from the sky by the earth, makes an object wetter,is pushed an umbrella to by a human,…,’ ‘Wind is air,
affects the direction of rain,… ,’ and ‘Air has no shape, notaste, no vitality, …,’ respectively. The special symbols‘*’ and ‘/’ are defined as (23) and (24) representing‘always’ and ‘no value’, respectively.
(λx)rain(x)↔(λx)(∃x1,x2,…)L(_,x,x1,x1,A41,Gt,_)∏L(Earth,x,Sky,Earth,A12,Gt,_)∏L(x,x2,p,q,A25,Gt,_)∏L(x3,x4,x,x,A19,Gt,x3)∧water(x1)(20)∧object(x2)∧human(x3)∧umbrella(x4)∧(p<q)…
(λx)wind(x)↔(λx)(∃x1,x2,…)L(_,x,x1,x1,A41,Gt,_)(21)∧air(x1)∧(L(x,x2,p,q,A13,Gt,_)∧rain(x2)…
(λx)air(x)↔(λx)(…∧L*(_,x,/,/,A11,Gt,_)∧…∧(22)
L*(_,x,/,/,A29,Gt,_)∧…∧L*(_,x,/,/,A39,Gt,_)∧ …)
X* ↔ (∀[p,q])X Π ε([p,q])(23)
L(…,/,…) ↔ ~(∃p) L(…,ω,…)(24)

4. Discussions and conclusions
The cross-media operations between texts in several
languages and pictorial patterns like maps weresuccessfully implemented on our intelligent system
IMAGES-M. Figures 5 and 6 show examples of Q-A on a
map and text-to-action translation by IMAGES-M,
respectively, where several kinds of intuitive postulates
such as PRS were effectively utilized. In this research area,
it is most conventional that conceptual contents conveyed
by information media such as language and picture arerepresented in computable forms independent of eachother and translated via ‘transfer’ processes which areoften specific to task domains [e.g., 7-9]. That is, at mybest knowledge, there is no other system that can perform
cross-media operation in such a seamless way as mine.

©ISAROB 2007

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Fig.6. Text-to-Action translation by IMAGES-M: ‘Sit
down AND wave your left hand’ was interpreted
as ‘Sit down BEFORE waving your left hand.’

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Geo-ontology (Compiled and edited by Jenny Harding), Ilkley
UK, Sept. 2002.
[2] Coventry, K. R., Prat-Sala, M., Richards, L. V.: “Theinterplay between geometry and function in the comprehensionof ‘over’, ‘under’, ‘above’ and ‘below’,” Journal of Memory
and Language, 4, 376-398, 2001.
[3] Yokota,M.: “An Approach to Integrated Spatial Language
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Bremen, Germany, Oct., 2005.
[4] Yokota,M.: “A psychological experiment on humanunderstanding process of natural language,” Trans. of IEICE
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[5] Yokota, M. & Capi,G.: “Cross-media Operations between
Text and Picture Based on Mental Image Directed Semantic
theory,” WSEAS Trans. on INFORMATION SCIENCE andAPPLICATIONS, Issue 10, 2, pp.1541-1550, Oct. 2005.
[6] Allen, J.F.: “Towards a general theory of action and time,”
Artificial Intelligence, 23-2, pp.123-154, 1984.
[7] Adorni, G.,Manzo,M.Di, Giunchiglia, F.: “Natural Language
Driven Image Generation,” Proc. of COLING 84, pp. 495-500,
1984.
[8]Shariff,A.R.,Egenhofer,M.,Mark,D.:“Natural-Language
Spatial Relations Between Linear and Areal Objects: The
Topology and Metric of English-language Terms,”
International Journal of Geographical Information Science, 123,pp.215-246, 1998.
[9]Coradeschi,S., Saffiotti,A.: “An Introduction to theAnchoring Problem,” Robotics and Autonomous Systems 43,
pp.85-96, 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Designing a Multi-modal Language for Directing Multipurpose Home Robots
Tetsushi Oka
Fukuoka Institute of Technology
oka@ﬁt.ac.jp.

Masao Yokota
Fukuoka Institute of Technology
yokota@ﬁt.ac.jp

Abstract

compositional semantics of logical forms and genericinference engines for natural language understanding
has been applied to directing a mobile robot, asking it
questions and giving it information by speech[1]. Thissystem requires considerable computational power tocheck the consistency of semantic interpretations ofuser utterances in real time.

This paper discusses an approach to realising multipurpose home robots a wide spectrum of people candirect by speech, physical contact and gesture. A good
spoken language interface allows ordinary people todirect robots without training in advance. However,many problems of Natural Language Understanding
must be addressed if a wide range of utterances areto be understood immediately. Therefore, we designa practical multi-modal direction language combining
a simple spoken language with nonverbal information.
The spoken language has a simpliﬁed grammar andlimited words, so that the robot should understandcommands without complex computation or a large
knowledge base. Nonverbal information makes commands more speciﬁc and eliminates ambiguity.

1

Introduction

Since the end of last century, more and more robots
are coming into homes and oﬃces to help ordinary
people. As the birthrates in advanced countries are
dropping, a robot that helps and cares elder or disabled people at home will be soon in demand in manysocieties. Such a robot is expected to understand what
the user wants it to do as soon as a command is given.
This raises an important issue of human-robot communication. For people who need helps, even computer
GUIs, or remote controls of TVs are not ideal interfaces. Some people may give up using the robot before
learning which commands all the buttons, sliders andlevers are linked with. In addition, if the robots are to
execute many kinds of commands, the user must learn
long sequences of operations.
A spoken language interface, on the other hand, canbe a good interface as it is not necessary to learn a
new language if robots understand spoken commands
in our language. However, there are still many diﬃcult problems to tackle to realize a natural language
interface by speech. For example, a spoken language
interface based on a linguistically motivated grammar,©ISAROB 2007

790

Recently, many robot systems implementing automatic speech recognition have been developed[2].
Many of them create word lattices from speech signals and interpret what the user means by means of
keyword spotting, creating semantic representationsof user utterances. In most systems, heuristic rules
to construct semantic representations are built by thedesigner so that it should not take much time for semantic analysis. However, it is not clear to users whatkind of an utterance the robot understands or misunderstands. Moreover, it is diﬃcult to identify themeaning by this method alone if a variety of commands
are given to the robot in many diﬀerent situations.
Therefore, this approach does not suit for multipurpose robots which help ordinary people at home.
Another way to reduce the computational cost of
semantic analysis is to employ a simple command language. The most straightforward way is to design
a command language which robots can directly execute, but the language would be diﬃcult for humans
to learn. Therefore, we design a command language
based on a natural language by reducing the number
and types of grammar rules, and the size of the lexicon. Obviously, a command language does not needto cover declarative and interrogative utterances. We
presume that it is possible to build a practical andcost-eﬀective spoken language interface by selecting
words and phrases and constructing rules carefully.
Besides spoken commands, gestures and physical contact provide a good means of intuitive direction. Nonverbal modalities of communication complement verbal communication and eliminate ambiguity. Thus,
we design a multi-modal language combining a spoken
language with nonverbal directions.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 20072
2.1

Target of the language
Multipurpose Home Robots

Home robots including pet robots like Sony AIBO
(http://www.aibo.com/) and healing robots, PARO
(http://www.paro.jp/) for instance, are already in themarket and helping ordinary people at homes in someways. Another example is a practical small robot available for cleaning our rooms (http://www.irobot.com).
Although it is still diﬃcult to build a robot which replaces a housekeeper or care worker, one can predictthat home robots will evolve in the coming decades
and be capable of doing many kinds of tasks given by
us. They will be connected to home computer networks and collect useful information for us. They will
move about in the home, bring something to us andmove heavy objects. They will help us doing physicaltasks, have a chat with us, control the air conditioner,
TV and lights, and so forth. In short, we predict multipurpose home robots will ﬁnd a place to help us.
In a way, a home robot can be thought of as a physical interface device with a home computer network or
an intelligent house which follows the user and provides a means of interaction with the computers. At
the same time, the robot is given physical tasks andhelps the user. Most importantly, the robot has sensors and actuators and can change its location. This
makes the way it interacts with a human utterly different from other interface devices.

2.2

Directing Mobile Robots

We believe that no matter how intelligent the robots
may be, in some cases we have to direct them step bystep telling them to stand up, turn back, go forward,
look left, raise the right arm, grasp an object and soon, especially when the robots are physically helping
them. Thus, the ﬁrst step would be designing a multimodal language which enables us to make robots turn,move forward/backward, look up/down and stop at
will, saying “Turn left!”, touching the robot, waving
to it and so on.
It looks straightforward to realize a simple spoken
language for this purpose, but things get a little complicated when we want to specify parameters of actions such as distance, angle and speed. On the one
hand, “Turn right slowly!” does not include detailed
information about the angle and speed, but on theother hand, “Turn 43 degrees clockwise within a second!” is not what we normally say. We could say “stopthere”, “faster”, ”a little bit more” after “go forward
slowly” or “turn left a bit” many times until the robot©ISAROB 2007

791

reaches a desirable position. Nonverbal signals such as
gestures can allow the user to give the robot detailed
information in a more natural manner. Thus, a multimodal language combining a simple spoken commandlanguage with gestures and physical contact opens up
possibilities to direct robots in natural ways.
The spoken language can be deﬁned by a set of simple grammar rules and a relatively small lexicon. Besides, one can realize a wider coverage of commands tohome robots using the same grammar rules and adding
words or phrases to the lexicon: “Turn on the TV!”,“Go to the kitchen”, “(Make the room) warmer!”,“Clean up the room”, “Check my mailbox!”, “Showme the weather forecast!”, “Lift the box” and so on.
Since in most of the environments of home robots, one
can assume that e.g. there is only one TV in the room
the user is in, little reasoning will be necessary to understand the commands if we properly design the language.

2.3

Directing Articulated Robots

Articulated robots with arms, hands and legs canperform many kinds of physical tasks. When using
such robots, we will often want to tell the robot moveits hands, arms or legs: “Stand up!”, “Sit down!”,“Walk a little!”, ”Raise the right arm higher!”, “Standon the left foot!”, “Wave the arms slowly!”, etc. These
directions specify either a primitive action using thearms and legs or movements of individual limbs, so
little confusion occurs in order to generate motor commands to the actuators, although a robust balance
control system is indispensable.
As a humanoid has many degrees of freedom, thediversity of its motions is much larger than that of awheeled mobile robot. Thus, we need a wider coverage in our multi-modal language. None the less, it
is possible to adapt the language for this purpose byenhancing the lexicon without adding grammar rules.
If we could easily direct humanoids’ various motions in a multi-modal language, it would be possible
to make them help us in physical tasks and teach them
how to move their limbs.

3
3.1

Spoken Language for Intuitive Direction of Home Robots
Grammar and Lexicon

Now, we discuss in more detail what kind of a spoken language is suitable for directing home robots.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

What we have principally in mind is a grammar
consisting of a small number of rules without recursions. For our purpose, we can select words necessary for directing robots. We presume that this is
the class we should employ for a wide coverage ofcommands to multipurpose home robots. The grammar restricts the number of acceptable commands, but
compositional semantics makes it to possible to interpret a large number of spoken commands and convert
them into robot actions. However, even a grammar inthis simple class generates unnecessary utterances, so
there are utterances which are grammatical but do notmake sense. Only utterances which are grammaticaland make sense should be converted into robot actions
and the other grammatical input must be recognised
as invalid at the stage in which their semantic representations are constructed. Thus, the robots needreact to spoken commands in three, at least, diﬀerent
ways.
There are many ways to deﬁne a language of thisclass to cover verbal directions. Designing our language, we do not adhere to linguistic grammars andlexical categories of natural languages. For our purpose, it is more important to consider to what extent
we should restrict directions to minimise the cost ofcomputation and how easy for humans the language isto learn. Here are some examples of grammar rules:S→

ACT ION P ARAMACT ION→turn

P ARAM→

DIRECT ION SP EEDDIRECT ION→

AN GLE lef t

ACT ION→OP ERAT ION OBJECTOP ERAT ION→

turn of f

OBJECT→

the T V

3.2→

DIRECT ION DU RAT ION

Semantic Analysis and Robot Actions

Once the syntax of our language for verbal directions is deﬁned, we need to give a meaning to eachutterance. For example, if “Move!” is grammatical,
we need to decide whether it is acceptable as a command or not and what actions it should be linked withdepending on nonverbal input. Although generally©ISAROB 2007

action(name(turn),dir(45.0),speed(50.0),time(now))

Semantic analysis is necessary to construct action representations from verbal and nonverbal input. Methods of compositional semantics can systematically construct semantic and action representaions
from speech input. We believe that it is important tohave a good framework for semantic analysis of multimodal directions. Mapping utterances into actions in
an ad hoc manner is not desirable for building a language for diverse directions. The Mental Image Directed Semantic Theory (MIDST) gives a frameworkfor semantic processing in multi-modal interactions
between a human and an articulated robot[3], and thus
will give one for our purpose.

3.3

Note that without recursions separate rules are necessary to allow the users to specify diﬀerent combinations of parameters.
P ARAM

speaking the meaning of a command can vary depending on the context, we should avoid allowing such an
ambiguous command. Instead, we should be able tochoose one action, if an acceptable combination of anutterance and nonverbal input is given.
If the user says “Turn right!”, the robot mustchange its orientation though it is not clear how muchand how fast without nonverbal input. Perhaps therobot should turn, say, 45 degrees clockwise at a moderate speed. To execute an action, it needs all the parameters of it. For our purpose, the language interface
should send action representations to the robot control system whenever the user commands the robot.
A simple example of action representations passed to
the robot system should look like the following:

792

Disambiguation

As mentioned above, ambiguity in spoken commands should be avoided as much as possible. First,
we can eliminate context dependent directions such as“Go there!” and “Approach it”. Secondly, we choose
default values of action parameters. For example, we
could link “Turn!” and an action to turn 360 degrees
slowly, or “Pick it up!” and an action to grasp anything near the robot and hold it. Thirdly, an utterance
can be interpreted in more than one way because ofmulti-sense words or syntactically ambiguous expressions. Although most of such utterances are ruled out
if the language is simple, we must consider this problem at both of the stages of designing the grammar
and lexicon and mapping utterances into actions.

4

Nonverbal Direction

Home robots will have many diﬀerent sensors forperceiving their circumstances and controlling their

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

bodies. Those sensors, especially tactile, proximity,
and vision sensors, are useful as user interface devices.
In our multi-modal language, nonverbal input throughsensors plays an important role for disambiguation,
ﬁlling slots of action representations. Pointing gestures disambiguate directions like “Go over there!”and “Touch that wall!”. Tapping a part of the robot
implies the direction, speed and duration of the action:saying “Turn!” and tapping the robots left arm threetimes can be interpreted as “Turn 90 degrees to theleft quickly!”.

5

spoken language based on Japanese using Julian andstudy its usability on our robot system.
Another target is a humanoid robot one can directusing our multi-modal language. We are developing
small humanoids for various objectives. The newest at
the moment is Syokabe (Fig.1), which has 27 DOFs including eﬀective yawing rotations at the body and legs.
Running Julian on our PCs, we can use the same spoken language interface for Lemon and our humanoids.

Robot Systems for Usability Studies

For usability studies, we build robot systems one
can direct in our multi-modal language and conduct
experiments involving people who are not familiar withcomputers and robots.
The robot systems consist of a multi-modal language interface and robot control system. The language interface receives sound signals and other sensory input. It comprises components for speech recognition, gesture recognition, analysis of tactile andproximity sensor readings, and syntactic and semantic
analysis. It sends the robot control system action representations which describe an action directed by theuser in real time .
We are currently implementing robot systems witha multi-modal command interface. Our ﬁrst prototype
was built on a Sony AIBO (ERS-210) using the Master
Studio SDK. The robot recognises about 50 Japanesewords, simple hand gestures moving a pink ball andtactile messages on its tactile and infrared sensors. Its
action repertoire includes standard action of the SDK,
standing up, walking etc., and our original actions created using Action Composer, a part of the SDK. Although it can only react to single-word commands, it
is possible to realise an intuitive interface to direct the
robot. The major limitations are due to the small setsof spoken commands and nonverbal directions.
Our next setup, Lemon (Fig.1), is basedon a new Sony AIBO (ERS-7M3), the Open-R
SDK (http://openr.aibo.com/) which enables us tobuild the on-board control system in C++ andMEdit to create robot motions to add new actions.
For speech recognition, we use Julian(http://julius.sourceforge.jp/en/julius.html), a grammar based recognition engine and its development kit
which allows us to develop context free grammars1 forspeech input and test them on our PCs. We develop a
1 The engine actually parses only regular languages.

©ISAROB 2007

793

Figure 1: Lemon (left) and Syokabe

6

SummaryThis paper proposed to design a practical multimodal language to direct multipurpose home robots.
We discussed basic features of the language, action
representations, and command interface. The language is based on a natural language and has asmaller set of grammar rules to cover directions ofhome robots. We have implemented some prototypes
and components of robot systems and are conducting usability studies using four legged robots and humanoids.

References[1] J. Bos and T. Oka “A spoken language interface
with a mobile robot” Journal of Artiﬁcial Life andRobotics, Vol. 11, 2007.
[2] R. Prasad, H. Saruwatari and K. Shikano, “Robots
that can hear, understand and talk” Advanced
Robotics, Vol. 18, pp. 533-564, 2004.
[3] M.Yokota and G.Capi, “Integrated multimedia understanding based on Mental Image Directed Semantic Theory” Proc. 11th International Symposium on Artiﬁcial Life and Robotics, OS6-1, 2006.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Tele-Immersive Collaborative Virtual Environment for Intuitive Interpretation
Nobuyuki KUKIMOTO
Detpt. of Infomatics
Tohwa University
1-1-1 Chikushigaoka Minami-ku
Fukuoka,815-8510,Japan

Yasuo EBARA
Academic Centerfor Computing and Media Studies
Kyoto University
Yoshida-Honmachi, Sakyo-ku
Kyoto,606-8501, Japan

Koji KOYAMADA
Center for the Promotion of
Excellence in Higher Education
Kyoto University
Yoshida-Nihonmatsucho, Sakyoku,
Kyoto,606-8501, Japan
Abstract
The purpose of tele-immersive collaboration is toenable people in separate locations to work together effectively by the Virtual Reality (VR) technology facilitating the human intuitive insight and interpretation.
Consequently, it is important to cogitate with figuresand characters as well as voice communication in Collaborative Virtual Environment (CVE). In this paper,
we constructed tele-immersive CVE with annotation
capability and free-hand drawing of characters andfigures into Immersive Projection Technology (IPT)
environment. In addition, participants from remotesites connected over a Wide Area Network (WAN)
were enabled to interact with each other by sharing
annotations via a PDA application. Moreover, we developed applications for scientific visualization and, forintellectual work based on KJ Method inside a teleimmersive CVE. From these results, we are able tomaintain essential information for thinking by making
annotation on IPT environment, and have proved thesystem can support intuitive understanding in remotecollaborative work.

1

Collaboration between multi-field researchers using visualization techniques become important in order toinspect these large-scale data sets efficiently, and it
can support human insight and interpretation.
In order to work smoothly with participants at remote sites via WAN, Computer Supported Cooperative Work (CSCW) tools, such as E-mail, WWW
browser or a TV Conference system, have been usedas general tools. However, CSCW tools are limited in
3D expression of data sets possessing depth information because 2D displays or projectors are generallyused.
We consider that effective remote collaboration using 3D contents requires a tele-immersive CVE [1, 2]
which enables high presence and reality. Collaboration in CVE is important to support problem solving
and intellectual discovery by intuitive observation of
3D contents from various view-points in a shared environment.
In this paper, we describe a tele-immersive CVE
which supports intuitive interpretation by making annotations within IPT environment. In addition, we describe our applications for scientific visualization andintellectual work in the CVE based on KJ Method.

Introduction
2Complexity and multiplicity of large-scale data sets
generated from High Performance Computing (HPC)
systems have been increasing at a proportional rate
to their computational power and network bandwidth.

©ISAROB 2007

794

SECI process and Collaboration

Knowledge is differentiated between “tacit” and“explicit” knowledge. Tacit knowledge, unable to be
expressed verbally, is an individual’s knowledge or ex-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Tacit Knowledge

Socialization

Internalization

Externalization

Combination

Explicit Knowledge

Tacit Knowledge

ImmersaDesk

Image
FASTRAK receiver
TCP/IP
Touch
screenExplicit Knowledge

Visualization
Application.

Figure 1: Knowledge Translation.
TCP/IP
FASTRAK
RS-232C
SEU

perience accumulated personally. In order to sharetacit knowledge with others, it needs to be converted
to a sharable explicit knowledge such as text or figures. On the other hand, the knowledge creation isa continuous process by dynamic interaction between
these different kinds of knowledge.
This process is called “SECI process” [3] which has
4 modes (Figure1). Socialization is to share a common
space through a communication between participants.
Externalization is to translate from tacit knowledge to
explicit knowledge. The explicit knowledge is sharedby other participants. Combination is the integrationof these into new more complex and systematic setsof explicit knowledge. Internalization is to absorb thisexplicit knowledge as tacit knowledge in practical use.
These four modes configure the spiral model in theprocess for knowledge creation.
We consider that collaborative works in CVE have
similarity to the SECI process. Socialization process
is same as the construction of shared virtual environment. Communication between participants in CVE
is similar to externalization and combination. Internalization looks like a simulation of work, and selfschooling in stand-alone mode is equivalent to thework in a VR environment. Externalization process
in particular requires various expression methods inorder to transmit their ideas and experiences. In addition, combination process requires the externalized
explicit knowledge to be converted to own knowledge.
Therefore, it is important to cogitate with figuresand characters as well as voice communication, because remote collaborative works in CVE has a tendency to use abbreviation or corresponding expressions. In order to support intuitive knowledge creation, we have constructed a tele-immersive CVE with©ISAROB 2007

795

trackdHost Computer

Cursor key
Delete drawn line
Immediate Draw

Figure 2: System configuration of our IPT environment.

annotation capability into IPT environment that consist of 3D contents. In this environment, we are able
to change the visualization parameter and to makeannotations by using PDA. We enabled collaborative
to be an understanding of visualization data with annotations which shared with co-researchers of remoteplaces.

