The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Program of The Eleventh International Symposium on

ARTIFICIAL LIFE AND ROBOTICS(AROB 11th '06)

January 23-25, 2006
B-Con plaza, Beppu, Oita, Japan
Editors: Masanori Sugisaka and Hiroshi Tanaka©ISAROB 2006

The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

THE ELEVENTH INTERNATIONAL SYMPOSIUM

ONARTIFICIAL LIFE AND ROBOTICS(AROB 11th '06)

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

OPERATED BY
Santa Fe Institute (SFI, USA)The Society of Instrument and Control Engineers (SICE, Japan)
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, 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)SPONSORED BY
The Commemorative Organization for the Japan World Exposition(’70)
Mitsubishi Electric Corporation Advanced Technology R&D Center
Oita Gas Co., Ltd.
ME System Co., Ltd.
Sanwa Shurui Co., Ltd.
Yatsushika Brewery Co., Ltd.
Yanai Denki

SUPPORTED BY
Kyushu Bureau of Economy, Trade and Industry, Ministry of Economy, Trade and Industry
Oita Prefecture
Oita City
Beppu City
The Federation of the Chamber of Commerce and Industry in Oita Prefecture
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 SHIMBUNThe Nishinippon Newspaper Co.
The NIKKAN KOGYO SHIMBUM. LTD.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Nihon Keizai Shimbun, Inc.
NHK Oita Station
Oita Broadcasting System
Television Oita System
Oita Asahi Broadcasting Co., Ltd.
Oita System Control Research Society

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, China)

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

INTERNATIONAL ORGANIZING COMMITTEE
K. Aihara (The University of Tokyo, Japan)
C. Barrett (Los Alamos National Laboratory, USA)M. Bedau (Reed College, USA)
Z. Bubnicki (Wroclaw University of Technology, Poland)
J.L. Casti (Technical University of Vienna,Austria)
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)©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

W.R. Wells (University of Nevada-Las Vegas, USA)
Y.G. Zhang (Academia Sinica, China)

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)
Z. Bubnicki (Wroclaw University of Technology, Poland)
A. Buller (ATR Network-Informatics Laboratories, Japan)
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)
I. Harvey (University of Sussex, UK)
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)
M. Kaneko (Hiroshima University, Japan)
O. Katai (Kyoto University, Japan)
M. Kono (University of Miyazaki, Japan)
H. Kawamura (Hokkaido University, Japan)
S. Kawata (Tokyo Metropolitan University, Japan)
S. Kitamura (Kobe 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)
H. Miyagi (The University of Ryukyus, Japan)
T. Nagashima (Muroran Institute of Technology, Japan)
M. Nakamura (Saga University, Japan)
K. Nakano (The University of Electro-Communications, Japan)
R. Nakatsu (Kwansei Gakuin 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 (ATR, Japan)
K-B. Sim (Chung-Ang University, Korea)©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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

LOCAL ARRANGEMENT COMMITTEE
X. Feng (National University of Singapore, Singapore)
S. Sagara (Kyushu Institute of Technology, Japan)
A. Loukianov (Baical State University of Economy & Law, Russia)
M. Rizon (Northern Malaysia University College of Engineering, Malaysia)
X. Fan (Oita University, Japan)
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 eleventh symposium will be held on
23-25 January, 2006, at B-Con Plaza, Beppu, Oita, Japan. This symposium invites you all to discussdevelopment of new technologies concerning Artificial Life and Robotics based on simulation andhardware 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 chaos©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Brain science
Cognitive science evolutionary computations
Complexity
Computer graphics
DNA computing
Fuzzy control
Genetic algorithms
Human-machine cooperative systems
Human-welfare robotics
Innovative computations
Intelligent control and modelingMicromachines
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 aninternational journal ARTIFICIAL LIFE AND ROBOTICS (Springer) and APPLIED
MATHEMATICS AND COMPUTATION (North-Holland). All correspondence related to thesymposium 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 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

MESSAGES

Masanori Sugisaka
General Chairman of AROB

Masanori Sugisaka
General Chairman(Professor, Oita University)©ISAROB 2006

It is my great honor to invite you all to the Eleventh International
Symposium on Artificial Life and Robotics (AROB 11th ’06).
The symposiums from the first (February 18-20, 1996) to the tenth(February 4-6, 2005) were organized by Oita University under thesponsorship of the Science and Technology Policy Bureau, the Ministry of
Education, Science, Sports, and Culture (Monkasho), Japanese Government
and Air Force Office of Scientific Research, Asian Office of Aerospace
Research and Development (AFOSR/AOARD), USA and co-operated by
Santa Fe Institute (USA), SICE, RSJ, IEEJ, ICASE, CAAI, ISCIE, IEICE,IEEE (Japan Council), and JARA.
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 Eleventh symposium is sponsored by The Commemorative
Organization for the Japan World Exposition(’70) and Japanese companies(Mitsubishi Electric Corporation Advanced Technology R&D Center, Oita
Gas Co. Ltd., ME System Co.Ltd., Sanwa Shurui Co. Ltd., Yatsushika
Brewery Co. Ltd. And others. I would like to express special thanks for The
Commemorative Organization for the Japan World Exposition(’70) and thecompanies 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 11th ’06 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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B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Hiroshi Tanaka
Program Chairman of AROB

Hiroshi Tanaka
Program Chairman(Professor, Tokyo Medical
and Dental University)©ISAROB 2006

On behalf of the program committee, it is my great pleasure and honor toinvite you all to the Eleventh International Symposium on Artificial Life andRobotics (AROB 11th ’06). 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 Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Y. G. Zhang
Vice Chairman of AROB

Y. G. Zhang
Vice Chairman(Professor, Academia Sinica)©ISAROB 2006

Dear all friends and all participants,
Here I would like to show my warm welcome to you all attending the
Eleventh International Symposium on Artificial Life and Robotics(AROB’06). 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 andRobotics” published by Springer in last eight 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 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 a very
great and difficult career, and need continuous and consistent efforts of more
and more scientists and engineers. All participants to AROB are the part ofteam and we are devoting to the same target.
Beppu, the place of held most AROB symposium, is very charmingcity in Japan, she has variety of hot spring (jijaoku), spa, beautiful bay andcolorful 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’06, 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 Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Ju-Jang Lee
ViceChairman of AROB
The 11th International Symposium on Artificial Life and Robotics (AROB)
will be held in Beppu, Oita, JAPAN from Jan. 23rd to 25th, 2006. This year's
Symposium will be held amidst the high expectations of the increasingly
important role of the new interdisciplinary paradigm of science andengineering represented by the field of artificial life and robotics that iscontinuously attracts wide interests among scientist, researchers, andengineers around the globe.
Ju-Jang Lee
Vice Chairman(Professor, KAIST)

Since the time of the very first AROB meeting in 1996, each year,
distinguished researchers and technologists from around the world arelooking forward to attend and to meet at AROB. AROB is becoming theannual excellent forum that represents a unique opportunity for the academic
and industrial communities to meet and assess the latest developments in thisfast 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 of applied research, present anddiscuss the latest development of new technologies and the relevant
applications. In addition, AROB offers offer the opportunity of hearing theopinions of well known leading experts in the field through the keynote
sessions, provides the bases for regional and international collaborative
research, and enables to foresee the future evolution of new scientificparadigms and theories contributed by the field of artificial life and robotics
and associated research area. The twenty-first century will become thecentury of artificial life and intelligent 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 11th AROB 2006 to welcome
everyone to this important event. Also, I would like to extend my special
thanks is owed to all the authors and speakers for contributing their researchworks, the participants, and to forget the organizing team of the 11th AROB.
Looking forward to meet you at the 11th AROB in Beppu-Oita and wish you
all the best.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Plenary talker:
PT1 Multi-locomotion robots -PDAC for intelligent controlT. Fukuda,
Masahiro Doi, Hideki Kajima, Yoshihiko Asano, Takayuki Matsuno(Nagoya University, Japan)
Yasuhisa Hasegawa (Tsukuba University, Japan),Prof. ToshioFukuda

In this paper, we introduce the novel control method for multi-locomotion
robot named Passive Dynamic Autonomous Control (PDAC). PDAC
assumes two following premises: 1) point-contact 2) interlocking. The
second premise means that the angles of robot joints are connected with theangle around contact point. The approach of PDAC is to describe the robot
dynamics as a 1-DOF autonomous system, which is the dynamics aroundcontact point. This approach makes it possible to generate natural motion.
The effectiveness of proposed method is confirmed by experiments using
multi-locomotion robot.
PT2 Present State and Future of Intelligent Space Discussion
on Implementation of RT in our Environments
Hideki Hashimoto
Institute of Industrial Science
University of Tokyo, Japan
http://dfs.iis.u-tokyo.ac.jp

Prof. Hideki Hashimoto“Intelligent Space” is a platform which we can implement advanced
technologies easily to realize smart services to human. We have developed
and reported the vision system based on color information, the handoverscheme networking multi cameras, the human following mobile robot systems,
the path generator based on human watching, etc. In this talk, I willsummarize the present state of Intelligent Space and try to describe a futurefrom a viewpoint of system integration. Also we are now introducing
RT(Robot Technology) to develop the Intelligent Space as an actual standard
platform which could be approved by Robotics Community.

PT3 Development of Future Intelligent Sweet Home for the Disabled
Ju-Jang Lee
Korea Advanced Institute of Science and Technology
Korea

Prof. Ju-Jang Lee©ISAROB 2006

This presentation introduces a new smart house, Intelligent Sweet Home,
developed at HWRS-ERC center of KAIST, Korea for testing advanced
concepts on independent living of the elderly and people with disabilities. The
work focuses on technical solutions for human-friendly motion/mobility
assistance and advanced human-machine interfaces that provide easy control ofboth assistive robotic systems and home-installed applications. To improve theinhabitant comfort, an intelligent bed, intelligent wheelchair and transferring
system between bed and wheelchair have been developed and tasted. The
solutions applied to their design comply with most of the user’s requirements
and suggestions collected by a special questionnaire survey among people withdisabilities. The intelligent sweet home behaves in accordance with the usercommands and/or the recognized intentions of the user. Various interfaces
based on hand gesture, voice, body movement and posture have been studied
and tested. The presentation shows the overall structures of the system andexplains the design and functionality of main system components. I hope that
this plenary talk will provide not only speech on the progress of newtechnologies but also the future vision of a better life for the disabled andelderly.

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B-Con Plaza, Beppu, Oita, Japan, January 23-25,2006

The Eleventh International Symposium on
ARTIFICIAL LIFE AND ROBOTICS(AROB 11th '06)

January 23 (Monday)
Room E: 10:40-11:00

Opening Ceremony
Chair: H.H. Lund (University of Southern Denmark, Denmark)Welcome Addresses
1. General Chairman of AROB

M. Sugisaka (Oita University, RIKEN, Japan)

2. Program Chairman of AROB

H. Tanaka (Tokyo Medical& Dental University,
Japan)Y. G. Zhang (Academia Sinica, China)

3. Vice Chairman of AROB
4. Vice Chairman of AROB

J. J. Lee (Korea Institute of Science and Technology,
Korea)

January 24 (Tuesday)
Hotel Shiragiku: 18:20-20:20

AROB Award Ceremony
Chair: K. Watanabe (Saga University, Japan)

BanquetChair: S. Sagara (Kyushu Institute of Technology, Japan)
Welcome Addresses
T. Hano (President of Oita University, Japan)
H.H. Lund (University of Southern Denmark, Denmark)©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

TIME TABLE
RoomA
1/22

13:00

(Sun)

17:00

RoomB

RoomC

RoomD

RoomE

Registration (Registration Desk)
Welcome Party (at Beppu Kamenoi Hotel 3th Floor)

1/23

8:00

(Mon)

8:40

Registration (Registration Desk)
GS2(4)

OS13(5)

GS21(5)

GS12(5)Chair D. Ai

Chair J .J. Lee

Chair S. Sagara

Chair S. Ishikawa

will end at 10:00
10:20
10:40

Coffee Break
Ceremony
Plenary Talk
PT1 T. Fukuda
Chair J .J. Lee

11:40
Lunch
12:40

14:00
14:10

16:30

GS3(4)

OS10(4)

GS22(4)

GS11(4)Chair X. Wang

Chair T.Yamamoto

Chair N. Homma

Chair J-Q. Liu

OS1(7)

OS4(7)

GS20(7)

GS4(7)

Chair F. Dai

Chair J. M. Lee

Chair C-Y. Lee

Chair Z. Shi

OS2(4)

GS7(5)

GS6(4)

Chair S. Omatu

Chair M. Eaton

Chair K. Ikeda

Coffee Breakwill end at 18:10
17:50
GS: General Session

OS: Organized Session

GS1 Artificial Brain Research
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life
GS5 Computer Graphics
GS6 Complexity
GS7 Cooperative Systems
GS8 Evolutionary Computations
GS9 Genetic Algorithms-I
GS10 Genetic Algorithms-Ⅱ
GS11 Human-Machine
GS12 Image Processing
GS13 Intelligent Control and Modeling-I & Cognitive Science
GS14 Intelligent Control and Modeling-Ⅱ
GS15 Innovative Computations-I
GS16 Innovative Computations-Ⅱ
GS17 Mobile Vehicles & Human-Welfare Robotics
GS18 Molecular Biology
GS19 Neural Networks©ISAROB 2006

GS20 Robotics-I
GS21 Robotics-Ⅱ
GS22 Robotics-Ⅲ
GS23 Others
OS1 Student’s Session-Recognition and Control for Robot
OS2 Intelligent Systems
OS3 Biomimetic Machines and Robots
OS4 Ubiquitous Automated Systems
OS5 Bio-inspired Approach and Application
OS6 Cognitive Robotics
OS7 Intelligent Systems and Learning
OS8 Vision, Automata and Neural Network
OS9 Robotics, Automata and Control
OS10 Soft Robotics and Information
OS11 Dynamics and Control for Complex Systems
OS12 Analysis and Implementation of Neural Systems
OS13 Intelligent Sensor and Algorithm
OS14 Robot Control and Application

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

RoomA
1/24

8:00

(Tue)

8:40

RoomB

RoomC

RoomE

Registration (Registration Desk)
GS9(5)Chair

A. Nakamura

will end at 10:20

GS17(5)OS7(6)

Chair P. Sapaty

Chair K. Abewill end at 10:20

10:40
10:50

Coffee BreakPlenary Talk
PT2 H. Hashimoto
Chair: K-B. Sim

11:30
Lunch
12:30

GS10(4)

GS8(5)

GS13(5)Chair H. Yamamoto
will end at 13:50

Chair M. Oswald

Chair J. S. Yang

OS11(5)

GS15(4)

GS14(3)Chair H. Suzuki

Chair T. Arita

Chair T. Nakashima

will end at 15:50

will end at 15:30

GS16(4)

GS19(6)Chair Y. Ishida

Chair Y. Kinouchi

will end at 17:10

will end at 17:30

14:10
14:30

16:10

Coffee BreakOS12(5)Chair T. Kohno
will end at 17:50

GS5(3)
Chair A. Niimi
will end at 18:10

18:20
AROB Award Ceremony (Chair K. Watanabe)
20:20

GS: General Session

Banquet -Hotel Shiragiku- (Chair S. Sagara)...... Welcome Address T. Hano / H. H. Lund

OS: Organized Session

GS1 Artificial Brain Research
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life
GS5 Computer Graphics
GS6 Complexity
GS7 Cooperative Systems
GS8 Evolutionary Computations
GS9 Genetic Algorithms-I
GS10 Genetic Algorithms-Ⅱ
GS11 Human-Machine
GS12 Image Processing
GS13 Intelligent Control and Modeling-I & Cognitive Science
GS14 Intelligent Control and Modeling-Ⅱ
GS15 Innovative Computations-I
GS16 Innovative Computations-Ⅱ
GS17 Mobile Vehicles & Human-Welfare Robotics
GS18 Molecular Biology
GS19 Neural Networks©ISAROB 2006

GS20 Robotics-I
GS21 Robotics-Ⅱ
GS22 Robotics-Ⅲ
GS23 Others
OS1 Student’s Session-Recognition and Control for Robot
OS2 Intelligent Systems
OS3 Biomimetic Machines and Robots
OS4 Ubiquitous Automated Systems
OS5 Bio-inspired Approach and Application
OS6 Cognitive Robotics
OS7 Intelligent Systems and Learning
OS8 Vision, Automata and Neural Network
OS9 Robotics, Automata and Control
OS10 Soft Robotics and Information
OS11 Dynamics and Control for Complex Systems
OS12 Analysis and Implementation of Neural Systems
OS13 Intelligent Sensor and Algorithm
OS14 Robot Control and Application

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The Eleventh International Symposium on Artificial Life and Robotics 2006(AROB 11th ’06),
B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

RoomA
1/25

8:00

(Wed)

8:40

RoomB

RoomC

RoomE

Registration (Registration Desk)
OS14(6)OS6(6)

OS5(6)

Chair S. H. Han

Chair M. Yokota

Chair I. Yoshihara

10:40
11:00

Coffee BreakPlenary Talk
PT3 J.J.Lee
Chair J. Du

11:40
Lunch
12:40
GS1(5)

OS3(6)

OS8(5)

Chair Y. G. Zhang

Chair K. Watanabe

Chair Y. Yoshitomi

will end at 14:40
14:20
OS9(6)

GS18(4)Chair

K. Ohnishi

will end at 15:40

Chair M. Kono

GS23(11)Chair

M. Rizon

16:20
Farewell Party (Room A)
17:00
GS: General Session

OS: Organized Session

GS1 Artificial Brain Research
GS2 Artificial Intelligence-Ⅰ
GS3 Artificial Intelligence-Ⅱ
GS4 Artificial Life
GS5 Computer Graphics
GS6 Complexity
GS7 Cooperative Systems
GS8 Evolutionary Computations
GS9 Genetic Algorithms-I
GS10 Genetic Algorithms-Ⅱ
GS11 Human-Machine
GS12 Image Processing
GS13 Intelligent Control and Modeling-I & Cognitive Science
GS14 Intelligent Control and Modeling-Ⅱ
GS15 Innovative Computations-I
GS16 Innovative Computations-Ⅱ
GS17 Mobile Vehicles & Human-Welfare Robotics
GS18 Molecular Biology
GS19 Neural Networks©ISAROB 2006

GS20 Robotics-I
GS21 Robotics-Ⅱ
GS22 Robotics-Ⅲ
GS23 Others
OS1 Student’s Session-Recognition and Control for Robot
OS2 Intelligent Systems
OS3 Biomimetic Machines and Robots
OS4 Ubiquitous Automated Systems
OS5 Bio-inspired Approach and Application
OS6 Cognitive Robotics
OS7 Intelligent Systems and Learning
OS8 Vision, Automata and Neural Network
OS9 Robotics, Automata and Control
OS10 Soft Robotics and Information
OS11 Dynamics and Control for Complex Systems
OS12 Analysis and Implementation of Neural Systems
OS13 Intelligent Sensor and Algorithm
OS14 Robot Control and Application

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TECHNICAL PAPER INDEXJanuary 23 (Monday)
Room E
11:00~11:40 Plenary Talk
Chair J. J. Lee （Korea Institute of Science and Technology, Korea）
PT-1 Multi-locomotion robots -PDAC for intelligent controlT. Fukuda (Nagoya University, Japan)

January 24 (Tuesday)
Room E
10:50~11:30 Plenary Talk
Chair K-B. Sim（Chung-Ang University, Korea）
PT-2

Present state and future of intelligent space –discussion on implementation of
RT in our environments
H. Hashimoto(University of Tokyo, Japan)

January 25 (Wednesday)
Room E
11:00~11:40 Plenary Talk
Chair J. Du (Beijing Technology and Business University, China)
PT-3

Development of future intelligent sweet home for the disabled
J. J. Lee (Korea Institute of Science and Technology, Korea)©ISAROB 2006

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January 23 (Monday)
8:00~Registration

Room A
8:40~10:00 GS2 Artificial Intelligence-Ⅰ
Chair: D. Ai (University of Science and Technology Beijing, China)
GS2-１ Locating static targets by matching image frames
X. Wang(Niihama National College of Technology, Oita University, Japan)
M. Sugisaka (Oita University, Japan)
J. Wang (Hebei University of Science and Technology, China)
GS2-2 Perceptual and introspective learning for developmental robot
Z. Shi, Q. Li (Chinese Academy of Sciences, China)
GS2-3 A robot control method using evolutive binary decision diagrams
M. Kanoh (Chukyo University, Japan)
H. Itoh (Nagoya Institute of Technology, Japan)
GS2-4 Waste incinerator emission prediction using probabilistically optimal ensemble ofmulti-agents and neural networks
D. Yamaguchi, M. Takahashi, E. Tazaki (Toin University of Yokohama, Japan)
K. J. Mackin (Tokyo University of Information Sciences, Japan)

12:40~14:00 GS3 Artificial Intelligence-Ⅱ
Chair: X. Wang (Niihama National College of Technology, Japan)
GS3-1 Model-based reinforcement learning for large-scale multi-agent games withsampling-based state estimation
H. Fujita, S. Ishii (Nara Institute of Science and Technology, Japan)
GS3-2 Analyzing robustness in multi-agent reinforcement learning-A comparison between profit sharing and Q-learning
T. Nehashi, K. Takadama(Tokyo Institute of Technology, Japan)
K. Miyazaki (National institution for Academic Degrees and University Evaluation,
Japan)
GS3-3 Natural policy gradient reinforcement learning method for a looper-like robot
Y. Nakamura, T. Mori, S. Ishii(Nara Institute of Science and Technology (NAIST),
Japan)
GS3-4 Vector control of induction motor using advanced hybrid system based on GA and
Bacteria Foraging
D-H. Kim (Hanbat National University, Korea)

14:10~16:30 OS1 Student’s Session-Recognition and Control for Robot
Chair: F. Dai (Matsue National College of Technology, Japan)©ISAROB 2006

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Co-Chair: J. Hayashida (Matsue National College of Technology, Japan)
OS1-1 Remote Control of robot using wireless LAN
N. Kawasaki, F. Dai, Y. Fujihara (Matsue National College of Technology, Japan)
OS1-2 Research on face recognition system by genetic algorithm
F. Dai, T. Kodani, Y. Fujihara (Matsue National College of Technology, Japan)
OS1-3 Research on autonomous mobile robot for visually handicapped humans
T. Nakamura, F. Dai, Y. Fujihara (Matsue National College of Technology, Japan)
OS1-4 Walking orbit of biped robot by using the simulation
K. Umezaki, M. Sugisaka (Oita University, Japan)
OS1-5 Motion trace through head control
E. Goubaru, M. Sugisaka (Oita University, Japan)
OS1-6 Characteristic measurement of artificial muscle
S. Ichikawa, M. Sugisaka (Oita University, Japan)
OS1-7 Motion control of biped robot
K. Kabata, M. Sugisaka (Oita University, Japan)

16:30~17:50 OS2 Intelligent Systems
Chair: S. Omatu (Osaka Prefecture University, Japan)
Co-Chair: H. Ido (Niihama National College of Technology, Japan)
OS2-1 Intelligent classification of bill money
S. Omatu (Osaka Prefecture University, Japan)
T. Kosaka(Glory TD Himeji, Japan)
M. Teranisi (Nara University of Education, Japan)
OS2-2 Intelligent smell classification by neural networks
S. Omatu,T. Fujinaka, S. Okazaki(Osaka Prefecture University, Japan)
T. Sumiya, M. Ono (Funai Electric Advanced Applied Technology, Research InstituteInc., Japan)
OS2-3 Graph construction with maximum number of trees by continuous edge addition
H. Ido, Y. Ohama, T. Takahashi (Niihama National College of Technology, Japan)
OS2-４ Graph Extension with Constant Connectivity
H. Ido (Niihama National College of Technology, Japan)
S. Omatu (Osaka Prefecture University, Japan)

Room B
8:40~10:20 OS13 Intelligent Sensor and Algorithm
Chair: J. J. Lee(Korea Advanced Institute of Science and Technology, Korea)
Co-Chair: M. Habib (Korea Advanced Institute of Science and Technology,
Korea)©ISAROB 2006

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OS13-1 Design and fabrication of tactile sensor system of FBG optical fiber sensors
J. S. Heo, J.J. Lee (Korea Advanced Institute of Science and Technology, Korea)
OS13-2 Implementation of an intelligent personalized digital library system based on
improved negotiation mobile multi agents,
Y. I. Cho (Suwon University, Korea)
OS13-3 Crowd simulation with virtual force model
C.Y. Lee (Kyungpook National University, Korea)
S. Baek (ETRI, Korea)
OS13-4 Driver fatigue detection using genetic algorithm
S. Jin, S-Y. Park. J. J. Lee(Korea Advanced Institute of Science and Technology,
Korea)
OS13-5 Virtual planes and active vision for fast 3D real time obstacle detection and avoidance
M. K. Habib, J. J. Lee(Korea Advanced Institute of Science and Technology, Korea)

12:40~14:00 OS10 Soft Robotics and Information
Chair: T. Yamamoto (University of the Ryukyus, Japan)
Co-Chair: H. Kinjo (University of the Ryukyus, Japan)
OS10-1 Identification using dynamical neural network with modified BP method
K. Nakazono, H. Kinjo (University of the Ryukyus, Japan)
K. Ohnishi (Keio University, Japan)
OS10-2 Nonholonomic system control using neurocontroller evolved by genetic algorithm
N. Ogino, H. Kinjo, K. Nakazono, T. Yamamoto(University of the Ryukyus, Japan)
OS10-3 Searching performance of real-coded genetic algorithm using biased probability
distribution functions and mutation
H. Nakanishi, H. Kinjo, N. Oshiro, T. Yamamoto (University of the Ryukyus, Japan)
OS10-4 A self-organizing model of place cells with periodically distributed receptive fields
N. Oshiro, K. Kurata, T. Yamamoto(University of the Ryukyus, Japan)

14:10~16:30 OS4 Ubiquitous Automated Systems
Chair: J. M. Lee (Pusan National University, Korea)
Co-Chair: S-C. Kim (Pusan National University, Korea)
OS４-1 On-line prognostics health maintenance for induction motors based on time-series data mining
J. Park, Y. Kim, Y-I. Kim, H. Bae, S. Kim (Pusan National University, Korea)
OS４-2 The curvature detect system of the lane using image
J-w. Kim, S-k. Ha, S-b. Cho, K-r. Baek (Pusan National University, Korea)
OS４-3 Implementation mass flow controller for adaptive PID
B. Cho, K. Baek (Pusan National University, Korea)©ISAROB 2006

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OS4-4 A force reflection electronic joystick using single hall sensor
S.K. Cho, S. S. Han, J.M. Lee (Pusan National University, Korea)
OS4-5 An self-localization of mobile robot in an RFID sensor space
B. S. Choi, H. S. Choi, J. M. Lee (Pusan National University, Korea)
OS4-6 Fuzzy posture control for biped walking robot based on force sensor for ZMP
K.C. Choi, M.C. Lee, J. M. Lee (Pusan National University, Korea)
OS4-7 Bluetooth network for distributed autonomous robotic system
S-H. Whang, I-H. Jang, K-B. Sim (Chung-Ang University, Korea)

16:30~18:10 GS7 Cooperative Systems
Chair: M. Eaton (University of Limerick, Ireland)
GS7-1 Mapping children and playgrounds into multi-agent systems
A. Derakhshan, F. Hammer, H. H. Lund (University of Southern Denmark, Denmark)Y. Demazeau (CNRS, Laboratoire LEIBNIZ, Institut IMAG, France)
GS7-2 Reference-position detection using fan beam laser for cooperative localization ofmultiple mobile robots
H. Takai, J.Mitsuoka (Hiroshima City University, Japan)
K. Tachibana(Osaka Gakuin University, Japan）
GS7-3 Learning method of cooperative team play using the immune system
N. Kogawa, K. Kobayashi, M. Obayashi, T. Kuremoto (Yamaguchi University, Japan)
GS7-4 A study on stability analysis of cooperative AGV systems using decentralized control
algorithm
J. H. Suh, K. S. Lee(Dong-A University, Korea)
Y. B. Kim(Pukyong National University, Korea)
GS7-5 Development of learning support system for piano-keying-Relationship between the activity of finger muscles and key release velocities of anexpertT. Tamei, T. Shibata, S. Ishii(Nara Institute of Science and Technology, Japan)

Room C
8:40~10:20 GS21 Robotics-Ⅱ
Chair: S. Sagara (Kyushu Institute of Technology, Japan)
GS21-1 Implementation of infrared wireless communication system for multi-mobile robots’team operations
H. Takai, T. Ninomiya (Hiroshima City University, Japan)
K. Tachibana (Osaka Gakuin University, Japan)
GS21-2 The development of robot palletizing simulator using pallet pattern algorithm andtrajectory optimization
S-n. Yu, S-j. Lim, H-k.Yoon, C-s.Han (Han Yang University, Korea)©ISAROB 2006

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GS21-3 Redundant arm positioning control by linear visual servoing based on linear
approximation of forward kinematics
S. Mukai, N. Maru (Wakayama University, Japan)
GS21-4 A study of an autonomous mobile robot for sewer inspection system
A. Ahrary, A. Nassiraei (FAIS-Robotics Research Institute, Kyushu Institute ofTechnology, Japan)
M. Ishikawa (Kyushu Institute of Technology, Japan)
GS21-5 Detection of face pose with neural networks
Y-Z. Chang, G-T. Hung (Chang Gung University, Taiwan)

12:40~14:00 GS22 Robotics-Ⅲ
Chair: N. Homma (Tohoku University, Japan)
GS22-1 A study on the development of actively controlled anti-seasickness bed
Y. B. Kim (Pukyong National University, Korea)
J. H. Suh, K. S. Lee (Dong-A University, Korea)
GS22-2 A modeling and position control based on two –mass system for machine standvibration system
S. Watanabe, F. Ohkawa, R. Oguro, J. Kobayashi (Kyushu Institute of Technology,
Japan)
GS22-3 Reinforcement learning of switching multiple controllers to control a real robot
Y. Tokita, Y. Nakamura, S. Ishii (NARA Institute of Science and Technology, Japan)
J. Yoshimoto (Okinawa Institute of Science and Technology Promotion Corporation,
Japan)
GS22-4 Real time control of humanoid-robot cooperative motion using a genetic algorithm
and neural network
K-J. Choi, Y-K. Hwang, D. S. Hong (Changwon National University, Korea)

14:10~16:30 GS20 Robotics-Ⅰ
Chair: C-Y. Lee (Kyungpook National University, Korea)
GS20-1 Navigation system for an autonomous robot using an ocellus camera in indoor
environment
T. Umeno, E. Hayashi (Kyushu Institute of Technology, Japan)
GS20-2 RAC for underwater vehicle-manipulator systems using dynamic equation
T. Yatoh, S. Sagara, M. Tamura (Kyushu Institute of Technology, Japan)
GS20-3 Digital tracking control of space robots using transpose of Generalized JacobianMatrixS. Sagara (Kyushu Institute of Technology, Japan)
Y. Taira (National Fisheries University, Japan)
GS20-4 Optimal parameter tuning in proportional control for the unicycle mobile robot:An experimental study©ISAROB 2006

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D. Hazry, M. Sugisaka (Oita University, Japan)
GS20-5 Development of an autonomous personal robot “System of recognition for work”
H. Mizoguchi, Y. Matsumoto, E. Hayashi (Kyushu Institute of Technology, Japan)

GS20-6 A model of Mckibben muscle actuator based on experiment
H. Zhao (University of Science and Technology Beijing, China / Oita University,
Japan)
M. Sugisaka (Oita University, Japan)
D. Yu (University of Science and Technology Beijing, China)
GS20-7 Offensive strategy of a billiard robot
J-S. Yang, J-T. Li (Tamkang University, Taiwan)
C.Y. Yang (Northern Taiwan Institute of Science and Technology, Taiwan)

16:30~17:50 GS6 Complexity
Chair: K. Ikeda (Kyoto University, Japan)
GS6-1 VeriSync:A verification system for firing squad synchronization protocols on
one-dimensional cellular automata
K. Matsumoto, H. Umeo (University of Osaka Electro-Communication, Japan)
GS6-2 State-efficient synchronization protocols for communication-restricted cellular
automata
T. Yanagihara, M. Kanazawa, H. Umeo (University of Osaka Electro-Communication,
Japan)
GS6-3 Analysis and simulation of group dynamics based on interaction between decision
making and Heider’s POX systems
T. Nomura (Ryukoku University, Japan)
GS6-4 Cut off of Zipfs power law in US cities
K. Yamasaki, M. Ohshiro, K.J. Mackin, E. Nunohiro (Tokyo University of information
Sciences, Japan)

Room D
8:40~10:20 GS12 Image Processing
Chair: S. Ishikawa (Kyushu Institute of Technology, Japan)
GS12-1 Visualization of an invisible space based on the MR technique
S. Hashimoto, J. K. Tan, S. Ishikawa (Kyushu Institute of Technology, Japan)
GS12-2 Face emotion recognition-a survey
M. Karthigayan, M. Rizon , S. Yaacob, R. Nagarajan（Northern Malaysia University
College of Engineering, Malaysia)
GS12-3 Moments in IC chip classification
M. Karthigayan, R.Nagarajan, S. Yaacob, P. Pandian, M. Rizon (Northern Malaysia
University College of Engineering, Malaysia)©ISAROB 2006

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M. Rozailan (Terengganu Advanced Technical Institute(TATI), Malaysia)
GS12-4 Dimensions reduction for face recognition using principal component analysis
M. F. Hashim, M. Rizon ,P. Saad, M. Karthigayan S. Yaakob, H. Yazid (Kolej
Kejuruteraan Utara Malaysia (KUKUM), Malaysia)
M. R. Mamat (Terengganu Advanced Technical Institute (TATI), Malaysia)
GS12-5 Automatic motion detection for surveillance
N. Mokhtar, M. Sugisaka (Oita University, Japan)

12:40~14:00 GS11 Human-Machine
Chair: J-Q. Liu (ATR Network Informatics Labs., Japan)
GS11-1 Surface based spatial awareness for mobile robots
K. Kanev (The University of Aizu, Japan)
S. Kimura (Kanazawa University, Japan)
GS11-2 Interactive musical editing system to support human errors and offer personal
preferences for an automatic piano- A system of inferring phrase expressionY. Hikisaka, E.Hayashi (Kyushu Institute of Technology, Japan)
GS11-3 Minimal design of “in situ” communication agents
N. Matsumoto, H. Fujii, M. Okada (ATR Network Informatics Laboratories, Japan)
GS11-4 A novel human-environment interface for conceptualizing spatial information in
non-speech audio
S. Nomura, M. Tsuchinaga, Y. Nojima, T. Shiose, H. Kawakami, O. Katai (Kyoto
University, Japan)
K. Yamanaka (Federal University of Uberlandia, Brasil)

14:10~16:30 GS4 Artificial Life
Chair: H.H Lund (University of Southern Denmark, Denmark)GS4-1 Emerging cell array based on reaction-diffusion
S. Miyashita (University of Zurich, Switzerland)
S. Murata (Tokyo Institute of Technology, Japan)
GS4-2 Biomimetic control of mobile robots based on the information processing model ofParamecium
A. Hirano, T. Tsuji, N. Takiguchi (Hiroshima University, Japan)
H. Ohtake (Osaka University, Japan)
GS4-3 Evolutionary simulation of an agent based mobility system using indirectcommunication
F. Zanlungo (University of Bologna, Italy)
T. Arita (Nagoya University, Japan)
GS4-4 Evolution, development and learning in the prisoner’s dilemma gameY. Ogawa, T. Arita (Nagoya University, Japan)
GS4-5 Describing metabolic pathways using an artificial chemistry based on pattern matching©ISAROB 2006

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and recombination
Y. Suzuki, K. Tominaga (Tokyo University of Technology, Japan)
GS4-6 Morphogenesis of 3D sheets exploiting a spatial condition
S. Miyashita (University of Zurich, Switzerland)
GS4-7 Sexual reproduction in digital organisms
D. Ai, X. Ban. Y. Yin, X. Tu (University of Science and Technology Beijing, China)

January 24 (Tuesday)
8:00~ Registration

Room A
8:40~10:20 GS9 Genetic Algorithms-Ⅰ
Chair: A. Nakamura (National Institute of Advanced Science and Technology(AIST), Japan)
GS9-1 Development of NC tool path generation system by absolute and incremental typeindividual
H. Yamamoto, R.B. Ramli (Gifu University, Japan)
GS9-2 A study of selecting optimal parameters for genetic algorithm
S.Y. Park, S. Jin, J.J. Lee (Korea Advanced Institute of Science andTechnology(KAIST), Korea.)
GS9-3 The new DFM approach based on genetic algorithm
M. Yoshikawa, H. Terai (Ritsumeikan University, Japan)
GS9-4 Design of an augmented automatic choosing control with constrained input by
extremizing a combination of Hamiltonian and Lyapunov functions
T. Nawata (Kumamoto National College of Technology, Japan)
H. Takata (Kagoshima University, Japan)
GS9-5 Colour quantisation using simulated annealing
G. Schaefer, L. Nolle (Nottingham Trent University, United Kingdom)
T. Nakashima( Osaka Prefecture University, Japan)

12:30~13:50 GS10 Genetic Algorithms-Ⅱ
Chair: H. Yamamoto (Gifu University, Japan)
GS10-1 Applying genetic algorithm to a programming training support system
E. Nunohiro, K. J. Mackin, M. Ohshiro, K. Yamasaki(Tokyo University of Information
Sciences, Japan)
GS10-2 Automatic generation of B-spline curve using the evolution technique
M. Saito, S. Serikawa (Kyushu Institute of Technology, Japan)
GS10-3 A genetic approach to the design of autonomous agents for futures trading©ISAROB 2006

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H. Kitano, T. Nakashima, H. Ishibuchi (Osaka Prefecture University, Japan)
GS10-4 Comparison between self-organization with sampling and genetic algorithms in
multi-modal function
T. Higo, K. Takadama (Tokyo Institute of Technology, Japan)
M. Katuhara, T. Majima (National Maritime Research Institute , Japan)

14:30~16:10 OS11 Dynamics and Control for Complex Systems
Chair: H. Suzuki(The University of Tokyo, Japan)
Co-Chair: S. Horai (The University of Tokyo, Japan)
OS11-1 A simple model of evolving ecosystems
T. Shimada, K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The
University of Tokyo, Japan)
Y. Murase, S. Yukawa, N. Ito(The University of Tokyo, Japan)
OS11-2 The behavioral adaptation and the diversification in ecosystem
Y. Murase, S.Yukawa, N. Ito (The University of Tokyo, Japan)
T. Shimada (Aihara Complexity Modelling Project, ERATO, JST, Japan)
OS11-3 Stability of equilibrium state of multi-type SIS model on network with certain property
N. Sugimine (Aihara Complexity Modelling Project, ERATO, JST, Japan)
K. Aihara ( Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)
OS11-4 On chaos suppression by resonant parametric perturbation
M. Sekikawa (Aihara Complexity Modelling Project, ERATO, JST, Japan)
N. Inaba (Utsunomiya University, Japan)
K. Aihara ( The University of Tokyo, Japan)
OS11-5 State estimation with finite data rates and information pattern
H. Shimokawa (Aihara Complexity Modelling Project, ERATO, JST, Japan)
K. Aihara (The University of Tokyo, Aihara Complexity Modelling Project, ERATO,
JST, Japan)

16:10~17:50 OS12 Analysis and Implementation of Neural Systems
Chair : T. Kohno(The University of Tokyo, Japan)
Co-Chair: Y. Hirata (The University of Tokyo)
OS12-1 Modeling of birdsong learning with chaotic Elman network
M. Funabashi (The University of Tokyo, Japan)
K. Aihara (The University of Tokyo, Japan)
OS12-2 Classification of the spike sequences by discriminating their sources of timecorrelation
K. Fujiwara (The University of Tokyo, Japan)
K. Aihara(The University of Tokyo, and ERATO, JST, Japan)
OS12-3 Spike train surrogates and dual coding
Y. Hirata, H. Suzuki (The University of Tokyo, Japan)©ISAROB 2006

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K. Aihara (The University of Tokyo, Aihara Complexity Modelling Project, ERATO,
JST, Japan)

OS12-4 Dynamic switching of neural coding schemes in a network with gap junctions
Y. Katori, K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, Japan)
N. Masuda (Aihara Complexity Modelling Project, ERATO, JST, RIKEN Brain
Science Institute, Japan)
OS12-5 MOSFET implementation of two-dimensional neuron model
T. Takemoto (The University of Tokyo, Japan)
T. Kohno, S. Tsuji (Aihara Complexity Modelling Project, ERATO, JST, Japan)
K. Aihara (Aihara Complexity Modelling Project, ERATO, JST, The University ofTokyo, Japan)

Room B
8:40~10:20 GS17 Mobile Vehicles & Human-Welfare Robotics
Chair: P. Sapaty (National Academy of Sciences, Ukraine)
GS17-1 A stepping motor control for electric vehicle
M. Sugisaka, K. Ahmad (Oita University, Japan)
GS17-2 Reinforcement learning with self-instruction by using dual Q-tables
O. Nishimura, H. Matsui, C. Hioki, Y. Nomura ( Mie University, Japan)
C. Hioki (Toyota Communication Systems Co., Ltd., Japan)
GS17-3 Position estimation of a mobile robot using U-SAT (Ultrasonic Satellites)
S.Y. Kim, C. S. Kim, S. M. Kim , M. H. Lee( Pusan National University, Korea)
GS17-4 Robotics safety issues for human welfare in an automated manufacturing system
B. Rajamony, M. Rizon (Northern Malaysia University College of Engineering,
Malaysia)
GS17-5 Asymmetry analysis of human motions for examining rehabilitation training
K. Noda, J.K. Tan, S. Ishikawa (Kyushu Institute of Technology, Japan)
N. Kito, K. Okumura (Kawashima Orthopedic Hospital, Japan)

12:30~14:10 GS8 Evolutionary Computations
Chair: M. Oswald (Vienna University of Technology, Austria)
GS8-1 Optimization of coordinated control parameters of traffic signals using ACO
M. Shinohara, K. Shoji, F. Toyama, J. Miyauchi (Utsunomiya University, Japan)
GS8-2 Evolution strategies based gaussian sum particle filter for state estimation of nonlinear
stochastic systems
K. Uosaki, T. Hatanaka ( Osaka University, Japan)
GS8-3 A new algorithm for obtaining molecular information based on TaqMan real time PCR
Z. Ibrahim (Universiti Teknologi Malaysia, Malaysia / Meiji University, Japan)
Y. Tuboi, S. Sasaki, T. Yamaguchi, O. Ono (Meiji University, Japan)©ISAROB 2006

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GS8-4 Automatic evolution of bipedal locomotion in a simulated humanoid robot with manydegrees of freedom
M. Eaton, T.J.Davitt (University of Limerick, Ireland)
GS8-5 Interpretability-accuracy tradeoff by multiobjective genetics-based machine learningfor pattern classification problems
Y. Nojima, H. Ishibuchi (Osaka Prefecture University, Japan)

14:30~15:50 GS15 Innovative Computations-Ⅰ
Chair: T. Arita (Nagoya University, Japan)
GS15-1 Recovering the network through mutual recognition and copying
Y. Ishida, Y. Sugawara (Toyohashi University of Technology, Japan)
GS15-2 Dynamics of spatial strategies in regulating copying strategies in a self-repair
network
Y. Ishida, M. Oohashi (Toyohashi University of Technology, Japan)
GS15-3 Sensor data mining system with multiagent approach for metrological data andpachinko system
A. Niimi (Future University-Hakodate, Japan)
GS15-4 Application of graduated PCR in concentration-controlled direct-proportional
length-based DNA computing
Z. Ibrahim (Universiti Teknologi Malaysia, Malaysia / Meiji University, Japan)
Y. Tsuboi, O. Ono (Meiji University, Japan)

15:50~17:10 GS16 Innovative Computations-Ⅱ
Chair: Y. Ishida (Toyohashi University of Technology, Japan)
GS16-1 Molecular computing approach for constraint assignment problem
Z. Ibrahim(Universiti Teknologi Malaysia, Malaysia / Meiji University, Japan)
Y. Tsuboi, O. Ono (Meiji University, Japan)
GS16-2 Matrix multiplication by DNA computing
N. Kasai,Y. Tsuboi, O.Ono(Meiji University, Japan)
Z. Ibrahim (Meiji University, Japan / Universiti Teknologi Malaysia, Malaysia)
GS16-3 Independent agents in a globalized world modelled by tissue P systems
M. Oswald (Vienna University of Technology, Austria)
GS16-4 A bioinformatics method for signaling pathways in cells
J-Q. Liu, K. Shimohara (ATR Network Informatics Laboratories, Japan)

17:10~18:10 GS5 Computer Graphics
Chair: A. Niimi (Future University-Hakodate, Japan)
GS5-1 3-D Modeling of remote dynamic situations using mobile stereo cameras
I. Yamaguchi, J. K. Tan, S. Ishikawa ( Kyushu Institute of Technology, Japan)©ISAROB 2006

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GS5-2 Tracking an object using an update type two-dimensional histogram
H. Uemura, J. K Tan, S. Ishikawa (Kyushu Institute of Technology, Japan)
GS5-3 Detecting method of coconuts location using circular hough transform
M. Rizon, H.Yazid, P.Saad, A.Y. M. Shakaff, A.R.Saad, S. Yaacob, M.Karthigayan,
M. F. Hashim (Northern Malaysia University College of Engineering, Malaysia)
M. Sugisaka (Waseda University, Oita University, Japan)
M. R. Mamat (Terengganu Advanced Technical Institute (TATI), Malaysia)

Room C
8:40~10:40 OS7 Intelligent Systems and Learning
Chair: K. Abe (Nihon University, Japan)
Co-Chair: N. Homma(Tohoku University, Japan)
OS7-1 Brain regions related to odor learning and memory in terrestrial slug, Inciralia
fruhstorferi:Two lobes of the cerebral ganglion show different spatio-temporal
activities
Y. Makino, H. Makinae, T. Obara, M. Yano (Tohoku University, Japan)
OS7-2 Natural intelligence: Noise-resistance of neural spike communication
N. Homma, M. Sakai (Tohoku University, Japan)
K. Fuchigami (Toshiba Med. Sys. Corp., Japan)
K. Abe (Nihon University, Japan)
OS7-3 Acquisition of deterministic exploration behavior by reinforcement learning
K. Shibata (Oita University, Japan)
OS7-4 Hierarchical Q-learning in POMDP environments
H. Kamaya (Hachinohe National College of Technology, Japan)
K. Abe ( Nihon University, Japan)
OS7-5 Improvement of finishing quality on blow mold’s constriction parts by using an
intelligent finishing robot
F. Nagata (Fukuoka Industrial Technology Center, Japan)
T. Hase, M. Omoto, Z. Haga (Meiho Co.Ltd., Japan)
K. Watanabe( Saga University, Japan)
OS7-6 Self-organizing neural networks for incremental category learning
M. Sakai, K. Takei, N. Homma (Tohoku University, Japan)
Y. Koyanaka (FANUC LTD, Japan)
K. Abe (Nihon University, Japan)

12:30~14:10 GS13 Intelligent Control and Modeling-Ⅰ& Cognitive Science
Chair: J. S. Yang (Tamkang University, Taiwan)
GS13-1 Simplified geometric models in skill-based manipulation for practical use
A. Nakamura, K. Kitagaki, T. Suehiro (National Institute of Advanced Industrial
Science and Technology (AIST), Japan)©ISAROB 2006

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GS13-2 Constructing technique of state space with low dependence on sensor configurations
for autonomous mobile robots
T. Shibuya, T. Hamagami(Yokohama National University, Japan)
GS13-3 Emergent Societies :An advanced IT support of crisis relief missions
P. Sapaty (National Academy of Sciences, Ukraine)
M. Sugisaka(Oita University, Japan)
R. Finkelstein(Robotic Technology Inc., USA)
J. Delgado-Frias (Washington State University, USA)
N. Mirenkov (University of Aizu, Japan)
GS13-4 Cognitive contour detection for negative filtering
Z. Petres, B. Resko (The University of Tokyo, Japan / Hungarian Academy of Sciences,
Hungary)
H. Hashimoto (The University of Tokyo, Japan)
GS13-5 Multistable perception model by multilayered bidirectional associative memory
K. Kitamura, T. Isokawa. H. Nishimura, N. Kamiura, N. Matsui (University of Hyogo,
Japan)

14:30~15:30 GS14 Intelligent Control and Modeling -Ⅱ
Chair: T. Nakashima (Osaka Prefecture University, Japan)
GS14-1 Comparative study on fuzzy and non-fuzzy cost-sensitive classification systems
T. Nakashima, Y. Yokota, H. Ishibuchi (Osaka Prefecture University, Japan)
G. Schaefer (Nottingham Trent University, United Kingdom)
GS14-2 Emergent design of a control system for cooperation between robots
K. Nakayama, K. Shimohara (ATR Network Informatics Lab., Japan)
H. Matsui (Mie University, Japan)
GS14-3 A design of mass estimated adaptive friction compensator for position control oflinear motor system
J. W. Lee, J. H. Suh, K.S. Lee (Dong-A University, Korea)

15:30 ~17:30 GS19 Neural Networks
Chair: Y. Kinouchi( Tokyo University of Information Sciences, Japan)
GS19-1 Implicit principle discovery in tourism geography information based on data mining
D. Junping (Beijing Technology and Business University, China)
G. Wensheng (University of Science and Technology, China)
GS19-2 Development of neural network based pattern classification system for cDNA microarray
D-W. Kim, C-H. Park, H-C.Yang, K-B. Sim (Chung-Ang University, Korea)
GS19-3 Remarks on tracking method of neural network weight change for adaptive typeneural network direct controller
T. Yamada (Ibaraki University, Japan)©ISAROB 2006

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B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

GS19-4 Situation recognition mechanism based on the fuzzy ART for a communication robot
K. Iwatsubo, Y. Hoshi, B. T. Nohara (Musashi Institute of Technology, Japan)
Y. Haga (Bandai Co.,Ltd., Japan)
GS19-5 Multi-module neural network system with concept formation and primitive
consciousness
Y. Kinouchi, K. Masuda (Tokyo University of Information Sciences, Japan)
S. Inabayashi (Pacific Technos Corp., Japan)
GS19-6 A bayesian approach to blind source separation with variable number of sources
J. Hirayama. S. Maeda, S. Ishii (Nara Institute of Science and Technology, Japan)

January 25 (Wednesday)
8:00~ Registration

Room A
8:40~10:40 OS14 Robot Control and Application
Chair: S. H. Han (Kyungnam University, Korea)
OS14-1 A controller design for a motion generator based on dual linear motors
D. H. Lee, S.Y. Seo, W. S. Choi, S-H. Lee, H. S. Kim (Kyungnam University, Korea)
OS14-2 Stiffness analysis of a limited-DOF parallel manipulator including the compliance ofboth joints and links
H. S. Kim, C-R. Shin, J-W. Kim, S-H. Lee (Kyungnam University, Korea)
OS14-3 A robust adaptive control of robot manipulator based-on DSPs
S. H. Han, U. Z. Na, W. S. Lee, H. B. Shin, Y. K. Kim (Kyungnam University, Korea)
OS14-4 A Study on image based visual feedback control of robot system
S. H. Han, U. Z. Na, H. R. Kim, Y. K. Kim, C. R. Shin(Kyungnam University, Korea)
OS14-5 Lateral-shearing interferometer for optical testing of DVD pick-up lenses
B. C. Kim, S.H.Kim. Y. K. Kwon, W. I. Kim (Kyungnam University, Korea)
OS14-6 Permanent magnet biased magnetic bearings and robotic applications
T. H. Son, U.J. Na (Kyungnam University, Korea)

12:40~14:20 GS1 Artificial Brain Research
Chair: Y.G. Zhang (Academia Sinica, China)
GS1-1 A free-scale model of knowledge growth
Y. G. Zhang, L. Tang (Academia Sinica, China)
M. Sugisaka ( Oita University, Japan)
GS1-2 Fundamental study of dielectric elastomer as artificial muscle
J. Hayashida, F. Dai, Y. Fujihara (Matsue National College of Technology, Japan)©ISAROB 2006

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GS1-3 A state space filter for reinforcement learning
M. Nagayoshi (Kobe University, Hyogo Assistive Technology Research and Design
Institute, Japan)
H. Murao, H. Tamaki (Kobe University, Japan)
GS1-4 Prediction of the aperiodic time series of a visual target by humans
M. Shikauchi, S. Ishii, T. Shibata (Nara Institute of Science and Technology, Japan)
GS1-5 Robotic emotion generation using dynamics-based information processing
M. Hashimoto (Shinshu University, Japan)
Y. Katoh (Harmonic Drive Systems Inc, Japan)

14:20~15:40 GS18 Molecular Biology & Bio-inspired Approach
Chair: K. Ohnishi (Niigata University, Japan)
GS18-1 Data manipulation of DNA relational database
Y. Kita, S. Kashiwamura ( Hokkaido University, Japan)
A. Kameda ( CREST, Japan Science and Technology Agency (JST), Japan)
M. Yamamoto, A. Ohuchi(Hokkaido University, CREST, Japan Science andTechnology Agency(JST), Japan)
GS18-2 Stability evaluation method of DNA tile structure
N. Iimura, F. Tanaka (Hokkaido University, Japan)
M. Yamamoto, A. Ohuchi(Hokkaido University, CREST, Japan Science andTechnology Agency(JST), Japan)
A. Kameda ( CREST, Japan Science and Technology Agency (JST), Japan)
GS18-3 Evolutionary emergence of the 16S/18S-ribosomal RNA from a poly-tRNA structure
containing a”(5’)tRNA(Gly)-spacer-tRNA(Cys)-spacer-tRNA(Leu)(3’)”region
K. Ohnishi, S. Hokari (Niigata Univeresity, Japan)
GS18-4 Stochastic analysis of schema distribution in multiplicative landscape
H. Furutani, S. Katayama , M.Sakamoto(University of Miyazaki, Japan)
T. Ito (Ube National College of Technology, Japan)

Room B
8:40~10:40 OS6 Cognitive Robotics
Chair: M. Yokota (Fukuoka Institute of Technology, Japan)
Co-Chair: G. Capi (Fukuoka Institute of Technology, Japan)
OS6-1 Integrated multimedia understanding based on mental image directed semantic theory
M. Yokota, G. Capi (Fukuoka Institute of Technology, Japan)
OS6-2 Associative learning method in hyper-column model
A. Shimada, R. Taniguchi (Kyushu University, Japan)
N. Tsuruta (Fukuoka University, Japan)
OS6-3 Incremental acquisition of behavioral concepts through social interactions with a©ISAROB 2006

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B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

caregiverT. Taniguchi, T. Sawaragi (Kyoto University, Japan)
OS6-4 A step towards artificial ‘Kansei’
K. Sugita, G. Capi, M. Yokota( Fukuoka Institute of Technology, Japan)
OS6-5 A spoken language interface to a mobile robot
J. Bos (Universita di Roma “La Sapienza”, Italy)
T. Oka (Fukuoka Institute of Technology, Japan)
OS6-6 Performance evaluation of evolutionary multiobjective optimization
G. Capi, M. Yokota ( Fukuoka Institute of Technology, Japan)
K. Bode (Polytechnic University of Tirana, Albania)

12:40~14:40 OS3 Biomimetic Machines and Robots
Chair: K. Watanabe (Saga University, Japan)
Co-Chairman: K. Izumi (Saga University, Japan)
OS3-1 Mobile robot navigation by visual perception with vision decision agent in an
unstructured environment
C. D. Pathiranage, K. Watanabe, K. Izumi(Saga University, Japan)
OS3-2 Guiding mobile robots in ubiquitous environment using binocular vision system andintelligent decision making
J. C. Balasuriya, K. Watanabe, K. Izumi (Saga University, Japan)
OS3-3 Kansei and human experience analysis for mobile robot navigation in ubiquitous environment
J. C. Balasuriya, K. Watanabe, K. Izumi (Saga University, Japan)
C. A. Marasinghe(University of Aizu, Japan)
OS3-4 A natural language based human friendly network robotic system
C. Jayawardena, K. Watanabe, K. Izumi (Saga University, Japan)
OS3-5 Some properties of coupled Van del Pol Oscillators with Inhibitory or excitory
connections
K. Izumi, A. Tajima, G. L. Liu, K. Watanabe (Saga University, Japan)
OS3-6 Optimal design method for region setting in fuzzy energy switching control ofunderactuated manipulators
K. Ichida, K. Izumi, K. Watanabe, N. Uchida (Saga University, Japan)

14:40~16:20 GS23 Others
Chair: M.Rizon (Northern Malaysia University College of Engineering, Malaysia)

GS23-1 Design and implementation of a novel part-feeding fixture mechanism for a stationary
robot
T. C. Manjunath (Indian Institute of Technology Bombay, India)
GS23-2 Remarks on texture synthesis and texture segmentation with the aid of multi-level
logistic model of Markov Random Field
T. Kubik (Wroclaw University of Technology, Poland)©ISAROB 2006

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M. Sugisaka (Oita University, Japan)
GS23-3 Tension-based movement corrections of the artificial agent
B. Matosiuk, B. Grzyb (Maria Curie-Sklodowska University, Poland)

GS23-4 Development of a mobile robot for video and thermal monitoring of railway tunnels
N. M. Bykova, S. V. Yeliseev, A. A. Loukianov, A. P. Khomenko (Irkutsk State
University of Railway Transport, Russia)
GS23-5 A unique design and implementation of a mobile octagon
D. Trivedi, A. Joshi, S. A. Patekar, T. C. Manjunath (Indian Institute of Technology
Bombay, India)
GS23-6 Design of an home/office automation for the physically handicapped using mobile
robotsT. C. Manjunath (Indian Institute of Technology Bombay, India)
GS23-7 An efficient path planning mechanism for a micro-robot
T. C. Manjunath(Indian Institute of Technology Bombay, India)
GS23-8 Design of a intelligent sensing interface for a flexible manufacturing system
T. C. Manjunath(Indian Institute of Technology Bombay, India)
GS23-9 Application of uncertain variables to knowledge-based decision making in complex
systems with uncertain and random parameters
Z. Bubnicki (Wroclaw University of Technology, Poland)
GS23-10 Firing Cell: An artificial neuron with a simulation of long-term-potentiation-related
memoryJ. Bialowas (Medical University of Gdansk, Poland)
B. Grzyb (Maria Curie- Sklodowska University, Poland)
P. Poszumski (Institute of Oceanology Polish Academy of Science, Poland)
GS23-11 Land-cover/land-use mapping based on color feature extraction
T. Kubik, W. Paluszynski (Wroklaw University of Technology, Poland)
A. Iwaniak, P. Tyｍｋoｗ(Agricultural University of Wroklaw , Poland)

Room C
8:40~10:40 OS5 Bio-inspired Approach and Application
Chair: I. Yoshihara (University of Miyazaki, Japan)
Co-Chair: M. Yasunaga (University of Tsukuba, Japan)
OS5-1 A functional motif scanning algorithm for invertebrate EST analysis
M. Ohkubo, F. Aranishi (University of Miyazaki, Japan)
OS5-2 Molecular clocks in aquatic invertebrates
F. Aranishi, T. Okimoto (University of Miyazaki, Japan)
OS5-3 Self-organizing map of species using ribosomal protein DNA sequences
Y. Higashi, M. Yoshihara, I. Yoshihara, K. Yamamori, N. Kenmochi©ISAROB 2006

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B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

(University of Miyazaki, Japan)
OS5-4 Non-transcriptional region analysis of dictyostelium discoideum using 1/f noise
M. Sato, I. Yoshihara, K. Yamamori (University of Miyazaki, Japan)
M. Yasunaga (University of Tsukuba, Japan)
OS5-5 A bio-inspired tracking-camera system
K. Hayashi, N. Aibe, Y. Yamaguchi (University of Tsukuba, Japan)
Y. Yamamoto (Yamamoto System Design Inc., Japan)
I. Yoshihara (University of Miyazaki, Japan)
OS5-6 A robot semantic recognition using support vector machine
H-C. Lai, C-T. Chou, S- J. Horng ,G-C. Chao (National Taiwan University of Scienceand Technology, Taiwan)

12:40~14:20 OS8 Vision, Automata and Neural Network
Chair: Y. Yoshitomi (Kyoto Prefectural University, Japan)
Co-Chair: A. Date (University of Miyazaki, Japan)
OS8-1 Flowering analysis based on image segmentation
H. Kamikokuryo, M. Yokomichi (University of Miyazaki, Japan)
OS8-2 Recognition of word expressed by sign language using thermal image processing
Y. Yamaguchi (KKC Information System Corp., Japan)
Y. Yoshitomi (Kyoto Prefectural University, Japan)
H. Fushimi (NTT DATA SANYO SYSTEM Corp., Japan)
OS8-3 Hierarchies based on the number of counters or the space allowed by four-dimensional
multicounter automata
M. Saito, M. Sakamoto, K. Iihoshi, T. Ito, H. Furutani, M. Kono (University ofMiyazaki, Japan)
K.Inoue (Yamaguchi University, Japan)
OS8-4 Iterative use of a pair of the self –organizing maps looking at data in two different ways
A. Date (University of Miyazaki, Japan)
K. Kurata (University of the Ryukyus, Japan)
OS8-5 Orbit control for the KEK 12GeV PS-MR by using NN algorithm
Y. Hitaka (Kitakyushu National College of Technology, Japan)
M. Shirakata, H. Sato (Proton Synchrotron Accelerator Division, KEK, Japan)
M. Yokomichi, M. Kono (University of Miyazaki, Japan)

14:20~16:20 OS9 Robotics, Automata and Control
Chair: M. Kono (University of Miyazaki, Japan)
Co-Chair: M. Yokomichi (University of Miyazaki, Japan)
OS9-1 Ｔrajectory for saving energy of direct-drive manipulator in throwing motion
A. Sato (Miyakonojo National College of Technology, Japan)
O. Sato, N Takahashi, M. Kono (University of Miyazaki, Japan)
OS9-2 Underwater moving object tracking and grasping with telerobotic system©ISAROB 2006

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B-Con Plaza, Beppu, Oita, Japan, January 23-25, 2006

P. Zhang, E. Shimizu, M.I to (Tokyo University of Marine Science and Technology,
Ｊapan)
OS9-3 A designing method research of the control system for autonomous underwater vehicle(AUV) using linear matrix inequalities(LMIs)
Y. Nasuno, E. Shimizu, M. Ito (Tokyo University of Marine Science and Technology,
Ｊapan)
T. Aoki, I. Yamamoto, S. Tsukioka, H. Yoshida, T. Hyakudome, S. Ishibashi (Japan
Agency for Marine-Earth Science and Technology (JAMSTEC), Japan)
OS9-4 Three-dimensional synchronized alternating turing machines
T. Ito, M. Sakamoto, M. Saito, K. Iihoshi, H. Furutani, M. Kono(University ofMiyazaki, Japan)
K. Inoue(Yamaguchi University, Japan)
OS9-5 Leaf-size bounded computation for four-dimensional alternating turing machines
K. Iihoshi, M. Sakamoto, T. Ito, M. Saito, H. Furutani, M.Kono(University ofMiyazaki, Japan)
K. Inoue(Yamaguchi University, Japan)
OS9-6 Existence and comparison for solutions in stochastic algebraic Riccati equation
N. Takahashi, M. Kono(University of Miyazaki, Japan)
M. Nakai (YASKAWA Electric Corp., Japan)
M. Ishitobi (Kumamoto University, Japan)©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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2

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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9©ISAROB 2006

11

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Present State and Future of Intelligent Space Discussion
on Implementation of RT in our Environment
Hideki HASHIMOTO
Institute of Industrial Science, University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 JAPANhttp://dfs.iis.u-tokyo.ac.jp

though such a complete system cannot be achieved
immediately, it is convinced that a useful system can beachieved with current technology by proper system
integration.
This paper is organized as follows. Section II introduces
the basic concepts and achievements of the iSpace. Section
III introduces the present state of iSpace as the artificial
spatial memory, real time vision and feature tracking,which have been developed for active human support.
Section IV explains the DIND data fusion with CovarianceIntersection(CI) that is ongoing research in iSpace. Section
V introduces the robot technology for human-iSpace
interaction. Finally, the directions for future work andconclusions are described in Section VI and Section VII,
respectively.

Abstract
Latest advances in network sensor technology and stateof the art of mobile robot, and artificial intelligence
research can be employed to develop autonomous anddistributed monitoring systems.
“Intelligent Space” is a platform which we canimplement advanced technologies easily to realize smart
services to human. We have developed and reported thevision system based on color information, the handoverscheme networking multi cameras, the human following
mobile robot systems, the path generator based on humanwatching, etc. In this talk I will summarize the present stateof Intelligent Space and try to describe a future from aviewpoint of system integration. Also we are now
introducing RT(Robot Technology) to develop theIntelligent Space as an actual standard platform which
could be approved by Robotics Community. I will discuss
how to use RT in our Intelligent Space and show our new
results.

2. Concepts and Achievements of iSpace
The iSpace is a space (room, corridor or street), which
has ubiquitous distributed sensory intelligence (various
sensors, such as cameras and microphones withintelligence) actuators (TV projectors, speakers, and mobile
agent) to manipulate the space, as shown in Fig. 1. The
iSpace propagates mobile robots in the space, which act inthe space in order to change the state of the space. These
mobile robots are called mobile agents. Mobile Agentscooperating with each other and with the core of the iSpace
to realize intelligent services to inhabitants. Mobile robots
become more intelligent through interaction with theiSpace. Moreover, robots can understand the requests (e.g.
gestures) from people, so that the robots and the space can
support people effectively.

Keywords: Intelligent space, spatial memory, vision,
tracking, mobile robot

1. Introduction'Intelligent Space(iSpace)' has been proposed by
Hashimoto lab. in university of Tokyo [1]. Intelligent
Space is an environmental system, which is able to support
human in informative and physical ways. Most ofintelligent system interacts with human in a passive space,but in Intelligent Space, a space, which contains human andartificial systems, is an intelligent system itself. Human andartificial systems become clients of Intelligent Space andsimultaneously the artificial systems become agents ofIntelligent Space. Since the whole space is an intelligent
system, Intelligent Space, a spatial system, is able tomonitor and to provide services to clients easily. Specifictasks, which cannot be achieved only by Intelligent Space,
are accomplished by utilizing its clients. For examples,Intelligent Space utilizes computer monitors to provide
information to the human, and robots are utilized to provide
physical services to the human as physical agents. Robot aswell as human is supported by Intelligent Space if it isnecessary. When a robot is lacking of sensors to navigate
around in Intelligent Space, the robot is treated as a clientof Intelligent Space and lacking information is provided to
the robot by Intelligent Space.
The ultimate goal of Intelligent Space project is toaccomplish an environment that comprehends human's
intentions and satisfies them. It seems that such a system ishardly achieved, since a huge number of functions should
be prepared and human-like intelligence is required. Even
©ISAROB 2006

Production facilities

Office , Hospital

Machine control
Wireless network

Agent control

Intelligent Space

Data
management

Health caremanagement

DIND platform
network
Status control

Wireless network

Status control

Health caremanagement
Outdoor

Structures

Home

Fig. 1. Vision of Intelligent Space, as a human support system for
more comfortable life.

The Intelligent Space can physically and mentallysupport people using robot and VR technologies; thereby
providing satisfaction for people. These functions will be
an indispensable technology in the coming intelligence
12The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

consumption society. Inhabitants in the iSpace areproducing intelligent reactions against instantaneous
situation. The iSpace evaluates situations (actionsreactions) from sensed information. The ranges of theservices of the mobile agents are from guiding in the
exhibition area to delivering parcels in a train station. For
both type of services, the mobile robots have to navigate ina human shared environment, which is very dynamic,
because the inhabitants changes its position quite often.
These intelligent devices have sensing, processing andnetworking functions, and are named distributed intelligent
networked devices (DINDs) [2]. These devices observe thepositions and behaviour of both humans and robots
coexisting in the iSpace. The information acquired by each
DIND is shared among the DINDs through the network
communication system. Based on the accumulated
information, the environment as a system is able tounderstand the intention of humans. For supporting
humans, the environment/system utilizes machines
including computers and robots.

Fig. 3. Experiment environment.

Camera system
In the iSpace, CCD cameras are adopted as sensors for
tracking the objects in the space. The cameras are placed sothat the whole area of the room is covered. The placementof the cameras can also be optimized to expand theviewable area of the cameras, which was a subject of ourprevious research [3].
In our currently used system, the human and robot
tracking is done by background subtraction and using color
information [4]. Skin color is used for human tracking. The
robot tracking makes use of color markers positioned on
top of the robot. Using the information from all cameras the
position of both the humans and robots can bereconstructed. The tracking software also provides a GUI
for easy operation.

3. Present State of Intelligent Space
3.1 New Intelligent Space Scheme
The Intelligent Space is constructed as shown in Fig. 2.
Figure 3 shows the present configuration of the Intelligent
Space in the Hashimoto Lab. Our laboratory room, which isabout 5 m in both width and depth, is used for the iSpace.
The present configuration involves eight pan-tilt-zoom
CCD cameras, handled by 4 sensing nodes (PCs), an
ultrasound positioning system and two mobile robots.
Moreover, the iSpace has a large size screen and speakers
for presenting information to the users of the space. All themodules are connected through the local area network.
Also, for achieving appropriate conditions for the operationof cameras, the lighting in the space can be easily adjusted.

Ultrasound positioning system
The ultrasound positioning system in the iSpace is used
to obtain the 3D position of objects in the space. In consistsof small size transmitters and 96 receivers positioned on theceiling and connected to the control unit. The transmitterssend an ultrasound signal, which is detected by thereceivers from which the position of the transmitter can becalculated. In order to obtain the information form thepositioning system the nodes on the network access a
network server connected with the system.

Sensor Network
DIND 3
DIND 2

DIND 4

DIND 1

DIND nCameraMonitoring,Comprehension

3D-UPS

Information,
Control

Mobile robots in the iSpaceIn the iSpace we currently use two mobile robots. One isa Pioneer 2-DX robot and the other a Pioneer 2-AT mobile
robot, both produced by ActivMedia Robotics(www.activrobots.com). A PC is mounted on each mobile
robot in order to provide processing of date from thesensors mounted on robots and for communication with theiSpace via wireless LAN.
For the detection of the robot position data fusion of
information from cameras, ultrasound system and robot
wheel encoders are implemented. Based on the obtained
position, tracking and position control of the robot are
performed. The observations from both cameras and theultrasound positioning system are also used to detect thehumans and obstacles in the space, which is in turn used forplanning the path of the robots.
3.2 Spatial Memory

Position,
Direction

Non physicalservicePhysicalService

Human

Agent
(Robot)Space

Fig. 2. Structure of Intelligent Space(iSpace).

©ISAROB 2006

13

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The spatial memory as an aid system for human activity
utilizing knowledge in iSpace has been proposed, as shownin Fig. 4. It is expected as a new interface between humanand iSpace. The spatial memory was designed in order toutilize information in the form of documents, images, web
pages, commands, etc. instantaneously and intuitively. The
spatial memory regards three-dimensional space like massstorage of computers, i.e. three- dimensional point istreated as an address of stored memory such as various dataand commands for machines. Consequently, we can accessa stored memory by indicating the point using our own
arms, which we named “human indicator”. We
implemented a prototype of the spatial memory system
which was supposed to be utilized for a desk work in
iSpace.
demo movies(e.g. in random environment any object can have the samecolor as the marker that confuses the existing system).
That is why Hashimoto Lab has endowed the existing
system with a shape recognition ability, which combinedwith the color based segmentation yields more reliable
results in less laboratory environments. The shaperecognition ability is based on an intelligent contouringmethod which was inspired by cognitive functions of
biological vision systems. For, both shape recognition andintelligent contouring is performed using vast parallel
computational architecture. The proposed architecture isbuilt up by numerous, simple computational elements thatcan perform only primitive functions such as addition,
subtraction. These computational elements are connected toeach other like the neurons in the brain. This architecture
can be much more efficient in certain tasks than thecomplex, classical algorithms as in spite of the fact thatthousands of simple operations are done, due to its special
architecture, they can be performed in a fully parallelmanner that can offer real time operation.
In contrast with conventional image processing
algorithms, the proposed model performs a pixel-to-object
transformation on the input image. The output data consistsof recognized line segments of certain properties that canbe mapped to predefined features of the object being
tracked [8]. In the Fig. 5, the recognized line segments of acertain property are together with sought colors. If the samecolor occurs in the neighbourhood of line segments ofdifferent characteristics, it will not be mistaken for thesought object.

Proceeding 2000
Proceeding 2004

Robot program

Commands for robot
Documents of robot

Fig. 4. Illustration of the spatial memory.

The implemented system was evaluated through two
types of experiments. First, the usability of the spatial
memory was investigated from the viewpoints ofaccessibility and effectiveness of memorizing [5]. The
results of the usability investigations showed that almost all
subjects could complete the access to all SKTs without
additional learning, in a time as short as in the second
performance in all subsequent performances. These results
confirmed that the accessibility was improved and thespatial memory improved memorizing the stored
knowledge once it was learned. Second, the performanceefficiency using the spatial memory was evaluated from theviewpoint of time required for completion of the task [6].
The efficiencies in two experiments performed by using the
spatial memory and by not using it were compared. The
results confirmed that the time required for the processes ofboth access and saving data is reduced by using the spatial
memory. Applying the t-test under the 5% level ofsignificance, significant difference of the input efficiency
in the two performances was confirmed.

Fig. 5. The input image and the line object recognized by the
proposed model.

3.4 Features Tracking
The schema that involves the use of a space doted byintelligence requires an environment capable of recognize
its own controllable components as mobile robots. The
process of gain knowledge about its own interaction layer
and the rest of the events in the space is a crucial task in theintelligent space objectives. The use of camera sensors as a
DIND in the proposed framework gives the Intelligent
Space a rich and huge amount of information. In the past
simple object recognition was achieved by means ofartificial beaconing or simple image descriptors [1, 2].
However, potential level of interpretation using visual
information and the recent achievements in computer
vision allows the intelligent space to reach a new level ofperception capabilities.
The inclusion of modern techniques of structure
recovery from multiples images [9] to the classicaldefinition and the successful use of modern probabilistic
approaches already used in other applications [10] allows a

3.3 Real Time Image Processing & Feature Extraction
The main source of information in Intelligent Space is
the image information of the distributed camera system.
Therefore, it is crucial for the robust and efficient operation
to adapt fast and accurate image processing methods.
Already color information based tracking algorithm [7] isoperating in our environment which requires a special
marker object to enable tracking. However, color based
segmentation can only be used in controlled environment©ISAROB 2006

14

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

new problem statement for robot localization in Intelligent
Space [11].

stochastic representation of all parameters in thesystem the development of data association uses a
probabilistic estimation for each landmark. The
estimation is modelled as a random process, so it ispossible to design a 'Mahalanobis' test to look for theclosest candidate in consecutive frames [13].

FeatureDetection

4. DIND Data Fusion with CovarianceTracking
Process

Visual Matching

Localization
Initialization processes
Process

Data association withstochastic modelIntersection(CI)Measurement updateStateestimation

Robot pose correction

Xˆ k ,k −1 Pˆk ,k −1

4.1 Distributed Data Fusion Network
One of the most important areas of research in the fieldof control and estimation is distributed data fusion. The
motivation for decentralization is that it can provide adegree of scalability and robustness that cannot be achieved
with traditional centralized architectures. In industrial
applications, decentralization offers the possibility of
producing plug-and-play systems in which sensors can be
slotted in and out to optimize a trade off between price andperformance. This has significant implications forintelligent space with network sensors as well because it
can dramatically reduce the time required to incorporate
new computational and sensing components into fighter
aircraft, ships, and other types of platforms.
The benefits of decentralization are not limited to sensor
fusion onboard a single platform; decentralization also canallow a network of platforms to exchange information andcoordinate activities in a flexible and scalable fashion thatwould be impractical or impossible to achieve with asingle, monolithic platform. Interplatform information
propagation and fusion form the crux of the distributed
intelligent network devices (DIND) for the intelligent
space. The goal of DIND is to equip all intelligent space
entities - car, ships, and even individual mobile agents with communication and computing capabilities to allow
each to represent a node in a vast decentralized commandand control network. The idea is that each entity candynamically establish a communications link with anyother entity to obtain the information it needs to perform its
multi-tasking role.

Robot pose and Structure

X k = ( xk , m1 L m m )Fig. 6. Scheme of localization.

Problem statement is defined as follows:• Any robot should be recognized by the environment
not because of complex structure recognition, instead
it will be recognized as an individual agent because of
its properties of be controlled. The only requirement of
the system should be this controllability of robotic
agents against the environment.
• No prior robot model is necessary and unknown metriclandmarks are available on it.
• The system obtains structural information of eachrobot at the same time the localization process isrunning.
To achieve the goals proposed is of absolute importancethe definition of a robust feature extraction and tracking.
The problem to address is to obtain an enough amount offeatures that belong to robot structure. This requirement istranslated into two main conditions. Both should define byconstruction what should be considered a feasible
landmark.
• A projection model of the feature is required, so it
should be related to a three dimensional concept
through its image detection• Such a feature should be capable of being tracked
during robot motion with unequivocal matching
between frames.
Natural landmark tracking is extensively used in many
essential tasks such as optical flow, camera calibration orpose computation. Depending on the prior information
available, two kind of algorithms used to obtain featuresfrom robot structure are possible, as shown in Fig. 6.
• Matching based on visual information: Landmark
extraction and tracking methods when there is no
additional information available apart from thesequence of images captured during object motion. A
complete tracker based only in image interpretation isdefined. The tracker starts using a classic approachborrowed from spare stereo matching approaches [12].
It should be modified to fit the specific conditions
which it should be exposed to such as hard clutteringand high projective changes during robot motion.
• Data association with stochastic model: Using a©ISAROB 2006

DIND
DIND
NODE

COMMUNICATIONLINK

SYSTEMMONITOR

Fig. 7. A distributed data fusion network. Each box represents afusion node. Each node possesses 0 or more sensors and isconnected to its neighbouring nodes through a set ofcommunication links.
15

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

constructs its local state estimate using CI to combine information
received from other nodes and a stochastic filter to incorporate
independent sensor measurements.

A distributed data fusion system is a collection ofprocessing nodes, connected by communication links, as
shown in Fig. 7, in which none of the nodes has knowledge
about the overall network topology. Each node performs a
specific computing task using information from nodes withwhich it is linked, but no “central” node exists that controls
the network. There are many attractive properties of suchdecentralized systems [16, 17], including:• Distributed systems are reliable in the sense that theloss of a subset of nodes and/or links does notnecessarily prevent the rest of the system fromfunctioning. In a centralized system, however, thefailure of a common communication manager or acentralized controller can result in immediate
catastrophic failure of the system.
• Distributed systems are flexible in the sense that nodes
can be added or deleted by making only local changes
to the network. For example, the addition of a node
simply involves the establishment of links to one ormore nodes in the network. In a centralized system,
however, the addition of a new node can change thetopology in such a way as to require massive changes
to the overall control and communications structure.

Assuming that the process and observation noises are
independent, the only source of unmodeled correlations isthe distributed data fusion system itself. CI can be used todevelop a distributed data fusion algorithm which directly
exploits this structure. The basic idea is illustrated in Fig. 8.
Estimates that are propagated from other nodes arecorrelated to an unknown degree and must be fused withthe state estimate using CI. Measurements taken locally are
known to be independent and can be fused using the
Kalman filter equations.

5. Implementation of RT in Intelligent SpaceWe have introduced state-of-the-arts in our intelligent
space project which is motivated to be adapted to a realenvironment to give more services to human. However,conventional robot system development has difficulty inintegrating these advanced robotics technologies which areremained in laboratory level. One of the reasons of it isoriginated from the lack of standard platform which should
be using the same protocol to integrate individual
technologies.
RT-middleware(AIST, Japan) [14] is the one of several
solutions in this moment and being supported by Japanese
government comparing with other middleware platforms
which are more to industrial applications. Therefore, we’vebeen working on implementing our intelligent space on the
RT-Middleware network platform [15]. Firstly, we
introduce preliminary approach on implementing telemicromanipulation system with RT-Middleware to collectthe knowledge which is inevitable for prospective
intelligent space system implementation issue. We have
proved flexibility which is required for building complex
system such as intelligent space using RT-Middleware

4.2 Using Covariance Intersection for Distributed Data
Fusion
The network consists of N DIND nodes whose
connection topology is completely arbitrary (i.e., it might
include loops and cycles) and can change dynamically.
Each node has information only about its local connection
topology (e.g., the number of nodes with which it directly
communicates and the type of data sent across each
communication link).
Correlated Informationfrom Other DIND Nodes
Measurement Fusion
with predicted position PDF

Physical support
for human

DIND Data
FusionAnalyze of humanbehavior

Support for
intelligent activeIntelligent Sapce

Covariance Intersect

RT-Middleware

Sensor A

State Estimate

Distributed Sensor Network Middleware
Sensorconfiguration
service

Sensor C

Sensor data
storage service

Human tracking Service robot A
service

Sensor data
analyze service

Sensor B
Sensor D

Stochastic Filter :Kalman/Particle

Actively addition, delete

Actively addition, delete

DIND nIndependent
Sensor Measurement

DIND 1

Fig. 9. Implementation of RT in Intelligent Space

DIND 2We also started to make RT-component of humandetecting sensor to be distributed to the intelligent space.

Fig. 8. A canonical node in a general data fusion network that
©ISAROB 2006

Service robot B

16

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The necessary features are currently being organized.
Sensors (ultrasonic, vision), actuators (mobile robot) are to
be implemented to RT-component in the first stage. Then,the ongoing algorithm development of spatial memory,feature tracking, real-time vision should be implemented on
RT-Middleware network infrastructure to improve theintelligent space. Figure 9 shows the implementation of RT
in iSpace.

independence of the estimates to be combined. Also, we
introduced robot localization based on natural landmark
tracking and RT(Robot Technology) to develop theIntelligent Space as an actual standard platform which
could be approved by Robotics Community.

References[1] J.H. Lee, G. Appenzeller, and H. Hashimoto, “An agent for
intelligent spaces: Functions and roles of mobile robots insensored, networked, thinking spaces,” in Proc. IEEE Conf.
Intelligent Transportation Systems, Boston, MA, pp. 983–988,
1997.
[2] J.H. Lee and H. Hashimoto, “Intelligent space -concept andcontents,” Adv. Robot., vol. 16, no. 3, pp. 265–280, 2002.
[3] T. Akiyama, J.-H. Lee, and H. Hashimoto, “Evaluation of
CCD camera arrangement for positioning system in intelligent
space,” in Proc. of Seventh Int. Symp. Artificial Life and Robotics(AROB’02), pp. 310-315, 2002.
[4] K. Morioka, H. Hashimoto, “Appearance based object
identification for distributed vision sensors in Intelligent Space”,
in Proc. of 2004 IEEE/RSJ Int. Conf. On Intelligent Robots andSystems (IROS’04), pp. 199-204, 2004.
[5] M.Niitsuma, H.Hashimoto and H.Hashimoto, “An Evaluationof Spatial Memory Based on Human Performance”, Proc. of the
31st Annual Conf. of the IEEE Industrial Electronics Society,pp.406-411, 2005.
[6] M.Niitsuma, H.Hashimoto and H.Hashimoto, “Efficiency
Evaluation of Human Performance Using Spatial Memory”, Proc.
of the 36th Int. Symp. on Robotics, 2005.
[7] K. Morioka, J. H. Lee, and H. Hashimoto. Human-following
mobile robot in a distributed intelligent sensor network. IEEE
Transaction on Industrial Electronics, 51(1), 2004.
[8] Zoltán Petres, Barna Reskó, Péter Baranyi, and Hideki
Hashimoto. Cognitive Psychology Inspired Distributed Intelligent
Network Devices. In Proceedings of SICE Annual Conference
2005 (SICE 2005), Okayama, Japan, pp. 3299–3304, 2005[9] O.D. Faugeras and Q.-T. Luong, The Geometry of Multiple
Images. MIT Press, 2001.
[10] A.J. Davison, “Real-Time Simultaneous Localization and
Mapping with a Single Camera”, In
Proc. International
Conference on Computer Vision, Nice, 2003.
[11] D. Pizarro, E.Santiso and M. Mazo, “Simultaneous
Localization and Structure Reconstruction of Mobile Robots with
External Cameras”, In International Symposium on Industrial
Electronics ISIE05, June 2005.
[12] Shi and C. Tomasi, “Good Features to Track”, In
Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition, pp. 593--600, 1994.
[13] J. Ñeira and J.D. Tardos. “Data association in stochastic
mapping using the joint compatibility test” , In IEEE Trans. on
Robotics and Automation, 2001.
[14] Noriaki ANDO, Takashi SUEHIRO, Kosei KITAGAKI,
Tetsuo KOTOKU, Woo-Keun Yoon, “RT-Middleware:
Distributed Component Middleware for RT(Robot Technology)”,2005 IEEE/RSJ Intl. Conf. On Intelligent Robots andSystems(IROS2005), pp.3555-3560, 2005.
[15]
Noriaki
ANDO,
MihokoNiitsuma,
Yoshihisa
TOSHIMA,”System Designof Intelligent Space by RTMiddleware”, RSJ2005, 2005(Japanese).
[16] Nicholson, D. and Deaves, R., Decentralized track fusion in
dynamic networks, in Proc. 2000 SPIE Aerosense Conf., 2000.
[17] J Black, T J Ellis, Multi Camera Image Tracking, Technical
Report no MVG99/1 Dept. EEIE, City University, London, 1999.

6. Future of Intelligent Space
The current level of Intelligent Space technology enables
the control of the robot motion. The future development of
iSpace technology will however enable us to control therobot manipulators as well. This fact will beside a vast
increase in possible applications have a large positive
feedback impact on the iSpace itself. When manipulator
control is possible within iSpace, sensors will namely be
mounted on manipulators as well. As a result a moreaccurate input data will be available.
An interesting topic within iSpace research is theinterface between human beings and iSpace. Improved
interface would enable humans to control object within IS
with voice or hand movement (depending on the type of
sensors used). As a result iSpace would be ideal tool for thedevelopment of systems capable of helping disabled
people. Robots would namely be able to guide blind people
or offer help to people in the wheelchair (bring a book froma shelf for example).
Another important area of iSpace research deals with theprospect of its usage outside closed buildings, which
represent a well-defined boundary. If there is no suchboundary, iSpace systems sensors are subjected to noisefrom different sources. Therefore there are currently noknown applications of iSpace outside buildings. When thedevelopment goes beyond this obstacle many new
applications will be feasible. iSpace together with its robots
could be used in areas which are dangerous for humanbeings because of different reasons (poisonous gases andextreme temperatures in fire fighting when large fires breakout for example).

7. Conclusion
This paper introduced the current research result onIntelligent Space Project. The Intelligent Space involves aubiquitous distributed sensory network, which can track
human and other object is the space; mobile robots, whatgives guiding support to the humans, and utilizes theobserved information of the sensory network; and artificial
spatial memory for human-iSpace interaction. At thepresent state, the distributed sensory network can track
several humans even the case of occlusion with other object
or with each other, based on observation from several
direction and global color model of the observed object.
The artificial spatial memory is an efficient technology tohandle the information exchange between human andiSpace, and gives robust gesture recognition of humanhand. And we discussed the extremely important problemof data fusion in iSpace. It described a data fusion/update
technique that makes no assumptions about the©ISAROB 2006

17

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development of Future Intelligent Sweet Home for the Disabled
Ju-Jang Lee
Department of Electrical Engineering and Computer Science
Korea Advanced Institute of Science and Technology
373-1, Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea(Tel.: +82-42-869-3432, Fax.: +82-42-869-3410, E-mail: jjlee@ee.kaist.ac.kr)

Abstract
This presentation introduces a new smart house,
Intelligent Sweet Home, developed at HWRS-ERC
center of KAIST, Korea for testing advanced concepts
on independent living of the elderly and people withdisabilities. The work focuses on technical solutions forhuman-friendly motion/mobility assistance andadvanced human-machine interfaces that provide easy
control of both assistive robotic systems andhome-installed applications. To improve the inhabitantcomfort, an intelligent bed, intelligent wheelchair andtransferring system between bed and wheelchair havebeen developed and tested. The solutions applied totheir design comply with most of the user’s
requirements and suggestions collected by a special
questionnaire survey among people with disabilities.
The intelligent sweet home behaves in accordance withthe user commands and/or the recognized intentions of
the user. Various interfaces based on hand gesture,voice, body movement and posture have been studied
and tested. The presentation shows the overall
structures of the system and explains the design andfunctionality of main system components. I hope that
this plenary talk will provide not only speech on the
progress of new technologies but also the future visionof a better life for the disabled and elderly.
1. Introduction
As the number of the elderly is drastically increasingalong with the number of the handicapped caused by avariety of accidents in the complicated and diversified
society, the demand for the benefits and support ofever-progressing scientific technology is also increasing
to lead more safe and comfortable lives as means ofcare-giving aids and to live well independently andconveniently along with people without disabilities withthe blessed feeling of equality. In particular, the elderlyor the handicapped has serious problems in doing acertain work with their own effort in daily life so thatsome assistive devices or systems will be very helpful if
it can assist or do the work instead of human beings
endowing as much independence as possible so as toimprove their quality of life.
If our ultimate goal is the realization of such awelfare state, a smart house for assisting the elderly andthe handicapped can be an alternative to solve dailyliving problems in individual activities since it
integrates systems for movement assistance of the
inhabitants carrying out necessary tasks, devices forcontinuously monitoring of their health status, andinterfaces for controlling home-installed devices in ahuman-friendly manner. Such a solution also has strong©ISAROB 200618

positive emotional impact on the inhabitants improving
their quality of life, giving them privacy and feelingthat they live in an ordinary house, not in a hospital, andreducing significantly the medical care costs per person.
Our work is distinguished in the sense of fully
supporting indispensable daily tasks with various
functionalities to comply with the needs of the users
from the statistical and questionnaire survey, and isbased on the idea that the technologies and solutions forsuch smart house should be human-friendly, i.e. smart
houses should possess high level of intelligence in theircontrol, actions and interactions with the users, offering
them high level of comfort and functionality. The effectof smart house toward its inhabitants is strongly
dependable on the list of the devices that build thehome environment and the efficiency of the automatic
system that synchronizes their operation. The way ofinteraction of the inhabitant with the home-installed
devices is also a significant aspect of smart house
design. As a solution, Intelligent Sweet Home consistsof several assistive systems and human-friendly
interfaces. Intelligent bed, intelligent wheelchair andtransferring system were designed and developed forthe movement assistance and active services since thepeople with movement limitations usually spend mostof time on bed or wheelchair. The conventional way forcontrol of home-installed devices by switches andremote controllers is not suitable for some categories ofpeople with movement disabilities. In order to offer analternative for such people, accessible interfaces using
hand gesture, voice, body movement and healthcondition were developed based an intention reading
technique. The information exchange between
subsystems is performed by the home network using
wired and wireless communications.
2. The KAIST’s Intelligent Sweet Home
2.1. History
The smart house idea started at KAIST in 1996 withthe design of a prototype robotic arm, KARES I,
mounted on a wheelchair. Various control modes andhuman-robot interaction were tested during the same
project. The smart house development continued with
the KARES II project. In the new project, a novel, very
flexible, and small robot was made. In parallel to that
research, various new interface systems were developed
and tested in other research projects of the HWRS-ERC.
The next project stage emphasized refining the smart
house concept, developing a common strategy forcontrol and information exchange, and designing new
advanced robotic modules. An experimental smart
house, called Intelligent Sweet Home, was arranged at

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

KAIST for practical testing of the new design solutions
and concepts.
2.2. Survey
To develop a strategy that offers a beneficial
response to some specific preferences and needs of thepotential Korean users, the research team was greatly
influenced by the results from a special questionnaire
survey that we conducted among patients of the
National Rehabilitation Center, district hospitals, andnursing homes. We involved 70 people between theages of 18 and 75 who have lower-limb dysfunctions or
are elderly (who form the main portion of the Korean
people with movement impairment). We sought togather users’ opinions regarding the effectiveness of theassistive systems that they use, the importance they
assign to various everyday activities, a list of importanttasks that cannot be supported by existing assistivesystems, the difficulties experienced by these users
when they perform certain tasks with assistive devices,
the activities for which they currently need assistancefrom an external helper, and directions for further
improvement of assistive devices. We also asked thereasons that make existing assistive devices
non-applicable or inefficient in a range of tasks (such as
too big, too small, too heavy, too noisy, requiring too
much external power, too complicated, and difficult tocontrol, etc). The survey results helped us to define theoutlines and target tasks of our smart house design. It
was found that people interviewed gave very highpriority to activities such as independence in goingoutdoors, assistance in meal preparation, eating,drinking, and control of home appliances from the bed
or wheelchair, and bringing/removing objects while
they are in the bed. The survey results revealed thatmost of the people interviewed consider it very
important to feel themselves comfortable in the bed andwheelchair where they spend most of their time. The
majority of those surveyed noted that they are indifficulties when they need to change their body postureor want to transfer between the bed and wheelchair.
2.3. The Intelligent Sweet Home Concept
From a functional point of view, the smart house canbe considered a large-scale robot capable of interaction
with its inhabitant(s) in a way that provides gentle
assistance, health monitoring of vital parameters, living
comfort (appropriate humidity, temperature, lighting),
and monitoring of home security. The smart house canrespond to situation changes or a user’s demands by
activating one or more different agents (hoist, roboticarm, bed, etc). Multiple assistive systems in the smart
house can cooperate in the execution of a single taskusing synchronized actions and information sharing.
Depending on the sensory information, each agent can
act autonomously in certain tasks and situations.
Cooperation between the systems may increase the listof tasks in which the user can be assisted. Given tasks
may also become more efficient and more precisely
executed.
We based our smart house design concept on thefollowing main considerations:
1)
USER-ORIENTED CONTROL: The algorithm
for processing task requests should prioritize
the user’s commands associated with urgent©ISAROB 2006

19

2)

3)

4)

5)

needs and sensor signals that indicate
worsening of the user’s state of health or safety
risks.
FLEXIBILITY: The smart house organization
should consider the nature of the disabilities of
the user. Customization, based on a modular
approach and common communication
protocol, can make the design process much
faster, easier, and more cost effective.
EFFICIENCY IN ASSISTANCE AND
HUMAN-MACHINEINTERACTION:
Home-installed technology should support
users efficiently, allowing them to perform
important everyday activities independently.
Human-machine interaction should require
minimal cognitive load, offering at the sametime high speed of information transfer.
HUMAN-FRIENDLY
DESIGN:
Human-machine interaction should be humanfriendly. The smart house should possess ahigh level of intelligence for control, actions,and interactions. The machine response should
consider the user’s health/emotional state,offering them a simple and natural means ofinteraction. The smart house environment
should include recognition of the direct user’scommands as well as detection and properinterpretation of the user’s intentions.
COMFORTABLEPOSTUREAND
MOBILITY: Smart house technology should
allow simple body posture change, either on
the user’s command or automatically, by
timing-based algorithms and information of thepressure distribution history. The design should
also consider technology for smoothtransportation to most places within the home
and wheelchair control manners that require
simple user’s instructions and automatic
steering. The smart house design should also
consider devices for effortless transfer of theuser between the bed and wheelchair.

2.4. System Architecture
Our smart house design includes several robotic
modules to assist motion and mobility, devices forhuman-machineinteraction,
sensors,andhealth-monitoring building blocks. A general block
diagram of the Intelligent Sweet Home is shown in Fig.
1. All home-installed components are integrated via acentral control unit. Referring to the sensory
information and the user’s commands, the central unit
generates a set of actions for each agent and thesequence in which these actions will be executed. Mostof the assistive modules developed (intelligent
wheelchair, robotic arm, etc) have been designed toperform their inherent functions as well as to cooperate
in tasks with other related systems. This approach has
several advantages: such modules can be used as
stand-alone devices, and their control can be based onhigh level commands when linked to another system.
The cooperation among home-installed systems leads tovery efficient and precise execution of given tasks andincreases the list of everyday tasks in which the
inhabitant can be assisted. When a new module is added
to the initial architecture, the management system

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

modifies the earlier control strategy to allow more
flexible and efficient use of each module. All systems
in the proposed smart house are connected via a home
network that includes both wired and wirelesscommunication modules.
The project exploits various ideas of human-machineinterfaces (HMI), emphasizing solutions that do not
require attachment of intermediate sensors to the user
and that reduce the user’s cognitive load in taskplanning, command setting, and receival of feedback
information from home-installed devices. For thispurpose, we explored interface concepts based on hand
gestures, voice, body movements, and an
intention-reading technique. The same HMI is used tocontrol various agents, for example, the gesture-based
soft remote control system can be used to control theTV, air conditioner, position of the mobile robot, etc.
Our smart house architecture contains two types ofrobotic modules: robots that help in manipulation, androbots for assistance in posture and mobility. The set ofrobots for assisting in manipulation includes a
bed-mounted robot, a wheelchair-mounted robot, and amobile robot for object delivery. The bed-mounted
robot is responsible for tasks related to serving the user
while they are in the bed (bringing objects, covering theuser with the quilt, etc). In our prototype, a MANUS
robotic arm was adopted and used as a bed-mounted
robot. In certain tasks, this robot is assisted by a small
mobile robot that transports objects to or from the bed.
The wheelchair-mounted robot (developed in the
KARES II project) is intended to assist the user inhandling various objects, opening doors, etc, while they
are in the wheelchair. A detailed description of thedesign and functionality of the robots for object
handling can be found in some of our previous papers.
The robots for manipulation assistance may be excluded
from the smart house structure when the user has
reasonable motion, adequate muscle strength in theupper limbs, and adequate hand dexterity.
The intelligent bed has been designed to provide
maximum posture comfort. The bed can change its
configuration automatically or upon command. The bed
also provides physical support for the user whenchanging posture via special force-sensitive bar
mechanism. The robotic hoist is a special autonomous
robot to transfer the user from the bed to the wheelchairand vice versa. The intelligent wheelchair provides easyuser access to various places indoors and out. The
sensor-based wheelchair facilitates user control byautomatic obstacle avoidance maneuvers. The robots
for assistance in posture and mobility are described ingreater detail in the next section.
An original ceiling-mounted navigation system,
called Artificial Stars, has been developed to provide
information about the positions and orientations of theinstalled mobile robotic systems. Originally, it wasapplied to the robotic hoist navigation. Later, the samenavigation information will be used for planning thepath of the intelligent wheelchair and mobile robot for
object delivery (links are shown in Fig. 1 with dashedlines). The pressure sensor system detects the user’s
posture in the bed. The pressure information is used tocontrol the bed configuration.

©ISAROB 2006

20

Fig. 1. Block diagram of the Intelligent Sweet Home
3. Intelligent Bed Robot System
Usually, one third or a quarter of life is spent forbeing in bed. Especially, peoples who need care usuallyspend the whole day for being in bed. If their
movements are measured and evaluated quantitatively,
not only robots can assist humans properly, but also
theses data can be used for a health monitoring and anevaluation of rehabilitation progress.
Body movements are very important for humans tolive. Moving the body makes humans take adaptive
behaviour to the outside world. It is said that physicalconditions and mental conditions are buried in the body
movements, because humans often move their bodies
when they are in good health, but move rarely theirbodies when they are in bad health. Therefore, it isthought that physical and mental conditions can be
estimated by measuring the body movements.
A measuring system which has sensors attached to ahuman body directly is usually for the body movement
or posture estimation. There is the system that canrecognize postures using accelerometers [2]. Although
this system can measure the body movement or postureaccurately, many sensors must be attached to the bodyto measure accurate movements. Since attaching many
sensors to the body produces mental burdens andrestricts person's activities, it is difficult to measure
unaffected body movements. In order to measure
unaffected body motions, sensors need to be attached
not to the body but to an environmental side as a bed.
Many researches are performed for body motion
tracking by using video camera [3], [4], [5]. However, it
is difficult for these systems using video camera to
extract motion features because the body is lost of sightin a quilt. Static charge sensitive bed is famous formonitoring the body movements in bed. It can measure
respiration, a heart rate and twitch movements [6].
Temperature sensors distribution bed can measure grossmovements such as body turns [7]. Pressure sensor
distribution bed is applied in many researches [8].
Harada et al. has applied Pressure sensor distribution
bed to estimate the body posture. His approach was
based on the body model. This approach estimated thesubtle posture and motion between main lying postures(supine and lateral posture). Since the body model had
lots of model parameter determined, it takes lots ofcalculation time to determining the posture.
In our system, the control of manipulator is
performed from the result of patient's posture andThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

motion estimation. Therefore, short calculation time isrequired to control the manipulator based on posture
estimation. In the previous paper, the systemdescription of IBRS, which is capable of estimating thepatient's posture and motion on bed in real-time andsupporting the patient using manipulator, wasintroduced [1]. In this paper, the control scheme ofrobot arm is focused on.
Section 4 briefly introduces the research background fordesigning IBRS and the proposed system. In Section 5,
control and measurement algorithm for the system is
described. Experimental results and conclusion are
followed in Section 6 and 7.
4. System Description
Most previous researches have a focus on the system
which can monitors the patient's posture and motion on
bed. In this paper, we will propose the robotic system
which can actively help the patient using robotic
manipulator. While there exists the patient on the bed,
the pressure sensors monitor his posture and motions.
When he moves on the bed, the robotic manipulator cansupport his body.
Before we design an intelligent bed robot system, we
conducted a survey of patients' opinion on IBRS at arehabilitation center and hospitals in Korea. At thesurvey, we made a questionnaire focused on activities
in bed, the functionality of IBRS, and difficulties indoing something without assistant. The survey said thatpeople with motor difficulties in their legs needassistant in doing body movement for posture change ortaking a ride on a wheel-chair. They also answered arobot system supporting the stability of body postureduring movement would be helpful.

Fig. 2 System architecture for IBRS.

Fig. 3. Intelligent Bed Robot System
To assist user’s movement while one is on the bed and©ISAROB 2006

21

allow gentle active support of the user when he/shechanges body posture, we attached a bar-type roboticarm as shown in Fig. 3. The same robotic arm can also
be used to deliver some objects on the tray mounted toit.
The robotic arm is shaped as a frame that is composedby two supporting bars (1 and 2) connected byhorizontal bar (3). A tray (4) is attached to the bar 3.
The height of the supporting bars (denoted as h in Fig.
3) can be actively controlled by 50W DC motor (A).
The frame is mounted to a mobile platform that allows
the robotic arm to reach and serve the whole area on the
bed by moving it along the bed (this motion is notedwith l in Fig. 3). Two 50W DC motors (B) on bothsides of the bed drive the moving platform. Two torquesensors mounted to each supporting bar measure the2-dimensional forces applied to the horizontal bar 3 asshown in Fig. 4. If the force, applied to the bar contains
only a horizontal component (fx), the torques τA andτB measured by each torque sensor have the same
directions, while application of vertical force
component fy causes torques with opposite directions.
When the user needs to change his/her posture on thebed, at first the user roughly positions the horizontal barof the robotic arm near his/her chest by using voicecommand. The horizontal bar comes close to the user
according to the position and the posture of the user asshown in Fig. 5. Then, the user grasps the horizontal bar
and adjusts the position/height of the bar by pushing or
pulling it. The forces coming from the user are sensed
by the torque sensors and cause movement of eachsupporting bars. This “follow” mode of robotic arm canbe change to “support” mode by voice command. In“support” mode, the supporting bars are fixed, and theuser can slightly change his/her position and postureleaning over the horizontal bar.
To position the horizontal bar near the user’s chest by
voice command and achieve automatic bending of the
bed according to the intention of the user, we applied
posture recognition and analysis of the history of user’s
motion on the bed. To this end, we distributed an arrayof pressure sensors over the bed surface as shown in Fig.
6. Force Sensing Resistors (FSRs) are used to measure
the pressure in the contact points. The FSR is a thinfilm sensor made from piezoresistive polymer which
resistance decreases in proportion to the force applied
on its active surface. We used pressure sensors that canmeasure forces up to 10kg/cm2. Sensors were uniformly
distributed with 70mm and 50mm spatial interval invertical and horizontal direction respectively. The
dimensions of the pressure sensitive mat are
1900mm×800mm×12mm. Since the mat covers a bed
which shape is changeable, the mat is divided into 3
parts, which dimensions coincide to the bed segments toprovide easy bending of the mat correspond to bendingof the bed. To get the pressure values, the control box
scans the pressure sensors one by one using
multiplexers, reads the pressure value of the selected
sensor, and transmits the data to the main computer as apressure distribution image. Sampling frequency of theimage is 10Hz and the resolution is 10 bit. Analyzing
the pressure data, the program recognizes both user’s
posture and motion.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

5. Control and Measurement
5.1. Measurement of patient intension usingpressure sensor mattress
The mass of the body and the force produced by themuscle generate the pressure on the FSR sensors
attached on the mattress. Normally, RBF neuralnetworks are widely used for function approximationand pattern recognition wherein the pattern dimension
in this application is usually small. But, the pressure
distribution image in our system has a high dimensional
property. The pressure distribution image has 336
features (equal to the number of FSR sensors). Due tothe high redundancy present in the pressure distribution
image vector, principal component analysis (PCA) wasused to reduce the dimensionality of data. The detailalgorithm was introduced in the previous paper [1].
5.2. Control of supporting manipulator
The robot arm has two kinds of operation mode. One is“support” and the other is “follow”. In the “support”
mode, the robot arm is operated based on kinematic
control. After the bed system estimates the position ofpatient, it controls the robot arm with the predefined
path. Otherwise, in the “follow” mode, two torquesensors measure the intension of patient on the
supporting manipulator.
5.2.1. Kinematic Control
In robotic motion planning and control, the solutions to
the kinematics problems are essential to achieve thegoals of a robotic operation. The forward kinematics
problem in robotics is concerned with thetransformation of position and orientation information
in a joint space to a Cartesian space described by aforward kinematics equationr (t ) = f (q(t ))(1)where q(t) ∈ Rm is joint variables, r(t) ∈ Rn isCartesian variables, and f(.) is a continuous nonlinear
function whose structure and parameters are known fora given manipulator. The inverse kinematics problem is
to find the joint variables given the desired positions
and orientation of the end-effector through the inverse
mapping of the forward kinematics equation (1)q (t ) = f −1 (r (t ))(2)
The inverse kinematics problem involves the existence
and uniqueness of a solution, and effectiveness andefficiency of solution methods. The inverse kinematics
problem is thus much more difficult to solve than theforward kinematics problem for serial manipulator. The
difficulties are compounded by the requirement of
real-time solution in sensor-based robotic operations.
The most direct way to solve (2) is to derive aclosed-form solution from (1). Unfortunately, obtaining
a closed-form solution is difficult for most manipulators
due to their nonlinearity of f(.).
The two robot arms of the developed bed robot system
are linked with the supporting bar. Although this
supporting could change its length, the maximum
length is defined. So, in the control procedure, thislimitation should be considered.
5.2.2. User intention analysis using torque sensors
In the “follow” mode, the patient can control the robot
arm. When he/she pull or push the supporting bar, robot
arm measures the variation of torque sensors, thencontrols motor A and motor B. As shown in figure 4,
two torque sensors are equipped for estimating user
intention. Desired position of user is calculated from the
combination of torque sensors.
The relation between torque sensors and user intension
can be expected intuitively. But, the structure of robot
arm system is not easy to explain mathematically.
Therefore, the fuzzy network was designed to infer thehuman intention. The rule table for fuzzy network is
introduced in Table 1. The motor A and B is controlled
based on rule table.

Fig. 4. Structure of supporting bar

Fig. 5. Various positioning of the horizontal bar©ISAROB 2006

Fig. 6. Pressure sensors-laid bed

22

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Table 1. Fuzzy rule table to infer the patient’s intension
from two torque sensors.
τB
Motor A
PL
PS
ZO
NS
NL
PL
ZO
ZO
PS
PL
PL
PS
ZO
ZO
PS
PS
PL
τA
ZO
NS
NS
ZO
PS
PS
NS
NL
NS
NS
ZO
ZO
NL
NL
NL
NS
ZO
ZO
τB
PL
PS
ZO
NS
NL
PL
PL
PL
PS
ZO
ZO
PS
PL
PS
PS
ZO
ZO
τA
ZO
PS
PS
ZO
NS
NS
NS
ZO
ZO
NS
NS
NL
NL
ZO
ZO
NS
NL
NL
* P: positive, N: negative, L: large, S: small, ZO: zero
Motor B

Fig. 8. Demonstration of Intelligent Bed Robot System
Table 2. Processing time and Recognition accuracy
according to the number of PC.
Structure
Number
Processing Recognitionof PC
time (ms)accuracy

6. Experimental Results

PCA
Mainly four kinds of postures on the bed (supine, rightlateral, left lateral, and sitting posture) are considered inthis paper. These postures were used for body posture
estimation process. In our system, discrimination thatthe current patient's posture belongs to the posture classis more important than the fact such as opening degreeof legs. The patient's motion and intension are
estimated from changes between the posture classes.
The proposed algorithm was applied to estimate poseand motion.
Figure 7 shows the postures to be estimated. The patientin a sleep takes main four postures.
Table 2 describes the processing time according to thenumber of principal components (PC). The processing
time for estimating the posture increases in proportion
to the increasing number of PC.
In order to finish the measurement and estimation at theprocessing rate 10Hz, the process should be fast andexact. As shown in table 2, proposed algorithm(PCA+RBFNN) with 20 PCs hits highest performance.
Though structure with higher PCs has higher accuracy,
the calculation cost is much higher.

5
10
20
30
50
PCA+RBFNN 5
10
20
30
50

13
18
22
29
57
21
30
44
63
82

85.1
87.2
91.6
91.8
90.9
87.9
90.8
93.6
93.7
93.4

The overall demonstration was performed as shown infigure 8. The developed bed robot system serves thepatient with bar-type supporting robot arm and foldable
mattress based on the sensor data which is obtained
from pressure sensor distributed mattress.
7. Conclusion
In this paper, the unrestraint human pose and motion
estimation system using the pressure sensor distribution
bed was described. This system is thought to be used
for analyzing the body pose and motion, assisting thepatient with robotic manipulator. Furthermore, it can beused for a health monitoring and evaluation ofrehabilitation progress and so on.
Acknowledgment
This paper was partially supported by HWRS-ERC(Human-Friendly Welfare Robot System Engineering
Research Center), Republic of Korea.
References[1] Kap-Ho Seo, Changmok Oh, and Ju-Jang Lee,
“User Pose Estimation Using Sensor Distribution
Bed”, IECON2004, pp. 598 – 602, November, 2004
[2] A. K. Nakahara, D. L. Jaffe, and E. E. Sabelman,“Development of a Second Generation Wearable
Accelerometric Motion Analysis System,” Proc.
2nd National Rehabilitation Research andFig. 7 Postures to be estimated.
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development Service Meeing, 2000.
[3] J. O'Rourke and N. I. Badler, “Model-Based Image
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[4] J. Rehg and T. Kanade, “Visual Tracking of HighDOF Articulated Structures: An Application to
Human Hand Tracking,” Proc. 3rd European Conf.
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[5] C. R. Wren, B. P. Clarkson, and A. P. Pentland,“Understanding Purposeful Human Motion,” IEEE
Int'l Conf. Automatic Face and Gesture Recognition,
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[6] J. Alihanka, K. Vaahtoranta, and I. Saarikivi, “A
New Long-term Monitoring of Ballistocardiogram,Heart Rate, and Respiration,” AM. J. Physiol., vol.
240, pp. 384-392, 1981.
[7] T. Tamura, J. Jhou, H. Mizukami, and T. Togawa,“A System for Monitoring Temperature Distribution
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[8] T. Harada, T. Sato, and T. Mori, “Estimation of
Bed-Ridden Human's Gross and slight Movement
Based on Pressure Sensors Distribution Bed,” IEEE
Int'l Conf. Robotics and Automation, pp. 3795-3800,
2002.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Locating Static Targets by Matching Image Frames
Xin Wang1;21

Masanori Sugisaka2

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

it is necessary to select relevant key areas around thesepoints, to ånd some interest points in these areas, andthen to get the optimal correspondences or possible solutions. In the next section, our algorithm to select thefeature points are introduced; Section 3 gives the principle about the statistical analysis in order to locate static
targets. Experimental results and discusions are in Section 4.
We denote I(x), fx=(x, y): x2 R2 g, as a frame in animage sequence, the ith frame as Ii (x), and the jth as
Ij (x), (i; j = 1; 2; . . . ; nji < j; i 6= n; j 6= 1). Two target
frames Ii (x), Ij (x), in which the targets are, are sampled
by their appeared orders in image sequence. The input
frame is Ii (x), output is Ij (x).

We present a new algorithm to locate targets by matching image frames taken from a moving platform. We havenoticed that an image point is environment sensitive, but
those energy changes of grouped points have their own
statistical similarities in two image frames within limited
time interval. This approach analyzes correspondence ofenergy points around every feature points between interframes in image sequence in order to decide those featurepoints. Successful results are given for a vide frames.

1. Introduction
Motion detection is made diécult as both the observerand some elements of the scene may be moving. But
detecting objects from a moving platform is one of the key
issues to the successful applications of mobile system, forexample, mobile robot system [2][3]. A common motion
analysis problem is to obtain comprehensive information
about moving and static targets presented in a scene.
Prior knowledge, which includes information about thecamera motion, mobile or static, and information about
the time interval between consecutive images for example
man-aid initial selection or varies information base, canalso help to decrease the complexity of analysis [1][4][5].
One of important diãerences between motion in moving
camera or moving platform and static camera is that all
is moving in the former case and only object are moving
in the later.
Generally, from the practical point of view, there arethree groups of motion-related problems: motion detection, object detection and location, and derivation of 3D
object properties. Our problem belongs to the second
group, i.e. tracking a static target from a moving platform [6][8]. Our method is to match corresponding feature points (F P ), and to locate them. The presented
method is based on calculating the local minimum energy points around a feature point, analyzing the image
tensor, and then deciding the corresponding relations between frame intervals. We discuss the case of a camera
on a moving platform and the target is static on the assumptions that motion is with maximum frame velocity constraint, which a target is scanned at discussing
frame time intervals, and with target mutual correspondence, which the target exhibits some stable points andnot all but part of these points correspond to exactly
some points in the next image frame. In the presented
approach, we build a contour model of a target to getsome low-energy feature points. In order to get the corresponding relation on the feature points between frames,©ISAROB 2006

Jin Wang3

2. Active Contour
The traditional active contour model, also named snake,is deåned as an energy minimizing spline{the snake energy depends on its shape and location within an image.
The main idea of snake model is to estimate an initialized curve, and use this curve to initialize the conventional snake equations in order to get solutions about its
Euler-Lagrange equation. It is recomputed to eliminate
high-energy segments and renew the low-energy contours
until constrains are satisåed. The energy functional isa weighted combination of internal and external forces.
The internal forces emanate from the shape of the snake,while the external forces come from the image and/orfrom higher-level image understanding processes. The
snake is deåned parametrically as v=v(s)=[x(s), y(s)],where x(s), y(s) are x, y co-ordinates along the contour
and s2[0, 1], v(s) can be approximated as a B-spline.
The energy functional to be minimized may be written
as:
Z 1
EsÉ =
Esna (v)ds
(1)
Z 1
0

=

(Eint (v) + Eext (v))ds(2)0where Eint (v) represents the internal energy of the spline
due to bending. External energy Eext (v) includes image
forces Eim (v) and external constraint forces Ecst (v):Eext (v) = Eim (v) + Ecst (v)(3)

Eim (v) is derived from the image data over which thesnake lies. Ecst (v) come from external constraints imposed either by a user or some other higher-level process
which may force the snake toward or away from particular features. We just select Ecst (v) = 0.
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig. 1: Initial control points on a rectangle.

Fig. 3: Feature points from the converged contour.
frame by parts of its feature points. Corresponding elements in A0m and A0n are recognized by simple directive
chain codes [6] with their neighborhoods. This process
are illustrated in Section . More details can be found in[14].
Let Am =fxm g be the set of all energy points in the årststarting image that is input state space, and An = fyn g
the energy points in the second image that is output state
space. Let cmn be a vector connecting points xm and yn(thus yn =xm +cmn ). Let the probability of correspondences of two points xm and yn be Ämn . Two points
xm and yn can be considered potentially corresponding
if their distance satisåes the assumption of maximum velocity

Fig. 2: Converged contour after 25 iterations.
From (2), the functional to be minimized is (1). Thenfrom the calculus of variations, the Euler-Lagrange condition states that the spline curve v(s) which minimizes
Esna (v) must satisfydEv Ä Ev = 0
ds s

(4)

jcmn j î çmax(6)

where Evs is the partial derivative if E(v) with respect todv(s)=ds and Ev is the partial derivative of Esna (v) withrespect to v. Then the Euler-Lagrange equation reducestowhere çmax is the maximum distance which a point may
move in the time interval between two consecutive images. Two correspondences of points xm yn and xk yl are
termed consistent of@v(s; t)@@v(s; t)
Ä
[ã(s)
]+
@t
@s
@s
@ 2 v(s; t)
@2
[
å(s)
] Ä 5Eext [v(s; t)] = 0@s2@s2

jcmn Ä ckl j î Åwhere Å is a pixel distance deviation. Consistency ofcorresponding point pairs will increases the probability
that a correspondence pair is correct. We determine thesets of energy points Am ö A0m ö Ii (x), An ö A0n ö
Ij (x), and construct a data structure as follows:(5)

where ã(s), å(s) specify the elasticity and stiãness of thesnake, which control the internal spline energy.
The process and one of our results by the describedmodel are shown in the ågures [1][6]. Fig. 1 shows an
initial contour with nine control points. The results after
25 iterations are given in Fig. 2. In Fig. 3, nine featurepoints about the rectangle target are made, which is fromthe converged contour shown in Fig. 2.

[xm ; (cm1 ; Äm1 ); (cm2 ; Äm2 ); . . . ; (EP C; EP P )]where EP C, and EP V are special symbols indicatingthat no potential correspondence was found. We iteratively calculate the probability of correspondence of apoint xm with all potential points yn as a weighted sumof probabilities of correspondence of all consistent pairs
xk yl , xk are neighbors of xm and the consistency of xk yl
is evaluated by xm and yn . To a function F [14]:3. Statistical Similarity Analysis
Feature points represent speciåc places in an image
that carry distinctive features of the object being studied. Assume that there be two local windows A0m ö I(x)and A0n ö I(x + Åx). Pay attention to the image energy
distribution around a feature point, two tracking windows with diãerent scales in input space A0m ö Ii (x) andoutput space A0n ö Ij (x) are selected. Let the elementsin the tracking windows be the ones around every feature point. If more than two of elements of A0m in A0n
are decided, the corresponding position of the x in Ij (x)can be estimated. If some corresponding points of x are
found in A0n , we can also estimate the target in this new©ISAROB 2006(7)

(sÄ 1)(sÄ 1)
Ä(s)mn = F (Ämn ; Äkl)(8)Those points that hold high probabilities that obviouslydiãer from those energy points without correspondences.
Intuitively, we give N local approximate locations in
Ii (x) by coarse hand initialization. After executing thealgorithm, an active contour model is formed årst andthen the contour converges to N low-energy positions.
There are N F P s in the state space Aïm . An input state
space Am , Am =fxm g, is constructed around a kernel inAïm . A0m gives a tracking window in Ii (x). We can compute the possible energy points around every F P in the26

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig. 4: Initial energy distribution.

Fig. 6: The energy distribution after 4 iterations.

tracking window, and build another tracking window
A0n including a corresponding output state space An =fyn g
in Ij (x). In this paper, we give the results of one F P between frames to illustrate our method. This principle
is also suitable for more ones. The area of A0n is up to
the maximum distance çmax in (6). Finally the corresponding relations are established by application of thealgorithm presented. We get a new F P in the frame A0n
including An =fyn g. More F P s, which are to form a new
input space state, can also be calculated by repeating thesame process. The target is matched by tracking its thesekernels in consecutive image frames. This process is illustrated in (9).

by black-white \+" where is the center of mass in A0n .
xm and yn are located by \+" symbols with white circle background in (e) or (h). All 13 pairs of elements
are decided between Am and An . (e) and (h) show thateven if we selected a smaller local area around a F P , itis still possible to catch some corresponding elements in
An since F P s locate on the low energy positions. To
show these results more clearly, (f) and (i) focus on thedetected corresponding part in (e) and (h), and give theirampliåed eãects. The results have given very good corresponding relations. The whole process is shown in Fig.
5(c) to Fig. 5(i) where the values of xm and yn are theirindexes in Am and An in their storage spaces.
The correspondence points are decided with errors lessthan 5 pixels because our calculating unit is 5 pixels.

(t1 )
(t2 )
(t2 )
1)
1)
Aï(t)
) Am
; A(t
) Aï(tmnm ) Am ; An
(tn+1 )n)n+1 ). . . ) Aï(t
; A(t
...
m ) Amn(9)

5. Conclusions

where tk , k = 1; 2; . . ., denotes sampling time at k. Theinterval is up to the velocity of the moving platform.

We proposed a new approach starting from some converged points by the snake principle. We depict the attributes of a target by its signiåcant energy points. Ouridea to understand a target is from its points of this target to its lines, from its points to its lines and areas.
This is related to manipulate our knowledge database in
our further works. The proposed energy point detection
approach also gives very satisåed results.
In this work, we proposed three processes to track astatic target from a moving platform where an camera
is moving and an target is static: active contour model,
which are used to estimate kernel points at the ånal converged contour position; the points detection, which isin a area around a feature point, along with measures ofimage energy, as the system input measurement; and astatistical similarity analysis method, which uses correspondence pairs detection, as the former system output
and the next system input. This approach takes us another advantage to occlusion problems, because the target/object can be estimated by parts of their features.

4. Experiments and Discussions
In this section, some experimental results based on thepresented method are illustrated. We use the same video
image [7] to execute our experiments. In Fig. 5, (a) is
the original input image frame 2; (b) is the output images frame 9; (c) gives the active contour model based on
the algorithm described in Section 2. The feature points
and an area of A0m are shown in (d). A0m ö R2 , there
are 24 Ç 24 (unit: 5 pixels) elements in it. (d) gives the
21 original energy points after the årst detection. 13 of
the 21 energy points are then kept after excluding thepoints on the edge; (g) shows the energy points in A0n ,A0n ö R2 , there are 100 Ç 50 elements in it. Am and An
are detected from A0m and A0n respectively, Am ö A0m ,An ö A0n . An is in a larger area so there are more elements i.e. energy points in it. The reason why a larger
A0n in (g) is selected is that the size of A0n up to the velocity constraints of Dmax in (6) resulted from the moving
camera. The larger the size is the more energy points aredetected, but also accompanied with much time cost. (e)and (h) give the detected results by using the algorithm
described in Section 3. The F P 0 s position is also marked©ISAROB 2006

Acknowledgements
This research was also done while Xin Wang was a staãat School of Engineering, the University of Tokushima.
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig. 5: Tracking a static target, a house corner, in image sequence from a moving platform.

References[9] B. J°
ahne, Image processing for scientiåc applications, CRC Press, New York, 1997.

[1] X. Wang, M. Sugisaka, W. Xu, \Model based recognition for artiåcial brain," Proc. of the 9th Int. Symposium on Artiåcial Life and Robotics，Vol, 2, pp.
605-609, 2004.

[10] D. Scharstien, \Stereo Matching with Nonlinear Diffusion," International Journal of Computer Vision,
vol.28, no.2, pp. 155-174, 1998.
[11] H. Zhang, S. Roth, it Camera aided robot calibration, CRC Press, Boca Raton, 1996.

[2] A. Kumar, A. Tannenbaum, and G. Balas, \Optical çow: a curve evolution approach," IEEE Trans.
Image Processing, vol.5, no.5, pp. 598-610, 1996.

[12] L. Torresani, C. Bregler, \Space-time tracking," Computer Vision - ECCV 2002, Part I, pp. 801-812,
2002.

[3] L. Robert, and O. Faugeras, \Curve-based stereo:
Figural continuity and curvature," Proc. CVPR'91,
pp. 57-62, 1991.

[13] W. Kropatsch, R. Klette, F. Solina, and R. Albrecht,
Theoretical foundations of computer vision, Springer
Wien, New York, 1996.

[4] S. B., Pollard, J. Mayhew, and J. P. Frisby, \Implementation details of the PMF stereo algorithm,"
3D Model Recognition from Stereoscopic Cues, MIT
Press, Cambridge, MA, pp. 33-41, 1991.

[14] X. Wang, M. Sugisaka, W. Xu, \Recognizing static
target in video frames taken from moving platform"
Proc. of the Int. Conference on Control, Automation, and System，Korea，pp. 223-227, 2003.

[5] R. Green, D. Gray, and J. Powers, Artiåcial Vision,
Academic Press, London, 1997.

[15] X. Wang, M. Sugisaka, W. Xu, \Image energy pilot, FA soil-salt recognition Approach," The 4th Int.
Conference on Advanced Mechatronics Toward Evolutionary Fusion of IT and Mechatronics, pp. 373376, 2004.

[6] D. A. Forsyth, J. Ponce, Computer Vision, Prentice
Hill, USA, 2003.
[7] X. Wang, M. Sugisaka, W. Xu, \Active contour model
based tracking analysis" Proc. of the 12th Int. Conference of Systems Engineering, UK，Vol, 3, pp.
347-352, 2003.

[16] W. Yu, K. Daniilidis, and G. Sommer, \Approximate orientation steer ability based on angular Gaussians," IEEE Trans. Image Processing, vol. 10, no.
2, pp. 193-205, 2001.

[8] M. Brown, D. Terzopoulos, Real time computer vision, Cambridge University Press, 1994.
©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Perceptual and Introspective Learning forDevelopmental Robot
Zhongzhi Shi Qingyong Li
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology,
Chinese Academy of Sciences, Beijing 100080, China
shizz@ics.ict.ac.cn
Abstract For the development robot learning strategies play very important role. Through learning the system
performance will be improved. A learning model which contains observation learning and introspective learning will be
proposed in this paper. Perceptual learning should be considered as an active process that embeds particular abstraction,
reformulation and approximation within the Abstraction framework. Introspective learning is an inside learning of brain,
which means without input information from outside environment. Perceptual learning and Introspective learning methods
will be described respectively.
Key words: developmental robot, learning model perceptual learning, introspective learning

1. Introduction
By a developmental robot means that the robot generates
its “brain” through online, real time interactions with its
environment[14]. For robots, the developmental program
starts to run at the ``birth'' time of the robotics, whichenables the robot to develop its cognitive and behavioral
skills through direct interactions with its environment
using its sensors and effectors. The basic nature ofdevelopmental learning plays a central role in enabling ahuman being to incrementally scale his or her level ofintelligence from ground up. In order to scale up therobot's capability to understand what happens around it,
the learning mechanism embedded in a developmental
program must perform systematic self-organization,according to what it sensed, what it did, action imposedby the human when necessary, the reward it received
from the humans, and the context. As a fundamental
requirement of scaling up, the robot must develop avalue system.
Learning is the basic cognitive activity andaccumulation procedure of experience and knowledge.
Through learning the system performance will be
improved. A general learning model has been proposed
as Fig 1.
In general machine learning focuses on observation
learning, that is inductive learning, analogical learning,case-based learning, explanation learning, evolutional
learning, connectionist learning and so on. A lot ofstatistical learning methods and algorithms have been
proposed and used in existing data mining system.
Introspective learning is also play very important role forhuman learning. The general learning model could show
the both learning activities.

©ISAROB 2006

Environment

Introspective
Learning Unit

Observation
Learning Unit

Knowledge
Base

Performance
Unit

Fig 1 Learning model

Perceptual learning should be considered as an active
process that embeds particular abstraction, reformulation
and approximation within the Abstraction framework.
The active process refers to the fact that the search for acorrect data representation is performed through several
steps. A key point is that perceptual learning focuses on
low-level abstraction mechanism instead of trying to rely
on more complex algorithm. In fact, from the machinelearning viewpoint, Perceptual learning can be seen as aparticular abstraction that may help to simplify complex
problem thanks to a computable representation. Indeed,
the baseline of Abstraction, i.e. choosing the relevant
data to ease the learning task, is that many problems inmachine learning cannot be solve because of thecomplexity of the representation and is not related to thelearning algorithm, which is referred to as the phasetransition problem. Within the Abstraction framework,
we use the term perceptual learning to refer to specific29The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

learning task that rely on iterative representation changes
and that deals with real-world data which human canperceive.

An important question in the study of visual
processing is how to identify the features used by humansubjects to classify visual stimuli: which features of animage are extracted and represented by the visual cortex?
It has previously been proposed that complex objects are
represented in the visual cortex in terms of sparse
structures such as sparse coding or Gabor basis
functions[11]. These 'building blocks' are universal in thesense that they are equally applicable to all natural
images. Alternatively, we investigate a new supervised
model, called here classification-oriented sparse coding(briefly as COSC) model, which encodes sparse andclass-specific features. Based on Olshausen’s sparse
coding scheme[12], COSC model incorporates anadditional constraint, named discriminability, to learn
the sparse and informative image features which are
specifically selected to support visual classification⎯
one of the basic tasks of visual perception. COSC
models probes into what information should be
represented under feedback of visual classification tasks
and provides a way of extracting sparse and informative
features from natural images though linear Hebbianlearning.
The consideration of granularity is motivated by thepractical needs for simplification, clarity, low cost,
approximation, and tolerance of uncertainty. As anemerging field of study, granular computing attempts toformally investigate and model the family of
granule-oriented problem solving methods andinformation processing paradigms.

In this paper Section 2 will discuss perceptual
learning. Introspective learning is described in Section 3.
The conclusions will be given the final section.

2. Perceptual Learning
After Hubel and Wiesel[5;6] first showed that neurals inmammalian primary visual cotex (V1) were optimallystimulated by bars and edges, a large part ofexperimental, computational, and theoretical studies
have been concerned with exploring the response
characteristics of neurons in V1 and in higher visualareas.
Aiming to reveal underlying relationships between theenvironmental information and internal representation of
the visual cortex, Olshausen and Field introduced alearning algorithm for sparse coding[11]. It was shown
that, by seeking sparse code for natural images, thenetwork could develop a set of receptive fields similar to
those simple cells found in the striate cortex (V1).
Though such network successfully captures sparsenature of the input data, it is a feedforward model and it
ignores one primary constraint on the visual system:
perception task. The tasks faced by the organism arelikely to be an even more important constraint. That is tosay, sparse coding states only that information must be
represented sparsely, it does not say anything about what
information should be represented.
In the classical feed-forward, modular view of visual
processing, the early visual areas (LGN, V1 and V2) are
modules that serve to extract local features, while higher
extrastriate areas are responsible for shape inference,
object recognition and so on. However, recent findings inprimate early visual systems reveal that the computationsin the early visual cortex are rather complex anddynamic, as well as interactive and plastic, subject toinfluence from global context, higher order perceptual
inference, task requirement and behavioral experience.
The evidence argues that the early visual cortex does not
merely participate in the first stage of visual processing,but is involved in many levels of visual computation[7;8].
The idea that various levels in cognitive and sensory
systems have to work together interactively andconcurrently had been proposed in more generalcomputational terms, particular by McClelland andRumelhart in terms of interactive activation neuralnetworks [10], by Grossberg in terms of adaptive
resonance theory[3], and Dayan et al in terms of theHelmholtz machine[2]. Hinton proposed a graphicalmodel and learning algorithms for perceptual system[4].

©ISAROB 2006

2.1 Linear image synthesis model
The starting point is from Olshausen[11]. A perceptual
system is exposed to a series of small image patches,
drawn from one or more large images, just like the CRF
of neurons. Imagine that each image patch, represented
by the vector I (numbered row-wise), has been formedby the linear combination of N basis functions. The basis
functions form the columns of a fixed matrix, A. The
weighting of this linear combination is given by a vector,
s. Each component of this vector has its own associatedbasis function, and represents a response value of aneuron in vision system. The linear synthesis model istherefore given by:nI ( x, y ) = As = ∑ s i * ai ( x, y )

i =1
(1)
In a cortical interpretation, the s model the responsesof (signed) simple cells, and the column of matrix A
closely related to their CRF’s.
This model can be represented by a simple neuralnetwork, where x is an n-dimensional vector denoting
the input to the network, si denotes the activity of the i-th

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

neuron, and ai (the i-th column of the A) is ann-dimensional vector composed of the connection
weights between the i-th neuron and the input.
The goal of sparse coding is to find a set of ai that
forms a complete code (that is, spans the image space)and results in the coefficient values being as statisticallyindependent as possible over an ensemble of natural
images. The reason for statistical independence havebeen elaborated else where [1], but it can be summarized
briefly as providing a strategy for extracting the intrinsic
structure in visual signal.

space efficient for representation.

2.3 Classification-oriented sparse model
A. Discriminability constraint
In order to code the sensory visual information
supervised by the visual classification task, it isnecessary to incorporate a constraint for theclassification task. Intuitively, it is very important for thecoded coefficients to be good for classification, so thecoefficients (or neuron responses) produced by thesparse coding model can be easily utilized by the higher
neurons which process such task. Linear discriminant
analysis, using within-class scatter and between-classscatter to choose coordinate for transformation, is
broadly used for pattern classification. We investigate asomewhat similar approach, and incorporate the‘discriminability’ cost function which constrains theneuron activities to be more valuable for classification.

2.2 Sparse coding model
In an influential paper, Olshausen and Field applied
two criteria to seek the optimal basis vector and thecoefficients [11]. One of the criteria is how well the code
describes the input. It can be measured by the squared
error between the input and its reconstruction by the
network:⎡⎤
Error ( s, A) = ∑ ⎢ I( x, y ) − ∑ si ai ( x, y ) ⎥x, y ⎣
i⎦

Supposed

i

2

and

2

s , 1 ≤ k ≤n

I i = [ s1 , s 2 , ..., s n ]jk, whereis thecoefficient produced by sparse coding model. N1 and N2
are the number of patterns in class X1 and X2.
The sparse coding model transforms the input stimuli
into code coefficient vectors, I. We define the distance
between two coefficient vectors as below:⎠(3)
where S(x) is a nonlinear function such as |x|,exp(-x2), and log(1+x2). The cost sparseness favors thecodes which consist of minimal number of non-zerocoefficients. As a result, the network seeks thecoefficients which are statistically independent eachother over an ensemble of input data. In the case that thedata contains some forms of higher-order statisticalstructure as found in natural images, it can be captured
by using this sparseness cost function.
So the search for a sparse code can be formulated as
an optimization problem by constructing the following
cost function to be minimized:

D( I 1 , I 2 ) =n∑ (s − s )
i =1

1

i

2 2

i

(5)
Within-class distance measures the distance between acoefficient vector and the center of class which includes
the vector. The formula isj~ )D w = D( I i , m
j
~
m

(6)

j
~ )
D B = D( I i , m
ĵ
~
m

(7 )

j
is the center of the class j. On the contrary,
where
between-class distance measures the distance between acoefficient vector and the center of class which excludes
the vector. The equation is

2

⎡
⎤
⎛s ⎞
E(s, A) = ∑ ⎢I( x, y) − ∑ si ai ( x, y)⎥ + λs ∑ S ⎜⎜ i ⎟⎟x, y ⎣
i
⎝σi ⎠
⎦
(4)

ˆ

j
represent the center of the excluding class.
where
In order to make the patterns be correctly classified,
we expect: 1) the within-class distance is smaller, so theclass is more compact in the N-dimensional coefficientspace; 2) the between-class distance is greater, thus theinterval between the class 1 and class 2 is bigger. That is
to say, we should maximize the between-class distance in

For the visual classification purposes we must seek areasonable transform method which maximizes the
distance between the classes while minimizing the
distance within each classes in coefficient state-space. So
the code coefficients (or features ) are efficient fordiscrimination, unlike the sparse code that seeks the©ISAROB 2006

1

represent the pattern sets,
here we only consider the two-class classification, and⎛ si ⎞⎝

1X 2 = {I 1 , I 2 , ..., I N 2 }∑ S ⎜⎜ σ ⎟⎟
i

that

2

(2)
As an additional criteria for sparse coding, Olshausen
and Field proposed the ‘sparseness’ cost for seeking
sparse codes. The sparseness cost function is given by
Sparseness( s, A) =X 1 = {I 1 , I 2 , ..., I N 1 }
1

2

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the same time minimize the within-class distance. So we
make a tradeoff to optimize a ratio. The ratio is given by

the current residual image using the basis function ai(x, y)and the weights. The second term applies the derivativeof sparseness. Especially, the third term incurs amovement which makes the si near the center of the
included class and apart from the excluded class.
The outer stage minimization over the A may befinished by simple gradient descent method. The
learning rule for it is given byDDR£½ WDB(8)
When we look into the Eq.(8) we can find that its
derivative for coefficient s is too complex to optimize.
So we smartly transform the magnitude of the ratio bylogarithm. The transformed ratio is as below:nn~ )2 ) - ln( (s j - m
Dis(s) = ln(DR ) = ln(∑(si - m∑ i ~ ĵ )2 )
j
2

j

i =1

i=1

(9)
As a result, the model produces an N-dimensionalcoefficient space in which the coefficients in the sameclass tightly locate in a subspace and are apart from theother class.

B. Learning
Learning is accomplished by minimizing the total costfunction:E(s, A) = Error(s, A) + λs Sparseness(s, A) + λd Dis(s)
(10)

λ
λ
where s and d are positive weights. The function
to be minimized, E ( s, A) , is the sum of three terms:
the first term computes the reconstruction error, which
forces the basis functions, A, to span the input space;
the second term incurs a penalty on the coefficient
activities, which encourages sparse representation; andthe third term calculates the discriminability which
drives the coefficients to be more efficient for patternclassification.

saliency
Response saliency is the response extent for a neuron
compared with a group of neurons which respond to thesame stimulus. The purpose of the response saliency is torepresent the conspicuity for every neuron in the same
perception level for a stimulus and to guide the selectionof attended neuron, based on the value of response
saliency[9]. The neuron response that has great response
saliency value will be chosen to further process. On thecontrary, the neuron that has small value will be omitted.
In the framework of sparse coding, the simple cells in
the primary visual cortex (V1) produce sparse code forthe input stimuli. That is say, simple cell takes very small(absolute) values or very large values often; tocompensate, it takes values in between relatively more
rarely. The sparse code focuses on the possibility
distribution of response. Intuitively, the response value
itself provides very useful information: the response
value is bigger, the information represented by theneuron is more important; otherwise, the information isless important. Obviously, the response value gives afoundation for the attention mechanism. Supposed herej ~j ~2*(si - m)
2*(si - m
λs si
dE
ĵ
j )
= −2bi + S′( ) + λd ( n
− n
)
σ σ
dsi
j ~
j ~
2
2
∑(si - mj ) ∑(si - mĵ )
i=1

(11 )
where

bi = ∑ x , y ( I ( x, y ) − ∑ s j a j ( x, y ))ai ( x, y )j.
According to Eq.(11), the si are drived by a sum of threeterms. The first term takes a spatially weighted sum of©ISAROB 2006(12)
where η is the learning rate. In the neural network
view, ai are updated by Hebbian learing between the
outputs coefficients, sj, and the resulting residual image.
Because there is no closed-form solution for the si interms of the input I(x, y), so si is calculated by recurrent
computation similar to an analysis/synthesis loop. An
intuitive interpretation for this algorithm is that in the
inner stage, the gradient of ‘sparseness’ sparsifies the
distribution of s by differentially reducing the value ofsmall coefficients more than great coefficient, at thesame time, the gradient of ‘discriminability’ makes thecoefficient near to homogeneous center and apart fromthe unhomogeneous center. Then, the ai learn on the error
induced by the sparseness criteria and discriminability
criteria, resulting in a basis function set which cantolerate sparseness and discriminability in the conditionof minimizing mean square reconstruction error.

2.4 Selective attention model based on response

The process for minimizing E ( s, A) can be divided
into two nested stages. In the inner stage, E is minimized
with respect to the si for a batch of pattern, holding the A
fixed. In the outer stage (i.e, on a long timescale, overmany image presentations), E is minimized with respect
to the A. The inner stage minimization over the si can be
performed by conjugate gradient method, so the si aredetermined by the differential equation:

i=1

⎤
⎡
∆ai ( x, y ) = η si ⎢ I ( x, y ) − ∑ s j a j ( x, y )⎥
j
⎦
⎣

32

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

position of reasoning in the system. Supervise
expectation failure during reasoning, the systemcompares results with corresponding expectation at
every step of reasoning to find out failure. Explain
failure, the system looks for the explanation for failure
according to the criteria of expectation failure and thetrace of reasoning, then puts forward a relevant advice toupdate the reasoning based on the reason found out foravoiding latter same failure. Repair failure, the measuresof repairing reasoning are attached to specificexpectation, thus the accessional measures can be put
forward when an expectation failure occurs. The
description of the measure cannot specify how and what
to repair, so the system should involve the mechanism ofmaking repair measure. The repair module makes actual
repair measures and repair according to the descriptionof failure and the method advised to repair.
The figure 2 is the general model of introspective
and Aj is represented as D ( Ai , A j ) , and the value forlearning. Besides the three process mentioned above, itD( Ai , Aj ) is a function of simple cell’s selective
includes knowledge base, trace of reasoning, expectation
model of reasoning and supervised protocol and so on.
characteristics: location (L), orientation (O) and
The supervised protocol regulates how and where tofrequency (F). The equation as below:
supervise the system’s reasoning and the switch of theD( Ai , A j ) = W1 * N£¨ ( Lix − L jx ) 2 + ( Liy − L jy ) 2£© system’s control power. The knowledge base involves
the relevant knowledge of reasoning, it is not only the+ W2 * N£¨ Oi − O j £©
+ W3 * N£¨ Fi − F j £©
base of reasoning but also blame assignment and explain(13 )failure. The expectation model of reasoning is a perfectmodel, it offers the criteria of reasoning expectation andDiff ( Ai , S ) =(1 − D(i, j ) * R j ) / Count£¨ S£©
becomes the central basis of blame assignment. The
A j ∈S
introspective learning units of intelligent knowledge(14 )system use existing knowledge about background, theHere, operation N(.) represents the normalization
expectation and the trace of reasoning to look over if
operator which makes the values between 0 and 1, and 0
expectation failure occurs at present state according to≤W1, W2, W3≤ 1 represents the weights, W1+W2+W3=1.
supervised protocol. Expectation failure occurs at two
Then, the response saliency (RS) value is thesituations that one is when an expectation of perfect state
weighted sum of norm response value and neuron
is not accordance with the present actual reasoning anddiscrepancy as following:
the other is when a catastrophic failure takes place in theRS( Ai ) = N( Ri ) + λ * Diff ( Ai , S )( 15 )
system. If the reasoning unit does not find out the
expectation failure, it means that all expectations are in
Here λ is a weight which determinates theaccordance with actual situation and the system will be
importance of each components
informed normal and regain the control power. If a
failure is found, the reasoning unit will make use of the
3. Introspective Learning
background knowledge, the trace of reasoning and theIntrospective learning is an inside learning of brain,
perfect expectation model to look for the preliminary
which means without input information from outside
reasons for the failure and explain the failure. When a[13]environment . Introspective learning process involves
failure is found, the available information is probably notthree steps: blame assignment, Explain failure, andsufficient for diagnosing and repairing. So, theRepair failure. Blame assignment, the system first makes
introspective learning unit may pause its explanation anddefinite and limited expectation of reasoning process,repairing, permit the system to continue working until
then compares the expectation with the actual reasoning
ample information acquired. If necessary information is
to find out the difference. The difference between
attained, the task of explanation and repair will restart
anticipant action and actual action is called asfrom the position of pause. The explanation of failure
expectation failure. The confirmation of failure means
can offer clues to the introspective reasoning unit forthat there are definite expectation values at the present
that Ai represents simple cell i, and Ri represents thesimple cell’s response.
Every simple cell (corresponding to the column of A
in equation 1) carries a specific pattern. Furthermore,
every such pattern is selective for location, orientationand frequency. Based on Gestalt similarity perception
principle and Hebb rule, we can get that the neurons withsimilar selectivity increase the response values; on thecontrary, the neurons with different selectivity decrease
the response values. So we can suppose that the response
saliency value of a neuron, which has great discrepancy
among a group of neurons responding to the same
stimulus, will decrease; and the value for a neuron which
has small discrepancy will increase relatively. The
neuron set responding to the same stimulus assumes as S,
S = { A1 , A2 , K, Am } ; the discrepancy between Ai∑©ISAROB 2006

33

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

repairing failure. The learning aim of repair failure ismade after explain failure, the repair failure module will
make the repair measures according to the learning aim
and update the reasoning. When the repair finishes orwhen the repair is found impossible, the system will
acquire the control power.
Knowledge
base

SupervisedBlameprotocolassignment

Without
expectation failure

Trace ofreasoning(No.2003CB317004), the 863 Project (2003AA115220)
and National Natural Science Foundation of China(No. 60435010).

References[1] Atick, J.J., Redlich, A.N. (1990). Towards a theory of
early visual processing. Neural Computation,
2:308-320.
[2] Dayan, P., Hinton, G.E., Neal, R.M, Zemel, R.S. (1995).
The Helmholtz machine. Neural Comput 7(5):889–904.
[3] Grossberg, S. (1987). Competitive learning: frominteractive activation to adaptive resonance. Cogn Sci
11:23–63.
[4] Hinton, G.E. (2005). What kind of a graphical modelis the brain? IJCAI2005, Award lecture.
[5] Hubel D, Wiesel T (1962): "Receptive fields,
binocular interaction and functional architecture in
the cat's visual cortex." J.Physiology (Lond)
160:106-154[6] Hubel D. H. and Wiesel, T. N. (1968). Receptive fields
and functional architecture of monkey striate cortex. J
Physiol. Mar;195(1):215-43.
[7] Lee T S, Mumford D, Romero R, Lamme VA. (1998).
The role of the primary visual cortex in higher levelvision. Vision Res 38:2429-2454.
[8] Lee T S. 2003. Computations in the early visual cortex.
Journal of Physiology 97:121–139.
[9] Qingyong Li, Jun Shi, Zhongzhi Shi. (2005). A modelof Attention-Guided Visual Sparse Coding. ICCI 2005:
120-125[10] McClelland JL, Rumelhart DE. (1981). An interactive
activation model of context effects in letter perception.
Part I: an account of basic findings. Psychol Rev
88:375–407.
[11] Olshausen B A and Field D J. (1996). Emergence ofsimple-cell receptive field properties by learning asparse code for natural images. Nature 381:607-609.
[12] Olshausen B.A, Field D.J. (2004). Sparse coding of
sensory inputs. Current Opinion in Neurobiology 14:
481-487.
[13] Zhongzhi Shi, Sulan Zhang, Case-based Introspective
Learning, IEEE ICCI 2005, 43-48.
[14] Juyang Weng. Developmental Robotics: Theory andExperiments. International Journal of Humanoid
Robotics, 1(2): 199-236, 2004.

Expectationof reasoning

Symptom
ReasonCase retrieval
of failure Lookfor reason of failure
select repair option
explain failure

Lack of information

Return the control to the problem-solving

Fig. 2 Introspective learning model

4. Conclusions
For the developmental robot two learning strategies
have been proposed in the paper. One is perceptual
learning. We have put forward a novel sparse coding
model, called classification-oriented sparse coding(COSC) model, for learning sparse and discriminablestructures in the natural images which is valuable for thehigher perception task: visual classification. Visionattention mechanism is an active strategy in information
processing procedure of brain, which has many
interesting characteristics, such as selectivity,
competition. Attention is everywhere in the visual
pathway. A typical scene within the neuron’s classic
receptive field (CRF) contains many different patterns
which compete for neural representation because of thelimited processing capacity of neurons in the visualsystem. In this paper, we also proposed an attention
model, that is saliency-based data-driven module, in theframework of efficient coding hypothesis.
Another learning strategy is introspective learning.
We proposed a introspective learning process involves
three parts that are blame assignment, explain failure andrepair failure. In the future we are continue working onlearning methods and algorithms for autonomous mental
development which enables robots to incrementally scale
its level of intelligence.

Acknowledgement
The research work in this paper is supported by the
National Basic Research Priorities Programme©ISAROB 2006

34

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Robot Control Method Using Evolutive Binary Decision Diagrams
Masayoshi Kanoh† and Hidenori Itoh‡
School of Life System Science and Technology, Chukyo University
101 Tokodachi, Kaizu-cho, Toyota 470-0393, Japan.
‡
Graduate School of Engineering, Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan.

†

Abstract

In this paper, we propose a learning-based method
for controlling robots using evolutive binary decision
diagrams (BDDs). BDDs are a ways of to represent
a logic function using a directed graph which consistsof binary switches having one input and two outputs,
and are mainly used to design LSI. Moriwaki et al.
proposed n-output BDDs (n-BDDs) which can evolve
BDDs, and they applied n-BDDs to food chain simulation of evolutional agents. In the evolution method,variable ordering of n-BDDs is ﬁxed, but they showedthat n-BDDs provided good performance for deciding
best output on some inputs. In this paper, we introduce a new genetic manipulation for avoiding the
problem of variable ordering to n-BDDs, and apply
the new n-BDDs to learn motion control of robots.
We demonstrate that our method is capable of providing good performance in this paper.

1

0

1

0

x1

x2
0
0

1

x2
1

x3 1
1

0

1

x3
0

1

1

2

2

(a) BDD..... n(b) MTBDD

Figure 1: Examples of BDD and MTBDD.
However, learning of robot’s motion control involves
very large state space, so that it is diﬃcult to provide
variable ordering for n-BDDs manually. In this paper,
we introduce a new genetic manipulation for avoiding
the problem of variable ordering to n-BDDs. To conﬁrm the eﬀectiveness of our method, we used action
learning of a mobile robot, LEGO Mindstorms. Moreover, as learning of high-dimensional state spaces, ourmethod was applied to motion control of a humanoid
robot, HOAP1 (Humanoid Open Architecture Platform). We demonstrate that our method is capable ofproviding good performance.

Introduction

Demand for robots is shifting from their use in industrial applications to their use in domestic situations. Such robots require sophisticated body designs
and interfaces to do this. Humanoid robots which havemulti-degree-of-freedom have been developed, capableof working with humans using a body design similar to humans. However, it is very diﬃcult to control intricately robots with human generated, preprogrammed, learned behavior. Learned behavior should
be obtained by the robots themselves in a human-likeway, not programmed manually.
Moriwaki et al. proposed n-BDDs which can evolve
multi-terminal BDDs (MTBDDs) [1], and they applied n-BDDs to food chain simulation of evolutional
agents. In the evolution method, variable ordering of
n-BDDs is ﬁxed, but they showed that n-BDDs provided good performance for deciding best output onsome inputs. We introduce n-BDDs to learn motion
control of robots in this paper. We consider that acontroller which is learned by a robot using n-BDDs
can be used for a controller of other similar robots.

©ISAROB 2006

x1

2

What are BDDs?

A binary decision diagram (BDD) is a data structure that is used to represent a Boolean function [2, 3].
Figure 1 shows examples of BDDs. The Boolean function is represented in a BDD as a rooted, directed,
acyclic graph where each nonterminal vertex is labeled
by a variable xi and has two directed edges connecting to child nodes. The left edge represents a variables
assignment to zero (0-edge), and the right edge represents an assignment to one (1-edge). A “1” or “2”labels all terminal vertexes. MTBDDs are an extension of BDDs, and the diﬀerence is that MTBDDs havemultiple outputs. For example, the MTBDD in Figure
1 has n outputs. Moriwaki et al. introduced to evolve

35

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0

x1

1

0

x2

x1

1

0

x2
0

x3
1

1

2

..... n

1

0

1

1

2

1

0

x1

1

0

..... n

1

0

x3

0

1

1

2

(a) insertion

1

0

x1

x2

x3

0

x1

x2

x3

0

x1

..... n

x2
1

0

x3

0

1

1

2

..... n

1
1

x3

0

1

1

2

(b) deletion

0

1

1

2

..... n..... n(c) mutation

Figure 2: Genetic operations of n-BDDs.
MTBDDs by using genetic operations, and they called
them n-BDDs [1].

33.1

Insert vertex
Input S: a set of already used variables in n-BDD.
1. begin2.
R := φ;3.
create a new nonterminal vertex V
which variable is xV ;4.
if xV is used as the root node5.
then go to 3.;
6.
if xV ∈ S
7.
then begin
8.
if there are edges e that can insert xV
9.
then add all candidates e to R as elements;
10.
else go to 3.;
11.
end;
12.
if R = φ13.
then insert V on an edge selected at random;
14.
else begin15.
select an edge e from R at random;
16.
insert V on e;
17.
end;
18. end.

N-BDDs for controlling robots
N -BDDs

Figure 2 shows genetic operations of n-BDDs [1].
Genetic operations, insertion, deletion and mutation
are deﬁned to operate an n-BDD as a gene.
Insertion inserts a new nonterminal vertex on anedge selected at random. Either of 0-edge or 1-edgeof the new vertex must connect to the vertex which
was connected before. The other edge connects to a
subordinate vertex at random.
Deletion deletes a nonterminal vertex selected at
random. The edge connected to the deleted vertex
must connect to one of the vertexes which was connected by the deleted vertex before.
Mutation changes the direction of one of the edgeof a nonterminal vertex selected at random. The noteconnected by the edge must be a subordinate vertex of
the mutated vertex. This restriction avoids the loop
and cycle of edges.

3.2

Figure 3: New insertion operation.

New Genetic Manipulation for Controlling robot

N-BDD for left motor

To avoid the problem of variable ordering in nBDDs, we introduce a new genetic manipulation for
insertion. Figure 3 shows the new insertion method.
Moreover, we consider the placement of n-BDDs.
Generally, the state space for controlling robots is tobe high-dimensional state spaces, because robots have
many motors. Only if we use an n-BDD for a robot,
the number of their output increases. Therefore, we
propose to place n-BDDs at each motor. Figure 4 is an
example placement of n-BDDs. Using this placement,
we can introduce crossover to evolution of n-BDDs.
Concretely, it is done by changing n-BDDs placed ateach motor between two robots. We consider that various individuals are carried out to next generation by
the crossover method.

©ISAROB 2006

0

x1

1

0

x1

x2
0
0

1

1

x2
1

x3 1

0
0

2

1

x3 1
1

2

N-BDD for right motor

Figure 4: N -BDDs placement.

4

Experiments

In order to conﬁrm the eﬀectiveness of our method,
we did two experiments: learning a line-tracing robot
and controlling a humanoid robot.

4.1

Learning Line-Tracing Robot

Consider a course shown in Figure 5. This environment has a track with black line. The learning robot

36The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

θB
θK

θW

y

θA

Figure 5: Course.

Figure 7: Learning motion.

Figure 6: Result.

Table 1: Sensed color and variable value.
Sensed color
black
gray
white

Figure 8: HOAP1.

Table 3: Sensed degree and variable value.

(x0 , x1 )(0, 1)(0, 0)(1, 0)

Sensed degree3(θmax − θmin ) ≤ θ < θmax4
13
(θmax − θmin ) ≤ θ < 4 (θmax − θmin )2
1(θmax − θmin ) ≤ θ < 12 (θmax − θmin )4θmin ≤ θ < 14 (θmax − θmin )(x0 , x1 )(1, 1)(1, 0)(0, 1)(0, 0)

Table 2: Actions for Mindstorms.
Terminal vertex
a0
a1
a2

Motor output-2 (backward)0
2 (forward)

Table 4: Actions for HOAP1.
Terminal vertex
a0
a1
a2

can sense ﬂoor color using a light sensor, and takes
action. Table 1 and 2 show ﬂoor colors sensed by the
robot and actions of the robot, respectively. In Table 1, gray is sensed on the border between black andwhite ﬂoors. The robot learn to trace gray color.
To simulate learning, we used the GURS [4] which
is a LEGO Mindstorms simulator. Fitness increases
if robot is on gray ﬂoor and its both motor does not
stop. We used 30 individuals in each generation. For
crossover, insertion, mutation and deletion rates, weused the value of 0.5, 0.3, 0.15 and 0.05, respectively.
In the evolution, one individual is carried out to next
generation (elite strategy), and the other 29 individuals are selected by roulette strategy.
Figure 6 shows a result of evolution with n-BDD at
50th generation. You can see that the robot can trace
the border between black and white well.

4.2

ing, we used the Open Dynamics Engine [5].
The robot’s state is sensed by the following variable
vector x(t):
W
K
K
A
A
B
B
x(t) = (xW
0 , x1 , x0 , x1 , x0 , x1 , x0 , x1 ).

(1)

K
A
where xW
i , xi and xi are variables for waist, knee
and ankle angles respectively, and xB
i is for the pitchof its body (Figure 7). Outputs of these variables are
determined by current degree θ of joint (see Table 3).
W= −80.0 [deg],
Here, the values in the table are θmin
W
K
K
θmax = 70.0 [deg], θmin = 0.0 [deg], θmax
= 120.0 [deg],
A
A
B
θmin
= −60.0 [deg], θmax
= 60.0 [deg], θmin= −60.0
B[deg], θmax = 60.0 [deg]. Actions of the robot are
determined as Table 4.
One generation terminates when the robot fell downor time passed over ttotal = 10 [sec]. Fitness is determined by height y [cm] of the robot’s breast (seeFigure 7). We used 30 individuals in each generation.
For crossover, insertion, mutation and deletion rates,
we used the value of 0.75, 0.15, 0.075 and 0.025, respectively. In the evolution, we used the same strategy
as section 4.1.
Figure 9 shows an evolution result at 1000th generation. You can see that the robot stood up while main-Controlling a Humanoid Robot

In this section, as learning of high-dimensional state
spaces, n-BDD is applied to a humanoid robot learning to stand up from a chair (Figure 7). The learning
was simulated using the virtual body of HOAP1. Figure 8 shows HOAP1. The robot is 48 centimeters tall,weighs 6 kilograms, has 20 DOFs, and has 4 pressure
sensors each on the soles of its feet. To simulate learn-©ISAROB 2006

Motor output-20.0 [deg/sec](backward)0
20.0 [deg/sec](forward)

37

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0.0[sec]

2.0[sec]

4.0[sec]

6.0[sec]

8.0[sec]

10.0[sec]

Figure 9: Result at 1000th generation.

Figure 11: N -BDD of waist.

Figure 12: N -BDD of knee.

BDDs. We did two experiments, and veriﬁed the eﬀectiveness of our method. The future work of this study
is veriﬁcation of eﬀectiveness of crossover of n-BDDs,
and is other motion generation, for example, walking.

Fitness

330
310

Acknowledgements
This work was supported in part by the Chukyo
University Research Fund.

290
270

0

200

References

400 600 800 1000
Generations[1] K. Moriwaki et al., “A genetic method for evolutionary agents in a competitive environment,” Soft Computing in Engineering Design and Manufacturing, pp.
153–162, 1997.