3
3.1

System Configuration
Scientific Visualization in IPT Environment

VR technology is one of the useful visualization
tools for intellectual discovery. We implemented a
PDA-based application for IPT such as CAVE or ImmersaDesk. It can control parameters and make annotations for scientific visualization without moving
between the IPT and host computer[4].
Figure 2 shows our system configuration. The hostcomputer generates visualization images and displaysthem on the ImmersaDesk display. We used the Visualization Tool Kit (VTK) and CAVE Library forvisualization. We established a private WAN by connecting several remote sites over the Japan Gigabit
Network II (JGN II). Participants in each site can in-

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

teract by making annotations via a PDA application
within IPT. Avatars represents other participants who
access from remote places. Coordinate data of eachparticipant’s head and PDA in the IPT environmentis transmitted between participants and each participant is represented as an avatar with motion.

3.2

Communication
CVE

inTele-immersive

Collaborative works between remote places uses a
lot of conversation[5]because participants in the IPT
environment have difficulty using input interface suchas keyboards or mice. Therefore,voice communication
is indispensable in achieving smooth communication in
CVE. We developed voice communication tools whichenable adjustment of volume and PAN level corresponding to speaker’s position in IPT environment.
In addition, we implemented two free-hand writing
methods to use in CVE since it has a high tendency
to use clipped words and anaphoric expressions. One
method is the drawing of annotations directly in IPTenvironment by using a PDA like a virtual pen enabling to move it freely while pressing a PDA’s button.
This method is suitable for drawing 3D figures or large
images. The other method is the use of PDA’s touch
screen to transfer a drawn annotation into the IPT.
This is appropriate for drawing characters or texts.
These drawing data are then sent to the IPT environments to be shared by all participants.

4

Application in Tele-immersive CVE

4.1

Scientific Visualization of Fluid Flow

We have implemented an environment for scientificvisualization in tele-immersive CVE. Scientific visualization in IPT environment is suitable for intuitive
interpretation of complex fluid data. Figure 3 shows
an overview of fluid visualization.
Each participant can change visualization parameters and can make annotations in CVE by using a PDA
application. Visualization parameter data and vertex
position of annotations are sent to each participant’s
IPT environment in real-time.

4.2

Visualization of Bedrock Destroying
Phenomenon

Traditional earthquake researchers have used threeorthographic views, however the use of 3D visualization technology has been considered effective for the©ISAROB 2007

796

Figure 3: Overview of fluid visualization with participant’s avatar.

Figure 4: Immersive bedrock destroying phenomenon
visualization and annotations.

analysis of destruction phenomenon. Taking this into
consideration, we attempted to use 3D visualization
technique to the destruction phenomenon of earthquake in an IPT environment.
Figure 4 shows an example of earthquake data displaying. In this visualization, earthquake sources are
represented by spheres and the intensities by colors.
The earthquake source location becomes easy to understand by interactively configuring the dense spheresin the IPT environment. In addition, researchers havebeen facilitated to understand the construction of fault
planes via intuitive observation by the virtual walkthrough to the underground structure.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 5: Overview of KJ Method application in IPT.

4.3

Intellectual Works by KJ Method in
IPT Environment

KJ Method is one of the well known techniques forknowledge creation. It makes groups for relationshipbetween each other and the various information are described as cards or “Post-it”. In addition, new ideas
created by using the KJ Method are described as writing to cards. Since the task of making cards using
the KJ Method is generally hard for the users, several
software have been released to support this task in a
Personal Computer (PC). However, users becomes interfered when the numbers of cards increase, because
there is a limitation of working area on traditionalPCs.
We implemented an application for using KJ
Method within an IPT environment in order to serve
as a CVE for knowledge creation. Figure5 shows theoverview of this KJ Method application in IPT. In thisenvironment, various image files, such as graph, picture or 3D visualization image, are used as the cards
in KJ Method. IPT environments have no limitation
regarding working area and are able to add more axisof grouping. Image and additional annotation data
in IPT environment are loaded from database system,
and are saved in database after the work. Therefore,
users are allowed to access the database from a WWW
browser and are enabled to carry out the continuationof the work in another environment.

5

Conclusion

In this paper, we presented a tele-immersive CVE
which we have developed with free-hand annotation©ISAROB 2007

797

capability via PDA in IPT environment which consistsof 3D visual contents.
Considering the use of annotations such as streamline drawing or marking the noteworthy point, we
could conclude that collaboration with annotations inside the CVE supports better communication between
participants. From these results, we are able to maintain essential information for thinking by making annotation such as characters or figures on IPT environment and have proved the system can support intuitive
understanding in remote collaborative work.
In the future, we believe that the task of making
3D annotations in CVE will be effective for enrichingcommunication tools during the collaborative investigation, and will be a more useful tools for human
intellectual works.

Acknowledgements
This work was executed as a part of the researchand development of Tele-immersive conference systems sponsored by National Institute of Information and Communications Technology (NICT). Special
thanks goes to Dr. Jorji Nonaka at Kyoto University
for his useful advices.

References[1] Leigh,J., Johnson,A., Vasilakis,C., DeFanti,T.
“Multi-perspective Collaborative Design in Persistent Networked Virtual Environments,” In Proc. ofIEEE Virtual Reality Annual International Symposium ’96, pp.253-260. (1996).
[2] Ki,B., Klasky,S.,“Collaborative scientific data visualization” Concurrency: Practice and Experience,Vol.9,No.11, pp1249-1259,(1997).
[3] Nonaka.I.,Takeuchi.H.,The Knowledge-Creating
Company: How Japanese Companies Create theDynamics of Innovation, Oxford Univ Pr.(1995).
[4] Kukimoto,N,, Furusho.Y.,Nonaka.J.,Koyamada.K.,
Kanazawa.M.,“PDA-based Visualization Control
and Annotation Interface for Virtual Environment,” In Proc. VIIP 2003, The 3rd IASTEDInternational Conference on Visualization, Imaging, and Image Processing, pp.975-980, (2003).
[5] Ruddle,R.A., Savage,J.C., Jones,D.M. “Verbal
Communication During Cooperative Object Manipulation”In Proc. of ACM Collaborative Virtual
Environments (CVE’02), pp.120-127,(2002).

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Next Generation Video Streaming System for Intuitive Remote Interaction
Kaoru Sugita Nobuhiro Nakamura Shinichi Baba Masao Yokota
Fukuoka Institute of Technology
sugita@fit.ac.jp, {mgm05008, mgm05011}@ws.ipc.fit.ac.jp, yokoya@fit.ac.jp
Abstract
In this paper, we propose a live video streaming system
based on virtual reality technologies for intuitive
interaction among people remotely located. This system is
one kind of sever-client system and can provide remoteusers with virtual 3D audiovisual fields in real time via avery high-speed network. The server captures audio andvideo data from its clients, compiles them into one 3D
audiovisual scene at a virtual conference and broadcasts itover the clients. At the present stage, our system captures
2 videos and creates one 3D video at a time. Our system
can play 3D audiovisual contents on Windows XP systems
as well as on CAVE systems.

1. Introduction
In recent years, live video streaming systems and TV
conference systems are very popular [1, 2, 3]. We havealso developed a WWW conference system in order toprovide WWW browsers with a live video communication
facility [4] and evaluated its performance [5, 6]. However,these systems are only to display flat pictures and thereby
the users hardly feel talking face-to-face with the otherparticipants at the same place.
On the other hand, there have been proposed several 3D
model reconstruction methods [7, 8]. These are applicableto videos as well as to photographs and can be
implemented on virtual reality systems such as CAVE
system [9] and, as a further extension, can provide theremote users with face-to-face realities.
This paper proposes a VR live video streaming system
in order to facilitate intuitive interactions among remoteusers and shows its implementation and evaluation basedon the experimental results.

2. VR live video streaming system
Our VR live video streaming system can provide theremote users with such a communication facility as shownin Fig.1 where 3D audiovisual fields are available on
Windows XP systems as well as on CAVE systems. The
participants can allocate their images arbitrarily in the©ISAROB 2007

798

video. The sound field, so called, Virtual Sound Field(VSF) is created so as to match the arrangement of theimages. Therefore, each participant can communicate withthe other users as if they were actually talking face-to-faceat the same place.
Our proposed system is one kind of sever-client system
as shown in Fig.2 and can provide remote users with
virtual 3D audiovisual fields in real time via a very highspeed network. The server captures audio and video datafrom its clients, compiles them into one 3D audiovisual
scene at a virtual conference and broadcasts it over theclients.
The server consists of 5 components as follows:(S1) User Interface - Provide windows to display 2D
videos and to set up video capture parameters(S2) Video Capturer - Take in 2D video and audio datafor 3D contents(S3) 3D Video Creator - Extract frames, detect
parallaxes and create polygons with textures(S4) 3D Video Stream Controller - Broadcast polygons
and textures controlling their numbers, frame sizes
and rates(S5) Sound Stream Controller – Broadcast sounds
controlling the sampling rates
Each client consists of 7 components as follows:(C1) User Interface - Provides windows to display 3D
videos and to set up 3D video parameters(C2) 3D Video Allocator - Arrange 3D videos in thevirtual world(C3) 3D Video Controller - Play 3D video with sounds
synchronized(C4) 3D Video Stream Controller - Receive polygons
and textures controlling their numbers, frame sizes
and rates(C5) Virtual Sound Field Creator - Play sounds
reflecting the arrangement of the 3D videos(C6) Sound Controller - Play sounds with 3D videos
synchronized(C7) Sound Stream Controller - Receive sounds
controlling the sampling rates

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig.1. VR live video streaming system.

!
!
!
!

candidate pairs of corresponding pixels so called‘Template for Candidate Projection Area (TCPA)’ as
shown in Fig.5. Usually such a candidate pair cannot be
determined uniquely due to the ambiguous z coordinate of
the object while it is prepared in advance by (4)-(7).
Therefore, the pair minimizing the difference between
TCPAs is selected as the most certain one.
" 2x + D %(4)
x1 = f $ 0'# 2z0 &"y %(5)
y1 = f $ 0 '# z0 &# 2x " D &(6)x2 = f % 0($ 2z0 '"y %(7)
y2 = f $ 0 '# z0 &

4. Virtual sound field
The virtual 3D sound field is intended to play soundreflecting the relation between the 3D video display point
and the viewpoint in the virtual world. As shown in Fig.6,
the sound source and the sound receivers are locatedaccording to the 3D video. In order to realize the verticalstereo effect, each receiver is divided logically into 2 subreceivers and each speaker plays the same sound twicereflecting the difference in distance and in vertical angle
between the sub-receivers. The volume and delay time at
the receiver is given by (8) and (9).

Fig.2. System architecture.

3. 3D video creation method
Figure 3 shows spatial relations of an object and its 2D
video frames. As known well, the object’s position (O)can be computed from the points (P1 and P2) projected on
the two different frames according to (1)-(3), whereO(x0,y0,z0), P1(x1,y1,z1), and P2(x2,y2,z2), respectively.
x0= (D/2d)(x1+x2)(1)y0= (D/d)y1=(D/d)y2(2)z0=(D/d)f(3)
A 3D model is composed of polygons each of which
consists of vertexes with 3D coordinates. Such a model
can be reconstructed by corresponding the 3D coordinates
specifying the object’s surface with the 2D coordinates inthe video frames. A 3D video supplies a time-sequenced
set of polygons and texture images. The details of 3Dmodel creation are shown in Fig.4.
In order for modeling in real time, we have developed a
new method to control the number of polygons forparallax detection. In this method, the parallax between
the frames is calculated efficiently using one set of©ISAROB 2007

799

Fig.3. Spatial relations of object and frames.

Fig.4. 3D model creation.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Vr = f v (Vss,Lsvx )f (L )Dr = d svx
c
!

!

(8)
(9)

where
Lsvx: the distance between the sound source and thesound receiver,
Vss: the volume at the source,
Vr: the volume at the receiver,fv(Vss, x): the volume at the distance x from the source,
Dr: the delay time at the receiver,c: the speed of sound, andfd(x): the delay time at the distance x.

5. Implementation
The server was implemented on Windows XP system
while each client was implemented on either Windows XP
system or CAVE system, employing the C Language, Intel
Open Computer Vision Library (Open CV) [10],WinSock2, Open GL, GLUT and CAVE Library. The
CAVE library was used to draw/display 3D videos and tosupport the head tracking on CAVE system only. The
others were utilized for both the systems in theFig.5. Parallax Detection.

©ISAROB 2007

800

conventional ways except that the Open CV was for
capturing videos and extracting video frames.
Figure 7 shows the videos from the left camera (a) andthe right camera (b) and the 3D video (c) composed on theserver. Logicool Qcam for Notebooks Pros were
employed as web cameras put at a space 25cm longenough for us to recognize easily the parallax between
Fig.8 (a) and (b). Figure 8-c was viewed at a client on
Windows XP System, where the user could control the
viewpoint and view angle through the keyboard.

6. Conclusion
The VR live video streaming system and its
implementation were described. At the present stage, oursystem captures 2 videos and creates one 3D video at atime. Our system can play 3D audiovisual contents on
Windows XP systems as well as on CAVE systems while
the existing live video streaming systems and TV
conference system are only to display the flat pictures.
Currently, we are evaluating our system in its precisionand performance and also implementing the soundmodules. In the future, we will introduce some highly
precise certainty calculation method.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Multimedia Network Systems and Applications(MNSA-2004), pp. 64-69, 2004.
[2] Y.Maita, K.Hashimoto, Y.Shibata: “A New TV
Conference System with Flexible Middleware for
Omni-directional Camera”, Proc. of the 7thInternational
Workshopon
Network-BasedInformation System (NBIS-2004), pp. 84-88, 2004.
[3] Y. Kato, D. Jiang and K. Hakozaki: “A Proposal of a
Streaming Video System in Best-Effort Networks
Using Adaptive QoS Control Rules”, Proc. of the
18th International Conference on Advanced
Information Networking and Applications (AINA2004), Vol.2, pp. 54-57, 2004.
[4] Kaoru Sugita, Noriki Uchida, Akihiro Miyakawa,
Leonard Barolli: “Implementation of WWW
Conference System for Supporting Remote Mental
Health Care Education", Proc of the SeventhInternational Workshop on Multimedia Network
Systems and Applications (MNSA2005), pp.686-692,
June.2005.
[5] Kaoru Sugita, Noriki Uchida, Akihiro Miyakawa,
Giuseppe De Marco, Leonard Barolli: “Performance
Evaluation of WWW Conference System for
Supporting Remote Mental Health Care Education",
Proc. of the 11th International Conference on Paralleland Distributed Systems (ICPADS-2005), Vol.1,
pp.271-277, July.2005.
[6] S.Baba, K.Sugita, N.Uchida, G. De Marco, L.Barolliand A.Durrusi: “Performance Evaluation of WWWbased Conference System ", Proc. of the 8thInternational
Workshopon
Network-BasedInformation
System(NBIS2005),
pp.50-56,
Aug.2005.
[7] Yin Li, et al.: “Stereo Reconstruction from
Multiperspective Panoramas ”, IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 26,
No. 1, pp.45-62, 2004.
[8] James Davis, et al.: “Spacetime Stereo: A Unifying
Framework for Depth from Triangulation ”, IEEE
Transactions on Pattern Analysis and MachineIntelligence, Vol. 27, No. 2, pp. 296-302, 2005.
[9] Cruz-Neira, C., Sandin, D.J., and DeFanti, T.A.:“Surround-Screen Projection-Based Virtual Reality:
The Design and Implementation of the CAVE. ”,
Proc of SIGGRAPH '93 Computer Graphics
Conference, pp. 135-142, August 1993.
[10] Intel Open Source Computer Vision Library,
http://www.intel.com/technology/computing/opencv/i
ndex.htm

Fig.6. Virtual Sound Field.

Fig.7. Implemented System.

References[1]

D.Nishantha,
Y.Hayashida
andT.Hayashi:“Application Level Rate Adaptive Motion-JPEG
Transmission for Medical Collaboration Systems”,
Proc. of the 6th International Workshop on©ISAROB 2007

801

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Face Recognition Across Illumination
Saleh Aly
Department of Intelligent Systems
Kyushu University
744, Motooka, Nishi-ku, Fukuoka, 819-0395
aly@limu.is.kyushu-u.ac.jp

Alaa Sagheer
Department of Intelligent Systems
Kyushu University
744, Motooka, Nishi-ku, Fukuoka, 819-0395
alaa@limu.is.kyushu-u.ac.jp

Naoyuki Tsuruta
Department of Electronics Engineeringand Computer Science, Fukuoka University
8-19-1, Nanakuma, Jonan, Fukuoka 814-0180
tsuruta@tl.fukuoka-u.ac.jp.co.jp

Rin-Ichiro Taniguchi
Department of Intelligent Systems
Kyushu University
744, Motooka, Nishi-ku, Fukuoka, 819-0395
rin@limu.is.kyushu-u.ac.jp

Abstract
Illumination variation in face images is one of themost difficult problems in face recognition system. The
performance of self-organizing map-based facerecognition system is highly degraded when theillumination of test images differ from training images.
Illumination normalization is a way to solve thisproblem. Both global and local image enhancement
methods are studied in this paper. Local histogram
equalization method is highly improving therecognition accuracy of the CMU-PIE face database.
Keywords—Face recognition, illumination, SOM

1Introduction

Important practical applications of automatic facerecognition have made it a very popular research area inthe last three decades [1]. Human-machine interface isconsidered as one of these applications. For example,
the interaction between robots and human requires afast and accurate identification of the person identity. In
spite of the expanding research in the field of facerecognition, many problems are still unsolved.
Recently, more researchers focus on robust facerecognition which is invariant to pose, expression,
illumination variations. Illumination variation is still achallenging problem in face recognition research area
especially for appearance-based methods [2]. The same
person can appear much differently under varying
lighting conditions. Varieties of approaches have been
proposed to solve the problem, and they can beclassified into three categories: preprocessing andnormalization [3, 4], invariant feature extraction [5],and face modeling [6].
Self-organizing map (SOM) [7] is a famous
unsupervised neural network. In this work, SOM isused for feature extraction and dimensionality
reduction. Two different shapes of SOM maps
including sheet, and cylinder shapes are used to learn
face manifold. However, the performance of SOM ishighly degraded when the lighting condition of the test
images differs from that of training images. Therefore,©ISAROB 2007802

it is proposed to improve its accuracy using
illumination normalization techniques.
To improve the performance of SOM, several
illumination normalization techniques are investigated.
According to the processing strategy, these methods canbe categorized into two classes. Global methods inwhich the whole image is processed at once. On theother hand, region-based methods process small regions
from the image separately. In this work, two wellknown image enhancement methods are used. Gamma
intensity correction (GIC) which normalize the overall
image intensity to a given intensity level and histogram
equalization (HE) which reassigns the intensity valuesof pixels in the input image such that the output image
contains a uniform distribution of intensities. Bothunnormailzed and normalized face images are projectedon the 2-dimensional space spanned by the two greatest
eigenvectors using principal component analysis(PCA); the structure of the projected manifold under
different lighting directions is analyzed, moreover, theseparability among different classes is explored.
The remainder of the paper is organized as follows:
In section 2, the learning algorithm of self-organizing
map neural network is discussed. Section 3 mainly
describes global and local illumination normalization
methods. Face database, experimental results, andconclusions are given in the following three sections.

2

Self-organizing Map

Consider the set of training images χ = {xi, 1 < i<M}, each image belong to N-dimensional space. SOM
is usually represented as a neural network sheet or map
whose units, usually called nodes or neurons, become
tuned to different input vectors xi. A weight vector wj,
sometimes called reference or codebook, is associatedwith each neuron j and the map weight vectors are
given by W = {wj, 1 < j <N}; such that N < M. In eachtraining step, the following two steps are repeated foreach input sample xi.
1.

Find the best matching neuron c using asimilarity measure between the input and all

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

the map’s neurons. This step name is winnertake-all (WTA), where c is the desired winnerand should satisfy:xi − wc = min( xi − w j )(2

2σ (t ))The SOM
characteristics:

codebook

has

the

3

Illumination variation of face patterns is extremelycomplex due to varying texture reflectance properties,
face shape, and type and distance of lighting sources.
Hence, in such a general setup, it is difficult to learn.
However, most of the variations can be described bydominant principal components, this motivates theillumination subspace analysis using PCA.
The original and the normalized image faces areprojected on the 2-dimensional space spanned by thetwo greatest eigenvectors using principal component
analysis (PCA). Figure 1 shows the distribution of Yale
B database face images [6] for two subjects under 64
lighting conditions. It is clear that the manifold of thesesubjects is highly overlapped. Thus, the discrimination
between different subjects will be a very hard task.