Figure 10: Fitness.

[2] S. B. Akers, “Binary decision diagrams,” IEEE Trans.
on Comp., vol. C-27, no. 6, pp. 509–516, 1968.

taining its balance. Figure 10 shows the ﬁtness per
generation with n-BDD. The results shown in the ﬁgure are averaged over 10 repetitions of the experiment.
In all experiments, the robot obtained the stand-up
motion. Figure 11, 12 and 13 show n-BDDs at eachjoint in a successful experiment. You can see that variable ordering of each joint diﬀers from the others.

5

Figure 13: N -BDD of ankle.

[3] R. E. Bryant, “Graph-based algorithms for booleanfunction manipulation,” IEEE Trans. on Comp., vol.
C-35, no. 8, pp. 677–691, 1986.
[4] J. Rasmussen, “The general unified robot simulator (gurs),” http://www.daimi.au.dk/%7Erepsej/ thesis.html.

Conclusions[5] R. Smith, “Open dynamics engine (ode),” http://
opende.sourceforge.net/ode.html.

In this paper, we proposed a learning-based method
for controlling robots using new manipulations for n-©ISAROB 2006

38

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Waste Incinerator Emission Prediction using Probabilistically Optimal Ensemble ofMulti-Agents and Neural networks
Daisuke Yamaguchi1
Kenneth J. Mackin2 , Muneo Takahashi1 and Eiichiro Tazaki1
1
Department of Electronics and Information Engineering Toin University of Yokohama
Aoba-ku, Yokohama , 225-8502 JAPAN
2
Department of Information Systems Tokyo University of Information Sciences
Wakaba-ku, Chiba , 265-8501 JAPAN
Abstract

Fujiyoshi et al. [1] has proposed applying fuzzy control to incinerator control to decrease the dioxin emission. Ichihashi et al. [2] has applied statistical analysis to calculate the correlation of various input signals
with dioxin emissions. Fukushima [3] has proposed
applying fractal fuzzy control in order to estimate andcontrol dioxin emission. For this research, we investigate the validity of applying neural network trained
multi-agents for the prediction of dioxin emission to be
used for the combustion control in order to decrease
the dioxin emission. Neural networks, as with othertraining based classiﬁers, inherently have a risk thatwhen classifying an untrained data set, the classifying error rate may be much worse than the training
result. In order to overcome this risk, we applied aprobabilistically optimal ensemble technique proposedby Imamura et al. [4] to N-version programming ofsoftware agents. Our purpose is to research the validity of using a training based classiﬁer method foremission prediction, compared against the results ofprevious statistical methods and fuzzy decision methods. We plan to continue this approach and apply
the proposed method to actual incinerator plant controllers to test the validity of the method.

Dioxin emission from waste incinerator plants is oneof the hottest ecological problems today. In waste incinerator plants, the chemical reactions in the incinerator occur under a very dynamic environment, making its control a very complex task, and current stateof-the-art incinerator facilities have not succeeded incompletely removing the dioxin emission. In this paper, we propose applying a probabilistically optimalensemble technique, based on fault masking amongindividual classiﬁer for N-version programming. Our
purpose is to research the validity of using a training
based classiﬁer method for emission prediction, compared against the results of previous statistical methods and fuzzy decision methods. One agent locates itwith one member in optimization in the multi agent.
We create an optimal ensemble of trained neural networks and use the majority voting result to predictwaste incinerator emission. In a probabilistically optimal ensemble, the members of the ensemble are chosen so that the members are correctly independent ofeach other. We show that an optimal ensemble ofmulti agent greatly improves the prediction error rateof emission of dioxins.
Keyword : Multi-agent, Neural Network, Emergence, Control Dioxin Emission, Incinerator Control,
Optimal Ensemble

12

introduction

For each of the diﬀerent types of incinerator sensor
data, there is an apparent correlation just described,
but direct correlation between the sensor data and carbon monoxide concentration is not very strong. Thisis because the environment in the incinerator is a complex dynamic environment in which the diﬀerent items
are dependent on each other, and is not a simple dependency relationship.
Artiﬁcial neural networks (ANN) can be character-

The emission of dioxins from waste incinerators is
one of 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. There has been past research in intelligent
estimation of dioxin emission from waste incinerators.
1©ISAROB 2006

Incinerator control using multi-agent
system39

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

ized by its ”black box” approach to learn and classify
complex data patterns. For this research, we propose
applying 3 layer network structure (1 input layer, 1hidden layer, 1 output layer) for the training of an
incinerator emission prediction agent, using the neural network to learn the complex relationship between
incinerator sensor data.
The proposed multi-agent system for incinerator
emission prediction is part of a larger incinerator controller system plan. The incinerator controller system will consist of two separate multi-agent systems,
the dioxin prediction section and the combustion controller section. Each section will use a separate, independently trained multi-agent system. The dioxin
prediction multi agent-system uses incinerator sensor
input and predicts the carbon monoxide (hence dioxin)
emission rate before the actual emission occurs. The
combustion controller multi-agent system uses the output from the dioxin prediction system as a trigger,
as well as incinerator sensors for input, and outputs
changes in incinerator control values in order to decrease the predicted carbon monoxide emission.
For this paper, we will propose methods applying
N-version programming using software agents to construct the dioxin prediction section. We will discuss
the combustion controller system in future works.

3

Dioxinagent

prediction

with

Figure 1: Preliminary experiment results of prediction error for untrained data

Figure 1 shows the training results of the preliminary experiment which predicted the carbon monoxide values directly using the neural network. From theresults of the preliminary experiment, we found thatthe prediction accuracy is completely diﬀerent between
normal range carbon monoxide values, and high carbon monoxide values. The network learned to accurately predict normal range carbon monoxide values
fairly quickly, but the same network failed to learn
abnormal (high) range carbon monoxide values during the same training period. When network training
was continued in order to increase the abnormal range
carbon monoxide prediction, this time the accuracyof normal range carbon monoxide prediction deteriorated. This ﬁnding conﬁrmed our initial estimate that
it would be diﬃcult to train the neural network due
to the complexity of the correlation between carbon
monoxide concentration and each of the other sensor
values.
For this reason, we decided to focus on detection of abnormally high carbon monoxide emission(Â 100ppm) as the preliminary goal of the dioxin prediction network. The network output was changed
from direct carbon monoxide concentration prediction
value, to binary output where 1 predicts high carbon
monoxide concentration (Â 100ppm) and 0 predicts
normal carbon monoxide concentration (¹ 100ppm).
As for the neural network input, we considered thepossibility that the large number of input nodes increases the problem domain and complicates the classiﬁcation, causing an adverse aﬀect on the network
training eﬃciency. With this assumption, we decided
to minimize the number of input nodes in order toﬁrst achieve a workable learning curve and prediction
accuracy.

softwareFirst we describe the basic software agent for dioxin
prediction. We considered the 3 layer artiﬁcial neural network (1 input layer, 1 hidden layer, 1 output
layer) as the basic training classiﬁer in the agent. We
use a sigmoid function for the synapse function of theneuron, with back propagation (BP) training of theincinerator data. The number of hidden neurons was
decided by results of preliminary experiments of theneural network.
As a preliminary experiment, we tested a neuralnetwork which took all of the sensor data except carbon monoxide concentration values for input, and thesingle output of the network was used to predict thecorrect carbon monoxide concentration. Time delay of
input data was not considered here.
For the network training we used the databaseof collected incinerator sensor data, and applied BP
training based on the diﬀerence between predicted carbon monoxide concentration and the actual carbon
monoxide concentration recorded for the same timeframe.

©ISAROB 2006

40

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

It can be assumed that there must be some relationship between oxygen concentration, ﬂapper angle
and carbon monoxide concentration, from the similarchanges seen in time-series data as in Figure 1. Basedon this assumption, for the initial model we use only
ﬂapper angle and oxygen concentration data as neuralnetwork input. Further, we can see that ﬂapper angle, oxygen concentration and carbon monoxide concentration each show a particular time delay in theirrelationship. For this reason, in order to predict the
carbon monoxide value for a given instance, time delay for the ﬂapper angle and oxygen values must be
taken into account. Data at some ﬁxed time frame
previous to the given output instance should be usedas the input data. Recurrent network structures could
be used to automatically treat such time sequence data
eﬀectively, but for the initial model, we map sequential data of ﬂapper angle and oxygen concentration of
speciﬁed time delay to individual input nodes to the
network. Speciﬁcally, we used 60 second delay for ﬂapper angle (t ¡ 60) and 30 second delay for oxygen data(t ¡ 30), to predict the emission for time t.

4

Figure 2: Prediction error of untrained data using single neural network

normal range carbon monoxide values, and 0.13 for abnormal range carbon monoxide values. As was seen in
the preliminary experiment, as the network trained todecrease the abnormal range output error, the normal
range output error in turn became higher.

Experiment results of single agent in
Neural network

5

We trained the software agent with the above described neural network using BP and evaluated theprediction accuracy. A standard sigmoid function wasused as the neuron’s base synapse function. The number of neurons used in each layer was 3 input neurons(1 ﬂapper input, 1 oxygen input and 1 ﬁxed input), 6
hidden layer neurons, and 1 output neuron.
For the training data, 100 cases of normal range
carbon monoxide data and 100 cases of abnormal(Â 100ppm) carbon monoxide data, for a total of 200
cases were randomly selected from the incinerator sensor database. For the untrained data used to plot thetraining curve of network accuracy, 100 cases of normal range carbon monoxide data and 100 cases of abnormal range (Â 100ppm) carbon monoxide data, fora total of 200 cases were randomly selected from theincinerator sensor database.
Figure 2 shows the change in output error for theuntrained dataset of the proposed neural network. The
absolute output error for normal range carbon monoxide values, absolute output error for abnormal range(Â 100ppm) carbon monoxide values, and absolute
output error for all values is graphed.
The absolute output error for a single neural network shown in Figure 2 was 0.18 for all values, 0.23 for©ISAROB 2006

Pobabilistically optimal ensemble ofmulti-agents

Neural networks, as with other training based classiﬁers, inherently have a risk that when classifying an
untrained data set, the classifying error rate may bemuch larger than the training result. In order to overcome this risk, we applied a probabilistically optimalensemble technique proposed by Imamura et al. [4]to N-version programming of software agents.
Fault masking in N-version programming assumes
that the individual members give completely independent results. If certain members give similar output,
then correct fault masking will not occur. In a probabilistically optimal ensemble, the members of the ensemble are chosen so that the members are correctly
independent of each other. This is realized by selecting members so that the measures error rate of theensemble comes closest to the expected error rate of
the ensemble. If the members are correctly independent of each other, then proper fault masking should
allow the measured error rate to become very close to
the expected error rate.
The expected binomab failure rate f of the probabilistically optimal ensemble can be calculated by theequation [4]41The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

)
n (
∑
n
(1 ¡ p)n−k pk
f=
k

6
(1)

In this research we applied a probabilistically optimal ensemble technique [4] to create an N-version
programming classiﬁer system using software agentstrained by 3-layer neural networks to predict incinerator emission. We were able to conﬁrm that by using
an optimal ensemble of independent software agents,
the classiﬁcation error rate can be greatly reduced.
For future works, we will consider methods to improve prediction accuracy of the individual neural network, including the increase in the types of sensor
input data, reevaluation of neural network structure,
combining fuzzy rules to treat input data, as well aseﬀect of using diﬀerent base synapse functions for neurons.

k=mwhere p is the failure rate of each individual, n is
the size of the ensemble, m is the minimum number offaulty outputs for an ensemble to fail. We assume thesame failure rate p for individuals for simplicity.
In the case where the ensemble has 3 members, majority vote (2 votes) for output, and p = 0.19 , thenf = 0.086 .
For our research we trained 6 neural network agentsusing diﬀerent initial weights, and the same training
set. Using the same training set, we compared themeasured ensemble failure rate for ensemble size 3, forall combinations of agents. Using individual failure
rate p = 0.19 , the expected ensemble error rate was
f = 0.086.
Table 1 shows the results of the measured ensemble
failure rate. The ensemble with the closest ensemble
failure rate ( 0.085 ) was selected as the optimal ensemble.
The selected optimal ensemble was evaluated using
untrained data. The resulting ensemble error rate was
f = 0.087, indeed very close the training ensemble
error rate, and vastly improved over the error rate 0.19
for the single neural network agent.

References[1] Makoto Fujiyoshi, Ryutaro Fukushima, Mitiharu
Masuya, Intelligent Control System for Fluidized
Bed Incinerator, Proceedings of 18th Fuzzy System
Symposium , Japan, pp.25-28, 2002
[2] H. Ichihashi, et. al, Fuzzy Bi-plot of Correlation Analysis for Waste Incinerator, Proceedingsof 19th Fuzzy System Symposium , Japan, 2003

Table 1: Measured ensemble failure rate
ANN no.
0,1,2
0,1,3
0,1,4
0,1,5
0,2,3
0,2,4
0,2,5
0,3,4
0,3,5
0,4,5
1,2,3
1,2,4
1,2,5
1,3,4
1,3,5
1,4,5
2,3,4
2,3,5
2,4,5
3,4,5

©ISAROB 2006

Conclusion[3] R. Fukushima, Fractal Fuzzy Intelligent Control
System, Proceedings of 20th Fuzzy System Symposium , Japan, 2004

ensemble failure rate
0.11
0.115
0.095
0.085
0.115
0.12
0.1
0.13
0.12
0.115
0.13
0.135
0.115
0.14
0.13
0.115
0.145
0.135
0.125
0.125

[4] Kosuke Imamura, Kris Smith, A Probabilistically
Optimal Ensemble Technique for Training BasedClassiﬁers, Proceedings of Joint 2nd International
Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems , Japan, 2004

42

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Model-based Reinforcement Learning for Large-scale Multi-agent Games
with Sampling-based State Estimation
Hajime Fujita and Shin Ishii
Graduate School of Information Science
Nara Institute of Science and Technology
8916-5 Takayama, Ikoma, 630-0192 JAPAN
Abstract

explicit learning of the environmental model to follow its
dynamics are necessary.
In this article, we present an automatic strategyacquisition scheme for large-scale multi-agent problems
with partial observability, and deal in particular with thecard game “Hearts”. To estimate unobservable states, weuse a sampling technique [13] based on Markov chain
Monte Carlo (MCMC) [2] for estimating the unobservablevariables with a plausible number of samples. To predictthe unknown environmental behaviors, we use a modelbased approach [11] in which the learning agent based on
our RL method has multiple action predictors for acquireing policies of the opponent agents. These ideas provide
us with an effective solution for large-scale partially observable problems and ability to apply for various multiagent settings; this is shown by computer simulations using expert-level rule-based agents. These results suggestthat our method is effective in solving realistic multi-agent
problems with partial observability.

We present a model-based reinforcement learning (RL)
scheme for large-scale multi-agent problems with partial
observability, and apply it to the card game “Hearts”, which
is a well-defined example of an imperfect information
game. To reduce the computational cost, we use a samplingtechnique based on Markov chain Monte Carlo (MCMC)
in which the heavy integration required for the estimationand prediction can be approximated by a plausible numberof samples. Computer simulation results show that our RL
agent can perform learning of an appropriate strategy andexhibit a comparable performance to an expert-level humanplayer in this partially-observable multi-agent problem.

1 Introduction
Reinforcement learning (RL) [12] has been devoted
much attention as an effective framework for strategic decision processes in multi-agent systems [9]. An optimal control problem in multi-agent environments, however, has ahigh degree of difficulty due to interactions among agents.
These may make the Markov property of the state space
fail because the changing behaviors of other agents provide
dynamic nature. Although several RL researches based on
the game theory have attained some remarkable results in areasonably sized state space [1, 6], it is difficult to deal withreal-world applications due to their serious complexity.
In addition, the environments often have partial observability; the agents cannot directly access internal states ofthe environment, but can get only observations which contain partial information about the state. Decision-making
problems in such a situation can be formulated as partially observable Markov decision processes (POMDPs)[5]. When introducing this framework to realistic problems, however, serious difficulties arise because not only
the estimation process for a large number of unobservablestates, but also computing the optimal policy depending on
the estimation require too heavy computation. To deal withlarge-scale multi-agent problems with partial observability,
an estimation method with an effective approximation and©ISAROB 2006

2

Preliminary

A POMDP [5] is a framework to make an agent learn
and act in a partially observable environment, and consistsof (1) a set of real states S = {s1 ; s2 ; · · · ; s|S| }, (2) a setof observation states O = {o1 ; o2 ; · · · ; o|O| }, (3) a set ofactions A = {a1 ; a2 ; · · · ; a|A| }, and (4) a reward function
R : S × A → R. The dynamics of the model is represented as transition probability P (st+1 |st ; at ) and observation probability P (ot |st ; at ). The objective of each agent
is to acquire the policy which maximizes an expected future reward in the partially observable world, in which thestate st is not observable for each agent; only the observation ot , which contains partial information about the state,is available.
The targets of this study are partially observable andmulti-agent problems; there are multiple agents in a common environment with partial observability. In this article, we then use the following notations. t indicates anaction turn of the learning agent. The variables (state, observation and action) for agent i (i = 0; : : : ; M ) are de-

43

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

noted by sit , oit and ait , where M is the number of opponent agents and i = 0 signifies the learning agent; st ,ot and at are the same as s0t , o0t and a0t , respectively. A
strategy of an agent i is denoted by φi . Note that we
make an assumption that there is only one learning agent
in the environment, and the other agents’ strategies φi(i = 1; : : : ; M ) are fixed, for the time being. This assumption will be loosened later. An action sequence of the opponent agents is denoted by ut = {a1t ; · · · ; aMt } and a history for the learning agent at its t-th action turn is given by
Ht ≡ {(ot ; ¡; ¡); (ot−1 ; at−1 ; ut−1 ); · · · ; (o1 ; a1 ; u1 )}.

and may be changed in a multi-agent setting; and (c) thesummation in equation (1) over possible current states andnext states has computational intractability because there
are so many candidates in a realistic problem. Some effective approximations, therefore, are required for avoiding
the above difficulties.
To avoid difficulty (a), we do not deal with thewhole history Ht but do a one-step history ht ={(ot ; ¡; ¡); (ot−1 ; at−1 ; ut−1 )}, which leads us to makean assumption (A): the belief state represents a simple onestep prior knowledge about states, but does not carry thecomplete likelihood information. The history Ht contains
two kinds of information. The first is about impossible
states at the t-th turn; for example, in the game Hearts, if
an agent played ♥9 after a leading card ♣3 in a past trick,the agent no longer has any club cards at the t-th turn andany state in which this agent holds club cards is impossible.
The second is about likelihood, considering the characteristics of the opponent agents; for example, in the same situation as above, it is unlikely for the agent to have any heartcard higher than ♥9. Although the belief state P (st |Ht ),which is the sufficient statistic for the history Ht , should involve these two kinds of information, we partly ignore thelatter kind by replacing the whole history Ht with a onestep history ht ; namely, the belief state P (st |Ht ) is approximated by the partial belief state P (st |ht ) in this study. No
impossible state, on the other hand, is considered in light of
the former type of information, but each possible state hasa one-step likelihood between the (t ¡ 1)-th and t-th time
steps. Although the maintenance of likelihood over possible states requires heavy computation and a large amountof memory in realistic problems, this assumption enables
us to estimate internal states easily at each time step.
To solve problem (b), the agent uses action predictors.
Since the state transition of usual multi-agent games depends on the other players’ actions, the transition probability P (st+1 |st ; at ) in equation (1) is calculated by the
product of action selection probabilities for M opponent
agents, that is,

3 ModelIn our RL method, an action is selected according to
the greedy policy: π(Ht ) = argmax at U (Ht ; at ), where
U (Ht ; at ) is the utility function at a time step t, defined by
X
U (Ht ; at ) =P (st |Ht )×X

st ∈StP (st+1 |st ; at ) [R(st ; at ) + V (st+1 )] ; (1)st+1 ∈St+1

where R(st ; at ) denotes an immediate reward at the time
step t + 1, and V (st+1 ) denotes the state value functionof the next state st+1 . In our application, the card game
Hearts, the reward is defined as R(st ; at ) = ¡n when theagent gets n penalty points (n may be 0) after the t-th play.
The value function V is approximated by a normalized
Gaussian network (NGnet) [8] with a feature extraction
technique to its 52-dimensional input; by considering thegame property, a state st is converted to a 36-dimensional
input pt before the value function is updated so as to approximate thePrelationship between the input pt and the13scalar output i=t R(si ; ai ) according to the Monte Carlo
RL method [12].
In large-scale problems, it is difficult to learn the valuefunction over the belief space [3]. We then use a completely observable approximation [7]; the agent maintains
the state value function so that the self-consistency equation holds on the underlying MDP, and calculates the stateaction utility value U (st ; at ) by a one-step-ahead prediction. After that, it calculates the history-action utility value
U (Ht ; at ) as an expectation of the state-action utility withrespect to the belief state under the knowledge that the optimal value function for the belief space can be approximatedwell by a piecewise-linear and convex function [10]. The
calculation of the utility function, however, includes three
difficulties: (a) the computation for constructing the belief
state P (st |Ht ) over possible current states is intractable
due to the large state space and high dimensionality; (b) the
prediction to possible next states is difficult because the environmental model P (st+1 |st ; at ) is unknown for the agent©ISAROB 2006

P (st+1 |st ; at ) ≈ P (st+1 |st ; at ; Φ̂)
=

M
Y
i=1

exp(F i (oit ; ait )/T i )
;
i i i
i
Ai exp(F (ot ; at )/T )P(2)

where Φ̂ = {φ̂1 ; : : : ; φ̂M }. Note that φ̂i is not a real policy φi but a policy approximated by the action predictor.
The learning agent maintains M action predictors corresponding to the M opponent agents. Note that the opponent agent’s observation oit is not observable to the agent,but it can be determined from the estimated current state ŝt
without any ambiguity in usual games whose observation
process is deterministic. F i (oit ; ait ) denotes the utility of

44

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

action ait for a given observation oit of the i-th agent, which
is an output of the action predictor. Ai denotes the set of
possible actions for agent i, and T i is a constant which
denotes the assumed randomness of the agent i’s policy.
Equation (2) represents the behavior model of the environment. The action predictors thus approximate the environmental dynamics for the learning agent. Each predictor is
implemented as Multi-Layered Perceptron (MLP), and its
input and output are reduced to reasonably sized vectors
by a feature extraction technique which is the same as ourprevious study [4].
To avoid the computational intractability problem (c),we use sampling-based approximation; the agent obtains
independent and identically distributed (i.i.d.) random
samples, ŝt and ŝt+1 , whose probabilities are proportional
to the partial belief state P (st |ht ) and acquired environmental model P (st+1 |st ; at ; Φ̂), respectively. By considering the two approximations described above, the utility
function in equation (1) can be calculated as0P−value

10

−3

10

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

3500

4000

4500

5000

5500

averaged aquired penalty ratio

0.3

RL agent

0.29

0.28

0.27

0.26

0.25

0.24

0.23

0.22

Rule−based agents
0

500

1000

1500

2000

2500

3000

number of games

Figure 1: Computer simulation result in an environment
where there are one learning agent trained by our RL
method and three rule-based agents.

N
K h
i
X
1 X
(i)
(j)
(j)
P (ŝt |ht )R(ŝt+1 ; at ) + V (ŝt+1 ) : (3)
K i=1
j=1

partially observable problems. In particular, they provide
an effective solution to multi-agent problems whose underlying state space is discrete, including various multi-agent
games.

Samples of current states ŝt are obtained by the MetropolisHastings (MH) algorithm, the most popular Markov chain
Monte Carlo (MCMC) technique [2], in the following threesteps: the first step is to sample a previous state ŝ∗t−1
so as not to violate the whole history Ht , no impossible state being sampled (according to the former type of
information described above); the second step is to calculate a one-step likelihood P (ŝ∗t |ŝ∗t−1 ; at ) by using theaction predictors according to equation (2); and the last(i+1)
according to the probstep is to replace ŝ∗t with ŝt(i) (i)∗ ∗ability p = min{1; P (ŝt |ŝt−1 ; at )/P (ŝt |ŝt−1 ; at )} and(i)otherwise ŝt remains. Note that a Markov chain is uniform according to assumption (A). These three steps areiterated N times, and the agent obtains estimated current(i)states {ŝt |i = 1; · · · ; N }. Samples of next states ŝt+1
are obtained by a simple sampling technique, according to(i)equation (2) given an estimated current state ŝt , an ac1
Mtion at and an action sequence ût = {ât ; : : : ; ât } by the(j)available fact that ŝt+1 can be determined without any ambiguity in usual games due to the deterministic nature of
P (st+1 |st ; at ; ut ). This is iterated K times for each of N
current states, and the agent obtains predicted next states(j){ŝt+1 |j = 1; · · · ; K} with the learned model. Two summations in equation (1) are simultaneously approximatedby using KN samples in equation (3). The three approximations described above (the partial belief state, action
predictor and sampling) enable us to solve large-scale and©ISAROB 2006

−2

10

0.31

U (Ht ; at )
≈

−1

10

4

Computer simulations

We applied our RL method to the card game Hearts,
which is a well-defined example of large-scale and multiagent problems with partial observability. To evaluate ourmethod, we carried out computer simulations where an
agent trained by our RL method played against rule-based
agents which have more than 65 general rules for playing
cards from their hands. The performance of an agent canbe evaluated by the acquired penalty ratio, which is the ratio of the penalty points acquired by the agent to the total
penalty points of the four agents. If the four agents haveequal strength, their penalty ratio averages 0.25. The rulebased agent used in this study is much stronger than theprevious one [4], due to the improvement in the rules, so
to have almost the same strength as an expert-level human Hearts player. The learning agent based on our previous RL method then remained much weaker than thisrule-based agent even after 100,000 training games. Since
the outcome of this game tends to depend on the initialcard distribution (for example, an expert player with a badinitial hand may be defeated by an unskilled player), we
prepared a fixed data set for the evaluation; the data set
is a collection of initial card distributions for 100 games,
each of which was generated randomly in advance. In
the evaluation games, the initial cards were distributed ac-

45

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

cording to this data set. Since performance is influenced
by seat position (that is, an agent may have an advantage/disadvantage based on its seat position if the agents
have different strengths), we rotated the agents’ positions
for each initial hand to eliminate this bias; each of the
100 evaluation games was repeated four times with thefour types of seating position. The performance of eachagent, therefore, is evaluated by the 400 fixed and unbiased games. Note that learning of the agent was suspended
during the evaluation games. Each learning run comprised
several sets of 500 games, in which initial cards were distributed to the four agents at random and seat positions of
the agents were determined randomly. In an experiment,
accordingly, 400 evaluation games and 500 learning games
were alternated.
Figure 1 shows the result. The abscissa of the lower
panel denotes the number of training games and the ordinate denotes the penalty ratio acquired by each agent. Eachpoint and error bar represents an average and standard deviation of the penalty ratio, respectively, for the 400 evaluation games over 17 learning runs, each consisting of 5,500
training games. The upper panel represents P-values of thet-test where the null hypothesis is “the RL agent has thesame strength as the rule-based agents” and the alternative
hypothesis is “the RL agent is stronger than the rule-based
agents”. The test was done independently at each point on
the abscissa. The horizontal line denotes the significancelevel of 1%. The constant T i in equation (2) was 1:0, andthe numbers of samples in equation (3) were N = 80 and
K = 20. The penalty ratio of the RL agent decreased withthe learning process, and after about 5,000 training games,
the agent became significantly stronger than the rule-based
agents. Since the agent showed a better performance thanthe expert-level rule-based agents after only several thousand training games, the new RL method based on a sampling method is a salient improvement over the previous
one, both in learning speed and in strength.

0P−value

10

−2

10

−3

10

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

3500

4000

4500

5000

5500

0.29

0.28

averaged aquired penalty ratio

RL agents
0.27

0.26

0.25

0.24

0.23

Rule−based agents
0.22

0.21

0

500

1000

1500

2000

2500

3000

number of games

Figure 2: Computer simulation result in an environment
where there are two learning agents trained by our RL
method and two rule-based agents.
complex setting. This ability is attributed to the fast learning owing to using three action predictors.

References[1] Crites, R. H. and Barto, A. G., Advances in Neural Information Processing Systems, Vol. 8, pp.1017–1023, 1996.
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[9] Shoham, Y., Powers, R. and Grenager, T., Proceedings of
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Vol. 21, pp. 1071–1088, 1973.
[11] Sutton, R. S., Proceedings of the Seventh International Conference on Machine Learning, pp. 216–224, 1990.
[12] Sutton, R. S. and Barto, A. G., MIT Press, 1998.
[13] Thrun, S., Advances in Neural Information Processing Systems, Vol. 12, pp. 1064–1070, 2000.

Figure 2 shows the result when two RL agents and two
rule-based agents played against each other. We executed
16 learning runs, each consisting of 4,000 training games.
The abscissa and the ordinate of the lower panel denote thesame as in Fig.1. The parameter values, other experimental setups and the representation of the upper panel are also
the same. The penalty ratios of both RL agents came toacquire a smaller penalty ratio than the rule-based agentsafter about 5,000 training games. In the previous experiment, our method was based on the POMDP approximation; it was assumed that there is only one learning agent in
the environment. In this experiment, our method was applied directly to multi-agent environments, in which there
are multiple learning and hence dynamic environment, andthe new RL method showed good performance even in this©ISAROB 2006

−1

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Analyzing Robustness in Multi-Agent Reinforcement Learning- A comparison between Profit Sharing and Q-Learning Tuyoshi Nehashi
Tokyo Institute ofTechnology,
4259 Nagatsuta-cho,
Midori-ku, Yokohama
226-8503 JAPANnehashi@cas.dis.titech.ac.jp

Kazuteru Miyazaki
National institution for Academic
Degrees and University Evaluation,
1-29-1 Gakuennishimachi,
Kodaira-city, Tokyo
187-8587 JAPANteru@niad.ac.jp

Keiki Takadama
Tokyo Institute ofTechnology,
4259 Nagatsuta-cho,
Midori-ku, Yokohama
226-8503 JAPANkeiki@dis.titech.ac.jp

simulation results of PS and Q-learning. Finally, ourconclusions are given in Section 6.

Abstract
This paper focuses on uncertainty of multi-agent
reinforcement learning and analyzes the robustness against
the uncertainty caused by state transitions of other agents thatprevents agents from appropriate learning. In this paper, wecategorize the uncertainty by analyzing what kind of factors
cause the uncertainty through a comparison of Q-learning
and Profit Sharing in simple environment. Through intensive
simulations, the following implications have been revealed:(1) a combination of a noise added to in environments andindirect interaction among agents causes the uncertainty; (2)random initial positions do not cause the uncertainty; (3) our
simulations derives inverse implications obtained by [Arai98], that is, the accumulated-based Profit Sharing is inferior
to Q-learning in terms of the average acquisition rewards.
This indicates that accumulating Q value does not cause theuncertainty of state transitions.

2. Uncertainty
2.1 The uncertainty of state transitions
The uncertainty is caused by an environment based on a
non-Markov decision process as shown in the following
figure. This figure shows a state transitions of three agents A,
B and C. S0 indicates the current state and S1~S4 indicate
the next state. After taking the action a0 by agent A in S0, thenext state becomes either of S1 to S4 by the action b0 or b1of the agent B and c0 or c1 of agent C. Here, if each agent
has not yet learned, the state transitions probability from S0
to S1 ~ S4 may change dynamically and a next state may
also change further.

Key words: multi-agent reinforcement learning, profit
sharing, Q-learning, uncertainty, state transition.

c0

S1

c1

S2

b0

a0

1. Introduction

S0

Research on multi-agent reinforcement learning has been
investigated in the context of multi-agent systems [Weiss,
1999]. As a typical research, [Arai 98] compared Q-Learning
and Profit Sharing in the multi-agent reinforcement learning
environments (pursuit problem) and reported the robustnessof Profit Sharing in such environments. The point of her
paper is that an uncertainty is caused by (1) the statetransitions of other agents that prevent agents fromappropriate learning, (2) perceptual aliasing by restricting an
input and (3) both influence. This claim shows a maindifficulty of multi-agent reinforcement learning.
However, the uncertainty claimed here is not described indetail, and thus it is not clear what kinds of factors cause theuncertainty. From this background, this paper aims at
categorizing causes of the uncertainty of state transitions andanalyzing reasons why cooperative behaviors of amulti-agent system becomes difficult.
This paper is organized as follows. Section 2 starts by
categorizing the uncertainty of state transitions. Details of
simulations are given in Section 3. Section 4 describes asimulation result and Section 5 discusses comparisons of the©ISAROB 2006

b1

c0

c1

S3

S4

Figure 1: The uncertainty of state transitions byexistence of other agents

2.2 Categorization
To clarify factors that cause the uncertainty, we propose
the following categorization.
(1) An environmental noise:(1-1)
Transportation (def., If an agent goes into a
specific place, it moves at the moment by acertain probability.)(1-2)
Random initial position (def., An initial positionchanges at random when an agent starts.)(1-3)
Random goal position (def., The area of the goalmoves or changes at random.)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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(2) Ability of agents:(2-1)
Restriction of action (e.g., An agent cannot
move to the right by a certain failure.)(2-2)
Restriction of percept (def., A sensing input islimited. e.g. perceptual aliasing)(3) Interaction:(3-1)
Indirect interaction (def., A cooperative behavior
which is restricted by other agents to achievecommon goals.)(3-2)
Direct interaction (e.g., A competitive behavior
in addition to cooperative.)

4. Simulation
4.1 Simulation cases
We divide simulations as the following four cases:(Case1) The predetermined initial position without torus
environment(Case2) The predetermined initial position with torus
environment(Case3) Random initial position without torus environment(Case4) Random initial position with torus environment

What should be noticed here is that the issues of the goal
random, restriction of percept, and direct interaction areinvestigated in [Arai 98].

In each case, we compare among Q-learning [Watkins 92],
the estimated-based PS (Profit Sharing) [Grefenstette 88],
and the accumulated-based PS [Miyazaki 94].
Q-learning updates Q values the following equation (1) inevery step.
Q(s, a) ← Q(s, a) +α(r + γ max Q(s' , a' ) − Q(s, a)) (1)3. Problem descriptiona'∈A( s ')Experiment environment is composed of 2x8 masses,
where the upper 1x8 masses are used for an agent A, while
the lower 1x8 masses are for an agent B. Each agents canmove to the right and left, and cannot stop. The 6th is theinitial position. Reward 10 will be given when both agentsare located at the 1st or 2nd position. After reward is given,
agents start from the initial position. One step is counted
when moving one mass, and one episode is a sequence ofsteps until agents get rewards.

In equation (1), s , s ' , a , a ' and Q(s, a) indicate thecurrent state, the next state where an agent will actually move,the current action, the next action taken at the next state, andQ value, respectively. r is the reward that an agent
acquires. The parameter α (0<α≤1) is the learning rate whichchanges the learning speed, while the parameter γ (0<γ≤1) isthe discount rate which decides the propagation rate of the
reward. A ( s ' ) is the set of actions which is able to be
selected at the next state s ' .
Profit Sharing (the estimated-based and accumulated-basedPSs) updates Q values according to the following equation(2) and (3).
• Estimated-based PS
Q(st , at ) ←Q(st , at ) + Cbid[r(t) − Q(st , at )] (2)

A
S

ｒ＝１０
ｒ＝１０

1

2

3

4

5

6

7

8

p％
ｒ＝０
ｒ＝０

S

ｒ＝１０
ｒ＝１０

• Accumulated-based PS

B

Q(st , at ) ← Q(st , at ) + r(t) (3)Figure 2: Image of experiment environment.
S: initial positions, r: rewards.

In equations (2) and (3), t , Cbid (0< Cbid ≤1), and r (t )indicate a step, the parameter correspond to α in the
Q-learning, and reinforcement function, respectively. The
rewards are added to Q value according to the reinforcement
function when acquiring rewards. In this simulation, we used

In this paper, we investigate the other factors listed in theabove items. (i.e. transportation, random initial position andindirect interaction.) Regarding transportation factors
transportation, if either of agents is located at the 8th position,
reward 0 is given by p% probability. In this simulation, we
set p=80. For random initial position, agents are set random
positions expect goal. Indirect interaction is finallyimplemented by torus environment where the 1st position isconnected the 8th position. In this environment, moving left
does not make sense because it cannot make agents step at
the 1st position, and thus agents have to acquire their actions
that move to goal at the same time.

t −1geometric increasing function (r (t ) = (1 / 2) × reward)
introduced by [Miyazaki 94]. What is the essential difference
from Q-learning is to update all Q value in the episode whenrewards are given. In this simulation, we set α=0.1, γ=0.9,
and Cbid =0.1.
All of three learning mechanisms employ Boltzmann
distribution as action selection. The parameter of the inverse
temperature is 0.1.

4.2 Simulation results
Figure 3 shows the simulation results on the acquired
rewards per one step averaged in every 10 episodes until
10000 episodes, which are also averaged from 10 times ofdifferent random seeds.

©ISAROB 200648

Q-learning (1)Accumulated-based PS (1)6The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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Transportation4
Non-transportation
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Non-transportation

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Non-transportation

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92 183 274 365 456 547 638 729 820 911
EpisodeCase 10
1Q-learning (2)100 199 298 397 496 595 694 793 892 991

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Episode6

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Non-transportation

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Non-transportation

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EpisodeCase 2101 201 301 401 501 601 701 801 901

101 201 301 401 501 601 701 801 901

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Non-transportation

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Non-transportation

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Transportation
Non-transportation

1
0

0
89 177 265 353 441 529 617 705 793 881 969

191 181 271 361 451 541 631 721 811 901 991
EpisodeCase 4
Figure 3: Simulation results©ISAROB 2006

Accumulated-based PS (4)65Transportation
Non-transportation

91 181 271 361 451 541 631 721 811 901 991
EpisodeQ-learning (4)Estimated-based PS (4)6

Episode1Case 3

Episode1

0

Episode100 199 298 397 496 595 694 793 892 991

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2Transportation
Non-transportation

1
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EpisodeThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The vertical axis indicates the average acquired rewards
per each one step, and the horizontal axis indicates theepisodes. The gray and blue lines indicate the results oftransportation and non-transportation environments,
respectively. From these results, we find the following
implications. (1) In the non-transportation environment, a big
difference is not seen in comparison of the estimated-based
PS and Q-learning. (2) However, in the transportation
environment, the difference is appeared in cases (2) and (4).

6. Conclusions
This paper presented our first attempt that analyzes therobustness in multi-agent reinforcement learning. Towards
this goal, we investigated the uncertainty of state transitions
introduced by [Arai 98] as one of the causes that makemulti-agent reinforcement learning complicated, andcategorized the uncertainty in more detail. Through intensive
simulations, the following implications were revealed: (1) acombination of transportation and indirect interaction is oneof the causes of the uncertainty of state transitions, (2) arandom initial position dose not cause the uncertainty of statetransitions, and (3) the robustness of Profit Sharing in
multi-agent environment is supported by its mechanism.
The following issues should be pursued in the near future,(1) detailed analyses of the difference between theestimated-based and the accumulated-based PSs in terms ofinfluence to multi-agent learning; (2) investigations of otherfactors that may cause the uncertainty of state transitions; (3)improvements of categorizing the uncertainty of statetransitions; and (4) experiments on other examples.

5. Discussions
5-1 Comparison of Profit Sharing and Q-learning▪ The transportation and indirect interaction
Simulation result shows that the estimated-based PS issuperior to Q-learning in terms of the average acquisition
rewards in cases (2) and (4), while the estimated-based PS
approximately equals to Q-learning in cases (1) and (3). Thissuggests that indirect interaction caused by torus withtransportation in multi-agent learning gives a big influence toQ-learning rather than Profit Sharing. That is, since
Q-learning updates Q values in order to identify environment,
Q-learning cannot always find the optimal solution even if it
finds the optimal solution at once and a convergence value isfar from the optimal solution. In contrast, since Profit Sharing
updates Q values of experimented situations, Profit Sharing
has a chance to reinforce episodes without going to thetransportation point. Therefore, Q-learning agents go to thetransportation position many time compared with Profit
Sharing agents, which derives that the convergence value of
Q-learning is worse than Profit Sharing.
▪ Random initial position
Simulation result shows that the estimated-based PS
approximately equals to Q-learning in terms of the averageacquisition rewards in cases (1) and (3) regardless of whether
transportation position is included or not in an environment.
This suggests that the factor of random initial position does
not derive the difference of Q-learning and Profit Sharing inthis environment. This is because Q values in all states are
estimated through learning, which enables agent’s selectcorrect action in any random initial position.

Acknowledgements
The research reported here was supported in part by aGrant-in-Aid for Scientific Research (B, No. 17360424)
from the Ministry of Education, Culture, Sports, Science andTechnology (MEXT).

References[1] Arai, S., Miyazaki, K. and Kobayashi, S.: “Methodology
in Multi Agent Reinforcement Learning -Approaches
by Q-Learning and Profit Sharing-”, Transactions of theJapanese Society for Artificial Intelligence, Vol.13, No.5,
pp.609-618 (1998). (In Japanese).
[2] Grefenstette, J.J.: “Credit Assignment in Rule Discovery
Systems Based on Genetic Algorithms,” Machine
Learning, Vol.3, pp. 225-245 (1988).
[3] Miyazaki, K., Yamamura, M. and Kobayashi, S.: “ATheory of Profit Sharing in Reinforcement Learning”,
Transactions of the Japanese Society for Artificial
Intelligence, Vol.9, No.4, pp.580-587 (1994). (InJapanese).
[4] Watkins, C.J.C.H. and Dayan, P.: “Technical Note:
Q-learning”, Machine Learning, Vol.8, pp. 55-68 (1992).
[5] Weiβ,G.: Multi-agent Systems -Modern Approach toDistributed Artificial Intelligence-, The MIT Press(1999).

5-2 Comparison of the estimated-based andaccumulated-based PSs▪ The robustness of Profit Sharing
Simulation result shows that the accumulated-based PS isinferior to the estimated-based PS in terms of the averageacquisition rewards in cases (2) and (4). [Arai 98] claimed
that the accumulated-based PS is robust to the uncertainty ofstate transitions, because accumulated rewards in Q values
are not sensitive in comparison of Q-learning. However fromthese results, we claim the robustness is caused bymechanism of Profit Sharing, in particular estimating Q
value at each updating. It is necessary to analyze this in thenear future.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Natural policy gradient reinforcement learning method for a looper-like robot
Yutaka Nakamura

Takeshi Mori
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan

Abstract

rameters is still diﬃcult, since parameters of the CPG
controller must be tuned depending on both the target
physical system (robot) and the environment. Therefore, autonomous learning framework for a CPG controller is necessary to realize the adaptability to dynamic environments. We formerly proposed an RL
framework called a CPG-actor-critic model for designing a CPG controller [5]. 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 theperformance indicator, the average reward per step for
example, with respect to the parameter (policy gradient) and this gradient can be obtained by interaction
with the environment. After learning, the CPG controller becomes to generate stable locomotion suited
to the environment surrounding the robot.
In the current study, we conﬁgure a looper-likerobot simulator and its CPG controller. Because alooper has an ability to move by simple and rhythmic telescopic motions, a looper-like robot would havean ability to behave in various environments. We apply our proposed method to the automatic acquisition task of ‘vermicular’ locomotion of the looper-likerobot. Computer simulations show that a good CPG
controller for the looper-like robot can be obtained byour proposed method. This adaptability is important
to develop a mobile robot which is able to overcome
unexpected circumstances to go through various roughterrains.

To develop a mobile robot which has the adaptability to various environments, we conﬁgured a looperlike robot simulator and a CPG controller for thisrobot, and applied a reinforcement learning (RL)
method to the automatic acquisition problem of ‘vermicular’ motions by this robot. Computer simulationshows a good CPG controller for the looper-like robot
can be obtained by our proposed 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 disturbances is necessary. RL is a framework for autonomous acquisitionof control rules and has been successfully applied tovarious automatic control problems, such as balancing
a cart-pole [1], swinging up an acrobot [2]. Because
real robots often have a large number of degrees of
freedom, RL methods to control them require somedevices to avoid the “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 [3][4]. In those studies, a CPG controller is implemented as a kind of recurrent neuralnetwork which generates rhythmic outputs, and thecharacter of the CPG controller is determined by connection weights in the network. Because the parameter of the CPG controller is designed to such that the
CPG controller and the robot interact with each otherand are eventually entrained into a stable limit-cycle
attractor, a robot controlled by a CPG controller isrobust against possible disturbances from the environment.
Although there have been some studies of designing a CPG controller, the determination of CPG pa-

2

Control scheme

Looper-like robot The looper-like robot comprises
two links each possessing a wheel, as depicted in Figure 1. The two links are connected to each other bya linear slider joint. This robot is controlled by threesignals: the force that expands or contracts the linear
slider, the brake for the head link’s (front) wheel, and
1©ISAROB 2006

Shin Ishii

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
Expand or contract

determinedby the spring constant and the link’s mass,
as 2π m/ks . Figure 2 shows the behavior of the ﬁrst
neural oscillator: phase portrait between y1 and y2 (a)and their time series (b).

Head link
Linear Slider

Brake (friction)

Figure 1: Looper-like robot simulator
y

2

0.5

that for the tail link’s (rear) wheel. The robot moves
in the forward direction, if the expansion of the linear
slider and braking of the rear wheel occur simultaneously, and subsequently the contraction of the linear
slider and braking of the front wheel occur simultaneously.
In the simulation, the friction coeﬃcient between
the link and the ground was assumed to be controlled
directly, instead of modelling the braking control of thetwo wheels. We also assumed that there is a springin the linear slider to keep the joint length within a
speciﬁc range. Then, the link movements are deﬁned
asmẍ1 = τ1 − ks (l − x1 + x2 ) − τ2 ẋ1
mẍ2 = −τ1 + ks (l − x1 + x2 ) − τ3 ẋ2

,

y1
y2

0.5
State

Tail link

0
−0.5

0
−0.5

−0.5

0
y

0.5

0

1

2
time

1

(a)

3

4

(b)

Figure 2: Behavior of the ﬁrst neural oscillator

Control scheme Figure 3 shows the control scheme
using a CPG controller. The CPG controller observes
the robot’s state x and outputs control signal τ . The
control signal τ is calculated as
τ1 = −0.3y1
τ2 = exp(y3 )/(1 + exp(y3 ))τ3 = exp(y5 )/(1 + exp(y5 )),(1)

where x1 and x2 denote the position of the head link
and that of the tail link, respectively. Here, m, l andks denote the mass of the link, the length of the spring
and the spring constant, respectively, while τ1 , τ2 andτ3 are control signals which correspond to the forceof the linear slider, the friction coeﬃcient of the headlink and that of the tail link, respectively. Constants
m, l, and ks were set to 0.1, 0.2, and 2.0, respectively.

where the sigmoid function is employed to prevent thefriction coeﬃcient from becoming negative.

CPG controller The CPG controller comprises
three neural oscillators, and each of them consists of
two neurons. The (2i − 1)-th and the 2i-th neurons’
dynamics are deﬁned as

Figure 3: Control scheme using a CPG controller1ẏ2i−1 = −y2i−1 + tanh W S y2i−1 +W I y2i +ui
c
 ,

1
I
S
ẏ2i = −y2i + tanh −W y2i−1 + W y2ic

CPG controller

τ

Robot

x
State

3
(2)

Learning method

In this section, we describe an RL algorithm for
the CPG controller, which we formerly proposed andcalled the CPG-actor-critic model. In a naive application of usual RL to training of a CPG controller, theCPG controller is regarded as an actor. In this case,the control signal depends not only on the state of thetarget 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-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 andwhere y2i−1 and y2i denote the (2i − 1)-th and the2i-th neurons’ states, respectively. W S , W I and c
are the self-excitatory connection weight, the mutualinhibitory connection weight and the time constant,respectively. Also, ui is an input to the i-th neural oscillator and calculated from the robot’s state x and the
CPG controller’s state y. W S , W I , and c were ﬁxed
at 1.1, 0.7, and 0.1, respectively. These parameters
were determined so that the frequency of the neuraloscillator’s output was similar to that of the looperlike robot’s intrinsic movements, which are physically©ISAROB 2006

Control signal

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Control signalu

CPG coupledsystemBasic CPG

Actor

for the expectation with respect to the stationary
distribution of the state-action pair (s, u). When
the Q-function is approximated by a weighted sum(s,u)≡
of bases ψ: Qw
θ
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 
, provides the natarg minw Qθ (s, u) − Qw
θ (s, u)ural policy gradient with no estimation bias for thegradient, so that the policy parameter can be updated[8][1] asτ
s=(x,y)_
State

Robot

Figure 4: CPG-actor-critic model
the actor, as depicted in Figure 4 [5]. The basic CPG
is a part of the CPG controller with ﬁxed connection
parameters, and its intrinsic properties, for example
frequency and amplitude of its basic oscillatory behaviors, is determined by these ﬁxed parameters, i.e.,
W I , W S , and c. We treat the physical system and thebasic CPG as a single dynamical system, and we callthis system the CPG-coupled system.
The actor is the controller for the CPG-coupled system. Since the actor turns out to be a feed-forward
neural network which has no its own dynamics, we
can easily apply usual RL algorithms to the trainingof the actor. The control signal u for the CPG-coupledsystem is conceptually represented asu ∼ π(u, s),θi := θi + η w̃i ,where η is the learning rate. Then, it is suﬃcient toobtain the optimal weight w̃ instead of to well approximate the Q-function itself which may be a highdimensional function.
When we introduce an approximate state-valuefunction V̂θ (s) ≡j vj φj (s), where φi for i =1, . . . , M are arbitrary basis functions of state s, and v
is the weight vector, the weight vectors, w and v, areestimated together based on the least square method:

−1

,
W = rϕT ϕϕT(3)

ψ(s(t),u(t))

Natural policy gradient method For the sakeof simplicity, we assume that Eqs. (1) and (2) are
discretized in time by an appropriate method, andthe learning system receives an immediate rewardr(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:p(u|s; θ), where θ is a parameter vector of the policy. We assume that πθ is diﬀerentiable with respect
to each parameter component θi , and there exists a
stationary invariant distribution of states under any
stochastic policy πθ .
The objective of RL here is to obtain the policy
parameter that maximizes the expected t−1 reward accu
r(s(t), u(t)) ,mulation deﬁned by ρ(θ) ≡ Eθtγ
where γ ∈ (0, 1] is a discount factor. The partial differential of ρ(θ) with respect to the policy parameterθi is calculated [6][7] by

4Experiment

The aim of this experiment is to obtain a control
rule which allows the looper-like robot to move in theforward direction. For this purpose, an immediate reward r(s(t)) was given byr(s(t)) = 5(ẋ1 + ẋ2 ).
We assumed the control signal was generated as
u 1 = θ1 X 1 + θ2 X 2 + 1
u 2 = θ3 y 1 + θ4 y 2 + 2 ,(7)

u 3 = θ5 y 1 + θ6 y 2 + 3
where X = {x1 − x2 − l, ẋ1 − ẋ2 }, and i (i = 1, 2, 3)
is a small random noise obeying a normal distribution. The policy parameter θ determines phase relations among the robot and the neural oscillators andis trained by RL.

(4)∂where ψi (s, u) ≡ ∂θ
ln πθ (u|s) and Qθ (s, u) denotesi 
the action-value function (Q-function).
· stands©ISAROB 2006

(6)

where ϕ(t) = φ(s(t))−γφ(s(t+1)) and W ≡ ( wv)[9][5][1]. Note that W is the parameter of the valuefunction (critic).

where π denotes the control policy of the actor ands ≡ (x, y) is a state of the CPG-coupled system.

∂ρ(θ) = ψi (s, u)Qθ (s, u) ,
∂θi

(5)

53

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
3

average reward

Basis functions of the state-value function Figure 2 showed the phase portrait between two neurons
which compose a neural oscillator. Because the phase
portrait presents a circle whose center is the origin,the angle ω1 = arctan(y2 /y1 ) carries essential information of the state of the ﬁrst neural oscillator. Similarly, ω2 = arctan(y4 /y3 ) and ω3 = arctan(y6 /y5 )are useful features of the second and the third neural oscillators, respectively. Furthermore, when thelooper-like robot repeats expansion and contraction,
ω4 = arctan((x4 − x3 )/(x2 − x1 )) is also an importantfeature of the robot’s movements. Because the phase
diﬀerence seemed to be more important than the phasein 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 andj = 1, 2, . . . , 16 and φ49 = 1.

position (m)

2

1

0

0

−2

10

20
30
time (second)

(a)

ω2−ω1
ω3−ω1
ω −ω

40

50

−4
0

4

9

18
27
time (second)

36

1

45

(b)

Figure 6: Control result
The position of the looper-like robot’s center (a)and phase diﬀerences of the feature variables fromthe ﬁrst neural oscillator ω1 .

the autonomous control problem of a real looper-likerobot and to develop a robust mobile robot are our
future work.

References

0

200

400
600
learning episodes

800[1] J. Peters, S. Vijayakumar and S. Schaal: “Reinforcement learning for humanoid robotics”, Third IEEE International Conference on Humanoid Robotics 2003,
Germany (2003).

1000

Figure 5: Learning curve[2] J. Yoshimoto, S. Ishii and M. Sato: “On-line em reinforcement learning”, IEEE-INNS-ENNS international
Joint Conference on Neural Networks(IJCNN 2000),
Vol. 3, pp. 163–168 (2000).

The horizontal axis denotes the number of learning episodes, and the vertical axis denotes the average reward in one episode. This ﬁgure shows
the moving average in 5 episodes.

[3] 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).

At the beginning of an episode, the robot was initialized to a motionless state, and was controlled by
the current actor for 10 sec. Figure 5 shows the learning curve, indicating that after about 200 episodes therobot was able to move forward. Figure 6(a) shows the
movements of the looper-like robot before learning andafter learning. Before learning the robot accidentally
moved backward, but after learning the robot was able
to move forward quickly. Figure 6(b) shows changesof the phase diﬀerence between ωi (i = 2, 3, 4) and ω1 .
These results suggest that the robot came to move fastby enabling the neural oscillator composing the CPG
controller to possess an appropriate phase relation.

[4] 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).
[5] Y. Nakamura, T. Mori and S. Ishii: International conference on parallel problem solving from nature (PPSN
VIII), pp. 972–981 (2004).
[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 learningwith function approximation”, Advances in Neural Information Processing Systems, Vol. 12, pp. 1057–1063(2000).

Conclusion[8] S. Kakade: “A natural policy gradient”, In Advances in
Neural Information Processing Systems, 14, pp. 1531–
1538 (2001).

In this study, we conﬁgured a CPG-based control
architecture for a looper-like robot, and applied RL toobtain a control rule for the robot simulator to movein the forward direction. Computer simulation showedthat a good CPG controller for this robot can be obtained by our RL method. To apply our RL method to©ISAROB 2006

2

−1
0

0.2

−0.2
0

5

4
Before learning
After learning[9] S. J. Bradtke and A. G. Barto: “Linear least-squares
algorithms for temporal diﬀerence learning”, Machine
Learning, 22, pp. 33–57 (1996).

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Vector Control of Induction Motor Using advanced Hybrid System based on GA and Bacteria
Foraging
Dong Hwa Kim
Dept. of Instrumentation and Control 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

nonlinear system strategies. On the other hand, sincenatural selection of bacterial foraging tends to eliminate
animals with poor foraging strategies for locating,handling, and ingesting food, optimization models can beprovided for social foraging where groups of parameters
communicate to cooperatively forage in engineering.
This paper focuses on dealing with an enhanced optimal
solution using a hybrid approach consisting of BA(Bacterial Foraging) and GA (Genetic Algorithm).
This paper provides optimal vector control method byviewing the relevant research in foraging theory,
foraging by communicating organisms (social foraging)which sometimes operate in swarms, and the relevanceof these areas to optimization.

Abstract
This paper is dealing with hybrid system (GA-BF) using
the conventional GA (Genetic Algorithm) and BF(Bacterial Foraging) which is social foraging behavior ofbacteria. To illustrate characteristics of the proposed
hybrid system, a variety of test function is introduced
and simulated to illustrate characteristics by mutation of
GA-BF, crossover of GA-BF, performance by variationof step size. variation of chemotactic step of GA-BF, andvariation of life time of GA-BF. This approach provides
us with novel models of their foraging behavior and withnew methods for distributed nongradient optimization,
and also provides a connection between evolutionary
forces in social foraging and distributed nongradient
optimization algorithm design for global optimization
over noisy surfaces. On the other hand, PID controllers
have been widely used in industrial systems such as
chemical process, biomedical process, and the main
steam temperature control system of the thermal power
plant. Very often, it is difficult to achieve an optimal PID
gain with no prior expert knowledge, since the gain of
the PID controller has to be manually tuned by a trial anderror approach. This paper proposes a hybrid approachby combining a Euclidian data distance (EU) based
Genetic Algorithm (GA) and bacterial foraging based
Optimization (PSO) method for tuning the PID controllerof an Automatic Voltage Regulator (AVR). Using the
hybrid EU-GA-PSO approach, global and local solutions
could be simultaneously found for optimal tuning of thecontroller parameters.

2. Novel Hybrid System Using GA and
Bacteria Foraging
1.1 Overview of Bacterial Foraging
This paper considers the foraging behavior of E. coli,
which is a common type of bacteria as in the previous
comment [4]. Its behavior to move comes from a set of
up to six rigid 100–200 rps spinning flagella, each driven
as a biological motor. An E. coli bacterium alternates
between running and tumbling. Running speed is 10–
20 µm / sec , but they cannot swim straight. When we cansummarize the chemotactic actions of bacteria as thefollowing description:- If in neutral medium, alternate tumbles and runs, itsaction is having search.
- If swimming up a nutrient gradient (or out of noxious
substances), swim longer (climb up nutrient gradient or
down noxious gradient), its behavior seeks increasingly
favorable environments.
- If swimming down a nutrient gradient (or up noxious
substance gradient), then search action is avoiding
unfavorable environments.
So, it can climb up nutrient hills and at the same time
avoid noxious substances. The sensors it needs for
optimal resolution are receptor proteins which are very
sensitive and high gain. That is, a small change in theconcentration of nutrients can cause a significant change
in behavior. This is probably the best-understood sensory
and decision-making system in biology.
Mutations in E. coli affect the reproductive efficiency
at different temperatures, and occur at a rate of about
10 −7 per gene and per generation. E. coli occasionallyengages in a conjugation that affects the characteristics

1. Introduction
In the last decade, evolutionary computation based
approaches have received increased attention from theengineers dealing with problems which could not be
solved using conventional problem solving techniques[1]. A typical task of a GA in this context is to find thebest values of a predefined set of free parameters
associated with either a process model or a control vector.
One of the active areas of research in GA is system
identification [2]. A recent survey of evolutionary
algorithms for the evaluation of improved learningalgorithm and control system engineering can be foundin [3]. The general problem of evolutionary algorithm
based engineering system design has been tackled invarious ways. GA has also been used to optimize
1©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006of a population of bacteria. Since there are many types oftaxes that are used by bacteria such as, aerotaxis (it are
attracted to oxygen), light (phototaxis), temperature(thermotaxis), magnetotaxis (it can be affected bymagnetic lines of flux. Some bacteria can change theirshape and number of flagella which is based on themedium to reconfigure in order to ensure efficient
foraging in a variety of media. Bacteria can form
intricate stable spatio-temporal patterns in certain
semisolid nutrient substances. They can eat radially their
way through a medium if placed together initially at its
center. Moreover, under certain conditions, they willsecrete cell-to-cell attractant signals so that they willgroup and protect each other. These bacteria can swarm.

When we where φ = [φ1,...,φ p ]T is 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 widthof the attractant signal. K repellant = Lattract is the heightof the repellant effect magnitude), and δ attract is ameasure of the width of the repellant. The expression of
Pc (φ ) means that its value does not depend on the
nutrient concentration at position φ . That is, abacterium with high nutrient concentration secrets
stronger attractant than one with low nutrient
concentration. Model use the function Par (φ ) torepresent the environment-dependent cell-to-cellsignaling as

2.2 Optimization by Bacterial Swarm Foraging

Par (φ ) = exp(T − P (φ ))Pc (φ )The main goal based on bacterial foraging is to apply
in order to find the minimum of P(φ ), φ ∈ R n , not in thegradient ∇P(φ ) . Here, when φ is the position of abacterium, and J (φ ) is an attractant-repellant profile.
That is, it means where nutrients and noxious substances
are located, so P<0, P=0, P>0 represent the presence ofnutrients. A neutral medium, and the presence of noxious
substances, respectively can be defined by
H ( j , k , l ) = {φ i ( j , k , l ) i = 1,2,..., N } .

where T is a tunable parameter. Model consider
minimization of P(i, j, k, l )+ Par φ i ( j , k , l ) , so that thecells will try to find nutrients, avoid noxious substances,
and at the same time try to move toward other cells, but
not too close to them. The function Par φ i ( j , k , l )implies that, with M being constant, the smaller P (φ ) ,the larger Par ( φ ) and thus the stronger attraction, whichis intuitively reasonable. In tuning the parameter M, it isnormally found that, when M is very large, Par ( φ ) ismuch larger than J (φ ) , and thus the profile of the search
space is dominated by the chemical attractant secreted by
E. coli. On the other hand, if T is very small, then Par ( φ )is much smaller than P (φ ) , and it is the effect of thenutrients that dominates. In Par ( φ ), the scaling factor ofPc ( φ ) is given as in exponential form.
This paper describes the method in the form of analgorithm to search optimal value of parameters.
[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 numberof 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 of the 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[substep a] For i =1,2,…,N, take a chemotactic stepfor bacterium i as follows.
[substep b] Compute fitness function, ITSE (i ,j, k,l).
[substep c] Let ITSElast=ITSE (i,j,k,l) to save thisvalue since we may find a better cost via a run.
[substep d] Tumble: generate a random vector∆(i) ∈ R n with each element ∆ m (i ), m = 1,2,..., p,a random number on [-1, 1].
[substep e] Move: Let(

Equation represents the positions of each member inthe 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 theith bacterium φ i ( j , k , l ) ∈ R n , and(2)

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
number of 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 cellto-cell signaling via an attractant and a repellant isrepresented by [5]N

Pc (φ ) =

∑P

i
cc

i =1

⎡
n
⎛
⎞⎤
⎜
i 2 ⎟⎥
⎢− L
−
−
exp
δ
(
φ
φ
)
j
j
⎜ attract⎟⎥⎢ attract⎜⎟i =1 ⎣⎢
j =1
⎝
⎠ ⎦⎥
N

=

∑

N

+

∑

⎡

∑ ⎢⎢− Ki =1 ⎣⎢⎛⎜

repellant exp⎜ − δ attract

⎜
⎝

n

⎞⎤

∑ (φ − φ ) ⎟⎟⎟⎥⎥,
j

j =1

i 2
j

⎠ ⎦⎥

(3)
2
©ISAROB 2006

)

(

(1)

φ i = ( j + 1, k , l ) = φ i ( j , k , l ) + C ((i )ϕ ( j ) ,

(4)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

φ i ( j + 1, k , l ) = φ i ( j , k , l ) + C (i )∆(i)technique was generalized to all ac drives, as torquevector control (TVC), and it was recognized as a viable
alternative to field-oriented control (FOC). Industrial
drives with DTC are present on the market today. DTC
abandons the stator current control philosophy,
characteristic of FOC and achieves bang-bang torque andflux control by directly modifying the stator voltage inaccordance with the torque and flux errors. DTC is
characterized by fast dynamic response, structural
simplicity, and strong robustness in the face of parameter
uncertainties and perturbations. It does not employ
current controllers and pulsewidth modulation (PWM),
and it is well suited for sensorless drives. Classic DTC
has still several drawbacks: It exhibits large torque, flux,and current ripple, produces annoying acoustical noise,
operates with nonzero steady-state torque error, hasdifficulties in controlling the flux at low speeds, and theswitching frequency is variable and lower than thesampling frequency [6]-[9].

∆ (i )∆ (i )TThis results in a step of size C (i ) in the direction of
the tumble for bacterium i.
[substep f] Compute ITSE (i, j + 1, k , l ) .
[substep g] Swimi) Let m=0 (counter for swim length).
ii) While m< N s (if have not climbed down toolong).
• Let m=m+1.
• If ITSE (i, j + 1, k , l ) < ITSElast ( if doing better),let ITSElast=ITSE (i, j + 1, k , l ) and let
φ i ( j + 1, k , l ) = φ i ( j + 1, k , l ) + C (i )∆(i)
∆T (i ) ∆(i )and use this φ i ( j + 1, k , l ) to compute the new
ITSE (i, j + 1, k , l ) as we did in [substep f]• Else, let m= N s . This is the end of the whilestatement.
[substep h] Go to next bacterium (i, 1) if i ≠ N (i.e.,
go to [substep b] to process the next bacterium).
[step 5] If j < N C , go to step 3. In this case, continue
chemotaxis, since the life of the bacteria is not over.
[step 6] Reproduction:[substep a] For the given k and l, and for eachi = 1,2,..., N , leti
ITSEhealth=

4. Simulation of Vector Control By GA-BF
In this paper, Test function F ( x) =
1

3

∑x
i =1

2
i

is introduced

for comparing the characteristic of chemotactic step inhybrid GA-BF (Genetic Algorithms- Bacteria Foraging).
This paper illustrates characteristics between the
proposed GA-BF and SGA (Simple Genetic Algorithm)
using test function, De Jong [6]. Fig. 1 is showing
characteristic GA-BF between objective function andgeneration (Generations: 1-70) to variation of life time ofbacteria Ns. Table I is function value to chemotactic step.

N c +1∑ ITSE(i, j, k , l )j =1

be the health of bacterium i (a measure of how many
nutrients it got over its lifetime and how successful it
was at avoiding noxious substances). Sort bacteria andchemotactic parameters C (i ) in order of ascending cost
ITSEhealth (higher cost means lowerhealth).
[substep b] The S r bacteria with the highest
ITSEhealth values die and the other S r bacteria with thebest values split (and the copies that are made are placedat the same location as their parent).
[step 7] If k < N re , go to [step 3]. In this case, we have
not reached the number of specified reproduction steps,
so we start the next generation in the chemotactic loop.
[step 8] Elimination-dispersal: For i = 1,2..., N , withprobability Ped , eliminate and disperse each bacterium(this 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. If
l < N ed , then go to [step 2]; otherwise end.

Table 1
Function value to chemotactic step
Chemotactic
step

100
500
1000

x1

x2

X3

Optimalobjectivefunction

Averageobjectivefunction-9.32E- 3.78E- -8.57E- 1.52E-13 1.59E-13
08
07
09
2.97E- 1.92E- 2.32E-08 1.79E-15 3.26E-15
08
08-1.70E- -1.44E- -2.31E- 5.01E-16 1.43E-15
08
08
09

3. Vector Control of Induction Motor
The direct torque and flux control for induction
machine drives has been developed as direct torquecontrol (DTC) and as direct self control (DSC). The

Fig. 1. Characteristic GA-BF to variation of Ns.
(Generations: 1-70)

3©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

control system engineering have been studying. The
general problem of evolutionary algorithm basedengineering system design has been tackled in various
ways because of learning time and local or suboptimal
solution. This paper suggested the hybrid system
consisting of GA (Genetic Algorithm) and BF (Bacterial
Foraging) and proved the characteristic of that systemusing test functions and vector control of induction motor.
This approach proposed in this has the potential to be
useful in practical optimization problems (e.g.,
engineering design, online distributed optimization in
distributed computing and cooperative control) as
models of social foraging are also distributed
nongradient optimization methods. Moreover, it remains
to be seen how practically useful the optimization
algorithms are for engineering optimization problems,
because they depend on the theoretical properties of thealgorithm, theoretical and empirical comparisons to othermethods, and extensive evaluation on many benchmark
problems and real-world problems.

Fig. 2. Characteristics of GA and GA-BF by test function.
(Generations: 1-70)
Fig. 2 illustrates characteristics of GA and GA-BF by
test function between objective function and generation(Generations: 1-70). If performance of test generations
Ns is a satisfactory result, the characteristic of BF(Bacteria Foraging) moves forward, and otherwise, BF
searches new direction to obtain optimal result fromgenerations after lifetime Ns. Fig. 3 is process of optimal
search in GA and GA-BF.

Reference[1] B. Maione, D. Naso, and B. Turchiano, “GARA: A genetic
algorithm with resolution adaptation for solving system
identification problems,” in Proc. Eur. Control Conf.,
Porto, Portugal, Sept. 4–7, 2001, pp. 3570–3575.
[2] C. M. Fonseca and P. J. Fleming, “Multiobjective
optimization and multiple constraint handling withevolutionary algorithms-Part I: A unified formulation;” “Part II: Application example,” IEEE Trans. System, Man,
Cybern. A: Systems and Humans, vol. 28, no. 1, pp. 26–47,
Jan. 1998.
[3] PASSINO, K. M., “Biomimicry of Bacterial foraging forDistributed Optimization,” University Press, Princeton,
New Jersey, 2001.
[4] Dong Hwa Kim, Jae Hoon Cho, “Adaptive Tuning of PID
Controller For Multivariable System Using Bacterial
Foraging Based Optimization,” Lecture Notes in Computer
Science Proceeding of Springer (SCI), June 12-15, 2005,
Poland.
[5] Dong Hwa Kim, Jae Hoon Cho, “Intelligent Control of
AVR System Using GA-BF,” Lecture Notes in Computer
Science Proceeding of Springer (SCI), June 12-15, 2005,
KES 2005, Melbourne, Australia.
[6] A. Benchaib, A. Rachid, E. Audrezet, and M. Tadjine,“Real-time sliding-mode observer and control of an
induction motor,” IEEE Trans. Ind. Electron., vol. 46, pp.
128–137, Feb. 1999.
[7] J.-O. Krah and J. Holtz, “High performance current
regulation and efficient PWM implementation for lowinductance servo motors,” IEEE Trans. Ind. App., vol. 35,
pp. 1039–1049, Sept./Oct. 1999.
[8] Ion Bolden, S.A. Nasar, “Electric Drives,” CRC Press,
1999.
[9] Andrzej M., Trzynadlowski, “Control of Induction
Motors,” ACADEMIC Press, 2001.

Fig. 3. Process of optimal search in GA-BF.
Table 2 shows initial condition and resulting parameter
simulated for test function. The value of Table III issimulated results based on range (searching range),chemotactic step, total number of chemotactic reaction ofbacteria, step size, basic unit for movement of bacteria
Ns, the number of critical reaction S, the number ofbacteria N, generations G, mutation Mu, crossover Cr.
Table 2Comparison by test function F1(x).
Optimalobjectivefunction

Averageobjectivefunction

7.22E- 5.07E- -9.43E- 7.87E-15
08
08
09-1.44E- -2.31E- 5.01E-16
GA-BF -1.70E08
08
09

8.03E-15

Chemotatic
step

x1

GA

x2

X3

1.43E-15

5. Conclusion
Recent many approaches of evolutionary algorithms
for the evaluation of improved learning algorithm and4©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Remote Control of Robot using Wireless LAN
Nobuya KAWASAKI, Fengzhi DAI, Yutaka FUJIHARA
Department of Control Engineering
Matsue National College of Technology

Abstract
Wireless LAN becomes rapidly widespread and its
communication speed is made extensive improvements.
In the conventional case of a remote control to therobot with the radio controller, the signal to control therobot is only transmitted unidirectional from the radio
controller and the data of sensors and images etc
captured by the robot can’t be returned to the hostcomputer for controlling the robot. But, it becomes easy
to send directly the data from the robot by using
wireless LAN instead of radio controller.
Therefore, two-way communication can be done
between the host computer and remote controlled robot,
and robots can be controlled by feed back control.
However, when PC of the desktop type or the note typeis slotted into the robot to use wireless LAN, it becomesdifficult to downsize the robot and to reduce the cost. At
present, the network connection became possible in thefield of the built-in microcomputer board, and Ethernet
can be easily and cheaply used.
In this research, the microcomputer board is installedin the robot to control wireless LAN and the Ad hoc
mode is utilized that can communicate directly among
wireless LAN devices; it also follows IEEE802.11b of
the long distance communication standard.
The communication is mainly controlled by thefirmware of the AVR microcomputer, and the wireless
LAN device uses the 16bit PC Card interface of 5V
power supply system. And the operation of transmitting
the signal from host computer is mainly checked.

Fig.1 Concept of system
In this paper, we propose a method to install wireless
LAN to microcomputer of robot as the basic study toachieve network robot system. Fig.1 shows the conceptof system.

2. Microcomputer on which mountedWireless LAN

1. Introduction

When a robot has a large size, it is possible that thedesktop type personal computer (PC) is installed into therobot to control wireless LAN. But, it becomes difficultthat a PC is installed in connection with a robot becomesa miniature.
Moreover, if PC is being included into every robot one
unit, the efficiency of the cost is bad.
A network connection has recently become possible by
the microcomputer board of embedded type and we canhandle Ethernet easily and cheaply.
Therefore, we propose a method to control wireless
LAN by microcomputer board embedded in the small
size robot as a network robot through Ethernet.

In the conventional case of a remote control to therobot with the radio controller, the signal to control therobot is only transmitted unidirectional from the radio
controller and the data of sensors and images etc
captured by the robot can’t be returned to the hostcomputer for controlling the robot. But, it becomes easy
to send directly the data from the robot by using
wireless LAN instead of radio controller.
Therefore, two-way communication can be done
between the host computer and remote controlled robot,
and robots can be controlled by feed back control.
Furthermore, the host computer can control the robot towork at the various situations by using the voices andimages from the robot.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2.1 Outline of Control
There are several communication protocols andcorrespondence procedures in wireless LAN. In thisstudy, using Ad hoc mode, we directly holdcorrespondence between the wireless LAN devices
according to IEEE802.11b that is a protocol of
long-distance communication.
We use an available 16bit PC Card interface of 5V
power supply for the wireless LAN device, and an AVR
microcomputer is used for the control of wireless LAN
and for the control of I/O by firmware.

2.2 AVR Microcomputer
An AVR microcomputer is the CMOS 8bit RISC
architecture microcomputer developed by Atmel
Corporation.
AVR microcomputer exhibits better performance than
PIC and H8 microcomputer. Because AVR
microcomputer has many kind of command and can
execute for about 90% of all commands within one
clock. And also its processing speed is about
1MIPS/MHz.
Moreover, the commands of AVR microcomputer areeasy to handle, and AVR microcomputer has many
registers and SRAM. Also it has a development
environment in which can handle an assembler, C++ andBasic can be programmed freely. And as usage ofprogrammer, these languages are divided into somesystems. So, programmer can easily handle theselanguages.
But, there are hardly materials and reference books
written in Japanese because this microcomputer is theminor handling in Japan compared with PIC and H8
microcomputer.
In order to handle PC Card as an outside memory, we
select the model number ATmega162 [1] among AVR
microcomputer for movement control.
ATmega162 is belonging to the 4414 or 8515 group of
AVR microcomputer. These groups are superior tohandle the outside memory and commands of I/O areespecially abundant. The pin arrangement of thismicrocomputer is shown in Fig.2 and basic movement
specifications are shown in Table.1.

Fig.2 Pin arrangement of AVR microcomputer

2.3

A 16bit PC Card interface of PCMCIA interface isused in this time. It is divided into "memory card" and"I/O + memory card" in this interface, and wireless
LAN card is being sorted by the latter. And, in the PC
Card, there are three access spaces that consist of thecommon memory space, the attribute memory space andthe I/O space.
Common memory is the usual memory space that canaccess freely from the host CPU, and the attributememory is the special memory space used only for a
configuration. Wireless LAN includes “I/O + memorycard”, therefore it is not necessary to mount a common
memory in the wireless LAN card. Therefore, a wireless
LAN card does the reading and writing of the data withalternately changing the attribute memory space and theI/O space [2].
In this study, it is possible that the PC Card can becontrolled by the data access of 8bit. Therefore, an extra
addition circuit is unnecessary so that the
microcomputer control the PC Card. Because the controlof the PC Card by the microcomputer becomes possible
by connecting directly each address port and the data
port of the PC Card to the microcomputer respectively,
and fixing the signal of other I/O ports of the PC Card
constantly.

Table.1 Specification of AVR microcomputer
SRAM
1KB
EEPROM
512B
Flash Memory
16KB
External
64KB
MemoryClock 16MHz
I/O
35ch

©ISAROB 2006

16bit PC Card

2.4 Wireless LAN
The correspondence procedures of wireless LAN
consist of an Infrastructure mode and an Ad hoc mode.
The former is the system that communication is done
through the access point, and the access point does all
the control of communication. Because it is suitable forthe exchange between more than one wireless LAN
devices, this system is used to control such as theautonomous robot.

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Fig.3 Circuit Diagram
The latter is the form that communication is done withthe wireless LAN devices, and we have to manage thecommunication by ourselves. And also, this form issuitable for controlling a robot directly because of thecommunication of one to one [3].
In this study, because our object is a movement
verification of one to one communication, we decided toadopt the Ad hoc mode.
And, also we used IEEE802.11b from the two reasons.
One reason is that it is prohibited by Japanese laws to
use the electric wave of the 5.2GHz band out of doors,
and the other is that the processing speed of the
microcomputer is low. Characteristics of IEEE802.11b
are shown in Table 2.

3. Method of control
First, each signal line of the PC Card is handled asfollows. A circuit diagram to satisfy this condition isshown in Fig.3 and signals of the PC Card are shown in
Table.3.
Next, the control of the PC Card is done by using I/O
by the firmware of AVR microcomputer. Therefore, we
describe how to handle the Ad hoc mode referring to thesource program of the general-purpose wireless LAN
driver of Linux [4]. We use UDP (User Datagram
Protocol) as Internet protocol for realizing the real timecommunication to directly control the robot. And we
process data of serial communication by using USART(Universal Synchronous Asynchronous Receiver
Transmitter) function of AVR microcomputer for I/O of
the outside.

Table.2 Specification of Wireless LAN
Speed
Bandwidth
MethodObstacle©ISAROB 2006

802.11a 802.11g 802.11b
54Mbps
11Mbps
5.2GHz
2.4GHz
OFDM
DSSS
OFDM
△
◎

4. Summary
In this study, we formulated and proposed a plan of themain part to control the mobile robot by wireless LAN.
In future, we must plan to mount both communication
forms of wireless LAN and also adjust a serial
communication to USB1.1 standard for increasing thecommunication speed.

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Furthermore, we should aim to improve this system by
mounting TCP (Transmission Control Protocol) ofInternet protocol; the reason is UDP is deficient inreliability.

Table.3 Signals of PC Card
Signal (Pin No.)
A0-A9 (29-22,12,11)
D0-D7 (30-32,2-6)
CE1# (7)
OE# (9)WE# (15)
IORD# (44)
IOWR# (45)
IREQ# (16)
WAIT# (59)
CE2# (42)RESET (58)
A10-A25(8,10,21,13,14,20,19,46-50,53-56)
REG# (61)
D8-D15 (64-66,37-41)
CD1# (36)
CD2# (67)
IOIS16# (33)
SPKR# (62)
STSCHG# (63)
INPACK# (60)
VS1# (43)
VS2# (57)
Vcc (17,51)
Vpp1 (18)
Vpp2 (52)
GND (34,68)

Control

CPU Control

Hi Level
Low Level

Non Connect

References[1] Atmel Corporation: ATmega162 Application Notes;
http://www.atmel.com/dyn/products/product_card.asp?p
art_id=2023[2] Hitoshi MIYAZAKI: Chapter1-9 PC Card;
Transistor Technology SPECIAL No.63 - All ininterface around personal computer, CQ Publisher,(1998/07/01)[3] Yukio MITSUIWA: Chapter4-8
802.11b
Experiment on Microcomputer Board for Wireless
LAN; Hardware Selection - Hardware control by LAN,CQ Publisher, (2005/05/15)[4] Kyousuke ISIKAWA: Wireless LAN PC Card
Control of Microcomputer;
http://www.asahi-net.or.jp/~qx5k-iskw/robot/wave.html©ISAROB 2006

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Research on Face Recognition System by Genetic Algorithm
Fengzhi DAI, Tatsuya KODANI, Yutaka FUJIHARA
Department of Control Engineering,
Matsue National College of Technology,
daifz (fujihara)@matsue-ct.jp

Abstract

improvement of the general template matching method.
Also, to search an image, lots of data need to beprocessed. GA has been considered as robust and global
searching method (although it is said that GA is not
used for finding the global optimization) [3]. In thispaper, the chromosomes generated by GA contain theinformation of the image data, and the genetic andevolution operations are used to obtain the best match in
an image to the template. Searching the best match is
the goal of the paper.
This thought emerged from the features of GA andthe need of easily and quickly recognizing the face ofpeople by an intelligent system. The single concept andfeature of image processing and GA will not be
introduced in the paper, for lots of books and papers
have been written about them.
In this paper, section 2 introduces the general methodof pattern recognition. The theory and GA encoding,experimental setting in this paper will be given. In
section 3, the experiment and the analysis are addressed.
Conclusions will be given in section 4.

Computer vision and recognition plays more
important role on modern intelligent control. Object
detection is the first and the most important step for theobject recognition. Traditionally, special object can berecognized by the template matching method. But therecognizing speed has always trouble. In this paper, theimprovement of the general genetic algorithm-based
face recognition system is proposed. Genetic algorithm(GA) has been considered as robust and global
searching method. In this paper, the chromosomes
generated by GA contain the information of the facialmodel, by which can we recognize it from an image.
The purpose of the paper is to propose a practicalmethod for face detection and recognition. Finally, theexperimental results, the comparison to the traditional
template matching method and some considerations arealso given.
Keywords: face detection, face recognition, genetic
algorithm, image processing, template matching1.

2. Theory and experimental setting

Introduction

For an image recognition system, first, the interested
part that has special features has to be detected from theoriginal image. This is called object detection. And after
that, this part will be compared to the template to see if
it resembles to the template or not, which is calledobject recognition. For example, if we want to find a
special person in an image, first, we have to detect
people in the image, and then recognize him/her whether
is the interested person or not. The whole procedure isshown in Fig.1.

If we search on the web or a conference proceedingabout intelligent control, lots of papers and applications
are presented. Among them, image processing andrecognition occupy very large percentage. The higher
the degree of intelligence is, the more important theimage detection and recognition technology are.
For an intelligent control system, it is necessary toautomatically acquire the information of the external
world by sensors, to recognize its position and thesurrounding situation. Camera is one of the mostimportant sensors for computer vision.
For controlling an intelligent system (autonomous
mobile vehicle, robot, etc), the most important part isthe control strategy. But before automaticallycontrolling it to move, image recognition is needed.
That is to say, the robot endeavors to find out what is in
an image (environment of the robot) taken by thecamera: traffic signs, obstacles or guidelines, etc.
Object detection is an important step in the object
recognition. Its reliability and time-response have amajor influence on the performance and usability of thewhole object recognition system [1]. The templatematching method is a practicable and reasonable way
used to the object detection [2]. And this paper gives the©ISAROB 2006

ResultImage

Preprocessing

Featureextraction

Patternmatching

Standard
patterntemplate

Fig.1 Object recognition system

One of the oldest techniques of pattern recognition ismatching filtering [4], which allows the computation of ameasure of the similarity between an image f ( x , y ) and atemplate h ( x , y ) . Define the mean-squared distance22

d fh = ∫∫ { f ( x, y ) − h ( x , y )} dxdy

63

(1)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

andTable 2 Settings for the experiment

R fh = ∫∫ f ( x , y ) h ( x , y ) dxdy , if the image andtemplate are normalized by
2

2∫∫ f ( x , y ) dxdy = ∫∫ h ( x, y ) dxdy

(2)

then
2

2

d fh = ∫∫ { f ( x, y ) − h ( x , y )} dxdy
22= ∫∫ { f ( x , y ) − 2 f ( x , y ) h ( x , y ) + h ( x , y )}dxdy (3)2= 2 ∫∫ f ( x , y ) dxdy − 2 R fh

In the right hand of above equation, the first term isconstant, thus Rfh can measure the least-squared
similarity between the image and template [5]. If Rfh is alarge value, then the image is judged to match thetemplate. If Rfh is less than a pre-selected threshold,
either rejecting this recognition or creating a new class,which means the similarity between the image and thecurrent template is not satisfied.
Statistical object recognition involves locating andisolating the targets from an image, and then identifying
them by statistical decision theory. The comparable
experiment of template matching method is given in
section 3.

x

8 bitsy

8 bitsrate

8 bitsѲ

8 bits

Pr
Pc

0.6
0.5

Pm

0.01

As shown in Table 2, one chromosome contains 4
bytes: the coordinate (x, y) of the center of the template,
the rate of scale and the angle Ѳ of rotation.
2.2 Experimental setting
The experiment is done by firstly given two files, theoriginal image file and the template file. By searching
the image file, GA is used to find whether in the image
there is the object of template or not. If the answer is
YES, then in the original image the result gives thecoordinate of it, the rate of scale and the rotating angle
to the template.
And for comparison, the general template matching
method is also presented [7]. The executive time showsthe effectiveness of the GA-based recognition method.
Fig.2 and Fig.3 are the original images and thetemplates for the experiment. The values are the width✕height pixels of the image.
In Fig.2, three images are presented, the content andthe size of which are different. Fig.2(a) has two faces,
Fig.2(b) gives the face that has the angle to theperpendicular. The person in Fig.2(c) wears the hat, andthe background is more complicate than (a) and (b).

2.1 Genetic encoding
As introduced above, the chromosomes generated by
GA contain the information of image data, so the firststep is to encode the image data into a binary string [6].
The parameters of the center of face (x, y), the rate ofscale to satisfy eqn.(2), and the rotating angle Ѳ areencoded into the elements of gene. Some importantparameters of GA used in this paper are given in Table 1
and the searching field is in Table 2.
Table 1 Some GA parameters
SourceFiles of the image and template

Fieldsx, y, rate, ѲGenerations

Max = 300 (the stopping criterion)

Population
Size

200

Reproduction(selection)
Crossover

Mutation©ISAROB 2006(a) 238✕170(b) 185✕196(c) 275✕225

Fig.2 Three original images

Pr of the best individuals will beselected to survive. The remained(1-Pr) will be treated by the genetic
operators (crossover and mutation)
Offspring is produced from parents
by exchanging their genes at thecrossover point, the ratio is Pc
Producespontaneous
randomchanges in various chroms. General
random change method at the rateof Pm(a) template-1
62✕62(b) template-2
60✕64

Fig.3 Templates for matching64

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The two templates in Fig.3 are not extracted fromonly one image. For the common use, the templateshould be extracted as the average of several feature
images. In Fig.4, the template (a)-0 is generated from(a)-1, (a)-2 and (a)-3, which is the average value of thegray levels from those three models. The same is alsofor (b)-0.

(a)-1

(a)-2

(a)-3

→

(a)

(b)

(c)

(d)

(a)-0
Fig.5 Result of searching by GA

(b)-1

(b)-2

(b)-3

→

Table 3 Results of searching by GA(b)-0

Fig.4 Generation of template

3.

Image in
Fig.4

Experiment and comparison

Original image is Fig.2(a), template is Fig.3(a).

The genetic operations and GA parameters arepresented in Table 1 and Table 2. The fitness is definedin eqn(4).

fitness =(a)

The 18th generation gives the result, fitness =0.916591, time = 2second.
(x, y) = (64, 97), rate = 1.31, Ѳ = 353o

∑ f ( x, y, rate,θ ) − template( x, y)Original image is Fig.2(a), template is Fig.3(b).

( x , y )∈R

(4)

(b)

In eqn(4), template(x, y) is the gray level of thecoordinate (x, y) in the template image file. f(x, y, rate,Ѳ ) is the gray level in the original image file, thecoordinate of which is calculated from (x, y) bytranslation, change of scale and rotation of the template
file. The field R is the whole region of the template.
The maximum generation is made 300, and thethreshold of the matching rate is set to 0.9 [6]. That is tosay, if within 300 generations the matching rate canreach 0.9, then it is said that the template is found in theoriginal image (the template is matched to the original
image within the threshold). Otherwise the result gives
the best match until the GA is trained to 300
generations.
The results of the GA-based face recognition are
given in Fig.5 and Table 3. Fig.5(a), (c) and (d) aresearched to match to the template of Fig.3(a), while
Fig.5(b) is matched to Fig.3(b).
In Fig.5, (a) and (b) reach the matching rate 0.9
within 300 generations, while (c) and (d) cannot reachthe matching rate 0.9 within 300 generations, but thebest match is given in Table 3.
In the images (a), (b) and (c) of Fig.5, we obtain thatthe result can match the template well. The coordinate(x, y), the rate of scale and the angle of rotation can becalculated correctly. But for Fig.5(d), the result is notsatisfied.

©ISAROB 2006

Explanation

The 2nd generation gives the result, fitness =0.901470, time = 0second.
(x, y) = (179, 112), rate = 1.45, Ѳ = 18o
Original image is Fig.2(b), template is Fig.3(a).

(c)

The 300th generation gives the result, fitness =0.889581, time = 39second.
(x, y) = (111, 103), rate = 1.72, Ѳ = 349o
Original image is Fig.2(c), template is Fig.3(a).

(d)

The 300th generation gives the result, fitness =0.832722, time = 39second.
(x, y) = (165, 120), rate = 1.00, Ѳ = 30o

The reasons is that (i) the template Fig.3(a) cannot
represent the recognized face commonly. That is to say,although the person to be recognized is the same, thetemplate cannot give all the features for this person at
all time, in all conditions (the generation of template isshown in Fig.4). (ii) is that the algorithm itself has someproblems. For example, by using GA-based recognition
method, the settings of the searching field (x, y, rate,Ѳ ), the determination of the genetic operations, theselection and optimization of the fitness function are allimportant to the recognizing result.

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the GA-based and the general template matching
method are presented in the paper, and the comparison
with the traditional pattern matching method shows thatthe recognizing is satisfied, although under someconditions the result is not very good (Fig.5(d)).
Based on the results of experiments in the paper,
further work will be major emphasized to (i) optimizing
the fields of GA chromosomes, (ii) improving the fitness
function by adding some terms to it. These works areimportant and necessary to improve the GA-based facerecognition system.

For the purpose to compare the effect of the GA-basedalgorithm, the result of the general matching method [7]is also presented. From Fig.6, we see that although boththe original image (the left-top image) and the template(the right-top image) are simplified by either decreasingor binarizing the image, the matching time is 1 minuteand 22 seconds, cannot within several seconds. The
recognized result is the left-bottom image in Fig.6.

References[1] M. Sugisaka, X. Fan: Development of a facerecognition system for the liferobot, Proc. of theSeventh Int. Symp. on Artificial Life and Robotics,
vol. 2, pp. 538-541, Oita, Japan (2002)[2] K. R. Castleman: Digital image processing,Original edition published by Prentice Hall, Inc., a
Simon & Schuster Company, Press of Tsinghua
University, China (1998)[3] H. Iba: Foundation of genetic algorithm – solutionof mystic GA, Omu Press (1994) (in Japanese)[4] K.
Deguchi,I.
Takahashi:
Image-based
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, pp. 375-380, Kyongju,
Korea (1999)[5] B. Jaehne: Digital image processing - concepts,
algorithms, and scientific applications, The Third
Edition, Springer-Verlag (1995)[6] T. Agui, T. Nagao: Introduction to image processing
using programming language C, Shoko-do press(2000) (in Japanese)[7] Ishibashi’s Studying Room of C++ (in Japanese).
http://homepage3.nifty.com/ishidate/vcpp.htm[8] M. Gen, R. Cheng: Genetic algorithms andengineering
design,A
wiley-Interscience
Publication (1997)[9] T. Agui, T. Nagao: Image processing andrecognition, Syokoudou Press (1992) (in Japanese)[10] H. Takimoto, Tasue Mitsukura, M. Fukumi, N.
Akamatsu: A design of face detection system basedon the feature extraction method, Proc. of 12th
Symp. on Fazzy, Artificial Intelligence, Neural
Networks
andComputational
Intelligence,
pp.409-410, Saga, Japan (2002)

Fig.6 Result by the general matching method

4.

Conclusions

In this paper, the GA-based image recognition method
is tested and the comparison with the general matching
method is presented.
As we know, GA starts with an initial set of random
solutions called population. Each individual in the
population is called chromosome, representing a solution
to the problem. By stochastic search techniques based on
the mechanism of nature selection and natural genetics,
genetic operations (crossover and mutation) andevolution operation (selecting or rejecting) are used tosearch the best solution [8].
In this paper, the chromosomes generated by GA
contain the information of 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 template, the rate ofscale and the angle Ѳ of rotation.
In fact, translation, scale and rotation are three main
invariant moments in the field of pattern recognition [9].
But for the face recognition, the facial features are
difficult to be extracted and calculated by the general
pattern recognition theory and method [10]. Even thesethree main invariant moments will not be invariant
because the facial expression is changed.
Thus the recognized result only gives the best
matching one upper the predetermined threshold. Both

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Research on Autonomous Mobile Robot for Visually Handicapped Humans
Takahiro NAKAMURA, Fengzhi DAI, Yutaka FUJIHARA
Department of Control Engineering,
Matsue National College of Technology
destination by GPS and automatically moves to thedestination by comparing the information of the current
place with that of the destination. But in the case of theurban area where the route of the autonomous mobile
robot is limited, the robot cannot be navigated safely
only by GPS data.
So the braille blocks that are laid widely in urban area
are utilized to navigate the robot safely. The images ofthe braille blocks are processed by Houghtransformation, and based on the data, the robot canarrive at the destination by fuzzy control. Furthermore,in order to assist the robot from the base station toensure the driver against danger, the communication
between the base station and the mobile robot by using
the wireless LAN is also proposed in this paper.

Abstract
In this paper, we propose the autonomous mobile
robot by improving the electric wheelchair for elderly
people. The research aims at automatically moving therobot to the destination by GPS and image processing.
Beforehand, the robot gets the information of thedestination by GPS, it automatically moves to thedestination by comparing the information of place where
it is now, with that of the destination. But the robot
cannot automatically move safely in this case.
The self-position recognition is a very importantfunction for the autonomous mobile robot. Under theindoor environment, the robot can easily detect the walls
or the pillars to recognize the self-position with simple
sensors. On the other hand, because in the outdoor
environment there are fewer steady objects forrecognizing the position of the robot, it is difficult for
the robot to recognize the self-position andautomatically move.
Thus we propose that the robot uses the braille blocks
that were laid on the sidewalk. The braille blocks areequipment to lead visually handicapped humans to walk,and it is laid widely in urban area. The robot can
automatically move safely on the sidewalk by driving
along the braille blocks.
So the autonomous mobile robot uses not only the
information of GPS but also the braille blocks that werelaid on the sidewalk. The images of the braille blocks
are processed by Hough transformation, and based on
the data, the robot can arrive at the destination by fuzzycontrol. Furthermore, we will set up the base station tomonitor the autonomous mobile robot with wireless
LAN for safety drive. We will assist the robot from thebase station to ensure the driver against danger.

2. Present state
2.1 The walking of the visually handicapped humans
individually
Now, the visually handicapped persons can
individually go out to walk by the helping of the careperson, the white walking stick, the guide dog and theauxiliary device.
Walking with the care person gives the big burden toboth the helpers and the handicapped persons
themselves, especially for their mental burden. The
visually handicapped persons must train themselves towalk safely with the white walking stick. However,
the white walking sticks are mostly used to get the
information of the road surface. But they are not enoughto sense the conditions of the direction of movement.
For example, when the visually handicapped persons
use the white walking stick, they often bang against thecar that is parked in the sidewalk of the road.
The guide dog is satisfied for the safety of a walk of
the visually handicapped persons. But if the visuallyhandicapped person says, “I want to go to the library”,
the guide dog cannot lead them to the destination [4].

1. Introduction
The visually handicapped humans can come and go
freely in the place where they usually visit, but theycannot go to other place alone. Typically, the helpers areneeded to help them. But such situations are big burden
for both helpers and handicapped persons. In particular,
it is said that mental burden of handicapped persons islarger. So we develop the autonomous mobile robot thatis improved from the electric wheelchair for the elderly
people [1][2][3].
The object of this study is to automatically navigate
the robot to the destination by GPS and image
processing. The robot previously gets the information of©ISAROB 2006

2.2 The braille blocks
The braille blocks are the emblem to lead the visuallyhandicapped humans to the destination and they are
installed consecutively in the sidewalk.
The braille blocks consist of the inductive blocks andthe admonitory blocks (in Fig.1). The inductive blocks
are marshaled salient of long square. The admonitory
blocks are marshaled salient of small circular form.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Their colors are yellow, and can be easily recognized by
the amblyopic persons.
The inductive blocks are consecutively installed andshow the route that the visually handicapped humans
can walk on them. The admonitory blocks are installedat the branch point and the dead end of the inductive
blocks, or the entrance of a building and so on.

3.2 Structure of the system
The structure of the mobile robot system developed inour study is showed in Fig.3.
The robot previously gets the information ofdestination by GPS and then automatically moves to thedestination by comparing the information of the current
place with that of the destination. At the same time, theimages of the direction of robot movement taken fromthe CCD camera are processed by the computer
mounted on the robot and the braille blocks can berecognized for navigation. In the image processing, thebinarization, the thinning, the elimination of the isolated
point and the edge detection are performed. After theseprocedures, the straight line of the forward direction isrecognized by Hough translation.
The autonomous mobile robot can decide thecontrolled variable of both the speed regulation and thesteering angle by the results of the recognition of thestraight line using Hough translation. The speed isadjusted by the distance to the next moving point. The
steering division uses the fuzzy rules to decide thecontrolled variables. When the DC motor moves, it
detects the angle by a variable resistor and the value ofthe angle is fed back to the controller.
Furthermore, the wireless LAN is used by the robot inorder to increase security and communicate to the basestation for monitoring the mobile robot. If the person at
the base station judges that there are some dangers to therobot from the monitoring image, he can directly control
the robot via LAN to avoid the danger state of the robot.

(a) The inductive blocks (b) The admonitory block
Fig.1 The braille blocks

3. The system of the autonomous mobile
robot
3.1 Introduction of the mobile robot
The electric wheelchair that is developed by the
SUZUKI company (Fig.2 (a)) is converted to the mobile
robot by installing GPS, computer, CCD camera forimage processing and other sensors for safety drive(Fig.2 (b)). By the Japanese traffic law, the electric
wheelchair is treated as wheelchairs for the physicallyhandicapped and is rated as walkers. Navigators do notneed to have the driver's license. Therefore, anyone candrive it [5]. The specification of the electric wheelchair is
as follows.
Dimensions: 1170×660×1060[mm]
Weight:79kg
Forward speed: 2～6km/h
Reverse speed: 2km/h
Drive system: Direct drive system with rear-wheel
drive
Braking system: Electromagnetic brake that uses
motor dynamic braking
Control system: Microcomputer variable speed
electronic control unit for accelerator leversensorGPS

wirelessLANcamerabase station
PC

D/A

motor driver

D/A

motor driver

A/D(a) Before conversion
variable
resistor

DCmotor
DCmotorGPS

handle

CCD camera

driving wheelsbase station

speed regulation

computer & wireless LAN

steering(b) After conversion

Fig.3 Structure of the system

Fig.2 The electric wheelchair and improved mobile robot©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

But only by this process, the isolated points remain in
the image, which must be removed. The result is shownin Fig.5(c). After the removal, the edge should be
detected, which is given by Fig.5 (d). This result is thenprocessed by Hough transformation, and the outline of
braille blocks is shown as two straight lines clearly, theresult of which is given by Fig.5 (e).

3.3 Image processing
The flowchart of image processing is shown in Fig.4.
First, the camera that is mounted on the autonomous
mobile robot gets the image of the braille blocks on
sidewalks. The image is processed by binarization andonly yellow color is left to be recognized. The input
image is shown in Fig.5 (a), and the binarized image isin Fig.5 (b).

3.4 Hough transformation

S ta rtHough transformation transforms the orthogonal
coordinate of each pixel of the object into the polar
coordinate system to recognize the input image.

R e a d t h e im a g e

ρ0

B in a r iz a t io n
θ0

r e m o v e t h e is o la t e d p o in t s

e d g e d e t e c t io n(a) XY-plane(b)ρ-θplane

Fig.6 recognition of a straight line by
Hough transformation

H o u g h t r a n s f o r m a t io n

A straight line in the XY-plane shown in Fig. 6(a) isexpressed by the equation (1) in polar coordinate.

d e c id e t h e c o n t r o lle d v a r ia b le fr o mt h e p r o c e s s s e d im a g e

ρ = x cosθ + y sin θ

Where,
ρ: The length from the origin to the straight line.
θ: The angle between the perpendicular line and the xaxis.
When the point (x, y) of the orthogonal coordinatestransforms to the polar coordinate, the point on theorthogonal coordinates correspond to the curve on ρ-θplane. If a lot of points on orthogonal coordinates aretranslated , a lot of curves are drawn in theρ-θplane.
The common point that the curves intersect canrepresent a straight line in the original x-y plane. The
straight line can be expressed by the equations (2), (3) [6].

Fig.4 Flowchart of the image processing(a) Original image(1)(b) Binarized image(c) Removal the isolated points (d) Edge detection

x=−

sin θ
ρ
y+
cosθ
cosθ

(2)

y=−

cosθ
ρ
x+
sin θ
sin θ

(3)

3.5 The decision of controlled variable for steering
angle
Only by the straight line obtained from Houghtransformation, it is still difficult for the robot tonavigate accurately in every situation. For example, therobot is controlled to move according to the centerlineof the braille blocks, but it often deviates from the(e) Hough transformation
Fig.5 Image processing©ISAROB 2006

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Gradecenterline at the place of sharp curve or crossing pointof a series of the braille blocks. Therefore, thecalculation of the objective vector, or the target vector tomove from the obtained features of the image isnecessary to navigate the robot autonomously.
Several parameters such as the radius of curve, thevelocity of the robot and so on are necessary to calculate
the angle of the target vector. So we propose thenavigation method that use the center of gravity of astraight line introduced by Hough transformation. The
center of gravity can be easily obtained [7].

NLθd [rad]In this paper, we propose the system of autonomous
mobile robot for welfare with image processing andGPS. Now we are in the process of developing the robot.
The structure of the whole system and the procedure ofimage processing are well explained and we have theplan to check that the robot can autonomously move todestination with safety and accuracy on the sidewalk of
the urban area by the presented fuzzy rules.

(5)

References

By using some membership functions from thesecomputational results, fuzzy rules can be applied toautonomously control the robot.

[1] Tomoaki Yoshida, Akihiro Ohya, Shin’ichi Yuta:
Autonomous Mobile Robot Navigation Using Braille
Blocks in Outdoor Environment, The Robotics
Society of Japan, Vol.22, No.4, pp.469-477, 2004
[2] Ikuko Yairi, Kentaro Kayama, Seiji Igi: Robotic
Communication Terminals as a Mobility Support
System for Elderly and Disabled People – Our Recent
Research Progress and Plan for Practical Use, The
18th Annual Conference of the Japanese Society for
Artificial Intelligence, 2004
[3] Koji Tokuda, Masanori Sugisaka, Mbaitiga
Zacharie: Automatic Control of an Electric Vehicle
using Visual Information, Proceeding of the TenthInternational Symposium on Artificial Life andRobotics, Session of Mobile Vehicles -Ⅰ, 2005
[4] http://www.e-switch.jp/cats/nakai2.htm[5] SUZUKI ET4E manual, Suzuki Motor Co[6] Shinichi Murakami: Image Processing Engineering,
University Press of Tokyo Denki university, 1996
[7] Takumi Kamioka, Yutaka Fujihara: Development ofTele-operational and Autonomous Rescue Robots,
Proceedings of The 6th China – Japan International
Conference on Industrial Management, pp.73-78,
2002[8]Yutaka Fujihara, Takumi Kamioka, Nobuo Takahashi,
Manabu Takao, Kazuhiro Uchimura, Fengzhi Dai,
Hiroyuki Uchida： Development of the Autonomous
Airship by Fuzzy Control, Proceedings of the SeventhInternational Conference on Industrial Management ,pp. 183-190, 2004
[9] Fengzhi Dai, Yutaka Fujihara: Competitive Learning
Algorithm for Fuzzy Rule Optimizing to a Mobile
Vehicle, Proceedings of the Seventh International
Conference on Industrial Management, pp. 172-176,
2004

Derivation of angle of target vector

Fuzzy control

Fuzzy control to navigate the robot is used in thispaper. Even if accurate mathematical models of systemcannot be obtained and sensor data is uncertain or
imprecise, the controlled variable can be decided withthe knowledge that is accumulated from the pastexperience. We define the desirability function by the
fuzzy rules of the form, IF Ai THEN Bi, where Ai
denotes a fuzzy logic formula from fuzzy predicates,
and Bi is a fuzzy set of controls. For example, when acar is driven through the hard curve, the fuzzy rule isexpressed by “if curve is hard then speed down”.
In this research, the steering angle of target vector
that is decided from the braille blocks is obtained by themembership function that has five triangular peaks
shown in Fig.8 [8][9].

©ISAROB 2006

PL

5. Conclusions

Then, the steering angle θ is calculated by the equation
(5).

4.

PS

Fig.8 The membership function

In Fig.7, the coordinate of target from the center ofgravity of (x1, y1), (x2, y2) of two straight lines iscalculated from the equation (4).
x0 = ( x1 + x 2 ) / 2, y 0 = ( y1 + y 2 ) / 2
(4)

Fig.7

ZR

0

3.6 Derivation of angle of target vector

θ = Tan −1 (YL − y 0 ) /(2 x0 − XL)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Walking orbit of biped robot by using the simulation
Koji UMEZAKI, Masanori SUGISAKA
Department of Electrical and Electronic Engineering, Oita University
700 Dannoharu, Oaza, Oita 870-1192 Japan
E-mail: msugi@cc.oita-u.ac.jp

Then, in this research, the position and movement

Abstracttransition of center of gravity and ZMP depict by using
the simulation. Then, we examined the result.

The movement robot is researched and developed bya lot of enterprises and research laboratories. Those2.

robots are expected to cooperate with human and workin a dangerous place in blast furnace and all that ofMetaphysic・ZMP(Zero Moment Point) [1]

atomic is enumerated as a reason. The biped robot is

It is a point of application when it substitute acting on

expected for the reason of excellent mobility and to be

one point with a normal element of the ground reaction

possible to act even in the small space.

that distributes the overall sole of feet. It is a point that

Then this research, the obit where the biped robot

the inertia force caused by gravity and the walking

walks used and demands the simulation. This time, we

motionconsider transmission of position of ZMP and correcting

and

the

reaction

force

from

groundequiponderates dynamically.

value in forward walking.

・Target ZMP [1 ]1.

Introduction

Target ZMP is a point that put over effort force f acting

The multilegged movement robot is researched andon center of gravity, and cross the floor. If target ZMP

developed by many enterprises and research laboratories.

has observed ZMP there, a target point is without the

As the reason, as for the mulitilegged, it is raised that

occurrence of overturning moment.

mobility in not flat ground is excellent compared withthe wheeled movement robot. Those robots, it is・Supporting leg polygon [1 ]expected to work cooperatively with work and human in

Supporting leg polygon isa dangerous place such as in atomic blast furnaces.

shown in Fig.1. This is the

Before, we generated operation by the biped robot

one that the parts that thewith a servo motor But, those were problems thefoot grounded was close

environment that can be operated by the difference ofround so as not to become

the influence of the friction of the back of the foot of theconcave. As for ZMP, thebiped robot and the floor must be limited, else thatarea that can be operated because of the feature isposition and those movement transitions of center oflimited within the area of the supporting leg polygon.]Itgravity and ZMP were not able to be understood.

©ISAROB 2006

Fig.1 supporting leg
polygon71

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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is impossible to come off the motion when target ZMP

The planned orbit of center of gravity is shown in Fig.2.

is not in the supporting leg polygon. Observed ZMP is

The orbit of target ZMP is shown in Fig.3.

out of synchronization when target ZMP is outside of
the supporting leg polygon. It’s contemplated that bipedrobot topples down by generating the overturning
moment.

・Equation of model [ 2 ][ 3 ]center of gravity is shown in equation (1) and equation

We take notice to the position of center of gravity, it(2).

moves smoothly. We take notice to the position of ZMP,

N y = m(xcg − x ZMP )(&z&cg + g ) − m(z cg − z gnd )&x&cg (1)it moves when center of gravity reaches on theN x = m( y cg − y ZMP )(&z&cg + g ) + m(z cg − z gnd )&y&cg (2)supporting leg. Moreover, the planned orbit of center ofgravity stops on the supporting leg. However, the orbit

Here, we hypothecate the height of center of gravity toof target ZMP has come off from the area of thebe constant. &z&cg = 0

supporting leg polygon. The purpose of this is to assume
that the sole of foot of the model has adsorbed the floor

Therefore, there become equation (3) and equation (4).

when we calculated target ZMP by the simulation.

~
N y = mg (xcg − x ZMP ) − m(z cg − z gnd )&x&cg (3)~N x = mg ( y cg − y ZMP ) + m(z cg − z gnd )&y&cg (4)3.

[N , N , N ] ：Moment that acts on
T

x

y

z

：Borland C++ Builder5[x , y , z ] ：Position of center of gravity
cg

Setting of simulation

Application programming language

center of gravity circumjacentT

cg

Model：weigh 3.0[kg]cg[x ZMP , y ZMP , z ZMP ] T ：Position of ZMP
・Planned obit

length of leg

0.29[m]length of thigh0.145[m]length of shin

0.155[m][ 2 ][ 3 ]Degree of freedom：Hip joint
Knee joint

The orbit generation run in the way hereinafter

3DOF
1DOF

Ankle joint 2DOF

prescribed.
Option1．We plan the landing position of sole of foot andNecessary time for one step 0.5[s]timing.

Rate of double support phase in one step

2．We settle on No.1 as required to comply with position

0.2

2

The maximum acceleration 0.5[m/s ]of center of gravity.

Distance from target orbit

3．We settle on No.2 in accordance with position of thewhole body.

©ISAROB 2006

Fig.3 the planned orbit oftarget ZMP

Fig.2 the planned orbit ofcenter of gravity

The balance of axis of x element circumjacent based on

to landing position of foot72

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4.

1
0.8
0.6
0.4
0.2
0

Result of simulation

The change in target ZMP, observed ZMP andcorrecting value when forward walking is shown from0

Fig.4 in Fig.9. The x element is shown from Fig.4 in

5000

10000

time(ms)

Fig.4 target ZMP (x element)

Fig.6 (Back and forth movement). The y element is
0.003
0.002
0.001
0
-0.001 0-0.002-0.003

shown from Fig.7 in Fig.9 (Right and left movement).
The comprising item of motion is shown. First, start up
a program. Next, it is state of rest for tow seconds.
Afterward, to do the forward walking, we keep pushing
the keyboard of five seconds. We release the keyboard.

5000

10000

time(ms)

We stop the program in three seconds later. The model

Fig.5 correcting value (x element)
1
0.8
0.6
0.4
0.2
0
-0.2 0

shifts from the state of both feet earth to the one feetearth with the left leg as for a reason that was state ofrest for two seconds in the start immediately after
beginning. Therefore, the correction is done. Then, it isbecause it was considered that this correcting value

5000

10000

time(ms)

become 0. The reason where the keyboard is separated

Fig.6 observed ZMP (x element)

and done the state of rest of the program in three

0.04

seconds is moreover that it decelerates gradually in no0.02

rest after motion separate the keyboard soon, and it

0

enters the state of rest.

-0.02

We think about correcting value. Correcting value Ux

0

5000

10000

-0.04

of x element is generated with start and finish of motion.

time(ms)

In these, the inertia force caused when the state of rest

Fig.7 target ZMP (y element)
0.003
0.002
0.001
0
-0.001 0-0.002-0.003one is moved and when do the state of rest the moving
one is causes. Correcting value Uy of y element isgenerated with start of motion. In this, the inertia force
when shifting from the state of rest in the state of
motion is a cause as well as the case of x element.

10000

time(ms)

Moreover, the similar amount of correction shape of

Fig.8 correcting value (y element)

waves has been repeatedly generated while both x

0.04

element and y element are walking. A constant speed

0.02

can be said that will repeat by these while walking

0thought is done the correction.

-0.02

We think from Fig.4 and Fig.7 about observed ZMP.

-0.04

0

5000

10000

time(ms)

The amount of the correcting value is extremely small©ISAROB 2006

5000

Fig.9 observed ZMP (y element)

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

compared with the amount of the change of ZMP and it
is not influenced in x element. It understands influenced
in y element thought it is a little at the beginning of
motion.

5.

Discussion

This time, we described the orbit of ZMP in theforward walking. The correction by inertia force is done
to start and finish of motion in x element as understoodfrom the simulation result. In y element, the correction
is done by inertia force at the beginning of motion.
These correcting values don’t become 0. However, we
think that secure walking motion can be done by lesscorrecting value.

Reference[1] http://www.ops.dit.ne.jp/~manava/robot[2] http://rogiken.org/newmanoid/index.html[3] T.Shinohara, “Real-time orbit generation of bipedrobot”, 2001

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Motion trace through head control
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

2. Experiment System

Recent years, researches about motion trace
moving object is extensively studied. If we canmotion trace-moving object, we can apply theskills of automotive control and so on.
For example, if obstacle avoidance ofmoving object instead of obstacle avoidance ofthe static object becomes possible, the control
becomes possible as for a more complex
situation.
Facial recognition was researched in mylaboratory, but, this research is weak of change
in light, so that my main research improve thatand to motion trace through head control ofmobile robot.

The robot which is being used in thisresearch was manufactured by incorporated
company DENKEN in 2000.The figure isshown in Fig.1.It consists of 2 drive 2 caster(2DC2W) systems.
The mobile robot is equipped on the rightand left with the driving wheel, auxiliary casterrings at the front and back, a driving wheel on
either side rotates by DC motor. Equipped withthe rotary encoder of resolution 80 (Pulse Per
Resolution) beside the driving wheel, andcounting the number of pulses, theright-and-left independence can be achieved
and a wheel can be controlled.
The difference in the rotation speed of aright-and-left driving wheel performs a steeringfunction. The CCD camera (EVI-G20: Sony) is
carried by the height of about 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 wasbean considered in various fields. Various
functions were bean required and the robot use
was expanded. One of the functions requires
environmental recognition and avoidance of an
obstacle. In order to develop such robot, thislaboratory, for some time, has mainly respondto the field of welfare and nursing. A mobile
recognition robot has been researched anddeveloped [1][2][3].
Researches about motion trace moving
object is extensively studied. In this research,
we used our laboratory robot. Image importfrom CCD camera, we compute feature pointand center point to track moving object, andimplement motion control of the head.

Fig. 1 View of The Mobile robot©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3. Image Processing

Fig.3 shows the triangle of points(r,g,b)=(R/(R+G+B),G/(R+G+B),B/(R+G+B))
that
satisfy r + g + b = 1. The center point W has
coordinates (1/3,1/3,1/3). We can find the angle
H = for any point P on the triangle by thegeometry of the triangle. Hue is a value
between 0 and 360 [4].
In order to improve the threshold value, HSI
value is corrected from the HSI image, thenconversion from RGB to HSI is performed.
After the conversion is performed. The
threshold value of HSI value is finalized.
Blue

3.1 Pixel Skipping
When the size of image is 300 by 300 [pixel],
it is difficult to implement in real timeprocessing, when the size of picture is small,moving object will be not detected.
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 the image isprocessing again by re-sizing and showing only
the taken pixel area. In this research, re-sizingof image is 30 by 30. Fig. 2 shows that graycolor is taken pixel area.

W(1/3,1/3,1/3)

H(b) Skipping Image

P(r,g,b)
Green(a) Original Image
Fig. 2Red

Pixel Skipping

Fig. 3 A slice of the color cube

3.2 Conversion From RGB To HSI

However, if HSI value is not threshold area,
the camera will not recognize the object. As thefirst step, we change the hue value to makeRGB relational expression.

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 independently as much as possible, thefollowing 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 a maximum total intensity of 1.

3.3 Labeling Technique
The label represents number. The number,which represents value from 0 to 255, isboundary value. The processed image is labeledseparate boundary value. Labeling-propagation
processing by using raster scanning is shown in
Fig. 4. Fig. 4 shows that the label represents byusing alphabet value. As a result, characteristicsof the moving object are calculated.

I=(R + G + B)/3
(1)
S=1-min{R,G,B}/I(2)
H=arccos{(P - W)*(R - W) /[|P - W||R - W|] (3)Let P be the point as shown in Fig. 3.

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A
1
1
1
1

1

1

1

1

1

1

1
1
1
1
1

(b) Skipping Image(a) Labeling Start

A
A
A
A
A

A

A

A

A
A
A
A
A

(c) Result Image
Fig. 5 Motion Detect Process

Fig. 4 labeling-propagation processing
by using raster scanning

Since the image (c) is shown in Fig. 5, center ofthe circle is calculated. Therefore, head of therobot rotate to center of the circle. However,moving object is difficult to detect in strong
light. When moving object and other object are
similar saturation, other object is detected, too.

4. Motion Detect Experiment

5. Conclusions

Motion detect experiment was actual
conducted in the environment. 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 movefollowing the direction of the red circle markmovement. As the first step of processing is
pixel skipping, second step is conversion from
RGB to HSI and third step is labeling. The
green point of the image (c) is shown in Fig. 5
is center of circle.

In this research, we are able to improvethreshold in light. However, moving object isdifficult to detect in strong light. When moving
object and other object are similar saturation,other object is detected, too. Therefore, wewould like to improve than this research instrong light and weak light, and we would liketo consider about occlusion.

B

B

B

(b) Labeling End

References[1]Syuiti Otsu, Masanori Sugisaka : Researchon the environmental recognition in a mobile
robot , Oita University Graduate School Master
Paper(2003)[2]Syohei Kuriyama, Masanori Sugisaka :Development of the mobile robot run systemusing dead reckoning and vision information ,Oita University Graduate School Master
Paper(2003)[3]Toshio Maeyama , Masanori Sugisaka : The(a) Original Image©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

obstacle avoidance run in the mobile robot
using the CCD camera, Oita University
Graduate School Master Paper(2002)[4]
http://www.cse.unr.edu/~looney/cs674/ch6
html.htm Chapter 6. Processing Color Images©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Characteristic measurement of artificial muscle

Satoshi ICHIKAWA

Masanori SUGISAKA

Department of Electrical and Electric Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan.
E-mail: msugi@cc.oita-u.ac.jp

Therefore, we research the support equipment by

Abstract

using the artificial muscle.

Recent years, artificial muscle is researched as awearable actuator that a user can use in comfort.

In this research, we made an artificial muscular robot.

Therefore, we research the support equipment by using

And, we measured the characteristic of each artificial

the artificial muscle. In this research, we made an

muscle.

artificial muscular robot. And, we measured the
２．Artificial muscle

characteristic of each artificial muscle. As a result, we

There are various kinds of artificial muscles. They are

were able to obtain the expansion and contraction

shown in the following.

characteristic of the artificial muscle.

・Actuator that used polymeric material
・Actuator that used shape-memory material

1. Introduction
Variouscommodities

that

supportthe・Actuator using electrostatic power

senior・Actuator that used air pressure

physically and psychologically appear by the coming ofIn this research, the air actuator use as a suitable

the aged society. Many of them are equipped directly by
the user.

actuator for the support equipment that the user equips,

As for such equipment, physical safety and a

the balance of the generative force and the contraction

psychological sense of security are requested.

percentage is good (It is possible to adjust it by the

To secure physical safety and a psychological senseof security, the following are needed.

designconcerning

the

air

pressure)and,（１）Even if controlling becomes impossible, it doesn't

viscoelasticity characteristic is similar to the muscle.

theThe McKibben type artificial muscle used by thisbecome a trouble.
（２）It is small and light. (The senior citizen can easily

research has structure that wrapped the rubber tube by amesh of a net plastic fiber, and fixed both ends.

carry possession.)（３）It flexibly fits the human body.

Pattern diagrams are shown in Fig.1. It expands（４）No operation sound.

radically when pressurizing it in an inside rubber tube as

Recently, the artificial muscle is paid to attention as ashown in Fig.2, and power to constrict axially is

wearable actuator that can be equipped the senior being

generated. If the state before pressurizing air pressure at

relieved.

artificial muscles is set to 100, after the maximum©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006⑦：gluteus maximus（305mm）

pressurization will be set to 70.

⑧：musculus iliopsoas（305mm）

The artificial muscle used from this by this research
has the expansion and contraction rate of 30%.
Flexible part100

Fig.1 Pattern diagrams before artificial muscle
is pressurizedFlexible part70（１）elevational view （２）left side viewFig.3 Pattern diagrams of artificial muscular robot

４．experimental apparatus

Fig.2 Pattern diagrams after artificial muscle
is pressurized

The outline chart of the apparatus used by thisresearch is shown in Fig.4.The air tube of the artificial

３．artificial muscular robot

muscle and the compress air supply tubes from the air

The model chart of an artificial muscular robot iscompressor are connected with the air pressure

shown in Fig.3.

controller. The control signal from the computer is given

The frame of the robot is wooden, and it has

to the air pressure controller, and the artificial muscle

articulatio coxae, the articulatio genus, and the ankle

can be expanded and contracted freely.

joint. Articulatio coxae and the ankle joint are madePC

from the articulatio spheroidea. The articulatio genus ismade from the hinge joint. The number of Fig.3
expresses arrangement of artificial muscles. Eachartificial muscle does the same role as person's muscle.

Pneumatic control system①：long peroneal muscle, short peroneal muscle（125mm）②：anterior tibial muscle, posterior tibial muscle（125mm）
Air compressor③：tibialis anterior（245mm）④：musculus triceps surae（400mm）
Artificial muscular robot⑤：musculus quadriceps femoris（330mm）⑥：hamstrings（330mm）©ISAROB 2006

Fig.4 Experiment device outline chart

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５．Expansion and contraction characteristic
measurement

100%
95%

used (225mm, 450mm). First of all, the load is not put

90%length(%)

The artificial muscle with different two lengths ison the artificial muscle and constant air pressure is
injected. The measurement result is shown in Fig.5 and

85%
80%
75%
70%
0.400

225(mm)
450(mm)

0.200
0.300
pressure(MPa)

100%

0.400

length(%)

90%
85%
80%
75%
0.100

0.200
0.300
pressure(MPa)

0.400

20%
15%

injection225(mm)injection50(mm)exhaust225(mm)exhaust450(mm)

10%
5%
0%
0.000

0.100

0.200
0.300
pressure(MPa)0.400

Fig.9 Expansion and contraction characteristic chart ofno loadInjection and exhaust in the same length showed the

Next, the load is not put on two artificial muscles andcharacteristic with a different each curve (Fig.9).

constant air pressure is exhausted. The measurement

As this cause, it was thought that elastic modification

result is shown in Fig.7 and Fig.8.

length(mm)

0.000

25%

Fig.6 pressure(Mpa)−length(%)

of the rubber tube that is the material of artificial

225(mm)
450(mm)muscles had influenced.
To this verification, 500g loads were hung on
artificial muscles, and the expansion and contraction
characteristic was measured. The result is shown in
Fig.10.

0.100

0.200
0.300
pressure(MPa)0.400

Fig.7 pressure (MPa)−length (mm)©ISAROB 2006

0.100

30%

225(mm)
450(mm)

95%

500
450
400
350
300
250
200
150
100
50
0
0.000

0.200
pressure(MPa)

These are collectively shown in Fig.9.
0.100

Fig.5 pressure(Mpa)−length(mm)

70%
0.000

0.300

Fig.8 pressure (MPa)−length (%)

Expansion and contraction rate(%)

length(mm)Fig.6.
450
400
350
300
250
200
150
100
50
0
0.000

225(mm)
450(mm)81

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Expansion and contraction rate(%)

30.0%
25.0%
20.0%
15.0%
225(mm)

10.0%450(mm)

5.0%
0.0%
0.000

0.100

0.200
0.300
pressure(Mps)0.400

Fig.10 Expansion and contraction characteristic chart ofload 500g
From Fig.10, if a certain amount of load was hung on
artificial muscles, the influence of hysteresis is
improvable.

６．Conclusion
In this research, an artificial muscular robot's creation
andexpansion

andcontraction

characteristic

measurement of artificial muscles were performed.
However, there are many things that should still bemeasured, such as the expansion and contraction
characteristic when hanging load. Therefore, othercharacteristics will be measured in the future. And, wemove it to an artificial muscular robot by using theresult.

References[1] S.Douno,A.Saitou,R.Kuwata“Knit Structure SMA Actuator for Wearable
Artificial Muscle Systems”[2]http://f-kawaraban.cside.com/find/kinoukaibou/muscl
es.htm©ISAROB 2006

82

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Motion control of biped robot
Kensaku KABATA

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

2. Specific of biped robot
Specific of biped robot are shown in Fig.1
and Table 1.

Recent years, researches about biped robot
are extensively studied. The vertical projection
area of biped robot on the floor side is small,therefore, biped robot can move crooked roadand narrow road.
So we think it is best system on life spacethat has many irregular ground.
In this paper, we made the robot with
servomotor, and generate walking motion by
application software.

1. Introduction
Fig.1 Photograph of biped robot
Recent years, researches about biped robot
are extensively studied at the enterprise and theuniversity. The purpose is human assistance,
but it aims entertainment now.
Modern society is created for human to live
easily. Therefore, to assist human, we needrobot that is suitable for life space.
In the past research, wheel type robot was amainstream. As a reason, the major factor iseasy and steady to control. But, the fault is notable to move at irregular ground. The verticalprojection area of biped robot on the floor sideis small, therefore, biped robot can movecrooked road and narrow road. So we think it
is best system on life space that has many
irregular ground.
In this research, we made the robot with
servomotor, and generate walking operation by
application software.

©ISAROB 2006

Table 1 Specific of biped robot

Actuator

Servomotor

ComputerIntel Pentium Ⅲ 487MHz

Mother board

HSWB-01

Application software

HSWB-01C
Height 449[mm]×Size

Width 200[mm]×Length 120[mm]83

DegreeHip joint 3×2

ofKnee joint 1×2

Freedom

Ankle joint 2×2

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3. Experiment
3.1 The method of motion creation

3.2 Walking motion

We use HSWB-01C to control the bipedrobot. This application 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 theposition of servomotor by the hour.
We can create various motions by the
position of the servomotor. We set position by
the trial and error.
In this paper, we use this method shown in
Fig.2 to move biped robot.

Figure of biped robot's joints are shown in
Fig.3.

STEP 1. Set home position.

Fig.3 Figure of biped robot's joints

Hip joint
Hip joint 2

Knee joint

Ankle joint
Ankle joint 2

STEP 2. Create the position ofAngle change graph of Right-and-Left (Hipjoint, Ankle joint 2) and Angle change graph of
Backward-and-Forward (Hip joint 2, Knee joint,
Ankle joint) are shown in Fig.4.
Position of servomotor (Fig.3) and the colorof graph (Fig.4) are corresponding.
Fig.4 (a) shows the change in Right-and-Left.
Fig.4 (b) shows the change in Backward-and-Forward.

servomotor by the hour.

STEP 3. Transmit it to the robot.

STEP 4. Motion
Fig.2 Method of motion creation

20
15

Angle[°]

10
Hip joint (Right)

5

Ankle joint 2 (Right)

0
-5

0

2

4

6

8

-15

Time[sec](a) Right-and-Left©ISAROB 2006

12

Hip joint (Left)
Ankle joint 2 (Left)

-10

-20

10

84

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

100

Angle[°]

80
60

Hip joint 2 (Right)

40

Knee joint (Right)

20

Ankle joint (Right)

0

-20 0

2

4

6

8

10

-40

12

Hip joint 2 (Left)
Knee joint (Left)
Ankle joint (Left)

-60
-80
-100

Time[sec](b) Backward-and-Forward
Fig.4 Angle change graph

3.3 Comparing with human walking
motion and robot walking motion

Table 3 Human’s walking cycle (static)

The rate of double stance phase and single
stance phase in biped robot's walking motion
are shown in Table 2. Double stance phase is aperiod standing with both legs, and switches
axopodium. Single stance phase is a period
standing with one leg, and goes ahead.

Single stance phase

41%

59%

40%

60%

Double stance phase

Single stance phase

20%

80%

4. Conclusion
In this paper, we researched about bipedrobot's walking motion. We transmit instruction
value to the robot, and generate walking motion
by application software.
Comparing with human walking motion androbot walking motion, the ratio of this result issimilar to the ratio of double stance phase andsingle stance phase in human's static walking.
But, we do not calculate ZMP (ZeroMoment Point), so the walking motion is notproven steady. In the future, we make thesimulation that uses ZMP.

The ratio of double stance phase and single
stance phase in human’s static walking isshown in Table 3.
The ratio of double stance phase and single
stance phase in human’s dynamic walking isshown in Table 4.
The result of this experiment is similar to theratio of double stance phase and single stancephase in human's static walking.

©ISAROB 2006

Single stance phase

Table 4 Human’s walking cycle (dynamic)

Table 2 Robot’s walking cycle
Double stance phase

Double stance phase

85

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Reference[1]

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

©ISAROB 2006

86

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Intelligent Classification of Bill Money
Sigeru Omatu+ Toshihisa Kosaka++, and Masaru Teranisi+++
Osaka Prefecture University, Sakai, Osaka 599-8531, Japanomatu@cs.osakafu-u.ac.jp++
Glory TD Himeji, Hyougo 670-8567 Japan+++
Nara University of Education, Nara, 630-8528, Japan+

Liras of 8 kinds, 1,000, 2,000, 5,000, 10,000, 50,000(new), 50,000 (old), 100,000 (new), 100,000 (old)
Liras with four directions A,B,C, and D where A and B
mean the normal direction and the upside down
direction and C and D mean the reverse version of A
and B. The simulation results show that the proposed
method can produce the excellent classification results.

Abstract
For the pattern classification problems theneuro-pattern recognition which is the patternrecognition based on the neural network approach has
been paid an attention since it can classify various
patterns like human beings. In this paper, we adopt thelearning vector quantization (LVQ) method to classify
the various money. The reasons to use the LVQ are
that it can process the unsupervised classification andtreat many input data with small computationalburdens. We will construct the LVQ network toclassify the Italian Liras. Compared with aconventional pattern matching technique, which has
been adopted as a classification method, the proposed
method has shown excellent classification results.
1.

2.

Competitive Neural Networks

We will explain the competitive neural networks
that are used to classify the bill money. The structureof a LVQ competitive network is shown in Fig. 1. The
input for the LVQ is bill money data where an original
image consists of 128x64 pixels and the input data to
the network is compressed as 64x15 pixels to decrease
the computational load. The output of the network
consists of the Italian Liras of 8 kinds, 1,000, 2,000,
5,000, 10,000, 50,000 (new), 50,000 (old), 100,000(new), 100,000 (old) Liras with four directions A,B,C,
and D where A and B mean the normal direction andthe upside down direction and C and D mean thereverse version of A and B.
In the input layer the original bill money data areapplied and all the units at the input layer areconnected to all the neurons at the output layer withconnection weight W ij . Wij denotes the connection

Introduction

Bill money classification by transaction machines
has been important to make progress the officeautomation [1]. Since sizes of bills are different
according to kinds of bills, the measurement data ofbills include various variations. Human being canclassify the bills correctly even if they are sufferedfrom those variations such as rotation and shift. But
usual pattern recognition using a conventionaltransaction machine cannot give us the correctclassification result under such cases since the basic
method is a pattern matching principle. Furthermore,
the conventional pattern matching method requires
many template patterns for many kinds of bills, whichtakes much time and needs much experience [1].
Recently, neural networks which are based on the
biological mechanism of human brain have been
focussed since they have intelligent pattern recognition
ability [2]. In this paper, we will apply the neuralnetwork approach to classify the bill money undervarious conditions by using transaction machines. The
learning vector quatization (LVQ) has been used toclassify the bills since it can treat high dimensional
input and has simple learning structure [3]. The LVQ
network adopted here has 64x15 units in the input layer
and many units at the output layer. The bills are Italianweight from the unit j in the input layer to unit i in the
output layer. The output layer will output only one
neuron which is called winner neuron. The winner
neuron is selected as the neuron with the minimum
distance between an input vector and its connection
weight vector. The connection weights Wij are set by
the random number at the beginning. Here, we set themean vector of the cluster plus small random number.
Then the following learning algorithm of theconnection weight vector is used.
LVQ algorithm
Step 1. Find the unit c at the output layer which has theminimum distance from the input data x (t)x(t ) − Wc = min x(t ) − Wii

©ISAROB 2006

87

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

LVQ, we havew c (t + 1) = w c (t ) + s (t )α c (t )(x(t ) − w c (t ))wheredenotes the Euclidean norm and t
denotes the iteration time.

= (1 − s (t ))α c (t )w c (t ) + s (t )α c (t )x(t )Step 2. If the input x (t) belongs to Category c, thenw c (t + 1) = w c (t ) + α (t )(x(t ) − w c (t ))andw c (t) = wc (t − 1) + s(t − 1)α c (t − 1)(x(t − 1) − wc (t − 1))

w i (t + 1) = w i (t ) , i ≠ c= (1− s(t − 1))α c (t − 1)wc (t − 1)and if the input x (t) belongs to the other Category j+ s(t − 1)α c (t − 1)x(t − 1)
Substituting the latter equation the former one, we havew c (t + 1) = (1 − s (t )α c (t ))(1 − s (t − 1)α c (t − 1)) w c (t − 1)(j ≠ c), thenw c (t + 1) = w c (t ) − α (t )(x(t ) − w c (t ))+ s(t)αc(t)x(t) + s(t − 1 )αc(t − 1 )( 1 − s(t)αc(t))x(t − 1 ).

w i (t + 1) = w i (t ) , i ≠ c

We assume that the optimal rate adjusts the effect ofx(t) and x(t-1) equally within the absolute value, thatis,
α c (t ) = (1 − s(t )α c (t ))α c (t − 1) .
Then we have
α c (t − 1).
α c (t ) =1 + s (t − 1)α c (t − 1)
From the above equation, we can see that the value ofα c (t ) become larger than 1 when s(t-1)= - 1, which
may make the learning algorithm unstable. Thus, wemust fix the α c (t ) to a boundary value α 0 when it
becomes larger than 1.
α c (t + 1) = α 0 if α c (t + 1) > 1 .
Using the above OLVQ1 algorithm, we will classify
the Italian bills in the following section.

where α (t ) is a positive function and denotes learningrate.
In the usual LVQ α (t ) is given bytα (t ) = α 0 (1 − )Twhere (0< α 0 <1) is a positive and T is a total numberof learning iterations.
The above algorithm for selection of new weightvector Wc (t +1) can be explained graphically as
shown in Fig. 2 .

Output Layer
Category１

Category２

If c belongs to thesame category asx(t)Category

Wc(t

Input
Fig. 1.

Wc(t+1)X(

Layer

Structure of the LVQ networks.

In the above LVQ algorithm, the learning rate α (t )plays an important role for convergence. To adjust theparameter, Kohonen has proposed an optimization
method without proof as follows:
α c (t − 1)
α c (t ) =1 + s (t − 1)α c (t − 1)
where s(t) =1 if x(t) belongs to the same Category cand s(t)=-1 if x(t) does not belong to the same
Category c. Here, α c (t) denotes the learning rate forthe pattern of Category C. In what follows, we will
prove the above relation. From the learning rule of the©ISAROB 2006

Wc(t)If c belongs to thedifferent
category withx(t)Fig.2. Principle of the LVQ algorithm where the righthand side shows the same category case of x(t) andCategory c and the left hand side denotes the different
category case.

88The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 20063.

Preprocessing Algorithm

The images obtained by transaction machine, there
are variations such as rotation or shift. Therefore, wemust adjust the images such that the variations may be
reduced as much as possible by using the
preprocessing. The flow char of the preprocessing
procedure is illustrated in Figure 3. In this figure, theoriginal image with 128x64 pixels are observed at thetransaction machine in which rotation and shit areincluded. After correction of these effects, we select asuitable aria which show the bill image andcompressed as the image with 64x15 pixels to theneural networks. Although the neural network of the
LVQ type could process any order of the dimension of
the input data, the small size is better to achieve thefast convergence result. Thus, we have selected theabove size of the image.
4.

A direction

Ｄ direction

Ｂ direction

Ｃ direction

Transfer direction
Fig. 4.

Four directions of bill money.

used for training of the network and the remaining 20
data sets are used to test the network. In order toreduce the misclassification, we have set the thresholdvalue dθ such that if dc > dθ , unit c is not fired. Thismeans that if the minimum distance is not less than dθ ,the input data is not classified. The parameters of theneural network used here are as follows:
Number of units in the input layer=960
Number of units in the output layer in the initialstate=32 where every 50 iterations the number has
been adjusted.
Total learning time T=150, α i (0) = 0.5 , i = 1, L M
Initial values of the weight vectors=mean vectors fortraining patterns dθ = min (mc + 4.5σ c )Italian Lira Classification

The bills used here are Italian liras, which have 8
kinds such as 1,000 Liras, 2,000 Liras, 5,000 Liras,
10,000 Liras, new 50,000 Liras, old 50,000 Liras, new
100,000 Liras, and old 100,000 liras. Those Lira bills
are used at the input of the transaction machine wherefour directions such as A, B, C, and D appear sincenormal direction, reverse direction, and their upside
down directions occur at the input as shown in Fig.4.
Thus, thirty-two bill images are one set of theclassification pattern of the experiment.

cAfter training the neural network, 20 data sets aretested how well the LVQ network could work. Tables
1 and 2 show those values at t=160. We can see theimprovement by learning. Table 5 shows the number ofthe neuron units at t=160 which are determined byincreasing them.

Original image(64x128 pixels)

Table 1.

Recognition rate (%) at t=160.

Directions
Rotation and shiftcorrection

Italian
Liras

1,000
2,000
5,000

Extractionoftheprocessing

10,00050,000(new)50,000(old)100,000(new)Input to NN

100,000(old)(15x64 pixels)

Ａ

Ｂ

Ｃ

Ｄ

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

95

95

100

100

90

100

100

100

95

90

ＮＮ
Fig. 3. Preprocessing algorithm.

From the original image data we can see that thedifference between 50,000 Lira old and new is slightand the difference between old and new100, 000 Liras

Total number of data sets is 30 and 10 data sets are©ISAROB 2006

89

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

as shown in Figs. 5 and 6. Therefore, it is rather
difficult to recognize them so perfectly. But in this casethe misclassification like old and new bills within thesame values is not serious. Thus, we have regarded thismisclassification as the correct one. Furthermore, wehave introduced the threshold value to prevent frommaking the misclassification. Thus, even if theminimum distance criterion results in the correctclassification, we have decided these bells are
unknown. Without threshold constraints, we could
obtain 100% classification rate.
Table 2.

Not fired rate (%) at t=160.

Directions
Italian
Liras

1,000
2,000
5,000
10,00050,000(new)50,000(old)100,000(new)100,000(old)

5.

Ａ

Ｂ

Ｃ

Ｄ

5

0

5

0

0

10

25

25

15

20

5

0

10

0

0

5

5

0

0

0

0

5

0

0

0

0

0

0

0

5

0

0

(a) 50,000 Lira(new)A direction(b) 50,000 Lira(old)A direction(c) 50,000 Lira(new)D direction(d) 50,000 Lira(old)D direction

Fig. 5.

Conclusions

New and old 50,000 Liras.

(e) 100,000 Lira(new)A direction(f)100,000 Lira(old)A direction(g) 100,000 Lira(new)D direction(h) 100,000 Lira(old)D direction

Fig.6. New and old 100,000 Liras.

We have proposed a new classification method of
Italian Liras by using the OLVQ1 algorithm. The
experimental results show the effectiveness of the
proposed algorithm compared with the conventional
pattern matching method.

Table 3.

Directions
Italian
Liras

1,000
2,000
5,000

References[1] S. Fukuda, T. Kosaka, and S. Omatu: Bill Money
Classification of Japanese Yen Using Time Series
Data, Trans. Of IEE of Japan, Vol.115-C, No.3,
pp.354-360, 9995(in Japanese).
[2] J. Dayhoff: Neural Network Architectures: An
Introduction, International Thompson Computer
Press, New York, 1990.
[3] T. Kohonen: Self-Organizing Maps, Springer, Berlin,
1997.

©ISAROB 2006

Number of units after learning.

10,00050,000(new)50,000(old)100,000(new)100,000(old)

90

Ａ

Ｂ

Ｃ

Ｄ

2

2

2

2

2

1

1

1

1

1

1

1

1

2

2

1

2

1

1

1

2

1

3

1

1

1

1

1

1

1

1

1

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Intelligent Smell Classification by Neural Networks
Sigeru Omatu+, Toru Fujinaka+, Seiji Okazaki+, Toru Sumiya++, and Masatoshi Ono+++
Osaka Prefecture University, Sakai, Osaka 599-8531, Japan: omatu@cs.osakafu-u.ac.jp++
Funai Electric Advanced Applied Technology, Research Institute Inc.,
Kawasaki, Kanagawa 210-0855, Japan

petroleum gas (LPG). The time series signals of the
MOGSs from the beginning to the time until the MOGSs
are fully absorbed the smell from each source of fire arerecorded and analyzed by the error back propagation (BP)neural networks and the k-means algorithms. The averageclassification rate of 99.6% can be achieved by using the
BP method with only a single training data from eachsource of fire. The results from the k-means algorithm canbe achieved 98.3% of correct classification that also
confirms the reliability of this new device to be able todetect various sources of fire in the early stage muchbetter than the results of Jame A. Mike [1].

Abastract
In this paper, a reliable electronic nose (EN)system designed from the combination of various
metal oxide gas sensors (MOGS) is applied to detect
the early stage of fire from various sources. The timeseries signals of the same source of fire in every
repetition data are highly correlated and each sourceof fire has a unique pattern of time series data.
Therefore, the error back propagation (BP) method
can classify the tested smell with 99.6% of correctclassification by using only a single training datafrom each source of fire. The results of the k-means
algorithms can be achieved 98.3% of correctclassification which also show the high ability of the
EN to detect the early stage of fire from various
sources accurately.
1.

2. Metal Oxide Gas Sensors for EN
A commercial MOGS has been developed widely formore than thirty years.
Solenoid Valves System
V
1

Mass Flow
Controller(MFC)

Introduction

V
2

OUT

IN

Air
Compressor

ExhaustA/Dconverter

FilterEvery year the damage from the household fire disasterbrings about not only severe loss to property assets, butalso physical and psychological injuries of the people.
Although most of the residences have installed the fire
detectors system such as smoke detectors, those devices
cannot detect the early stage of fire since their warning
signals are triggered by the high smoke density or thehigh air temperature. In this paper, the reliability of a newelectronic nose (EN) system developed from various
metal oxide gas sensors (MOGSs) to specify the smell
from various sources of fire is presented.
Jame A. Milke [1] has proved that two kinds of
MOGSs have the ability to classify several sources of fire
more precisely than conventional smoke detector.
However, his results can be achieved only 85% of correctclassification. In this paper, a new EN that has beensuccessfully applied to classify not only the same smell
from different brands, but also the same smell at different
concentration levels [2] is applied to measure smells fromvarious sources of fire such as household burningmaterials, cooking smells, the leakage from the liquid©ISAROB 2006

EN
Unit

V
3

Sampling System

IN

OUT

Sampling box
IN

Data recording
andAnalyzing system

OUT

Head space

IN

OUT

BubbleIN

OUT

Sampling bag

Fig. 1 Schematic diagram of the electronic nose system.

91

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Generally, it is designed to detect some specific smell inelectrical appliances such as an air purifier, a breath
alcohol checker, and so on. Each type of MOGS has its
own characteristics to response to different gases. When
combining many MOGSs together, the ability to detect
the smell is increased. An EN system shown in Fig. 1 has
been developed based on the concept of human olfactory
system by using the combination of MOGSs from FIS Inc.
listed in Table I as the olfactory receptors in the humannose. The MOGSs unit is combined with the air flow
system to flow the air and the tested smell into the
MOGSs unit. The data logger converts the analog signals
to digital signals and stores them in the data recording
system before being analyzed by multivariate analyticalmethods, such as the BP method and the k-means
algorithms.
The main part of the MOGS is the metal oxide
element on the surface of the sensor. When this element isheated at a certain high temperature, the oxygen isabsorbed on the crystal surface with the negative charge.
The reaction between the negative charge of the metaloxide surface and deoxidizing gas makes the resistance ofthe sensor vary as the partial pressure of oxygen changes[3]. Based on this characteristic, we can measure the total
voltage changes during the sensors absorbing the tested
odor.

value, v air , is used as an air reference point. After that,the voltage signals of the sensors when absorbing tested
smell, v smell ,t , are collected every two minutes for eachsmell sample. Finally, the total change in signals at eachperiod, Vsmell ,t , is calculated by

V smell ,t= v smell ,t − v air
where t is the time from 1 to 120s.
After testing one smell the MOGSs need to be cleanedby removing the tested smell and supplying only the fresh
air until the MOGSs return to stable point before testing
the new sample. This process is just like the human nose
which need to breath the fresh air before able to recognize
the new smell accurately. Some time series data from theexperiment in Fig.2 show that all smells approach thesaturation stages within the measuring periods. The
signals from the same source of fire in every repetition
data are similar in most data sources. The results using the
BP method and the k-means algorithm to analyze the timeseries data from each source of fire every two seconds
and
the average signals during the saturation
stages(time 100-120s) are discussed in Section V.
Table II

Sources of fire

Table I List of MOGSs from the FIS Inc.
Sensor Model
Main Detection Gas
SP-53
Ammonia, Ethanol
SP-MW0Alcohol, Hydrogen
SP-32
Alcohol
SP-42A
Freon
SP-31
Hydrocarbon
SP-19
Hydrogen
SP-11
Methane, Hydrocarbon
SP-MW1
Cooking vapor

Abbreviation

Since the MOGS is sensitive to the temperature and thehumidity, the MOGSs unit is put in a small chamber thathas a heating system to increase the air temperatureduring winter season. The heating unit can also decrease
the air humidity in the chamber. The clean water ismanually sprayed into the chamber when the humidity
drops lower than the control level. In this experiment thetemperature in the chamber is kept between 20-30°C andthe humidity is kept between 30-40%RH. The tested
smell is sucked to mix with the fresh air before passing to
the MOGSs unit. The distance from the tested smell to the
MOGSs unit is approximately 1.5 m.

Steam from boiling water

Steam

Burning joss stick

Joss

Burning mosquito coilMos

Aroma oil

Aroma

Aroma candle

CandleFlame from liquid petroleum gas(LPG)

FlameLeakage of LPG

LPG

Steam from Japanese soup called “oden”

Oden

Boiling vegetable oil

Oil

Toasted bread

Toast

Burning paper

Paper

Burning wood

Wood

4 Correlation of the Experimental Data
Before classifying each source of data, the correlationof each data source is investigated by using the similarity
index (SI) and the principal components analysis (PCA).

3. Experimental Data Collection
The smell from twelve sources of fire listed in Table II
are measured by the EN system explained in previous
section. Each source of fire has been tested with forty
repetition data measured in different days in order tocheck the repeatability response of the MOGSs to thesame smell.
For each data, the voltage signal of the normal air ismeasured every second for one minute and its average©ISAROB 2006

List of Burning Materials in the Experiment4.1. similarity index
In the statistical application, the correlation value
developed mainly by Karl Pearson is widely used to findthe relationship between two random variables. In thispaper, we call the correlation value as a similarity index

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

(SI). The SI value varies from -1 to 1. Two randomvariables with a SI of either 1 or 1 are highly correlated
because knowledge of one provides precise knowledge ofthe other. However, the SI provides information only
about linear relationships between random variables.
Random variables could have a nonlinear relationship but
still have a SI close to 0 [4]. Therefore, we make anassumption on this application that each data pattern has
nearly linear relationship to the other data patterns. The SI
value between two data is calculated by

In this paper, the well known PCA is applied to analyze
two cases of the experimental data. The full time series
data case uses the data signals every two seconds, but thesaturation stage data case uses only the average data fromtime 100 to 120s for analyzing.
The plots of two main components are shown in Fig.3.
The distribution of the paper and the wood burning smell
are more scattered than the other kinds of smells
especially in the case of saturation stage data. Most of thetested data are separated into their own clusters with someoverlap zones between different data source.

5. Experimental Results and Discussion
5.1

3.5

Two case of data are analyzed by the BP method andk-means algorithm. The full time series data (TSD) caseuses the data from all MOGSs every two seconds as the
input data. The saturation stage data (SSD) case uses only
the average value from time 100 to120s of all sensors as
the input data.
The BP structure contains three layers. The input layerof the TSD case, and the SSD case consists of four
hundred eighty nodes (8 sensors x 60 periods), and eight
nodes(average signal from 8 sensors), respectively. For thehidden layer, we have tried with several values and thesize that gives a good accuracy and a reasonable training
time for both data cases is forty nodes. The output layer
contains twelve nodes, each node represents one data
source. The learning rate, the momentum rate, and theminimum mean square error (MSE) during the training
period are set by trial and error method to 0.1, 0.001, and
0.0003, respectively.
Based on the information during investigating thecorrelation of the data, most data sources are highly
correlated to their repetition data with high SI values.
Therefore, only one data that has the highest average SI
value to the other repetition data from each sources offire are used as the training data for the BP and the rest of
the data are used as the test data. We assume that a pattern
is classified correctly if (output ≤ 0.7 and target=1) or(output ≤ 0.3 and target =0). For the k-means algorithm,
the training data of the BP method are used as theinitialize data and then assigns the data patterns to thenearest cluster center by calculating the Euclidean
distance. After that, the new cluster center is recalculated.
The process continues until the position of the cluster
center is not changed. The final results of this experimentare shown in Table III.
The results using the TSD from both the BP method
and the k-means algorithm are sufficiently effective. The
data signals from the MOGSs are affected by manyfactors, such as the sampling condition, the inconsistency
burning rate, the fluctuation from the standard air, and soon. Therefore, the saturation stages of the data are variedby those factors. By including the signal before
approaching the saturation stage, the accuracy to classify
all smell is increased.

3

Smell data

2.5
2
1.5
1
0.5
0
1

6 11 16 21 26 31 36 41 46 51 56
Time[s]SP-MW1
SP-32
SP-19

SP-53
SP-42A
SP-11

SP-MW0SP-31

Fig.2 Time series data from some sources of fire in theexperiment.
n

rxy =

∑ x y − nx y
i =1

i

i

n

n

i =1

i =1

experimental result(∑ xi2 − x 2 )(∑ yi2 − y 2 )where rxy is the SI value, x and y are the comparing data,

1 n
1 n
x = ∑ xi , y = ∑ yi , and n is the size of eachn i =1n i =1
data which equals 480 (60 periodsx8 sensors).
By using the SI to find the relationship between therepetition data of each data source, we found that all data
sources except the paper and the wood have high average
SI values above 0.99. During the experiment, the paper
and the wood have inconsistent burning rates, therefore
the signals from the repetition data of these sources are
more fluctuated than the other sources that have better
consistent burning rate.
4.2 principal components analysis

5.2 Discussion©ISAROB 2006

93

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Although the distribution of PCA shown in Fig. 3
cannot clearly separate similar smell such as the aroma oil
and the aroma candle, the BP method and the k-means
algorithms are able to classify them perfectly as shown in
Table III. The results of TSD using the BP method have
only two incorrect classified data. These two data are not
misclassified as the other smells. Only the output valuesof their paper node are not high enough to classify themas the paper. The output values of these two data on thepaper node are only 0.4951, and 0.4799, respectively andthe output of the others output nodes are nearly 0. The
results are much better than the results from [1] whichused two kinds of MOGSs to classify several sources offire into three fire condition levels, flaming, smoldering,and nuisance, with only 85% of correct classification. The
smoke density of the tested data is not high enough totrigger the alarm of the smoke detector. In case of unusual
burning smells in the residences such as the wood burning,flaming from the LPG, or the leakage LPG, it is necessary
to have a proper device to detect these sources before
unable to stop the fire. We can conclude that the new EN
system shown in this paper is a proper device for thisapplication.

6. Conclusions
We have proposed a new EN system designed fromvarious kinds of MOGSs. The EN has the ability toidentify various sources of fire in the early stage withmore than 99% of accuracy by using only a single
training data in the BP case. The results from thek-means algorithm are also able to predict the sources offire with more than 98% of accuracy. It can beconcluded that the EN is suitable for detecting the earlystage of fire.

REFERENCES

Fig.3. Two main components of the experimental datausing the PCA.

Table III
Sources

TSD
BP

[1]

Jame A.Mike, "Applicaiton of Neural Networks for
Discriminating Fire Detectors", International Conference on
Automatic Fire Detection, AUBE'95, 10th, Duisburg, Germany,
pp.213-222, April 4-6, 1995.
[2] Bancha Charumporn, Michifumi Yoshioka, Toru Fujinaka, andSigeruOmatu, "An Electronic Nose System Using Back
Propagation Neural Networks with a Centroid Training Data Set",
proceeding of the Eight International Symposium on Artificial Lifeand Robotics, Japan, pp.605-608, January 24-26, 2003
[3] General Information for TGS sensors, Figaro Engineering,M.
available at www.figarosensor.com/products/general.pdf.
Tanaka, “Modeling of Mixtures of Principal Component Analysis
Model with Genetic Algorithm,” Proc. 31st Int’l Symposium on
Stochastic Systems Theory and Its Applications, pp. 157-162,
Yokohama, Japan, 1999.
[4] William L. Carlson and Betty Thorne, Applied Statistical Methods,
Prentice Hall International, 1997

©ISAROB 2006

Experimental Results

Steam
Joss
Mos
Aroma
Candle
Flame
LPG
Oden
Oil
Toast
Paper
Wood
Average

94

True
39/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
38/39
39/39

%
100
100
100
100
100
100
100
100
100
100
100
95
100
99

SSD
k-means

True
40/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
35/40
37/40

%
100
100
100
100
100
100
100
100
100
100
100
88
93
98

True
38/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
39/39
38/39
38/39
31/39
32/39

%
97
100
100
100
100
100
100
100
100
97
97
80
82
96

True
39/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
40/40
37/40
40/40
28/40
28/40

%
98
100
100
100
100
100
100
100
100
93
100
70
70
94

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Ggraph Construction with Maximum Number of Treesby Continuous Edge Addition
Hideshi Ido+, Yosuke Ohama++, and Tomoki Takahashi+++ Electrical Engineering and Information Science, ++Electrical Engineering
Niihama National College of Technology,
7-1,Yagumo-cho, Niihama, 792-8580, Ehime, Japan+ ido@ele.niihama-nct.ac.jp

number of trees by adding new edges continuously to theoriginal graph. Also we constructed graphs with theminimum number of trees on the similar way. And theratio of the maximum number of trees to the minimum of
those is about from 1 to 6 for two types of starting graphs
which are a tree of series edges and a star-shaped tree fornodes n=7 and 8.
In the subsequent chapters, we show the algorithm forgraph construction and results about the ratio of the
maximum number of trees to the minimum for theseexamples.

Abstract
The simple computational algorithm is presented toconstruct the graph with maximum number of trees byadding edges one by one. The number of trees of a graphwould become an index to estimate the overall reliabilityof probabilistic communication networks with same linkprobabilities. Our procedure Max-trees selects one edge
which gives the maximum number of trees among edges
not included in the original graph. And this process is
continuously repeated in each step of adding edge, we get
the sequence of new edges to be added. As examples ofexecution results, the edge sequence and the maximum
number of trees are shown for each starting graph of two
types which are a tree of series edges and a star-shaped
tree for nodes n=7 and 8.
To see how many trees these graphs have, the minimum
numbers of trees for graphs with the same number ofedges are similarly calculated by the minimum version
algorithm Min-trees. An edge sequence of Max-trees
makes long cycles, and Min-trees makes 3-cycles. And,the ratio of the maximum number of trees to the minimumof those is about from 1 to 6 within these examples.
Keywords: Continuous edge addition, Maximum andMinimum number of trees, Node determinant, Clique
1

2

Algorithm of the Maximum Number of TreesFirst we introduce some graph theoretical terms. A
graph denoted by G (V , E ) is composed with a node set
V and an edge set E. Each edge denoted by (i, j )connects a pair of nodes i and j . A graph is said to be
simple if it has no parallel edges, no self-loops and no
directed edges. In this paper, we treat only simple graphs
as shown in Fig.1 (a). A tree is a subgraph which connects
all nodes of G and includes no closed circuits, as shown in
Fig.1 (b).
v5

Introduction

v5

v2

v4
v1

v1

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 same [1].
Some analytical methods have been reported for thenumber of trees of graphs obtained from the completegraph by deleting several types of subgraphs[2],[3]. Other
ones are to maximize the number of graphs when someedges or subgraphs are deleted from the completegraph[4],[5]. Another one is to get the expressions of thetrees of a graph which is obtained by connecting graphs of
special structures[6].
These analytical methods are based on obtaining theexpressions derived from a node determinant of the target
graphs. And the graphs need to have any special structures.
Therefore simple expressions haven’t been known for
arbitrary graphs with no structural conditions so far.
On the other hand, it is well known that the numericalcalculation of the number of trees can be performed by
the node determinant for any graph. So, we have tried tocomputationally construct graphs with the maximum©ISAROB 2006

v4

v3

v3

v2

(a)
(b)
Fig.1.(a)Simple Graph, (b)Tree
The complete graph of n nodes has one edge (i, j )between every node pair i , j , and includes

1 n( n − 1)
2edges. The node determinant T of a graph G with n
nodes is a determinant of the matrix A = aij , where for( )1 ≤ i, j ≤ n − 1 , each diagonal element aii is the number ofedges incident to node i , and each non-diagonal element
aij is –1 if there is an edge between two nodes i and j ,otherwise 0. Then, T represents the number of trees of
the graph G. Since the value of T is always an integer
for any graph, the determinant T must be computed byusing double precision floating point number for
matrix A .And usual Gaussian elimination is available forthis purpose.

95

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Algorithm: Max-trees

3.1

step1. Find the set P of position (i, j ) where there isn’t

Edge sequences shown in Fig.3 and Fig.5 obtained byan edge (i, j ) in the given original graph G (V , E ) .

Characteristics of Edge Sequence

1

Now, set G0 ← G (V , E ) and k ← 0.

8

4
7

Step2. Calculate the number of trees T * of each graphobtained by adding an edge (i, j ) to the graph Gk ,3

for each position (i, j ) ⊂ P , respectively.
Step3. Find the edge (i, j ) that gives the maximum

5

2

6

(a) 1st ~ 4th.

(b) 5th ~ 8th.

*

number of trees among T ’s in step2. And replace
Gk +1 ← Gk + edge (i, j ) , P ← P - position (i, j ) ,11

and k ← k+1.
Step4. If the set P is empty, then terminate, otherwise
go to step2.

149(c) 9th ~ 12th.

(d) 13th ~ 15th.