3.1

PC2

-3
-4
-5
-20

-10

-5
PC1

Histogram equalization is most widely used method

803

0

5

10

Histogram Equalization
15
10

PC2

5
0
-5
-10
-15
-6

-4

-2

0
PC1

2

4

6

Figure 2: Normalized Face images distributions using
histogram equalization method.
to enhance biased contrast image that some pixels
are concentrated on a narrow range of the pixel
intensity. The result is obtained using cumulative
density function of the image as a transfer function. The
result of this process is that the histogram becomesapproximately constant for all gray values. As shown in
Figure (2), the face distribution after applying HE
algorithm is highly regularized.

3.2

Gamma Intensity Correction (GIC)

Gamma correction is a technique commonly used in
the field of computer graphics. It concerns how todisplay an image accurately on a computer screen.
Images that are not properly corrected can look dark.
Gamma correction can control the overall brightness ofan image by changing the Gamma parameter. The
gamma transform of an image is a pixel transform inwhich the output and input are related byexponentiationf (I (x , y )) = I (x , y )γHistogram Equalization (HE)©ISAROB 2007

-15

Figure 1: Unnormailzed face images distributions under
64 illumination conditions along the first two principle
components.

The probability Distribution Function (PDF)of the codebook is a good approximation for
the PDF of the training data.
The topographic order of the training data ispreserved in the codebook, even if thedimensionality of the SOM is smaller than thatof training data.

Normalization Methods

0
-1
-2

following

The second characteristic means that similar facial
features are mapped to nearby positions in the feature
map. This ordering takes place automatically without
external supervision based on only the internal relationsin the structure of the input patterns and thecoordination of the neuron activities through the lateralconnections among the neurons.

∗

1

(3)

where hcj(t) is the neighborhood kernel function aroundthe winner c at time t, α(t) is the learning rate and isdecreased gradually toward zero and σ 2 (t ) is a factor
used to control the width of the neighborhood kernel.

2.

2Update the weight vector of the winner c andalso all its topological neighborhood in the
map towards the prevailing input according to
the rule:w j (t + 1) = w j (t ) + hcj (t )[ xi (t ) − w j (t )](2)hcj (t ) = α (t ).exp rc − r j

1.

3

(1)

j

2.

No Normalization
4(4)

Depending on the value of γ the output image isdarker or brighter. In GIC, the image is gamma

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007
Gamma Correction

transformed as to best match a canonically illuminatedimage IC(x, y). Predefine a canonical face image, IC,
which should be lighted under some normal lighting
condition To find the value of γ the following equationmust be solved
γ*

2

(5)

5

0

x ,y

This is a nonlinear optimization problem, and it canbe solved using Golden section search algorithm.
As shown in Figure (3), face distribution using GIC
normalization algorithm still overlapped and face data
still have bad separation.

-5

-10
-4

-3

-2

-1

0

1

2

3

PC1

Figure 3: Normalized Face images distributions using
Gamma Intensity Correction method.
Region-based Histogram Equalization

Region-based HE and GIC

5

It is obvious that both HE and GIC are global
transforms over the whole image area. Therefore, they
fail when side lighting exists. To solve this problem,
region-based method is proposed to process the face
images based on different local regions. That is,
performing HE or GIC in some predefined face regions
in order to alleviate the highlight, shading and shadow
effect caused by the unequal illumination. As we know,
face image is symmetrical around the vertical axis andcontains different parts around the horizontal axis;
therefore, it is proposed to partition it into four regions.
As shown in figures (4, 5), region based normalization
methods increase the separation between different
subjects, thus the recognition accuracy will be
improved.

4
3
2
PC2

3.3

10

PC2

γ = arg min ∑ [I (x , y ) − I C (x , y )]
γ*

15

1
0
-1
-2
-3
-4

-3

-2

-1

0

1

2

3

PC1

Figure 4: Normalized Face images distributions using
Region-based histogram equalization method
Region-based Gamma Correction
654Face Database

4

CMU-PIE face database [8] is available for studying
pose, illumination, and expression problems in facerecognition. There are 68 individuals under 43 different
lighting and 3 different facial expressions for 13 poses.
Since this work mainly deals with the illumination
problem, frontal images under varying lighting
conditions are selected, these which includes the images
under 21 different directional flashes. Example imagesof one person in frontal pose are shown in Figure (6).
The images are divided into four subsets according to
the angle that the light source direction makes with thecamera axis— Subset 1 (f06~f09, f11, f12, f20), Subset
2 (f05, f10, f13, f14, f19, f21), Subset 3 (f04, f15, f18,f22), and Subset 4 (f02, f03, f16, f17).

5

Experimental Results

In this experiment, subset 1 is used as the training set(gallery images) and other subsets are used for testing(probe images), all images are rescaled to the size of
48x48 in order to accelerate the computation time. All
cropped images in the database are photometricallynormalized using histogram equalization, gamma©ISAROB 2007

804

PC2

3
2
1
0
-1
-2
-3
-3

-2

-1

0

1

2

PC1

Figure 5: Normalized Face images distributions using
Region- based Gamma Correction.
correction, and region-based methods.
Figure (7) shows the canonical face image used to
calculate the γ coefficient of the GIC method. This
image is the mean image of the training set enhancedwith histogram equalization. In the region-based
methods, the face images divided into four regions, andthese regions are normalizing with the same algorithmof global HE or GIC.
Figure (8) shows results of applying different
normalization methods for one subject in the CMU-PIE

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Subset 1

(a)

(b)

(c)

(d)

(e)

Figure 8: the processed images after applying different
illumination normalization methods for one image in
the CMU-PIE face database. (a) Unnormalized (b) GIC(c) RGIC (d) HE (e) RHE.
Subset 2.

100

Subset 3.

Accuracy80

Subset 4.

Figure 6: sample images of an individual divided into
four subsets.

60

40

20

Non
HE
GIC
RGIC
RHE

0
Subset 2

Subset 3

Subset 4

All

(a)
(b)
Figure 7: (a) Canonical face image (b) Facial four
regions.

Figure 9: Recognition rate comparisons of different
illumination normalization methods on CMU-PIE Face
Database using cylinder SOM map.

face database. Region-based normalization methods
increase the contrast of the image rather than global
normalization methods. It is clear that the quality of theimage is improved and feature parts such as eyes, noseand mouth are made more clearly.

6

The local normalization method has the disadvantagethat the output is not necessarily realistic. However, inthe problem at hand, the objective is not to have arealistic image but to obtain a representation of the facethat is invariant to illumination, while keeping theinformation necessary to allow a discriminative
recognition of the subjects. With this idea in mind, it
makes sense to use local illumination normalization
methods for this type of application.
The normalized gallery images are used to train two
different shapes of SOM, sheet and cylindrical. Eachgallery image is represented by the index of the winner
neuron; in this experiment 2-dimensional map is used
for training. The winner of the normalized probe image
is considered as the feature vector of the face image,this vector is compared with all gallery feature vectors.
Nearest neighbor classifier based on the Euclidean
distance is employed for classification. Figure (9)shows the recognition accuracy of applying all
normalization methods to process all images before
training and testing. The recognition accuracy for eachsubset and the accuracy for all subsets are calculated. It
is clear that RHE improve the accuracy of recognition.
By studding the performance of two different shapesof SOM. Cylinder map shape gives good results than
the sheet map shape, because of the circular shape ofthe face manifold in the image space.

©ISAROB 2007

805

Conclusion

The experimental results on CMU-PIE face databasereveal a number of interesting points,
1.
2.
3.

SOM performance highly degrades when thetest sample lighting condition is different fromthe training samples condition.
Region-based
illumination
normalization
outperforms the global normalization methods.
The cylinder shape of SOM is better than the
sheet shape because face manifold of the
training images tend to be circular.

References
[1]
[2]
[3]

[4]

[5]
[6]

[7]
[8]

W. Y. Zhao, R. Chellappa, A. Rosenfeld, and P. J. Phillips.
“Face recognition: A literature survey”, UMD Technical Report
CAR-TR-948, 2000.
S. Z. Li, A. K. Jon, “Handbook of Face Recognition”, Springer,
2005.
Bo Du; Shiguang Shan; Laiyun Qing; Wen Gao, “Empiricalcomparisons of several preprocessing methods for illumination
insensitive face recognition” , IEEE International conference on
Acoustic, speech, and signal processing , vol.2,pii/981-ii/984,
2005.
W. Chen, M. J. Er, and S. Wu,“ Illumination Compensation andNormalization for Robust Face Recognition Using Discrete
Cosine Transform in Logarithm domain”, IEEE Trans. On
Systems, Man, and Cypernetics-part B: Cypernetics, vol. 36, No.
2, April 2006.
P.N. Belhumeur, J. Hespanha and D.J. Kriegman, "Eigenfaces
vs. Fisherfaces: Recognition Using Class Specific Linear
Projection," IEEE PAMI, Vol. 19, pp. 711-720, 1997.
A. Georghiades, D. Kriegman, and P. Belhumeur.” From few tomany: Generative models for recognition under variable poseand illumination” IEEE Trans. on Pattern Analysis and MachineIntelligence, 40:643--660, 2001.
T. Kohonen, “Self-Organizing Maps”, Springer, 1997.
Sim, T.; Baker, S.; Bsat, M. “The CMU pose, illumination, andexpression database”, IEEE Trans. Pattern Anal. Mach. Intell.,vol.25, no. 12, pp. 1615-1618, 2003

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Construction of super-micro sense of force feedback
and visual for an elastic body
Takamasa Sonoda

Eiji Hayashi

Graduate School of Computer Science
And System Engineering
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
sonoda@imcs.mse.kyutech.ac.jp

Department of Mechanical Information
Science and Technology
Kyushu Institute of Technology
680-4, Iizuka City, Fukuoka, Japan, 820-8502
haya@mse.kyutech.ac.jp

Abstract
This research aims to develop a combined sense systemthat uses the sense of force feedback and the visual by
the shape of microscopic features of a micro sample. The
visual and the sense of force feedback were expanded inan effort to improve the efficiency of the skill to operate
the micro sample by creating conditions similar toactually feeling samples that are large enough to actuallygrip by finger. In this basic research, we use a haptic
device that presents the expanded reaction force
generated from virtual object of a micro sample.
Based on a feedback force presentation experimentusing a reaction force, a feedback force transmission
simulation model of actual force transmission wasconstructed. In this experiment, two control approaches
were used such as position control and hybrid control.
We discussed our experimental results considering thesystem’s advantages and problems.

This study describes the development of a compoundsystem that utilizes sight with a microscope and a force
feedback manipulator.
This fundamental research utilizes a haptic device toshow the amplified feedback force from a virtual minutesample. We also describe an interface that displays agraphic in a microscope and commands the x-y stage on
the microscope.

2. System Structure
2.1 Summary of Structure
The structure of the system is shown in Figure 1. Thissystem consists of a metallographic microscope, anautomatic x-y stage on the microscope, a feedback stage
controller to control the x-y stage, a hapic device fortransmitting force feedback (Fig. 2), a minute power
sensor (Fig. 3), and the PC with which I control andoperate them.

Key words: Force feedback, Haptic interface, Simulation
Motor driver

1. Introduction
Force sensor

Currently, bioindustry, the medical field, and the fieldof semiconductor production expect to see advancementsin the efficiency of micro-technology. Recently, a current
study focused on post-genomics, the field thatinvestigates the use of the enormous amount of
information analyzed. The field of semiconductor
research also has incorporated nanoscale productive
processes since 2004. It is difficult to manipulate
micro-technology directly, however, because of its
dependence on sight and the need for a considerable
mastery of skills.
As a method for solving this problem, the reaction force
of a minute sample improves the efficiency of functions
such as identification and operation by utilizing the
feedback force to the worker.

©ISAROB 2007

806

X-Y stage

Haptic device

Main computer

Microscope
Feedback stage controller

Fig. 1. Schematic diagram of system structure

Fig. 2. Haptic device

Fig. 3. Micro force sensor

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

2.2 Microscope Image Interface

Horizontal element

Therefore, we are able to check for deformation ineach minute sample using microscope images and thecomputer simulation.

ｚｚｚ

Fig. 4. Image of interface

3. Construction of virtual elastic bodyFig. 5 shows the construction of the virtual elastic bodyused in this study. It includes nine mass points and thespring-damper elements. The horizontal element andvertical element are constructed from a parallel model
made with a spring and damper. The virtual elastic bodycontains fixed mass points, except the center mass point
which acts as a condition of constraint.
We give the sample displacement and measure thereaction force to identify the elastic coefficient andviscous modulus on the simulation. We measure theelastic coefficient on the basis of the relation between thereaction force and the displacement, and the viscous
modulus on the basis of the relation between time anddisplacement.

mass

Vertical element
Fig. 5. Schematic diagram of dynamic model

4. Feedback Force Presentation Experiment
In this Chapter, we used the haptic device described in
Chapter 2. We attempted to evaluate whether a worker
can grasp an amplified micro reaction force form virtual
object with the haptic device.
The experimental method of force feedback
transmission involves hybrid control with a master-slave
method. A worker touches the virtual object described in
the previous chapter in PC. Then, the generated torque isamplified and transmitted by the haptic device.
These experimental results are shown in Fig. 6, Fig. 7,
and Fig. 8. Fig. 6 and Fig. 7 use the same time line. In
Fig. 6, the horizontal axis represents time in (seconds)
and the vertical axis represents the angle (in degrees) of
the master and slave. In Fig. 7, the horizontal axis
represents time and the vertical axis represents the torque(milli newton meters) supplied by the reaction force inthe slave and the torque transmitted to the master. In
Fig.8, the horizontal axis is time (in seconds), the verticalaxis is the torque (milli newton meters) supplied by thereaction force in the slave and the torque amplified tentimes in the master.
25

20
Angle[degree]

In this paper, we constructed the microscope image
interface shown in Figure 2. The functions of thisinterface are:• to display the graphic shown from a microscopethrough a digital camera• to move the x-y stage by pushing a button on theinterface• to measure the reaction force between a sample andsensor by a micro force sensor• to display the virtual object through simulation andcheck any deformation of it.

15

10

5

Angle(Master)Angle(Slave)

0

0

5

10

15

20

Time[sec]

Fig. 6. Experimental result of hybrid control (Angle)©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Acknowledgement

32Torque[mN･m]

1
0
0

5

10

15

20

25

-1
-2
-3

Torque(Master)
Torque(Slave)-4-5
Time[sec]

Fig. 7. Experimental result of hybrid control(Torque)

30
20

Torque[mN・m]

10
0
0

5

10

15

20

25

-10
-20
-30

Torque（Master)-40

Torque(Slave)-50
Time[sec]

Fig. 8. Experimental result of hybrid control(Torque is amplified 10 times.)

We can confirm that the torque of the master was
extended to 10[mN / m] according to Fig. 8. In addition,
we can feel the elasticity to a slight degree by touching avirtual object with a haptic device that we actually built.
However, we can not feel the softness when we touch asample with haptic device. Considering the causes thatcan not feel the softness, it is likely that the mechanismof the haptic device is not pertinent, and the reaction
force of a visual object is too small to induce with ahaptic device.

5. Conclusion
In this paper, we evaluated whether force feedback is
amplified by touching a virtual object with a haptic
device in a PC. We can amplify the reaction force but we
can not present a large enough reaction force for aworker to feel its elasticity.
In the future, I plan to build a system to show a minute
reaction force extended precisely. Future research will
focus on a system that will detect and express reaction
forces more precisely. The system will be tested using asmaller sample. Therefore, I will build a manipulator anddevelop a system around it.

©ISAROB 2007

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The authors would like to acknowledge the advice andassistance of Professor Tadashi Kitamura of Kyushu
Institute of Technology.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A System Allowing Concurrent Design and Implementationof Both Virtual and Real Robots
Toshiaki OOMORI, Norihiro ABE
Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Email: oomori@sein.mse.kyutech.ac.jp
Hirokazu TAKI
Wakayama University
930 Sakaedani, Wakayama-shi
Wakayama 680-8510, Japan

Shoujie He
Eastman Kodak Company,
Plano, Texas, USA

Abstract

physical low, we aim at building the virtual robotthat can operate as if it were the real machine. In
addition, we want the system to assist a naiveuser in both designing a virtual robot anddebugging the software.

By the development of the VR technology in recent
years, virtual debugging that uses a virtual environment
to debug the robot is paid to attention. However, at thepresent stage the reliability of virtual debugging is low,
and it is difficult to create and operate virtual robots
without exclusive knowledge. Then, in this research, we
use a virtual space according to rigid body dynamics,
and aim the systems that we can create and operate
virtual robots without exclusive knowledge.
Keywords: VR, Virtual Robots, Virtual Space

1

Introduction

In late years, more large-scale and complex
embedded software is required. This makes it
difficult to develop high-quality embedded
software in a short term, and the delay ofdevelopment is caused because developers cannot
debug software until a real machine is completed.
Consequently, a virtual machine should be built
concurrently with a real one. Then thisconcurrent development allows developers towrite and debug using a virtual robot thesoftware to be installed into the corresponding
real one. This makes it possible for developers tofurther examine the real machine in detail bydownloading the software developed with thevirtual one to the real one.
And as robots developed so far are supposed toact in safe environment, developers do not try to
make them work in dangerous environment forfear that they may be broken owing to a tumble
on a convex-concave road or falling from a highposition.
In such cases, virtual robots will help them
analyze the cause of accident, and improve fragile
components of robots or incomplete control
program leading robots to destruction.
In this research, reproducing the motor and thesensor, etc. in a virtual space according to a

©ISAROB 2007

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2

Construct of Virtual Space

A virtual machine designed based on the
traditional static does not contribute to develop
the corresponding real one, as it does not work
well in real environment comparison with the factthat the virtual one works well in virtualenvironment because of the lack of dynamics
including inertia. A real time solid body physics
engine VORTEX (developed by CMLabs
Simulations, Inc.) introducing dynamics into
virtual environment is exploited to solve theabove problem.
VORTEX provides following two restrictions:
Joint, which joins two elementary parts toconstruct an articulated body.
Contacts, which restricts movement of twogeometrical models within tolerance.
As a sample of VORTEX, the robot arm is shownin Figure 1. This simulation faithfully reproduces
the gravity, friction, and the torque of the motor,
etc. in real time. Using VORTEX, we can executethe simulation of complicated models with a lot ofphysical parameters in real time.

Figure 1: Robot arm simulation by VORTEXThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3

Development of Virtual Robot

4

VORTEX has many features but some of them
are unnecessary for constructing a virtual robot.
Then we took out only function necessary for the
robot, and redefined them as the C++ class forfacilitating to construct a virtual robot. As aresult, we can change the specification of virtual
robots, and shorten the debugging time. Using
this class, we made a four-legged virtual robot
and a two-legged one shown in Figure 2 andFigure 3, respectively.

Virtual Sensor

The robot needs a variety of kinds of sensors forboth detecting its pose and localizing itself. Then,a virtual distance sensor and a virtual gradientsensor were invented. We configured the functionof these two virtual sensors as C++ classes tomake it easily for a developer to exploit them.
This system allows a developer to implement the
movement algorithm of the robot easily.

4.1

Virtual Distance Sensor

We made a sensor with high directivity like thelaser distance sensor. And by combining thesensors together, a low directional sensor is
obtained. Figure 4 shows these two sensors.
Figure 2: Four-legged robot

High directivity
Low directivity
Figure 4: Distance sensor

Figure 3: Two-legged Robot
To model a servomotor of virtual robot, a hinge
joint of VORTEX is used and controlled. The
hinge joint restrains the two parts to rotate
around one axis as shown in Figure 4.

4.2

Virtual Gradient Sensor

The mechanism of this sensor is very easy.
Non-graphical two objects are set up in the model
to be measured its gradient, and the grade iscalculated from coordinates of the two objects.

Figure 4: Hinge-joint image
Moreover, we built in the walking program inboth robots. It is difficult to have a two-legged
robot walk stably though it is easy to have afour-legged robot walk. But, to make afour-legged robot pass the centerline of a corridor,
a virtual gradient sensor is needed. Virtual
sensors introduced into the system will be shown
below.

Figure 5: Gradient sensor

5

Installation of Virtual Sensor to Virtual
Robot

Because a developer can make the sensors ofvarious specifications expeditiously, the virtual©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

sensor class makes it brief to devise themovement algorithm of the robot by the trial anderror.
Figure 6 shows a four-legged virtual robot in
which four distance sensors are installed. This
robot measures the distance to the wall with thesensor, and it is possible to walk without
knocking against the wall. A developer can easilyadjust the number and the time base range etc. ofthe sensor, and this leads the developer to thebest operational condition. For instance, when we
install two sensors on the forefoot of the robot, as
it collides against the wall, it was not able to walk
well. It was, however, possible to walk well when
4 sensors were installed on all feet of the robot.
Thus, the developer can judge appropriate
allocation of the sensors using a virtual robot
instead of a real one.
Figure 7 shows a two-legged virtual robot
equipped with a gradient sensor at the center of
its body. So as not to fall, this robot corrects theangle of the joint by detecting the inclination of
the body. It is very useful for making a real robot
to make such a correction algorithm in a virtual
one. The reason is clear considering the casewhere the algorithm is developed from thescratch using a real machine. The virtual debug
allows a developer to operate both a real andvirtual robot at ease, as the result the realmachine debugging will complete briefly. The riska real machine falls will decrease, and its
breakdown also decrease as a result. On the other
hand, it is difficult to expect the same result
when a real machine is debugged from thescratch.