Fig.3. Edge sequence by Max-trees
for Series-edge tree of n=7.
1
2

4

8
7

3

(b) 5th ~ 8th(a) 1st ~ 4th13

11

Two algorithms described above can be adapted torelatively small simple graphs with nodes n ≤ 20 . Thesealgorithms are programmed in C and run on sx5
computer.
When the algorithm starts, it usually needs the initialgraph although it works to make a star shaped tree as an
initial graph. Therefore, we examined two types of trees,
one is a series edge and the other is star shaped as shownin Fig.2.

12
14

9
15

10

(d) 13th ~ 15th.
(c) 9th ~ 12th.
Fig.4. Edge sequence by Min-trees
for Series-edge tree of n=7.

3

1

4

6
2

(a) 1st ~ 4th.

785(b) 5th ~ 8th.

(b) Star-shaped tree.
14

Fig.2. Two types of trees as initial graphs.

12

Results of edge sequences are represented by numbers
near dotted lines as shown in Figs.3-Fig.6 for Max-trees
and Min-trees, series edge and star-shaped of nodes n=7.
And the numbers of trees are shown in Table 1 and 2
comparing Max-trees with Min-trees according to edge
sequences for nodes n = 7 and 8 , respectively.

11©ISAROB 2006

5
6

Examples and Results(a) Series-edge tree.

13

12

In this algorithm, step2 and step 3 will be repeateduntil Gk reaches to the complete graph.
To compare the maximum number of trees with theminimum number of trees on the same number of edges,
we make the minimum version algorithm Min-trees
derived from Max-trees algorithm. Namely, in theminimum version, “the maximum number of trees among
T * ’s” is replaced by “the minimum number of trees
among T * ’s” in step3. Then we call the maximum
version algorithm as Max-trees, and the minimum version
algorithm as Min-trees, respectively.
3

15

10

9

10
13

(c) 9th ~ 12th.

15(d) 13th ~ 15th.

Fig.5. Edge sequence by Max-trees
for Star-shaped tree of n=7.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Table 2. Comparison of Trees for n = 8 .
1

4

Initial tree

2

Added
edges
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

5
3

7

6

8
(b) 5th ~ 8th.

(a) 1st ~ 4th.

11

13
15

12

9

14

10
(d) 13th ~ 15th.

(c) 9th ~ 12th.

Fig.6. Edge sequence by Min-trees
for Star-shaped tree of n=7.
Max-trees mean that it is effective to make long cycles as
many as possible for getting more trees. Similarly, edge
sequences shown in Fig.4 and Fig.6 obtained by Min-trees
mean that it is effective to make 3-cycles as many as
possible for getting less trees. Namely, thesecharacteristics can be clearly observed at the early part ofeach edge sequence. Particularly Min-trees is enlarging asize of a clique(a complete subgraph) by added edges.
3.2

Characteristics of the Number of Trees©ISAROB 2006

3
9
27
72
168
377
841
1,537
2,800
4,928
8,056
12,44019,20028,80040,80057,60080,640110,592
147,456
196,608
262,144

3
8
16
40
75
125
300
540
864
1,296
3,024
5,292
8,232
12,005
16,80738,41665,856
100,352
143,360
196,608
262,144

3
9
27
72
168
377
841
1,537
2,800
4,928
8,056
12,44019,20028,80040,80057,60080,640110,592
147,456
196,608
262,144

3
8
16
40
75
125
300
540
864
1,296
3,024
5,292
8,232
12,005
16,80738,41665,856
100,352
143,360
196,608
262,144

Ratio Max./Min.

2.5
2
1.5

Star-shaped
1

Max-trees Min-trees Max-trees Min-trees
7
19
51
117
231
408
720
1,200
1,840
2,800
4,200
6,125
8,575
12,005
16,807

3
8
16
40
75
125
300
540
864
1,296
3,024
5,292
8,232
12,005
16,807

3
9
27
63
144
320
576
1,024
1,792
2,688
4,032
5,880
8,575
12,005
16,807

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Added edges

3
8
16
40
75
125
300
540
864
1,296
3,024
5,292
8,232
12,005
16,807(a) Series-edge of n=7.
2.8
2.6
Ratio Max./Min.

Added
edges
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Max-trees Min-trees Max-trees Min-trees

3

Table 1. Comparison of Trees for n = 7 .
Series-edge

Star-shaped

3.5

Numbers of trees for each edge sequence obtained by
Min-trees in both Table 1 and 2 are the same values. The
reason for this phenomenon is that, as mentioned above,
Min-trees make a clique of the same number of edges andthe other edges are remained as parts of the original tree.

Initial tree

Series-edge

2.4
2.2
2
1.8
1.6
1.4
1.2
1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Added edges(b) Star-shaped of n=7.

97

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

numbers of trees have often happened at more than one
node-pair in the process of algorithm. Then, it may be thecase of isomorphic graphs. If those are not isomorphic,
we will select one with the maximum after additional
calculation steps for a next edge.

Ratio Max./Min.

6
5
4
3
2

4. Conclusion
21

19

17

15

13

11

9

7

5

3

1

1

We presented the simple algorithm Max-trees that gives
the maximum number of trees by adding edges to theoriginal graph one by one. The number of trees becomesan index to estimate the network reliability.
Edge sequences obtained by Max-trees for examples
indicate that long cycles are needed for more trees. And
also, the minimum version algorithm Min-trees givesedge sequences which make a clique. And these edge
sequences get the same minimum numbers of trees forany original trees.

Added edges(c) Series-edge of n=8.
4

Ratio Max./Min.

3.5
3
2.5
2

References
1.5[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 Edges”,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 Method forConnection Graph of Cycles from General Graph(inJapanese)”, Proc.2003 IEICE Gen.Conf.,A-1-6,2003.

21

19

17

15

13

11

9

7

5

3

1

1
Added edges(d) Star-shaped of n=8.
Fig.7. Ratio of Max-trees/Min-trees.
Therefore, if we start Min-trees from any different trees,
this phenomenon always happens and the number of trees
depends upon the number of edges, apart from the numberof nodes. Then, Min-trees of this case will give the global
minimum edge sequences.
Fig.7 shows the ratio of Max-trees to Min-trees
according to the number of added edges. These figures(a)-(d) show that if we want to strengthen the networks byadding edges continuously, the over all reliability varies ina wide range according to an edge sequence.
3.3

Additional Considerations

The same maximum numbers and the same minimum©ISAROB 2006

98

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Graph Extension with Constant Connectivity
Hideshi Ido+, Sigeru Omatu+++Electrical Engineering and Information Science, Niihama National College of Technology,

7-1,Yagumo-cho, Niihama, 792-8580, Ehime, Japan
ido@ele.niihama-nct.ac.jp++
Computer and Systems Sciences, Graduate School of Engineering,Osaka Prefecture University, 1-1 Gakuen-cho, Sakai City, 599-8531
omatu@sig.cs.osakafu-u.ac.jp

be denoted as V and E , respectively and let thecommunication network be a simple graph denoted by
G (V , E ) . The connectivity of the graph G (V , E ) is shownas ω (G ) and the edge between nodes s, t is denoted by( s, t ) . To open the edge is called as remove of the edge
and we define the degree of node by the number of edges
which have been connected to the node.
The minimal graph is the graph with ⎡(nk + 1) / 2⎤ edges
where n is the number of nodes and k (2 ≤ k < n) is theconnectivity and ⎡ ⎤ denotes the Gaussian notation.

Abstract
According to scaling up the communication networks,
numbers of communication stations(nodes) and thecorresponding communication links(edges) are rapidly
increasing. Then the reliability of the networks has
become important. To keep the reliability of the networks,
the connectivity(invulnerability) of the networks should
be increased. In this paper, a method of extending theconnectivity of a graph with constant connectivity has
been proposed and some examples have been shown torealize the networks.

3

Keywords: Network Reliability, Connectivity, Graph
Extension.
1

For the construction method of the graph withpre-assigned connectivity k ( 2 ≤ k < n) and node n ,Hakimi’s report is the first one[3]. Tutte has proposed amethod[4] to extend the nodes n for the 3-connected
graph under some constraint on the edges. We haveproposed a method to enlarge k step by step[5] and amethod to extend the nodes n with a connectivity k .
We have proposed the following construction
algorithm of new graph which preserve the connectivity
when we add a new node. Note that only when n and k
are both odd, only one node has the degree k + 1 and theremaining ones have the degrees of k .
Then we have use the following construction
algorithm to extend the nodes n of the network by one.
Construction algorithm

Introduction

Communication networks have been extended year
after year to speed up the information transmission andgrade up the quality of information. Then the reliability ofcommunication networks has become more important tomake the network system more robust. Since thecommunication networks could be represented as a graph,
the graph theory could be applied to analysis of thereliability.
We have considered the robustness of the networks
from viewpoint of connectivity of the graph and proposed
an extension method of a network with a constantconnectivity when the nodes of the network have been
increased by one more node[1].
In this paper, we extend the result when thecommunication network has been extended by more than
one nodes with the same connectivity based on theconcept of the maximum connectivity of the graph theory.
First, we will introduce some concepts of graph theory
and summarize the results on the graph with maximum
connectivity. Then we will state the results to construct
the extended graph with constant connectivity for thecommunication network with more nodes. Finally, someexamples are shown to show the effectiveness of thepresent extension method.
2

Step1. Set the removable p edges ( v i , v j ) : vi , v j ∈ Wsuch that the degree of node v i ≥ k − 1 , for eachvi ∈ W where W = {v1 , v 2 ,..., v h } for different h
nodes of the graph G (V , E ) . Let G1 (V , E1 ) be thegraph which was deleted p edges from G (V , E ) .
Step2. Let A = {v1 , v 2 , ⋅ ⋅ ⋅ , v h , ⋅ ⋅ ⋅ , vl } be different l( h ≤ k ≤ l ) nodes of the graph G1 (V , E1 ) such as
W ⊂ A ⊂ V . Add to G1 (V , E1 ) a new node u ∉ V
and l edges (u , vi ) : v i ∈ A, 1 ≤ i ≤ l and set the newgraph as G * (V * , E * ) .
For the above construction algorithm, the following
Lemmas have been proved in [4] which show that theconnectivity by the above construction method is k .
[Lemma 1] For a graph with connectivity k ≥ 2 if we

Definition of the Graph Connectivity [2]We regard the stations and the links of communication
networks as a set of nodes and a set of edges of a graph,
respectively. Let the sets of nodes and edges of the graph©ISAROB 2006

Preliminary Results

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connectivity k1 = ω (G1 ) and k 2 = ω (G2 ) respectively,

apply the construction algorithm to the extension of the
network and the graph G1 (V , E1 ) satisfies ω (G1 ) ≥ k − 1 ,k1 > k 2
and| V1 | ≥ (k1 − k 2 )+ | V2 | are
whereassumed.
[Theorem 1] As shown in Fig.2, for each node u i of G2 ,then ω (G * ) ≥ k holds for the graph G * (V * , E * ) .
[Lemma 2] For the minimal graph G (V , E ) with n nodes
and the connectivity k ( k ≥ 2) , we assume that h = l = k
and the construction algorithm is adopted for theextension under the following conditions(a) p = (k + 1) / 2 when n and k are both odd(b) p = ⎡k / 2⎤ when n or k is even.

select the distinct set Vdi of (k1 − k 2 ) nodes of G1 .
And

add

new

(k1 − k 2 )edges

betweenthese(k1 − k 2 ) nodes of each distinct set Vdi and the nodeu i of G2 respectively. Then we have got the resultant

Then if ω (G1 ) = k − 1 , then the graph G * (V * , E * ) is theminimal graph with number of nodes (n + 1) andgraph G * of connectivity ω (G * ) = k1 .

(k1 − k 2 ) edgeω (G * ) = k .
v4
v1

(a)

v3

v2

G2 (V2 , E 2u

G1 (V1 , E1 )v4

Fig.2. More general extension of the graph
G1 including G2 .

v1

(b)

(Proof) We want to show that all remaining nodes of G *are connected after removing any set of (k1 − 1) nodes.

v3

v2

Let A be the set of (k1 − 1) nodes to be removed from

u

G* .

v4

(1) Case of A ⊂ V1 . Since new edges added between

(c)

v1

v2

G1 and G2 are incident to at least(k1 − k 2 ) + k 2 = k1 nodes of G1 , then even if(k1 − 1) nodes of A are removed from G * , thev3remaining graph is connected.
(2) Case of A ⊂ V2 . All of the remaining nodes of G2

Fig.1. Construction of minimal graph where (a) is originalgraph with three cycle, (b) added graph by a new nodeu and three edges, (c) the 3-connected minimal graph.

are connected to the connected graph G1 .

A ⊂ V1 U V2 except (1) and (2). The
remaining subgraph of G1 is connected, and all theremaining nodes of G2 are connected to G1 .

(3) Case of
To see the meaning of these Lemmas, we consider thecase of k = 3, n = 5 as shown in Fig.1(a). If we add the
new node u and we select three cycle {v1 , v2 , v3 } at thenode v1 with the node degree 4 as shown in Fig.1(b), we
can remove two redundant edges (v1 , v4 ) and (v2 , v3 )denoted by the dotted lines[6]. Thus, we obtain theminimal graph with the connectivity 3 by adding the newedges (u, vi ), i = 2 ,3,4 as shown in Fig.1(c).
4(Q.E.D)
This Theorem 1 gives a sufficient condition so that theresultant graph G * keeps connectivity k1 . Therefore

G * may have some redundant edges for connectivity k1 .
This will be shown in some examples below.
The next theorem establishes the extension method
connecting two graphs with same connectivity k .
[Theorem 2] When two graphs G1 and G2 have the sameconnectivity k1 = k 2 = k , select each set Vd and U d ofMore General Extension Theorems

For more general extension of the original graphincluding plural nodes we propose two theorems. Let
G1 (V1 , E1 ) and G2 (V2 , E 2 ) be the simple graphs with©ISAROB 2006

distinct k nodes from G1 and G2 respectively and100

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[Example 3] Fig.6 shows the case of k1 = 2 and k 2 = 2 .

connect by new k edges between a node of Vd and oneof U d on 1-1 correspondence. Then the resultant graph

2 new edges are added between G1 and G2 , then the

G has connectivity ω (G ) = k .
*connectivity of the resultant graph G * is k1 = 2
Theorem 2.

*

Fig.3 shows the location of k edges by theorem 2.
The proof for this theorem is clear and omitted.

1

by

1
2

2

k

k

k edges
G2

G1

G2 (V2 , E 2 )G1 (V1 , E1 )Fig.5. k1 = 2 and k 2 = 1 .

Fig.3. Connection by k edges for G1 and G2of same connectivity k.
It will be easily observed that theorem 2 takes the similaredge location as theorem 1 in case of (k1 − k 2 ) = 1 .
5

ExamplesSome examples are shown in Fig.4 ~ Fig.10 to see
previous two theorems. For simplicity G1 and G2 are
restricted to smaller graphs with | V1 |= 4 , k1 = 2 and 3,| V2 |= 3 , k 2 = 0,1,2, and 3,respectively.
[Example 1] Fig.4 shows the case of k1 = 2 and k 2 = 0 .
6 new edges are added, then the connectivity of theresultant graph G * is k1 = 2 (Theorem 1).

G1

G2

Fig.6. k1 = 2 and k 2 = 2 .
[Example 4] Fig.7 shows the case of k1 = 3 and k 2 = 0 .
9 new edges are added between G1 and G2 , then theconnectivity of the resultant graph G * is k1 = 3 byTheorem 1.

G2

G1

Fig.4. k1 = 2 and k 2 = 0 .

G1[Example 2] Fig.5 shows the case of k1 = 2 and k 2 = 1 .

Fig.7. k1 = 3 and k 2 = 0 .

3 new edges are added between G1 and G2 , then theconnectivity of the resultant graph G * is k1 = 2

G2

by[Example 5] Fig.8 shows the case of k1 = 3 and k 2 = 1 .

Theorem 1. And G has a redundant edge among 3 newedges.

6 new edges are added between G1 and G2 , then the*connectivity of the resultant graph G * is k1 = 3 byTheorem 1. And G * has a redundant edge among 6 new©ISAROB 2006

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edges.

connectivity, we first added edges between G1 and G2 .
But, there may be another procedure that first increase theconnectivity of G2 from k 2 up to k1 , then next add

G1edges between G1 and G2 . According to the latter, theexample 6 shown in Fig.9 needs only 6 new edges,
although the example 4 shown in Fig.7 need 9 new edges.
When we are going to connect 3 or more graphs, it
may be useful to apply repeatedly theorems describedabove. In this case, the order which graphs are to beconnected becomes an important problem to be
determined
Related to the total number of edges, the minimal
graph with minimum edges for nodes n and connectivity
k is important. as described at Introduction. When G1

G2

Fig.8. k1 = 3 and k 2 = 1 .
[Example 6] Fig.9 shows the case of k1 = 3 and k 2 = 2 .

and G2 are both minimal graphs, then the resultant
graph may become minimal, if the process of detecting
and removing redundant edges succeeds. So, it needs toestablish the reliable procedure to distinguish the set ofnecessary edges and the set of redundant edges.
Another problem is to extent these theorems to the
network with link capacities or link directions.

3 new edges are added between G1 and G2 , then theconnectivity of the resultant graph G * is k1 = 3 byTheorem 1.

7

The simple procedure for graph extension is proposed.
It becomes useful when the network is required to enlarge
its communication stations or to connect with an anothernetwork. This method has the advantage to be able toapply repeatedly, so we can make the large network
starting from small ones.
The problems to detect redundant edges and to get theminimal graph are left for further considerations.

G2

G1

Conclusions

Fig.9. k1 = 3 and k 2 = 2 .
[Example 7] Fig.10 shows the case of three graphs G1 ,References

and G3 with k1 = 3 , k 2 = 2 and k 3 = 2 ,respectively. First, 3 new edges are placed between
G1 and G2 , then another 3 edges of dotted lines are

G2[1] Ido H., “An Extension of Networks with a ConstantConnectivity”(in Japanese), IEICE Trans. Vol. J71-A,
No.10, pp.1983-1984, 1988.
[2] Harary F., “Graph Theory”, Addison-Wesley, 1972.
[3] Hakimi S.H.: “An Algorithm for Construction of
the Least Vulnerable Communication Network or theGraph with the Maximum Connectivity”, IEEE
Trans. On Circuit Theory, CT-16, No.5, pp.229-230,
1969.
[4]. Tutte W. T., “A Theory of 3-connected Graphs”,
Indagations MATHEMATICS, Vol.23, pp.441-455,
1961.
[5] Ido H., Sasaki T., “On the Construction of Systems
with the Minimal Structure” (in Japanese), IEICE
Trans. Vol. 63-A, No.6, pp.398-405, 1980.
[6] Halin R., “A Theorem on n-connected Graphs”,
Journal of Combinatorial Theory, Vol.7, pp.150-154,
1969.

added between G2 and G3 . The connectivity of theresultant graph G * is k1 = 3 by Theorem 1.

G1

G2

G3

Fig.10. k1 = 3 , k 2 = 2 and k 3 = 2 .
6Further Considerations
About the extension of the graph keeping its©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Design and Fabrication of Tactile Sensor System of FBG 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

Jung-Ju Lee
Dept. of Mechanical Engineering
Korea Advanced Institute of Science and Technology,
Daejon, Korea 305-701
leejungju@mail.kaist.ac.kr

Abstract
2. The Structure of the Flexible FBG Sensor

This paper describes the flexible 3x3 array force
sensors using fiber Bragg grating (FBG) and silicone
rubber for the tactile sensor to detect the distributed
normal force. This sensor has the simple principle. Once,the external force is applied to the transducer which ismade of flexible silicone rubber, the reflective
wavelength of the fiber Bragg grating which isembedded to the transducer is changed. Then the changeof reflective wavelength, so called, Bragg wavelength
indicates external force change. The chirping effect andthe light loss for micro bending must be considered todesign this flexible transducer. The performance of thistransducer can be simulated using finite element method(FEM). The prototype is fabricated and the performanceof prototype is evaluated. The temperature compensation
is applied to this prototype for more precise measuring.
And this array sensor is attached to the silicone rubber
which is similar to the human skin to check theapplicable possibility of the intelligent robot system.

2.1 The Principle of the FBG sensor
Fiber Bragg grating (FBG) sensors based on
wavelength-division multiplexing (WDM) technology
are attracting considerable research interest and appear
to be ideally suitable for structural health monitoring ofsmart structure[5]. FBG sensors are easily multiplexedand have many advantages such as linear response andabsolute measurement. As the spectral response of the
FBG sensor signal renders the measurement free from
intensity fluctuations, it guarantees reproducible
measurements despite optical losses due to bending or
connectors. The basic principle of a fiber Bragg grating(FBG)-based sensor system lies in the monitoring of the
wavelength shift of the returned Bragg-signal, as afunction of the measurand (e.g. strain, temperature andforce). The Bragg wavelength is related to the refractive
index of the material and the grating pitch. Sensor
systems involving such gratings usually work by
injecting light from a spectrally broadband source into
the fiber, with the result that the grating reflects a narrowspectral component at the Bragg wavelength, or intransmission this component is missing from theobserved spectrum. Fig.1 shows this simply andschematically. The intensity of the reflected opticalsignal is a function of the Bragg grating wavelength that
relates to the applied strain on the fiber Bragg grating.
Therefore, the dynamic strain can be derived from theintensity change measurement as function of the
wavelength of the reflected optical signal. The operationof a FBG is based on a periodic, refractive index changethat is produced in the core of an optical fiber by
exposure to an intense UV interference pattern. Thisgrating structure results in the reflection of the light at a
specific narrow band wavelength, called Bragg
wavelength. The Bragg condition is given by

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 3x3 array force sensor using flexible
optical fiber sensor and newly designed small transducer
that can be easily applied to the curved surface
minimizing the wiring.

©ISAROB 2006

λB = 2ne Λ (1)where λB is the Bragg wavelength of the FBG, ne is theeffective index of the fiber core, and Λ is the grating
period. The shift of the Bragg wavelength due to strain
and temperature can be expressed as[∆λB = λB (α f + ξ f )∆T + (1 − pe )∆ε

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

⎛ n2 ⎞
pe = ⎜⎜ ⎟⎟[ p12 − ν ( p11 − p12 )] (3)⎝ 2 ⎠where αf is the coefficient of the thermal expansion(CTE), ξf is the thermo-optical coefficient, and pe is thestrain-optical coefficient of the optical fiber. The value of
pe =0.227 was measured experimentally and used for thisstudy. If there is no temperature change, we can measure
the strain from the wavelength shift as

ε=

∆λB
1
(1 − pe ) λB

(4)Fig. 2 Micro bending effect of Bragg wavelength
In the case of using fiber Bragg grating sensor, FBG isfrequently experienced non-uniform strain distribution.
This non-uniform strain distribution makes the distorted
Bragg wavelength, so called, chirping (Fig. 3) whichinterferes to find the exact Bragg wavelength. Therefore,
the transducer must be designed to avoid this chirping
effect.

Fig.1 FBG sensor encoding operation

2.2 The Structure of Flexible FBG Force Sensors
In general case of small force sensor, the diaphragmtype transducer which can be easily deflected by theapplied force is mostly used. But, this diaphragm typetransducer can be occurred the chirping which is causedby the non-uniform strain distribution and the loss ofreflected light by micro bending of the optical fiber.
These two effects have influence on the performance ofsensor. Therefore, the minimizing these two effects
must be considered when the small sensor is designed.
First of all, the light loss caused by micro bending ismuch significant than that by the macro bending of
optical fiber when the smaller sensor is fabricated. As thesensor size is minimized, the possibility that the local
deflection of optical fiber is appeared is increased. As
shown in Fig. 2, if the micro bending is occurred in the
optical fiber, the intensity of the reflected light isremarkably decreased. Then, the proper Bragg
wavelength can not be detected. Therefore, thetransducer must be designed to minimize this micro
bending effect.

©ISAROB 2006

Fig. 3 The chirping effect of FBG
To avoid these drawbacks, the transducer is designed
as shown in Fig. 4. The transducer which looks like thebridge can be extended symmetrically to the optical fiber
direction when the external normal force is applied to the
top of that. This extension of the transducer makes theuniform elongation of the Bragg grating which is
attached under the transducer. As this structure can beextended to only the fiber direction, there is no micro
bending. Then, the Bragg wavelength shift occurs
without the chirping and the light loss of Bragg
wavelength.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig 4. Newly designed transducer using fiber Bragg
grating sensor

3. Design of the Transducer using FEM
First of all, the capacity of flexible force sensor must
be decided. As this sensor will be applied to the tactile
sensor system, maximum force range of 10N that issimilar to the capacity of human skin is accepted. The
length of FBG which is used in this sensor is 2mm. The
shorter FBG can be used, the shorter spatial resolution
can be realized when this force sensor can be utilized as
the tactile sensor. Some researches introduce the dozens
of µm FBG length [6]. Therefore, the spatial resolutionof this array sensor can be improved more and more
keeping pace with the development of minimized grating
length. The 5mm spatial resolution of this array sensor isdesigned considering the size of transducer. Fig. 5 shows
the designed dimension of transducer. And the transducer
is made of beryllium copper (BeCu) which is often usedas the spring material. As the extension to the opticalfiber direction must be increased, the thickness of siderims is minimized as possible as we fabricate. And thediameter of holes which are on the center of the each rim
is 0.3mm. That is suitable size to insert the optical fiber
sensor which has 250µm diameter.

(a)
Estimated displacement

0.018

Estimated ∆λB
4

0.014
0.012
0.010
0.008

2

0.006

Estimated ∆λB (nm)

Estimated dispalcement (mm)

0.016

0.004
0.002
0

0.000
0

2

4

6

8

10

Force (N)(b)
Fig. 6 Finite element analysis of the transducer: (a)FEM model, (b) Estimated extension and Bragg
wavelength shift by the external normal force

4. Fabrication and evaluation of the taxel
As shown in Fig. 7, the alignment system using the
microscope and the precision stage is made use of in
fabricating the taxel (the element of the tactile sensor) to
avoid the fracture of the optical fiber sensor. Aligned
fiber Bragg grating sensor is fixed by the epoxy.

Fig. 5 The dimension of the transducer
If the extension of the transducer where the fiber
Bragg grating sensor is attached can be simulated, theshift of the Bragg wavelength can be also estimated byusing the equation (4). Using the finite element method,the extension of the transducer can be calculated like Fig.
6-(a). As the structure of transducer is symmetric, thehalf geometric model is used and 2-dimensional element
model is applied. And Fig. 6-(b) shows the estimatedextension of the transducer and the shift of Bragg
wavelength by the applied external normal force.

©ISAROB 2006

Fig. 7 Fabrication of the taxel
The fabricated prototype taxel is calibrated by theverified uniaxial loadcell as show in Fig. 8. The
broadband light source having the 1527~1602nm
wavelength is incident in the optical fiber and the light isseparated by the 2 by 1 coupler. The isolator cannot
transfer the reflected light to the broadband light source
source. The separated light is transmitted in the Bragg

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

made. Especially, to avoid the chirping and light loss by
micro bending effect, the transducer of taxel is newly
designed. Using the FEM, the Bragg wavelength shift isestimated and this estimated result is well matched to theexperimental results. There is linear relation between theapplied force and the Bragg wavelength shift in eachtaxel. The resolution of the taxel is about 0.002N. The
fiber Bragg grating sensors are multiplexed using
wavelength division multiplexing method.

grating. And the reflected light is experienced theapplied load is sent the tunable Fabry-Perot filter (TFPF)
which can detect the Bragg wavelength. The prototype
sensor is set on the z-stage and is aligned to the loadcell.
As the z-stage moves up, the mesa of the prototype
sensor is pressed by the loadcell. That is, the same force
is applied to the loadcell and the prototype sensor andthen the prototype sensor can be calibrated by comparing
with the data of verified loadcell.

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[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
[5] C. Y. Ryu, C. S. Hong, C. G. Kim, S. B. Lee, and S.
S. Choi, “Strain measurement of the laminatedcomposite using attached fiber Bragg grating
sensor”, Proceeding of the 3rd optoelectronics andcommunications conference, pp272~273, 1998
[6] Meng-Chou Wu, R. S. Rogowski, and K. K.
Tedjojuwono, “Fabrication of extremely shortlength fiber Bragg grating for sensor application”,
Sensors, Proceeding of IEEE, vol. 1, pp 49~55,
2002

Fig. 8 Calibration system of prototype taxel

4.5

Estimated ∆λB

4.5

4.0

Experimental ∆λBLinear fit

4.0

3.5

3.5

3.0

3.0

2.5

2.5

2.0

2.0

1.5

1.5

1.0

1.0

0.5

0.5

0.0

∆λB (nm) Experimental Results∆λB (nm) EstimatedFig. 9 shows the Bragg wavelength change by theapplied force. There is linear relation between the Bragg
wavelength change and the force. But some difference
between the estimated and the experimental shift of the
Bragg wavelength comes out. This difference resultsfrom the exclusion of the optical fiber in the FEM model.
Using this relation, we can find the exact force detecting
the Bragg wavelength. The accuracy of the prototype
sensor is 99.9% and the resolution is 0.01N. The
resolution of this optical force sensor depends on that of
the tunable Fabry-Perot filter which detects the Bragg
wavelength (0.02nm). Namely, if the resolution of the
TFPF will be improved, that of the force sensor will beenhanced.

0.0
0

2

4

6

8

10

Applied Force (N)Fig. 9 Estimated and Experimental Bragg wavelength
shift vs loadcell signal

5. Conclusion
In this paper, the force sensor using fiber Bragg
grating for the tactile sensor is newly designed andexperimentally verified. The size of sensor is 5mm andthe used length of FBG is 2mm. If shorter FBG is used,then smaller size of the flexible force sensor will be©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Implementation of an Intelligent Personalized Digital Library System based on
Improved Negotiation Mobile Multi Agents,
Young Im Cho

Dept. of Computer Science, Suwon University
Wauri 2-2, Bongdap-up, Hwaseong, Kyeongki-do, KOREA
e-mail : ycho@suwon.ac.kr
conventional library, in order to serve information fromdatabases on the web to users, according to the developmentof computers and the related fields.
However, there are several problems in the searching ofdata of the existing digital libraries. First, as the searching
method is one dimensional and distinguishes the existence ofthe searching keyword from the database, the result is very
simple. Secondly, the results may contain unnecessary
information under a condition that was not given the priorinformation about the user. Thirdly, whenever a client
connects to the servers, he has to receive the certification andbe under the dominant power of the influence of network.
To overcome such problems, I proposed a new platform ofmobile multi agents for a personal digital library in this paper.
For developing a new platform, I combined the existing
DECAF (Distributed Environment Centered Agent
Framework) multi agent framework [2] with Voyager which
is a mobile ORB (Object Request Broker). Also a newnegotiation algorithm and a scheduling algorithm areproposed, so that I developed a PDLS (Personal Digital
Library System) using this new platform. Although thepartial studies for a personal digital library have been carriedout, there has been none about the integrated and systemized
personal digital library.
For the higher relationship among searched documentsfrom mobile servers, an unsupervised neural network is
applied. For the user's preference, some modular clients areapplied to a neural network. A multi agent platform and amobile 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.
PDLS is different from the electronic paper service system
which is supplied only to members. It is a more intelligent
system that is capable of establishing a database in users’
computer by learning the interests of those individuals. In
this paper, I tried to set up the theoretical structure of themulti mobile agents and develop an algorithm of themodified intelligent negotiation agent for inducing
interaction among multi agents.
This paper is composed of five chapters. Multi agents and
DECAF framework is explained in chapter 2. PDLS based ona new mobile multi agent platform is explained in chapter 3.
The simulation results of PDLS are explained in chapter 4,
and finally the conclusions are in chapter 5.

Abstract
There are several problems in the searching of
database of the existing digital library. To overcome someproblems, I proposed a new platform of mobile multiagents for a personal digital library. For developing a
new platform, I proposed DMMAF agent
For the higher relationship among searched documentsfrom mobile servers, an unsupervised neural network is
applied. 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 ascheduling algorithm are activated for the effectiveness of
PDLS.
PDLS is more intelligent system capable of establishingdatabase in his computer by learning interests of users.
We tried to set up the theoretical structure of the multi
mobile agents and develop an algorithm of the modified
intelligent negotiation agent for inducing interaction
among multi agents.

I. Introduction
Recent developments of the internet and network
technologies evoke the technical change of the dataprocessing 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 in a
distributed processing environment by using agent systems
for efficient network management. An agent system has thefollowing 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 the number of agents, oreach agent manages it independently, and then they solve theproblems by analyzing the results. In addition, it has someadvantages such that intelligent agents reflecting thetendency of users make no limitation of movement in a
network, and it remarkably decreases the network traffic [1].
There are so many application areas of agents in the realworld. One of these areas is a digital library system. The
digital library is called an electronic library or a virtual
library. This is a library developed to replace the©ISAROB 2006

2. Multi Agent System
2.1 DECAF framework and Voyager

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

DECAF (Distributed Environment-Centered Agent
Framework) is a conventional framework to design a lot of
intelligent agents [2]. DECAF is a kind of operating system
including agent communication, planning, scheduling,
monitoring, coordination, diagnosis, and learning among
agents. DECAF makes a socket program by itself, andpresents some building blocks which makes messages andcommunicates between agents. Therefore, users or
programmers can produce agents without having someknowledge about API approaches. Also, users or
programmers do not need to make the communication codes
directly to communicate among agents. DECAF produces a
KQML protocol automatically which sends messages andsearches other agents and interacts between agents. Agentsystems have been developed using various languages andplatforms, 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 and Simulation)
algorithms.

A digital library serves a lot of information on-line [6,7].
The advantages of digital libraries are user friendly, on-site
service and accessibility. However, in case of not having
standardized platform, the search of heterogeneous
information from digital libraries may be hard, as well as
impossible. If it does not have or learn about the user’s
information, unnecessary or useless information will appear
in the searched results from the digital library.
Each agent can access DBMS and search documents
according to the user profile. And then each agent
categorization of the searched results.

3. Personal Digital Library System based on a
DMMAF Agent Platform
3.1 System Structure
The proposed system, in Figure 2, is a Personalized Digital
Library 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 is used, and to activate amobile framework, Voyager is used here.

Figure 1. DECAFGPGP is for improving of PGP which acts as acoordination algorithm of multi agents [3]. The firstadvantage of GPGP is that it reduces the system 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 can makeheterogeneous 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(Task
Analysis Environment Modeling and Simulation)[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 fordeveloping agent’s applications, whereas DECAF is a nonmobile agent’s framework. Voyager is an interactive
framework with Java programming. Also, Voyager canactivate any Java class in remote sites, and it makes use of
network bandwidths effectively.

2.2

Figure 2. PDLS Structureb
The PDLS is composed of two parts: client group andserver group. The client group is composed of three modules.
First, a user interface module lets users make use and control
the library. Second, a user profile control module learns theuser’s preferences by neural network (SOM [8]), and makes
databases accordingly. Third, a PLA (Personal Library
Agent) module makes multi agents in real time, and searches
information from the library according to the user’s profile,and stores the searched results into a database. Theinteractions among detailed modules in PDLS are explainedin Figure 3(b).
As shown in Figure 2, PLA 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 PLA, the monitoring agent module

The Concept of Agent based Digital Library©ISAROB 2006

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relationships between methods and tasks and between
methods and resources, such as Enable, Facilitate, Produce,
Consume, Limits and so on. In the negotiation algorithm, if
the agents in the same levels do the different actions, thenmax operation is operated to produce the output of the agents,
and if the agents in the lower levels do the different actions,then min operation is operated.

checks whether the servers are available or not. After that, it
makes some agents, and passes them to the servers. The
searched results are saved in a temporary repository. They
are filtered by negotiation agents, and the final results are
saved in the result repository.
/* improved negotiation algorithm of multiagents */Switch(one of 5 relations) { /* relation_name(sender,
receiver) */case 1: Add_R(Ai_Mi, Aj_Mj)/* (i≠j), Ai=agent, Mi=method */while(Aj_Mj is finished)
no-operation (Ai-Mi); break;case 2: Compensate_R(Ai_Mi, Aj_Mj)
Ai_Mi and Aj_Mj are operated in real situation
by call(inference) function;
Inference() is from user profiles or user information
repository;
break;
case 3: Replace_R(Ai_Mi, Aj_Mj)
Aj_Mj=Ai_Mi;
break;
case 4: Contradict_R(Ai_Mi, Aj_Mj)
Aj_Mj=Aj_Mj;
break;
case 5: Activate_R(Ai_Mi, Aj_Mj)
if wait(Aj_Mj) then awake(Aj_Mj) and restart;
break;
default:}

3.3. The Construction of User’s Profile
The construction of the initial user’s profile is constructedby the user’s first input information. According to the user’s
searched results, PDLS endows the user’s keywords toweight values, and updates user’s profile information by
SOM (self organizing map) network in real time [7].
SOM model

categorization
Map

Abstract
Generate Input vector

Kohonen Network

Figure 4. SOM network
SOM is an unsupervised neural network, In this paper, 2layered SOM network is used here.
1 2 .…j….…k1
2 ….i………n

Input
nodes

Column represent jth pan-in weight vector

Figure 5. Weight Vector in SOM
In Figure 5, wij means a weight vector from node i to node
j in SOM network. In SOM, a weight vector having thenearest Euclidian distance is categorized when searching
documents.
The user interface of PDLS is composed of four windows.
The user’s window is for entering the user’s information andfor recalling the user’s profile from databases. The
monitoring window is for checking agent’s activities. The
remote window shows the final results, and finally the local
window shows the constructed hard disk information of theuser’s computer by PDLS.

Figure 3. Negotiation Algorithm among Multi AgentsIn the proposed platform, the relationship among multiagents in negotiation agent module is in Figure 3.
Agent_Task Group generates Task_1, Task_2 and Task_3
according to Agent 1, Agent 2, and Agent 3. Tasks areautomatically decomposed into methods and do theirassigned tasks. Each method has five types of methods’
relationships. Add_R is to add the results of actions to theresults of the other methods. Activate_R is to let the running
method run continuously, Compensate_R is the relationshipthat the results among methods need to be compensated.
Replace_R is to replace the results of receiving methods withthe results of sending methods. Contradict_R is to disregard
the results of receiving methods. Also, there are lots of©ISAROB 2006

4. Simulation Results
The user interface is composed of four panes, and eachpane is interconnected. The login window and query window
is for user login. The user pane is for checking the agents’
activation states including monitoring. The remote pane is forrepresenting the information of remote servers and searched
results. The local pane is for representing the building statesof a local library. The scenario for PDLS simulation is in
Figure 6.

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2
3
4
5
6
7
8
9
10

mobilejava
multi
personalized
distributed
environment
neuralarchitecture
autonomous

neuralarchitecture
networksimulation
layer
agent
artificial
associativealgorithm

libraryindexing
structure
distributed
agent
retrievalautonomous
neuralmulti(a) Module Relations
Table I shows the categorized simulation results. As aresults, there are 3 categorization by PDLS: agent system,
neural network, digital library. 10 keywords in eachcategorization is classified by SOM network. For example, if
the words such as framework, mobile, jave are found in any
documents, the documents are classified “Agent System”
group in PDLS.
A ll

A gent S ystem

36

1

11

50

7
4

7

29

32

(b)

PDLS Simulation Scenario

25

60

10

16

1

4

Figure 6. PDLS Interface and Simulation Scenario① User login by user interface② The monitoring agent(MA) is activated, and MA checkthe current connected remote digital library. At the same time,a user profile is read from database.
③ The queries according to users are sent to PLA.
④ MA makes a search agent 1and registers it in ANS.
⑤ The search agent 1 is sent to the remote library.
⑥ The agent 1 is activated by receive the parameters fromPLA.
⑦ The results from the agent 1 with information such asname and index and abstract of the remote library are sent to
PLA. And then they are sent to the negotiation agent(NA) for
negotiation. At the same time, they inform to the MA andANS.
⑧ A is clustered using SOM network according to thereceived results⑨ The results by the user profile can be shown in the remoteplane.
⑩ user can move to his local library after receiving his
necessary results.
⑪ The user profile’s update is activated.

1

7

36

4

6

15

24
8

5

15
3

4

4
2

2
6

2

1

15

9

26

D igital Library

N eural N etw ork
21

10

25

36

1

10

3

Figure 7. SOM Categorization Results
Figure 7 shows the simulation results according to therandom documents in servers. The parameters used in SOM
are like the followings. 2 dimensional array for output
display and radius 2 for classification are used here. Also, thelearning times and learning rate are 500 times and 0.05,
respectively. The computer specification is CPU 1.8
GHZ(RAM 1024MB), and window NT based Mysql are used
for simulation.
In Figure 7, the numbers in matrix mean the number of theclassified documents. If the sum of the numbers in eachcategory, the total will be 100, as we simulated using 100
documents.
The searching times between the proposed PDLS and the
traditional client-server model are shown in Figure95. Astime passed, PDLS showed a faster search time as well as amuch safer search than the client-server model. The result
showed that as the numbers of servers were increased, thesearching time was decreased in PDLS.

Table 1. Keyword based Categorized Results
Category Agent System Neural Network Digital Library
1©ISAROB 2006

framework

learning

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[3] Keith S. Decker, Victor R. Lessor Generalizing the partial
global algorithm. Intelligent Cooperative Information
Systems, 1(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 LibraryServices, October 2002
[8] Kohonen, Self Organizing Feature Map, 1995

User: “컴퓨터” 키워드입력(a) Actual Results(b) Comparison of Simulation Results
Figure 8. Simulation Results

5. Conclusions
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
Environments, and to make it operate effectively by the
propose of a new negotiation algorithm and a new schedulingalgorithm. From the simulation results of PDLS, theperformance and the user’s satisfaction of this system ishigher than any other information search systems as of now.
Also, as the numbers of servers and agents are increased, thesearched time of PDLS is lowered. And the degree of theuser's satisfaction is increased four times than theconventional client-server model. In the future, PDLS needs
to be compensated in order to be activated in the real world.

Acknowledgements
This paper is carried out by supporting of KOSEF(KOrea
Science and Engineering Foundation) during 2003 to 2005 as
an Eminent Wonan 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 inIntelligent Agents IV, Agent Theories, Architectures, andLanguages Springer-Verlag, 2000, Nicholas Jennings,
Yves Lesperance, Editors©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Crowd simulation with virtual force model
Choon-Young Lee
School of Mechanical Engineering
Kyungpook National University
Daegu, 701-702, Korea
cylee@knu.ac.kr

Seongmin Baek
Digital Actor Research TeamETRI
Daejeon, Korea
baeksm@etri.re.kr
This paper presents a method for simulating crowd

Abstract

scene by virtual force model. The result of thissimulation can be converted into natural humanlocomotion by multi-layer posture.

This paper presents a method of creating crowd
scene by simulation with a virtual force model. The

Virtual force generates various interactive situations

simulation results can be converted to a realistic crowd

between agents. These interactions give more vivid

scene by incorporating motion capture data. Virtual

motions of agents.

force is used to generate a specific motion of character

information for using these postures easily andagent. Model of virtual force is based on the Helbing’s

efficiently in assigning motion capture data to a nparticle model and reflection force of the proposed

agent. Our method takes advantage of the fact that it

method can be controlled by a set of parameters. Our

can create variable motions using a few motions andexperiments show that the generation of somewhat

generate a natural human-crowd scene. Experimental

exaggerated crowd scene in movie can be easily done.

results show various situations can be generated with

Posture database stores importantthe proposed force model.
The organization of this paper is as follows. In

1. Introduction

section 2, we explain related works for crowd
simulation. Virtual force based simulation scheme is

These days, the techniques for creating human-

described in section 3. The experimental results are

crowd scene have been studied actively because theshown in section 4. We summarize future direction indemand of a large-scale crowd scene increases in thesection 5.

film industry. Previous researches focused on collision
detection using particle or bounding volume, human-

2. Related Worksbehavior, and using motion capture data. However,these methods are somewhat insufficient to human
crowd. For example, the trajectory that generated by

There has been a great deal of past research in a

the simulation of particle is natural but it is very

number of crowd simulations, especially, collisiondifficult problem to convert the simulation result into

detection that is known by bottleneck in computer

human motion. In other problem, it has disadvantage

simulation. I-Collide[2] is adopted a tow-levelthat the performing actions are limited when applying

approach to control complex and the number of objects

to the crowd.

in a large scale environments.

©ISAROB 2006112

Hubbard[3] proposed

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

progressive refinement method to guarantee accuracy

weights. Park et al.[10] suggested a motion transition

while maintaining steady and high frame rate. Kim[4]graph to solve on-line motion blending and transition

presented an efficient collision detection algorithm

simultaneously. Rose at el.[15] presented technique foramong spheres moving with unknown trajectory.

interpolating between example motions. They used

Reynolds[13][14],verb, parameterized motions, and verb graph with

Xiaoyuan[18],Niederberger[9]smooth transition between motions.

solved a collision detection about non-human creatureThalmann[16] and Ulicny[17] presented the method

and focused on the behaviours of creature.
In Improv[11], it is suggested a method that control

that control the crowd by real time in virtual

the interactive actors by interpolation after defining theenvironment. Musse et al.[8] described a model for

range of joint angle using script language. Improv

simulation of human crowd by a hierarchy composedconsists of two subsystems, an animation engine and aof virtual crowds, groups and individuals in real time.

behaviour engine. The combined system provided an
integrated set of tools for authoring the minds and

3. Virtual Force Modelbodies of interactive actors. Lee et al.[6] presented thata connected set of a human-like character is able to becreatedfrom

non-linear

sequencesofmotion,The social force model by Helbing [19] was aautomatically organized for search, and used real-timefoundation of the proposed virtual force model. For acontrol of an avatar using three interface techniques:

crowd consisting of N agents, the force acting on the i-

selecting from a set of available choices, sketching ath agent is denoted bypath, and acting out a desired motion in front of afi = fi S +camera. Kovar et al.[5] constructed a directed graphcalled a

N

∑
j =1
i≠ j

motion graph that includes connections

among the database for creating realistic, controllable

where

motion. The motion graph consists of original motions

f ij I +

M

∑f

ik

O

(1)

k =1

f i S denotes the self-driven force which

forces the agent toward desired location with desired

and automatically generated transitions. Li et al.[7]speed, f ij I denotes the interaction force on agent i

described motion texture that consists of LDS(linear
dynamic system) for synthesizing complex humanwith agent j, and f ik O denotes the obstacle avoidance

character that is statistically similar to the originalforce due to the k-th object.

motion captured data. Pullen et al.[12] discussed aHere, we focused on the modeling of interaction

method for creating animations by setting a small

force

number of key-frames and used motion capture data to

f ij I . Commonly, this force is modeled byenhance the animation. Arikan et al.[1] created theexponentially decaying function with distance in radial

new motion performs the specified actions at specified

direction. Oval like shape is used as equi-force line fortimes by assembling the motion capture data frominteraction force and the shape is controlled by threemotion database and sketch on the timeline withindependent parameters.

annotations.

f ij I = A exp(− g (d ij ) / B)n ij

Motion blending method produces new motions
from example motions according to time-varying©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

where

g (d ij )represents

effective

distance

conversion for calculating interaction force. Helbing
used g (d ij ) = d ij so that the resulting interaction
force reduces according to the Euclidean distance.

4. Simulation Results
We conducted three kinds of experiment using the
proposed virtual force model. The first case is to get
together at the specified position for all agents (Fig. 1).
The second case is to move randomly and the shape of
virtual force is controlled interactively with the user

Fig. 3. Virtual Force for a group of ranks

during simulation (Fig. 2). The final case is to crack agroup of ranks by a virtual force model (Fig. 3). Fig. 4
shows a crowd scene generated by the virtual force
model.

Fig. 4. A crowd scene made by the proposed method

5. Conclusions

Fig. 1. All agents get together at some position

To create a human crowd scene in a movie, an easy
and efficient method is needed. The crowd motions
should be looked like those of real humans andconformed to the scenario continuity. In this paper,
simulation result by a virtual force can be mapped tothe human motions and a few experiments for crowd
scenes were conducted.
The general formulation of virtual force by using
spline curve will be our further research topics.
Fig. 2. Random Movement©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Acknowledgements[10] Park, S. I., Shin, H. J., and Shin S. Y. year. On-line

This work was supported by ETRI Research Fund,

Locomotion Generation Based on Motion Blending. In

2005.

Proceedings of the 2002 ACM

References

SIGGRAPH/Eurographics symposium on Computer[1] ARIKAN, O., FORSYTH, D. A., AND O’BRIEN, J. F.

animation, pp 105-111, 2002.

2003. Motion Synthesis from Annotations. In[11] Perlin, K., and Goldberg, A. 1996. Improve: A System

Proceedings of ACM SIGGRAPH 2003.

for Scripting Interactive Actors In Virtual Worlds. In[2] J. D. Cohen, M. C. Lin, D. Manocha, and M. K.

Proceedings of ACM SIGGRAPH 1996, pp205-216,
1996.

Ponamgi, I-COLLIDE: An Interactive and Exact
Collision Detection System for Large-Scale[12] Pullen, K. and Bregler, C., “Motion Capture assisted

Environments. In Proc. of Symposium on Interactive

animation: Texturing and synthesis”, In Proceedings of

3D Graphics: pp189-196, 1995.

SIGGRAPH 02, 2002, pp. 501-508[3] P. M. Hubbard. Collision Detection for Interactive[13] C. W. Reynolds. Flocks, herds, and schools: Agraphics applications. IEEE Transactions on

distributed behavioral model, In Proc. of SIGGRAPH

Visualization and Computer Graphics, 1(3): pp218-230,

97, pp25-34, 1987.

1995.

[14] C. W. Reynolds. Steering Behaviors for Autonomuos[4] H. K. Kim, L. J. Guibas, and S. Y. Shin, Efficient

Characters. In Conference Proceedings of the 1999

Collision Detection among Moving Spheres with

Game Developers Conference, pp763-782, 1999.

Unknown Trajectories, CS-TR-2000-159, 2000.

[15] Rose C, Cohen MF, Bodenheimer B. Verbs and[5] Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion

adverbs:mulidimensional motion interpolation. IEEE

Graphs. In Proceedings of ACM SIGGRAPH 2002,

Computer Graphics and Applications 1998; 18(5): 32–

pp473-482, 2002.

40.

[6] J. Lee, J. Chai, Reitsma, P.S.A., HODGINS, J. K., AND[16] D. Thalmman, S. R. Muss, F. Garat. Guiding and

POLLARD, N.S. 2002. Interactive control of avatars

Interacting with Virtual Crowds. In Proc. ofanimated with human motion data. In Proceedings of

EUROGRAPHICS Workshop on Animation and

ACM SIGGRAPH 2002, pp491-500, 2002.

Simulation, pp22-34, 1999.

[7] LI, Y., WANG, T., AND SHUM, H. Y. 2002. Motion[17] B. Ulicny and D. Thalmman. Towards Interactive Real-

Texture: A Two-Level Statistical Model for Character

Time Crowd Behavior Simulation. Computer Graphics

Motion Synthesis. In Proceedings of ACM SIGGRAPH

Forum 21(4), pp767-773, 2002.

2002, pp465-472, 2002.

[18] Tu, Xiaoyuan, and D. Terzopoulos, Artificial Fishes:[8] S.R. Musse, D. Thalmann. Hierarchical Model for Real

Physics, Locomotion, Perception, Behavior. In Proc. ofTime Simulation of Virtual Human Crowds. IEEE

SIGGRAPH 94, pp43-50, 1994.

Transactions on Visualization and Computer Graphics[19] D. Helbing, I. farkas, and T. Vicsek, “Simulating

7(2), pp152-164, 2001.

dynamical features of escape panic,” Nature, Voll. 407,

[9] C. Niederberger and M. Gross. Hierarchical andpp. 487-490, 2000.

Heterogeneous Reactive Agents for Real-Time
Applications. Computer Graphics Forum 22(3), 2003.

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Driver Fatigue Detection using Genetic Algorithm
Shanshan Jin and So-Youn Park 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
shanshan@odyssey.kaist.ac.kr, arashi@odyssey.kaist.ac.kr, jjlee@ee.kaist.ac.kr
Abstract

image recognition technology. There are several kindsof factors that reflect driver’s fatigue[2]. Driver fatigue

Nowadays, a lot of traffic accidents occurred due
to driver fatigue. Driver fatigue detection basedon computer vision is one of the most prospective
applications of image recognition technology. There
are several kinds of factors that reflect driver’s
fatigue. Many efforts have been done on developing
fatigue monitoring, but most of them focus on onlya single behavior, feature of eye, or head motion, or
mouth motion, etc. When the fatigue monitoring is
implemented on a real model, it is difficult to predictthe driver fatigue accurately or reliably only based on
driver’s single behavior. Additionally, The changesof driver’s performance are more complicated andnot reliable. In this paper, we represent a modelthat simulates a space in a real car. A web camera
as a vision sensor is located to acquire video imagesof the driver. Three typical characteristics of driver
fatigue are concerned, pupil shape, eye blinking
frequency, and yawn frequency. As the influencesof these characteristics to the driver fatigue arequite different from each other, we propose a genetic
algorithm(GA) based neural network(NN) system tomake fusion of these 3 parameters. We use GA todetermine the structure of neural network system.
Finally, Simulation results show the proposed fatigue
monitoring system detects driver fatigue probability
more exactly and robustly.
Keywords : Driver Fatigue, Genetic Algorithm, Neural
Network.

Figure 1: Driver fatigue detection system

1

Introduction

detection heavily relies on facial expression recognition and tracking. Much research has been done on
facial recognition technology, but most of them focus
on only a single behavior of facial expression, suchas feature of eye, or head motion, or mouth motion,
etc[3]. When the fatigue monitoring is implemented
on a real model, it is difficult to predict the driver

Nowadays, a lot of traffic accidents occurred due
to driver fatigue. Developing systems for actively detecting driver’s fatigue and warning the tired driver israther necessary[1]. Driver fatigue detection based on
vision is one of the most prospective applications of©ISAROB 2006

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fatigue accurately or reliably only based on singularelements of driver’s behavior. The system relying ona single behavior may encounter difficultly when thedetection of one facial expression cannot be acquired
accurately or reliably due to the uncertainty of realmodel. In order to make the fatigue monitoring system
more stable and reliable, the 3 main characteristics ofdriver’s facial expression are used. Pupil shape, eyeblinking frequency, and yawn frequency are detected,
but the influence of these 3 elements on driver fatigue
is quite different and nonlinear. Using these 3 kinds ofdriver’s facial information, we propose the GA basedneural network system to achieve the fatigue warning
system. In this paper, we use back propagation learning algorithm to train weight value and apply genetic
algorithm to optimize the structure of neural network.
The driver fatigue detecting system is shown as Fig.1.
The rest of paper is organized as follows: Section
2 outlines the facial expression extraction for fatigue
detection and section 3 describes the driver fatigue detection system using neural network and genetic algorithm. Simulation results for driver fatigue monitoring
are given in section 4. Finally, conclusion is presented
in section 5.

2tion. For the trajectory of each facial feature, thisconstraint removes random jumping due to the uncertainty in the image. Combining the Kalman filteringwith the pupil motion can predict the current locationof each feature [4]. The Gabor wavelet based method
is used to identify each feature in the tracking initialization. Linear and nonlinear filters have been used
for eye detection: oriented Gabor wavelets form anapproximation of the eye gray level images; non linear filters are applied to color image[3]. Based on theabove algorithms, we can detect the facial expression
and obtain the data of each motion. Fig.2 shows theeye and mouth field regions; the facial feature in theseregions is highly considered. The parameters neededin this paper for fatigue detection is as follows:

Facial Expression Extraction
Figure 3: Pupil shape

In order to detect fatigue probability, we should extract the facial expression parameters first. As fatigue
level can be properly characterized by eye and mouth
movement, a vision sensor is needed to recognize andtrack eye and mouth. We can use a common video
camera as a vision sensor on the premise that environment is bright enough. In this paper, pupil shape, eyeblinking frequency, and yawn frequency can be measured using a Logitech web camera (QuickCam Pro
3000).

• Pupil ShapeThe degree of eye opening can be detected by theshape of pupil. When the pupil gets occluded by
the eyelids, their shapes get more elliptical. So
we can test the ratio of pupil ellipse axes to characterize degree of eye opening. We use parameter
X1 which is defined as (1) to describe pupil elliptical degree. Pupil shape is described in Fig.3.
X1 = y/x(1)• Eye Blinking Frequency
When people concentrate on specified work, eyeblinking frequency is much lower than they are
not tired. Eye blinking frequency(per minute) as
one parameter is detected to compute the fatigue
probability.
• Yawn Frequency
Besides pupil shape and eye blinking, another parameter that can potentially capture one’s fatigue
probability is frequent Yawning. We can focus
on monitoring mouth movement to detect yawning. Another parameter of yawn frequency is the

Figure 2: Eye and mouth regions from input image
Kalman filtering is widely used for object tracking.
It can impose a smoothing constraint on the face mo-©ISAROB 2006

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occurrence frequency of yawning over time(perminute).

3

Driver Fatigue Detection System
Configuration

As the diver environment and fatigue generation arequite complicated, it is difficult to determine driver’s
fatigue probability only by one performance or by simple combination of several performances. Moreover,the degree of each eye and mouth movement to express fatigue is so different and uncertain. Due to theabove reason, here, we propose a neural network system to monitor driver’s fatigue. In addition, we use
genetic algorithm for selecting the optimal structureof neural network system.

Figure 4: Neural Network System

3.1

Figure 5: Genetic Algorithm Process

4

Neural Network System

Neural network can simply solve the nonlinear complicated problem and results in quite accurate approximation of unknown system. Here, we use feedforward
neural network with backpropagation learning algorithm. It is illustrated as Fig.4.

3.2

In order to simulate the GA based NN fatigue detection system discussed above, we collected 60 data
set of different people’s pupil shape, eye blinking frequency, and yawn frequency for training the system.
Furthermore, 50 set of extra data is used as the performance detection data. The initial weight of neuralnetwork is randomly generated, and the signal function of hidden layer is sigmoidal function. Here we use
one hidden layer for simulation. The specified parameter value is given in Table1.
Driver fatigue probability is detected by the given
system. Probability above 0.5 means the driver is very
tired and should be warned. Fig.6 represents the detected fatigue probability compared with the desired
value of performance data set. It clearly shows thatthe system totally detects the driver fatigue probability very well. From the simulation results, we know

Neural Network Structure Tuningwith Genetic Algorithm

As GA is stochastic and less likely to get trapped inlocal minima, it is an appropriate optimization algorithm to identify both structure and parameter of neural networks[5]. The Hidden layer number and neuron
number of each hidden layer can be optimized by GA.
The fitness function is neural network performance.
Individuals of GA represent the layer and neuron number. GA process in detail is given in Fig.5.

©ISAROB 2006

Simulation

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With the proposed system, we did the simulation
using samples of different people’s facial expression
parameters. The results showed that our driver fatigue monitoring system just detected the driver fatigue quite exactly. Due to the intricate and uncertain
real car environment, we should learn more information about the driver fatigue, and then our system may
detect the fatigue level more correctly and appropriately.

Table 1: Simulation parameters
Parameter
Value
Input nodes
3
Output nodes
1
Hidden layer
1
Neuron number range 1∼ 30
Epoch number
500
Population size
20
Generation
20

Referencesthat the best individual of neuron number is 4, and actually there are no obvious changes between distinct
generations. As the data set we considered is not ascomplicated as the real situation, the fatigue system
may be learned rather easily. If we consider more difficult and uncertain circumstance, the fatigue detection
must be more relied on the increasing of generation.

[1] Wang Rong-ben, Guo Ke-you, Shi Shu-ming and Chu
Jiang-wei, “A monitoring method of driver fatigue behavior based on machine vision,” Proc. IEEE Intelligent Vehicles Symposium, 9-11 June 2003, pp. 110-113.
[2] Haisong Gu, Qiang Ji and Zhiwei Zhu, “Active facial tracking for fatigue detection,” Proc. Applicationsof Computer Vision, Sixth IEEE Workshop, 3-4 Dec.
2002, pp. 137-142.

Fatigue propt. approximated by GA based neural network
0.9
desireddetected

0.8[3] T.D’Orazio, M.Leo, P.Spagnolo and C.Guaragnella,“A neural system for eye detection in a driver vigilance
application,” Proc. Intelligent Transportation Systems,
7th International IEEE Conference, 3-6 Oct. 2004 pp.
320-325.

Fatigue propt.

0.7
0.6
0.5

[4] Zhiwei Zhu and Qiang Ji, “Real time and non-intrusive
driver fatigue monitoring,” Proc. Intelligent Transportation Systems, 7th International IEEE Conference,
3-6 Oct. 2004 pp. 657-662.

0.4
0.3
0.2
0.1

0

10

20

30

40

[5] Muhammad Firdaus Hashim, Puteh Saad, Mohd Rizon Mohamed Juhari and Shahrul Nizam Yaakkob, “Aface recognition system using template matching andneural network classifier,” 1st International Workshop
on Artificial Life and Robotics., 14-15 May 2005, pp.
1-6.

50

samplesFigure 6: Fatigue Probability approximated by NN
and GA

5

Conclusion

In this paper, we configured a driver fatigue detection system with genetic algorithm. The system wasdisplayed as a neural network, and the optimal structure of the neural network was tuned by genetic algorithm. First, the facial expression of the driver was
extracted by a video camera, so 3 main parameters:
driver’s pupil shape, eye blinking frequency, and yawn
frequency can be obtained. Second, Neural network
system based on the input of these 3 parameters was
combined with genetic algorithm.

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Virtual Planes and Active Vision for Fast 3D Real Time
Obstacle Detection and Avoidance
Maki K. Habib and Ju-Jang Lee
Department of Electrical Engineering and Computer Science
Korean Advanced Institute of Science and Technology (KAIST),
373-1, Guseong-dong, Yuseong-Gu, Daejeon, 305-701,
Republic of Korea

maki@ieee.org
environments that are characteristically more unstructured
and unpredictable. These applications range fromsurveillance of buildings and supervision of plants,
transport patients and delivery items, cleaning houses andguiding people, maintenance and repair in dangerous andinaccessible places, undersea operations, space exploration,mining operations, to hazardous-waste disposal. The use of
autonomous mobile robots in such application allows
humans to remain in a safe environment while acting in asupervisory capacity. In door mobile robots may need tocarry out in populated and natural human habitat
environments.

Abstract
This paper presents the development of a fast 3D virtual
planes and active vision based concept that can support
real time obstacle detection and avoidance in a dynamic
environment. This work enables an autonomous mobile
robot to detect and avoid moving and static objects that
may appear in front of the robot while navigating in realtime. The developed approach encodes the space coarsely
by using small number of 2D laser spots projected throughfiber grating based active vision on the scene in front of anautonomous mobile robot to locate, track and avoid any
object that may appear along the course of the robot
movement. Efficient algorithm is developed to represent thedetectable depth in front of the sensor system with dynamic
resolution and decompose spatially the space along that
depth into a number of parallel virtual planes that areperpendicular to the line of the robot movement. To
facilitate the real time tracking of dynamic objects, each
virtual plane is divided into five zones and the laser spots
within each zone are used as a base for the tracking. The
distance between the virtual planes represents the spatial
decomposition of spot’s movement space, i.e., spot’smoving path on the image plane. The detection of a spot ata certain virtual plane indicates the availability of an
obstacle or object at the range of that plane with respect the
robot. The longer a spot moves along its path the closer
virtual plane to the robot activated. Accordingly, the virtual
plane closer to the robot has a higher priority in thedetection and tracking process than the others. The laser
posts, depth dynamic resolution, space spatial
decomposition into virtual planes and the zones within each
virtual plane have the advantage of reducing significantly
the computation time of 3D information that is required forreal time detecting, avoiding and tracking objects indynamic environment. The paper discusses and illustrates
the developed concept along with results analysis.

1

Autonomous mobile robots are designed with aim to govern
themselves, manage its resources including power, findpaths between any two random point within its
environment, avoid obstacles while navigation these paths,
collect information about the environment, adjust strategies
and adapt to changes based on the surrounding or in itself,
learn or gain new capabilities, and make decisions without
outside assistance. It is necessary to achieve these
activities efficiently and safely. Autonomous mobile robots
must rely upon on-line sensory information to take actions
and be able to react appropriately to unforeseen changes inthe environment or to unpredictable objects blocking theirtrajectories. This requires having significant sensing
capabilities, to reason about sensing data, and be flexible.
They must be able to detect and track moving and nonmoving targets as a function of time, to report their status
and approach a proper decision to avoid or track them asnecessary. Therefore, it is necessary to have an efficientsensor system that can fulfill these tasks properly and inreal time. A common property shared by all types of sensors
is their noisy responses. This sensor uncertainty, together
with the inaccuracy of the robot’s actuators and theunpredictability of real environments, make the design ofmobile robot controllers a difficult task.
Some robot navigation systems may rely on global maps
for global path planning and navigation. Other navigation
systems use maps locally, to plan collision-free paths, toavoid obstacles, or to identify environment features for
localization. Local navigation techniques are the ones

Introduction

Autonomous mobile robots are useful for many
applications in indoor and outdoor environments and in©ISAROB 2006

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responsible to achieve these reactive issues. These
techniques are sensory-based approaches, using only local
sensory information, or an additional small fraction of theworld model. Humans and other animals have a remarkableability to coordinate their actions with complex, changing
environments. With little conscious effort, we routinely
reach or walk through cluttered scenes, avoiding obstacles,
reaching goals, and intercepting moving targets safely andeffectively. The change in the state of the robot action is afunction of its current state and collected information about
the environment. Behavior arises from interactions between
the components of this mutually coupled system andreflects the constraints of both components. There aremany successful navigation algorithms for various types ofmobile agents and for various situations. The challenge
thus becomes one of identifying behavioral variables
relevant to the situations and the task goals that evoke thedesired behavior in the system to shape the appropriate
behavioral dynamics [1, 4]. Important approached for local
navigation and reactive collision avoidance methods havebeen studied. This includes: the dynamical steering method,
the artificial potential field, the vector field histogram, thecurvature-velocity method, the dynamic window approach,and the global dynamic window approach [1-3, 7, 10, 11].

ahead of the sensor system as shown in Figure 1. For the
purpose of this work, the author selected a fiber of 20 µ m
in diameter and 10 mm in length [5, 8].
The developed sensor hardware is based on this principles
and it consists of a fiber grating based vision sensor, aframe memory, an image processing and decision making
board, laser driving circuit, alarm indicators, and interfacing
capabilities with a higher-level controller along with a user
friendly interface when it is used as an independent
module. The fiber grating based vision sensor consists of abright laser-spots array projector and a CCD camera. The
laser-spots array projector consists of a semiconductor
laser light source that is compact and lightweight in design(laser diode of 830 nm wavelength, and 30 mW output
power) and a fiber grating. Typically, the wavelength
generatedbythesolid-state device in therange 750 - 850 nm . The laser diode, the collimating lenses,
and the FG are integrated and mounted into one unit.

Laser Diode
Side

Among the popular sensors that have been actively
studied actively many fields including robotics research
include: laser range finders, sonar sensors, infrared sensors
and vision sensors. Vision sensor has numerous
advantages for achieving such a task. Nevertheless, they
have their own drawbacks. Vision systems are costly andvery computationally challenging given the complexity of
most of the algorithms developed by researchers. One of
the traditional approaches in mobile robot research has
been the use of stereovision systems to extract range
information from pairs of images. Applying such approachtime real time navigation in a dynamic environment faces thedifficulty of the intrinsic computational expense of
extracting three-dimensional 3D information from stereo
pairs of images and this leads to limits the number of points
that can be tracked. These limitations initiate the work inthis paper to explore the use of an alternative sensor that
could deliver 3D range information directly and facilitate realtime detection and avoidance of obstacles.

2

The FG

Fig. 1 2D laser spot pattern generated through twooverlaid FG sheets irradiated by a laser beam

3

Optical Arrangement
Principles

andWorking

Fig. 2 shows the layout of the optical arrangement for theFG vision sensor. The coordinate system is based on thelens of the CCD camera and the U-V coordinate is based on
the image plane. The spot array projector is located parallelto the CCD camera at (0, - d , 0), and the image plane islocated at (0, 0, l ). With this arrangement, the FG vision
sensor treated as one unit, positioned and oriented as
required with respect to the plane of projection [6, 7, 9].

FG Based Active Vision Sensor

When a fiber-grating projector constitutes two-overlaid
fiber sheets arranged at a right angle and irradiated by laser
light, a sphere-like lens formed at each intersection of two
fiber gratings. Spherical waves interfere with one anotherand generate a tetragonal 2D array of laser spots. Then, thegenerated 2D laser spots array is projected onto a plane©ISAROB 2006

FG SideWith reference to Fig. 2, a 3D coordinate system isdefined and the physical coordinates of any disturbed spot

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on an object can be expressed by ( Xs , Ys , Zs ), s for spot. It
is possible to clarify the measurement space of a detectableobject height or range in relation with the opticalarrangement as follows: When projecting a bright spot on a
person or on an object, the position of the projected spottranslates from its original reference in a particular direction
on the image plane from A ( u , v ) to B (u , v + δ ) . The

the reference image plane coordinates of a spot (i.e., theoriginal coordinates of a spot before moving).
Each spot has a certain path to move along it, and thispath is represented by a certain number of pixels . The
number of pixels is in a spot is moving is proportional to thedepth calculated from the virtual reference plane. Table 1
shows the experimental data that compares a spot’s
movement limit in pixels for different type lenses.

number of pixels that represent spot’s translation (shift inposition) due to the presence of an object is determinedfrom the ( u , v) coordinate values on the image plane and isTable 1
Lens
Focal
Lengthused to calculate the 3D location ( Xs, Ys, Zs ) of a reflected
spot at which the beam was projected:

mm
4.8
6.5
8.0

4

Pixels36
46
54

The Generation of the Virtual Planes

For the purpose to help an autonomous mobile robot
detects and avoids in real time dynamic or static obstacles
such as human or objects while navigating, the developed
optical sensor was integrated with the robot. Before
deciding the number of virtual planes and the distance
between them, it is important to analyze the relation
between the detectable depth of an object in front of therobot and the distance ( d ) between the laser spot projector
and the CCD camera. Fig, 3 illustrate this relation. As the
distance d increases the maximum detectable object depth
will decrease (calculated from a selected reference plane infront of the robot), i.e., the undetectable depth from therobot will increase and this is valid for all types of lenses.
Also, as the distance ( d ), the resolution of the FG based
sensor will improve.

Fig. 2 FG vision sensor optical arrangement
The physical location ( Xs, Ys, Zs ) of a reflected spot at
which the beam was projected can be calculated using thefollowing equations,
h 2δ
Zs =
(1)
dl + h δ
Xs = u ( h - Zs)
(2)
l
v+δ
Ys =
( h - Zs )(3)lWhere: h is the depth of the center of the CCD camera lensfrom the surface of the virtual reference plane. δ is the shiftquantity in pixels that describe the translation in spot
position on the image plane. l is the distance from the lensto the image plane; d is the center distance from the laserspot generator to the lens of the CCD camera; u and v are©ISAROB 2006

Movement Limit ofSpot’s Path with FG
rotating by anAngle φFig. 3 the relation between the maximum detectable depth,
the FG-CCD Distance and the sensor resolution.

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Therefore, for detecting and avoiding obstacles in real time
the author selected following parameters:a. Lens with 8.0 mm focal length attached to the CCD
camera.
b. 12 mm distance (d ) for proper pixel resolution
related to the application, andc. 200 cm depth from the robot (h ) between thereference virtual plane surface in front of the robot
and the sensor on the robot.

shown in Fig. 4-a. As mentioned earlier, the targetedreference virtual plane is selected at 200 cm from the sensor
on the robot and the space between this reference and therobot has been divided into 9 virtual planes with distanceof 20 cm between each of them to cover a depth between 40
cm to 200 cm from the robot. The first virtual plane starts at
a distance of 40 cm from the sensor edge on the robot. The
dimension of the area covered by each of the virtual planes
is inversely proportional with the focal length of the lens
attached to the CCD camera and it is directly proportionalwith its distance from the CCD camera. This leads to thenecessity to adjust the positions of the virtual planes andrecalculate the area covered by them when having any of
the following circumstances:i. Change the lens attached to the CCD camera with alens of different focal length and,ii. Reconfigure the distances between the virtual planes
and with respect to the CCD camera and the change in
the number of the virtual planes.
Fig. 4-b and c illustrates the generated virtual planes andtheir covered areas with different projection for lens with
8.0 mm focal length.

X
The OpticalSensor180 cm40 cm

5 Operational Software Development

The moving directionof the robot

The developed system software consists of the following
main parts:
i. For the purpose to enhance spot’s area, rejectbackground noise, minimize tis effect, and optimize
searching for and deciding a spot, an efficient dynamic
floating threshold algorithm has been developed.

(a)

(b)
Fig. 4 (a, b and c) shows the concept and the alignment of
virtual planes
Fig. 5 The identified laser spots as seen on the reference
plane.

The sensor is fixed on the robot and aligned in a waythat can achieve a horizontal projection of the laser spots ahead of the robot while selecting a reference virtual plane infront of the robot at the maximum detectable depth between
the sensor on the robot as and the reference plane. The
space in between is divided in to several virtual planes as©ISAROB 2006

ii.

123

The generation of a reference spot frame. The aim of
this part is to identify all possible laser spots and to
calculate the position in image coordinates and its
brightness for each of the identified spot. Since theThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

area of each spot is not uniform, the position of a spot
( u ref , v ref ) is calculated by determining the center ofA virtual plane that is covered by an image planeat a selected distance from the CCD camera

gravity for the extracted spot’s area (See Fig. 5).
iii. During the calculation of spot’s reference frame, therelevant laser spots on each virtual plane and theirtranslated movements in pixels are experimentally
determined and tabled. The laser spots associated withthe first virtual plane (the one closer to the robot)
demonstrate largest translation in spot position thanother planes to reflect the closeness of an obstaclewith respect to the robot, i.e., the obstacle is within 40
cm from the robot. While the laser spots associatedwith the last virtual plane (the one at the far end fromthe robot), demonstrate the smallest translation in spot
position than other virtual planes. This reflects thedetection of an obstacle at the far end of the marked
depth distance, i.e., between 180-200 cm. The laser
spots and the associated spots’ translation on theother virtual planes reflect the detection of an obstacle
at a depth distance between the mentioned two ends. It
is clear from the alignment in Fig. 4-a that,

Upper
Left-2Lower
Left-1

Lower
Left-2Upper
Center

Lower
CenterUpper
Right-2

Upper
Right-1

Lower
Right-2

Lower
Right-1

Fig. 6 the available zones within each virtual pane.

6 Conclusion

a. The virtual plane at the far end from the sensor
on the robot has more laser spots associated withit than the virtual plane at the near end from while
their spot’s translation in pixels is smaller. This istrue when the area covered by the virtual plane at
the near end can’t accommodate all of the
projected laser spots.
b. The laser spots associated with a virtual planecloser to the robot is a subset of the laser spots
associated with a virtual plane just before it in thesequence toward the far end.
iii. Real time detection, avoiding, tracking, communicating
and reporting alarms to a different level of control.

In this paper, a 3D fiber grating based vision sensor todetect and track static and moving objects (human of
obstacles) in indoor environment has been presented. The
main feature of the introduces work is the use of virtual
planes with FG based active vision to decompose the spacein front of the robot into multiple layers and the partitioningof each virtual plane into zones. This helped to detect andextract in real time 3D information about any object that
may appear within the active view of the sensor. The same
concept can be applied to track and monitor humanbehavior in a realistic environment. The author isconsidering to apply computational intelligence techniques
to conclude the targeted details about the obstacles
location and movement directions

To facilitate the detection and the directional movement of
any obstacle or object in front of the robot during thenavigation task, each of the virtual planes is divided into 10
zones as shown in Fig. 6. These zones are: Upper-Right 1,
Upper-Right 2, Upper Center, Upper-Left 2, Upper-Left 1,
Lower-Left 1, Lower-Left 2, Lower Center, Lower-Right 2
and Lower-Right 1. The partitions into zones and the widthof each zone are decided to facilitate fast detection andtracking of an obstacle. Also, the number of zones and theirwidths can be changed according to the need andrequirements of the task at hand. To save time and have aquick decision, the searching process for any moved spot
starts from the lower zones. Also, the author used statebased events to contract the transitions that will lead toconclude the current status for the space in front of therobot in relation to the appearance of an object.

©ISAROB 2006

Upper
Left-1

References[1] B.R., Fajen and W.H., Warren, “A Dynamical Model ofVisually-Guided Steering, Obstacle Avoidance, andRoute Selection”, International Journal of Computer
Vision, Vol. 54, 3, 2003, pp. 13–34.
[2] C.R., Weisbin, G., De Saussure, J.R., Einstein, F.G., Pin,and E., Heer,” Autonomous mobile robot navigationand learning”, IEEE Computer, Volume 22, Issue
6, 1989, pp. 29 – 35.
[3]
O. Khatib, “Real-time obstacle avoidance formanipulators and mobile robots”, International Journalof Robotics Research, Vol. 5, No. 1, 1986, pp. 90–98.
[4] S., Suzuki, M. K., Habib, J., Iijima, and S., Yuta,” How to
Describe the Mobile Robot’s Sensor-Based

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Behavior”, Robotics and Autonomous Systems, 7,
1991, pp. 227-237.
[5] H., Machida, J., Nitta, A., Seko, and H., Kobayashi,”High Efficiency Fiber Grating for Producing Multiple
Beams of Uniform Intensity”, Applied Optics, Vol. 23,
No. 2, 1984, pp. 330-332.
[6] K., Nakazawa, S., M., Nakajima,, and H., Kobayashi, “Development of 3D shape measurement system using
Fiber Grating”, Trans. IEICE, Vol. j69-D, No. 12, 1986,
pp. 1929-1935.
[7] J. Borenstein and Y. Koren, "The vector field histogram:
fast obstacle avoidance for mobile robots", IEEE
Transactions on Robotics and Automation, Vol. 7. No.
3, 1991, pp. 278-288.
[8] Machida Endoscope Co. Ltd., Tokyo, Japan, 1996.

©ISAROB 2006

[9]

J. Yamaguchi, H. Gou, and M. Nakajima, “Finding
Intruders System using Hologram Disk”, Technical
Digest of the 8th Sensor Symposium, 1989, pp. 83-86.
[10] D. Fox, W. Burgard. and S. Thrun, “The Dynamic
Window Approach to Collision Avoidance”, IEEE
Robotics and Automation Magazine, Vol. 4, No. 1, 1997,
pp.23-33.
[11] O. Brock and O. Khatib, “High-Speed Navigation Using
the Global Window Approach”, in IEEE Intl. Conf. on
Robotics and Automation ICRA’99, Detroid, Michigan,
1999, pp.341-346.

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Identification Using Dynamical Neural Network with Modified BP Method
Kunihiko Nakazono
University of the Ryukyus
Senbaru 1, Nishihara, Okinawa. 903–0213
nakazono@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
Abstractpared to that in layered neural networks with a training algorithm.
Here, we propose a dynamical neural network(DNN) that realizes a dynamical property and has a
network structure with the properties of inertia, viscosity, and stiffness without time delayed input elements. In a previous study, the proposed DNN wasconstructed with a training algorithm that used error backpropagation (BP) method [5]. However, theBP method modified only the connecting weights, andthe property parameters for the DNN had to be determined by trial and error. We therefore designed a
GA-based training method [6] to train both the connecting weights and the parameters of the DNN [7, 8].
But the GA-based training method took the executing
time of computer program that evolved in GA simulation.
The validity of the proposed DNN was verified by
identifying periodic functions such as a simple oneperiod sine waveform and several periodic sine waveforms [7], and time series signals [8]. In the present
paper, it is verified by identifying the time series signals of an unknown system with strong nonlinearity.
Simulation results show that the proposed DNN provides higher performance than the conventional neuralnetwork.

In the present paper, we propose a dynamical neural network (DNN) with the characteristics of inertia,
viscosity, and stiffness and a training algorithm basedon the backpropagation (BP) method. In a previous
study, we proposed a modified training algorithm forthe DNN based on the error BP method. However,in the previous method it was necessary to determine
the values of the DNN parameters by trial and error. So, in the proposed DNN, the BP method is designed to train not only the connecting weights but
also the property parameters of the DNN. Simulation
results show that the DNN with characteristics of inertia, viscosity, and stiffness trained by the modified BP
method obtains good training performances for timeseries signals generated from an unknown system. In
the present paper, we compare the DNN with a conventional neural network in order to verify the validityof the DNN.

1Introduction

Recently, recurrent neural networks and spiking
neural networks have attracted more research interest
than layered neural networks having static mapping
capability [1, 2, 3, 4]. The recurrent neural network is
a possible candidate for improving the system dynamics because it incorporates a feedback structure in theneuron unit and takes time delayed inputs into consideration. Research on spiking neural networks is also
ongoing. Spiking neural networks treat spike trains
and process the signals based on spike pulses. However, the network structure in recurrent neural networks and spiking neural networks is complex com-

©ISAROB 2006

2

Structure of DNN

In the present paper, we propose a DNN using aneuron having the properties of inertia, viscosity, andstiffness. In this neuron model, we assume that the image output from the neuron possesses the properties ofinertia, viscosity, and stiffness, and that the output ispropagated in the next neuron. The proposed DNN is

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3composed of three hierarchy layers, and the proposed
neuron adopts only in a hidden layer. The structureof the DNN is shown in Figure 1.






wij

Training algorithm based on modified BP method

First, we derive a minimizing sequence of the measurement of error function E:

wjk

E=

N

N

k=1

k=1

K
K
1X
1X
e2k =
(dk − yk )2
2
2

・・・

・・・

・・・

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 derivation of the BP method is shown in thissection. The connecting weights and the propery parameters of the DNN are updated sequentially basedon the steepest descent method.

Fig. 1 Structure of DNN

∂E
∂w
∂E
(old)
= Kj
−η
∂Kj
∂E
(old)
= Dj
−η
∂Dj
∂E
(old)
= Mj
−η
∂Mj

w(new)yi= ui ,yj= Kj fj (netj ) + Dj f˙j (netj ) + Mj f¨j (netj )
(2)

netj

=

NI
X

wij yi ,(j = 1, 2, · · · , NJ )= w(old) − η

(new)
Kj

The equations for the DNN are expressed as follows:(i = 1, 2, · · · , NI )

(6)

(new)
Dj

(1)

(new)
Mj

(7)
(8)
(9)
(10)

η is the training rate (η > 0).
The derivations of ∂E/∂wjk and ∂E/∂wij are described as follows.

(3)

i=1

yk

= fk (netk )

netk

NJ
X

=

wjk yj ,

∂E
∂wjk
∂E
∂wij

(4)
(k = 1, 2, · · · , NK )

(5)

j=1

=

∂E
∂netk
∂E
·
=
· yj
∂netk ∂wjk
∂netk
∂E
∂netj
∂E
·
=
· yi
∂netj ∂wij
∂netj

(11)
(12)

In the upper expression, the derivations are defined aswhere ui is the input value to the DNN, and yi , yj ,and yk are the output values in the input, hidden,
and output layers, respectively. The connecting weightfrom unit i in the input layer to unit j in the hidden
layer is denoted by wij . Similarly, wjk is a connecting
weight from unit j in the hidden layer to unit k inthe output layer. The total sum of the products of theconnecting weight wij and wjk and the output value isdenoted by netj and netk , respectively. Mj , Dj , andKj are the property parameters of inertia, viscosity,and stiffness, respectively, and NI , NJ , and NK are thenumber of neurons in the input, hidden, and output
layers, respectively. The activation function fj (x) inthe hidden layer uses a sigmoid function in the therange of [0, 1]. Also the activation function fk (x) in
the output layer is a linear function.

©ISAROB 2006

=

δk

=

δj

=

∂E
∂netk
∂E
∂netj

(13)
(14)

and δk and δj are calculated, respectively. First, δk is
expanded asδk∂E∂E ∂ek
∂yk
=
·
·
∂netk
∂ek ∂yk ∂netk
dfk (netk )= ek · (−1) ·dnetk
0= −ek fk (netk )=δk finally becomes equation (15).

127

(15)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

When δj is calculated in the same way, it becomesEquation (16).
δj

In this way, the connecting weights and property parameters of the DNN are updated based on the conceptof the modified BP method.

∂E
∂netj

NK 
X
∂netk ∂yj
∂E ∂ek∂yk····∂ek ∂yk ∂netk
∂yj
∂netj

=
=

k=1

NK
X

=

k=1

∂yj
δk · wjk ·
∂netj

(16)

Numerical simulation

4.1

System identification

The effectiveness of the proposed DNN is verified bynumerical simulation in order to identify a time series
signal. The method by which a time series signal froman unknown system is identified is shown in Figure
2. The DNN is structured to have two input (u(t)∂yj /∂netj is expressed as equations (17)–(20).
∂yj
∂netj
= Kj

4

∂ f˙j (netj )∂ f¨j (netj )∂fj (netj )
+ Dj
+ Mj
∂netj
∂netj
∂netj
(17)

Unknown
System

d(t)

+

u(t)

∂fj (netj )∂netj∂ f˙j (netj )∂netj¨∂ fj (netj )∂netj= fj0 (netj )

(18)

˙ j
= fj00 (netj ) · net

(19)

e(t)

z −1

−
DNN

˙ 2j + fj00 (netj ) · net¨ j= fj000 (netj ) · nety(t)Fig. 2 System identification(20)
Next, the derivation of ∂E/∂Kj is described as follows.
∂E
∂Kj

=

and d(t − 1)) and single output. The input signal u(t)both of the DNN and the unknown system is a periodic
signals expressed in Equation (24).

NK
X∂E∂netk ∂yj
·
·
∂netk
∂yj
∂Kj

u(t) = 0.1 sin(2πf t),

k=1= fj (netj ) ·NKX

wjk δkWhen the property parameters Dj and Mj for theerror function E are calculated in the same way, it
becomes Equations (22) and (23).
=

NK
X
∂E
∂netk ∂yj
·
·
∂netk
∂yj
∂Dj
k=1

=

f˙j (netj ) ·NKX

wjk δkd(t) = 0.5 sin(2d(t − 1)) + 0.1 sin(2d(t − 2)) + u(t)
(25)

(22)

k=1

∂E
∂Mj

=

4.2

NK
X
∂netk ∂yj
∂E
·
·
∂netk
∂yj
∂Mj

f¨j (netj ) ·NKX

wjk δk(23)

k=1©ISAROB 2006

Simulation results

At this time, the number of neurons NJ in the hidden layer is 5 units and the training rate is set toη = 0.1. The training involved 10,000 iterations. The
result of the error function E is shown in Figure 3.

k=1

=

(24)

The periodic input signal which has three frequencies
f = 0.01, 0.05, 0.1 is inputted every five cycles sequentially.
The desired signal, namely the training data d(t), is
the output signal of the unknown system. In numericalsimulation, the validity of the proposed DNN is verified by identifying the unknown system, for example,
as a nolinear system, expressed in Equation (25).

(21)

k=1

∂E
∂Dj

f = 0.01, 0.05, 0.1

128

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
103

5

102

In the present paper, the proposed DNN, exhibiting
the effectiveness of dynamical neuron with properties
of inertia, viscosity, and stiffness, was configured. The
training algorithm adopt the modified BP method. We
designed a modified BP method to train both the connecting weights and the parameters of the DNN. Simulation results showed that the DNN trained by the BP
method realized good training performance for timeseries signals generated from the unknown system withstrong nonlinearity.

Error function

101
100
10−1
10−2
10−3

1000

0

4000

3000
2000
Iteration

5000

Conclusion

References

Fig. 3 Error function

1 D. E. Rumelhart, J. L. McClelland, and PDP Research Group (1989), Parallel distributed processing, MIT Press.
The figure shows the result of the error function in therange of [0, 5000]. The result of the simulation showsthat the DNN provides good performance. The error
function E decreases gradually and E almost converge
about 0.01.

2 R. J. Williams and D. Zipser (1989), A Learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1, No.2, pp.270–
280.
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.

The result of regenerating the signal utilizing thetrained DNN is shown in Figure 4. The output y(t) of

Output signals

1

DNN
d(t)

4 W. Mass and C. M. Bishop (1999), Pulsed neuralnetworks, pp. 16-53, MIT Press.
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.

0

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 networkwithn GA-based training, AROB 9th ’04, Vol.1, pp.
75–78.

-1

0

100

200

300
t

400

500

600

Fig. 4 Regenerated signal

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.

the trained DNN negligibly deviates from the desired
signal d(t). The DNN trained by the modified BP
mothod obtained good training performance for thetime series signal generated from the output of thenonlinear system.

©ISAROB 2006

129

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Nonholonomic System Control Using Neurocontroller
Evolved by Genetic Algorithm
Norikazu Ogino, Hiroshi Kinjo, Kunihiko Nakazono
and Tetsuhiko Yamamoto
ogino,kinjo,yamamoto@mibai.tec.u-ryukyu.ac.jp
Faculty of Engineering, University of the Ryukyus
Nishihara, Okinawa 903-0213, Japan
Abstract

used when the solution is traped in the local optimum.
The training performance of the BP depends on thesequence of training patterns. We use a genetic algorithm (GA) for the NN training. GA training is easierthan BP from the viewpoint of mathematics.
In this study we use a two-wheel vehicle, which isthe simplest nonholonomic system, as a control object.
We construct a tracking system of the two-wheel vehicle using a neural network controller (NC) with GA
training.

In this paper, we propose a design method of tracking control using a neurocontroller for a two-wheel vehicle optimized using genetic algorithm(GA). The twowheel vehicle is a class of nonholonomic system. For
nonholonomic systems, there are many control methods that utilize a chained form by applying stability
theories. In this paper, we propose a design method
for a nonholonomic system controller without chained
form conversions. The controller is a neural network
controller (NC). The training method of the NC utilize a genetic algorithm. In this paper, we use NC
for tracking control. Simulation shows that the NC
trained using the GA has a good control performancefor the two-wheel vehicle.
Keywords: nonholonomic system, tracking control, genetic algorithm, neurocontroller, two-wheel vehicle

1

2
2.1

Nonhoronomic restraint of two-wheel
vehicleThe two-wheel vehicle that is used in this study isshown in Figure 1. (x, y) denotes the coordinates ofthe middle point P between both wheels of the vehicle,
and θ denotes the angle between the horizontal axis
and traveling direction of the vehicle. η denotes the
direction of the vehicle. Rl and Rr are the radii ofthe right and left wheels, respectively. The distance
between the wheels is assumed to be 2W .

Introduction

The control system design of a nonholonomic system is difficult and complex. It is impossible to stabilize the system using a static consecutive feedback
loop. It is necessary to construct uncontinuous ortime-varying feedback system. Recently, many control
methods for control system design have been proposed[1]-[5]. One of the solutions to the problematic control
system design of the nonholonomic system is to utilizea chained form conversion of the control objects. However, the chained form conversion has some limitationsof the controllable ranges in the state variables.
We propose a neurocontroller for the nonholonomic
system. The neurocontroller is a controller using aneural network (NN). The neural network can obtaina solution close to the most suitable solution by learning. The most populare learning methd for NN isback propagation (BP). However, the BP cannot be©ISAROB 2006

Two-wheel vehicle

W

Rl

θ
y
η

P

Rr

x
Figure 1: Two-wheel system

130

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the vehicle system is X = [x, y, θ]T . The input to the
NC is (X r − X) and the output is U . Here, X r is thedesired value. The NC is trained using the GA. We
use a real-coded GA to modify the connecting weightsof the NC.

The relationships between speed dη/dt and speed
dx/dt in the direction of x, and speed dy/dt in the
direction of y are described by the following equations.
dη
dy
dη
dx
=
cos θ,
=
sin θ
dt
dt
dt
dt

(1)

3.2

The speeds dx/dt and dy/dt have the folowing relation.
dydx=
tan θ.
(2)dt
dt
This is a geometrical restraint shown at the positionand speed of two-wheel vehicle.

2.2

We use a three-layered NN as controller. The numbers of neurons are 3 for the input layer, 5 for thehidden layer, 2 for the output layer. For the activity function of NC, the input and output layers use alinear function, and the hidden layer uses a sigmoid
function.

System equation of two-wheel vehicle

3.3

Let u1 and u2 be the control inputs. u1 is the speedof the vehicle dη/dt and u2 is the angular speed dθ/dtof the wheel, respectively. It can be shown that the
inputs areu1 =dηR l ωl + R r ωr=dt
2Construction of NC

Learning method
Desired position
after t seconds(3)−Rl ωl + Rr ωr
dθ
=
,
(4)
dt
2W
where ωl and ωr are the angular speeds of the left andright wheels, respectively.
The state equation of the two-wheel vehicle is described by

 

 
x
cos θ
0
d 
y  =  sin θ  u1 +  0  u2 .
(5)
dt
θ
0
1

Initial desired positionu2 =Initial vehicle posiiton

Figure 3: Tracking method
Figure 3 shows the tracking method. In Fig. 3, vehicle is set to the initial state and the bullet denotes
the desired state and the dotted line the desired trajectory.
We consider the problem that the vehicle traces thedesired trajectory after t[s]p(x − xr )2 + (y − y r )2
|θ − θr |
+
(6)
t=
0.2
0.1
Here, x, y and θ are the current states. In this study,
t is limited to 0.01 ≤ t ≤ 0.1.
We train the NC to control the vehicle to trace thedesired trajectroy using the GA. The fitness functionused in the GA is defined byThis formula is called the driftless system.

3
3.1

Control system
Constitution of a controller
GA

Xr +
-

NC

U Two-wheel
system1,(7)
E
where E is an error function described the following
equation.

Xf=

E (t) = (x(t) − xr(t) )2 + (y (t) − y r(t) )2 ,Figure 2: Control system

(t)

(8)

where x and y express states after t seconds. Likewise, xr(t) and y r(t) express the state of the desired
value after t seconds.

Figure 2 shows the control system. U = [u1 , u2 ]Tis the input to the two-wheel vehicle. The output of©ISAROB 2006

(t)

131

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Before moving the vehicel, NC is tarined based on
the error function E (t) . If E (t) < 10−5 then finish
the training and control the vehicle at step 1. Aftere
moving the vehicle, we check the vehicle state and perform the NC training. We iterate the NC training andvehicle moving until the end of the control time.

0.3
0.2
0.1

y [m]

4
4.1

Vehicle value
Desired value

Simulation of tracking control

0Vehicle start

Desired start-0.1

Method of simulation

End

-0.2
-0.3
-0.3

The plant that is described by the continuous timemodel is calculated by the Runge-Kutta method witha time step of 0.01[s] and zero-order hold control. The
desired position (xr , y r ) is assumed to turn counterclockwise along the circumference of a circle with aradius of 0.25[m] centered on (x, y)=(0,0). The circumferential velocity is set to 0.2[m/s]. The desired
angle θr is the angular difference between the directionof the desired position and the horizontal axis. The
initial state of the desired value is set to x=0.25[m],y=0.0[m] and θ= π2 [rad].
Table 1 shows the parameters of the GA training.

-0.2

-0.1

0.1 0.2
x [m]

0

0.3

0.4

0.5

0.6

Figure 4: Tracking result

0.1
0.01

Error

0.001
0.00011e-05
1e-06

Table 1: GA parameters

1e-07

4

3

2

1

0

Population
Parents
Range parameter of αGenerations

5

t [s]

50
15
0.6
1000

Figure 5: Error

4.2

Number of times of learning

70

Simulation 1

Figures 4-8 show the control results from the initial state x=0[m], y=0[m] and θ= π2 [rad] of the vehicle.
Figure 4 shows the control trajectory. Figure 5 showsthe error between the desired and control trajectories.
Figure 6 shows training iteraton. Figure 7 shows thesystem states. Figure 8 shows the control inputs.
From Figure 4, it can be seen that the vehicle traces
the same line as that desired. From Figure 5, it is observed that there are very small errors after 0.5 seconds. From Figure 6, it can be seen that there aremany learning iterations when the error is large (refer
to Figure 5). From Figure 8, we can see that the control inputs u1 and u2 be come approximately constantafter 0.5[s], that is, u1 = 0.2[m/s] and u2 = 0.8[rad/s].
u1 and u2 in stady state are alomst the same as the
speed of the desired values.

©ISAROB 2006

60
50
40
30
20
10
0

0

1

3

2

4

5

t [s]

Figure 6: Learning situationn

4.3

Simulation 2

Figure 9 shows the control result from the initialstate x=0.5[m], y=0.5[m] and θ= π2 [rad] of the vehicle.
It can be seen that tracking control is well performed.

132

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
0.3

Vehicle value
Desired value

0.3

0.2

Vehicle value
Desired value

0.1

x [m]

0.2
0
-0.1

0.1

y [m]

-0.2
-0.3

3

2

1

0

4

5

0

Desired startt [s]Vehicle start

-0.1
0.3

Vehicle value
Desired value

0.1

y [m]

End

-0.2

0.2

-0.3
-0.3

0

-0.2

-0.1
-0.2

-0.1

0

0.1 0.2
x [m]

0.3

0.4

0.5

0.6

Figure 9: Tracking result

-0.3

3

2

1

0

4

5

t [s]

5

6
5

By this study, we proposed a tracking control
method for a nonholonomic system using a neurocontroller evolved using a GA. As a result of simulation,
we succeeded in the tracking control of the nonholonomic system. There is no limitation in the controlable
ranges because this method does not use chained form
conversion in the control objects. It is shown that theneurocontroller has suficient performance in the control of the nonholonomic system. the application ofthis neurocontroller to a more complicated object isthe subject of future work.

θ [rad]

4
3
2
1
0
-1

5

4

3

2

1

0

t [s]

Figure 7: System stats3
2.5

References

u1 [m/s]2
1.5[1] M. Sanpei (1997), Feedback Control of Nonholonomic Systems (in japanese), Journal of SICE, Vol.
36, No. 6: 396-403[2] Y. Nakamura (1997), Perspective of Nonholonomic
System Control (in japanese), Journal of SICE,
Vol. 36, No. 6: 291-296[3] C. Samson (1995), Control of Chained Systems
Application to Path Following and Time-Varying
Point-Stabilization of Mobile Robots, IEEE Trans.
Automatic Control, Vol. 40, No. 1: 64-77[4] Z. Jiang and H. Nijmeijer (1999), A Recursive
Technique for Tracking Control of Nonholonomic
Systems in Chaind Form, IEEE Trans. AutomaticControl, Vol. 44, No. 2: 265-279[5] M. Yamada, S. Ohta, Y. Syumiya and Y. Funahashi (2002), Multirate Sampled Data Control Form Based on Periodic Switching, Trans. of
SICE, Vol. 38, No. 4: 369-378

1
0.5
0
-0.5

0

1

2

3

4

5

3

4

5

t [s]

20
15
10

u2 [rad/s]

5
0
-5
-10
-15
-20
-25

0

1

2

t [s]

Figure 8: Control inputs©ISAROB 2006

Summary

Vehicle value
Desired value

133

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Searching Performance of Real-Coded Genetic Algorithm Using Biased
Probability Distribution Functions and Mutation
Hiroki Nakanishi, Hiroshi Kinjo, Naoki Oshiro, Tetsuhiko Yamamoto
hiroki,kinjo,yamamoto@mibai.tec.u-ryukyu.ac.jp
Faculty of Engineering, University of the Ryukyus
Nishihara, Okinawa 903-0213, Japan
Abstract

The proposed method involves the use of biased probability distribution functions (BPDFs) in a crossoverprocess. The crossover with BPDFs frequently produces offspring that are close to the best individuals
in the current generation and it is highly likely thatthese offspring will offer the best solution to the problem. Furthermore, we propose a mutation that has aconstant and extended range that is wider than thatof the UNDX. The cause of the evolutionary stagnation is that the search area becomes smaller with theprogress of the generation. The mutation maintains
the search area constant.
In a simulation study, we apply the proposed
method to the optimization problems of three test
functions: the Sphere function, Rastrigin function,
and Rosenbrock function. The results show that the
proposed method has a good searching performancefor the test functions.

One of the crossover methods for the real-coded genetic algorithm (RGA) is the unimodal normal distribution crossover method (UNDX). The UNDX issuperior to the blend crossover method (BLX). However, the UNDX does not have a higher performance
than the BLX in terms of searching speed and conversion stagnation. In this paper, we present a method of
improving the searching performance of the RGA. We
propose the use of biased probability distribution functions (BPDFs) in the crossover process. The crossoverwith BPDFs frequently produces offspring that areclose to the best individuals in the current generationand it is highly likely that these offspring will offer
the best solution to the problem. Furthermore, we
propose a mutation that has a constant and extended
range that is wider than that of the UNDX. Simulations show the efficiency of the proposed method.
Keywords: Real-coded GA, crossover method,
unimodal normal distribution crossover (UNDX), biased probability distribution function, optimization
problems.

2

Unimodal normal distribution
crossoverP3

1

Introduction

C

D

Recently, the real-code genetic algorithm (RGA)
has been well used for solving problems. A well-known
crossover method of the RGA is the blend crossover
method (BLX). The BLX uses a uniform distributionfunction for breeding offspring. An excellent crossover
method is the unimodal normal distribution crossover
method (UNDX). The UNDX uses a Gaussian distribution function. Generally, GA solution searchingtakes a long time. Sometimes, using a GA results inevolution stagnation. In this paper, we present an
improvement in GA solution searching performance.
We present the first algorithm for solution searchingthat uses a variable distribution for breeding offspring.

©ISAROB 2006

P2

Pp
P1

Fig.1 Unimodal Normal Distribution Crossover,
UNDX
Figure 1 shows the original UNDX. The mechanismof breeding offspring in the UNDX
is described byn−1
X
c = pp + ξd + D
ηi ei ,(1)i=1

ξ ∼ N (0, σξ2 ), ηi ∼ N (0, ση2 )134

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

generation and it is highly likely that these offspring
will offer the best solution to the problem.

where c is the offspring individual, Pp is the center between the individual parents p1 and p2 , d is the subtraction vector between the individual parents. ei isthe orthonormal basis vector, n is the dimensions ofsearch space and D is the distance between the parent
p3 and main searching line. ξ and ηi are the normal
distribution functions with the mean zero deviationsσξ2 and ση2 , respectively. The UNDX uses two normal distribution functions in the offspring production
process. The first and second terms denote the main
search ingredient. The third term is the subsearch ingredient.

3.2

Mutation

P1

L
3ση

L

Fig.3 Mutation

Crossover method with BPDF

Figure 3 shows the mutation method. L is a constant that widens the offspring generation range. Generally, the search area R in the generation field decreases with progress of the evolution. Sometimes, R
is very small. The reduced field causes the evolution
stagnation. In this mutation method, the search field
is guaranteed to any constant width because there isthe constant range L in the crossover field.

PbR(a) Pb is outside of range R

4

Performance test

4.1

Test functions

In this section we investigate the searching performance of the proposed method for optimization problems for three two-variable functions: the Sphere function, Rastrigin function, and Rosenbrock function.
The searching performance is measured using successful evolution rates obtained from the minimum values of the following three functions: the Sphere function, Rastrigin function, and Rosenbrock function.
The Sphere function is described byPbR(b) Pb is inside of range R

Fig.2 Crossover using biased probability density
functions (BPDFs) (n = 1)

f1 (x, y) = x2 + y 2 .

Figure 2 shows the crossover with BPDFs. The
crossover with BPDFs produces offspring biased tothe individual pb based on a triangle distribution function. Figure 2 (a) shows the case when pb is outsideof the range R. Figure 2 (b) shows the case when pb
is inside of the range R. R is an offspring production range determined using the same process as the
UNDX. The pb is the best individual in the generation
field. The crossover with BPDFs produces offspringthat are close to the best individuals in the current©ISAROB 2006

3σξL

Biased probability distribution functions and mutation

3.1

P2

3σξ

3σ η

L

3

Pp

(2)

The minimum value of this function is 0.0 and occurs when (x, y) = (0, 0). The sphere function is thesimplest case.
The Rastrigin function is described by
f2 (x, y) = 20 + {x2 − 10 cos(2πx)}+{y 2 − 10 cos(2πy)}.

(3)

The minimum value of this function is 0.0 and occurs when (x, y) = (0, 0). The Rastrigin function

135

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

f3 (x, y) = 100(x − y 2 )2 + (y − 1)2 .

S[%]

has many peaks. There are many suboptimum solutions. The function has multimodal characteristics.
The function is of the separation type without a dependence relation between variables.
The Rosenbrock function is described by(4)

The minimum value of this function is 0.0 and occurs when (x, y) = (1, 1). The Rosenbrock function
has a single-peak shape. The solution of the function
is a deep valley in a bend. The function has an interdependence relation between variables and has strong
nonseparation characteristics.

4.2

S[%]

(5)

Here, Cf is a constant. We set Cf = 100 for functions
f1 and f2 and Cf = 500 for function f3 . The population numbers are set to 20 for functions f1 and f2 and50 for function f3 .
We use the crossover method with BPDF with theprobability of α = 20%. The mutation rate is set to
β = 10%.
In this experiment, we compare the evolutionary
performances of four methods: the original UNDX,
the addition of the BPDF with rate α, the additionof the mutation with rate β, and the addition of boththe BPDF and mutation with rates α and β. We callthe latter three methods methods A, B, and C, respectively.

100
90
80
70
60
50
40
30
20
10
0

UNDX
Method A
Method B
Method C
0

10 20 30 40 50 60 70 80 90 100
Generation(b) Range of initial values is [−5.0, −4.0]
Fig.4 Evolution performance for Sphere function

Figure 5 shows the evolution performance for the
Rastrigin function. The range of the initial parent
individuals in Fig. 5 (a) includes the solution to the
problem. The range of the initial parent individuals in
Fig. 5 (b) does not include the solution to the problem.
The lines show the mean of 1000 evolution trial iterations . From Fig. 5 (a), the success rate of method A
is higher than that of the original UNDX during early
generation. In late generation, there is no differencein success rate between method A and the original
UNDX. In this test, methods B and C, i.e., those usedwith mutation, increase searching performance. From
Fig. 5 (b), the success rates of three proposed methods are higher than that of the original UNDX. Boththe original UNDX and method A converge to a local
optimum. Methods B and C have higher performances
than the original UNDX and method A. We can observe that the mutation has an important role in the
search solution for the multimodal function.
Figure 6 shows evolution performance for the
Rosenbrock function. The range of the initial parent individuals in Fig. 6 (a) includes the solution to
the problem. The range of the initial parent individ-

Results

Figure 4 shows the evolution performance for the
Sphere function. Figure 4(a) shows the case of therange of the initial parent individuals [−5, 5]. Figure4(b) shows the case of the range of the initial parent
individuals [−5, −4]. The range of the initial parent
individuals shown in Fig. 4 (a) includes the solution to
the problem. The range of the initial parent individuals in Fig. 4 (b) does not include the solution to the
problem. The horizontal axis denotes generation. The
vertical axis S denotes the success rate. The condition
for successful evolution is f1 < 0.001. The lines show
the mean of 100 evolution trial iterations. In Fig. 4(a), there is no difference between the four methods.
In Fig. 4 (b), we can observe that the proposed methods are faster than the original UNDX.

©ISAROB 2006

10 20 30 40 50 60 70 80 90 100
Generation(a) Range of initial values is [−5.0, 5.0]

Parent individuals are chosen by roulette selection
using a fitness function based on the function f . The
fitness function F is defined in the next expression.

4.3

UNDX
Method A
Method B
Method C
0

Experiment parameter

F = Cf − f (x, y)

100
90
80
70
60
50
40
30
20
10
0

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70
60
50
40
30
20
10
0

S[%]

S[%]

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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UNDX
Method A
Method B
Method C
0

200

600
400
Generation

800

0

1000

S[%]

S[%]
UNDX
Method A
Method B
Method C
0

200

600
400
Generation

800

200

600
400
Generation

800

1000

(a) Range of initial values is [−0.25, 1.5](a) Range of initial values is [−5, 5]
100
90
80
70
60
50
40
30
20
10
0

UNDX
Method A
Method B
Method C

100
90
80
70
60
50
40
30
20
10
0

UNDX
Method A
Method B
Method C

100
90
80
70
60
50
40
30
20
10
0

0

1000

200

600
400
Generation

800

1000

(b) Range of initial values is [−5, −4](b) Range of initial values is [−0.5, 0.5]

Fig.5 Evolution performance for Rastrigin function

Fig.6 Evolution performance for Rosenbrock functionuals in Fig. 6 (b) does not include the solution to the
problem. The lines depicting the success rate S arecalculated for the mean of 1000 trials. From Fig. 6,
in the case that the range of the initial parent individuals includes the solution, it can be seen that thefour methods obtain the solution. In the case that therange of the initial parent individuals does not include
the solution, it can be seen that only methods B andC obtain the solution.

References

5[1] L. Davis (1991), Hand Book of Genetic Algorithms, Van Nostrand Reinhold[2] L. J. Eshelman and J. D. Schaffer (1993),“Real-Coded Genetic Algorithms and IntervalSchemata”, Foundations of Genetic Algorithms 2,
Edited by L. Darrell Whitley, Morgan Kaufmann
Publishers[3] H. Kita, I. Ono and S. Kobayashi (2000), MultiParental Extension of the Unimodal Normal Distribution Crossover for Real-Coded Genetic Algorithms (in Japanese), Transactions of the SICE,
Vol. 36, No. 10, pp. 875-883

Summary

In this paper, we propose the crossover method of
the real-coded GA to speed up solution searching in
the UNDX. The method uses biased probability distribution functions (BPDFs) in the crossover. Furthermore, we proposed a mutation that avoids stacking ina local optimum and evolutionary stagnation. In thesimulation study, we apply the method to the optimization problems of the Sphere function, Rastriginfunction, and Rosenbrock function. The results showthat the proposed method has a good searching performance for the test functions.

©ISAROB 2006

[4] D. E. Goldberg (1989), Genetic Algorithms
in Search, Optimization & Machine Learning,
Addison-Wesley[5] H. Okamura, K. Kitasuka and T. Dohi (2002), Perfomance Evolution of Genetic Algorithms Based on
Markovian Analysis (in Japanese), Transaction of
ISCIE, Vol. 16, No. 7, pp. 303-312

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A self-organizing model of place cellswith periodically distributed receptive fields
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

ceptive field’ shows periodic grid structure in the area
within which rats are allowed to walk around. A receptive field of a position cell is a set consisting of
positions at which the cell fires. It is striking that thereceptive field of a position 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. Also in
our preceding paper [2], the place cells had connected
receptive field.
In this paper, we show that SOMs connected via
anti-Hebbian synapses form this grid-structured receptive field. This new representation of position may
contribute to more precise robot navigation.

In this paper, we proposed new information separation method with inhibitory self-organizing map(SOM). SOM has topological structure and dimension,and inhibitory-connected SOMs are able to extract hierarchical structure of inputs has same dimension ofmap structure. We realized learning mechanism using this composition of SOM similar to some results ofrecent study that spatial inputs caused periodic distribution of receptive field in entorhinal cortex.

1

Introduction

Self-Organizing Map (SOM) can extract major informations from multi-dimensional information [1, 2].
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 [3]. The model was applied
to extract position and rotation from visual information acquired by a camera on a mobile robot ina room surrounded by walls of different colors. 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 theother SOM consisting of one-dimensional array of cells
is assigned for one-dimensional information of direction of the robot. Numerical simulations showed that
position-sensitive and direction-insensitive cells areformed on the two-dimensional SOM, and directionsensitive and position-insensitive cells are formed on
the one-dimensional SOM.
Such cells are found in real animal brains. In recent study of cortical micro circuits, it was found thatspatial information are represented by ‘grid cell’ inthe dorsocaudal medial entorhinal cortex (dMEC) [4].
Grid cells are a kind of position cells but their ‘re-

©ISAROB 2006

2

Kohonen’s SOM algorithm

The self-organizing map (SOM) was proposed by
Kohonen [1, 5] as a model of the cerebral cortex andits self-organization. It was successful at reproducing
a functional map of the visual cortex [6, 7], and wasapplied to many kinds of data as a statistical tool ofnonlinear auto-regression [5].
A SOM usually consists of a two-dimensional arrayof neuron-like units, each of which has an reference
vector mi , which is n-dimensional, as well as input
vectors x.
The SOM algorithm is as follows:(SOM1) Assign random values for the reference vectors.
(SOM2) Choose or generate an input vector x insome random manner.
(SOM3) Find the unit whose reference vector is theclosest to the input vector x in the sense ofn-dimensional Euclidean distance. The unit iscalled the winner.

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(SOM4) Apply the following learning rule for the reference vectors:
mi := mi + hci (x − mi ),Superior layer(1)
Robot field2

krc − ri k
hci = αexp −
2σ 2

!

,

(2)

Anti-Hebbian-connection toinferior layer(x,y)

where r i is the two-dimensional position of theunit i on the unit array, and c is the index assigned
to the winner unit.

Spatial inputsof robot(SOM5) Return to (SOM2) and repeat (SOM2) (SOM4) many times.

Inferior layerThis is called neighborhood learning, because a modification of the reference vectors takes place mainly inthe neighborhood of the winner, as is represented byhci , which is called the neighborhood function. As aresult of this learning rule, the reference vectors arescattered all over the input signal region and neighboring units tend to grow similar reference vectors, whichmeans a topological map of the input signal region isself-organized on the unit array.

3

Layer composition
SOMs

of

Figure 1: Structure of the proposed model. Layers aredesigned for formation of grid structure of spatial information. Each layer consists of hexagonal layout of cells
and one-directional suppression. Locational informationof robot x = (x, y) given to both layers.
to zero. As iterative learning proceeds, the learning parameters α and β are updated as follows:(2) (2)α := α0 (1−t(1) /t(1)/tmax ). (3)max ), β := β0 (1−tconnected

where α0 , β0 are initial value of α, β, and t(l) ,(l)tmax (l = 1, 2) are iterative learning times andmaximum of iterative learning times respectively.
This update equation leads stability of learning.

We prepare two layers, the superior and inferior layers, for representing spatial information. Each layer
consists of cells arranged in two-dimensional hexagonal array. We assume each of the cells in the inferior
layer receive inputs from all of the cells in the superior
layer via anti-Hebbian synapses.
All the cells on both layers receive a same locational
information x = (x, y) (Figure 1). The superior layer
receives no inputs from the inferior, so it works justas usual SOM for the two-dimensional positional information. On the other hand, the learning process of
the inferior layer is influenced by the superior throughthe anti-Hebbian synapses.
The robot moves randomly within a room, and its
position is represented by two-dimensional vector x
and supplied as input to the model.
The learning rule is detailed as follows:(SOMAH2) Set the position x = (x, y) of the robot
randomly.
(SOMAH3) Find the winner, c(1) in the superior
layer for the input x.
(1)c(1) = argmin mii(4)Then find the winner c(2) in the inferior layer for
the input, considering the inhibitory input sj,c(1)from the winner in the superior layer.


2
(2)
mj − x + sj,c(1) . (5)c(2) = argmin
j(SOMAH4) Update the reference vectors of all theunits in both layers,


(l)
(l)
(l)
(6)
mi := mi + hc(l) i x − mi ,(SOMAH1) Assign random values for all reference(1)(2)
vectors in the both layers mi , mj , where i =1, ..., N (1) , j = 1, ..., N (2) , and superscript (1)stands for the superior layer and (2) for the inferior layer. Initialize inhibitory connections sj,i

©ISAROB 2006−x .

(l = 1, 2).

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(SOMAH5) Update the inhibitory connections,sj,i := sj,i + ∆sj,i ,(1) (2)∆sj,i = β yi yj −

0

1

(7)
1
N (1) N (2)



.

(8)

(a) Superior layer

where β is learning parameter of inhibitory connections, and
1, i = c(l) ,
(l)
yi =
(9)
0, otherwise,(l = 1, 2).

(b) Inferior layer(10)
Figure 2: Learning results with one-dimensional input
for two one-dimensional SOMs. (a) Superior layer:
Reference vectors (filled circle) and receptive fields(rectangular mark) of cells distributed widely in the
inputs range. (b) Inferior layer: Receptive fields are
not connected but consist of five small segments.

(SOMAH6) Return to (SOMAH2) and repeat(SOMAH2) ∼ (SOMAH5) many times.
We can naturally let the both layers self-organize simultaneously as described above, but the learning process of the superior layer is independent of the inferior.
We, therefore, simulate only the learning process of thesuperior layer first until it reaches the equilibrium, andthen the learning process of the superior is stopped or(1)
all the reference vectors mi are fixed, and the learning process of the inferior layer and the anti-Hebbianconnections is started using the output of the superior
layer.

(2)

In the equilibrium of the learning process the learning rules (9) and (10) assure that the distribution of
the two winners on the two layers should be statistically independent of each other or the joint probability of the two winners should be uniform. If the
two SOM layers were one-dimensional array, the twodimensional input would be decomposed into two onedimensional components and mapped on each of the
two SOMs. In this case, however, the two SOM layers
are two-dimensional as well. So, the superior layer issufficient to represent all of the input information, andnothing seems to be left for the inferior layer.

σ (2) = 1.5, tmax = 480, 000 for inferior layer. Range of
input x = x is [0, 1].
In Figure 2, the receptive fields and the reference
vectors of the five cells in each layer are shown. Because the superior layer is a usual SOM, the receptive field of each cell is a connected segment aroundthe reference vector, which are nicely ordered on the
input range [0, 1]. As for the inferior layer the receptive field of a cell is not connected but consists of fivesmall segments, each of which is contained in one of
the receptive fields of the cells in the superior layer.
Thus, the receptive field of a cell in the superior layer
is divided into five small segments, each of which arecontained in the receptive field of each cell in the inferior layer. The reference vectors of the inferior layer
show little correspondence to the receptive fields. Thismeans the anti-Hebbian connections from the superior
layer play an important role in realizing these unusual
receptive fields. We can easily see that the distributionof the two winners are statistically independent in thisresult. This representation is equivalent to two-digit“pental” number system.

4.1

4.2

4

Simulation results

One-dimensional example

We used two SOM layers each of which consistsof 19 cells arranged in a hexagonal array (N (1) =N (2) = 19). Learning parameters of this experiment are α(1) = 0.02, σ (1) = {0.8 (0 ≤ t(1) ≤(1)
15, 000), 0.2 (15, 000 < t(1) ≤ tmax )} and α(2) =(1)0.0001, β (2) = 0.005, σ (2) = 1.2. Also, tmax = 40, 000,
(2)
tmax = 260, 000. Input vector x = (x, y) is generated

Before showing our main results, we will elucidate
the our model’s property by more simple situation,
where we selected one-dimensional input and set thedimension of the two SOM arrays two as well. Each
SOM has same number of cells N (1) = N (2) = 5, and(1)learning parameters are α(1) = 0.1, σ (1) = 0.6, tmax =(2)(2)
20, 000 for superior layer, α = 0.001, β = 0.005,©ISAROB 2006

Two-dimensional positional inputs

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layer forms disconnected receptive fields showing a periodic grid structure, which is very similar to what wasobserved in dMEC of rats by Hafting et al. [4]. According to our model, there must be some other position cells with wider and connected receptive fields insomewhere in rats’ brain.
We have to compare our results with Hafting’s observation in more details. Multi-ary representation obtained in our model has advantage of higher resolution
with fewer cells, which would be nice for robots as wellas rats.

Acknowledgment
The authors thank Dr. Akira Date for drawing ourattention to Hafting’s works.

References[1] T. Kohonen, “Self-Organized Formation of Topologically Correct Feature Map”, Biological Cybernetics, 43, pp.59-69, 1982.

Figure 3: Receptive fields of the cells in both layers for
spatial inputs. The whole input area is divided into thereceptive fields of the cells in the superior layer (boldline), each of which is subdivided into smaller area
(thin line), which constituting the receptive fields ofinferior layer. The receptive field of the 9-th cell in theinferior layer is shaded. We can see it is disconnected
and periodically arranged.

[2] K. Kurata, N. Oshiro, “Separating visual information into position and direction by SOM”, Artificial
Life and Robotics, 8 (1), pp.5–8, 2004.
[3] N. Oshiro, K. Kurata, “Separating visual information into position and direction by two inhibitoryconnected SOMs”, Artificial Life and Robotics, 9(2), pp.86–89, 2005.

randomly with uniform distribution within a circle ofradius 1.
Learning results are shown in Figure 3. As in onedimensional simulation, the receptive fields of the superior layer are connected and cover the whole input
area altogether. Each of the receptive fields of theinferior layer is not connected but consists of manysmall areas, each of which are contained in one of thereceptive fields of the superior layer. The small areas constituting the receptive field of the inferior layer
show very regular periodic arrangement, which is very
similar to that observed for the ‘grid cell’ in dMEC of
rats.

5

[4] T. Hafting, M. Fyhn, S. Molden, M. Moser andE. Moser, “Microstructure of a spatial map in theentorhinal cortex”, Nature, 436, pp.801–806, 2005.
[5] T. Kohonen, Self-Organizing Maps, SpringerVerlag, Berlin, 1994.
[6] E. Erwin, K. Obermayer and K. Schulten, “Modelsof Orientation and Ocular Dominance Columns in
the Visual Cortex: A Critical Comparison”, NeuralComputation, 7, pp.425-468, 1995.
[7] H. Shouno, K. Kurata, “Formation of a Direction
Map using Kohonen’s Self-Organization Map byProjection Learning” , Biological Cybernetics, 85,
pp.241-246, 2001.

Conclusion

We showed that two SOM layers connected via antiHebbian synapses can form an expression similar to‘multi-ary’ number system, and when two-dimensional
position vector is supplied to the model, the inferior©ISAROB 2006

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On-Line Prognostics Health Maintenance for Induction Motors
Based on Time-Series Data Mining
Jinsu Park, Yountae Kim, Young-In Kim, Hyeon Bae, 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: soblessed@dreamwiz.com, dream0561@pusan.ac.kr, kyi97@pusan.ac.kr, Baehyeon@pusan.ac.kr,
sskim@pusan.ac.kr
Abstract − The motor is an essential part inmanufacturing systems. The issues of preventive andcondition-based maintenance, online monitoring,system fault detection, diagnosis, and prognosis are ofincreasing importance. This paper introduces an online prognostics health maintenance (OPHM) for
induction motors. The proposed OPHM algorithm isbases upon stator currents from induction motors. The
stator currents are measured by the current meters andstored in the time series data. Preprocessing methods
are applied to treat the signals, because the intact timeseries data is not suitable to represent the current
signals. After preprocessing of the signals, the features
have to be extracted by the time series data mining
methods that include synchronization and wavelet
analysis. The discovered features are constructed to themotor faults templates for the template matching
method. The test results for based on real data showthat the proposed approach is very useful and powerful
method to detect the features in the signals.

the following symptoms:• Unbalanced air-gap voltages and line currents• Increase torque pulsations• Decreased average torque• Increase losses and reduction in efficiency• Excessive heating

Keywords: fault detection, feature extraction, timeseries data mining

In this research, we use several time-series data
mining techniques for fault diagnosis. First, we introduce
a synchronizing method, which is a very important issue
in digitalized time-series measurement equipment.
Wavelet transform is a method for time varying or nonstationary signal analysis, and uses a new description ofspectral decomposition via the scaling concept. Wavelet
theory provides a unified framework for a number oftechniques, which have been developed for various
signals processing applications. One of its features ismulti-resolution signal analysis with a vigorous functionof both time and frequency localization. This method iseffective for stationary signal processing as well as nonstationary signal processing. Mallat’s pyramidalalgorithm based on convolutions with quadratic mirror
filters is a fast method similar to FFT for decomposing
the original signal in an orthonormal wavelet basis ordecomposing of the signal in a set of independent
frequency bands. The independence is due to theorthogonality of the wavelet function [3]. However,
performance changes according to the mother wavelet.
Unfortunately, selecting a suitable mother wavelet isvery difficult. To overcome this problem, we developed anon-supervising method, which supports the selection of

The diagnostic methods to identify the above faults
may involve several different types of fields of scienceand technology [1], [2]. Several methods are applied todetect the faults in induction motors as the following:• Electromagnetic field monitoring• Temperature measurements• Infrared recognition• Radio frequency (RF) emissions monitoring• Noise and vibration monitoring• Chemical analysis• Acoustic noise measurements• Motor current signature analysis (MCSA)• Model, AI and NN based techniques

1. Introduction
The most popular way of converting electricalenergy to mechanical energy is an induction motor.
This motor plays an important role in modern
industrial plants. The risk of motor failure can begreatly reduced
routine maintenance is completed,such as scheduling motor replacement or repairs andwarnings of impeding failure can be recognized. In
recent years fault diagnosis has become a challenging
topic for many electric machine researchers and we
introduce a real-time diagnosis algorithm for motors
called on-line prognostics health maintenance (OPHM).
The major faults of electrical machines can broadly
be classified as follows [1]:• Abnormal connection of the stator windings• Broken rotor bar or cracked rotor end-rings• Static and/or dynamic air-gap irregularities• Bent shaft (akin to dynamic eccentricity)• Shorted rotor field winding• Bearing and gearbox failure
Faults in electric machines produce one of more of©ISAROB 2006

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These atoms also remain normalized. The wavelet
transform of f belonging to L2(R) at the time b and scale
aj is:

the most suitable mother wavelet for fault diagnosis.
2. Synchronization with Hilbert Transform
2.1 Hilbert Transform
The Hilbert transform of the signal, x(t), is defined
to be the signal whose frequency components are allphase shifted by -π/2 radians.

+∞

Wf (b, a j ) = f ,ϕ b ,a j = ∫ f (t )
−∞

a

j

ϕ*(

t −b
)dt
aj

(6)

A real wavelet transform is complete and maintains anenergy conservation as long as the wavelet satisfies aweak admissibility condition which is:+∞∫0

Fig. 1 Hilbert transform.

Ψ ( w)w

20

dw = ∫−∞

Ψ ( w)w

2

dw = Cψ < +∞(7)

When Wf(b, aj) is known only for a<a0 to recover f weneed a complement of information corresponding toWf(b, aj) for a<a0. This is obtained by introducing ascaling function φ that is an aggregation of wavelet atscales larger than 1. ψˆ ( w) and ϕˆ ( w) are Fouriertransforms of ψ(t) and ϕ(t) respectively. ψ(t) is a band
pass filter, and ϕ(t) is a low-pass filter. Taking positivefrequency into account ϕˆ ( w) has information in [0, π]
and ψˆ ( w) in [π, 2π]. Therefore they both have complete
signalinformation
without
any
redundancy.
Decomposition of the signal in [0, π] using Mallat’salgorithm gives:

The resulting signal is denoted by(1)xˆ (t ) = H {x(t )}xˆ (t ) is produced by passing x(t) through a filter witha transfer function.
(2)
H ( f ) = − j sgn( f )The magnitude and phase of H(f) are given by(3)
H( f ) =1∠H ( f ) = −

1

π

(4)
sgn( f )2
This phase shift is very useful for synchronizing
each measured signal.

h(n) = 2 − j ϕ (2 −1 t )ϕ (t − n)

2.2 Synchronization
We use the Hilbert transform to align the phases ofeach measured signal. The phase of the original signal
can be established by using the arctangent from thecreated phase shift term that results from the Hilberttransform and the original signal. Each signal isaligned to zero degrees, as shown in Fig. 2.

g (n) = 2 − jψ (2 −1 t )ϕ (t − n)j = 0, 1, 2, K

(8)

Wavelet decomposition does not involve the signal in[π, 2π]. In order to decompose the signal in whole
frequency band, wavelet packet can be used for thispurpose. After decomposition for l times, we will get 2l
frequency bands each with the same bandwidth. That is:⎡ (i − 1) f n if n ⎤⎢ 2 , 2⎥⎣⎦i = 1, 2,K, 2 j(9)where fn is the Nyquist Frequency, in the ith frequency
band. Wavelet packet de-composes the signal into one
low-pass filter h(n) and 2l-1 band-pass filters g(n),provides diagnosis information in 2l frequency bands.
Functions h(n) and g(n) can be obtained by innerproduct of ψ(t) and ϕ(t).
h( n) = 2 − j ϕ ( 2 −1 t )ϕ (t − n)g ( n) = 2 − j ψ ( 2 −1 t )ϕ (t − n)
A j ( n) = ∑ k h(k − 2n) A j −1
D j ( n) = ∑ g g ( k − 2n) A j −1

Fig. 2 Synchronization of signal.
3. Wavelet Transformation
A wavelet is a function ψ belonging to L2(R) with azero average. It is normalized and centered in the in
the neighborhood of t=0. A family of time-frequency
atoms is obtained by scaling ψ by aj and translating is
by b:
−j

⎛t −b⎞j ⎟⎝ a ⎠ψ a ,b = a 2 ψ ⎜©ISAROB 2006t ∈ R, n ∈ Z(10)

n = 1, 2, 3,K (11)where A0(k) is the original signal and Aj is the low
frequency approximation at the resolution j. Dj is calledhigh frequency detail signal. After de-composition of jtime, we can obtain one approximation signal Aj and D1,
D2, …, Dj detail signals.
Wavelet packet decomposition is:

(5)

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x2 n (t ) − 2 ∑ h(k ) x n (2t − k )kLast, four fault templates were made consisting of abroken rotor bar, bearing fault, misalignment, andunbalance. The fault templates were based on 40 test sets.
Figure 5 shows that the template was produced byfinding the difference between the averaged fault signal
and healthy signal.

(12)x2 n+1 (t ) − 2 ∑ g (k ) xn (2t − k )kwhere x1(t) is the original signal. Comparing (12) with
(10), we can find that Aj in (10) is decomposed but also
Dj in (10) is decomposed in (12).
Wavelet and wavelet packet decompose the original
signal that is non-stationary or stationary into
independent frequency bands with multi-resolution.
4. Experimental Results
4.1 Current Signals and Data Preprocessing
The motor rating used in this paper depended on theelectricity conditions. The rated voltage, speed, andhorsepower were 220V, 3450RPM, and 5kw,
respectively. Motor specifications include the numberof slots, the number of poles, slip, and other factors.
The actual motor specifications used were 34 slots, 4poles, and 24 rotor bars. Slip is determined by
calculating the actual motor speed and the rated speed.
The input current signal was sampled 16,384 tines, hada maximum frequency 3kHz, and measurement
internal of 2.1333. The faults that were studied were abroken rotor, faulty bearing, a bowed rotor, unbalance,and static and dynamic eccentricity.
If the wavelet decomposition is used to detect faults
in induction motors, the unsynchronized current phaseproblem should significantly influence the results. If
target signals are not synchronized as shown in Fig. 3,
unexpected results will appear in the wavelet
decomposition.

Fig. 5 Fault templates.
These produced templates have high frequency terms
related to the characteristics of each fault. For this reason,
wavelet decomposition was used to extract the detailterms. It thought that this improved performance and isintroduced in the next section.
4.2. Applying wavelet decomposition and finding asuitable mother wavelet for diagnosis
Coming signal
Preprocess
De-noising andNormalize

Synchronize

Waveletdecomposition
Fault templates

BF template

Correlation withBR template

MA template

UB template

Find
best matching rate

Fig. 3 Original current signals.
Diagnosis result

Therefore the signals were re-sampled bysynchronizing the signals using phase 0 by using
Hilbert transform. The average value divided by one
cycle signal was calculated in order to reduce the noiseof the original signals as shown in Fig. 4.

Fig. 6 Flow chart of diagnosis processes.
The wavelet decomposition was applied to the
produced templates. We use a produced-detail coefficientin the first step of decomposition. Figure 7 shows thecharacteristics of discrete wavelet transform. The detailcoefficient produces a high frequency source signal.
These become new fault templates and are used in atemplate matching method. A correlation is used todetermine the matching rate. The flow chart in Figure 6
shows the entry process of our proposed diagnosis. First,
an unknown signal preprocessed. The results aremathematically correlated with the four templates. But,

Fig. 4 Data synchronizing and filtering.

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before this process, we need to find most suitable
mother wavelet for the motor diagnosis. The mostsuitable mother wavelet is taken from the various
wavelet sets in Table 1.

5. Conclusion
The proposed OPHM algorithm is based upon several
time-series data mining techniques. The stator current ismeasured by a current meter and stored in the time series
data. Preprocessing methods are applied to the signals,
because the intact time series data is not suitable forrepresenting current signals. After preprocessing thesignals, the features have to be extracted by the timeseries data mining methods which include
synchronization and wavelet analysis. The resulting
features are matched to the motor faults templates.
Wavelet analysis is a possible method for detecting faults
in induction motors, but it depends on locating the mostsuitable mother wavelet. We have established that the
Daubeches wavelet is the most suitable mother wavelet
for diagnosis of motor. It can produce a diagnostic
accuracy rate of about 97.5%. In future work, we willdevelop the OPHM based stand-alone hardware fordiagnosing inductive motors and try to design the
optimal mother wavelet for diagnosis.

Source‘db1’ mother wavelet

cA Low Frequency

cD High Frequency

Downscale 1/2
Approx. coef.

ACKNOWLEDGEMENT
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.

Detail. coef.

References[1] P. Vas, Parameter Estimation, Condition Monitoring,and Diagnosis of Electrical Machines, Clarendron
Press, Oxford, 1993.
[2] G. B. Kliman and J. Stein, “Induction motor fault
detection via passive current monitoring,”
International Conference in Electrical Machines,
Cambridge, MA, pp. 13-17, August 1990.
[3] K. Abbaszadeh, J. Milimonfared, M. Haji, and H. A.
Toliyat, “Broken Bar Detection In Induction Motor
via Wavelet Transformation,” IECON’01: The 27th
Annual Conference of the IEEE Industrial
Electronics Society, pp. 95-99, 2001.
[4] Masoud Haji and Hamid A. Toliyat, “Pattern
Recognition-A Technique for Induction Machines
Rotor Fault Detection Eccentricity and Broken Bar
Fault,” Conference Record of the 2001 IEEE
Industry Applications Conference, vol. 3, pp. 15721578, 30 Sept.-4 Oct. 2001.
[5] S. Nandi, H. A. Toliyat, “Condition Monitoring andFault Diagnosis of Electrical Machines – A
Review,” IEEE Industry Applications Conference,
vol. 1, pp. 197-204, 1999.
[6] B. Yazici, G. B. Kliman, “An Adaptive Statistical
Time-Frequency Method for Detection of Broken
Bars and Bearing Faults in Motors Using Stator
Current,” IEEE Trans. On Industry Appl., vol. 35,
no. 2, pp. 442-452, March/April 1999.

Fig. 7 Characteristics of DWT.
Table 1 Mother wavelets for DWT
Wavelets

Window size

Daubechies

1-15

Symlets

2~15

Coiflets

1~5

Meyer

.

Each mother wavelet is used to match the signal to awavelet to times. The number of hits is used toevaluate the diagnosis. Table 2 shows that the
Daubeches wavelet of window size 1 (Harr wavelet) is
the most suitable mother wavelet for diagnosis. The
Daubeches wavelet has an accuracy rate of about
97.5%. Diagnosis misalignment and unbalance was
especially accurate at 100%
Table 2 A list of results of diagnosis test sets in best 4
mother wavelets (Each 40 times).
FaultsWavelets
Total
BF
BR
MA
UB
‘db1’
37
39
40
40
156
‘coif1’
31
38
20
33
122
‘db2’
28
38
19
34
119
‘sym13’
25
31
5
18
79

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The curvature detect system of the lane using image
Kim Jun-won
Dep. of EE.
Pusan National
University.
Busan, Republic ofKorea.
nonfast@pusan.ac.kr

Cho Seog-bin
Dep. of EE.
Pusan National
University.
Busan, Republic ofKorea.
chosb@pusan.ac.kr

Ha Sung-kil
Dep. of EE.
Pusan National
University.
Busan, Republic ofKorea.
skha@pusan.ac.kr

Baek Kwang-ryul
Dep. of EE
Pusan National
University
Busan, Republic of
Korea
krbaek@pusan.ac.kr

various applications includes the resolution of camera
image getting better very, the method using image is
receiving footlights.
In this paper, it added from the image processor inequipment of existing and the algorithm it will be able toacquire information in traveling and moving easily it
proposed. It proposed the algorithm which minimizes theoperation which is whole algorithm and it guaranteed areal-time characteristic.

Abstract
In this paper, we research real-time algorithm for the
extraction of lane information. The algorithm in thispaper based on EDF(edge distribution function), thatgive priority to lane direction and curvature information
detect which are toughness and reliable.
And itcomposes a system optimization for the efficient conductof lane curvature information detect algorithm base on
EDF.
At first, this paper converts image using
IPM(inverse perspective mapping) for reliable laneinformation detect algorithm. In order to search thedirection and a curvature of the lane from the image
where the IPM is applied it divided an image equallywith y direction, against each part histogram of the edgemagnitude which relates in the direction angle of theedge, an edge distribution function defined. It becomes
to extracted lane information and scatter diagram usingedge-pixel that agrees with peak value of the EDF forgets lane information from divided image.

2 The Pretreatment Algorithm for an Image
2.1 Camera calibration
Camera calibration entails solving for a large number ofcalibration parameters, it requires large scale nonlinear
search. For example, focal distance from lens until CCD
camera, vision angle of lens, the lens distortion.[1] In
addition errors appear when mapping from 3D to 2D isnonlinear, the error appear when it acquire image data. In
this paper, camera calibration use DLT(direct linear
transformation) equation by M. Bretozzi and A.
Broggi.[2]Keywords image, transformation, curvature, detect,lane, EDF

1 Introduction
Recently, automation equipment is developed that
substitutes the human. The AGV(automatic guided
vehicle) is one example, it is used at harbor of the worldwide various nations. Also small-sized automationequipments are tendency which increase in industry.
There is to control of automation equipments that it isimportant that acquires information of the location andprogress direction. Various techniques is used in order to
get these information, there are technique using magnet
tape and technique using RF & laser beacon. However,there are fault difficult of the foundation andmodification, moreover it is expensive. So image
processing technique is increased. It is useful in current
environment and need not additional equipment. In that
one is the location and direction detect using image. It ismany difficult in data processing because the image data
is many too much at the past. Also the efficiency of thecamera cannot be good, it did not use well. But now theoperation speed of the processor comes to be quick and©ISAROB 2006

Fig. 1. Relationship between the two coordinate
systems
The parameter acquires data using coordinates anddirection angle of the camera, vision angle and resolutionof the lens. This method the calculation is simple for dataacquisition; it has the advantage of real-time. W-image
and I-image are shown in Fig. 1.

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2.2 Image conversion algorithm
It does pretreatment that convert the images from thecamera into top-view for lane detect. It applies
mathematics algorithm and it is calculated and there is apossibility of making the vector table where in actual
location it corresponds. The data which is converted it isacquired information about a traveling direction and acurvature. It use Sobel mask for a traveling direction anda curvature, it use accumulation distribution function andimage division for a curvature.

(a)(c) Remapped image using inverse transform
Fig. 2(a) is I-image and Fig. 2(b) is a image by themapping W-image from I-image. Namely, it must
interpolation for original W-image reconstruct. Fig. 2(c)is remapped image using inverse transform that it used
the method which summons the coordinate of theW (u , v, z = 0) from the I ( x, y ) . Show equation (4a) and(4b).

location [3]Two Euclidean spaces are defined for convert I-image
into W-image in Fig. 1.
• W = {( x, y, z )} ∈ E3 , representing the 3-D world space⎡ h × sin γ ( x, y, 0 ) ⎤arctan ⎢⎥ − (θ − α )y−d⎣⎦u ( x, y, 0) =
2α
n −1

(1)

⎡y−d ⎤− (γ − α )arctan ⎢x − l ⎥⎦⎣=,,0v(x y )
2αn −1

2.2.2 Convert top-view image

The image which it is having actually from two space Iimage and the W-image is the I-image.



(4a)

(4b)

The value of the tangent becomes infinite from theangle which is parallel with the surface in z = 0 . This
point is a disappearance point. When it mapping fromlower region of disappearance point, the image become
top-view image with the Fig. 2(c) it is same.

viewpoint : camera position is C = ( l , d , h ) ∈ W
viewing Direction : optical axis oˆ is determined by
the following angles(c)Fig. 2. (a)Original image (b) Remapped image

2.2.1 Interrelation of image coordinate and actual( world-coordinate) , where the real world is defined.
• I = {( u, v)} ∈ E2 , representing the 2-D image space( screen-coordinate) , where the 3-D scene is projected.

(b)

(2)

γ : the angle formed by the projection (defined by versor η )of the optical axis oˆ on the plane z=0 and the x axis

θ : the angle formed by the optical axis oˆ and versor η

3 Lane Information Detection Algorithm

Aperture : camera angular aperture is 2α
Resolution : camera resolution is n × n

3.1 The edge detect algorithm [4]And when it applies the equation for a conversion withfollow equation it is same and when it applies this, itshow in Fig. 2.
2α ⎤⎡cos ⎢(θ − α ) + u
n − 1 ⎥⎦⎣x (u , v ) = h ×
+l
2α ⎤
⎡
tan ⎢( γ − α ) + v
n − 1 ⎥⎦⎣
2α ⎤⎡sin ⎢(θ − α ) + u
n − 1 ⎥⎦⎣y ( u, v ) = h ×
+d
2α ⎤
⎡
tan ⎢( γ − α ) + v
n − 1 ⎥⎦⎣
z=0
u , v = 0, 2," , n − 1

The edge is defined slope of the change which image
brightness. The 1st differential from location of image,
namely slope vector it is equation (5).
⎡ ∂f ⎤⎡Gx ⎤ ⎢ ∂x ⎥∆f = ⎢ ⎥ = ⎢ ⎥⎣G y ⎦ ⎢ ∂f ⎥⎢⎣ ∂y ⎥⎦

Here, slope vector ∇f in point ( x, y ) is maximum
variable direction of f ( x, y ) , amplitude and direction areequation (6a), (6b).

(3)∆f ( x, y ) = Gx2 + G y2

(6a)

⎛ Gy ⎞⎟⎝ Gx ⎠(6b)α ( x, y ) = tan −1 ⎜This paper used Sobel mask for algorithm.

©ISAROB 2006

(5)

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become Fig. 5.

(a) 60D

Fig. 3. Sobel mask(b) 60D and 120D(c) 45D and 135D

Fig. 5. CDF of the edge direction for lane image⎧⎪ f ( x, y ),x, y ) = ⎨⎪⎩0,
TH : Threshold
f

G(

if

f ( x, y ) > THiff ( x, y ) ≤ TH

(7)

3.3.1 Direction angle detect using CDF
There is a possibility of getting LMP(local maximum
point) using CDF. This is made by the pixels where theedge direction is from the converted image.

After X-coordinate and Y-coordinate masking, it store
data α ( x, y ) by threshold. Finally, it applies with thedata about a slope.

{Γ = ( x , y ) α ( x, y ) = θ}(9)Here, α ( x, y) is edge direction from ( x, y ) . There isgetting Fig. 6. by gathering the data which gets near to
the LMP.

3.2 Convert one-loop algorithm
It is repeated two loops when it applied Eq. (4), (5). So,
it make vector table because image information isunchanged. The process receives 9 data whichcorresponds to the location of the I-image from thebuffer it shows Fig. 4.

{

Γ = ( x, y ) LMP − δ ≤ α ( x, y ) ≤ LMP + δ = θ

}

(10)

δ is constant value, when the value becomes larger
and the noise comes to be large, when the value becomes
lover and the information comes to be lack.

(a)Fig. 4. Sobel mask using vector array

(b)

(c)

Fig. 6. (a) Original image (b) Remapped image
And then it store final operation value (vector location,(c) Edge direction of the lane boundaryα ( x, y ) ).

3.3.2 Curvature information acquisition of the lane
This paper used PABLF(principal axis-based line
fitting) for acquisition of slope information in image.
This method must assume which lane is a straight line.

3.3 Cumulative distribution function [5]Suggest CDF(cumulative distribution function) for edge
direction of lane. it show follow equation.
(8)

F ( d ) = ∑ ∆f ( x, y )θn( d )

Here, n(d ) means number of pixel that edge direction
is α ( x, y) = d , range of angle d is d ∈ (0 ,180 ) .
And it use gaussian filter for remove noise, thenD

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Ministry of Education
Development in Korea.

&

Human

Resources

5 Reference[1] Roger Y. Tsai, “A Versatile Camera Calibration
Technique for High-Accuracy 3D Machine Vision
Metrology Using Off-the-Shelf TV Cameras andLenses” IEEE journal of robotics and automation, vol.
RA-3, NO.4, August 1987.
[2]-[3] Massimo Bertozzi, Alberto Broggi, “GOLD: A
Parallel Real-Time Stereo Vision System for GenericObstacle and Lane Detection”, IEEE Transactions on
image processing, Vol. 7, NO. 1, January 1998.
[4] J. W. Lee, U.K. Yi, and K.R. Baek, “A cumulative
distribution function of edge direction for road-lane
detection”, IEICETRANS. INF. & SYST., Vol.E84-D,
No.9 September 2001.
[5] A.D. Bimbo, L. Landi, and S.Santini, “Determinationof road directions using feedback neural nets”, Signal
process., Vol32, pp.147-160, 1993

Fig. 7. Image division
It detect gravity center using (p+q) difference
moment for detect region of each part.
m pq = ∑ x p y q f ( x, y )(11)( x , y )∈Γm pq

is (p+q) difference moment, Γ is pixel off ( x, y ) . So, gravity center with follow it is same.

x=

m10
m
, y = 01
m00
m00

(12)

Follow equation is slope of principal axis by center
moment.
µ = ∑ ( x − x ) ( y − y ) f ( x, y )
p

q

(13a)

( x , y )∈ΓΦ=⎛ 2 µ11 ⎞1tan −1 ⎜⎟2⎝ µ 20 − µ02 ⎠(13b)

The next is the equation using PABLF method.
y = tan Φ ( x − x ) + y(14)

Finally, it is bought the slope of each region. It searches
the slope change quantity of each region then it is known
the change the slope from the whole image.

4 Conclusion
This paper describes an image processing algorithm
capable of extracting lain-related information (curvature,
direction) using an edge distribution function (EDF),
which is designed for the model function of the road lane.
When image transformation is processed, Sobel masking
is processed at the same time. So, performance of thesystem is improved. And without the change of external
circumstance, the algorithm can apply to the driven
system for gathering road information.
But, it cannot be applied when lane is damaged. And it
needs improvement because there is an error in uphilland downhill road.

Acknowledge
This work was supported by "Research Center for
Logistics Information Technology (LIT)" hosted by the©ISAROB 2006

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Implementation mass flow controller for adaptive PID
Bongsu Cho

Kwangryul Baek

Dept. of Electronics Engineering in Pusan Natl’

Dept. of Electronics Engineering in Pusan Natl’

University

University

BUSAN, KOREA, ZIP : 609-735

BUSAN, KOREA, ZIP : 609-735
e-mail : krbaek@pusan.ac.kr

e-mail : mscho97@pusan.ac.kr

Abstract

few problems. The flow rates of fluid are related to apressure drop and a cross-sectional area of pipe, when afluid flows through a pipe. Also the flow rates are related
to physical parameters of fluid [2].

MFC(Mass Flow Controller) is a equipment thatmeasures and controls flow rates of fluid. The MFC
control problem includes the operating pressure and thephysical parameters of fluid when the MFC system
operates. Also, commonly, the piezo valve makes use of
the control valve. The piezo valve controls a detailed
displacement and is not sensitive for a temperature and apressure. But, when the piezo valve assembles the MFC
system, a distance error generates. Though this distanceerror is very small distance, a large error of flow rates
generate.
In this paper, we implement MFC system and propose
adaptive PID controller with self tuning. It makes use ofadaptive PID controller with self tuning in order tocompensate these problems of the MFC system. The
adaptive PID control algorithm makes use of a method toestimate the parameters of a MFC system. This paper
implements MFC system using Ziegler & Nichols's
method and the proposal adaptive PID. And, it makes acomparative analysis of the proposal adaptive PID andthe traditional PID.

P = P1 − P2 =Qm = ρ Q

(1.1)
(1.2)

where P1 = pressure at inlet of pipe, pascal
P2 = pressure at outlet of pipe, pascal
P = pressure drop flrom inlet to outlet, pascal
u = absolute viscosity of fluid,pascal second
l = length of the pipe, meterd = inside diameter of the pipe, meter
Qm = mass flow rate, kilogram/second
Q = volume flow rate, cubic meter/second

ρ = fluid density, kilogram/cubic meter
Due to this reasons, the parameters of MFC system arechanging according to the operating condition of the
MFC system. When the MFC system control, a operatingpressure and a kind of fluid become important variables.
Also, the piezo valve has the characteristic ofnonlinearity that includes a hysteresis characteristic. The
maximum stroke of the piezo valve is 50 um, actually,
20um distance is enough in the flow control. But, when
the piezo valve assembles the MFC system, a distanceerror generates. This distance error is about 5~20um.
Though this distance error is very small, the operating
point of piezo valve is enough to change. A change of
operating point has a effect on the system characteristic,
because piezo valve has hysteresis characteristic.
It is very difficult to find out the value of parameters of
the MFC system. When the MFC system implements
using the PID algorithm, a man of ripe experience must

Keywords: MFC(mass flow controller), adaptive PID,
self tuning

1. Introduction
Recently, it is important to measure and control flow
rates of fluid in many industries. The mass flow control
is an important factor for a decision of the quality of
product in many industries. Therefore, the high speed
and highly accurate control of mass flow controller havedemanded on the industry.
Most of MFC are still using the PID algorithm. The PID
controller shows superior performance on the MFC
system. But the PID controller in the MFC system has a©ISAROB 2006

128ul
Q
πd4

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system changes, the new parameters are estimated andparameters of PID controller are redesigned. This paper
proposes adaptive PID controller system that parametersof the MFC system estimate from operating data of the
MFC system using the method of least mean squares,
and parameters of PID controller are readjusted using
these estimated parameters.

be repeatedly tune the different PID coefficient about
each of the MFC system.
In this paper, we implement MFC system and propose
adaptive PID controller with self tuning. The mass flow
measurement makes use of the heated bypass capillarythermal mass flow meter, and the mass flow control
make use of piezo valve in the MFC system [4,5]. It
makes use of adaptive PID control algorithm with self
tuning in order to compensate these problems of the
MFC system. The adaptive PID algorithm estimates theparameters of MFC system. And parameters of PID
controller are updated by this estimated MFC systemparameters [1,3]. The adaptive PID algorithm
compensates the operating condition due to using theestimated parameters for operating MFC system. And
this paper implement MFC system using Ziegler &Nichols's method as traditional PID controller andproposal adaptive PID. And, it makes a comparative
analysis of the traditional PID and the proposal adaptive
PID.

AF BF

r (t )r (n)e( n )u (t )y (t )u (n)K P Ti Tdy ( n)

a1 a2 b1 b2

Figure 2.1 Adaptive PID controller system
2.3 Estimation of MFC system

2. Design adaptive PID controller

If continuous equation of MFC system that express Eq(2.1) convert discrete equation with sampling time T,
discrete equation express Eq (2.5).

2.1 Design PID controller
The MFC system assumes that it approximates a second
system. A transfer function of the MFC system is given
by Eq (2.1), and a transfer function of PID controller isgiven by Eq (2.2). If express Parameters for PID
controller as Eq (2.3), A transfer function of close loop
become Eq (2.4). Therefore, if it gets a parameters of the
MFC system, the close loop system become first systemwith a gain 1 and time constant τ .

(2.5)
y[n] = a1 y[n −1] + a2 y[n − 2] + bu
1 [n −1] + b2u[n − 2]
−

T

−

T

where, a1 = e p1 + e p2 , a2 = − e
−

T

−

−(

1 1
+ )T
p1 p2

,

T

p e p1 − p1e p2
c= 2,
p1 − p2

K( p1s + 1)( p2 s +1)(2.1)

b1 = K ( − a1 + c + 1) , b2 = K (− a2 − c )⎛⎞1
Gc ( s ) = k p ⎜ 1 ++ Td s ⎟⎝ Ti s⎠(2.2)

In this paper, I propose method as follow. It expresses
relation between input and output of MFC system as Eq(2.5). The parameters are estimated by input and output
data. The parameters are defined a vector as Eq (2.6).
And the input and output data y[k-1], y[k-2], …, y[k-n],u[k-1], u[k-2], …, u[k-n] are defined a vector as Eq (2.7).
Using the method of least mean square, the estimatedparameters calculate Eq (2.8).

G p (s) =

Td =

T
p1 p2
, Ti = p1 + p2 , k p = i
p1 + p2
Kτ

(2.3)

G(s)
1
=
1 + G ( s) τ s +1

(2.4)

Gclose −loop ( s) =2.2 Structure of adaptive PID controllerθ = [ a1 a2 L an b1 b2 L bn ]T©ISAROB 2006

(2.6)

f [k] =[ y[k −1] y[k −2]Ly[k −n] u[k −1] u[k −2]Lu[k −n]] (2.7)

Parameters of the MFC system change according tooperating pressure, a kind of fluid and assembly error of
piezo valve. When operating conditions of the MFC

T

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006−1θˆ = ⎡⎣F( N )T F( N ) ⎤⎦ FT ( N )y ( N )3.1.1 Measurement part
The measurement part consists of the constanttemperature control thermal mass flow meter. And thesensor for the mass flow measurement makes use ofcoiled platinum wire around bypass capillary.
Advantages of this thermal mass flow meter are tomeasure mass flow of gases and liquids regardless of atemperature and a pressure, and have lower sensitivity
related a surrounding thermal changing, high response
and superior stability.

(2.8)

where, F( N ) = [f T (n) f T (n + 1) Lf T ( N )]T
Because Eq (2.8) includes inverse matrix, estimating
new parameters need many calculations. So we use
recursive least mean square method. It expresses
recursive equation as Eq (2.9) ~ (2.12)[1,3].

P( N ) = ⎡⎣F( N )T F( N ) ⎤⎦

−1

(2.9)
−1

L(N +1) = P(N)f (N +1) ⎡⎣1+ f T (N +1)P(N)f (N +1)⎤⎦

3.1.2 Control valve
A control valve uses a piezo valve. The piezo valve
controls a precision displacement regardless of atemperature and a pressure. And a response speed is lessbelow 1ms.

(2.10)

θˆ (N +1) = θˆ (N) + L(N +1) ⎡⎣ y(N +1) − f T (N +1)θˆ (N)⎤⎦ (2.11)

P( N +1) = P( N ) − L( N +1)f ( N +1)P( N )T

3.1.3 Control and signal processing
The control and signal processing part consist of a
microprocessor and a peripheral device in order tocontrol the MFC system. And it includes an analog
circuit for sensing signal.

(2.12)

Using this method, parameters of MFC system become
Eq (2.13)

3.2 Design using Ziegler & Nichols's method

b +b
T
T
p1 = −, p2 = −, K= 1 2
ln r1
ln r2
1− a1 − a2here,

r1 , r2 =(2.13)As design PID controller using Ziegler & Nichols's
method as traditional PID controller, PID parameters
are K P = 0.24, K I = 0.001073, K D = 13.41 andstep response is Figure 3.2.

a1 ± a12 + 4a2
2

3. Actual experiment
3.1 MFC system
The MFC system consists of a measurement part, acontrol valve part and control and signal processing part.
Figure 3.1 is the block of MFC system.

Figure 3.2 Ziegler & Nichols's step response
If operating conditions of the MFC system change, PID
controller is redesigned. Without redesign, the step
response becomes to change according to operatingpressure, a kind of fluid and assembly error of piezo
valve. This response is Figure 3.3.

Figure 3.1 : block of MFC system©ISAROB 2006

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4. Conclusions
This paper implements the MFC system using thermal
mass flow meter and piezo valve. Control algorithm use
proposed adaptive PID algorithm instead of Ziegler &Nichols's method. Proposed algorithm is that parametersof the MFC system are estimated from operating data of
the MFC system using the method of least mean squares.
Parameters of PID controller are readjusted using theseestimated parameters. As result of the algorithm it shows
more superior response than Ziegler & Nichols's method
as traditional PID. In proposed algorithm, when
operatingconditions
of
the
MFC
systemchange(operating pressure, a kind of fluid and assembly
error of piezo valve), system step responses show similar
step response.

Figure 3.3 Operating condition change
3.3 Design adaptive PID controller
As proposed adaptive PID controller design, step
response is Figure 3.4. Because proposal adaptive PID
controller includes feedforward, it shows fast step
response and improved transient response. As operating
conditions of the MFC system change, step response is
not a little change. Figure 3.5 is step response that
operating conditions are changing.

Acknowledgment
This work was supported by "Research Center for
Logistics Information Technology (LIT)" hosted by the
Ministry of Education & Human Resources
Development in Korea.

References[1] Charles L. Phillips, H. Troy Nagle (1995), Digital
Control System Analysis and Design. pp 404~413[2] Robert N. Bateson (1996), Introduction to control
system technology. pp 118~140[3] Simon Haykin (2002), Adaptive filter theory. pp
610~635[4] Kaoru H, Masayoshi E (2002), “Stainless steel-based
integrated mass-flow controller for reactive andcorrosive gases.”, Sensors and Actuators, pp 33~38[5] J. Schuurmans, T. Jones (2002), “Control of mass
flow in a hot strip mill using model predictive control.”,
Proc. of the 2002 IEEE int. Conf. on Control
Applications, pp 18~20

Figure 3.4 : Adaptive PID controller’s step response

Figure 3.5 : Operating condition change in adaptive PID©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Force Reflection Electronic Joystick Using Single Hall Sensor
SeungKeun Cho
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
skcho04@pusan.ac.kr

SunSin Han
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
ranger112@pusan.ac.kr

JangMyung Lee
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
jmlee@pusan.ac.kr

master site to the slave site. It is used to control thecontact force, position, or velocity of the teleoperated
robot, and it is commonly called teleoperation with force
reflection. The general method of implementing teleoperation systems is to provide visual information of theremote environment to the human operator. This method
can provide the visual information needed to perform
simple task, but can not ensure safe operation wherevisual information is limited be camera’s narrow viewing
angle, bad weather or surrounding obstacle
Therefore, the operator can robustly control in any
circumstance if the physical information about theworking environment is supplied to the operator alongwith vision information. [1]In this paper, we studied bilateral control that usedboth a teleoperated mobile robot and a joystick with twodegrees of freedom, In addition to the Virtual MassSpring-Damper Model was used. The relationshipbetween the mobile robot and the objects in the remoteenvironment were modeled to get collision vector. [2]Specially, A Hall sensor was used in the electronic
joystick instead of a potentiometer. The use of a noncontact electronic joystick solves the performancedegradation problem due to a track wear, the non-linear
problem, and complexity of the mechanism structure.
Chapter 2 describes the composition of the entire
system especially master joystick, Chapter 3 describes
the method that provides the operator with virtual force
using suggested collision vectors. Chapter 4 describes
the results from the experiment using the mobile robot,
And chapter 5 concludes the paper..