6

Stereo Vision System

It is difficult for robots to detect complex
external world only by the distance sensor, thenstereovision system is often built into it recently.
This system calculates the distance to the object
by the image data processing with two cameras.
At first, the parallax is calculated by extracting
corresponding parts (called stereo matching) from
two images captured with two cameras. Next, thedistance to the object is obtained by using theacquired parallax and the distance between two
cameras. For example, Figure 8 shows result of astereo matching.
For simulating the stereo matching in virtual
space, the aspects (two frame buffer of GL)
captured with two cameras corresponding to therobot’s eyes must be converted into the bit map
images. Next a stereo matching algorithm isapplied to them. This procedure is shown in
Figure 9.
C++ class for implementing the stereovisionsystem is so that the user might easily make eyesof virtual robots.

Left camera Image
Depth image
Figure 8: Stereo matching example

Figure 6: Four-legged robot with distance sensor

Figure 9: Procedure of stereo vision system

Figure 7: Two-legged robot with gradient sensor©ISAROB 2007

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The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

7

Combine virtual robot and stereo vision

We expect a virtual robot equipped with thestereo vision to following behavior.
A four-legged virtual robot will successfully
avoid obstacles with the stereo vision system andreach the destination. The distance measurement
using both stereo vision and virtual distancesensor will help the robot make an elaborate
plan.
We want a two-legged virtual robot go up anddown the stairs of which height and size areunknown. Many robots which can go up and down
the stairs are given in advance such parameters.
On the other hand, when our robot finds thestairs, it measures the height with the stereo
vision. If there is enough space on the stair andthe height is less than the tolerance, it will go up
on the stair. At the moment, the robot willmeasure only the vicinity of its feet for conducting
measurement of high-speed and high-accuracy.

8

Conclusions and Future Work

This research has aimed at making a virtual
robot with the reality. Building virtual sensor andservomotor, etc. into VORTEX, we can makevirtual robot with high reproducibility. And, by
creating a variety of C++ classes, developers canmake and change a virtual robot easily. In the
future, various sensors and actuators should be
provided to virtual machines and this will help us
develop complex virtual robots that we cannot
construct up to now. We would like to show how
concurrent robots development would be able tosolve various problems including development of
a complex robot like a humanoid.

Acknowledgements

We greatly appreciate the aid of Ministry ofInternal Affairs and Communications (MIC) and
the Grant-in-Aid for Scientific Research.

References[1]Yi PAN, Norihiro ABE, Kazuaki TANAKA ： The
virtual debugging system for embedded softwaredevelopment, ICAT2002, pp.119-p124, 2002
[2]Yi PAN, Norihiro ABE, Kazuaki TANAKA：The
Virtual Debugging System for Embedded Software
Development, SCI2003,pp78-84 , 2003
[3]Yi PAN, Norihiro ABE, Kazuaki TANAKA：The
Virtual Debugging System for Embedded Software
Development, VRAI2003, 2003
[4]Yi PAN, Norihiro ABE, Kazuaki TANAKA, Hirokazu
TAKI：The Virtual Debugging System for Developing
Embedded Software using Virtual machinery,©ISAROB 2007

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EUC2004[5]Toshiaki Oomori, Norihiro Abe, Kazuaki Tanaka,
Hiroaki Taki, Shoujie He "Concurrent Developmentof Virtual Robots and Real Robots Based on Physical
Law",
15th
International
SymposiumonMeasurement and Control in Robotics (ISMCR2005)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Real-time Interactive Dialog System between Human and Virtual Agent
Shunji UCHINO,
Norihiro ABE
Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Email: s_uchino@sein.mse.kyutech.ac.jp
Hirokazu TAKI
Wakayama University
930 Sakaedani, Wakayama-shi
Wakayama 680-8510, Japan

Shoujie He
Eastman Kodak Company,
Plano, Texas, USA

Abstract

verbal information using the voice and the non-verbal
information using a gesture, and verified the validity ofthe system. It permits a user and a virtual humanrendered in display to communicate each other using
pointing action and utterance. The configuration of thissystem is shown in Figure 1.

In this research, a dialog environment between
human and virtual agent has been constructed. With theactual VR technologies, special devices have to be used
for the interaction with a virtual environment. This
makes it extremely difficult for general users tomanipulate an object in the virtual environment. In ourdaily life, when an object is out of our reach, we usually
ask someone with direct access to the object tomanipulate it on our behalf. If there is such a helper who
has direct access to objects in a virtual space, we may do
the similar thing. The question, however, is how tocommunicate with the helper, namely, the virtual agent.
This paper presents a solution to the question. The basic
idea is to utilize speech and gesture recognition systems
and to integrate the verbal and non-verbal information.
Experimental results have proved the effectiveness of theapproach in terms of facilitating man-machineinteraction and communication. The environment
constructed in this research allows a user tocommunicate by talking and showing gestures to apersonified agent in virtual environment. A user canuse his/her finger to point at a virtual object and ask theagent to manipulate the virtual object.
Keywords: Virtual Agent, real time, voice and gesturerecognition, interaction.

1

Introduction

Recently, toward a ubiquitous network society,products are developed that have some excellent
functions based on Information Technologies. In future,
it is expected that these products will be much more
complex to provide multifunction. Nevertheless, maininterface of computer such as mouse and keyboard willremain unchanged and it will make it difficult for elderly
person to operate it. Further it will become a cause ofdigital divides. Also, it will be difficult even for experts
in operating computer as instrument developers to use it
when they grow older. So, developing new interface is
expected which helps everyone to operate a computer
easily. In this paper, a dialog environment between
human and computer is proposed which unifies the©ISAROB 2007

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Figure 1: Concurrent processing among 3 PCs.
This system is divided into three parts: The
language processing part which recognizes user’s voice
picked from mike. In the vision part, user’s action isrecognized based on image information obtained fromcameras. The display part shows a virtual human andmakes him speak and act responding to requirementfrom a user. This system realizes actual dialog
environment using both utterance and gesture byconnecting these three parts with a high speed network
called Scram Net+ which makes the time needed to send
information among computers less than one millisecond.

2

Space for conversation

We have developed a system in which an avatar
responds to a user as a salesman in a virtual fountain pen
store. The user tells him the pen he wants to buy using
utterance and a pointing action. The typical user's
utterance with pointing action is like, "Please give this tome". The system recognizes the appropriate pen basedon the verbal and non-verbal information. If the pen thatthe user wants cannot be located with the pointing action,
the avatar must uniquely determine the pen with somequestions. However, if he asks question many times, theuser feels much annoyed. So some devices are necessary
to reduce questions. We have already reported theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

method using a decision tree to have the avatar ask goodquestion leading to unique identification of the pen.

3

Interaction from user to system

Pointing action is required to synchronize withutterance such as ‘What is this?’ In this system, thespoken language processing and the gesture recognition
are conducted concurrently to identify an object ofpointing action. There is a possibility that the avatar may
misunderstand user’s instruction because pointing action
involves ambiguity and thus pick up a wrong pen. In
the case, the user has to immediately interrupt the actionof the avatar and rectify wrong behavior.

3.1

Spoken language processing

Voice is analyzed with Julius for Windows version
v3.3p4_jl2-1. It is possible to recognize a given
utterances at about 1.1-1.3 times of the utterance time.
The starting and ending time of each demonstrative
pronoun appearing in utterance detected with Julius areused to judge the temporal relation between utterance ofa demonstrative pronoun and pointing action. Thisfunction successfully finds temporal relation even if
utterance includes several demonstrative pronouns, andseveral pointing actions are given.
The user does the pointing action toward the screen.
The system extracts user's finger with a vertical stereo
vision system excelling in detecting of a sidewise vector,
and acquires the direction and coordinates of the user’s
fingertip and knuckle.

Retinachip
cameraCCD
cameraof a hand stops to make the object of pointing action
clear, the still image of the hand is captured with a retina
chip camera using images deference function. Imagesof two CCD cameras corresponding to the above retina
chip image are examined to measure the direction andcoordinates of the user’s fingertip and knuckle.

3.2

Extraction of Skin Area

3.2.1

HSV Conversion

Generally color information data obtained fromcamera is RGB. It is difficult to exract a target object
with peculiar colors from the environment illuminatedwith strong light. HSV color system helps an image
processing system extract the target from suchenvironment. Here, H of HSV, S and V means Hue,
Saturation and Value of Brightness, respectively. The
RGB color system is convered into the HSV color
system. To extracts skin-colored area, it is necessary to
set threshold values in terms of HSV value. Each ofthreshold values for extracting skin-colored area areshown below.
Hue:
0 < H < 40 [Range: 0 < H < 360]
Sarturation: 0 < S < 140 [Range: 0 < S < 255]
(1)
Value of Brightness: 0 < I < 255 [Range: 0 < I < 255]

3.2.2

Labeling processing to remove noises

Even if an effectual color values is given to extractskin-colored area, areas not corresponding to a hand arealways extracted. The labeling process is useful to
exclude all regions but the hand. This method is asfollows.
1). Sequentially applying the label to eachskin-colored region.
2). Extracting region of which area is the maximum
value among them.
3). Making the image binary to remove noise except
for the region with the maximum area.

Prism
Figure 2: Combination of CCD and retina chip camera.
For the real-time processing, the system using only
CCD color cameras is unsuitable. To reduce the cost ofimage processing, retina chip camera is used to narrow
the image region to that including just a forefinger. The
retina chip camera realizes a super-parallel image
processing (“Moving object detection” and “Edgehighlighting”…) with analog circuits embedded in eachpixel. As shown in Figure 2, a ray passing through a
prism is divided into two rays each of which is input to
CCD and retina chip camera respectively. As the motion©ISAROB 2007

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Figure 4: Extraction of skin colored area

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3.3

Agreement of voice and action

The object in virtual space that the user specifies
using pointing action can be determined based on theinformation through the spoken language processing.
Even if a pronoun cannot be detected, when the pointing
action is successfully found from the corresponding timespan, it is possible to find the pronoun using theverbal-nonverbal relation described above and viceversa.

4

interrupts his action to inform him of negation. This
makes him under-stand what the user wants. For
confirming his favorite pen, he grasps the pen (3).

Result of conversation

Consider the case a user talks with an avatar invirtual space including 30 fountain pens in a showcase.
Every fountain pen includes 6 attributes such as Size,
Length, Color, Weight, Cost, and Product buy. It is
possible to construct a decision tree and use the tree todeicide which pen a user wants to buy. But the method
does not allow the user to specify freely his favorite
attributes but forces him to answer whether he likes or
dislikes a peculiar attribute which the decision tree
algorism selects.
Instead, our system permits him to select his favorite
pen by pointing action with the utterance such as“Would you show me this pen?” or “Would you take thisand that pen here?” The first utterance must include
just one pronoun and one favorite pen must be specified
with pointing action. On the contrary, the second one
include two pointing action but the discrimination ofthem is easy although it is not easy to find two pens if
two pointing action specifies neighboring regions.
In both cases, the avatar has to find a set ofcandidate pens. For the first case, he can select two ways
to determine what a user wants to buy. The first way is
to ask if the user likes the one pointed or not withpointing one of candidates. This method is adequate if
the number of the candidates is a few, otherwise theavatar may have to enumerate all of them. The second
one is to make a decision tree from the set of candidates
and then to ask the user which values his favorite pen
has in terms of the attribute designated on the current
node tracing the tree downward from the root. In thiscase, the number of inquiries to be asked will besmallest.
Here, the third one is proposed. Let us see an
example. The flow of the conversation is shown in
Figure 5. It is to use interrupt mechanism. Consider thefollowing dialog in which a user specifies his favorite
pen by pointing action (1). At this time, as 6 pens are
selected as a candidate because of the ambiguity ofpointing action, he asks the color to qualify a favorite
pen. As a result, two red pens are left. Though he cannot
decide which one is specified, instead of asking
additional question he is unintentionally or intestinally
going to grasp one of hem (2). Instantaneously, the user

©ISAROB 2007

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Figure 5: Flow of the conversation.
Avatar: Which pen do you want to buy?
User: Please show me this one. (Pointing a red pen) (1)Avatar: Which color do you like?
User: Please give the red one. (The avatar is going toreaching his hand to one of two red pens.) (2)User: No, it is wrong.
Avatar: Is this one? (Grasping another red pen) (3)User: Yes.
Avatar: Thank you for purchasing this pen.
As a result, a user can feel actually as if he weretalking to a real man. Thus, by constructing a naturalconversation system with an avatar, it is possible formany users to use it as an easy interface instead of themouse and the keyboard.

5

Dialog system withsocket communication

In the existing system, SCRAMNet+ was used toconnect the PCs, which is costly. For this reason, wedeveloped a system that enables the communication
between a user and a virtual agent through the socket
communication, instead of the SCRAMNet+. This
makes it possible that the regular PCs are used to achieve
the same effects.
We use UDP communication protocol for thesynchronization among the three PCs. In order toimprove reliability, memory of each PC is always
updated to the fresh data whenever the system writes
data into memory.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

5.1

6Multicast

UDP can simultaneously transmit data to multiple
destinations with the multicast option.

Figure 6: Multicast
The way of the multicast works is that the data istransmitted to the PCs that participate in the multicast
group (Figure 6).
Compared to TCP that transmits data to each PC
sequentially, UDP had better performance in terms of thetime needed for the data transmission.

5.2

Reliability of data

UDP is a high-speed communication, but it is lackof reliability. The following approach has been taken to
improve the reliability.

5.2.1

Distinction of Information

In this dialog environment, we introduced a virtual
agent named avatar. So, we constructed the dialog
environment in the real time between the user and thecomputer with the voice and gesture recognitions.
Therefore, we developed the system that enables
everyone to operate the computer easily. And, we
completed a system that enables the communication
through the socket communication, instead of the
SCRAMNet which is costly. This result was able to be
executed by equal ability with SCRAMNet. This made it
possible that the regular PCs are used to achieve thesame effects. However, in a current system, the humanuser cannot freely manipulate the virtual object.
Therefore, we should construct the communication
channel between the virtual space and the real world sothat the virtual object could be manipulated. The
manipulation includes translocation, rotation, andexpansion and contraction of the object. This is an issue
in the future.

Acknowledgements
We greatly appreciate the aid of Ministry of Internal
Affairs and Communications (MIC) and theGrant-in-Aid for Scientific Research.

References

We made a two-way transmission port from each
PC. If we use one single port, information collides andthe data loss will occur at the time that data istransmitted from Avatar, Voice, and Vision unit (Figure
7).

[1]

[2]

[3]

[4]

Figure 7: Collision of data
To avoid the collision, we prepared three ports,
exclusively for Avatar, exclusively for Voice recognition
system, and exclusively for Vision system (Figure 8).
This way prevents the data collision from happening.

[5]

[6]

[7]

Figure 8: Each port

©ISAROB 2007

Conclusion

816

Ministry of Economy, Trade and Industry, "Society(e-Life strategy society) basic strategy reportconcerning strategy of making to market of
information appliances - e-Life initiative -", 2003.
N. Abe, and Tsuji, “A consulting system which
detects and undoes erroneous operations by
novices”, Proc. of SPIE, pp.352-358, 1986.
N. Abe, T. Amano, K. Tanaka, J.Y.Zheng, S. He,
and Taki, “A Training System for Detecting
Novice's Erroneous Operation in Repairing Virtual
Ma-chines“, ICAT, pp.224-229, 1997.
N. Abe, J.Y.Zheng, K. Tanaka, and Taki, “Atraining System using Virtual Machines for
Teaching Assembling/Disassembling Operations to
Novices“, Inter-national Conference on System,
Man and Cybernetics, pp.2096-2101, 1996.
K. Tanaka, T. Ozaki, N. Abe, H. Taki,“Verbal/Nonverbal communication between Man
and Avatar in Virtual Mechanical AssemblyTraining System”, Proc. VARI, pp.202-207, 2002.
T. Ozaki, “Verbal and Non-Verbal Comunication
Using Virtual Reality“, Kyusyu Institute ofTechnology, Master Thesis, 2001.
T. Yagi, “Silicon visual area”, Japanese Neural
Network Society, Vol.8, No.2, pp.65-69, 2001

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Design of the Tactile Sensor Element Using Microbending Optical Fiber Sensors
Jin-Seok Heo
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Daejon, Korea 305-701
dandyheo3070@kaist.ac.kr

Cheol-Hee Han
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Daejon, Korea 305-701
rapha012@kaist.ac.kr

Jung-Ju Lee
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Daejon, Korea 305-701
leejungju@mail.kaist.ac.kr

Abstract
This paper describes the taxel for the flexible tactile
sensor using the microbending optical fiber sensor. Thissensor utilizes the microbending of the optical fiber
which drives the light loss from the optical fiber. And thestructure of this taxel with the optical fiber embedded in
the silicone rubber is very simple: once the external
force is loaded on the contact mesa of this taxel, theoptical fiber experiences the microbending which makes
the light loss, and then, the intensity of the transmitted
light is decreased. Therefore, detecting this intensitychange of the transmitted light, the external uniaxial
force can be calculated. Compared with the other opticalfiber sensor system, this sensor system can be easily
evaluated using simple optical measuring system.
Moreover, as the sense of touch of this taxel is similar tothat of the human skin, it can be applied the intelligent
robot system to the artificial skin.

1. Introduction
Sensory information of human skin for feeling somematerials and determining many of their physicalproperties is provided by sensors in the skin. This tactile
information is a kind of sense of touch that is one of thefive senses including the sense of sight, hearing, smell
and taste. Nowadays, many researchers try to apply thefive senses to the intelligent robot system. Especially,
many kinds of the tactile sensor combined small force
sensors are introduced for intelligent robots,
teleoperational manipulators and haptic interfaces. Thesetactile sensors which are able to detect the contact force,the vibration, the texture and the temperature can berecognized as the next generation information collection
system.
Some tactile sensors using the MEMS(Micro ElectricMechanical System) technology have been introduced[14]. Although these sensors have a good spatial resolution,
they remain some problems to apply the practicalsystem: they are not enough flexible to attach the curved
surface and the more elements of sensor connect, themore wires they need. Therefore, in this paper, we willshow the flexible uniaxial force sensor can be used theflexible optical fiber tactile sensor that can be easily
applied to the curved surface minimizing the wiring.

©ISAROB 2007

817

2. The Structure of the Taxel
2.1 The Basic Principle of the Taxel
In general case, there is little light loss when the
optical fiber is bended with a large radius of curvature.
However, as shown in Fig.1, the conspicuous light loss ishappened at the microbending of the optical fiber withthe small radius of curvature. Although thismicrobending light loss is not effective to the opticalcommunication, this can be utilized as the optical fiber
sensor.
To be concrete, using this microbending loss, theenvironment change can be detected. This kind of sensor
is called the microbending optical fiber sensor which is asort of the intensity based optical fiber sensor. As thisintensity based optical fiber sensor uses the lightintensity change at the light detector, the opticalmeasurement system of this type of sensor is composedof a simple light source and a light detector which cost
too little.

Fig.1 Microbending effect of the optical fiber.

2.2 The Structure of Taxel and the Tactile Sensor
In general case of small force sensor, the periodic
zigzag transducer which can be easily microbended by
the applied force is mostly used. But, this zigzag typetransducer can be occupied large area considering thesize of the optical fiber. Therefore, the minimizing theThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

size of the transducer must be needed. In this study, we
propose the cross fiber structure which is embedded in
the silicone rubber such as Fig. 2. Once the contact force
is applied to the contact mesa, the upper optical fiber andthe lower optical fiber are microbended simultaneously
by the change of the inner stress of the silicone rubber.
Using this phenomena, we can extend the tactile sensor
with the fabric structure of the optical fibers embeddedin the silicone rubber like Fig. 3.

Microbending at upper fiberLight loss
SiliconerubberUpper optical fiber

3.

Fabrication of the TaxelFirst of all, the dimension of this flexible force sensor
must be decided. As this sensor will be applied to thetactile sensor system, the thinner we fabricate this sensor,
the better we can apply it for the artificial skin. In thisstudy, we decide the depth of this sensor to be 2mm bytrial and error. 2mm depth can fix the optical fiber
without the exposure of the optical fiber from thesilicone rubber. The fabricate process is very simple asshown in Fig. 4. At first, a molding frame which canmake the shape of the taxel must be prepared. And theoptical fiber is aligned under the contact mesa position.
Next, liquid silicone rubber is poured to the molding
frame to the depth of the taxel. After cured the silicone
rubber, the flexible taxel can be made removing themolding frame.

Lower optical fiber

Microbending at lower fiber
Load
2 mm
SiliconerubberUpper optical fiber

1mm
1mm

2mm

Lower optical fiber

Supporter

Light loss

Fig. 2 Structure of the taxel using the microbending
effect of the optical fibers.

(a) Molding frame(b) Aligning optical fiber(c) Curing PDMSLight Input(d) Detaching the frame

1
2
3
4
5
6
7
8

1 2 3 4 5 6 7 8

Fig. 3 Dimension of the taxel and its fabrication.

Light output

Fig. 3 Structure of the tactile sensor using the
microbending effect of the optical fibers.

If the some area is contacted by some material, thecontacted mesas make the microbending of the opticalfibers in the silicone rubber which decreases the outputof the light intensity of optical fiber. The numbers of the
optical fiber contains the position of the contact area, andthe light intensity informs the change of the contact force.
Therefore, using this structure, the distributed contact
force can be measured.

©ISAROB 2007

818

Fig. 4 Fabricated prototype taxel.

The fabricated prototype taxel is shown in Fig. 4.
The prototype taxel is so flexible for the silicone rubber
transducer that it can apply for the artificial skin for theintelligent robot system.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

The fabricated prototype taxel is calibrated by theverified experimental equipments composed of auniaxial loadcell and a fatigue test system for small load
as show in Fig. 5.

The output signal of this prototype taxel is shown in
Fig. 7. The light intensity of this taxel is decreased for its
microbending loss as the contact load is increased. The
sensitivity of this sensor is -20 gray scale value / N andthe resolution of this prototype sensor is 0.05N. The
calibration process is very simple. By multiplying thesensitivity to the light intensity change, we can calculate
the applied contact force as shown in Fig. 8.

10

500

8

400

6

300

4

200

Load (N)Fig. 5 Experimental setup for the evaluation of the taxel.

as shown in Fig. 6. A power LED(Light emitted diode) isused as the light source of this sensor, and a CCD(charge
coupled device) plays a role of the light detector of thissensor system. Using the CCD as the light intensity
detector, the intensity changes of each optical fiber canbe measured when we apply this taxel for the tactile
sensor. This means that a CCD makes it possible tominimize the optical measurement system. And we use
the gray scale value from the output signal of CCD toevaluate the light intensity of the optical fiber.

Light intensity change
Loadcell load signal
Taxel load signal

2

0
0.00

0.02

0.04

0.06

100

Intensity (gray scale value)

4. Evaluation of the taxel

0
0.08

0.10

Time (sec)Fig. 8 Calibration of this prototype taxel.
Fig. 6 Optical system of this tactile sensor system.

550

500

480

CCD gray scale

Light intensity (gray scal value)

500

Experimental results
Lineat fitting

450

400

-20.13388

460

440

350

420
300

400
250
0

2

4

6

8

0

10

1

2

3

4

5

6

Load (N)Load (N)Fig. 7 Experimental verification of the prototype taxel.

Fig. 9 Hysteresis of the prototype taxel.

As mentioned above, the optical system is very simple

And the hysteresis error of this sensor is about 6.3%

©ISAROB 2007

819

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

such as Fig. 9. This hysteresis error results in thecharacteristic of the silicone rubber which is the materialof its transducer. And the error of repeatability is about
2%.
Next, we verified the maximum capacity of thisprototype sensor. As shown in Fig. 10, the linearity
between the light intensity change and the applied load isbroken after 17N is applied. This non-linearity can be
estimated as the abrupt stress change of the silicone
rubber by the insertion of the contact mesa in Fig. 11.
Therefore, the maximum capacity of this sensor is 17N
which the linear response of the light intensity
guarantees.

hysteresis error exists for the material of its transducer,
silicone rubber. This prototype sensor is sufficient for its
application of the artificial skin which includes thetactile sensor.

Acknowledgement
This research (paper) was performed for theIntelligent Robotics Development Program, one of the
21st Century Frontier R&D Programs funded by the
Ministry of Commerce, Industry and Energy of Korea.

Reference
700

Intensity change (gray scale value)

600

500

400

300

200

100

17N

0
0

5

10

15

20

25

30

Applied load (N)Fig. 10 Non-linear change of the light intensity by theapplied contact load.

Fig. 11 Insertion of the contact mesa after 15N

5. Conclusion
In this paper, the force sensor using microbending
light loss for the tactile sensor is newly designed andexperimentally verified. The structure of this sensor withthe crossed optical fiber embedded in the silicone rubber
and the optical system which is composed of a simple
power LED and CCD chip are very simple. The linear
light intensity change by the applied load is verified by
the experimental results. And this prototype sensor has agood performance: the resolution of this sensor is 0.05N
for its maximum capacitance 17N. However, a little©ISAROB 2007

820

[1] T. Mei, W. J. Li, Y. Ge, Y. Chen, L. Ni and M. H.
Chan, “An integrated MEMS three dimensional
tactile sensor with large force range”, Sensors andActuators (A), Vol. 80, pp155-162, 2000
[2] M. J. Yoon, K. H. Yu, G. Y. Jeong, S. C. Lee and T.
G. Kwon, “Development of a Distributed Flexible
Tactile Sensor System”, J. of KSPE, Vol. 19, No. 1,
pp. 212~218, 2002
[3] N. Futai, N, Futai, K. Matsumoto and I.
Shimoyama, “A flexible micromachined planar
spiral inductor for use as an artificial tactile
mechanoreceptor”, Sensors and Actuators (A), vol.
111, pp293~303, 2004
[4] U. Paschen, M. Leineweber, J. Amelung, M.
Schmidt and G. Zimmer, “A novel tactile sensor
system for heavy-load applications based on an
integrated capacitive pressure sensor”, Sensors andActuators (A), vol. 68, pp294~298, 1998

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

A Localization of Mobile Robot based on Ultra-sonic Sensor
using Dynamic Obstacles
Byoung-Suk Choi

Ju-Jang Lee

Dep. of EE and CS
Korea Advanced Institute of Science and Technology,
Daejeon, Republic of Korea 305-701
bs_choi@kaist.ac.kr

Dep. of EE and CS
Korea Advanced Institute of Science and Technology,
Daejeon, Republic of Korea 305-701
jjlee@ee.kaist.ac.kr

Abstract

door recognition. This method requires feature extractionof static obstacle or landmark. However, unfortunately,
most real environments are dynamics with state changes
occurring at a range of different speeds. Thus, for mostreal-world application, it is desirable that mobile robots
are capable of exploring or moving within a dynamics
environments[4].
This paper considers the situation where a mobile
robot is moving an unstructured environment, there is
only moving object. In this paper, we present a method
for solving previous problem using ultrasonic sensor,
which is able to measure the distance between themoving object and the mobile robot. The movements ofobject can be detected by sonar sensor, and then theposition of the moving object is estimated. Using the
distance data, the robot’s position can be estimated.
This paper is organized as follows. Section 2 shows akinematics modeling and position estimation of themobile robot. In Section 3, the method that detects themoving object using ultrasonic sensor is described, theposition correction technique with Kalman-filter isshown. In Section 4, the experiments environment andcomputer simulation results are shown to prove thevalidity of the proposed method. Finally, in Section 5,
conclusion and further research topic are presented.

Localization is most important and necessary
technology for mobile robot to work well. The robots
need to recognize their position and pose in known
environment as well as unknown environment. In the
future, the robots will be human-friendly robots that areable to coexist with humans in dynamic space. The
localization includes several restrictions which arise
from dynamic obstacles-people, moving chair, and so on.
It is desirable for a mobile robot to estimate his position
using dynamic obstacles. In this paper, we propose themethod for the localization of the mobile robot using amoving object. Throughout the computer simulation
experiments, its performance is verified.

Keywords Mobile robot; Moving object; localization;
position estimation;

1 Introduction
Localization of the mobile robot is one of the mostimportant issues for successful autonomous navigation;
therefore, a great number of localization methods havebeen proposed and developed so far[1][2].
The difficult problem that has a substantial impact on
the localization of the mobile robot is the environment.
The environments can be static or dynamics[3]. Static
environments are environments where the only variable
quantity state is the robot’s pose. Put differently, only the
robot moves in static environment. All other objects inthe environments remain at the same location forever.
Static environments have some nice mathematicalproperties that make them amenable to efficient
probabilistic estimation. Dynamics environments possess
objects other than the robot whose location orconfiguration changes over time. Specially, the changes
persist over time. Examples of more persistent changes
are people, and moving object such as furniture, chairs,
and so forth. A good example of dynamic environments
is shown in figure 1. Because of various uncertainties
and limitation of sensor information for dynamicchanges, localization in the dynamics environments is
obviously more difficult than localization in the static
ones. Therefore, there are two general methods for
estimation of location under each environment. Under
static environment, the localization of the mobile robot isbased on landmark method using the wall, corner and©ISAROB 2007

821

Fig. 1. The mobile robot in dynamic environments.

2 Robot Modeling
2.1 Kinematics modeling of a mobile robot
The modeling of a mobile robot is shown in fig. 2,
where
x R : the y-component of the mobile robot position;y R : the x-component of the mobile robot position;
θ R : the orientation of the mobile robot;
v L : the velocity of left wheel;v R : the velocity of right wheel;
v1 : the linear velocity of the mobile robot;

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007transition of the mobile robot can be described in termsof currents state and inputs as follows[ ]:v + vR(5-a)
cos θ k ,x k +1 = x k + T R

v 2 : the angular velocity of the mobile robot;l : the width of the mobile robot.

R

vL

v1

θR
yR

l
2

( xR , y R , θ R )

X

xR

R

(5-b)
(5-c)

where T is the sampling period.
Note when the position of the mobile robot is estimated,state estimation error is included, and represented asfollow:
k +1
k(6)
Pˆrobot= f ( Probot, u (k )) + ν (k ) ,

vR

v2

l

R

2
vR + vRk +1
ksin θ Rk ,yR = yR + T
2v − vR .
θ Rk +1 = θ Rk + T RlYFig. 2. Kinematics model of a mobile robot.
A mobile robot with differential driving mechanism
has two wheels on the same axis, and each wheel iscontrolled by an independent motor. On the twodimensional X − Y cartesian coordinates, position andorientation of the mobile robot is described by statevector as follow:(1)
Probot = [x R y R θ R ] T
The linear and angular velocities of the mobile robot canbe described as follows:v + vL ,(2)
v1 = R2
2( v R − v L ) .
(3)
v2 =
l
Now the kinematics model of the mobile robot can be
represented as [5]⎡cos θ R 0⎤
⎡v ⎤
(4)
&
Probot = ⎢⎢ sin θ R 0⎥⎥ ⎢ 1 ⎥v⎢⎣ 0 1⎥⎦ ⎣ 2 ⎦Kinematics analysis aims at the proper velocity
assignment to each wheel to drive the mobile robot to adesired position and orientation.

where u (k ) is the current input, ν (k ) denotes
estimation error as a noise term. The estimation error is
unexpected components, when position is calculated[6].
The error can be corrected by applying Kalman filtering
technique in Section 3.

3 The Localization by Ultrasonic Sensor
3.1 Detecting moving object using sonar sensor
In this section, we describe the procedure ofdetecting a moving object through a distance obtained byultrasonic sensor. When the object is moving, it can bedetected by sonar sensor, and then distance information
between the object and the robot is available to robot.
The position of the mobile robot is updated. Fig. 4. show
the coordinates of the moving object and the mobile
robot with 12 ultrasonic sensors.
Y

1
Pobj

2
Pobj

0
Pobj

Object
d1

d0

d2

d3

Mobile robot
Ultrasonic sensor

2.2 Position propagation
In previous session, we studied that the states of themobile robot with differential driving mechanism arechanging according to the two wheel velocities.

0
Probot1
ProbotX

vL

Fig. 4. The outline of motion of object and robot.

vL

vR

vL

vR

B
k
Probot= [x Ry R θ R ]k

obj

C

A
T

k +1
Probot= [ xRvRyR θ R ]k +1

T

k +n
Probot= [xRyR θ R ]k + n

TFig. 3. Position propagation of the mobile robot.
In Fig. 3, when the mobile robot is moving from A whereTk
the robot is located on Probot= [xR yR θ R ]k at time=

to C where the position is onkT= [xR y R θ R ]k + n at time = k + n . The state

k +n
robot

P

©ISAROB 2007

As shown fig. 4. the initial position of the object and0mobile robot is precisely given as P 0 , Probot,822

respectively. Also, the distance d between the object
and the mobile robot is denoted.
There are three steps in procedure of detecting themoving object.
Initially, the mobile robot and the object is kept
stationary. When the object is moving, distanceinformation at time k and k+1, respectively, generate thedistance differences. When distance differences exceed
the specified threshold value, thus, resulting in thedetection of motion of the object.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Secondary, in case that a distance information is
obtained by neighboring sensor due to movement of theobject, the displacement and angular data with respect tomeasuring time, TM of the object can be calculated asshown in fig. 5. And then, the current state of the moving
object is estimated thought initial state and obtained
information.
Finally, the mobile robot moves along a free path
after estimation of the moving object is finished. The
inverse method is used to estimate the position of themobile robot. When the object is kept stationary and themobile robot moves, the displacement and velocity withrespect to the measuring time, TM of the mobile robot
can be obtained. The state vector of the mobile robot isestimated.
To obtained the sequential position estimation, we canuse proposed step recursively. The outputs of step arechosen as the estimated state vector of the moving object
and mobile robot.
vobj
Ultrasonic sensor

d1

Δs _ obj

Mobile robot
d0
Objectϕwhere Φ k is the state transition matrix of xˆ (K−+) 1 , w k is
the model noise of the system, where Q k is thecovariance matrix of w k .
The measurement step is represented as(12)z k = H k xˆ k + v k
where z k is the measurement vector, H k represents
the relationship between the measurement and the statevector, and v k is the measurement error.
In the final correction step, the state vector and theestimate error are corrected to a new value based on themeasurement value of the measurement step. The
formula is represented as
K k = P(k− ) ( H k ) T [ H k P(k− ) ( H k )T + R k ]−1 (13)xˆ (k+ ) = xˆ (k− ) + K k [ z k − H k xˆ (k− ) ](14)

P(k− ) = [ I − K k H k ]P(k− )(15)where R k is the covariance matrix of the measurement
noise, and K k represents the Kalman gain. The optimalfilter gain K k minimizes the estimate errors by thecovariance matrix of the estimate error P(k− ) , theαFig. 5. The motion of object.
3.2 Correction using Kalman filter
The estimated position of the moving object includes
some unexpected estimation error. This leads to failure
in localization of the mobile robot. An Kalman filter[7]can be one of the good method to tackle this problem. To
apply the state estimation of a moving object to the
Kalman filter, eq.(7) and (8) of the state transition matrix
are required. The Kalman filter minimizes the estimation
error by modifying the state transition model based on
the error between the estimated vectors and the measured
vectors, with an appropriate filter gain. The state vector
which consists of a position on the x-y plane, thedirection, linear and angular velocity can be estimatedusing the measured vectors representing the position of
the moving object[8].
(7)x k = Φ k x k −1 + w k −1
(8)z k = H k x k + v k −1
The Kalman filter is a recursive algorithm to determinex̂ k , the optimal estimation value of state vector, x k in a
linear dynamic system. Kalman filtering is divided into
the three steps of prediction, measurement, andcorrection.
In the prediction step, the next state vector x(K−+) 1 andthe covariance matrix of the estimated error P(k−+) 1 are
predicted. The symbol (-) means that the values don’t
correct through measurement. The covariance matrix of©ISAROB 2007

the estimated error is just like eq.(9).
(9)
P(k−+) 1 = E[( x k − xˆ k )( x k − xˆ k ) T ]The projected estimates of the covariance matrix of theestimated error and the state vector in the prediction step
are represented as(10)xˆ k +1 = Φ k xˆ (k+ ) + w k(11)P(k−+) 1 = P(k+ ) + Q k

823

measurement matrix
H k , and the covariance matrix of measurement noise
R k in eq.(20). Next time, the estimate of the state vectorkxˆ (k+ ) from the measurement z is expressed as eq.(21).
The Kalman gain functions as the weighting between themeasurement and the estimate value when the statevector x k is corrected. In the end, as in eq.(22), thecovariance matrix of the estimated error is corrected.

4 Simulation Experiments
4.1 Experimental environment
The proposed approach is implemented on computer
simulation program. The experimental parameters 1 forcomputer simulation are listed in Table.
Let assume some conditions for finding the positionof moving object and localization of the mobile robot.
1) The maximum velocity of the mobile robot isfaster than that of moving object.
2) There is no interference between other ultrasonicsensors3) The initial position of the robot and object is given.
4) The path of the robot and object is piecewise
continuously differential.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

4.2

Table 1. Simulation parameter
Parameter list

Value

5 Conclusions

Size(diameter) of Robot

0.5 m

Size(diameter) of object

0.15 mNumber of ultrasonic sensor
Maximum detecting range ofultrasonic sensor
Directivity of ultrasonic sensor

12

In this paper, an state estimation method for a mobile
robot with ultrasonic sensor was proposed using themoving object. The estimated errors is reduced using
Kalman filter. It was demonstrated that localization of
the mobile robot on computer simulation.
The localization is one of the fundamental functions
for intelligent mobile robot. The mobile robot has tohandle various dynamic uncertainties for localization
robustly. In further research, proposed method will beverified throughout the real experiments with a robot
system. And the effective localization of the mobile
robot will be needed to improve the estimation accuracy.

3 (m)10 (deg)

Experimental results and discussions

The initial position of the moving object and the mobile
robot was set as (0.5, 3, 0o) and (1, 2, 10 o), respectively.
Fig. 6. shows the path of the moving object and themobile robot.

Fig. 6. The path of the moving object and robot.
The estimated state vector of the mobile robot by
only dead-reckoning method with uncertainty is shownin fig. 7. And in fig. 8, reduced error with proposed
algorithm is shown.

Fig. 7. Position estimation error of the mobile robot
using only dead-reckoning method.

Fig. 8. Position estimation error of the mobile
robot using moving object©ISAROB 2007

824

References[1] Anastasios I. Mourikis, and Stergios I. Roumeliotis,“Performance Analysis of Multirobot Cooperative
Localization,” IEEE Tras. Robotics, vol. 22, no. 4,
pp. 666-681, Aug. 2006.
[2] Dongheui Lee, and Woojin Chung, “Discrete-StatusBased Localization for Indoor Service Robots,”
IEEE trans. Industrial Electronics, vol. 53, no. 5, pp.
1737-1746, Oct. 2006.
[3] Probabilistic Robotics, Sebastian Thrun, Wolfram
Burgard, and Dieter Fox, the MIT Press, 2005.
pp.191-199.
[4] Zhihua Qu, Jing Wang, and Clinton E. Plaisted, “A
New Analytical Solution to Mobile Robot Trajectory
Generation in the Presence of Moving Obstacles,”IEEE Trans. Robotics, vol. 20, no. 6, pp. 978-993,
Dec. 2004.
[5] Hiromas Takahashi, Hiroaki Nishi, and Kouhei
Ohnishi, “Autonomous Decentralized Control for
Formation of Multiple Mobile Robots Considering
Ability of Robot,” IEEE Trans. Industrial
Electronics, vol. 51, no. 6, pp. 1272-1279, Dec.
2004.
[6] Cheol-Ho Hwang, Sang-Hun Lee, Jae-Pyung Ko, andJang-Myung Lee, “Optimal path planning for thecapturing of a moving object,” in Proc. Int. Conf. ofControl, Autonomous and Systems (ICCAS),
Gyeongju, Korea, Oct. 22-25, 2003.
[7] G. Dudek and M. Jenkin, Computational Principles ofMobile Robotics, Cambridge university press, 2000,
pp. 75-79.
[8] Jang Myung Lee, K. Son, M. C. Lee, J.W. Choi, S. H.
Han, Man Hyung Lee, “Localization of a Mobile
Robot using the Image of a Moving Object,” IEEE
Tras. Industrial Electronics, vol. 50, no. 3, pp. 612619, Jun. 2003.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

On the periodic sequence of a discrete sliding mode control system
for a single-link robot arm
Sung-Han Son* and Kang-Bak Park*** Department of Guidance and Control, Agency for Defense Development, Korea(Tel : +82-42-484-9049; E-mail: shson@add.re.kr)** Department of Control and Instrumentation Engineering, Korea University, Korea(Tel : +82-41-866-8140; E-mail: kbpark@korea.ac.kr)
Abstract: A novel sufficient condition for a sampled-data system with constant-gain discrete
sliding mode controller (SMC) to be globally uniformly ultimately bounded (GUUB) is proposed. It
is shown that the stability of the overall system can be known by checking out the one parameter.
Simulation results for a single-link robot arm are presented to verify the feasibility of the proposed
method.
Keywords: Sampled-Data System, Discrete Sliding Mode Control, GUUB.

1. INTRODUCTION

system input, and a1 , a2 are elements of a system

Almost all of studies of sliding mode control (SMC)
have been proposed in the continuous-time domain[1-2].
In the actual system, however, controller is implemented
in the discrete time domain since they use microprocessors and/or digital computers. Recently, discretetime sliding mode control (DSMC) has been studied
extensively to address various controllers using specificprinciples [3-6]. However, the research of discretizing acontinuous-time SMC for digital implementation has
not been fully explored. Furthermore, it is also wellknown that a control system designed in the continuoustime domain may become unstable after sampling.
Recently chaotic behaviors were found in discretizing
continuous SMC systems by X. Yu [7-8]. Yu and G.
Chen proposed the sufficient conditions for discretizedsystem to be GUUB [9]. But these sufficient conditions
can be only applied to limited sampling period andspecialized cases.
In this paper, therefore, a novel sufficient condition
for discrete sliding mode controller (SMC) to be
globally uniformly ultimately bounded (GUUB) is
proposed. It is shown that the stability of the overallsystem can be known by checking out the magnitude ofone parameter which is an element of the discretizedsystem matrix. The ultimate bounds of the system statevariables are also derived. Simulation results for asingle-link robot arm are presented to show theeffectiveness of the proposed method.