Abstract
A force feedback electronic joystick for the efficient
remote control of a mobile robot is proposed andimplemented in this paper. There are several limitations
in controlling a mobile robot in tele-operation mode
based on CCD vision, which is a very popular method
for the tele-operation of a mobile robot. For examples,shadows and curved areas cannot be viewed by thenarrow view-angle camera, especially in bad weather
conditions like snows or rains. To overcome this problem,a virtual force is generated according to the distance
between the obstacle and the robot and a collision vector
estimated from the ultrasonic sensor data. Therefore, theoperator can control the position and speed of the mobile
robot smoothly using both visual and haptic information.
The sixteen ring – structure ultrasonic sensors areattached onto a mobile robot that was manufactured forthis experiment, and two small-size DC motors areattached at each axis of the joystick. Althoughpotentiometers have generally been used to measure themotion of the joystick bar, a non - contact electronic
joystick was used so that a hall sensor would resolve thedegradation problem caused by track-wear. Finally, theaddition of the force feedback mode has been shown tobe very helpful for experimentally avoiding obstacle.

Keywords Force Feedback, Collision Vector, Teleoperation, Hall sensor, Potentiometer

1 Introduction
2 Composition of the Tele-operation

The robotics industry has changed because of thedevelopment of the micro processor and sensor. The
robotics ability to do dangerous or simple tasks insteadof human being in industry has been greatly improved.
However, recently developed robots are limited in theirability to recognize all dangerous situations andunexpected incidents unlike human beings. Teleoperation allows used human beings to operate robots fortasks that require the judgment and selection. The
service robot, which is used in a hospital or hotel, and
the AGV, which is used in the Autonomous Port orfactory, are based on an autonomous driving, but they
can also be tele-operated when emergency situations
occur. The latest teleoperation systems use a bilateralcontrol method which transfer information from a slave
site to a master site as we transfer information from the©ISAROB 2006

In this study, the entire system is composed of thehuman operator, master joystick, slave mobile robot andcommunication block. The schematic diagram is shownin figure 1. If a human operator give force to theelectronic joystick, displacement occurs as Force, This
displacement is then read by the Hall sensor andtransferred to the control part. The control part thenperform on A/D conversion and finally the speed order,
which is calculated by the control part, is given to themobile robot. The mobile robot moves giving PWM
which corresponds to that speed. The repulsive force,
which corresponds to the distance between robot andobstacles is detected by ultra sonic sensor and transferredto the motor drive. The human operator can use the154The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Bh = λ (θ ) B sin(θ )repulsive force. Finally, the mobile robot follows
reference point given by the human operator while
supplying information about obstacles. The humanoperator uses feedback concerning the force, whichcorresponds to the distance and approach velocity
between a teleoperated mobile robot and obstacles in the
remote environment, so a telepresence is realized.

Joystick

Main
Controller

Because of the distribution feature of the magnetic
flux of the permanent magnet, a non-linear element
exists as λ (θ ) . If the direction of the flux is parallelwith the direction of the joystick bar, λ (θ ) = 1 . Due tothe increasing rotation angle, θ , the horizontal
vector B h does not consistently increasing, but
decreases when a specific rotational angle is exceeded.
So a non-linear element, λ (θ ) , is defined as a

Mobile Robot
433
MHz
RF
Moudledecreasing function about the rotational angle θ .
2UltrasonicSensorHuman
Operator(1)λ (θ ) =Motor
Driver(2)

Where, k is a constant value corresponding to thegeometrical features of the permanent magnet andjoystick mechanism, and the magnetic flux density.
The output voltage of the hall sensor has a linear
relationship with a horizontal vector of magnetic flux
density, so the output voltage satisfies Eq. (3). Where,
C is a ratio constant value.

VisionCameraEnvironment

1
1 + kθ 2

Fig. 1. The Total System architecture.

2.1 Electronic joystickVx = CBh cos(α ) = Cλ (θ )B sin (θ ) cos(α )In this paper, a non – contact electronic joystick withsingle hall sensor is used to tele-operate a mobile robot.
The performance of the existing contact electronic
joystick using a potentiometer deteriorated due to track
wear. To solve this problem a non-contact joystick wasdeveloped using a hall sensor. It has better accuracy anddurability[4]. The Fig 2 shows a model of the noncontact joystick.
As can be seen in Fig 2, direction of the magnetic
flux is indicated at the rotational center of the joystick.

Vy = CBh sin (α ) = Cλ (θ )B sin (θ ) sin (α )(3)The output voltage of the hall sensor is processed
through an A/D converter in the Micro Processor, so Eq.
(3) can become follow Eq.(4). [5].

sin (θ )× cos(α )1 + kθ 2
sin (θ )× sin (α )ADy = ς ×
1 + kθ 2
ADx = ς ×

(4)

Z

θ
α

Y

Bx
B

(a) Electronic control part(b) Hall sensor module

Bh
By
X

Fig. 2. The Mechanism of the Joystick.

θWhen a permanent magnet is inclined at an angle ofdegrees, the horizontal vector B h of magnetic flux

density B is detected by the hall sensor, and Bx , By is(c) Joystick mechanism part

determined from X,Y –axis direction. The hall sensor
generates a voltage signal corresponding to Bx , By

Fig. 3. Joystick System.

which has a phase difference is 90°. The relationshipbetween B and B h is expressed in Eq. (1)©ISAROB 2006

Where, ς is a constant value corresponding to the155The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Vd ( s) = K m ⋅ X m ( s)(5)
X d ( s ) = K v ⋅ V d ( s ) ⋅ ds
(6)
where, K m and K v are constant scaling values,
and ds is a control period, and X m and Vd are theamplifier value of a signal conversion circuit with sensor
output. In Eq. (2) and (4), the constant values k and ς
are essential features of the joystick system. Both k andς change when the mechanism specifications change.
The mechanism and controller of the joystick isshown at Fig. 3.
The permanent magnet is made of alnico and is1800~2000 Gauss.
The perpendicular distance between the permanent
magnet and the Hall sensor is 13mm. The length of thetotal joystick bar including permanent magnet is 31mm.
Fig. 4 is a block diagram of joystick system. A
potentiometer is used to verify the usefulness of the Hallsensor. The joystick controller is designed using PIC
Micro-Controllers. This repulsive the force at the
joystick based on the force values, Fx , Fy , which are

position and velocity of next reference point.

3.2 Virtual Force Feedback
The virtual force, Fb , which feedbacks to theoperator can be derived from the next equation:

where,RFM o ud le

B utton d a ta

p o te n tio m e te r

d sP ICP o sition d a ta

P o sitio n d ata
to S im u la to r

Fod and Fos are derived from next

sensor.
The method for deriving C (collision vector) is
introduced in chapter 3.3.
In Eq. (9), Fb , which is generated from a mobile

Fig. 4. The Joystick System block Diagram.

3 Virtual Force Feedback

robot at a remote site, is transferred to the operator in themaster site.
(9)τ b ( s ) = sat ( K b ⋅ Fb ( s ))3.1 Order value Acquisition of human operator
When the operator gives a force, Fh , to the joystick,⎧x, if x ≤ xmax
(10)
sat ( x) = ⎨
sgn(
x
)
⋅
x
,
otherwisemax⎩
where, K b is the constant of force feedback gain.

the position coordinates of the joystick are changed tothe degree of displacement, X m , using Eq. (5) and (6)as shown in Fig. 5. The displacement, X m , generates the

In spite of limited visual information due totransmission delays, limited bandwidth, or camera
trouble, the operator has the distance between themoving robot and the obstacles not seen on the visualdisplay according to the force, τ b , felt from the joystick.

desired velocity Vd by the following equation.
X
Joystick Handle

X mx

Fy

(7)

(8)
As can be seen in Eq. (8), the virtual impedance
method is a form of generating virtual force using
distance and velocity, the modeling robot and a reference
point, and the relationship between the robot and the
obstacles as a spring and d damper. This method differsfrom the general impedance method[6,7,8]. We suggest a
new virtual impedance method which uses a discretevalue (approaching velocity) as damper:
where, ρ o is the measurable range of the ultrasonic

M oto rs

Jo ystic k
C o ntro ller
(P IC 18 F 4 5 2 )ios = 0⎧⎪∑ {K ( ρ − C )C unit − D (∆ C )C unit }, when C < ρ 0=⎨⎪0, otherwise.
⎩

the power.

H a ll S e n so r

iod = 0Fod , s ( s ) = K ( X ( s )) + D ( X& ( s ))F y . The L6203 DC Motor drives are used to amplify

F orc e da ta
from M ob ile R ob o t

nosequation:sent from the controller to the operator. For this purpose,it drives two DC motors by sending out PWM (Pulse
Width Modulation) output corresponding to Fx and

PW M

nod

Fb = { ∑ Fod ( s ) + ∑ Fos ( s )}Xm

3.3 collision vector detection for obstacle

Fh

Fx
X my

The ultra sonic sensor (SRF 04) consist of 16 ring
structures place at 22.5° intervals on the robot. A decoderand multiplexor are used to reduce interference among
the ultra sonic sensors. In this paper, the collisionY

Fig. 5. Joystick axis and coordinates©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

vector ( C ) which is oriented toward the mobile robot's
center, and is vertical to a tangent line of obstacle, issuggested in order to derive the virtual repulsive force
using the suggested virtual impedance method.
Three situations exist when an obstacle is recognizedusing the ultrasonic sensor.
First a small cylindrical obstacle or human isdetected by a single ultrasonic sensor.
Second, a bigger obstacle is detected by two
ultrasonic sensors. And third, an obstacle is detected bythree ultrasonic sensors, in which case this may be a wall.
1. The collision vector for the obstacle in the firstsituation is simply the distance to from the detecting
sensor:Fig. 7. The Mobile Robot Figure

C = L

(11)
2. For the second case, when there are two vectors, L1and L2, which were detected by two neighboring
ultrasonic sensors, the collision vector can be obtainedby finding a perpendicular line from the center of robot

Fig. 8 (a) and (b) show the results of the teleoperation. Fig.8 (a) is the result from using only thecamera, Fig. 8 (b) is the result from using the force
reflection joystick with the camera.
The results show that tele-operation of the robot
using force reflection is more efficient and stable thanwhen using only the camera.

to the line of obstacle location, P1 P2 .
The second case is illustrated in Fig. 6.

Fig. 6. The get collision vector of an obstacle
3. The collision vector for the third situation can bederived using the same method as the second situation.

(a) Without Force Reflection

4 Experiments and Results
The mobile robot used in this experimental isequipped with CCD camera, encoders, ultra sonicsensors and two DC motors. Each wheel of the mobile
robot is operated by two DC motors. The motor specs
are 24V, 35W, 0.8A, 20rpm(10 : 1 Reduction gear),
30kgf-cm torque. The figure of robot is shown at Fig. 7.
The mobile robot was tele – operated in the hallwayof the special building in Pusan National UNIV. The
remote site had dark lighting and limited sight so that
distances could not be ascertained using the camera.
The experiment was performed to demonstrate theusefulness of the suggested virtual impedance control
with collision vector using force reflection joystick.

©ISAROB 2006(b) With Force Reflection
Fig. 8. The Experimental results

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Three operators worked the robot during theexperiment will similar results from all three. When thecamera alone was used, there were 1 to 2 collisions withobstacles. When the force reflection joystick and thecamera were used, there were no collisions and the timeneeded to navigate to the goal was reduced by 13~14
seconds.
Fig. 9 shows the trajectory of the joystick bar inorder to compare the performance of the dual
potentiometer and the single Hall sensor. Because theoutput of the Hall sensor and the movement of joystickbar has non – linear characteristics, the motion of thejoystick bar con not correctly represented.
The result of the trajectory is almost circular whenusing a single Hall sensor.

improved and can be widely used in fields that require
remote control mobile robot or precision work.
If information is acquired using a hybrid method
which mixes various sensor types with ultra sonicsensors, although a mobile robot is located at a remotesite, it can be accurately tele-operated by a humanoperator.

Acknowledge
This work was supported by "Research Center for
Logistics Information Technology (LIT)" hosted by the
Ministry of Education & Human Resources
Development in Korea.

References[1] S. S. Lee and J. M. Lee, "Haptic interface design for
the tele-surgery", Proc. of the CIDAM Workshop on
Service Automation and Robotics, Hong Kong, pp.
81-91, June 19-20, 2000.
[2] S. K. An, S. J. Han, H. R. Hur, and J. M. Lee,
"Implementation of a Remote Peg-in-HoleOperation Using a Two Degrees of Freedom ForceReflective Joystick," J. of Electrical Engineering
and Information Science, vol. 4, no. 3, pp. 315-322,
Jun.. 1999.
[3] “Angular Position Sensing Measurement with
Sentron Hall ASIC" Sentron Application Note. No
17, 2001.
[4] H. C. Kim, D. H. Kang, J. M. Lee, "Development ofa Non-contact electronic Joystick using Single Hall
Sensor", KACC 2005. pp. 68-73, 2005.
[5] Jay L. Devore, “Probability and Statistics forEngineering and the Science" Publication by
Duxbury, pp. 247-271, 1995.
[6] T. Arai, H. Ogata, and T. Suzuki, "Collision
Avoidance Among Multiple Robots Using Virtual
Impedance", Proc. of IEEE/RSJ Int. Workshop on
Intelligent Robots and Systems, pp.479-485, Sept.
1989.
[7] J. Ota, T. Arai, E. Yoshida, D. Kurabayashi, and T.
Mori, "Real Time Planning Method for Multiple
Mobile Robots", Proc. of IEEE Int. Symposium on,
Assembly and Task Planning, pp. 406-411, 1995.
[8] Ming ouhyung, Wu-Nan Tasi, Ming -Chang Tsai, "A
Low-Cost Force Feedback Joystick and its use in pc
video games", IEEE Transactions on Consumer
Electronics, Vol.41.No.3. August 1995(a) Using Hall Sensor(b)Using Dual Potentiometer
Fig. 9. Compassion of the performance of the Hallsensor and Dual Potentiometer5 Conclusion
In this paper, the efficiency of tele-operation using
the force reflection joystick is established through anexperiment. When the mobile robot is being driven,
information about obstacle is measured using ultra sonicsensor.
This information is communicated to the operator
as feelings in the joystick. This method can improve thestability and controllability of robot through informationacquired through repulsive force. This method isdifferent than the existing method which uses only vision
information. Also, using the single Hall sensor instead ofdual structure potentiometer that has a non-linear section
the displacement of the stick, can accurately bemeasured. The performance of the joystick was©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

An Self-Localization of Mobile Robot in an RFID Sensor Space
ByoungSuck Choi
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
woodc@pusan.ac.kr

HyoSik Choi
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
chs3040@pusan.ac.kr

displacement is small. However, when wheel slippage
occurs during travel, errors in estimation increase andthey aggregate so that the position is not precisely
calculated[5]. To remove the problem of accumulative
errors, ultrasonic sensors, IR sensors, vision sensors, andGPS have been recommended to establish the absolute
position of the mobile robot based on the measured
position and visual information[6]. However, there arealso several problems with these systems. For example,
the GPS-based positioning system is not effective after
the mobile robot enters a building and the ultrasonicsensor based positioning system suffers from theinterference of neighboring sensors. Also, a vision sensor
is so sensitive to illumination and color that it is very
expensive to maintain a suitable sensing environment.
Another severe shortcoming of vision sensor is that theamount of computations required increases requires aheavy computational burden with the increase ofresolution and number of cameras increases. To resolve
all of these problems, a new concept of an ‘intelligent
space’ or ‘sensor network space’ has been introduced[7][8]. In the sensor network space, the robot recognizes
its position not by sensors attached its body but by thesensors that exist in the network space.
In this paper, a new sensor network space is
proposed. This space is formed by RFID tags and readers.
Several RFID tags are attached to the floor, which areread by the RFID reader attached to the bottom of themobile robot. Since each tag provides its position
information, the robot’s position can be established.
Usually the RFID tags have formed located a square
pattern on the floor. However, in this paper, we propose atriangular pattern which provides better accuracy than
the square pattern with the same number of tags in thesame space. In section 2, the constitution of the RFID
sensor space has been illustrated, and section 3 describes
the position estimation scheme in the sensor space andthe allocation pattern of the RFID tags. In section 4, therobot position has been estimated by simulation andexperiments and section 5 concludes and summarizes themain contributions of this paper.

Abstract
This paper presents an efficient localization scheme
for an indoor mobile robot using RFID tags on the floor.
The mobile robot carries an RFID reader at the bottom of
the chassis, which reads the RFID tags on the floor to
localize the mobile robot. Each RFID tag stores its own
absolute position which is used to calculate the positionand velocity of the mobile robot. Locating the RFID tags,
which constitutes an intelligent sensor space, may
require several factors: economics, feasibility, andaccuracy. In this paper, the optimal allocation scheme of
the RFID tags on the floor to satisfy the accuracy
constraint has been proposed and verified by theexperiments. Based on reading the RFID tags, the mobile
robot navigation has been successfully demonstrated to
avoid obstacles and to reach the goal within a prespecified time.

Keywords RFID system, tag, mobile robot, allocation

1 Introduction
Recently robots have been developing rapidly withthe strong support from the constitutional technologies
such as integrated circuits, sensors, artificial intelligence,
image processing, computers and microprocessors. As
a result, intelligent robots, which recognize theenvironment, and ubiquitous robots, which are connectedto the network environment, are receiving attention
instead of industrial robots, which perform repetitive
labor[1]. Mobile robots, which are completely different
from the manipulators, have expanded the fields thatthey can be applied to their mobility. Several
applications of the mobile robot can be easily found: avacuum robot which frees housekeepers, a service robot
which guides visitors and explains articles in a museum,or a surveillance robot which performs dangerous tasks.
For successful task execution and autonomous
navigation, the position and orientation of the mobile
robot itself must always be quickly and precisely
recognized within the environment[2]. With a precise
estimate of its position/orientation, the mobile robot canplan a trajectory to its goal. To estimate the position of
the mobile robot, encoders and gyroscope sensors havebeen used to calculate its position based on its known
initial position[3][4]. This method is effective when theinitial position information is precisely given and©ISAROB 2006

JangMyung Lee
Dep. of EE.
Pusan National University.
Busan, Republic of Korea.
jmlee@pusan.ac.kr

2 RFID Sensor SpaceIn this section, an RFID sensor space is illustrated toestimate position of a mobile robot.

2.1 Position estimation system of robot

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computer system installed on the mobile robot processes
the position information from the tags to estimate theposition of the mobile robot. Figure 3 illustrates thecomposition of an RFID system for the localization of amobile robot.

In the conventional approaches, several different
kinds of sensors are attached to the mobile robot torecognize its location as shown in fig. 1-(a). To
efficiently estimate position, a variety of sensors areneeded, which implies that the robot must process all of
the resulting data in to locate. Generally it is notguaranteed to utilize the same position estimation systemfor each robot. That is, the position estimation systemshould be matched to each robot’s localization capability
when there are multiple robots working together.
On the contrary, the position estimation of each robot
in the sensor space can be accomplished through the
distributed sensors in the space as shown in fig. 1-(b).

YX
RFID Tag
Mobile Robot
with RF Reader

CCD Camera

Fig. 2. An RFID sensor space.

Ultra - sonic sensor
IR - sensor

RFID TagLASER sensor

RFID

RFID Tag

Encoder

MobileReader

RF Comm.

Robot(a) A conventional position estimation system.
Computer

Sensor< Sensor space >SensorAntennaFig. 3. Composition of an RFID system.
SensorSensor3 Position Estimation of a Mobile Robot

MobileRobot(b) A position estimation system in a sensor space.

3.1 Position of a mobile robot

Fig. 1. Types of position estimation systems.

The center coordinates of a mobile robot can be
represented in a two dimensional X-Y plane asfollows[9] :Each mobile robot establishes its position
coordinates when it detects and uses a sensor’s position
information. Since each mobile robot does not need tocarry sensors, but only a reader, other robots operating inthe sensor space cannot interfere with its position
estimation. That is, each robot can be free from otherrobots in recognizing its own location. Even though
there are obstacles in the space, the position estimation
process can be continued without any degradation.

P = [x R
where

θR ]T

(1)

θ R represents the orientation of the robot.

In Fig. 4, the position and velocity of a mobile robot
moving on a two dimensional space have been illustrated.
When the mobile robot is moving along a curved pathwith a constant curvature, the position and velocity of
the robot can be derived from the mobile robot
kinematics. When a mobile robot is moving fromlocation A to B, where location A is denoted as( xR , yR , θ R ) at time = t, and location B is demoted as( x ′R , y ′R , θ R′ ) at time = t + δ t , the followinginteresting relationships in the circular motion of themobile robot occur:

2.2 RFID sensor network system
Figure 2 illustrates the RFID sensor space proposed
for the localization of a mobile robot in this paper. The
RFID tags are regularly allocated on a two-dimensional
X-Y plate on which a mobile robot with an RFID reader
moves on. To form a wireless communication channel
between an RFID reader and the tags, a suitable antenna
pattern is engraved in each reader and tag. When thecommunication channel is connected, that is, thedistance between the reader and tags becomes smallenough for electric induction, the RFID reader provides
electric power by RF to the tag and receives the position
information that has been pre-stored in the tags. The©ISAROB 2006

yR

l
l
(2)
:R+
2
2
l ⎛ v + vL ⎞(3)⎟
R = ⎜⎜ R
2 ⎝ v R − v L ⎟⎠Where vL and vR represent the velocity of the left
vL : vR = R −160

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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and right wheels, respectively, R is the rotation radiuswith respect to ICC(Inertial Center of Circle), andrepresents the distance between the two wheels.

Y( xN , y N )Read recognition
area
X

ICC

( x3 , y 3 )RFID TagvL

ϕ( x1 , y1 ) ( x 2 , y 2 )
vR

X

R
B

vL

l
2

A

d

Fig. 5. RFID reader’s recognition area.

( x ′R , y ′R , θ R′ )When the position of the mobile robot is estimated asshown in fig. 5, fig. 6 illustrates position estimation error.

( xR , y R , θ R )vRY0

RFID Tag

Fig. 4. Position and velocity of a mobile robot.
Also the coordinated of ICC, the travel distance andangle, d and ϕ , can be obtained as follows:

Read recognitionarea

ICC = [ x R − R sin(θ R ) , y R + R cos(θ R ) ] (4)t+ δ t v + v
L
R
(5)
d =∫
dt
t
2

Fig. 6. Estimation error in RFID sensor space.

(a )In fig 6. the position of reader-coordinates of themobile robot-is estimated to be the same position in figs.
6-(a) and 6-(b), because the tags have the same
coordinate data.
However, the real position of mobile robot is not
same. The estimation error occurred between the realposition of robot and estimated position. Recovery of theestimation error is related to the gap between the RFID
tags. Figure 7 illustrates the relationships between theestimation error and gap between the RFID tags.
If the X-dimension only is considered for the tags,
then it is assumed that the each tags from left to right has
coordinates, a1 , a 2 , a 3 , L , a n −1 , a n , a n +1 , and the gap

t+δ t(v L + v R ) dt
d ∫t
ϕ= =
(v R − v L )Rl (v L + v R )

(b)

(6)

Using the rotation radius, travel distance and angle
from the equations of (4)~(6), the linear and angularvelocities of the mobile robot in the circular motion also
can be obtained[10].

3.2 Position estimation in RFID sensor space
The RFID sensor space for estimating the position ofa mobile robot was observed in the previous chapter. If
the tags are arranged at regular intervals and the distance
between them does not exceed the range of reader, thenthe reader’s recognition area for the tags would be
similar to that in figure 5.
The position of the mobile robot ( x est , y est ) that has

between the tags is d tag .
RFID Tag
R1

a reader antenna at its bottom, can be obtained throughthe tags that are located within the reader’s recognition
area.

R2

X
a1

max{ x1 , L , x N } + min{ x1 , L , x N } (7)2max{ y1 , L , y N } + min{ y1 , L , y N } , (8)y est =
2x est =

a2
dtag

a3

L

an −1

an

an +1

RFID reader
recognition area

Fig. 7. Estimation error and gap of tag.
Also, the left boundary of the reader’s recognitionarea is R1 , the right is R 2 . That is, the RFID reader
can detect tags located between R1 and R 2 .
The estimation coordinate, R est , and the real center

where N represents the number of tags detected by
the reader and x1 , x 2 , x 3 , y1 , y 2 , L represents thecoordinate information of the tags.

position of the reader R real , illustrated in fig. 9, are©ISAROB 2006

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represented as follows:

and R 2 are represented in fig. 9 as follows:

a 2 + a n −12
R1 + R 2
R real =2

R est =

b1 < R1 < a 2
⎧
⎪
d
⎨ tag
⎪⎩− 2 < R1 − a 2 < 0⎧ bn −1 < R 2 < a n
⎪
d tag
⎨
⎪⎩0 < R 2 − bn −1 < 2

(9)

(10)

The Estimation error, e est , is defined as follows:

eest = Rest − R real =R1 + R 2 a 2 + a n −1−22

(14-a)(!4-b)(11)

RFID Tag
R1

where the coordinates
represented as follows:

of

R1

and

R2

X

a1

⎧ 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

b1(12-b)d tag / 2

b2

b3

an

an −1

L

L

bn −1

an +1

bn

bn+1

X1

RFID reader
recognition area

Fig. 9. Estimation error decrease in triangularpattern.

( R1 − a 2 ) + ( R 2 − a n −1 ) 1≤ d tag2
2

As shown in fig. 9, the estimation error in x-direction
decreases as follows:(13)

eest =

Equation. (13) shows that the estimation error is
proportional to the gap between the tags and maximum
value is half of the gap.
Therefore, if the gap between the tags is reduced,
then the accuracy of the estimation is improved. Thissolution increases costs because it increases the numberof tags. The optimal allocation of the RFID tags in thesensor space proposed in this paper aims to improve theaccuracy of the position estimation without increasing
the number of tags. Traditionally tags have been
allocated in a square pattern (fig. 5), but a triangularpattern (fig. 8) decreases the estimation error without
increasing the number of tags.

( R1 − a 2 ) + ( R 2 − bn −1 ) 1≤ d tag2
4(15)

4 Experiments and Results
Before conducting a real experiment with a mobile
robot, the position estimation accuracy of the two tag
allocation patterns are simulated and compared. It is
assumed that the mobile robot precisely follow theprescribed path. As can be seen in fig. 10, the position of
the mobile robot is estimated every 0.1 seconds along a
constant curvature Path-1 and Path-2. To compare theestimation accuracy, the RFID tags are allocated in two
different patterns, square (fig. 10-(a)) and triangular (fig.
6-(b)). The RFID reader’s recognition area was set as 2.5
m * 2.5 m

Y

4.1 Simulation( xN , y N )Read recognition

For the simulation experiments, two different path
are set as Path-1 and Path-2 as shown in fig. 10. For the
given path, the velocity and position of the mobile robot
can be estimated using equations (2)~(4), and the pathcharacteristics are summarized in Table 1.
As illustrated in fig. 6, the two allocation patterns of
RFID tags are tested under the same conditions, and theresults are summarized in Table 2 for Path-1 and in Table
3 for the Path-2. Through the close observation of theestimation accuracy of the two pat-terns in Tables 2 and3, it can be concluded that the triangular pattern provides
better accuracy, especially in the x-coordinate estimation.

area( x3 , y 3 )RFID Tag( x1 , y1 ) ( x2 , y 2 )X

Fig.
8. RFID tag allocation and reader’srecognition area in triangular pattern.
Figure 9 illustrates the decrease in estimation error
in the triangular pattern space. The coordinates of R1©ISAROB 2006a3

a2

(12-a)

From (11) and (12), the estimation error can be
represented as follows:

eest =

R2

are

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Table 3. Position estimation data of Path-2(simulation)
Position(a) Motion trajectory in square pattern.

(b) Motion trajectory in triangular pattern

Starting Point(0.2, 0.8)

(0.2, 0.2)

Ending Point(0.8, 0.2)

(0.7, 0.7)

Rotation Radius

0.6 m

0.5 m

Velocityv L = 0.28 m / sv R = 0 .2 m / sv L = 0 .2 m / sv R = 0 .3m / s

ICC(0.2, 0.2)

(0.2, 0.7)

Table 2. Position estimation data of Path-1(simulation)
Position

Square pattern

Triangular pattern

X(m)

Y(m)

X(m)

Y(m)

X(m)

Y(m)

0.2
0.27
0.34
0.4
0.48
0.56
0.61
0.66
0.7
0.74
0.76
0.78
0.79
0.8

0.8
0.8
0.78
0.76
0.73
0.68
0.63
0.58
0.52
0.46
0.42
0.37
0.31
0.2

0.2
0.3
0.3
0.4
0.5
0.6
0.6
0.65
0.7
0.75
0.8
0.8
0.8
0.8

0.8
0.8
0.8
0.8
0.7
0.7
0.65
0.6
0.5
0.45
0.4
0.4
0.3
0.2

0.2
0.28
0.33
0.4
0.5
0.6
0.63
0.65
0.7
0.75
0.75
0.8
0.8
0.8

0.8
0.8
0.8
0.8
0.7
0.7
0.6
0.6
0.5
0.45
0.4
0.4
0.3
0.2

©ISAROB 2006

X(m)

Y(m)

X(m)

Y(m)

X(m)

Y(m)

0.2

0.2

0.2

0.2

0.2

0.2

0.27

0.21

0.3

0.2

0.28

0.2

0.35

0.22

0.3

0.25

0.33

0.25

0.42

0.25

0.4

0.2

0.43

0.2

0.48

0.29

0.5

0.25

0.5

0.25

0.52

0.32

0.55

0.3

0.5

0.3

0.58

0.37

0.6

0.4

0.58

0.4

0.60

0.41

0.6

0.4

0.6

0.4

0.63

0.45

0.6

0.4

0.63

0.4

0.67

0.54

0.7

0.5

0.68

0.5

0.70

0.62

0.7

0.6

0.7

0.6

0.70

0.69

0.7

0.7

0.7

0.7

The simulation results were verified through the realexperiments. A mobile robot was utilized to carry an
RFID reader at the bottom of the chassis, which reads
the positions of the mobile robot when-ever the reader
passes a tag.
Figure 11 shows the RFID reader and a tag used forthe real experiments. The main frequency of the RFID
system was 13.56Mhz, the position coordinates of thetags were pre-stored, and the tags were regularly
allocated at specific locations following a designed
pattern. To show the superiority of the triangular pattern,
the tags are allocated every 0.1m in a row for both of thesquare and triangular patterns. Figure 12 shows themobile robot and RFID sensor space for the experiments.
To allow a comparison with the simulation, the mobile
robot followed the Path-1 and Path-2 while the RFID
reader recognized and read the position data from thetags.

Table 1. Characteristics of Path-1 and Path-2
Path-2

Triangular pattern

4.2 Real experiments

Fig. 10. Allocation of RFID tags and therecognition area of reader.

Path-1

Square pattern(a) RFID Reader(b) RFID Tag

Fig. 11. RFID reader and tag for the experiments.
The estimation error is represented through graphs infigure 13. As can be seen by comparing the error values,
error for the triangular pattern has a lot smaller error than
the square pattern for both of the Path-1 and Path-2.

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be estimated precisely without any interference fromenvironments. Therefore, this scheme is very effective
for estimating the position of any object in the sensor
space and it could be a good tool to form an ubiquitousenvironment.

Acknowledge(a) Mobile Robot(b) Sensor SpaceThis work was supported by "Research Center for
Logistics Information Technology (LIT)" hosted by the
Ministry of Education & Human Resources
Development in Korea.

Fig. 12. Mobile robot and sensor space for theexperiments.

References[1] Ren C, Luo, Kuo L. Su, Shen H. Shen and Kuo H.
Tsai, “Networked Intelligent Robots Through theInternet: Issues and Opportunities,” Proceedings ofthe IEEE, Vol. 91, Issue 3, pp. 371-382, March 2003.
[2] San Mok Lee, “Using Vision Sensor and Sonar
Sensor of Mobile Robot Localization,” Chungnam
National University, 2001.
[3] Komoriya, K., Oyama, E., “Position estimation of amobile robot using optical fiber gyroscope,”Intelligent Robots and Systems IROS ’94.
Proceedings of the IEEE/RSJ/GI International
Conference on,, Vol. 1, pp. 143-149, 12-16 Sep.
1994.
[4] Sooyong Lee, Jae-Bok Song, “Robust mobile robot
localization using optical flow sensors andencoders,” Robotics and Automation, 2004.
Proceedings. ICRA '04. 2004 IEEE International
Conference on Vol. 1, No. 26, pp. 1039-1044, April
2004.
[5] Young Shick Ro, Kyung Sup Park, “A study of
Optimal Position Estimation for Mobile Robots,”
Journal of Engineering Research, Vol. 26, No. 1, pp.
243-257, 1995.
[6] Ohno, K.., Tsubouchi, T., Shigematsu, B.., Maeyama,S., Yuta, S, “Outdoor navigation of a mobile robot
between buildings based on DGPS and odometry
data fusion,” Robotics and Automation, 2003.
Proceedings of ICRA '03. IEEE International
Conference on, pp. 1978-1984, Sept. 2003.
[7] Joo-Ho Lee, Hideki Hashimoto, “Controlling
Mobile Robots in Distributed Intelligent Sensor
Network,” IEEE Transaction on Industrial
Electronics, Vol. 50, No. 5, Oct. 2003.
[8] Kazuyuki Morioka, Joo-Ho Lee, Hideki Hashimoto,“Human-Following Mobile Robot in a Distributed
Intelligent Sensor Network,” IEEE Transaction on
Industrial Electronics, Vol. 51, No. 1, Feb. 2003.
[9] W. E. Dixon, D. M. Dawson, E. Zergeroglu and A.
Behal, Nonlinear Control of Wheeled Mobile
Robots, Springer, 2003, pp. 1-4.
[10] G. Dudek and M. Jenkin, Computational Principlesof Mobile Robotics, Cambridge university press,
2000, pp. 15-22.

(a) Estimation error in path-1(b) Estimation error in path-2
Fig. 13. Estimation error of the each pattern.

5 Conclusion
This paper proposes a new pattern of RFID tag
allocation for the efficient and accurate estimation of themobile robot position in an RFID based sensor space.
This pattern overcomes the shortcomings of theconventional absolute position estimation schemes andimproves its efficiency and accuracy. The importers aredemonstrated by a simulation of a moving object over an
RFID-based sensor space. To illustrate the improved
accuracy and economics of the position estimation
scheme, the square and triangular tag patterns have beensurveyed. The triangular pattern is shown to be thesquare pattern for position estimation of a mobile robot.
Note that based on the approach described in an RFID
sensor space, the absolute position of a mobile robot can©ISAROB 2006

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Fuzzy Posture Control for Biped Walking Robot Based on Force Sensor for ZMP

K. C. Choi

M. C. LeeJ. M. Lee

Department of Mechanical andIntelligent systems Engineering,
Pusan National University, Korea

School of MechanicalEngineering,
Pusan National University, Korea

Department of Electronics
Engineering, Pusan National
University, Korea

A posture control algorithm using fuzzy logic controls
posture of the robot as using the obtained ZMP
information.
The section 2 describes system configuration andkinematics of the biped walking robot which ismanufactured for this research. Section 3 introduces
analytic ZMP acquisition problem and then explains
method obtaining ZMP based on the force sensors.
Section 4 introduces posture control algorithm by fuzzy
logic, and Sections 5 and 6 show experiment result andconclusions.

Abstract
In these days, biped walking robots are concentrated
on a service robot. Biped walking robots are suitable ina human life work such as walking up and down stairs
and acrossing a threshold because they have two leg
mechanism like a human. However, it is very difficult
subject that robot maintains valance in walking. One
way to solve this problem is using an accurate dynamic
model of biped robot in 2-Dimension and 3Dimension. Another method to solve this problem ismodeling biped robot simply and controlling the robot
with an adaptive control, intelligent control and robust
control algorithm and so on. ZMP(zero moment point)concept was invented in this field.
In this paper, ZMP trajectory following for bipedwalking robot based on force sensors and posturecontrol using fuzzy algorithm are studied. The posturecontrol of the biped robot is performed by ZMP
information in static standing. In this paper, posture of
biped walking robot is controlled by fuzzy algorithm
referring to the obtained ZMP trajectory information.
The result of this study is confirmed by experimentapplied fuzzy posture control to against disturbanceand keep valance on a slope in static standing

2. SYSTEM CONFIGURATION AND
KINEMATICS ANALYSIS
2-1. System configuration
The developed biped walking robot is shown in Fig.
1. It’s height and weight are 28㎝ and 3.2kg,
respectively. The biped robot is composed of 12
degrees of freedom. Fig. 2 shows coordinates of thedeveloped biped robot.
The system can be divided into a control unit, asensor module, and a motor module. The main control
system is a Pentium Ⅳ computer and has trajectorycompensation algorithm of the biped walking robot andsend command to control motor module of each joint.
The sensor module consists of a ATMEGA 128 thathas 4-channel of A/D converter. Force data is acquiredby using FSR(force sensor resistor) sensors. ZMP is
calculated using force data and sent to control unit. The
RS485 communication is used between the PC and the
motor and the sensor module.

Keywords: Biped robot, ZMP, Force sensor, Fuzzy,
Posture control

1. INTRODUCTION
A biped robot has a structure similar to human’s leg
and has higher mobility than conventional wheeled
robots. However stability of a biped walking robot is
not yet solved completely and has to be solved to use
advantage of a biped walking robot[1]. The bipedwalking robot is a multi-body system and has complex
dynamics and many non-linear factors. Therefore it isvery difficult to move a biped robot stably.
Concept of ZMP has been introduced as importantcriterion for the stability of a biped walking robot[2]. If
ZMP is measured only by using force sensors, we don’t
need consider complicate dynamic analysis of a bipedwalking robot. The measured ZMP gives advantagethat can directly recognize state of surface of land
without the other sensors such as acceleration sensor
and gyro-sensor[3]. The robot can recognize own
posture by using ZMP by ground reaction force. In thispaper, ZMP is measured to judge stability of a bipedrobot by using force sensor attached on a sole of a foot.

©ISAROB 2006

Fig. 1 The developed biped robot

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3-2. ZMP measurement based on force sensor
If we use the attached force sensor on a sole of afoot of the biped walking robot, position of the ZMP isfound by simple calculation.
Fig. 3 shows the force sensors attached on a sole of afoot of the designed biped robot. The force sensors
measure ground reaction force from the sole. ZMP of
the actual biped walking robot is calculated by using
the detected force from force sensors. The sensors
consist of 8 force sensors attatched on each sole of afoot corner. Formula for acquiring ZMP is as
8∑ fi × ri

ZMP = i =18

(1)

∑ fi
i =1

Because force sensor has some measurement noise,
3rd Chebyshev lowpass filter is used to acquire ZMP.
The cutoff frequency and sampling frequency are set as
2Hz and 100Hz, respectively.

Fig. 2 The coordinates of the biped robot

2-2. Kinematics
In trajectory generation of the biped robot, forward
and inverse kinematics equation are used to calculate aposture of the robot and angle of each joint.
Kinematics analysis is based on DenavitHartenberg's kinematics notation. The biped robot has
12 degrees of freedom. Coordinate system of the bipedrobot is shown in Fig. 2. The base coordinates “BASE”
is located on a ground surface in the middle point of
the feet. The trunk coordinates “Trunk” is located on
the middle point of waist directly above “BASE”. The
left “PLT” and right “RLT” coordinates are located on
the tip of the foot.

Fig. 3 The force sensors attached on a robot sole

4. POSTURE CONTROL OF ROBOT
4-1. Fuzzy Posture algorithm

3. ZMP MEASUREMENT BASED ON
SENSOR

This study is to keep stability of biped robot withoutchanging walking trajectory. So a proposed algorithm
is carried out by compensator which changes thecoordinate of “Trunk” without coordinate change ofother joints.
Posture of a biped robot is stable when ZMP existsin ‘Desired area’ which is assumed as the most stablearea according to ZMP. If ZMP does not exist in‘Desired area’, robot has to move a ZMP to the‘Desired area’. Fuzzy algorithm compensatescoordinate of “Trunk” to move the measured ZMP tothe desired ZMP, which calls ZMP compensator. Robot
has a new reference angle of each motor, which is
obtained to analyze inverse kinematics for stability ofrobot by ZMP compensator. ZMP compensation isaccomplished by driving motor of each joint.
Coordinate of “Trunk” is compensated for acumulative
error. Fig. 4 is schematic diagram for posture control
using ZMP compensation by fuzzy algorithm based onforce sensor.

3-1. Problem of Analytic ZMP
The biped robot is supposed that robot is doing
accurate action for analytic ZMP acquisition. That is,position or angular velocity, angular acceleration ofeach joint should be controlled correctly and each linkmust be a perfect rigid body. However control error ofa biped walking robot exists clearly and a bipedwalking robot is not a perfect rigid body.
Specially, because each link of the biped walking
robot is connected, the error due to connection affects
stability of a biped robot. If we use actuator with thehigh torque and high accuracy or manufacture the robot
using a rigid-body, this error can be reduce. But when
the biped robot is standing on the sloped bottom, therobot may falls down because obtaining analytic ZMP
in real time is so difficult.

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Nine rules are constructed in a 3x3 fuzzy table, which
involves x1 and x2 in order to stabilize the bipedwalking robot by ZMP compensation. If x1 is P and x2
is N, posture of robot incline forward and movebackward to reference ZMP. Therefore, posture ofrobot can keep the ‘Desired area’ of ZMP for stability
without ZMP compensation. The membership function
for the coordinate of “Trunk” compensation isdefuzzified by the centroid method. The defuzzified
output values are compensated by reference coordinate
of “Trunk” for inverse kinematics.
Table 1 The rule base to trail the desired ZMP
x2

P

Z

N

P

NB

NS

Z

Z

NS

Z

PS

N

Z

PS

PB

x1

Fig. 4 The schematic diagram for posture control using
ZMP by fuzzy algorithm

5. EXPERIMENTS

4-2. Fuzzy rule for compensating coordinate of“Trunk”

An experiment is carried out in single support phaseand in static standing. A comparison of stability is
carried out between posture control with ZMP
compensation algorithm and without it against
disturbance. Experiment is also carried out in single
support phase on a slope. Desired ZMP is fixed on a
zero position which is center of sole.
First, when the robot received disturbance forward
and backward, the obtained ZMP trajectory is shownin Fig. 7 and Fig 8. We can see that xzmp decreases andincreases when disturbance is given backward andforward. If disturbance is given backward, “Trunk”
moves forward to move desired ZMP under backward
disturbance until ZMP is compensated. Whendisturbance is removed, ZMP trajectory is shown in theforward area because “Trunk” has moved forward tocompensate for incremental error as shown Fig. 8. If
ZMP is not compensated by fuzzy algorithm under
backward disturbance, although disturbance issuddenly removed, ZMP trajectory is not shown in
forward area as shown Fig. 7.
We experiment to evaluate the stability of the robot
on a slope(incline is a direction to clime a slope,decline is a opposite slope). First, when the proposed
posture control algorithm using only position sensor isapplied, ZMP trajectory is shown proportionallyaccording to angle of decline and incline as shown in
Fig. 9. If an angle of inclination is more increased,
robot is fallen down because ZMP is not compensated.

Three membership functions are constructed for x1
and x2, where x1 is error between measured ZMP anddesired ZMP and x2 is error between measured ZMP
variation and desired ZMP variation. Each of the input
variables has three fuzzy sets: N, Z, P. Membershipfunctions consist of overlapped isosceles triangles asshown in Fig. 5. All membership functions have equalbase length. The universe of discourse is assumed as–a ≤ x1 ≤ a(cm) and –b ≤ x2 ≤ b(cm/sample time)
for the two variables, which the universe of discourse
is decided by displacement of robot “Trunk” for x1 andby moving velocity of robot for x2. Output value offuzzy logic, which is to compensate coordinate of“Trunk”, is constructed in five membership function
for u_com as shown in Fig 6.
N

Z

-a
-b

0

P

a

x1

b

x2

Fig. 5 Fuzzy membership functions of input variables

Fig. 6 Fuzzy membership functions of output variables©ISAROB 2006

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Fig. 10 shows the result of experiment by fuzzy posturealgorithm using ZMP information according to incline
and decline. It is more stable than posture control
algorithm without ZMP information. If slope isdeclined, ZMP trajectory is moved to the desired ZMP
because ZMP compensator compensates coordinate of“Trunk”. After then, as slope is moving to horizontal
direction, ZMP trajectory is shown in the backward
area temporary because posture of robot can not becompletely compensated due to time delay by lowpass
filter, a slow sampling time. And fuzzy membershipfunction is not yet optimal. After then robot again
move to compensate to the desired ZMP on ahorizontal land.

Xzmp for forward and backward disturbance6

ZMP of X Axit (cm)4(1) backward disturbance
2

0

-2

(2) forward disturbance

-4

-6
0

2

4

6

8

10

12

14

16

18

Time(sec)

Fig. 7. The ZMP trajectory for disturbance(without fuzzy posture algorithm)

6. CONCLUSIONS

Xzmp for disturbance forward and backward
8

6

ZMP of X Axis (cm)4

The fuzzy posture control algorithm using ZMP
information was introduced. The ground reaction force
and the ZMP information was acquired as using force
sensors. And we could find position of center ofgravity of the biped walking robot as using theacquired ZMP and could control in real time so that
ZMP always exists within ‘Desired area’ by fuzzy
posture algorithm. The fuzzy posture algorithm using
ZMP is able to control the posture suitably even if thebottom of road has a slope. Further works on the
proposed ‘fuzzy posture algorithm using ZMP
information’ improve posture stability by choosing the
optimal fuzzy membership function and decrease timedelay. So, robot has stability on a dynamic environment
in walking(1) backward disturbance(2) forward disturbance

2

0

-2

ZMP reference

-4

-6
5

10

15

20

25

30

35

Time(sec)

Fig. 8 The ZMP trajectory for disturbance(with fuzzy posture algorithm )8

Xzmp on a slope

Slope is decline

ZMP of X Axis (cm)64

ZMP reference(desired ZMP)

2

Acknowledgment

0This work was supported (in part) by the Ministry of
information & Communications, Korea, under the
Information Technology Research Center(ITRC)
Support Program.

-2

Slope is incline

-4
5

10

15

20

25

30

Time(sec)

References

Fig. 9 The ZMP trajectory on a slope
without fuzzy posture control[1] J. Furusho and M. Masubuchi (1986). “Control ofa Dynamical Biped Locomotion System for SteadyWalking”,
JournalDynamic
Systems,
Measurement and Control, vol. 108, pp. 111-118.
[2] C. Zhu and A. Kawamura (2003). “Walking
Principle Analysis for Biped Robot with ZMP
Concept, Friction constraint, and Inverted
Pendulum
Model,”
Proc.
ofIEEE/RSJInternational Conf. on Intelligent Robots andSystems, pp. 364-369.

8

Xzmp on a slope

ZMP of X Axis (cm)6

Slope is decline

4

Slope is horizontality

20

ZMP reference(desired ZMP)

-2

-4

Slope is horizontality
10

20

Slope is incline3040[3] J. Furusho and A. Sano (1990) “Sensor-BasedControl of a Nine-Link Biped,” Journal of
Robotics Research, vol. 2, no. 2, pp. 83-98.

Time(sec)

Fig. 10 The ZMP trajectory on a slopewith fuzzy posture control©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Bluetooth Network for Distributed Autonomous Robotic System
Se-Hee Whang
In-Hun Jang
Kwee-Bo Sim
School of Electrical and Electronics Engineering
Chung-Ang University
221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea
Abstract
Distributed Autonomous Robotic System (DARS)
is a system that independent autonomous robots in
the restricted environments infer their status from preassigned conditions and operate their jobs through thecooperation with each other. In the DARS, a robot
contains sensor part to percept the situation aroundthemselves, communication part to exchange information, and actuator part to do a work. Especially, inorder to cooperate with other robots, communicating with other robots is one of the essential elements.
Because Bluetooth has many advantages such as low
power consumption, small size module package, andvarious standard protocols, Bluetooth is rated as oneof the efficient communicating technologies which canapply to small-sized robot system. In this paper, we
will develop Bluetooth communicating system for autonomous robots. For the purpose, the communication
system must have several features – separated module,
flexible interface. We will discuss how to construct andwhat kind of procedure to develop the communicating
system.

has the ability of independent operations. We will design the system to organize a network scheme and tomaintain the network scheme in the small sized communication module. In addition to these features, thenetwork module should serve a kind of standard interface which commonly used by the most people. If
the Bluetooth network is developed in this way, development convenience will be increased because of independent operation of each part in the robots andthis Bluetooth network will be applicable to other embedded systems easily. Except the previous hardware
features, many considerable view points are remained– about network scheme, constructing sequence, androuting problem. We should deliberate on how to organize the network system, what kind of network form
to be constructed, and what sequence to be used.
The features of Bluetooth are briefly demonstrated
in the chapter 2. The constitutions of network module
are described in the chapter 3. The problems related
to network formation are investigated in the chapter 4.
The conclusions and future works about the Bluetooth
network system are denoted in the final chapter.

1

2

Bluetooth

2.1

Features

Introduction

In the Distributed Autonomous Robotic System(DARS) [1] [2], each independent individual robot understands its situation through the surroundings, andthen operates its jobs through cooperation with eachothers. For these cooperative works, the ex-change ofthe information between the robots should be essential
and communication module is an important element toconstruct a distributed autonomous robot.
Bluetooth is a kind of short distance wireless communication methods and is developed for a portabledevice. Because of its features, Bluetooth is regarded
as the most suitable wireless technology for build up
the small-sized robots. In this paper, we will introduce Bluetooth network for distributed autonomous
robotic system. First of all, the Bluetooth network©ISAROB 2006

Bluetooth is designed for portable devices using thepower of a battery, so it satisfies the conditions – lowcost, low power, and compact size. With these reason,
Bluetooth is recognized as suitable wireless communication technology for applying to robotics. Besides,
this technology commonly supports various standard
protocols. Basically most of Bluetooth modules serve
HCI level interface and higher protocol stacks are implemented by software. Sometimes, the rest serve RFCOMM level interface – higher protocol than HCI –in the Bluetooth module. The following figures show
com-positions of Bluetooth Stack in the typical systemusing Bluetooth.

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2.2

Network Support

use the same voltage of Bluetooth module, exchanging
ATmega128 for ATmega128L can solve the interfacing
voltage problem.

Fundamentally, Bluetooth is developed to support
master and slave structure. One master should haveup to 7 slaves. Master and slave role can be operated
in a module. There are two kind of network concept
in Bluetooth. The first, a piconet is made up of only
one master and several slaves. The next, a scatternet,
which consists of piconets, is larger than the piconet.

3.2

Adaptation to Robots

In our lab, we are developing an agent robot for
DARS. In this agent robot, Bluetooth network module
takes a role of communication part and uses UART
as an interface with other parts. Fig. 2 Shows theconstruction of the agent robot.

Figure 1: Bluetooth networks (Piconet/Scatternet)

3

Network Module

Bluetooth communication system consists of Bluetooth network modules – define as a hardware module in this paper – and each module operates independently of the embedded system connected to it.
In a word, Bluetooth network module takes charge of
constructing wireless networks. Since only Bluetoothmodule cant construct and maintain networks, Additional circuital elements are required. Moreover when
this network system is applied for other system, standard interfaces are necessary for serving more convenience to developers.

3.1

Figure 2: Adaptation of Bluetooth Network Module

3.3

Bluetooth module which we use supports several
kinds of interfaces such as UART, USB, and SPI. In
order to test Bluetooth network system in the sameenvironments that robot operates, we had better directly program to the robot. However, if only the operation of Bluetooth module is inspected, the result
would be similar to that of direct programming. So
we recommend the following procedures.

HardwareBluetooth network module is basically composed ofa Bluetooth module and a net-work controller to organize a network system with the Bluetooth modules.
We use the Bluetooth module which contains CSR
Bluetooth chipset and use ATmega128 as a network
controller. This Bluetooth chip-set supports UART
interface and is the most commonly used. An ATmega128 contains about 128KB memory and supports
2 UART interfaces and is widely used in the world.
The network controller communicates only HCI commands with the Bluetooth module. Interfacing with
network controller and Bluetooth module, logic voltage difference problem may occur. However, it is easily solved by a level shifter device. If external UART©ISAROB 2006

Development & Test

1. The operation procedures of every Bluetoothmodule should be designed in order of time sequence.
2. After that, to test the designed procedures is executed. In this stage, we recommend test with serial dongles, because HCI level command is standard and HCI level test program is offered.

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3. If thus much is correctly performed, network controller may be programmed. After the programming, test of each Bluetooth network module andentire Bluetooth network system should be examined. Passing the test, the Bluetooth network
system would be operated well.

tree. In the star structure, BlueStar [7] is the representative algorithm. Star structure is connected to anumber of piconets through the bridge node. In thering structure two kinds of algorithms exist by whether
the basic unit is node or piconet. Nevertheless, theconnected form of basic units is a ring.

4.2

Formation of Bluetooth Network

In this paper, we use the tree structure of the network formation, because tree formation is easy andlight to adapt for small embedded system. Besides,
in order to implement self-organized Bluetooth networks, self-organizing algorithm is essential and usually has four procedures – discovery, organization,
maintenance, and re-organization.
1. Discovery phase: All of Bluetooth modules are initialized andreceive unique module name from network controller. After initializing, each module sends inquiry command to detect neighbor modules andstores information about neighbor modules.
2. Organization phase: Using stored information about neighbor modules, Bluetooth network structure would be constructed. At this time, efficiency of the structure is affected by which is the root of the tree.
To estimate the efficiency thorough the tree root
candidate, we define simple fitness function. The
Bluetooth module, which has the largest Fitness
function value, becomes the root of the tree.

Figure 3: Bluetooth Development and Test

4

Bluetooth Network

In this paper, we use the tree structure of the network formation, because tree formation is easy andlight to adapt for small embedded system. Besides,
in order to implement self-organized Bluetooth networks, self-organizing algorithm is essential and usually has four procedures – discovery, organization,
maintenance, and re-organization [8] [9].

4.1(a) initialize variables : sum = 0, fcandidate = 0(b) number of neighbors around candidate module :sum+ = nneighbor , fcandidate + = sum(c) candidate is replaced with neighbors during
the origin candidates neighbors and operates
the followings.

Researches in Scatternet

There are three major parts about researches inscatternet – scatternet structure formation algorithm,
routing algorithm, and scheduling in the bridge node.
Routing and Role scheduling is dependent on the network structure, so these three parts of scatternet researches are organically associated with each others.
There are three kinds of representative formation
algorithm by the shape of the scatternet structure– tree, star, and ring. Representative tree structure
researches are MIT-TSF [3], MIT-BSFA [4] [5], andBluetrees [6]. This structure has high extensibility but
also has potentiality of bottleneck at the root of the©ISAROB 2006

- initialize variables : sum = 0- fitness function adds multiplied sum bycoefficient of neighbor order :fcandidate + = (1/2)order · sum(d) Unless the neighbor module does not exist,
repeat the 3 procedure
3. Maintenance phase: Each module updates the information aboutneighbor module through periodic inquiry command.

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4. Re-organization phase: Through the updated information, recalculate
the simple fitness function value of every Bluetooth module. If necessary, change the structureof the tree.

4.3

algorithm. We should examine what kind of algorithm
is really suitable for the Bluetooth network system.

Acknowledgements
This research was supported by the Brain Neuroinformatics Research Program by Ministry of Commerce, Industry, and Energy.

Routing of Bluetooth Network

Bluetooth transmit rate is up to 1MBPS in Bluetooth core specification 1.1. Hence, transmission of
the massive packet – such as image data – could leadany problems. Also, because of the structural problems, the larger numbers of modules exist, the heavier
transmission load is concentrated at the root of thetree. As these reasons, we should use the pre-processed
and light-weight packet data.
Except these problems, data packet usually hops
a number of steps. Accordingly, we must design thetunneling packet structure and related algorithm.

5

6

References[1] H. Asama, T. Fukuda, T. Arai, and I. Endo, Distributed Autonomous Robotic Systems, Springer
Verlag, 1994.
[2] H. Asama, T. Fukuda, T. Arai, and I. Endo, Distributed Autonomous Robotic Systems 2, Springer
Verlag, 1996.

Conclusion and Future Works[3] G. Tan, A. Miu, J. Guttag and H. Balakrishnan, “Forming Scatternets from Bluetooth Personal Area Networks,” In MIT Technical Report
MIT LCS-TR-826, 2001.

In this paper, we develop the Bluetooth network
system for distributed autonomous robotic system. In
this system, we use Bluetooth modules, because of
its advantages – low power consumption, cheap price,
small size, and various standard protocols. To guarantee the applicability and flexibility of the hardware
module, we design Bluetooth network module witha microcontroller and additional circuits. Also To
build simple and light weight structure, we select treeshaped network scheme and define a simple construction algorithm. Nevertheless, this Bluetooth module
has several problems. After understanding about thismodules features, we could use the Bluetooth network
system.
This Bluetooth System has numerous rooms for
performance improvements in the circuit, network formation algorithm, and routings. We should try to increase the performance in various ways – hardware
improvement and other network structure formation
algorithm.
For hardware improvements, there are several possibilities. We can replace the Bluetooth module withanother module which has more high speed transmission rate. Also circuital modification is needed forconvenience of usage. If the higher profile stack andapplication program interface (API) are contained in
the network controller, it is certain that easier andmore flexible than that of the old system.
In the network scheme, we should compare a tree
formation algorithm with the other network formation©ISAROB 2006

References[4] C. Lay and K.Y. Siu, “A Bluetooth Scatternet Formation Algorithm,” Proc. of the Conf. on Global
Telecommunications, Vol. 5, pp. 2864-2869, 2001.
[5] C. Law, A.K. Mehta and K.Y. Siu, “Performanceof a New Bluetooth Scatternet Formation Protocol,” Proc. Of ACM Symp. on Mobile Ad Hoc Networking and Computing, pp. 182-192, 2001.
[6] G. Zaruba and S. Basagni, “Bluetrees-Scatternet
Formation to Enable Bluetooth-Based Ad Hoc
Networks,” Proc. of the IEEE International Conf.
on Communications, Vol. 1, pp. 273-277, 2001.
[7] Petrioli, S. Basagni and I. Chlamtac, “Configuring BlueStars: Multihop Scatternet Formation forBluetooth Networks,” Proc. of the IEEE Trans. on
Computers, Vol. 52, Issue 6, pp. 779-790, 2003.
[8] C. Chevallay, R.E. Van Dyck, T. A. Hall, “Selforganization Protocols for Wireless Sensor Networks,” Conf. on Information Sciences and Systems, 2002.
[9] G. J. Pottie. “Wireless Sensor Networks,” in IEEE
Information Theory Workshop, June, 1998, pp.
139-140.

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Mapping Children and Playgrounds into Multi-Agent Systems
Alireza Derakhshan? , Frodi Hammer? , Henrik Hautop Lund? , Yves Demazeau†?

The Maersk Mc-Kinney Moller Institute for Production Technology
University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark†
CNRS, Laboratoire LEIBNIZ, Institut IMAG
46, avenue Félix Viallet, F-38031 Grenoble Cédex, France
kianosh@mip.sdu.dk

frodi@mip.sdu.dk

hhl@mip.sdu.dk

Abstract

the children’s interest in the playground and therefore
proportionally increase the physical activity level ofthe children.
By introducing this form of dynamic behaviour onto
the playground, the playground should have the possibility to adapt in favour of each individual child playing on it making it as interesting as possible as a whole.
These adaptations could be as small as changing thecolour of a LED to a child’s favourite colour, or tolarger adaptations which change the morphology ofthe elements on the playground according to e.g. thechild’s build. This would essentially allow for equalplay on the playground along its users irrespective toage, weight, speed, etc.

We present how to adapt a playground using multiagent systems based upon live human agents. The
playground consist of tiles having sensing capabilities
and abilities to communicate with their four neighbours. We propose a mapping of the children playing
on the playground to a multi-agent system inhabitedby BDI agents, which use an artificial neural network
to learn the children’s desires and intentions and identify these within seven categories; thus, enabling theplayground to adapt accordingly. The playground has
been developed, implemented and tested on a group ofchildren. Based on a 6×6 environment we show that
it is possible to learn and recognise childrens desires
and intentions based on the behaviour they exhibitedon the playground.

1

1.1

Playground Prototype

To allow dynamic behaviours on the playground a
new playground prototype has been developed. This
prototype consists of several building blocks or tiles(see fig. 1). These tiles allow a high degree of freedom
for children to create their own game depending on e.g.
the assembling of the tile, thereby making the games
depend on the playgrounds morphology. The assembled playground then contributes to the play (here bysound and/or visual interaction) allowing an interesting and even dynamic storyline to emerge with which
the children can e.g. merge with their own imaginary
stories. Additionally, by embedding ambient intelligence into the tiles the playground gets the characteristics of being personalizing, adaptive and anticipatory
according to the interactions exhibited by the children.
The tile prototype’s dimensions are 21cm × 21cm ×6cm (width, height, depth) and it consists of a rubberlike shell, a sandwich plastic case, a PCB and a force
sensor as illustrated in fig. 2. The rubber-like shell

Introduction

In the western society there exists increasing problems related to obesity in the population. These problems cannot only be confronted by being increasingly
aware of proper diet, but also physical activity needs tobe taken into consideration. One attempt is to introduce IT known from robotics, artificial intelligence andmultimedia on playgrounds in order to increase children’s interest in these and thereby increasing their
physical activity [1, 2]. For instance, could some of
the techniques known from today’s computer games
be taken into consideration on the playgrounds; computer games are dynamic universes versus the static nature of playgrounds. These dynamic elements
could be introduced on the playground, by transforming them into a physical form which are capable ofphysical interactions. This would indeed help keeping©ISAROB 2006

yves.demazeau@imag.fr

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Fig. 1. An example of four tiles assembled into a playground.

has a ”bump” indicating the location of the force sensor and a Plexiglas mark for light output. The PCB
uses an Atmel ATmega 128 microcontroller; a general purpose microcontroller supporting 128kB of selfprogrammable flash memory which runs up to 16MHz.
A 4-way communication bus is interfaced to the serial USART on the microcontroller via a Quad UART
chip (TL16C754BPN). The Quad UART is furthermore interfaced to a multi-channel line driver/receiver(MAX211) in order to support RS-232 level connections between the tiles. Four bright Light Emitting
Diodes, capable of showing red, green and blue colours
at different intensities are connected to the microcontroller. A Force Sensitive Resistor (FSR) sensor (FlexiForce A201) is interfaced of the A/D converter on the
microcontroller which supports up to a 10-bit resolution measurement of the sensor value.

1.2

Fig. 2. The tile consist of a rubber-like shell, a FSR sensor, aplastic case, a PCB and some tightening screws.

through different worlds. The KidsRoom is based on
a 24 × 18 ft. room modelled as a real child’s bedroom.
In addition the room has monitoring capabilities that
can track and analyse the behaviour of the children;thus, by using sound, images, animations and music,
the room can communicate with the children. In addition the child or children can be part of the story
narrators, making them able to change the journey’s
course by their own behaviour and interaction withthe room.
The relating part of the KidsRoom to the playground is the amount of freedom the child is given
and the unawareness the child has of the room’s capabilities. Therefore the child does not have to pushcertain buttons or do anything extraordinary for thestoryline to proceed. In a sense, the room triggers agame and the child then decides how he or she wants
to play this game and the room adjusts its behaviour(storyline) by analysing (by using computer vision) the
child’s behaviour.
The vessel berthing system [5] uses a multi-agent
system to enhance the efficiency of a container terminal operation. The essence is that the intelligent
agent can dynamically adapt to the changing environment. For example one agent (schedule agent) mustassign limited number of berths and resources (Cranes,
Trucks, etc.) in a terminal to a number of vessels. Oneof the interesting aspects is that the agents are modelled with a generic BDI architecture extended with an
intelligent entity making them capable of learning andRelated WorkAs the work presented here is being both novel andinterdisciplinary, the area of related work is limited tosome technical and conceptual aspects.
The Smart Floor [3] was developed for transparent user identification and tracking. The Smart Floor
measures the force exerted on a tile in order to identify
the person according to the footstep’s force signature.
The system assumes that the footstep signatures canbe uniquely recognized among a small ”enough” group(approximately 15 people), but gets error prone when
the number of people becomes large. Before any identification can take place, each user must put their footstep signature into the system database – new users
cannot be seamlessly integrated.
The KidsRoom [4] is a perceptually-based, multiperson, fully automated, interactive, narrative playspace where children are taken on a fantasy journey©ISAROB 2006

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adapting; hence, making the agent a better decision
maker. This intelligent entity is a mixture of an artificial neural network and fuzzy logic, named ”Adaptive
Neuro Fuzzy Interface System” (ANFIS) [6].

2Children, Playgrounds and MultiAgent Systems

One way of optimizing the activity level of the children playing on the playground is to increase or maintain the level of fun over time. In order to do this it isnecessary to have knowledge about each child’s playing
behaviour. This could be knowledge of favourite moving patterns, such as jumping, running, doing somersaults etc. It could be imagined that these moving patterns could be mapped to specific games that favours
these behaviours. E.g. in the ”smash game”[7] a location is indicated to the user that the user have to”smash” e.g. by jumping on this location. In this typeof game, if a child favours running instead of jumping,
the locations to smash should be located farther apartincreasing the running in the game and thus also increasing/maintaining the fun for that child.
Since all children are different their individual interest in the game are also more or less different. Hence,in order to increase/maintain a child’s interest in thegame, the playground should be able to adapt accordingly. This implies that it must be possible to recognise and/or predict the behaviour of a child playing at
the playground. However, when several children areplaying together on the playground, the playgroundneeds to be adapted in order to maintain several interests; thus, also the collective behaviour of the children
needs to be recognised and/or predicted.
We state that a playground can be viewed froma multi-agent system (MAS) perspective. In general MAS are considered consisting of agents, environments, interactions and organizations [8]. This could
be mapped into the playground as shown in fig. 3
where the children are considered as being agents, theplayground being the environment, the interactions
are those between the children and the playground orother children respectively and finally the organizations – if any – are due to the formations of the children. We now need to consider how the environment
and the agents can be modelled in the MAS.

2.1

Fig. 3. Mapping the children and playground into a MAS.

The possible input sources of the tiles are the FSR
sensor and the communication lines; hence pressure
can be measured and movements can be calculated.
Pressure can be read directly from the FSR sensor oneach tile, and by using the communication lines, two
successive pressure points can be used to calculate thedistance travelled and the time the movement took.
Of course, the distance can only be calculated as longas some a priori knowledge about the topology of thetiles is known. These three physical units are illustrated in fig. 4.

Fig. 4. The three physical units that can be measured in theenvironment. P P1 and P P2 are the pressure points.

Combining the pressure, the distance and the timeseveral interactions with the environment can be defined. For simplicity we have reduced the numericalspan of the physical units from infinite to two leaving
us with the following:
Time (t) is either slow or fast.
Pressure (p) is either soft or hard.
Distance (d) is either near or far.

With these three units eight interactions can becomposed as listed in tab. 1. The definitions of p,t and d are all relative and can thereby be specified
according to any appropriate threshold function.

Modelling the Environment
2.2

The playground defines the environment. Since theplayground consists of several tiles, the possible interactions with the environment are defined by the tiles.

©ISAROB 2006

Modelling the Agents

When modelling human or cognitive behaviour, theBDI agent architecture is the most commonly used in

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Interaction
Walk
Touch
Step
Tug
StretchStroke
Jump
Run

∆t
slow
fast
slow
fast
slow
fast
slow
fast

∆p
soft
soft
hard
hard
soft
soft
hard
hard

∆d
near
near
near
near
far
far
far
far

is, the organisation defines which interaction patterns
comply in between the agents. This agent–agent interaction, which defines the group behaviour, originate from the desires and intentions of the individual
agents. Thus, from having individual desires and intentions, the agents also have joint desire and intentions.
The agent–environment interactions (or agent actions) listed in tab. 1 define the basic entities of thepossible behaviours on the playground. A sequenceof actions corresponds to a plan being, which whenbeing executed complies with the desires and intentions of the child playing by exhibiting these actions.
So, by recognising or learning this plan, the desires
and intentions of the child playing could be identified. This would allow for an agent only observing
the agent–environment (children–playground) interactions to learn to recognize and/or identify the child’s
desires and intentions in order to adapt the playgroundto serve these.
In order to design an agent architecture that allows
for this, we follow Shoham et al’s ”AI agenda” [11]and look for an optimal (or at least effective) agent
design for the environment amongst the agents inhabiting it – that is the children. Thus, we propose anagent architecture where the desires and intentions of
the child can be learned by an artificial neural network (ANN). The input to the ANN then corresponds
to the child’s belief set which can be decomposed into aplan (that is, a sequence of actions) and the perceived
environment (some limitation of the surrounding environment). Having this action/perception correlation
also indicates the effect of a given action performed in
the environment to the agent.

Tab. 1. The interactions defined according to the parameters
time (t), pressure (p) and distance (d).

the literature. BDI is an abbreviation for belief-desireintention and the agent model was first proposed byBratmann [9] and can be illustrated as in fig. 5.

Fig. 5. The BDI agent architecture [10].

The beliefs correspond to the information the agent
has perceived from the environment which can be incomplete or even incorrect. The desires hold information about the objectives the agents has to accomplish
and their priority – the desires are often referred toas the motivational attribute of the agent. Intentions
represent the deliberative component of the agent (i.e.
which are the desires that the agent has committed
to achieve). When an agent has committed to somedesire, it tries to achieve this intention until it beliefs
that the intention is satisfied or until it beliefs thatthe intention no longer is achievable. The plans consist of a library of plans that specify courses of action
which achieve a specific intention. The interpreter is
responsible for the execution cycle of the agent. It
updates the beliefs according to new perceptions fromthe environment, generate new desires according to
new beliefs, select which of the current active desires
should act as intentions, and finally selects an action
to perform according to the agent’s current intentions
and plan library.
When several agents, i.e. several children playing together on the playground, a collective behaviour emerges from the organisation of the agents; that

©ISAROB 2006

3

Observing Childrens PlayIn order to collect data a playground consisting of
36 tiles arranged in a 6 × 6 grid was used. A play called”Bug Smasher” inspired from the ”Smash” game [7]was implemented. The play shows three bugs (indicated by green LEDs) moving around randomly keeping a certain distance to the child. The child thenmust try to squeeze the bugs by stepping on them.
Several responses according to the actions in tab. 1
were implemented.
To observe the children playing, two types of test
were performed; an individual test which settles thechildrens individual desires and intentions, and a group
test which settles the joint intentions of two children
playing together.

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The individual test was performed with 23 children
aged 5-6 years. In general two major behaviours wereobserved. A ”Playing” behaviour was defined according to children whos behaviour was under influenceby the environment. A ”Not Playing” behaviour was
defined according to children who were not under influence by the environment (i.e. playing their own
imaginary play). In the test, 10 of the children weregenerally ”Playing” while the remaining 13 were generally ”Not Playing”.
Eight of the children also participated in the group
test. All of them had a general ”Playing” behaviour.
However, the playing behaviour varied from being cooperative initially to becoming occasionally competitive, e.g. some of the children pushed each other trying
to squeeze a bug (see fig. 6).

Fig. 7. The cycle of adaptation on the playground.

in fig. 9 the percentages by which the classifications
are given are not as unambiguous as desirable – an example is Silje; indications are that the children change
behaviours during the play. By producing a graphical representation of how the agent classifies epochsof 36 actions, we see that Silje is indeed switchingdifferent behaviours as expected (see fig. 10). Also
notice, that the individual behaviours can be distinguished from group behaviours by the agent; meaningthat the agent also is capable of recognizing joint intentions that is agent–agent interaction, by observing
the agent–environment interaction.

Fig. 6. Two children are competing to squeeze the bugs in thegroup test. Each test lasted 5 minutes and was performed in aneutral environment (here a kindergarten).

4

Learning desires and intentions

In order to adapt the playground three main disciplines must be considered, namely; observation, classification and finally adaptation (see fig. 7). An agent
being able to observe the child’s interactions with theenvironment can learn the child’s desires and intentions and use these in order to increase/maintain thechild’s physical activity by adapting the playgroundaccordingly.
From the tests, behaviours complying with theseven desires and intentions were defined analyticallyas shown in fig. 8. A sequences of 36 actions in the collected data was selected as defining the behaviours in
the training set of the agents ANN as unambiguously
as possible1 . In verifying the trained agent, a test set
including the rest of the collected data was all correctly classified (see fig. 9). However, as can be seenFig. 8. The seven classifications of the ANN corresponding todifferent criteria. The continuously and discontinuously complies with the behaviour being predictable or not.

5

We have presented an approach to adapt playground inspired from multi-agent systems; modellingchildren as agents, the playground as the environment,
allowing children– playground (agent–environment)
and children–children (agent–agent) interactions andfor the children as being organised. This approach enabled the children as being view upon as BDI agents
where their actions are determined by their desires andintentions.

1 Only 5 of the analytically 7 behaviours existed in the collected data.

©ISAROB 2006

Conclusion & Future Work

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Acknowledgements
The hardware platform was partially developed by
T. Klitbo. The design of the tiles was made by C. Isaksen from Isaksen Design. KOMPAN A/S participatedin the development of the tiles. C. Balslev Nielsencreated some of the contacts with the children fromwithout the results would not have been possible.

References[1] H. H. Lund and C. Jessen. Playware – intelligent technology for children’s play. Technical Report TR-20051, Maersk Institute, University of Southern Denmark,June 2005.
[2] H. H. Lund, T. Klitbo, and C. Jessen. Playware technology for physically activating play. To appear in
Artificial Life and Robotics Journal, 9, 2005.

Fig. 9. Selected agent classifications of the test set. Bold
indicates the classification, while underline indicates the target.

[3] Robert J. Orr and Gregory D. Abowd. The smart
floor: a mechanism for natural user identification andtracking. In CHI ’00: CHI ’00 extended abstracts on
Human factors in computing systems, pages 275–276,
New York, NY, USA, 2000. ACM Press.
[4] A. Bobick, S. Intille, J. Davis, F. Baird, C. Pinhanez,
L. Campbell, Y. Ivanov, A. Schutte, and A. Wilson. The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment. Technical
Report 398, E15, 20 Ames Street, Cambridge, MA
02139, December 1996.
[5] P. Lokuge and D. Alahakoon. Enhancing efficiency ofcontainer terminal operations: Using intelligent bdi
agents with adaptive neuro fuzzy inference systems.
In Proceedings of the 23st National IT Conference,
pages 34–43, Colombo, Sri Lanka, 2004. AAMAS.
[6] Jyh-Sing Roger Jang. ANFIS: Adaptive-NetworkBased Fuzzy Inference System. IEEE Transactions
on Systems, Man, and Cybernetics, 23:665–684, 1993.

Fig. 10. Classifications of Silje’s behaviour by the trained
agent. The orange curve shows ”I. Not-Playing Cont.” while
the purple shows ”I. Not-Playing Disc.”. The yellow is ”I. Playing Slow”.

[7] Robert Headon and Rupert Curwen. UbiquitousGame Control. In Ubicomp Workshop on Designing
Ubiquitous Computing Games, 30 September 2001.
[8] Yves Demazeau. From interactions to collective behaviour in agent-based systems. In Proceedings of theFirst European conference on cognitive science, pages
117–132, Saint Malo, France, 1995.

Seven desires and intentions of the children were defined by their behaviour, from a combination of how
many children were playing and of how much they
were under influence by the environment. A training
set consisting of selected examples from the collected
data representing each of the defined behaviours wasconstructed and an ANN capable of classifying the behaviours was trained.

[9] M. E. Bratman, D. J. Israel, and M. E. Pollack. Plans
and resource-bounded practical reasoning. Computational Intelligence, 4:349–355, 1988.
[10] Michael Wooldridge. Practical reasoning with procedural knowledge. In FAPR, pages 663–678, 1996.

Having the first two phases of fig. 7 the last phaseis to use this knowledge about the children to adapt
the playground in their favour, an aspect which we
present in another paper.

©ISAROB 2006[11] Yoav Shoham, Rob Powers, and Trond Grenager.
Multi-agent reinforcement learning: A critical survey.
Technical report, Stanford University, 2003.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Reference-position detection using fan beam laser forcooperative localization of multiple mobile robots
Hiroyuki Takai*, Jun Mitsuoka* and Keihachiro Tachibana***Graduate School of Information Sciences, Hiroshima City University
3-4-1 Ozuka-Higashi, Asa-Minami, Hiroshima 731-3194 JAPAN** Graduate School of Computer Science, Osaka Gakuin University
2-36-1 Kishibe-Minami, Suita, Osaka 564-5811 JAPAN

Abstract

1. Introduction

In recent years, mobile robot systems that
perform team operations such as repairing
industrial equipment have been researched.
However, since a number of robots have towork in a small indoor workspace for cooperation, they are prone to collisions and sensor signal interference. We formed a hypothesis that their collisions were caused by
the ambiguity of their positional information.

Recently, multiple autonomous mobile robots that can perform team operations havebeen developed. These robots are used formaintenance of nuclear reactors, repairing
industrial equipment, and so on.
When the number of robots increases in theworkspace for cooperative operation, they
will often suffer from collisions or signal interference. One of the proposed collision
avoidance methods of mobile robot teams isthe exchange of their action programs
through inter-robot communication. Communication signal interference known in wireless communication as "the hidden terminal
problem" or "the exposed terminal problem"
occurswhen
theserobotstransmitomni-directional signals simultaneously.
Occurrences of these robots’ collisions or signal interference depend on their mutual location. Inaccurate positional information is
one of the causes of mobile robots’ collisions
or signal interference.

Since each robot has its own coordinate
axis, they cannot compare their positional
information. In order that each robot can
detect the same position in the same coordinate axis correctly, they need to decide reference positions in their workspace. We considered detecting "corners / edges" whichwere formed by walls and pillars that are in
most buildings as indoor reference positions.
In order to detect direction of "corners /edges" clearly, we propose to use the fanbeam laser that can create a visual representation of the object’s geometrical features.
Since image sensors can capture the image of“the corner / edge”, the direction of "the tip ofthe corner / edge" can be detected using image processing algorithms. Because morethan two robots can capture the image of “thesame corner / edge” illuminated by the fanbeam laser simultaneously, they can detect
direction of “the tip of the corner / edge” asthe direction of the reference position.

Accurate outdoor positional information
can commonly be detected using GPS (Global
Positioning System). However, indoor mobile
robots cannot use GPS because they cannot
receive signals from GPS satellites. In addition, since artificial landmarks also are hardto prepare for in advance in unknown workspace, accurate detection of positional information on global coordinate axis in indoor
workspace is quite difficult. Therefore, we
studied how to acquire the global coordinate
axis among teammate robots in their workspace.

The direction detection method using a fanbeam laser was proven and successful results
were obtained. Each robot can acquire accurate positional information using triangulation based on detected reference position's
direction and mutual localization among
teammate robots.

©ISAROB 2006

One of the proposed mutual localization
methods uses teammate robots as landmarks,
if these robots can detect direction andmeasure distance between each other. We

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

have developed an infrared wireless communication system that can detect the angle ofarrival (AOA) of communication signals fromthe communicating partner. Normally, animage sensor and ultrasonic sonar also havebeen attached to remote controlled mobile
robots as embedded sensors. These robots canmeasure the distance to the partner based on
the time difference of arrival (TDOA) of infrared communication signals and ultrasonicwaves provided the communication system isused with ultrasonic sonar simultaneously.
Consequently, each robot can compute it’s
relative location between each other by triangulation ranging based on detected direction and measured distance.