2. SYSTEM DESCRIPTION
Consider a second-order system of the following
form
1 ⎤
⎡ 0
⎡0⎤
x = Ax + bu = ⎢x + ⎢ ⎥u ,
(1)
⎥
⎣1 ⎦
⎣ −a1 − a2 ⎦where

x ∈ R 2 is the state vector,©ISAROB 2007u ∈ R1 is the825

matrix. Let the sliding surface be σ = cT x = [ c1 1] x ,where c1 > 0 is assumed to be designed such that thesliding dynamics, σ = 0 , are asymptotically stable.
From σ = 0 , we can easily obtain the equivalent
control law asσ = cT x = cT Ax + cT bu = 0 ⇒ ueq = −(cT b) −1 cT Ax.
From the sliding mode existence condition, σσ = 0 ,we have the following equivalent control based SMC:u = ueq + us(2)
= −(cT b)−1 cT Ax − α (cT b)−1 sgn(σ ( x)),where α > 0 is a control gain, and sgn(⋅) is a signum
function. It’s assumed that cT b is nonsingular.
To discretize the overall system, we convert thecontinuous-time system (1) under the zero-order hold
(ZOH) to the discrete-time systemx(k + 1) = e Ah x(k ) + ∫ e Aτ dτ bu (k ),
h

0

(3)

where
u (k ) = ueq (k ) + us (k )(4)= −cT Ax(k ) − α ⋅ sgn(σ ( x(k ))),h is a sampling period, and the index k indicates thek - th sample.
As the system state x(k ) evolves, the switching
function sgn(σ ( x(k ))) forms a sequence of binary
values of −1 and +1 . For simplicity, we denote
sgn(σ ( x(k ))) as sk ∈ {−1, 1} .
Then the discretized system can be described byx(k + 1) = Φx(k ) + αΓsk

⎡ γ ( h) ⎤⎡1 v ( h) ⎤x(k ) + α ⎢ 1 ⎥ sk ,=⎢⎥⎣ 0 d ( h) ⎦⎣γ 2 (h) ⎦(5)The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

where Φ = e Ah − ∫ e Aτ dτ bcT A , Γ = ∫ e Aτ dτ b .
h

h

0

0

Equation (5) can be rearranged as
x1 (k + 1) = x1 (k ) + vx2 (k ) − γ 1α sk ,x1 (k + 1) =(6)

x2 (k + 1) = dx2 (k ) − γ 2α sk .

(7)

3. STABILITY CONDITION OF DISCRETESLIDING MODE CONTROLLER
Generally, the asymptotic stability can be guaranteed
if the sliding mode controller with a constant gain is
implemented in the continuous-time domain. For thediscrete-timesystem,however,theultimateboundedness can be ensured. In the following theorem,
we derive conditions for the stability of the closed-loopsystem with discrete SMC (4).
Theorem 1: For the discretized systems (6)~(7) withthe discrete SMC (4), the overall system is globally
uniformly ultimately bounded (GUUB) if| d (h) | < 1 .
(8)andv(h)γ 2 (h)+ γ 1 (h) > 0.
(9)
1 − d ( h)Furthermore, ultimately bound of the system statevariables are given bylim | x1 (k ) | ≤ | γ 1α | +(c1−1 − v) | γ 2α |k →∞lim | x2 (k ) | ≤k →∞| γ 2α |.
1− | d |1− | d |

,

(10)

(11)

Proof: From (7), It is clear that (8) has to besatisfied because the pole of the system (7) should belocated inside the unit circle. It is obvious that theultimate bound of x2 can be obtained as (11). Whenthe state x2 reaches its ultimate bound, we say x2
is on the equilibrium line, (6) can be rewritten asvγ α sx1 (k + 1) = x1 (k ) − 2 k − γ 1α sk .
(12)
1− d
In order to the state x1 converges to the sliding
surface, the last two terms of (12) should satisfy thefollowing inequality:vγ α svγ− 2 k − γ 1α sk ⇒ 2 + γ 1 > 0.
(13)
1− d
1− d
The ultimate bound of the state x1 can be derived byconsidering the switching points – intersection of thesliding surface and the equilibrium lines. Since thepoints are on the sliding surface and equilibrium line,
x2 should have a value of its ultimate bound, and x1
has to satisfy
x1 (k ) = −c1−1 x2 (k ).
(14)
Substituting (14) into (6) gives©ISAROB 2007826

(c1−1 − v) | γ 2α | sk
− γ 1α sk .
1− | d |(15)

From (15), therefore, the ultimate bound of x1 canbe obtained as (10).
Theorem 1 needs two conditions, (8) and (9), tocheck the stability of the discretized system. Actually,
the condition (9) contains four variables, v, d , γ 1 , γ 2 ,whereas the condition (8) is composed of only one
variable d . In the following theorem, we show that
it is sufficient to check the condition (8) to guarantee thestability of the overall system.
Theorem 2: For the closed-loop system (5), if theinequality (8) holds, then the inequality (9) is alsovγ 2satisfied. That is, | d | < 1 implies+ γ 1 > 0.
1− dProof: Due to the page limitation, the proof isomitted.

Remark 1: From Theorem 1 and 2, the stability ofthe overall system can be obtained by checking up themagnitude of d .
Remark 2: In the conventional digital system, thestability of the closed-loop system depends on thesampling period, and is given by one inequality. For theconstant-gain sliding mode controller for the sampleddata system, however, the stable region of the sampling
period may be composed of several supports. For
example, for a single-link robot arm, the stable region
for the sampling period h is given as a shaded region
as in Figure 1.

Fig. 1 Stable sampling period. ( | d (h) | < 1 ).

4. SIMULATION STUDIES
Consider the model of a pendulum without damping.
A second-order system of the pendulum can be
represented as

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

g
1
u.
(55)
l
m
where g is the acceleration of gravity, l = 1 m andm = 1 kg are the length and mass of pendulum. Assume
that θ is small enough that sin θ ≈ θ .
Figures 2~5 are results when h = 0.1 second. From
Figure 1, it’s clear that the system is stable for the
sampling period. Figure 2 shows the phase portrait. It’svery similar to that of continuous-time system. In
addition, it is shown that, in the steady state, the system
states are bounded by the region given in Theorem 1:
x1 (∞) ≤ 0.61, x2 (∞) ≤ 0.67 .
It is also can be seen in Figures 3~4.
Figures 6~9 are results when h = 1.5 second. From
Figure 1, it’s obvious that the system is stable for the
sampling period. For this sampling period, in the steadystate, the profile of the state variables show a kind oflimit cycle; periodic solution (Fig. 6), and their bounds
given in Theorem 1 are as follows:
x1 (∞) ≤ 0.86, x2 (∞) ≤ 0.46
Figures 7~8 show that the state variables show theperiod-12 profiles.
Figures 10~12 are results when h = 0.9 second.
From Figure 1, it’s easy to know that the system isunstable for the sampling period although the sampling
period is shorter than the another stable one, h = 1.5 .
The unstable profile can be seen in Figures 10~11.

θ = − sin θ +IEEE Trans. Ind. Electron., vol 42, no.2, pp. 117122[7] X. Yu and S. Yu (2000), Discrete sliding mode
control design with invariant sliding sectors, ASME
Journal of Dynamic Systems, Measurement andControl, vol. 122, no. 4, pp. 776-782.
[8] X. Yu (2001), Discretizing Sliding Mode Control:
From Chattering to Unstable Motion, Proc. 40 thIEEE Conf. on Decision and Control, USA, pp.
914-918[9] X. Yu and G. Chen(2003), Discretization Behaviors
of Equivalent Control Based Sliding-Mode Control
Systems, IEEE Trans. on Automat. Contr., vol. 48,
no. 9, pp.1641-1646

Fig. 2 Phase portrait when h = 0.1

5. CONCLUSION
In this paper, a condition to be GUUB for the sampleddata system with discrete SMC has been presented. The
proposed scheme gives a simple way to check up thestability of the closed-loop system for a given sampling
period.
REFERENCES[1] V. I. Utkin (1977), Variable Structure Systems with
Sliding Modes, IEEE Trans. on Automat. Contr.,
vol. AC-22, no. 2, pp.212-222.
[2] K. Furuta and Y. Pan (2000), Variable structure
control with sliding sector, Automatica, vol. 36,
pp.211-228[3] S. Z. Sarpturk, Y. Istefanopulos, and O. Kaynak(1987), On the stability of discrete-time skidding
mode control system,” IEEE Trans. on Automat.
Contr., vol. 32, no. 10, pp. 930-932.
[4] K. Furuta (1990), Sliding mode control of a discrete
system, System & Control Letters, vol 14, pp. 145152.
[5] P. Myszkorowski (1990), Robust controller for the
linear discrete-time system, Proc. 30th IEEE Conf.
on Decision and Control, England, pp. 2972-2973[6] W. Gao, Y. Wang, and A. Homaifa (1995),
Discrete-time variable structure control systems,©ISAROB 2007

827

Fig. 3 State variable x1 when h = 0.1

Fig. 4 State variable x2 when h = 0.1

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Fig. 5 Switching variable s when h = 0.1

Fig. 9 Switching variable s when h = 1.5

Fig. 6 Phase portrait when h = 1.5

Fig. 10 State variable x1 when h = 0.9

Fig. 7 State variable x1 when h = 1.5

Fig. 11 State variable x2 when h = 0.9

Fig. 8 State variable x2 when h = 1.5

Fig. 12 Switching variable s when h = 0.9©ISAROB 2007

828

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Stable Adaptive Neural Control for a Nonlinear Robot Systemin the Presence of Actuator Failures and Uncertainties
Jin-Ho Shin∗1,

Kap-Ho Seo+,

Min-Soeng Kim+, Ju-Jang Lee+,
Moon-Noh Lee∗3

Won-Ho Kim∗2,∗1,2

Department of Mechatronics Engineering, Dong-eui University,∗1,2
995 Eomgwangno, Busanjin-gu, Busan 614-714, Korea∗1E-mail: jhshin7@deu.ac.kr+
Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science andTechnology, +373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea∗3
Department of Computer Engineering, Dong-eui University,∗3
995 Eomgwangno, Busanjin-gu, Busan 614-714, Korea
Abstract
The bounded nonlinear time-varying actuator torquecoefficients as well as uncertainties may deteriorate theperformance of a robot. This work presents a design
methodology of a stable adaptive neural controller toovercome the performance degradation for an uncertain
nonlinear robot system with actuator failures. The
proposed control scheme is based on the Lyapunovstability approach for adaptive control using a GFN(Gaussian function network) to approximate a nonlinear
dynamic terms. The proposed controller can improve
performance degradation and achieve task completion
despite actuator failures and uncertainties. Simulation
results are shown to verify the validity and robustnessof the proposed control scheme.
Keywords: Stable adaptive neural control, Robotsystem, GFN, Actuator failure, Uncertainty.

1

Introduction

Hardware or software failures at actuators as well as
uncertainties such as parameter variations anddisturbances cause performance degradation of robots.
Fault detection and fault tolerance for robots weredealt with and discussed in [1-4]. The adaptive, robust,and neural control issues for robot systems withuncertainties were discussed in [5-10].
Both actuator failures and uncertainties, which are
two factors of performance degradation in a robot
system, need to be considered at the same time in thecontrol system design. In most of the previous works,
control methods overcoming these two factors at thesame time were not yet actively discussed.
In this paper, a stable adaptive neural control
scheme for a nonlinear robot system in the presence ofactuator failures and uncertainties is developed withguaranteeing the stability, based on adaptive control
technique and a GFN. The validity of the proposed
scheme is verified through simulation.

The dynamic model of an n-link robot system withactuator failures and uncertainties can be described as
M (q )q + F (q, q ) = u + d (t ) = Ka uc + d (t )(1)nwhere q ∈ ℜ
is the position vector of joint
coordinates, M (q ) is the symmetric positive definite

inertial

matrix,C (q, q )q represents the centrifugal and Coriolis torques,Ffr (q ) = Fvq + Fd (q ) + Fs (q ) is the vector of friction

forces/torques including the viscous, coulomb and static
friction forces/torques, respectively, and G (q ) isgravitational torques, u is the actual joint torquevector, uc is the commanded torque vector orcontroller output vector, and d (t ) is an external
disturbance vector bounded as d (t ) ≤ θ d where θ dis an unknown positive constant. An unknown matrix
Ka ∈ ℜn×n representing the current joint failure’s
situation is a bounded nonlinear time-varying diagonal
matrix consisting of actuator torque coefficients, inother words, K a (t ) = diag ( K a1 (t ), K a 2 (t ), " , K an (t ) ) ,and Ka ≤ ka with an unknown positive constant ka .
The block diagram of the robot control system withactuator failures is shown in Fig. 1.

Fig. 1. The robot control system with actuator failures.
The boundary property for each dynamic term of theabove dynamic equation (1) is shown as follows.
Property 1: There exist positive constants mmin ,mmax ,

fg ,≤ mmax , and2f f , andf c such that mmin ≤ M (q )F (q, q ) ≤ f g + f f q + f c q

2

[11].

As an ideal case, Ka is an n × n identity matrix

Robot System©ISAROB 2007

F (q, q ) = C (q, q )q + Ffr (q ) + G (q ),829

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

and then u = uc , and it means no actuator failures.
On the contrary, in real robot systems, thecommanded torques uc may not be precisely delivered
to the actual torques u due to the hardware orsoftware actuator failures at some joints.
The actuator failures may deteriorate the robot
performance. If fault tolerance is not considered in thecontrol system, the more time passes the more degraded
the system performance is, ultimately the desired task
performance may not be achieved.
In this paper, it is considered that the actuator torquecoefficient matrix Ka is not accurately an identity
matrix and thus experiences the actuator failures, but it
is assumed that Ka does not have any zero diagonal
elements. This kind of failure is called as the partial
actuator failure.

3

φ = L(x )w + b + ea (t, x ) ,(7)
where x ∈ ℜ , (m = 5n ) is the input variable vector
such as q, q, qd , qd and qd , getting into the network,and L(x ) ∈ ℜn×nr is the matrix that has the rGaussian functions. w ∈ ℜnr and b ∈ ℜn are thedesired unknown constant weight and desired unknown
constant bias to very approximate the GFN (7) to thenonlinear function φ (6), together with L(x ) ∈ ℜn×nr .
ea (t, x ) ∈ ℜn is the approximation error.
The above GFN is illustrated in Fig. 2. L(x ) in Eq.
(7) is shown as follows.
m⎡N 1 N 2⎢⎢00⎢
L(x ) = ⎢
⎢
⎢
⎢0
0
⎢⎣

Stable Adaptive Neural Control

where η = (Ka Ka−1 − I n ) vc + d (t ) , φ (q, q, qd , qd ) =−F (q, q ) − M (q )qd + M (q )Λe , and I n is an n × n
identity matrix.
Assumption 1: There exists an unknown constantc ≥ 0 such that K Kˆ −1 − I ≤ c < 1 .
a

0

a

n

0

Let us consider the following Lyapunov function
candidate to develop a GFN approximating a nonlinear
function.
V1 = sT M (q )s 2 .
(4)
Differentiating Eq. (4) along the solution of the
dynamic equation (3),V1 = sT (vc + φ + η ) = sT (vc + η) ,(5)
where the lumped uncertainty η is η = φ + η , andφ(q, q, qd , qd , qd ) = φ (q, q, qd , qd ) + M (q, q )s / 2(= φ1 φ2 … φn) ∈ℜ .
T

The unknown nonlinear function
expressed by the following GFN.

©ISAROB 2007n

(6)

φ can be

830

0

0

0

0

N1 N2

Nr

0

0

0

0

0

0

0

0
0⎤
⎥
0 ⎥⎥
⎥
⎥
⎥
N r ⎥⎥⎦N1 N2

The joint position error and the augmented error are
denoted by e = q − qd and s = e + Λe , respectively,
where qd is a desired position vector and Λ ∈ ℜn×n
is a positive definite constant diagonal matrix.
The dynamic equation for the augmented error s
is obtained as follows.
M (q )s = −F (q, q ) − M (q )qd+M (q )Λe + Ka uc + d (t ) .
(2)
Now, uc is defined as uc = Ka−1vc , where Ka
represents a guessed nominal model for Ka .
Substituting the commanded torque for Eq. (2) andwriting it again, it is as follows,
M (q )s = vc + η + φ ,
(3)

Nr

,

(8)

Fig. 2. The structure of the used GFN.
Assumption 2: There exist an unknown positive
constant vector θe and a known positive function
ψe (t, x ) such that ea (t, x ) ≤ ρe (t, x ) = θeT ψe (t, x ) .

From the structure of the nonlinear function φ (6)and the GFN (7), it can be easily shown that the above
assumption is very reasonable. We can find that thenorm of φ (6) is bounded by a positive function from
Property 1. In Eq. (7), the norm of the GFN is also
bounded by a positive constant. Therefore, from
Property 1, Eqs. (6) and (7), a positive function
ψe (t, x ) can be obtained as follows.

(ψe (t, x ) = 1

q

q

2

qd

e

) ∈ℜ .

(q s )

T

6(9)

Theorem 1: Under Assumptions 1 and 2, if thefollowing control law and adaptation law are applied to
the robot system (1), then the joint position and velocity
errors are globally uniformly ultimately bounded.
Control law:⎛s ⎞⎟uc = Kˆa−1vc = Ka−1 ⎜⎜⎜−Kcs − Lwˆ − bˆ − ρˆ⎟ , (10)⎝s + ε ⎠⎟(

ρˆ = θˆT ψ , ψ = s

w

b

ψaT

)

T

∈ ℜ10 ,

(11)

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

( 1) ∈ ℜ ,
ψ = (1 qqqTψa = ψeT

(12)

7

2

e

d

( q s ))

Te

∈ ℜ6 ,(13)

where Ka is a guessed nominal actuator torquecoefficient matrix as mentioned above and it is usually
initially defined as an identity matrix. The gain
Kc ∈ ℜn×n is a positive definite constant diagonal
matrix, w is an estimation weight vector of w andb is an estimation bias vector of b , ε is a small
positive constant, and θ ∈ ℜ10 is an estimate for aparameter vector θ ∈ ℜ10 .
Adaptation law:
⎛ψ s2
⎞

− σθˆ⎟⎟⎟ ∈ ℜ10 , (14)wˆ = Γw LT s, bˆ = Γb s, θ = Γθ ⎜⎜⎜⎝ s +ε⎠where Γw ∈ ℜnr×nr , Γb ∈ ℜn×n and Γθ ∈ ℜ10×10 are
positive definite constant diagonal matrices, and σ > 0
is a small constant.
Proof: Let us define an overall Lyapunov function
candidate using (4).
V = V1 +Va = z T Pz / 2 ≤ λmax (P ) z 2 / 2 , (15)

(

where

z = sTθTwTbT) , V = (1 − θ )θ Γ θ /2
T

T

a

3

−1
θ

+wT Γ−w 1w /2 + b T Γb−1b /2 , 0 ≤ θ3 = c0 < 1 , θ = θ − θ,w = w − w, and b = b − b .
Taking the time derivative of V along the solutionof the system and substituting the robot dynamics forthe augmented error s (3), the upper-bound on thenorm of lumped uncertainty based on Assumption 1 andAssumption 2, and the control laws (10)~(13) and theadaptation laws (14), the following result is madethrough some manipulations.
V ≤ −sT Kcs − (1 − θ3 ) σθT θ + h (ρ, ρ, s )≤ −λmin (Q ) z 2 / 2 + h(ρ, ρ, s )where ρ = θT ψ, θ ∈ ℜ10, ψ = ( se⎛2Kc⎛ s ε ⎞⎟ ⎡⎜h (ρ, ρ, s ) = ⎜⎜⎜⎟⎟ ⎢⎣ρθ3 + ρ (1 − θ3 )⎤⎥⎦ , Q = ⎜⎜⎜⎝ s + ε⎠
⎝⎜ 0

⎞⎟
⎟⎟,
(1−θ3) σI10⎠⎟⎟⎟(Teψa = ψ

) ∈ ℜ , ψ = (1 q

1

T

7

e

q

2

qd

ψaT

b

T

T

0

argument, and h(ρ, ρ, s ) = (1 − θ3 ) σθT θ /2 +h (ρ, ρ, s ) .
From Eq. (15), Eq. (16) is expressed asV ≤ −µV + h(ρ, ρ, s ) ,(17)
where µ = λmin (Q ) λmax (P ) , Q and P are positive
definite matrices, and λmax (⋅) represents the maximum
eigenvalue of its argument.
From the boundedness of the Lyapunov function(15), the augmented error s(t ) , the parameter errors©ISAROB 2007

andb (t ),

Consequently, since the augmented error s(t ) andthe parameter errors θ(t ), w(t ) and b (t ) areglobally uniformly ultimately bounded(GUUB), thestable dynamics s = e + Λe guarantees that theposition and velocity errors e and e are also
globally uniformly ultimately bounded, respectively. J
Remark 1: In the controller (10), it is reasonable thatthe guessed nominal actuator torque coefficient matrix
Ka is usually initially defined as an identity matrix,
because it is hard to expect any kind of actuator failures
initially in a robot system and a user can consider the
initial actuator states as the normal state.

4

Simulation Study

The proposed controller is applied to a three-linkrobot moving on a horizontal plane shown in Fig. 3.
The robotic parameters such as length (Li ), mass (mi ),moment of inertia (I i ), and center position of mass (Lci )are Li = 0.5(m ), mi = 1(kg ), I i = 0.02083(kgm 2 ) and
Lci = 0.25(m ), i = 1,2, 3, respectively. The viscous,
coulomb and static friction coefficients at each joint are
0.01(Nm ) andassigned to be 0.05(Nm sec/ rad ),0.02(Nm ), respectively.

10

and λmin (⋅) represents the minimum eigenvalue of its

θ(t ), w(t )

1/ 2

(16)

) ∈ℜ ,( q s )) ,w

, θ (t ) ≤ ⎡⎢2V / (1 − θ3 )λmin (Γ−θ 1 )⎤⎥⎣⎦−1 ⎤ 1/ 2−1 ⎤ 1/ 2⎡⎡
w(t ) ≤ ⎣2V / λmin (Γw )⎦ , b (t ) ≤ ⎣2V / λmin (Γb )⎦ .
1/ 2

s(t ) ≤ [2V / λmin (M )]are bounded as follows.

831

Fig. 3. A three-link planar robot.
The values of the nominal dynamic parameters usedin the controllers are set to 50 % of the values of theactual dynamic parameters. The nominal values of eachfriction coefficient are set to zero values. The external
disturbance is the random noise of which norm isbounded by d (t ) ≤ 3 /2 .
The control objective is the joint position control
from the initial joint positions q1(0) = 0(rad ),q 2 (0) = 0(rad ), and q 3 (0) = 0(rad ) to the final desired
joint positions q1d = 5π / 6(rad ), q 2d = π / 2(rad ), andq 3d = −2π / 3(rad ) . The initial joint velocities q(0) andthe desired joint velocities qd and accelerations qd of
all joints are zeros.
In the Gaussian functions, the 15 basis functions areused. The center positions are randomly initialized andupdated by a k-means clustering method. Weights andThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

biases are initialized with very small random numbers
and updated by the adaptation laws of the proposed
control scheme. The widths are determined as
widk = d max 2 ⋅ N cen , where d max is the maximum
distance between the chosen centers and N cen is thenumber of centers.
The simulation scenario is given as follows. Total
execution time is 10 (sec). Initially, no actuator failures
are assumed. From the initial time to 3 (sec), the robot
has the normal states without failures. The actuator
failures at all joints occur at 3 (sec) and continue from 3(sec) to 10 (sec). The actuator torque coefficients areshown in Fig. 4.
The guessed nominal actuator torque coefficientmatrix Ka is defined as an identity matrix.
In this simulation, the proposed adaptive neuralcontrol scheme and PD control method are compared,and Fig. 5 and Fig. 6 show the simulation results foreach control method.

5This work has presented a stable adaptive neuralcontrol scheme for a robot system with performancedegradation due to actuator failures and uncertainties.
The proposed controller can improve the performancein the presence of actuator failures and uncertainties,
and it achieves task completion.
It has been observed that the proposed control
scheme is valid and robust under the actuator failures
and uncertainties through simulation study.
This work is applicable to an uncertain remote robot
system of which independent actuators are partially
failed. In other words, for an uncertain robot withpartially failed actuators in remote sites, the actuator
failures are compensated and the task can be completed
by the proposed controller, without human intervention.

References[1]Fig. 4. Actual actuator torque coefficients ( K a ) .

Fig. 5-(a). Joint position
errors (e) .

Fig. 5-(b). Joint velocity
errors (e) .

Fig. 5. Control results using the proposed controller.

Fig. 6-(a). Joint position
errors (e) .

Fig. 6-(b). Joint velocity
errors (e) .

Fig. 6. Control results using the PD controller.
As shown in Figs. 5-(a) and 5-(b), it is found thatthe position and velocity errors decrease to very smallneighborhoods of zero even though the actuator failuresoccur under the uncertainties. From Figs. 6-(a) and6-(b), it is shown that the position and velocity errors do
not satisfactorily converge to very small neighborhoodsof zeros due to actuator failures and uncertainties.

©ISAROB 2007

832

Conclusions

English J.D. and Maciejewski A.A. (1998), Fault
Tolerance for Kinematically Redundant Manipulators:
Anticipating Free-Swinging Joint Failures. IEEE Trans.
on Robotics and Automation. 14(4):566-575.
[2] Visinsky M. L., Cavallaro J.R. and Walker I.D. (1995),
A Dynamic Fault Tolerance Framework for Remote
Robots. IEEE Trans. on Robotics and Automation.
11(4):477-490.
[3] Groom K.N., Maciejewski A.A. and Balakrishnan V.
(1999), Real-Time Failure-Tolerant Control ofKinematically Redundant Manipulators. IEEE Trans. on
Robotics and Automation. 15(6):1109-1116.
[4] Dixon W.E., Walker I.D., Dawson D.M. and Hartranft
J.P. (2000), Fault Detection for Robot Manipulatorswith Parametric Uncertainty: A Prediction-Error-BasedApproach. IEEE Trans. on Robotics and Automation.
16(6):689-699.
[5] Dawson D.M., Qu Z. and Lewis F. L. (1992), Hybrid
Adaptive-Robust Control for a Robot Manipulator. Int.
J. of Adaptive Control and Signal Processing.
6:537-545.
[6] Yoo D.S. and Chung M. J. (1992), A Variable Structure
Control with Simple Adaptation Laws for Upper
Bounds on the Norm of the Uncertainties. IEEE Trans.
on Robotics and Automation. 37(6):860-864.
[7] Fu L.C. (1992), Robust Adaptive Decentralized Controlof Robot Manipulators. IEEE Trans. on AutomaticControl. 37(1):106-110.
[8] Sanner R.M. and Slotine J.-J.E. (1992), Gaussian
Networks for Direct Adaptive Control. IEEE Trans. on
Neural Networks. 3:837-863.
[9] Sanner R. M. and Slotine J.-J.E. (1991), Stable
Adaptive Control and Recursive Identification Using
Radial Gaussian Networks. Proc. IEEE 30th Conf. on
Decision and Control, Brighton, England, 1991, pp.
2116-2123.
[10] Mizerek R.T. (1994), Gaussian Neural Network for
Direct Adaptive Control of Robotic Systems. Proc.
IEEE 33rd Conf. on Decision and Control. Lake Buena
Vista, FL, USA, 1994, pp. 3504-3505.
[11] Lewis F.L., Abdallah C.T. and Dawson D.M. Control of
Robot Manipulators. Macmillan Publishing Company,
1993.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Intelligent Automatic Community Grouping System by Multiagents
Young Im Cho
Dept. of Computer Science, The University of Suwon,
San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, KOREA
ycho@suwon.ac.kr
Abstract
We developed an intelligent ubiquitous web
based e-learning system based on multi agents.
The proposed system, Intelligent Ubiquitous Web
based e-learning Multi Agent System, is used the
new distributed multi agent framework and neuralnetwork for e-learning grouping. The proposedsystem implements user’s individual satisfaction
network from analyzing the satisfaction degree
among learners in groups on web environment.
The satisfaction network is personalized byproviding weights to the learners’ satisfaction
degree on e-learning grouping. So, it constructs
the learners' satisfaction network model about thee-learning grouping. Based on this network model,
the proposed system can decide if the group isremained or reorganized or break down for nexttime, and the system learn about the above states.
Keywords: multiagents, e-learning, community

1. Introduction
The dropout rate in e-learning is higher thanthat in traditional face-to- face learning due to its
low degree of continuity. In order to lower this
dropout rate, many researches have been done toheighten the degree of learners' satisfaction and toprovide them with motivation [1,2,3,4,5].
The agent system, which began to appear in
the 1990s, is a system that is automaticallymanaged and self-operative. It is a very intelligent
concept that can manage the information of eachlearner in the e-learning system, and recommend
and search information that fits the inclination ofeach individual [6,7]. By applying the concept of
the agent to the e-learning system, we can develop
the next generation's technology which willcontribute to the increase of the degree ofsatisfaction of learners, as well as the degree oflearning achievement, by analyzing theinclination of each individual learner andreflecting its result in each group.
In this paper, as we recognize the importanceof a learning community and intend to form a©ISAROB 2007

833

learning community which is strong and at thesame time, the most feasible, we will develop an
intelligent web based e-learning multi agent
system through the questionnaire called theinclination test with the method of intelligencebased agent which will reflect the inclination andcharacteristic of an individual learner.
This paper is consisted of the followings.
Chapter 2 will explain the proposed system’s
overview, module specification and the algorithmof the proposed system. Chapter 3 will evaluate
the proposed system. And finally a conclusion isin chapter 4.

2. The Proposed System
2.1 System Structure
On the basis of the above researches, firstly we
present a list of homogeneity and heterogeneous
items for inclination testing for the effectivenessof online e-learning community.
As for a questionnaire, 10 items are included
which are considered to be adequate for grouping,according to the characteristics of each category.
Information from individual learners throughsecurity and certification procedure as seen in
Figure 1 is inputted to the system, Intelligent
Ubiquitous Web based e-Learning Multi Agent
System (IMAS), to be proposed in this paper, and
IMAS creates each user's profile from theinformation. Based on it, learning community
grouping suitable to each individual is
automatically executed by using Self Organizing
Feature Map (SOM) learning algorithm via multiagents.
In IMAS, the grouping and the learning is
automatically performed on real time by multiagents, regardless of the number of learners. A
new framework has been proposed to generate
multi agents, and it is a feature that efficient multiagents can be executed by proposing a newnegotiation mode between multi agents.
Overall structure is composed of the user
information (user, learner), user profile in which
user's tendency is saved, e-learning database,The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

which processes digitalized learning informationand distributed multi agent framework (DIMAF),
which generates multi agents, as well as multiagents that are comprised of grouping agent
deciding a learner's group form DIMAF, user
profile update agent who continuously updates
learner's information continuously and learning
evaluation agent who automatically informs
learning evaluation as seen in Figure 1.
End User (Client)
Results

IMAS User Interface(Web page)
Query

User Profile DB

Behavior

Distributed Multi Agent Frame work (DIMAF)
Agent Manager

UpdateNegoti ati on

Multi Agent Creation

Groupi ng
Agent

User Profile Update
Agent

RequestEvaluati on
Agent

Monitoring
Agent

Response

E-Learning Online Community

E-Learning DB

ServerFigure 1. IMAS system configuration
2.2 Main Functions
2.2.1 User Profile Learning
On of multi agents in IMAS, Grouping agent
is generated by learner's drawing up a distribution
map related to items using Kohonen's SOM
learning algorithm, based on inputted information
for homogeneity and heterogeneity. Whenhomogeneous and heterogeneous items are
inputted respectively, input vector is generated inorder pairs, each. Then, learning grouping is
automatically executed with the weight provided
by drawing up a categorization map on real timethrough SOM network [8].
The explanation of user profile drawing
method regarding each number is as follows. ①
Input vector is generated with regard to learner's
input value for 1st (homogeneous) and 2nd(heterogeneous) categorization criteria. ② A
distribution map is drawn up by providing weight
to detailed homogeneous and heterogeneous items
via SOM network.
2.2.2 Multi Agent Framework
When an agent is generated, DIMAF consistsof the negotiation algorithm between the agent©ISAROB 2007

834

name server (ANS) providing agent ID, an agent
manager controlling and monitoring generation,
execution and movement of agent and multiagents.
The negotiation algorithm is greatly required
for suitable grouping from grouping list by
searching learned user profile with user input item
inputted at an early stage in the grouping agent. In
the grouping list, the user (ID), group number (G),satisfaction degree (SD) and team information(TI) are recorded. TI is recorded as a value
among maintenance (M), don't care (D) and break(B). Maintenance (M) is the case wheresatisfaction degree of previous group members isvery high, which means the value is required to be
maintained
constantly,
notdesiring
tobreak. Don't care (D) is the value meaning that it
may be changed, according to learners' responses
in the normal position. Break (B) means the group
to be regrouped, after breaking existing group,
since satisfaction degree of the previous groupmembers is very low.
The detailed negotiation process by negotiation
algorithm proposed in this paper is as follows:
Step 1: Grouping agent searches concerned
individual (ID), group number (G), satisfaction
degree (SD) and team information (TI) fromgrouping list by inspecting user profile from thehomogeneous items categorized primarily. If a
concerned ID's TI value is M, the concerned
grouping is maintained without executing 2nd
step and you need to move to step 4. If TI value is
D or B, you need to move to step 2 and continue.
Step 2: From the table saved in the temporarystorage, grouping agent (GA) calculates G andSD which performed grouping by SOM learningalgorithm by using user input homogeneous item.
If TI value was B, you need to move to step 4,
beyond step 3.
Step 3: When the grouping result value
performed by GA in step 2 and the G value ofgrouping list are different, concerned groupingshould be maintained in the user ID with priority
in the result of grouping list in the user
profile. However, concerned user should judge
by showing the group member list to concerneduser (ID).
Step 4: Show grouping information andmember list to each learner.
Four multi agents are generated basically in
the IMAS system. Grouping agent (GA) is theagent that generates user profile using 1st
categorization criteria (homogeneous) and 2nd
categorization criteria (heterogeneous). GA is in

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

charge of grouping. User profile update agent(UA) is the agent that saves user history and helps
grouping performance, while consulting GA.
Evaluation agent (EA) evaluates learningsatisfaction degree of user and grouping members
and decides whether to maintain, don't care or
break this group, according to satisfaction degreevalue.
Monitoring agent (MA) is the agent to identify
state of a learner (user) by monitoring the numberof grouping, number per group and satisfaction
degree per group graphically through monitoringof learners' learning status.
In IMAS, grouping forms group via grouping
agent.
The
grouping
agent
indicates
homogeneous and heterogeneous distribution
from homogeneous and heterogeneous items
selected by a user through the use of SOM
algorithm. Automatic grouping is made bylearner's input with this distribution.
Among homogeneous items as you seen in
section 2, a user selects detailed items (i.e. major
subject is Korean) regarding each item. In the
IMAS, 6 input nodes and random 10*10(=100)
output nodes are provided for a learner to learn
using learning algorithm of the SOM network
regarding each detailed input value of the userinput homogeneous value. Here, the reason why
100 output nodes are provided is because
maximum number of cases in which homogeneity
can be generated is limited to 100.
With regard to input value, values wererandomly generated in order of major subject,favorite sports, etc, giving priority to each item by
valuing homogeneous values numerically. For
example, ID: yicho1234, major subject: Korean,teaching experience: 1-5 years, favorite sport:
swimming, hobby: movie, favorite food: Chinese
food, favorite color: yellow were selected, they
are expressed in the following data structure orderby with priorities in order.
Among four heterogeneous items inputted bya user, nodes with regard to four detailed input
values selected by the user and random 6*6(=36)
output nodes are provided, and the user learned in
the SOM network. Here, the reason why 36 nodes
were provided is because maximum number ofcases where heterogeneity can be generated waslimited to 36. With regard to input values, they
have been generated randomly with a priority inorder of area and gender based on the priority ofeach item by valuation of heterogeneous values.
The input values regarding four detailed items
weregenerated
randomly
byvaluing©ISAROB 2007

835

heterogeneous
valuesnumerically.
Likehomogeneity, each detailed item selected withregard to four items was valued numerically andthen learned.
For example, if ID: yicho1234, area:
Seoul/Kyeonggi-do, gender: female, computer
using hours: 1-2 hours, online training experience:
yes were selected, the data structure of input nodeof the concerned ID is as follows. Here, users canlearn through 10,000 inputs in the input nodes.
Like homogeneity, output group distribution isformed in relation to input node. The distribution
formed
like
this
forms
heterogeneous
categorization map as seen in Figure 2(right). In
the figure, N=10,000, and a~d are the number ofheterogeneous items and m is the group numberin the heterogeneous categorization map. In thiscase, the number of group was 16. A user with ID
1 was categorized into heterogeneous group 4 inthis instance.

Figure 2. The process of group formation(left: Homogeneous, right: Heterogeneous)Group is generated through input of learners
with the distribution generated by homogeneousand heterogeneous SOM learning. The number of
total group is decided by the number of people ina group, which is performed by manager's
input. The input items of a learner are made by
selecting 10 input items (homogeneous,heterogeneous). In order to meet homogeneity andheterogeneitywithhomogeneousandheterogeneous distributions learned through 10
input vectors, final learner's group meetinghomogeneity and heterogeneity is generated byproviding weight to each vector. The size oflearning group (size of community) can be
designated by manager randomly.
Finally, the homogeneous categorization map
M generates the final group G for learning byrandomly taking among groups excluding m towhich concerned learner ID belongs in Figure 4.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

3. Performance Evaluation

4. Conclusions

In this paper, a pilot test was conducted toevaluate actual users of the IMAS system. As aresult, the evaluation of user satisfaction degree
per group is seen in Figure 3. If the scale indicates
1 in satisfaction degree, it means very satisfactory,
if the scale is 5, then it means very unsatisfactory.
The scale from 1 to 5 with regard to 5 categories
was expressed as value. Here, satisfactory meansthat members of a group or learning desire show avery positive result.

To do development of e-learning community
system, we have made an inclination test
questionnaire for the formation of effective andefficient online learning community. And then,we have implemented and realized an automaticgrouping system with information of learners thatappear through the questionnaire and by using an
intelligent agent.
The results of our experiment with 1,000
people in reality by means of developing thegrouping system have shown that 151 groups areautomatically formed.
In the future, it is necessary to improve
services
concerningthecommunication
between users by supplementing the grouping
system and to continue research on which multiagent system can be achieved effectively in
automatic grouping.

Satisfaction Degree

5
4
3
2
1
0
1

8

15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148
Number of Final Learning Groups
Average Satiafaction Degree per Group

Figure 3. The satisfaction degree per group

References

duration time(nsec)

As a result of surveying users' groupsatisfaction degree regarding 151 groups, we cansee the average was distributed around scale value
2 of satisfaction degree. Accordingly, when
automatic grouping was performed by agent,learners were generally satisfied. When they
desired to maintain their group according to thevalue 2, around 51 groups of total 151 groups(34%) showed in favor of maintenance, while
61% showed don't care and less than 5% showed
break.
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
-0.5 1

2

3

4

5

6

7

8

9

10

Time Series
MA

GA

EA

UA

Figure 4. The average duration time and cycle
in IMAS
The agent’s duration (or activation) time by
DIMAF framework on time slice in IMAS isdrawn in Figure 6. In Figure 6, after GA is
activated, UA is activated. But MA is activated all
the way except the activation time of GA. EA isactivated after GA’s activation. This cycle isrepeated in IMAS e-learning system. Owing to
DIMAF, IMAS can activate intelligently among
many users in ubiquitous environment.

©ISAROB 2007836

[1] M. J. Rosenberg, E-learning: Strategies for
Delivering Knowledge in the Digital Age,
McGraw Hill, New York, pp.28-29, 2001.
[2] A. P. Rovai, "Building sense of community
at a distance". International Review of
Research in Open and Distance Learning,vol.3, no.1, 2002.
[3] B. Winson, and M. Ryder, Dynamic
learning communities: An alternative todesigned instructional system. 2001.
[4] B.Winson, and M. Ryder, Distributed
learning communities: An alternative todesigned instructional system. derived fromhttp://cudenver.edu/~bwilson, 1998.
[5] D.P. Fulford, and S. Zhang, "Perceptions ofinteraction: The critical predictor in distanceeducation" The American Journal ofDistance Education. vol.7, no 3, pp.181-198,
1993.
[6] Stuart Russel, Peter Norvig, Artificial
Intelligence: A Modern Approach, PrenticeHall International Editions, 1995.
[7] John R. Graham, Keith S. Decker, Towards
Distributed, Environment Centered Agent
Framework. Appearing in Intelligent Agents
IV, Agent Theories, Architectures, andLanguages
Springer-Verlag,
NicholasJennings, Yves Lesperance, Editors, 2000.
[8] Kohonen, Self-Organizing Maps, Springer
Series in Information Sciences, vol.30, 1995;
Third, extended edition, 2001.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

PID Controller Design Using Double Helix Structured DNA Algorithms with
Recovery Function
Jeong-Jung Kim and Ju-Jang Lee
Electrical Engineering and Computer Science
Korea Advanced Institute of Science and Technology(KAIST)
373-1, Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea
justi@odyssey.kaist.ac.kr, jjlee@ee.kaist.ac.kr
Abstract
PID controller has been widely used in industrial
fields. Since PID parameters have a great influenceon the stability and the performance of the control
system, many approaches have been proposed todetermine them. In this paper, we propose double
helix structured DNA algorithms to design the typeof PID controller and optimize PID parameters.
The double helix structured DNA algorithms employ DNA encoding method based on a base-64
notational system to represent PID parameters,
defines various mutation methods, and has a recovery
function to preserve a DNA strand that has good
fitness value. Computer simulation shows that we
can get satisfactory results with the proposed method.
Keywords : DNA Algorithms, PID Controller.

1

Introduction

It is a well known fact that proportional integral
derivative (PID) controller has been widely used inindustrial fields, because, despite their simplicity, it
can assure an adequate and satisfactory performancefor a wide range of processes. Since PID parameters
have a great influence on the stability and the performance of the control system, many approaches havebeen proposed to determine them.
One of the approaches that can obtain global optimization solution is GAs. Although this approaches
can get optimal PID parameters, they have some disadvantages. As GAs’ size of the chromosomes andpopulations increase, their computation time also increase, and a chromosome that has a good fitness value
can be lost by a mutation.
The DNA(deoxyribonucleic acid) is a nucleic acidthat contains the genetic instructions for the biological development of a cellular form of life. That is,©ISAROB 2007

837

DNA is a blueprint of living things, because DNA
makes RNA that makes proteins. If we implement
DNA into a computer, we can mimic the process that
occurred in organic life and can overcome the limitations of the traditional GAs. Some studies havebeen conducted to design controller inspired from theDNA. Yongsheng Ding et al.[1] designed the generalized memberhip-type Takagi-Sugeno fuzzy control system using DNA algorithms, Chu-Liang Lin et al.[2]suggested self-organizing PID control design based on
DNA computing method and Huang Yourui et al.[3]proposed optimization for parameter of PID based on
DNA Genetic Algorithm.
In this paper, we present a new method to design
type of PID controller and optimize PID parameters
by using a double helix structured DNA algorithms.
More specifically, we use DNA encoding method basedon a base-64 notational system to represent PID parameters, define various mutation methods, and suggest a recovery function to preserve a good DNA
strand that has good fitness value. This paper is organized as follows. In Section 2, biological basis and implementation are given. Simulation results are shownin Section 3, and the conclusion is made in Section 4.

2

Biological Basis and Implementation

The nucleic acid DNA(deoxyribonucleic acid) serves
as the genetic material in all living organisms andmakes RNA that makes proteins. Base on the biological information about the DNA in [4], we implement
DNA in a computer to design type of PID controller
and optimize PID parameters.

2.1

DNA Encoding Method

The DNA molecule exists in cells as a long, coiled
ladder like structure described as a double helix. EachThe Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 1: The encoding of stands corresponding to thetype of the PID controller and PID parameters
strand of the helix consists of a linear polymer madeup of genetic building blocks called nucleotides, ofwhich there are four types. Nucleotides vary, depending upon which of four nitrogenous base is part ofthe molecule - A(adenine), G(guanine), T(thymine),or C(cytosine). A triplet code of nucleotide bases specifies the codon, which in turn contains a specific anticodon on transfer RNA(tRNA) and assists subsequenttransmission of genetic information in the formation ofa specific amino acid. A chromosome consists of combinations of the above four bases and can represent
different genes.
A single strand of DNA can be likened to a string
consisting of a combination of for different symbols,
A, G, C, T. Mathematically, this means we have afour-letter alphabet to encode information, which ismore than enough, considering that an electronic computer needs only two digits, 0 and 1, for the same purpose. In the Fig.1, the first part of the DNA strand
is the type of PID controller, and the second part is
PID parameters. The type of PID controller is determined by only a nucleotide, while each PID Parameter is determined by three codons that use a base-64
notational system. Because each parameter is composed with three codons, a maximum decimal value
is 262144(64x64x64). The length of DNA strand andmaximum value are can be modified to meet a specification.
The double helix structured DNA is used to preserve good DNA strand. Each DNA strand is a template for synthesizing a new strand which is nearly
identical to the previous strand. When one of strand is
modified by a mutation operation, the modified strand
is evaluated. If the modified strand is better than theoriginal strand, the original strand is changed to modified strand. Otherwise, the modified strand is recovered from the original strand.
Further description about usage of double helix©ISAROB 2007

838

Figure 2: Description of each mutation
structured DNA is shown in section 2.5.

2.2

Genetic Operators

Two genetic operators,DNA mutation operations
and a PCR operation, are developed to modify ourDNA.
Errors that occur in the synthesis are called mutations. Mutations are the results of the cells’ attempts to repair chemical imperfections in this process, where a base is accidentally skipped, inserted, orincorrectly copied, or the chain is trimmed, or added
to. Only three mutations, ‘Modification’, ‘Inversion’,‘Extension’, are used to mutate DNA. One of the mutation methods is selected randomly. Description ofeach mutation is shown Fig.2. The Modification mutation is a point mutation that changes in one baseof the DNA sequence. A randomly selected point ischanged among A, G, T, C. In inversion mutation, asection determined by a randomly selected start point
and an end point is inversed. After inversion, the orderof the strand in the inversion region is reserved. An
extension, last mutation method, is a kind of infectionthat influences adjacent codons.

2.3

DNA Shuffling

The polymerase chain reaction(PCR) is a rapid
method of DNA cloning that has extended power ofrecombinant DNA research and eliminated the needfor host cells in DNA cloning. PCR generates many
copies of a specific DNA sequence through a series of avitro reaction and can amplify target DNA sequences
present in infinitesimally small quantities in a population of other DNA molecules. The PCR is implemented in a computer to shuffle DNA. A process of
PCR is shown Fig.3. It is a process of the exchange ofgenetic information. All individuals are shuffled withbest DNA by saving previous best individual.

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007

Figure 3: A process of PCR

2.4

Fitness Function

After decoding the type of PID controller and PID
parameters that was encoded in a DNA strand, thePID controller design can be evaluated. A fitness function that minimizes rise time 15 steps, overshoot 5
percent, and settling time 50 steps is set. The fitness
function is defined as (1) to evaluate decoded parameters. C1 , C2 and C3 are determined by their priority.

Figure 4: Overall process of the double helix structured DNA algorithms

ff it = C1 × RT Gap + C2 × OSGap + C3 × ST Gap (1)

RTGap represents gap between real rise time and15steps, OSGap represents gap between real Overshoot and 5 percent and STGap represents gap between real Settling Time and 50 steps.

2.5

Overall Process

An overall process of the double helix structured
DNA algorithms is shown in Fig.4. The process firstinitialize each individual, and do mutation, DNA shuffling, evaluation, and recovery or change until maximum generation. An individual is the same as DNA,and has two strands. When one of strand is modified by a mutation operation, the modified strand isevaluated. If the modified strand is better than theoriginal strand, the original strand is changed to modified strand. Otherwise, the modified strand is recovered from the original strand. This process is possible
because the double helix structured DNA was used.

3

Simulation

To verify the PID controller design method that isdesigned in section 2, a motor model (2) is used tosimulate.
G(z) =

0.02937z 2 + 0.0153205z + 4.64302 × 10−5z 3 − 1.03869z 2 + 0.0386917z − 8.99251 × 10−8

(2)

©ISAROB 2007

839

Table 1: Simulation parameters
Parameter
Maximum number of shuffling
Mutation rate for PID parameters
Mutation rate for type of PID controller
Number of individual
Maximum generation

Value
10
0.1
0.005
500
1000

The fitness function that rise time has high priority,
overshoot has medium priority and settling time has
lower priority is set. And maximum fitness value is set
to 10000.
The maximum number of shuffling for each process
is 10, mutation rate of PID parameters is 0.1, mutation rate of PID controller type is 0.005. Furthermore
the number of individual is set to 500, and Maximum
is 1000. The specified parameter value that we use
is given in Table1. Convergence behavior of the fitness function is shown in Fig.5. Initial fitness value is
10000, but it converges into 124 at the 1000 generation.
Some unit step response of the each PID controller
designs are shown in Fig.6, and the PID parameters and specifications of PID controller are given in
Table2.
Computer simulations show that after convergence

The Twelfth International Symposium on Artificial Life and Robotics 2007(AROB 12th ’07),
B-Con Plaza, Beppu, Oita, Japan, January 25-27, 2007of the double helix structured DNA algorithms, we
can always find a group of parameter values for thetype of PID controller and PID parameters that obtainsatisfactory control performance.

4

Conclusions

This article uses the double helix structured DNA
algorithms to design the type of PID controller and optimize PID parameters. The double helix structured
DNA algorithms use DNA encoding method based ona base-64 notational system to represent PID parameters, define various mutation methods, and suggest arecovery function to preserve a DNA strand that has
good fitness value.
The proposed method is well demonstrated and verified by simulations.
The Double helix structured DNA algorithms canbe used for not only to design PID controller but also
to design other controllers.

Figure 5: Convergence of the fitness function

References[1] Chun-Liang Lin, Horn-Yong Jan and Thong-Hsing
Huang, “Self-organizing PID control design based on
DNA computing method,” Proc. IEEE International
Conference on Control Applications, 2-4 September
2004, pp. 568-573.
[2] Yongsheng Ding and Lihong Ren, “DNA genetic algorithm for design of the generalized membershiptypeTakagi-Sugeno fuzzy control system,” Proc. IEEE
International Conference on Systems, Man, and Cybernetics, 8-11 October 2000, pp. 3862-3867.

Figure 6: Unit step response of the each PID controller
designTable 2: Some the PID parameters and specificationsof PID controllers
P
PD
PI
PID
3.2827 2.1724
1.96883.0337Kp-0.0059 0.00391
Ki
12.3657
6.5205
Kd
15
15
18
15
Rise Time
0.000
0.000
1.945
0.000
Overshoot
35
30
29
36
Settling Time

©ISAROB 2007

840

[3] Huang Yourui, Chen Xiuqiao and Hu Yihua, “Optimization for parameter of PID based on DNA genetic
algorithm,” Proc. IEEE International Conference on
Neural Networks and Brain, 13-15 October 2005, pp.
859 - 861.
[4] William S. Klug and Michael R. Cummings, “Essentialof genetics fifth edition,” Pearson Prentice Hall, 2005.

Authors’ Index[A]

Cheung

Y.

597

Abe

N.

376,442,809,813

Chigoi

Y.

276

Abe

K.

390

Cho

C-W.

33

Adachi

T.

639

Cho

J.H.

296

Adzni

A. R.

272

Cho

S.

474

Ahmad

F.

410

Cho

Y.I.

740

Ai

D.

280

Choi

B-S.

821

Aibe

N.

665

Choi

J.H.

474

AiharaI.

617,613

Choi

W.

244

AiharaK.

549,553,557,561,

Chunh

J.

478

565,569,601,605,

CollierT.

256

609,613,617,597

Cui

G.

673

Aikawa

M.

657

Aly

S.

802

Ando

N.

183

Dai

F.

639,643

Aoki

Y.

736

Date

A.

210

Araki

O.

693

Date

T.

458

Arita

T.

689

Delgado-Frias

J.

709

Arof

H.

410

Dong

X.

627

ArroyoF.

494,500

Doya

K.

106

Awano

T.

665

Du

J.

191

[D]

[B]

[E]

Baba

S.

798

Eaton

M.

88

Balasuriya

J.C.

752

Ebara

Y.

794

Ban

X.

280

El-Sousy

F.F.M

37

Bando

T.

458

Bank

S.C.

756

Beak

G.

426

Faizal

E.R.M.

356

BlumsteinD.

256

Fernandez

L.

494,500

Bode

K.

589

Filipe

J.

709

Bravo

G.

500

Finkelstein

R.

171

Freund

R.

509

Fubio

T.

430

[C]

[F]

Capi

G.

589

FujiharaY.

635,639

Chang

J-Y.

364

Fujii

K.

744

Chang

Y.Z.

60

Fujita

Y.

657

Chang

W-C.

33

Fujiwara

Y.

484

Chau

S.D.

320

Funaya

H.

531

Chen

L.

561597,609

FurutaniH.

131,135,140,144,652

Cheon

S.

196

[G]

HitakaY.

214,218

Garcia

I.

494

Homma

N.

390

Girod

L.

256

Hung

G.T.

60

Gobaru

E.

402

Hwang

S.T.

446,462

Goto

H.

442

Goto

N.

284

Goto

R.

308

Ichida

K.

764

Guo

J.

673

Ichikawa

S.

577

Gutmann

M.

553

Ido

H.

156.160

Ihara

M.

527

IihoshiK.

131,135,140,144

[H]

[I]

Habib

M.K.

768

Ikeda

K.

96,531,778,782

Ham

O-K.

418

Ikeda

S.

131,135,140,144

Hamid

A.

719

Imabeppu

T.

732

Han

C-H.

817

Inoue

K.

131,135,140,144

Han

M.

46

Irie

T.

380

Han

S.

304

Ishibashi

T.

390

Han

S-H.

230,234

IshibuchiH.

372,422

HanamotoT.

635

IshidaY.

581,736

Hariharan

H.

384

Ishii

J.

214,218

Hayashida

J.

635

Ishii

S.

84,197,527,312,458

Hane

Y.

72

Ishikawa

S.

76,380Hara

M.

204,577,593

Ishitobi

M.

152

Hashimoto

H.

183

Isoi

R.

430

Hashimoto

K.

613

Ito

K.

308

HashimotoT.

187,260

ItoT.

131,135,140,144

Hatanaka

T.

669

Izumi

K.

748,752,756,764,768

HayashiE.

51,72,80,264,276,
284,348,352,368,[J]

450,470,806

Jaber

A.Q.

513

Hayashi

K.

661

Jeong

H.

304

Hazry

D.

384,406

Jia

Y.

316

He

G.

561

Jiang

Y.

715

He

S.

376,442,813

Jie

W.R.

191

Hema

C.R.

719

Ju

C.W.

252

Henta

H.

80

Jung

B-W

196

Heo

E.C.

249

Heo

J-S.

817

HikisakaY.

264

KabataK.

593

HikizuM.

344

KanasugiA.

440

Hiranuma

K.

148

Kang

S.H.

64

HirataY.

565,601

KamiyaY.

344

Hirayama

J.

312

Kamohara

K.

752

[K]

Karthigayan

M.

406

Konno

T.

187

Katayama

K.

539

Kono

M.

131,135,140,144,148,152,226

Katayama

S.

652

Koo

H.S.

244

KatoriY.

565

KoyamadaK.

794

Katuhara

M.

585

Kubik

T.

394

Kawakami

H.

206

KukimotoN.

794

kawamuraH.

56,68,92

KurataK.

210,328

KawanaF.

728

Kuribayashi

K.

760

KawatoM.

484

KuritaT.

376

KawawakiD.

484

Kusumoto

Y.

760

Katai

O.

206

Kwon

Y.K.

249

Khoa

T.Q.D.

631

Kim

B.C.

249

Kim

H-D.

573,740

Labhart

N.

454

Kim

H-S.

239

Lambert

D.

171

Kim

H-Y.

239

Le

X-T.

230,234

Kim

J.B.

478

Lee

C-H.

418,426,462

Kim

Jungmin

196

Lee

D.D.

252

Kim

Jinyong

196

Lee

J.

304

Kim

J.J.

837

Lee

J.J.

817,821,829,837

Kim

J-W.

239

Lee

J.M.

300,466,474

Kim

J-Y.

426

Lee

K.S.

300

Kim

C.S.

64

Lee

M.H.

60,300,446,462

Kim

D.H.

296

Lee

M-N.

829

Kim

M-S.

829

Lee

S.

478

Kim

S.

196,418,426

Lee

S.H.

478

Kim

S.H.

249

Lee

S-H.

244

Kim

S.Y.

300

Lee

S.T.

60

Kim

W-H.

829

Lee

W-S.

230

Kim

W-I.

230

Li

T-Y.

364

Kim

Y.

418,426

Lim

G.H.

478

Kim

Y.D.

296

Lim

H.

466

Kim

Y.K.

234

Liu

G.L.

768

Kinjo

H.

320,324

Lund

H.H

6

Kitagaki

K.

179

Kitahata

H.

617

[M]

Kobayashi

S.

260

MackinK.J.

104,116,517,539,774

Kohno

T.

569

Maeda

H.

605

Koizumi

N.

661

Maeda

S.

527

Kondo

E.

46

Maeshiro

M.

320

Kondo

T.

100

Majima

T.

585

Kondo

T.

123,438

MakinoK.

84

KonishiO.

308

Manjunath

T.C.

288

[L]

Marasinghe

C.A.

752

E.

517,539,774

MatsukiT.

438

Matsumoto

M.

440

[O]

Matsumoto

S.

621

OffesenM.

6

Matsuo

M.

549

Oh

S-B.

230,234

Matsushita

K.

517,539

Ohama

Y.

156

Matusita

K.

774

Ohashi

H.

292

Meng

D.

316

OhdairaT.

292

Minagawa

Y.

56,68

OhkusaY.

605

MirenkovN.

709

OhnishiK.

324

MishimaM.

705

OhshiroM.

517,774

MiyachiM.

152

OhtakeH.

110

Miyamichi

J.

430

OhuchiA.

56,68,92

Miyashita

S.

454

Oka

T.

790

Mizoguchi

H.

348

Okada

N.

46

Mochizuki

T.

450

Oku

M.

557

Moriki

T.

214

Okuma

C.

681

Morozoy

A.

171

Omatu

S.

156,160,163,167

Murakami

J.

681

Onitsuka

T.

693

Ono

S.

685,732

Oomori

T.

809

[N]

Nunohiro

Na

U.J.

252

OosawaC.

621

NagamizuY.

131,135,140,144

Osaga

S.

312

NagataF.

760

OshiroN.

328

NaitohK.

677

OswaldM.

509

Nakagawa

M.

631

NakamuraA.

179

Nagarajan

R.

406,719

Park

D.

418

NakayamaS.

685,732

Park

K-B.

825

NakazonoK.

320,324

Park

H.G.

446

NakamuraM.

119,728

Park

Y.T.

478

NakamuraN.

798

Pagliarini

L.

13

NakamuraY.

84

Pathiranage

C.D.

748

NakashimaT.

372,422

Paulraj

M.P.

384, 719

Narita

K.

96

Peng

J.

488

NawataT.

523

Pillai

B.

22

Neba

Y.

635

Pu

Y.

627

Ngo

T.D.

42

Nii

M.

372

Niimi

A.

308

Ramli

R.

513

NiimotoK.

470

RaposoH.

42

Nishioka

R.

92

Rizon

M.

406,384,719

NomuraY.

782

Rodriguez

A.

494

[P]

[R]

Rozailan

M.

406

Ryu

G.G.

478

394,398,402,406,577,

Ryu

J.H.

446,462

593,643,709[S]

SugisakaM.

1,171,204,356,384,

Sugita

K.

798

Suh

I.H.

478

Saito

M.

535

Sui

M-L.

545

SakamotoM.

131,135,140,144,652

Suziki

H.

565

Sakai

M.

390

SuzukiR.

689

SagaraS.

336,360

SuzukiY.

506

Sasaki

H.

430

SallehK.

344

SapatyP.

171,709

Tabuse

M.

222

Sato

A.

226,697

Tachibana

K.

268

Sato

M.

484

Taira

Y.

336

Sato

T.

106

Takadama

K.

585

Sato

O.

148,226

Takata

H.

523

Saudi

A.

272

TakayaT.

56,68,92

Savageau

M.A.

621

TakahashiN.

148,152,226

SazaliY.

719

Takebayashi

R.

268

Seki

H.

344

Takehara

Y.

222

Seo

K-H.

829

Takei

K.

390

SchaeferG.

422

Takemoto

K.

621

SchiolerH.

42

Takenouchi

T.

197,312

Shah

N.M.

410

Taki

H.

376,442,813

Shang

T.

414

Takiguchi

N.

110

Serikawa

S.

535

TamakiT.

131,135,140,144

Shibasaki

H.

119

TamakiE.

332

ShibataT.

84,484,458

TamuraM.

360

ShimadaH.

308

TamuraT.

693

Shimamoto

M.

206

Tan

J.K.

76,380

ShimonoM.

352

Tanaka

H.

204

Shin

J-H.

829

TanakaK.

376,430,809

Shin

H.B.

234

TanakaYoshito214,218

Shin

T.Y.

300

TanakaYutaka214

Shinohara

M.

430

Tanaka-Y

M.

701,705

Shiose

T.

206

Tang

L.

1

Shoji

K.

430

Taniguchi

R.

802

Sim

K-B.

573,740

Taylor

C.

256

Soh

Z.

110

Tejedor

J.A.

500

Son

S-H.

825

Teo

J.

272

SonodaT.

806

Tokuoka

S.

701

Su

X.

340

Toma

N.

332

Sugi

T.

119

TomozoeN.

131,135,140,144

[T]

Tone

M.

368

YamaguchiT.

116

ToshimaY.

183

Yamaguchi

Y.

665

Tou

K.

380

Yamamori

K.

657,661

Trifa

V.

256

YamamotoM.

56,68,92

Tsugimura

K.

76

Yamamoto

Y.

681

Tsuji

T.

110

Yamamoto

H.

513

TsumuraH.

18

YamamotoT.

320,324,328

TsurutaN.

802

Yamamoto

Y.

665

TsusuiS.

647

YamasakiK.

517,774

YamasakiT.

685,778,782

Yamashita

T.

197

[U]
UchibeE.

106

YamashitaO.

484

Uchida

N.

764

Yang

H-C.

573

Uchino

S.

813

Yang

J.

244

UeharaR.

46

Yasunaga

M.

661

Ueno

J.

100,123,438

Yasunaga

M.

665

Uewaki

T.

51

Yatoh

T.

360

Umeo

H.

268

Yokomichi

I.

214

UosakiK.

669

YokotaM.

786,790,798

YokotaY.

422

YoshiharaI.

657,661,665

[W]
Wada

C.

214

Yoshikawa

K.

617

Wada

M.

308

Yoshizawa

M.

390

Wang

B.

728

Wang

D.H.

244

Wang

M.

340

Zhang

C.

724

Wang

R.

609

Zhang

S.

280

Wang

S.

414,715

Zhang

T.

119

Wang

W.

280

Zhang

Y.G.

1

Wang

X.

398,728

Zhang

Y-I.

545

Wang

Y.

488

Zhao

H.

639,643

Watanabe

D.

585

Zhao

M.

340

WatanabeK.

748,752,756,760,

Zhao

N.

724

764,768

Zhao

X.

597

Watanabe

Y.

689

Won

J.S.

252

L.

724

Yaacob

S.

384,406

Yamada

T.

127

[X]
Xiong

[Y]

[Z]