2. Multi-robot mutual localization using
inter-robot communication system
When the number of robots increases toaccomplish complicated tasks in a small indoor workspace, they are prone to collisions
and sensor signal interference. Occurrencesof their collisions or signal interference depend on their mutual location and can be
decreased by inter-robot communication using directional communication medium suchas infrared rays or millimeter waves. We
have designed an infrared wireless communication system for inter-robot communication.
When these robots move or rotate, usual
infrared wireless communication often loses
communication links owing to the directivityof infrared rays. In the system that we designed, in order to maintain communication
links, several infrared transceivers are puton the circumference of the robot body andfaced outwards. Figure 1 shows the arrangement of transceivers.

However, in order to avoid collisions with
fixed obstacles and to form smooth action
programs, these robots need to acquire theirabsolute positions in their workspace. These
robots need to find out stationary reference
positions used as the basis that determines
their coordinate axis in their workspace toacquire their absolute positions. We considered detecting "corners / edges" which wereformed by walls and pillars that are in mostbuildings as indoor reference positions.
Since more than two robots have to detect
direction of “the same corner / edge" in orderto share its positional information, we propose to use the fan beam laser that can create
a visual representation of the object’s geometrical features. Image sensors can capture
the image of “the corner / edge” created by
fan beam laser and can detects its direction
as a reference position using image processing algorithms. The reference position can becalculated by triangulation ranging based on
its detected direction, since each robot hascomputed its relative location mutually using
the inter-robot communication system. These
robots can exchange calculated positional
information of reference positions throughtheir inter-robot communication network. A
map of their workspace also can be createdfrom shared positional information of reference positions.

Fig.1 Arrangement of transceivers
In order to maintain a communication linkby tracking the communicating partner, eachtransceiver receives a communication signal
and the transceiver detects the AOA of thecommunication signal simultaneously. Thesetransceivers are also switched to adjacenttransceivers that face communicating partners when these robots move or rotate.
This communication system is able tocommunicate in parallel with different partners in different directions by using different
transceivers simultaneously. As a result,space-division communication can take place.
These robots create a communication network by relaying information among them.
The created communication network is called

In this paper, the method of multi-robot
mutual relative localization using the infrared inter-robot wireless communication system and the method of reference position detection using the fan beam laser are described.
©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 3 shows mutual localization using
triangulation based on the AOA of communication signals. The robot detects AOA ( θ01,
θ12, θ23 ) of communication signals and computes coordinate P(x,y) by triangulation.

ad hoc network, since each robot is independently mobile and may change position
depending on their tasks. Figure 2 shows aninter-robot communication network.

We tested this mutual localization method
using PSD (Position Sensitive Device) PIN
photo diode (Hamamatsu S6560) that can
detect incidence angle of infrared rays as thereception devices. In an experiment of AOA
detection, angle detection error is approximate 0.5 degrees and this result is able todetect an AOA precisely. The accuracy ofmutual localization is approximately 90%
with the true value based on the detected
AOA.

Fig.2 An inter-robot communication network
These robots can detect direction of partner
robots using this infrared communication
system and can also measure distance between partners using ultrasonic sonar withsupport of the communication system. Consequently, these robots can compute theirmutual location by triangulation using information about their direction and distance.

3. Reference position detection using
fan beam laser
Although mutual location information isextremely useful for local mutual cooperation
among adjacent robots, it is not suitable forthe use that needs to point to appointed positions such as stationary obstacles in theirworkspace. In order to point to appointed
positions, these robots have to decide a global
coordinate axis based on several reference
positions in their workspace.

The computation method of mutual location requires three known partners’ locationin the workspace. These partners’ locations
are arranged on coordinates from P1(x1,y1) toP3(x3,y3), and P2 is the origin. When, the coordinate of the robot is P(x,y) and the movement direction of the robot is θ, these parameters are computed from equation 1.

"Corners / edges" which were formed bywalls and pillars can be used as indoor reference positions in most buildings, since artificial landmarks are hard to prepare for inadvance in unknown workspace. The fanbeam laser that can create a visual representation of the object's geometrical features
is suitable for detection of "corners / edges".
Figure 4 shows a model of “corner / edge”
detection using fan beam laser.

x = p 2 p cos f
y = p 2 p sin f

q = f - q 01 - q 12 + p(1)Parameters are computed as follows.
f = tan -1
p2 p =

p1 p 2 sin (q 12 + a ) sin q 23 - p 2 p 3 sin q 12 sin q 23

p1 p 2 cos (q 12 + a ) sin q 23 + p 2 p 3 sin q 12 cos q 23

sin q 23 cos f + cos q 23 sin f
p 2 p3sin q 23a = tan -1

y1
x1

z

y

P1(x1,y1)

θ01

θ

θ12

P(x,y)
θ23xα
φ
P2(x2,y2)

P3(x3,y3)yxFig 4. A model of “corner / edge” detection

Fig 3. Localization using triangulation©ISAROB 2006

fan beam laser emitter

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

We probed the feasibility of the "corners /edges" detection method using fan beam laser
with experiments. The laser emitter (Coherent, LVM3 Laser-line micro 670nm 2.5mW
60deg) and the image sensor (RF system Lab,
PRO-5 2CCD color wireless TV camera withlens) were used in experiments. The laser
emitter and the image sensor were attached
to a motorized turntable (SIGMA KOKI,
SGSP-160YAW) horizontally at a 10cm interval vertically. The image sensor can capture 320 (horizontally) ´ 480(vertically) pixel
images and its horizontal view angle is approximately 23 degrees. We also measured
angle detection error of this image sensor
using a captured image. The angle detection
error is approximately 1.5 degrees.

Table 1 Results of triangulation

Pa
P1
P2
Pb
P3
P4
Pc
P5
P6

54.8 71.5 44.2
86.1 71.5 44.2

References
1. ASAMA H., et al (Eds.) (1994) “Distributed
Autonomous Robotic Systems” (DARS), Springer,Tokyo
2. TAKITA Y, et al (1995) “Navigation and Recognition of the Unknown Space Using Infrared-Following Servo System for Moving Robots”,
Transactions of the Japan Society of MechanicalEngineers, vol.61, no. 590, series C, pp.209-216
3. TAKAI H., et al. (2005) “A Space-Division WirelessCommunication System for Ad Hoc Networking
and Cooperative Localization of Multiple Mobile
Robots”. 16th IFAC World Congress 2005. IFAC,
ID:05004

Pc

P1

x

4. TAKAI H. et al (2002) “A Geometric Arbiter Selection Algorithm on Infrared Wireless Inter-robot
Communication”, Distributed Autonomous Robotic
Systems 5 (DARS 5), Springer, Tokyo, pp.61-70

P5
P6
Fig. 5 Experiment environment©ISAROB 2006

70.4 63.3 89.0
78.3 63.3 89.0

This work is supported in part by Japan
Society for the Promotion of Science
Grant-in-Aid for Scientific Research (C) (No.
17500118).

P3

qc fc

68.8 49.8 51.0
73.4 49.8 51.0

Acknowledgementsfaqa

reference

If this method is used effectively, the global
coordinate axis of the robots' workspace will
be acquired.

Pb

Pa

50
30
65
90
70
100
43
-7.5
-7.5

x’ y’

We proposed cooperative localization
methods using inter-robot communicationand fan beam laser. Also we examined thepositioning method and successful results
were obtained. In this method, accurate reference positions were detected.

qb
P2

angle

Conclusion

y

fb

y

Length: cm, Angle: deg

We also examined the positional detection
method based on the "corners / edges" detection. A motorized X-Y axis linear stage pair
(SIGMA KOKI, SGSP46-500(X) and SGSP651200(X)) was used for a simulation of a mobile robot that moves around an obstacle of
cuboid set on the flat floor. The turntable inwhich the laser emitter and the image sensor
were attached was put on the X-Y axis linear
stage pair. The robot moved from P1(x1,y1) toP6(x6,y6)and the position of the cuboid's edge
Pa(xa,ya), Pb(xb,yb) and Pc(xc,yc) were measured. Figure 5 shows experiment environment with an obstacle. Table 1 shows resultsof this experiment. In the table 1, “x” and “y”
are arrangement coordinates. Detected angle q and f are shown in “angle”, “x’”and ”y’” are results of triangulation. The detection errors are less than 3% in this experiment.

P4

x
50
0
0
63.5
10
10
72
35
75

182

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183

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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186

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Study on Stability Analysis of Cooperative AGV Systems using
Decentralized Control Algorithm
Jin Ho Suh

Dept. of Electrical Engineering

Dong-A University

Young Bok Kim
Dept. of Control and MechanicalEngineering
Pukyong National University

840, Hadan-dong, Saha-gu, Busan,
Korea

San 100, Yongho-dong, Nam-gu,
Busan, Korea

840, Hadan-dong, Saha-gu, Busan,
Korea

suhgang@donga.ac.kr

kpjiwoo@pnku.ac.kr

kslee@dau.ac.kr

Dept. of Electrical Engineering

Kwon Soon Lee

Dong-A University

Keywords: Automatic guided vehicle (AGV), Passive velocity field control (PVFC), Decentralized control⎛ x2 ⎞ ⎛ x2 − h sin θ 2 ⎞ ⎛ xc + L cos ϕ ⎞⎟⎟⎜⎜ ⎟⎟ = ⎜⎜⎟⎟ = ⎜⎜⎝ y2 ⎠ ⎝ y2 + h cosθ 2 ⎠ ⎝ yc − L sin ϕ ⎠Abstract
In this paper, we propose the stability analysis to apply
decentralized control to cooperative AGV systems whosesubsystems are under nonholonomic constraints and convey acommon rigid object in a horizontal plain. Moreover, it isshown that multiple AGV systems ensure stability and thevelocities of augmented systems converge to a scaled multipleof each desired velocity field for cooperative mobile robot
systems. Finally, the application of proposed virtual
passivity-based
decentralizedalgorithm
via
systemaugmentation is applied to trace a circle.

(2)

θ2

ϕ
2L

θ1

1. Introduction
A wheeled mobile robot named automatic guided vehicle(AGV) is an important element to convey work in the present
flexible material handling system of factory automation androbots with the various kinds of payloads are developed.
Therefore, we take a great interest in the cooperative
object-transportation system by multiple vehicles.
However, the passive velocity field control (PVFC)
algorithm applied to a single manipulator could not extend tomultiple AGV systems [2]. Especially, multiple robotic systems
can execute various tasks which could not be done by a single
manipulator such as the handling of a heavy object, forexample, transportation of port containers using cooperative
AGV systems. Therefore, many control algorithms have been
proposed for the coordinated motion control of multiple robot
systems. Moreover, various decentralized control algorithms
have been proposed to overcome some problems of thecentralized control algorithm in which each robot system iscontrolled by its own controller without explicit
communication among cooperative systems.

Fig. 1 Configuration of 3-wheeled AGV systems for moving arigid object
If we define the constraint force, the actual dynamic
equation of whole system can be decomposed into thefollowing equations
H i ( βi )η& (t ) + fi ( βi ,ηi ) = Gi ( βi )τ mi − J iT λi(3)

M o &x&o = − J oT λ , I oϕ&& = − J ϕT λ

(4)Then, we can define a local control input given byτ mi = Gi−1 ( H i vi + f i + J iT λi ) − Gi−1H i J iT λi(5)where vi (i = 1, 2) is new input. If we inject new control input
vi into cooperative mobile robots. Therefore, the actualdynamic equation of whole system is represented by
M w &x&w = Gwv(t ) − J wT λ
(6)

2.2. Decentralized control algorithm

2. Theproposeddecentralized
controlalgorithm for cooperative AGV systems
2.1. Cooperative AGV systems
We consider a case where two AGVs convey a rod forsimplicity, however, the similar discussion can be applied forcases where AGVs arre carrying a general planer rigid object.
In this section, we describe the configuration of cooperative
AGVs as shown in Fig. 1. In considered systems, an object
denoted by a rod is connected to each mobile robot by a free
joint without friction and the length of an object is 2L.
From the kinematic constraints by the passive joints, theholonomic constraints between the generalized coordinates aredefined as follows:⎛ x1 ⎞ ⎛ x1 − h sin θ1 ⎞ ⎛ xc − L cos ϕ ⎞⎟⎟⎜⎜ ⎟⎟ = ⎜⎜⎟⎟ = ⎜⎜(1)⎝ y2 ⎠ ⎝ y1 + h cosθ1 ⎠ ⎝ yc + L sin ϕ ⎠©ISAROB 2006

Fig. 2 Configuration of cooperative AGV systems with
virtual-based flywheel system
The proposed scheme is shown in Fig. 2. Also, if the actual
control input is defined asν 1 = ν 1′ − (I 2× 2 02×2 )( Jϕϕ&& + J&ϕϕ& )
(7)

187

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

ν 2 = ν 2′ − (02×2

I 2×2 )( Jϕϕ&& + J&ϕϕ& )2
1
dH ai′ = ∑ ( X& aiT M ai′ X&&ai + X& aiT M ai′ X& ai )
2
dt
i =1

(8)

Adding the dynamic equation of xo , we can describe themotion equation of an object as follows:( I 2×2 + M o + I 2× 2 ) &x&o = ν 1′ + ν 2′
(9)

2
1 2
&
= ∑ ( x&oT M i′&x&o + x& fwi M fwi &x& fwi ) + ∑ X& aiT M ai′ X& ai
1
4
2
43
1
4
2
4
3
2 i =1i =1=ν fwiFirst of all, the procedure in order to apply an individual PVFC
algorithm can be designed that the motion equation in Eq. (9) is
separated as the following virtual dynamic equation
M i′&x&o = ν i′ (i = 1, 2)(10)= ∑ X& aiT Gai′ X& ai + X& aiT Rai′ X& ai = 014243 14243
i =1

where ρi (i = 1, 2) is load sharing coefficient and it is(20)

Since H a′ (0) ≥ 0 , the system is passive with respect to the
supply rate.
Proof of (2): Given α ∈ ℜ , let’s define the positive definite

where ν fwi is the coupling control input to the flywheel. Thus,

storage function Wα as follows:Wα =)x& fwi T is the velocity of the augmented

1 2 T∑ eαi M ai′ eαi
2 i =1(21)&
Differentiating Eq. (21) and the fact that M ai′ + 2Gai′ is skew

system, ν ai′ is the augmented control input, and M ai′ is theaugmented inertia matrix and is defined by
⎛M′
0 ⎞
⎟
M ai′ = ⎜ i(13)⎜ 0 M fwi ⎟⎠⎝For each X ai , we can define the kinetic energy of thesymmetric, we obtaind
1 2
Wα = ∑ (e&αTi M ai′ eαi + eαTi M ai′ e&αi + eαTi M ai′ e&αi )
1424
3
dt
2 i =1=0

2= −∑ α iγ i (4 H ai′ Ei − << Vai , X& ai >> 2ai ) ≤ 0

augmented dynamic system, H a′ which is expressed in local
coordinate by2
1
H a′ = ∑ X& aiT M ai′ X& ai(14)
i =1 2
Theorem 1: Consider the decentralized PVFC as shown in Fig.
2 where the motion equation is given by Eq. (10), and theindividual PVFC control law consists of the virtual dynamic
augmentation Eq. (11) and coupling control law Equation.
Furthermore if the control input about control internal force isdefined by⎛F ⎞ν Ii′ = (1 + k f )⎜⎜ di ⎟⎟
(15)
⎝ 0 ⎠
and an actual control input about given system ν i′ is also
defined byν i′ = ν ai′ + ν ′Ii (i = 1, 2)
(16)
′
′
′
where ν Ii is desired internal force and satisfies ν I 1 + ν I 2 = 0 .

(22)

i =1

Since Wα is a positive definite function of α i -velocity error
eαi , we know that the augmented α i -velocity error eαi = 0
is Lyapunove stable of the error dynamics using Barlalet’s
lemma [3]■3. Conclusions
In this paper, we propose a new control methodology forcooperative AGV systems systems convey a rigid object, andthe proposed decentralized control algorithm is also analyzed
using passive velocity field control algorithm. Especially, theclosed-loop input/output systems for multiple AGV systems are
passive with the environment force as inputs, the systemvelocities as outputs, and the environment mechanical powers
as supply rates as if it is similar to an original passive velocity
field contorl.

Acknowledgement

Then the passivity and convergence properties of decentralized
PVFC are summarized as follows:(1) The augmented feedback system is passive with respect tothe supply rate defined bys ( F , x& ) =< F , x& >= F T x&
(17)
where F and x& are input and output.
(2) For the augmented α i -velocity error eαi , the velocity ofis a Lyapunov stable solution in the
an object X&

This
work
was
supported
by
grant
No.
R01-2005-000-10418-0 from Korea Science and Engineering
Foundation in Ministry of Science & Technology.

References[1]ai

absence of environment forces.
[2]

Proof of (1): The derivation of kinetic energy defined by
2
1
H ai′ = ∑ X& aiT M ai′ X& ai = Ei > 0(18)
i =1 2Then,©ISAROB 2006

(19)

=0

0

the dynamics of the augmented system are composed asfollows:
M ai′ X&&ai = ν ai′ (i = 1, 2)
(12)

(

=0

Therefore, upon integration of Eq. (19), we can obtaint &∫ H ai′ (t )dt = H a′ (t ) − H a′ (0) = 0 > − H a′ (0)satisfied with ρ1 + ρ 2 = 1 .
Also, the dynamics of the virtual flywheel is given by(11)
M fwi &x& fwi = ν fwi (i = 1, 2)

where X& ai = x&o

=0

2

[3]
[4]

188

G. Campion, G. Bastin, and B. D’Andrea-Novel, “Structuralproperties and classification of kinematic and dynamic models ofwheeled mobile robots,” IEEE Trans. on Robotics andAutomation, Vol.12, No.1, pp. 47-62, 1996.
P. Y. Li and R. Horowitz, “Passive velocity field control (PVFC)
Part I –Geometry and Robustness-,” IEEE Trans. on AutomaticControl, Vol.46, No.9, pp. 1346-1359, 2001.
J. J. Slotine and W. Li, Applied Nonlinear Control. Prentice-Hall,
New Jersey, 1991.
J. H. Suh, et. al., “Decentralized control of cooperative 3-wheeledmobile robots using passive velocity field control,” In Proc. of Int.
Sym. on AROB, pp. 433-436, 2004.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development of learning support system for piano-keying- Relationship between the activity of finger muscles and key release velocities
of an expert Tomoya Tamei

Tomohiro Shibata

Shin Ishii

Graduate School of Information Science, Nara Institute of Science and Technology
8916-5, Takayama, Ikoma, Nara, 630-0192 Japan
tomo-tam@is.naist.jp

Abstractsound. How to relax muscles during the play is hard tolearn even for students who have studied piano-playing
for a long time.

This study aims at comparing behaviors of anexpert piano player between two contrasting pianokeying cases: when a player applies significant force
to keys (case A) and when the same player relaxes
their muscles as much as possible (case B). For this,we developed a simultaneous measurement system forthe motions of an index finger and a key, and for Electromyographic (EMG) signals of the index finger. The
results acquired in a pilot experiment show the clear
difference in the key motion and the EMG signals between the two cases. Additionally, we found the strong
correlation between extensor muscle activities and the
key-release velocities. The results suggest that the developed measurement system has a possibility to evaluate the proficiency of pianists in particular for relaxed
keying motions.

1

Therefore, we confine our interest to the simple
piano-keying with an index finger, and investigate thedifference in skill of piano-keying between beginners
and experts, and search for indices for muscle relaxation of players during piano-playing.
There are some previous studies related to thisstudy. Parlitz et al. measured keying force from expert and amateur pianists at ‘tied finger exercise’ bymeans of a pressure sensor sheet placed beneath keys
[1]. Their results indicated that amateur pianists often take much amount of force to achieve the same
tempo and volume as those of experts. They also
found that experts strike keys with less effort thanamateurs. Their study, however, did directly focusedon relaxed playing, nor measure player’s muscle activities. Oshima et al. investigated the relationship between key-release velocity and sound proficiency witha note of the importance of the relaxation in player’s
muscles [2]. They, however, did not measure muscle
activities and henceforce were not concerned the relationship between the muscle activities and key keyrelease velocities.

introduction

Observing and imitating the motions of an expert
of, for example, sports and playing an instrument are agood starting point for amateurs. It is, however, necessary to know the time course of appropriate force
for each muscle movement, because it is generally
thought that the motor program of experts is optimized to achieve highest accuracy with a minimum
effort, whereas amateurs often give much amount offorce, which hinders appropriate learning. Furthermore, trainers often refer to the importance of relaxation of muscles because uneconomic use of force hinders learning, and may incur injuries. To our knowledge, there are very few motor learning support systems which is not only for learning motions of experts
but also for learning appropriate time course of force
to be suitable to learner’s muscles.
The arguments above are applicable to pianoplaying. It is often said that the relaxation of player’s
muscles is important to achieve deft and fine finger movements or rich expression by producing sweet©ISAROB 2006

This article first presents our simultaneous measurement system for joint angles, displacements ofkeys, and Electromyographic (EMG) signals of aplayer’s index finger to make their concern clear. We
then show the results of recording behaviors of an expert piano player using this apparatus. We show oursystem successfully captures salient and direct featuresof an expert, indicating the relationship between the
key-release velocity and muscle relaxation.

189

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Electromyograph
120 HzAmp
A/D
3000 Hz

Figure 2: Marker loFigure 3: Electrode locations
cations

Laser displacement
sensorFigure 1: System configuration2
2.1

piano instructor with years of experience. She understood our research aim.
She sat on a chair and placed from her elbow toahead of wrist (locality of carpus) of the right armon the armrest in order to use only three joints ofan index finger without using other joints such as ashoulder, elbow and wrist as much as possible. She
was instructed to be relaxed as possible as she wasable to do, (Figure 3).
The subject was instructed to let the key down synchronized with a metronome under four conditions asfollows:• ARCH1: let a key down every one meter in timewith the tempo of M.M.=50 with a natural form(arched) of fingers.

Methods
System configuration

Figure 1 shows our developed system for measuring
simultaneously motion, EMG and the displacement ofa key. We used an optical motion capture system Vicon512 (Oxford Metrics) for measurement of motions
at a sampling frequency of 120 Hz.
EMG signals were measured by an electromyograph
BA1104m (DEGITEX LABORATORY) with activetype electrodes. The displacement of a key was measured with a laser displacement sensor CD3-250 (SICKOPTEX). The outputs of them were input to an A/D
converter of Vicon512, which enabled synchronizedrecording of motion data and other data. The sampling frequency was 3000 Hz for recording of the EMG
and the displacement, while 120 Hz for motion as described above. The postprocessing of EMG signals will
be described below.

2.2• ARCH2: let a key down every two meters in timewith the tempo of M.M.=50 with natural form(arched) of fingers.
• EXTE1: let a key down every one meter in timewith the tempo of M.M.=50 with the PIP and
DIP joints being extended, but by moving mainly
the MP joint.

Measured joints and finger muscles

In this study, MP, PIP and DIP joints of the rightindex finger were measured. We attached infrared reflection markers for motion capturing to five points:
fingertip, DIP, PIP and MP joints and the back of
the subject’s right hand as shown in Figure 2. We
measured EMG signals of external muscles of the index finger at four points as shown in Figure 3. Twoof them were flexor muscles: flexor digitorum superficialis (FDS) and flexor digitorum profundus (FDP),
and the other two were extensor muscles: extensor
digitorum (ED) and extensor indicis (EI).

• EXTE2: let a key down every two meters in timewith the tempo of M.M.=50 with the PIP and
DIP joints being extended, but by moving mainly
the MP joint.
Each task consisted of keyings of 10 times for eachof the two cases: the subject was instructed to apply
significant force to a key (case A), or was instructed torelax her muscles as possible as she can during keying(case B).

2.3

2.4

Procedure

We conducted experiments where a subject was requested to strike a piano key repetitively with an index finger of the right hand by moving the three finger
joints.
The subject was a healthy adult woman who majored at a music college in piano-playing and was a©ISAROB 2006

Data processing

The motion data of an index finger were used to
calculate the joint angles of MP, PIP and DIP basedon the acquired three-dimensional information of the
5 markers. The joint angle data were smoothed by asecond-order Butterworse filter at a cuttoff frequencyof 3 Hz.

190

100

0.6

80

0.4
0.2
11

12

13

14

15

16
time [s]

17

18

19

20

21

40
20
0
20

0.2
0
11

12

13

14

15

16
time [s]

17

18

19

20

4.5

5

5.5

6

6.5

7
time [s]

7.5

8

8.5

9

9.5

5

5.5

6

6.5

7
time [s]

7.5

8

8.5

9

9.5

21
1500

6
5

1000

4
2

3
2
1
11

12

13

14

15

16
time [s]

17

18

19

20

21

100
80

500
0
500
1000

60
40

1500

20
0
11

12

13

14

15

16
time [s]

17

18

19

20

the key was pressed. The extensor muscles, however, were not activated in case B during key-release
whereas they were activated in case A (indicated byarrow in the third panel of Figure 4).
It appears less difference between the two cases
when the subject stroke a key, while the difference during key-release is clearly shown; the displacement of
the key changed more gently in case B. We calculated
the key velocities by taking the difference of adjacent
key displacements to investigate if there was differencein key-touch velocity or key-release velocity between
the two cases. An example result of ARCH1 is shownin Figure 5. The top panel shows the time course of
key displacements, and the bottom panel presents thecorresponding key velocities. The solid lines denote
the results in case A and the dashed lines case B. In
the bottom panel, positive peaks correspond to keytouch timings, while the negative peaks correspond to
key-release timings.
We found that there is seemingly a strong relationship between extensor muscle activities and keyrelease velocities at a key-release period in both cases.
Figure 6 actually depicts that there is a strong correlation between the extensor muscle (ED) activities andthe key-release velocities. This figure shows normalized maximum key-release velocity at the peak EMG
of ED during the key-release period, acquired in every
task. Each line presents a regression line over data
points in each task. Moreover, in order to examine
the difference in key-release velocity in the case B overdifferent experimental conditions, i.e., the finger form
and the number of keying meters, we performed twoway analysis of variance (ANOVA), and found:• the finger form: no significant difference (p-value:
0.665),The EMG signals were down-sampled by taking the
average of every 25 points after full-wave rectification. The sampling rate of the EMG signals henceforce became 120 Hz which was the same as the sampling rate of motion capturing. Additionally, we performed smoothing by taking the moving average over
five points:
2

EM Gma (t) =1 X
EM Gave (t − i).
5 i=−2(1)

Next, the data were filtered by a second-order lowpass filter proposed by Akazawa [3] which is known tobe effective in approximating the tension in a muscle.
The impulse response function of the Akazawa filter¡¢is:h(t) = 6.44 × e−10.80t − e16.52 .
(2)
Both the key displacement and motion data weresmoothed by a second-order Butterworse filter at a
cuttoff frequency of 10 Hz.

Results

A sample result acquired in condition ARCH2 isshown in Figure 4. The top, the second, the third
and the bottom panels present time course of the MP
joint angles, flexor muscles (FDS and FDP), extensor
muscles (ED and EI) and the key displacement, respectively. In the bottom panel, higher displacement
corresponds to keying down. For all panels, solid linesand dashed lines denote the results of case A and caseB, respectively.
As shown in Figure 4, EMG signals in case A are
generally higher than in case B. In both of the cases,
the flexor and extensor muscles were activated when

©ISAROB 2006

4.5

Figure 5: Key-touch and key-release velocities duringcondition ARCH1

21

Figure 4: Sample time course of joint angles, EMGs,
and key displacements during condition ARCH2

3

60

0.4

Key velocity [mV/s ]Key displacement [mV]

Key displacement [mV]

0.8

EMG(ED,EI) [mV]

EMG(FDS,FDP) [mV]Joint Angle(θMP)[rad]The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

191

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

sults would support a common belief among pianiststhat a certain sweet sound on a key can be generated
by feeling the force to the finger applied by the key
during releasing.
As contrasted to the key-release period, there wereno such difference in the activation pattern between
the flexor muscles and the extensor muscles during
the key-touching period. Both muscles were simultaneously activated in both case A and B, while theactivity of the flexor muscles were higher than that of
the extensor muscles, which simply means the stiffnessof joints were higher in case A than in case B.
Key-release velocities in case B were slower than incase A in either task, as clearly shown in Figure 7.
We further found that there is a strong correlation
between the extensor muscle activities and the keyrelease velocities as shown in Figure 6. As a consequence, measuring extensor muscles during the keyrelease period gives a useful index of key-release velocity involving in generating certain sweet sound (cf.
[2]).
In addition, the subject reported a difficulty in
ARCH1 and EXTE1 tasks in which the subject wererequired to let a key down every one meter; letting
down a key every one meter requires higher key-releasevelocity compared to the tasks to do the same thing
every two meters. Figure 7 actually reflects what she
mentioned.
Integrating sound recording and analysis into oursystem is necessary because we need to define a certain
goal to be achieved as a sound.

1.6

ARCH1
ARCH2
EXTE1
EXTE2

Normalized maximum key-release velocity

1.4

1.2

1

0.8

0.6

0.4

0.2
1

ARCH1 : R = 0.912
ARCH2 : R = 0.947
EXTE1 : R = 0.843
EXTE2 : R = 0.885
1.5

2

2.5

3

3.5
EMG(ED)

4

4.5

5

5.5

6

Mean of normalized maximum keyrelease velocity

Figure 6: Correlations between extensor muscle activities and maximum key-release velocities
1.1
1
0.9
ARCH in case A
EXTE in case A
ARCH in case B
EXTE in case B

0.8
0.7
0.6
0.5
0.4
0.3

1

2
Number of keying meter

Figure 7: The averaged normalized maximum keyrelease velocity• the number of keying meters: a significant difference (p-value: 0.000),• interaction: no significant difference (p-value:
0.212).
These results indicate that the velocity difference wasdue to the number of keying meters, but not to theother factors. The averaged normalized maximum
key-release velocity for each experimental condition isshown in Figure 7.

4

Acknowledgement
We thank Dr. Oshima at ATR Media Information
Science Laboratories, for giving us a lot of useful comments.

References[1] D.Parlitz, T.Peschel, and E.Altenmular. Assessment of dynamic finger forces in pianists: Effects oftraining and expertise. J Biomechanics, 31:1063–
1067, 1998.

Discussion

In this study, the most salient difference in activation patterns between flexor and extensor muscles
were observed during the key-release period in case A.
As shown in the third panel of Figure 3, the extensor muscles activated not only during key-touching
periods but also during key-releasing periods indicatedby arrows in the figure in case A (cf. solid lines), but
not in case B (cf. dashed lines), while such secondary
activations are not observed for the flexor muscles aspresented in the second panel of this figure. These re-©ISAROB 2006

[2] C.Oshima, K.Nishimoto, and M.Suzuki. Effects
of key-release velocity in piano performance -basic
analyses on features of low key-release velocity
points-. 2004-MUS-056:15–20, 2004.
[3] Y.Koike and M.Kawato. Estimation of dynamic
joint torques and trajectory formation from surface
electromyography signals using a neural networkmodel. Biological Cyberbetics, 73:291–300, 1995.

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Implementation of infrared wireless communication
system for multi-mobile robots’ team operations
Hiroyuki Takai*, Toshio Ninomiya* and Keihachiro Tachibana***Graduate School of Information Sciences, Hiroshima City University
3-4-1 Ozuka-Higashi, Asa-Minami, Hiroshima 731-3194 JAPAN**Graduate School of Computer Science, Osaka Gakuin University
2-36-1 Kishibe-Minami, Suita, Osaka 564-5811 JAPAN

Abstract

1. Introduction

Recently, mobile robots’ team operation has
been proposed as one of the effective solutions that enable the execution of complicated tasks [1]. These robots that join team
operations need to communicate mutually forsmooth task execution. Although radio waves
are usually used for mobile robot's remotecontrol or communication of mobile computer
terminals, one drawback is that they spreadout signals omni-directionally that can cause
communication interference.

In recent years, multiple mobile robots thatcan perform team operations have been developed. These robots are used for SAR(search and rescue) operation, surveillance,
patrol and so on. When the number of robots
increases in the same workspace to accomplish their tasks, they will often collide orinterfere with adjacent robots. Therefore,
they need to communicate to decrease theircollisions in order to achieve their taskssmoothly. Radio waves or infrared rays areused as carriers in inter-robot communication.

We have proposed an infrared wirelesscommunication system that can detect angleof arrival (AOA) of infrared signals from
communicating partners for mobile robots'team operations. Infrared rays have strong
directivity and limited beam width. They areadvantageous to communicate between adjacent robots, since they can decrease communication interference. Signal intensity
decreases with the square of the communicating distance. Also the reception capability
is in inverse proportion to the data transmission rate. Transmission efficiency andreception capability are also restricted by theperformance of the circuit elements such as
infrared LEDs and photo diodes.

Usually, radio waves are used for mobile
robots’ remote control or communication ofmobile computer terminals. However, radio
waves spread out signals omni-directionallyand cause communication interference that is
known in wireless communication as "thehidden terminal problem" or "the exposed
terminal problem". This signal interference
can be decreased using directional communication mediums such as infrared rays or
millimeter waves.
Infrared rays have strong directivity andlimited beam width. Moreover, infrared rays
can be used in circuits easier than millimeterwaves. However, infrared wireless communication often loses communication links
when mobile robots move or rotate. In order
to execute tasks smoothly, we have designed
an infrared wireless communication systemthat can maintain their communication links
by tracking the partner robot. This communication system communicates with partner
robots and simultaneously detects an incidence angle of infrared rays that points to thepartner’s direction using PSDs in order totrack the partner.

A position sensitive device (PSD) PIN
photo diode (HAMAMATSU S6560) that can
detect incidence angle of infrared rays is usedas the reception device of this infrared wireless communication system. This PSD has 2
current output electrodes and these ratios
show the incidence angle. We implemented
communication
signalamplifiers
andthreshold decision mechanisms that can restore digital pulse signals from received
analog signals. These electronic circuits were
tested and successful results were obtained.

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The PSD photo diodes (HAMAMATSU
S6560) that are reception devices have 2
current output electrodes and these ratios
show the incidence angle. Figure 2 shows aschematic view of this PSD.

In this system, several transceivers used
PSDs as reception devices are put on the circumference of the robot body and faced
outwards. These transceivers detect angle ofarrival (AOA) of the communication signals
in order to maintain communication links byswitching to adjacent transceivers that facecommunicating partners.

Anode
a

Infrared signal intensity decreases withthe square of the distance between thecommunicating partners. Also the reception
capability is in inverse proportion to the datatransmission rate. We designed amplifiers
and threshold decision mechanisms that can
restore digital pulse signals from received
infrared signals.

Cathodecommonqa bCathodecommonInfraredsignals

Anodeb

Fig.2 A schematic view of the PSD
The electric current output ‘a’ and ‘b’ of the
PSD has the relations of the equation 1 with
the incidence angle of the infrared signals q .

This paper describes the design and implementation of the infrared wireless inter-robot communication system.

q=(a–b)/(a+b)

2. Basic design of the infrared wirelessinter-robot communication system(1)

The electric current output 'a' and 'b' of the
PSD are feeble analog signals that need amplification. Figure 3 shows a block diagram ofa signal processing amplifier. In figure 3, thesignal processing amplifier outputs not only adigitized incidence angle but also serial dataof communication signals.

Infrared rays have strong directivity andlimited beam width. Although it can eliminate communication interference, usual infrared wireless communication often loses
communication links when mobile robots
move or rotate. We have designed an infrared
wireless communication system that canmaintain communication links. The communication system is formed by several infraredtransceivers that are put on the circumference of the robot body and faced outwards.
Figure 1 shows the arrangement of thetransceivers.

a

I/V

+

PSD

b

I/V

¸

P/H

P/H

+

-

¸

incidence
angle
A/D
serial data

Fig. 3 Block diagram of amplifier
The detected incidence angle also encoded
to the 2bit direction code in order to selecttransceivers that are facing to communicating partners. Figure 4 shows the direction
code in an infrared transceiver.

Fig. 1 Arrangement of transceivers
In this system, these transceivers used
PSD photo diodes that can detect incidence
angle of infrared rays as reception devices.
The incidence angle of infrared rays is usedin order to find the partner's direction.

©ISAROB 2006

Fig. 4 The direction code and detected angle

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When a partner is in the left, center, andright view of a transceiver, the partner's direction is encoded to the direction code '10','11', and '01', respectively. When a partnermoves or rotates, these transceivers detect
the partner's direction in order to switch toan adjacent transceiver that is facing thepartner. The direction code is used in order tomaintain the communication link by switching these transceivers. Figure 5 showstransceiver exchange circuit. In figure 5, theserial data that is received by each transceiver is on the left side, and the direction
code that is outputted from each transceiver
is below. Since the serial data can be received
from selected transceivers continuously, theinfrared communication system can maintaintheir communication links.

3. Feasibility study of the infrared
wireless communication system
As previously mentioned, the infrared
wireless communication system is able toadapt for the inter-robot communication, if
its mechanism is designed appropriately.
However, infrared signal intensity decreases
with the square of the distance from thetransmitter to the receiver. Also the communication capability is in inverse proportion to
the data transmission rate. We studied theperformance of the inter-robot communication system.
In order to operate multiple mobile robots
that perform team operations such as SAR
operation or surveillance by remote control
smoothly, the inter-robot communication
system needs high-speed data transmission.
The communication system has to send not
only its positional information but also gathered information such as compressed video
images, direction of thermal sources, gasconcentration data and so on. We determined
384 kbps as a data transmission speed of thecommunication system in order to design thecircuit, because it is similar to the speed used
for the uplink from mobile phone terminals of
walking users that transmit compressed
QVGA video images.

Fig. 5 Transceiver exchange circuit.
Since infrared rays have strong directivity
and limited beam width, infrared communication suffers from hardly any interference, if
partners are in different directions. This
communication system can communicate
with different partners in different directions
by using different transceivers simultaneously. As a result, space-division communication can take place. When each robot relays
information between different partners, acommunication network is created in theirworkspace. It is an ad hoc communication
network because each robot is independently
mobile and may change position dependingon their task. Figure 6 shows an inter-robot
communication network.

We implemented an infrared transmitter,
two types of infrared receivers and two typesof threshold decision circuits for feasibility
study of this communication system. The infrared transmitter uses two infrared-LEDs(STANLEY DNF324U, 850nm) driven by
2SA1300 transistor. One of the infrared receivers uses a PSD (HAMAMATSU S6560,
960nm) that can detect an incidence angle ofinfrared rays. Another one uses a photo diode(TOSHIBA TPS703, 960nm) that has similarcapabilities to the S6560 (except its angle
detection) in order to test the design ofamplifiers.
Two threshold decision circuits that restore
serial data from received infrared pulse signals were tested. Figure 7 shows the threshold decision circuits. In figure 7, the circuit(A) calculates the median value of high side
peak and low side peak as threshold voltage.
Also in the circuit (B), the difference between
high side peak and low side peak is divided
into three in order to give hysteresis effects.

Fig. 6 An inter-robot communication network©ISAROB 2006

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The photo-diode used for infrared reception
detects both an infrared signal and background noise. In addition, the duty ratio ofreceived signals influences the nonlinear
character of the photo-diode, and makes amplification of signals unstable. The design of
the amplifier circuit that suited the characterof the photo-diode will be needed forhigh-speed long-distance infrared wirelesscommunication.

High-side

P/H
PD

I/V

P/H Low-side(A) a median value threshold
High-side

P/H
PD

I/V

Set

FF
P/H

Low-sideReset

Acknowledgements(B) a hysteresis threshold
Fig. 7 Threshold decision mechanisms

This work is supported in part by Japan
Society for the Promotion of Science
Grant-in-Aid for Scientific Research (C) (No.
17500118).

The infrared receiver that uses TPS703
photo diode and is connected to MOSFET(2SK241) amplifiers was used for the test. In
the test, the receiver detected infrared signals of 460kbps from the infrared transmitterin 1m distances and outputted 400mVpp approximately.

References
1. ASAMA H., et al (Eds.) (1994) “Distributed
Autonomous Robotic Systems” (DARS), Springer,Tokyo

In this test, the threshold decision circuits
were possible to recover to digital pulse fromthe received signal voltage of 100mVpp, andit was also possible to receive signals up to
460kbps. However, when the outputted signals of the infrared receiver were less than
400mVpp, the wrong serial data was received
because errors were included in the digitalpulse sequence. At a slow transmission speed,these circuits were impossible to recover tothe correct digital signals, because thethreshold value is changed by the electric
discharge of peak-hold capacitors.

Conclusion
We proposed and designed an infrared
wireless communication system that is suitable for multiple mobile robots. We also implemented an infrared transmitter, two typesof infrared receivers and two types ofthreshold decision circuits in order to study
the feasibility of the communication system.

3. TAKAI H., et al. (2001) “A Space-Division OpticalWireless Communication System for Fully Distributed Multiple Autonomous Mobile Robots”. In:
K.SCHILLING, H.ROTH (Eds.). Telematics Applications in Automation and Robotics 2001
(TA2001). PERGAMON press, pp.333-338
4. TAKAI H. et al (2002) “A Geometric Arbiter Selection Algorithm on Infrared Wireless Inter-robot
Communication”, In: ASAMA H., ARAI T., FUKUDA T., HASEGAWA T (Eds.). Distributed
Autonomous Robotic Systems 5 (DARS 5),
Springer, Tokyo, pp.61-70
5. TAKAI H, et al (2002) “Development of aSpace-Division Infrared Receiver for Fully
Distributed Inter-robot Communication Network”,
2002 IEEE International Conference of System,
Man and Cybernetics, IEEE, ID:WA1E3
6. TAKAI H, et al (2003) “Function Integration forTeam Operations of Mobile Robots: Inter-robot
Communication and Mutual Localization”, Proceedings of the 11th International Conference ofAdvanced
Robotics(ICAR2003),
IEEE,
pp.1431-1436

The infrared receivers were able to detect
infrared signals of 460kbps from 1m distances and outputted 400mVpp approximately in this experiment. The threshold decision circuits were able to operate in thesignal of 100mVpp 460kbps. However, when
the outputted signals of the infrared receiver
were less than 400mVpp, wrong serial data
was received.
©ISAROB 2006

2. YOSHIDA E, et al (2000) “Performance Analysis ofLocal Communication by Cooperating Mobile Robots”, IEICE Transactions of Communication, series EB, vol. E83-B, no. 5, pp.1048-1059

7. TAKAI H, et al (2005) “A Space-Division WirelessCommunication System for Ad Hoc Networking
and Cooperative Localization of Multiple Mobile
Robots”, Preprints of the 16th IFAC World Congress, ID:05004

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The Development of Robot Palletizing Simulator using Pallet Pattern Algorithm andTrajectory Optimization
Seungnam Yu, Sungjin Lim
Dep. Precision Mech. eng.
Han Yang University
Seoul, 133791, Korea

Heukwon Yoon
Dep. Indus. Eng.
Han Yang University
Seoul, 133791, Korea

Abstract

majority of palletizing application. Actually, thenumber of case patterns that can be
programmed into a fully automatic palletizer isvirtually limitless; however, when a pattern isprogrammed into a palletizer, it is not simply asoftware change in the controller itself. One of
the biggest problems of manufactures is building
loads to customer’s specifications.
We propose the off-line robot palletizing
simulator to combine Fast algorithm and 3D
robot simulator. Fig.1 shows basic schematic of
the simulator.

Palletizing task is necessary to promote
efficiency of keeping and shipping task. This is,however one of the most monotonous and heavylaborious work in the factory. So, many types of
Robot palletizing system have been developed.
But many robot motion commands is still
depends on the teach pendent. That is, theoperator input the motion command line one byone. It is very troublesome and most of all, user
must know how to type the code. So we propose
the new GUI Interface of Palletizing System thatcan be used more conveniently. To do this, we use
the algorithm of PLP, "Fast Algorithm" andrealize the 3D Auto-Patterning Visualization.
The usages of 3D pattern are following. First, an
operator can identify the result of own task andedit it. Second, pass the position of object to arobot simulator. Using that position, palletizing
operation can be simulated. We use the"Hyundai HHI series" and analyze thekinematics and dynamics to realize into a robot
simulator. In this paper we propose the 3D
patterning algorithm and 3d robot palletizing
simulator. Future work is optimal pathgeneration of robot manipulator and connecting
this simulator to a real robot controller.
¾
¾
¾
¾
¾

Fig. 1 Proposed schematic of Palletizing simulator

2. Fast Algorithm
2.1 Steudel’s Heuristic Algorithm

PLP(Pallet Loading Problem)
Fast Algorithm
Pallet loading Pattern
Robot Simulator
Trajectory Optimization

The objective of the pallet loading problem(PLP) is to maximize the number of products
loaded onto a pallet that is used for thetransportation and storage of products. We canreduce the distribution and storage costs of pro
duct by increasing pallet utilization.

1 Introduction
This paper will focus on palletizing, which isa form of unitizing. Palletizing refers to a
uniform load stacked on a wooden pallet, using a
pre-determined case pattern sequence and given
number of layers. The process of palletizing
typically involves the stacking of boxes, trays,
bundles, bags, pails or drums in a predeterminedpattern configuration. This paper will focus on
the use of boxes only, as they make up the vast

©ISAROB 2006

Changsoo Han
Dep. Mechanical Eng.
Han Yang University
Seoul, 133791, Korea

Fig. 2 Steudel’s Heuristic Algorithm
Fast Algorithm proposed in this study is

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basically, improved algorithm compare with the
4-block pattern heuristic algorithm which
proposed Steudel. Typical pattern of Stuedel’sheuristic algorithm represented Fig.2. This
heuristic finds the 4-block pattern where eachblock is a homogeneous pattern with the samebox orientation [1]. This heuristics consist of twophases. First, an initial solution is made with
Li andWi thatthe combination of
maximizes the utilization of all four pallet edges.
Dynamic programming was applied to find the
combination and the initial solution has one of
the four patterns shown in Fig.3. An initialsolution that has a central hole sufficiently large
to load more than one box in the case of P1 andP3, or an infeasible pattern, such as P2 and P4,
due to the overlapped area,¾¾¾

Fig. 5 Treatment of the Fast Algorithm
In the first method: the boxes that are cut by thetwo horizontal edges of the overlapped area.
In the second method: the boxes that are cut by thetwo vertical edges.
In the third method: the boxes that are cut by theleft vertical edge and the lower horizontal edge.

2.2.2 Schematic of Fast Algorithm
We do not consider the all size of blocks;
therefore this algorithm has more rapid
calculation time. First phase’s initial solutions
find the combination rather than using DP and
define the 4 parameters. (Fig.6)

a : When maximize the length of block anddispose the boxes lengthwise, maximal possible
number of box = 5l

a : When maximize the length of block anddispose the boxes lengthwise, minimal possible
number of box = 2lFig. 3 Four patterns of the initial solutionb : When maximize the width of block andthis will involve the second phase. In the second
phase, Treatment 1 is Fixing (L3, W3) and (L4,W4), resize L1 and L2 and Treatment 2 is Fixing(L1, W1) and (L4, W4), resize W2 and W3. We
can choose the better solution to compare thefirst and second method.

dispose the boxes lengthwise, maximal possible
number of box = 8w

b : When maximize the width of block anddispose the boxes lengthwise, minimal possible
number of box = 2 w(a)(B)
Fig. 4 Treatment of Steudel’s Algorithm
Treatment 1, (b) Treatment 2

2.2 The Fast Algorithm

Fig. 6 A parameters of Fast Algorithm

2.2.1 Definition

In the first phase, make combinations of (L1, W1)

The Fast Algorithm which we proposed has
similar process to generate the initial solution of
4 patterns. And additionally, we adapt ourTreatment 3 to apply the heuristic recursively tothe central hole in the following three methods
to remove the overlapped area. (Fig.5)

such as ( a, b) , ( a, b) , ( a, b ) and ( a, b) and given( L1 , W1 ) is the width and length of the other blocks
can be determined.

⎛ ⎡ L − L1 ⎤ ⎡W − W1 ⎤ ⎞( L2 , W2 ) = ( L4 , W4 ) = ⎜⎜ ⎢⎥ w, ⎢⎥l ⎟⎟⎝⎣ w ⎦ ⎣ l ⎦ ⎠( L3 ,W3 ) = ( L1 , W1 )After making the four initial solutions in thefirst phase, redefine these solutions by applying©ISAROB 2006

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the three treatments in the second phase

2.2.3 Computational Experience

We implemented the algorithm in Visual C++
6.0 and compiled it with the maximized-speed
option.
Table 1 The test result problem of small size
(L,W,l,w)
Amount of box loaded(1000,1000,205,159)
30(1000,1000,200,150)
33(22,16,5,3)
23(30,22,7,4)
23(14,10,3,2)
23(53,51,9,7)
42(34,23,5,4)
38(57,44,12,5)
41(87,47,7,6)
97(1200,800,176,135)
38(L: Length of Pallet, W: Width of Palletl: Length of Box, w: Width of Box)

Fig. 8 Box target position (Robot Coordinates)

3. The Development
Simulator

of

3D

Robot

3.1 Overview
For an industrial robot, to generate thepalletizing task, several methods had been
introduced and used. First one is On-line
teaching the robot using teach pendent or mimic
and memorize the worker’s motion. Second one
is Off-line method which is to generate task datausing computer and download it to the robot
controller. Especially, we focus to the Off-line
task generation and simulation using robot
simulator. In this phase, we represent three
dimensional robot simulator based on thedimension data of our real target machine,
HX300 which is a 6-axis industrial robot ofHyundai Heavy Industrial Co. This robot modelis realized by commercial CAD modeler, and GUI
interface is developed using OpenGL® and MFCof Microsoft Visual C++®.
To solve and analyze the forward and inverse
kinematics equation, we use D-H table andLagrangian Dynamic Equation. And thissimulator is possible to compute and display thejoint torque, angle, angular acceleration
simultaneously.

Upper result acquired from a computer of K6350MHz CPU and 64Mbyte RAM. All problems
were calculated within 1 second and resulted in
optimal solutions. [1] This algorithm test isabout two dimensional pattern generation ofboxes and its calculation speed. To use thisalgorithm,
however,considerthethreedimensional space and physical constraint of box
loading. To realize this, we consider the threeconstraint condition of palletizing, which isapproach direction of robot hand to the pallet,
multiple box gripping and weight center ofpalletized boxes. More information about thiscondition is referred to [2].
To use this algorithm to the industrial robot
system, we developed GUI interface for a palletloading problem using Fast Algorithm (Fig.7),
and Fig.8 shows the calculated target position ofeach box.

3.2 Computation
Robotic system

andDevelopmentof

We solve the transformation matrix of eachjoint space and derive the inverse kinematicalequation. And we compute the energy equationof potential and kinetic energy of system using
Lagrangian equation; finally we acquire thefollowing equations (More detail equations arereferred to [3])U ij =

Fig. 7 Pallet Pattern Generation Software©ISAROB 2006

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∂Ti ∂ ( A1 A1 L A j L Ai )
=
∂q j
∂q j

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The performance test of upper simulator
combined with Fast algorithm is explained to [2].

K i = ∫ dK I
⎤
⎡ i i
1
T
= Trace ⎢∑∑ U ip ( ∫ ri ri dmi ) ⋅ U irT q& p q& r ⎥2⎦⎣ p =1 r =1

4. Conclusion and Future work
In this paper, we proposed Fast Algorithm for
PLP, 3D robot simulator and schematic of
combination of two systems. To develop total Offline palletizing simulator, however, the functionof Trajectory generation and optimization of task
are required. Fig.10 shows current status of ourresearch. As a future work, we study the
trajectory generation algorithm using path
planning and obstacle avoidance. Especially, two
topics will be considered. First issue is CartesianCurve Fitting for a path defined by user andsecond one is Collision-Free Path Planning(CFPP) for automatic path generation.

( Ai : transformation matrix of i th joint spaceqi : i th joint angle)
L=K+P=
+

6
1 6
2
&
I
q
−
[− mi q T ⋅( 0 Ti ri )]∑ i ( act ) i ∑2 i =1i =1( Ji :‘

1 6 i i∑∑∑ Trace(U ip J iU irT )q& p q& r
2 i =1 p =1 r =1

Pseudoinversematrixtoexpress∫ r r dm ’ term)
T

i i

i

To build the dynamic equation, we
differentiate each term and we derive thefollowing equations.
6

Ti = ∑ Dij q&& j + I i ( act ) qi6 + ∑∑ Dijk q& j q& k
j =1

Fig. 10 Development Process
6∑ Trace(U J U )Dij =p = max(i , j )Dijk =

pj

p

T
pi

References[1] Young-Gun G, Maing-Kyu Kang, 2000, “A
fast algorithm for two-dimensional palletloading problems of large size”, European
Journal of Operational Research 2001,
pp.193-202[2] Seung-Nam Yu, Heu-Kwon Yoon, Sung-Jin
Lim, Young-Hoon Song and Chang-Soo Han,
“The development of Robot Palletizing S/Wusing “Fast Algorithm and 3D Robot
Simulator”, KSME 2005, pp.1663-1668[3] Sung-Jin Lim, 2004, “A Study on analyzing
inverse kinematics and inverse dynamics of
6 DOF articulated robot”, Hankuk Aviation
University[4] John J. Craig, 1989 “Introduction to
Robotics, Mechanics and Control Second
Edition”, Addison Wesley, pp230~246[5] Naak-hyung
Lee,
1998,
“A
DesignExperience on PC-Based Open Control for
Robot Systems”, Han yang University[6] T. Lozano-Perez, “A Simple Motion Planning
Algorithm for General Robot Manipulators”,
IEEE Journal of Robotics and Automation
1987, Vol. RA-3, No.3,[7] C.W Warren, J.C. Danos, and B.W. Mooring,“An Approach to Manipulator Path
Planning”, The International Journal of
Robotics Research, Vol.8, No.5, pp.87-95

6∑ Trace(U pjk J pU Tpi )p = max(i , j , k )6

Di = ∑ − m p q T U pi rp
p =i

Ti is angular acceleration inertia, Dij isinertia of actuator, Dijk is Coriolis term andCentrifugal force and Di is gravity term. Using
these equations, we realize the three dimension
robot simulator as Fig.9.

Fig. 9 Robot Simulator for Palletizing Task

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A Study of an Autonomous Mobile Robot for Sewer Inspection System
1,2

Alireza Ahrary, 1,2Amir Nassiraei, 2Masumi Ishikawa
1
FAIS-Robotics Research InstituteCollaboration Center 4F, 2-1 Hibikino, Wakamatsu-ku, Kitakyushu City, 808-0135, JAPAN
2
Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Kitakyushu City, 808-0196, JAPAN
ali@ksrp.or.jp, amir@ksrp.or.jp, ishikawa@brain.kyutech.ac.jp

Abstractmonolithic KARO robot prototype resembles much astandard non-autonomous sewer inspection robot, and it
is tethered via a cable to a surveillance unit. Using
inclinometers and an on-board control program, KARO
is able to automatically correct for tilt in its pose andwheel slippage when driving inside a sewer pipe, thus
freeing the human operator from this crucial control task.
The main innovation of KARO is its wide range of novel
inspection and navigation sensors, namely a microwave
sensor and a 3D optical sensor, complementing thestandard video camera and some ultrasound transducers.
A step forward in completely autonomous sewer
inspection robot is a semi-autonomous tethered sewer
inspection robot. Piper [6] is good example close to thisarea. Piper's operating range is limited by the mandatory
cable, having a length of about 45 meters. Twouser-programmable microcontrollers provide forcontrolling locomotion and for automatically adjusting
pan and tilt of the video camera head to the robot's
locomotion speed. The video images can be either tapedor captured on a computer. Its dimensions (9 inches
width and 13.5 inches height) prohibit its application insmaller pipes.
KURT [7] is a six wheeled autonomous robot ofapproximate dimensions 30x45x30 cm. With its pivoted
ultrasound sensor, KURT1 is able to classify the type of apipe junction, i.e. whether it is L-shaped, X-shaped, orT-shaped. A special innovative method for navigation
under uncertainty enables KURT1 to detect and correct
errors due to false classification or due to errors while
performing turns at pipe junctions.
MAKRO [8] is the prototype of a fully autonomous,
self-steering articulated robot platform. It is designed forautonomous
navigationinroughlycleaned,non-man-entry sewer pipes within a diameter range of
300 to 600 millimeters at dry weather conditions.
MAKRO is consisting of six segments connected by
five motor-driven active joints, allows for simultaneously
climbing a step and turning, e.g. at a junction consistingof a 600 millimeter pipe and a branching 300 millimeter
pipe with equal top levels. MAKRO carries all theneeded resources on-board. Standard NiCd batteriesThis paper describes KANTARO, a prototype
autonomous mobile robot designed for inspecting sewer
pipes. It is able to move autonomously in 200-300 mm
diameter sewer pipes. KANTARO can also carry various
sensors such as a camera for detecting faults and a 2D
laser scanner for recording the pipe features.
Keywords: Autonomous mobile robot, Sewer inspection,
Sewer system, Navigation, Fault detection system.

1

Introduction

Pipe walls in sewer systems are prone to be damaged
due to aging, traffic and chemical reactions, throughwhich inflow such as rainwater and groundwater seeps
into the pipe systems. Regional city government reports[1] state that this inflow amounts to approximately 30%of the total flow. In addition to the inflow of groundwater
into the sewer pipes, outflow from damaged systems also
occurs, contaminating the surrounding environment[2]-[4].
The current state of the pipe maintains is carried out
using a cable-tethered robot with an onboard video
camera system. These platforms are connected to theoutside world by a cable that gets used for energy supply,
for transmission of commands from the human operator
to the device, for data transmission back to the operator,
as a life-line in case the device gets stuck in the pipe, andas a measuring line for the distance traveled. By thisvideo-supported visual inspection, any notable damages
or abnormalities are recorded in the video stream. The
reliability of this system depends on the experience of an
operator. The system is also prone to human error.
Consequently, effective autonomous robot with online
techniques to identify and extract objects of interest suchas cracks from sensory signals is of immediate concern.
There are some approaches on optimizing the design,
handling and sensors abilities of these cable-tethered
robots. An example related to this area is KARO [5]. The©ISAROB 2006

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and sensor reading.
To achieve to wiring less and easy to assemble anddisassemble robot, we designed the robot as a module
system. As it showed in Fig. 1 robot consists of two mainparts, upper box and bottom box that can connect to eachother by a main connector.

provide the power for its 21 motors, the sensors, and theelectronics, including an industry standard PC/104+
computer system and seven microcontrollers.
The work we report here is an autonomous mobile
sewer inspection robot, KANTARO. KANTARO differsfrom above conventional robot in two aspects: First it issmaller than others and able to allowing for anautonomous uptime of about nine hours. Second, it isable to detect various types of faults automatically.
KANTARO is able to move autonomously in
200-300 mm diameter sewer pipes and it can also carryvarious sensors such as a camera for detecting faults anda 2D laser scanner for recording the pipe features.
The paper is organized as follows. First, we describe
the KANTARO's hardware design in Section 2. Then we
sketch an outline related to the task of navigating anddetecting the faults autonomously through the sewer
system in Section 3. We conclude this paper in the lastsection.

Figure 1: The KANTARO mechanism.

2

KANTARO's hardware design
2.2

The sewer system is no easy place for such a robot towork. It is narrow, slippery, dirty and wet. The system
must be constructed such that it is able to drive throughlong pipe sections quickly and with a minimum of energy
consumption. Turning at narrow and slippery junctions
must be possible with a minimum of mechanical wear
out.

2.1

Basically, if some sensor redundancy is desired, thebenefit of having more sensor information must be
weighed against the computer power required for
exploiting it.
In our case, one should use few sensors and implement
domain dependent and cheap, yet robust algorithms
instead, which compensate for any uncertainty in thesensor interpretation and the resulting incompleteness of
the internal representation.

Mechanism

2.2.1

With regard to current sewer inspection technology, allcommercial robots are capable to move in straight pipes
but not any kind of pipe-bends such as “curves”, “joints”
and “T-junctions”. However, because of the high range ofpipe-bends in the sewer pipe system, pipe-bends pose amain problem for sewer inspection robots. Design a robotthat can move into the straight pipes and pass pipe-bends
will be a great industrial progress in sewer inspectionindustry.
We designed a compact and a novel moving
mechanism for the sewer pipe inspection robots. The
design is based on the concept of passive adaptation ofrobot wheels to the bends in the pipe. This isaccomplished by proper wheel orientation and passive
damping of springs. A single robot, include of this new
moving mechanism, can carry the controller, electronics,
motors and sensors and it is able to pass any kind of thepipe bends, moves in different size of pipe, goes step
down and passes the obstacles with no intelligent control©ISAROB 2006

Sensors

CameraIn many computer vision applications including robot
navigation, a camera with extremely wide-angle view isrequired. The conventional camera, however, has alimited angle of view. Therefore “fish-eye” lens isemployed in this paper. Omni-directional view has
recently become popular in mobile robots.
Here in our robot, we use a fish-eye lens CCD camera
located in center with surrounded by circle type white
LED light system. Fig. 2 shows the KANTARO camera
and the lighting system.
The environment in the pipe is less affected bychanges of external light. The lighting condition iscontrolled by a white LED array around the camera toacquire the best quality image.

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First, computing the current position based on
distinetivation local features such as manholes and pipe
joints, and finds a path. Second, follow this path in thereal sewer pipe system.
Existing map of the sewer pipe system specify pipes,
manholes and other local features is helpful to solve thisovercoming problems. The high slip in sewer pipes
caused unreliable odometry for an autonomous sewer
robot. Furthermore, the metric information from thesewer pipe system map is not applicable in navigation foran autonomous sewer robot. Note that data from Global
Positioning System (GPS) is inaccessible in undergroundsewer pipe system. Consequently, an autonomous robot
has to compute the current position based on local
features.
In our previous works [9]-[11], various methods were
proposed for the detection of local features andcomputing the current position. We conducted
experiments for sewer robot navigation in a dry sewer
test field at RRI, Kitakyushu. They succeeded in
detecting local features by use sensors information. We
use a fast and accurate method to compute the distancedata between local features and robot. Finally, the refinedcurrent distance can be used for autonomous sewer robot
navigation.
In other works, we presented the detection of local
features in sewer pipes based on fusion of laser scanner
data and image data [12]. We still have to perform further
experiments with different pipe material, different
diameters and test these methods in real sewer pipe
system.

Figure 2: The KANTARO vision system.
2.2.2

Laser scanner

2D and 3D laser scanners are excellent distancesensors, because they can recognize the shape of adetected object accurately. Since conventional scanners
are large and heavy, it is difficult to install them in
KANTARO that moves through pipes with diameter of
200-300 mm. Because of this, we develop a special 2D
laser scanner that can measure the distance in sewer
pipes and the angle of rotation and can be installed in
KANTARO. The 2D laser scanner can locate features
such as manholes and junction points. Fig. 3 shows theexternal view of the 2D laser scanner.

4

By current video-supported visual inspection system,
any notable damages or abnormalities are recorded in thevideo stream. The reliability of this system depends onthe experience of an operator. The system is also prone tohuman error. Consequently, effective autonomous online
techniques to identify and extract objects of interest suchas cracks from sensory signals are of immediate concern.
In our previous works, we described a method for
detecting faulty areas in a sewer pipe system based onimages acquired by KANTARO [13] [14]. The proposed
method is able to detect various types of faults
automatically and can be implemented in a real timesystem. The proposed approach demonstrates highperformance of detection and it is able to detect evenfaint faults in small areas.
Graphic user interface (GUI) for the fault detection is
illustrated in Fig. 4. Some additional information such asedge image and correlation variation is displayed to help
experts doing data analysis.

Figure 3: 2D laser scanner.
The triangulation method is used to measure thedistance inside of pipes by the semiconductor 2D laser
scanner using rotary scanning. It receives the reflected
light by a light detecting element located at the axis ofrotation, and measures the distance and the angle ofrotation using a lens installed at the axis of rotation. Datais transmitted to a computer with the sampling speed of
10 KHz. The network is connected with the transmission
rate of 1Mbps. The distance error is ±1mm or less.

3

Navigation

One of the central issues in developing an autonomous
sewer robot is its navigation with the following problems.

©ISAROB 2006

Automated fault detection system

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1990.
[4] Rudolph, U., Wellnitz, J., “Zustand undSanierungsbedarf der Abwasserkanale in den neuen
Bundeslandern”, Korrespondenz Abwasser, 38 Nr.
12, pp. 1625-1630, 1991.
[5] http://www.iitb.fhg.de/servlet/is/1604/[6] http://www.angelusresearch.com/piper.htm[7] http://www.kurt2.de/[8] http://www.ais.fhg.de/projects/Makro/makro.html[9] A. Ahrary, L. Tian, S. Kamata, M. Ishikawa, “Anautonomous sewer robots navigation based on stereo
camera information,” Proc. of the 17th IEEE Int.
Conf. on Tools with Artificial Intelligence, Hong
Kong, 2005.
[10] A. Ahrary, M. Ishikawa, “Detecting pipe featurepoints for sewer pipe system based on image
information,” Proc. of the 10th Int. Conf. on
Mechatronics
andInformation
Technology,
Chongqing, China, 2005.
[11] A. Ahrary, L. Tian, S. Kamata, M. Ishikawa, “A
cooperative stereo matching algorithm for sewer
inspection robots,” Proc. of the IASTED Int. Conf. on
Robotics and Applications, Cambridge, USA, 2005.
[12] A. Ahrary, M. Ishikawa, “Fusion of laser scannerand image information data for landmark detection insewer pipe system,” Proc. of the 9th Int.Conf. on
Mechatronics Technology, Kuala Lumpur, Malaysia,
2005.
[13] A. Ahrary, M. Ishikawa, “Visual fault detection forautonomous sewer robots,” Proc. ITC-CSCC 2005,
vol.1, pp.201-202, Jeju, Korea, 2005.
[14] A. Ahrary, M. Ishikawa, “An automated fault
detection system for sewer inspection robots,” Proc.
of the 36th Int. Symposium on Robotics, Tokyo, Japan,
2005.

Figure 4: The GUI for fault detection.

5

Conclusions

The RRI dry sewer test field is definitely real world,
but it is not an operational sewerage system. Although
we have made clear that we have respected many
constraints in our test scenario that are present in the realapplication area, it is obvious that it is simplified in a
number of aspects and this simplification was deliberate.
In consequence, we do not claim to have a fully
operational autonomous sewer system robot.
Future work should aim to develop our robot mucheasier to use in the real world. Finally we hope that
KANTARO use into the city sewer system in near future.

Acknowledgements
This research was supported by the FAIS-Robotics
Research Institute, funded by the Organization for Small& Medium Enterprises and Regional Innovation, JAPAN(SMRJ).

References[1] Material provided by Sewer System Administration
Dept., City of Kitakyushu, Japan.
[2] Durbeck, W., “Stand der Eigenkontrollverordnung
in den Landern der Bundesrepublik Deutschland ausder Sicht der Industrie”, Dokumentation 4. Int.
Kongress Leitungsbau, Hamburg, Germany, pp.
153-166, Oct. 1994.
[3] Keding, M. Riesen, S. van Esch, “Der Zustand deroffentlichen Kanalisation in der Bundesrepublik
Deutschland. Ergebnisse der ATVUmfrage 1990”,
Korrespondenz Abwasser, Nr. 10, pp. 1148-1153,©ISAROB 2006

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Detection of Face Pose with Neural Networks
Yau-Zen Chang and Gau-Tin Hung,
Medical Augmented Reality Research and Development center,
Department of Mechanical Engineering,
Chang Gung University, Tao-Yuan 33302, Taiwan
face, and the image-based methods directly investigate
or learn those known/given images.
In [6], an image-based face detection approach is
proposed to detect upright and frontal views of humanface using neural networks. In [1], a detection method is
proposed by searching for elliptical contour andconsidering color distribution within the region
enclosing the contour. [2] proposed a method to detect
critical features, i.e. eyes, by the morphology techniquesof image processing. In [3], triangles are used to model
the relation between eyes and mouth, cooperated withthe color information nearby the vertices of them. [4]adopted YCrCb color space to detect the human face inorder to reduce the effects of illumination. As theseworks detect human face with skin color informationand the outline of the face, they are classified asfeature-based methods accordingly.
In this work, face pose, in addition to its position, is tobe estimated. This task is vital in an active display
system, surveillance system or a robot with intelligent
interface, etc. We will resort to both feature-based andimage-based methods to solve this tough task.
Considering its tremendous power in modeling the
mapping between two sets of data, artificial neuralnetworks (ANN) are used. Two neural networks areused to arbitrate the existence of face region and theposition of features in consecutive operations. The firstneural network is adopted to classify the connected
region of the rest pixels into three categories, i.e. eyes,mouths and others. The second neural network is thenimplemented to arbitrate the most possible compositionof two eyes and one mouth. After the feature positions
are found, the pose of the face can be derived by thegeometric relationship between them.

Abstract
In this paper, we propose a strategy to detect theposition of a face and estimate its pose using two neuralnetworks. At the first stage of the arrangement, the color
information was adopted to extract skin regions fromgrabbed images and the potential face regions arecropped according to vertical and horizontal projectionof the segmented image. From this cropped image, we
implement two neural networks to arbitrate the existenceof face region and the position of features in consecutive
operations. The first neural network is used to classify
critical regions of the cropped image into three
categories, i.e. eyes, mouths and non-of-them. The
second neural network is then implemented to arbitrate
the most possible composition of two eyes and one
mouth. After the feature positions are found, we canderive the pose of the face by the geometric relationshipbetween eyes and mouth. Experimental results show thatthe proposed method can successfully and robustly
detect the existence and inclination of a face fromimages.
Keyword: Face Detection, Artificial Neural Network.

1. Introduction
Face detection from complex and arbitrary
backgrounds has been under intensive study in image
processing and computer vision. Early efforts in facedetection can be traced as early as the beginning of the
1970s. Due to constant progress in computing power, theamount of research into face detection has grown inrecent years.
A survey of face detection is reported in [5], where thetechniques are roughly divided into two categories:
feature-based and image-based. The feature-based
methods consider the nature characteristics of human

2. Face Pose Detection
The developed system of face pose detection consistsof three parts: skin color segmentation, feature
localization, and estimation of face pose. In thefollowing sections, we will introduce our approach indetail.

Yau-Zen Chang is with the Mechanical Engineering
Department ,Chang Gung University, Tao-Yuan 33302, TAIWAN(corresponding author to provide phone: +886-3-211-8800; fax:
+886-3-211-8050; e-mail: zen@mail.cgu.edu.tw).
Gau-Tin Hung is with the Mechanical Engineering Department,
Chang Gung University, Tao-Yuan 33302, TAIWAN (e-mail:d9322006@stmail.cgu.edu.tw).

©ISAROB 2006

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selected 3 candidate features, 9 attributes were selected,
including the relative distances of them, and relative
coordinates in the images. The first 3 attributes aredefined as:

2.1 Segmentation of Skin Color
In this research, the color information was adopted to
extract skin regions from grabbed images. As the RGB
color space is sensitive to illumination, the segmentation
is based on pixels represented in the CIE Lab color space.
Table 1 shows the color values classified as skin. The
potential face regions are cropped according to verticaland horizontal projection of the segmented image. Fig.1
shows the results of skin color segmentation and its
projection. At this stage, the system finds potential
regions where human face might appear.

(((

where Peye1 Peye 2 , Peye1Pmouth and Peye 2 Pmouth stand for the
distances between those three critical features. The rest 6
attributes are defined as:x⎧ X in4 = Peye
1 / Sx⎪ 5yX=Peye1 / S y⎪ inx⎪⎪ X in6 = Peye
2 / Sx, S x , S y = sizeof (Cropped Image )⎨ 7y⎪ X in = Peye 2 / S yx⎪ X in8 = Pmouth
/ Sx
⎪ 9
y
⎩⎪ X in = Pmouth / S y[

2.2 Localization of the Face Features
For scenarios with complex background, potential
face regions should be verified firstly. We implement
two neural networks to arbitrate from this cropped image
the existence of face region and the position of featuresin consecutive operations. Fig.1 demonstrates typicalresults of skin color segmentation and its projection. At
this stage, the system finds out the region which humanface might appear.
At the first stage, we eliminate the skin color pixels
from the cropped image to exaggerate important features.
In order to discriminate rest pixels for feature regions,
we search and label all of the connected regions [7]within the eliminated image. Fig.2 shows the results ofthe elimination and labeling of the connected regions.
The first neural network is employed to classify theconnected feature regions into three categories: eyes,mouths and others. We randomly selected 528 images ofeyes and 636 images of mouths, each of 15 by 8 pixels,
from the MIT-CBCL face recognition database [8] andour own database to train the network. Some of the
training data are shown as Fig.3.
The network is of the two-layered Multi-layered
Perceptron (MLP) architecture with 15 neurons in thefirst hidden layer and 8 neurons in the second hidden
layer. Three output neurons were used to classify the
input images into eye, mouth, and others. A standard
back-propagation algorithm [9] was used to train this120-15-8-3 network for 2000 cycles.
However, due to limited training data, theclassification performance of the ANN is not stable. As
shown in Fig.4, similar images may result in different
categories. In this figure, squares represent the verticesof the mouth region, and the diamonds stand for eyes,
respectively. To solve this problem, we design thesecond ANN to improve the correctness in featuredetection.
The second neural network is then implemented toarbitrate the most possible composition of two eyes andone mouth. In order to clearly verify the combination of©ISAROB 2006)))

⎧ X in1 = Peye1 Peye 2 sum Peye1 Peye 2 + Peye 2 Pmouth + Peye1 Pmouth⎪ 2⎨ X in = Peye 2 Pmouth sum Peye1 Peye 2 + Peye 2 Pmouth + Peye1 Pmouth⎪X 3 = P P
eye1 mouth sum Peye1 Peye 2 + Peye 2 Pmouth + Peye1 Pmouth⎩ in]where S x and S y denotes the size of the cropped image.
Two output neurons were used to indicate whether acandidate combination is a correct configuration of twoeyes and one mouth, and they are in reasonable relative
positions.
In this work, we randomly selected 430 sets of proper
combination and 325 sets with wrong combination.
Typical combinations of the marked features are shownin Fig.5. Also, the back-propagation algorithm was used
to train this 9-8-5-2 network for 2000 cycles.
Fig.6 shows an experimental result that demonstrates
the effectiveness of the second ANN to detect criticalfeatures and to estimate face pose.

3. Conclusion
We propose a novel strategy using ANN to reliably
detect the position of human face and estimate the facepose at the same time. The first neural network is used toclassify the connected region of the rest pixels into three
categories, i.e. eyes, mouths and nothing. The second
neural network is then implemented to arbitrate the mostpossible composition of two eyes and one mouth.
After the feature positions are found, we can derive
the pose of the face by the geometric relationshipbetween eyes and mouth. Experimental results show thatthe proposed method can successfully and robustly
detect the existence and inclination of a face fromimages with a frame rate of 30 frame/sec.

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

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References[1] 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.
[2] C.C. Han, H.Y. Liao, G.J. Yu, and L.H. Chen, “Fastfacedetectionviamorphology-based
pre-processing,” Pattern Recognition, Vol.33, pp.
1701-1712, 2000.
[3] C.H. Lin and K.C. Fan, “Triangle -based approachto the detection of human face,” Pattern
Recognition, Vol.34, pp.1271-1284, 2001.
[4] R.L. Hsu, ”Face detection in color images,” IEEE
Transaction on Pattern Analysis and MachineIntelligence, Vol.24, pp.696-706, 2002.
[5] Erik Hjelmas and Boon Kee Low, “Face Detection:
A Survey,” Computer Vision and Image
Understanding, 83, 236-274, 2001.
[6] Henry A. Rowley, Shumeet Baluja, and Takeo
Kanade, “Neural Network-Based Face Detection,”
IEEE Trans. Image on Pattern Analysis andMachine Intelligence, vol. 20, issue 1, pp. 23-38,
1998.
[7] Rafael C. Gonzalez and Richard E. Woods, Digital
Image processing, 2nd edition, Prentice-Hall, 2001.
[8] http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html[9] Fredric M. Ham, Ivica Kostanic, Principles of
Neurocomputing for Science & Engineering,
McGraw-Hill, 2001.

(a)

(b)

(c)
Fig.1 (a) Original image (b) results of color
segmentation and (c) its projection

Table 1. Values of the skin color in CIE Lab space
Set 1
Set 2
Set 3
Set 4
Set 5

L
142.56168.33156.67143.56134.33a140.44142.78138.11
143.22139.44b125.56136.44132.78125
123.33

(a)

(b)
(c)
Fig.2 (a) Elimination of the skin color in a cropped
image (b) labeling of the connected region by different
colors©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(a)(a) Images of various eyes(b)(b) Images of various mouths

Fig.5 (a) Correct and (b) incorrect combination of criticalfeatures

Rotated Angle =-4o(c) Images of objects other than eyes and mouths
Fig.3 Training data of ANN corresponding to theregions of eyes, mouth and others
Fig.6 Detection of the face and estimation of its pose ina cluttered environment

Fig.4 The results of ANN classification. Similar images
may result in different categories©ISAROB 2006

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A Study on the Development of Actively Controlled Anti-Seasickness Bed
Young Bok Kim
Faculty of Mechanical Engineering
Pukyong National University
San100, Yongdang-Dong
Nam-Gu, Busan 608-739, Korea
kpjiwoo@pknu.ac.kr

Jin Ho Suh
Department of ElectricalEngineering
Dong-A University840-1, Hadan-Dong, Saha-Gu,
Busan 604-714, Korea
suhgang@hanmail.net

being developed to achieve the necessary performancesin the several points of view.[9]In ship operation and etc, wave, wind and other facts
induce undesirable ship motions. For examples, roll,
pitching motion and heaving are included in this category.
These motions cause seasickness and degrade theperformance of mechanical and personnel effectives.
In the many articles [1-7], excellent solutions havebeen introduced where fins, tanks, rudders and flaps areused to reduce and suppress rolling and pitching. These
strategies are mainly focused on improving the shipstability. In other word, the ship herself is the plantconsidered. Therefore the physical points for human are
not considered in a sense.
In the paper [8], a vibration reduction skill for main
hulls is introduced using semiactive absorbers. In thisarticle, the considered fact is the motion(vibration)
oscillated by repeated excitations from the propellers,
main engine and wave load. Because, the high-pitch
propeller and long-stroke diesel engine have been more
widely used, widespread problems relating to main hull
require attention. Furthermore such vibrations affect the
comfort of passengers and crew, damage the structure
and impair the fighting efficiency of war ships.[8]In this paper, we do focus on the very specialized
category or equipment which will bring the riding
comport to the human. This is extended idea appeared inreference [10]. Especially, we design a bed to cope with
unpredictable environment like winds and waves etc.
The designed system is composed of sensors and several
electric equipments. Comparing with hull control system,
it is very simple and there exists easy application
properties. If we consider the characteristics of the ocean
vehicle, we can see that a fact of easy application to thereal system is absolutely necessary condition. So, we
introduce and verify the system properties of proposedsystem from experimental study.

Abstract
In ship operation the consequence of roll andpitching motion can seriously degrade the performanceof mechanical and personnel effectiveness. So manystudies for the roll stabilization and trimming control
system design have been performed and good results
have been achieved where the stabilizing fins, tanks,
rudders and flaps are used.[1-7]
However the ultimate objective of such approach mustbe focused on improving the boarding sensitivity. But
there exist many weak points, for examples, increasingof navigation resistance, ship control performancedegradation and increasing of system complexity. And,the achieved control performance could not give us
enough comfortable boarding sensitivity. The residualrolling and pitching motion are main drawbacks.
To get rid of these disadvantages, the main hull control
system design approach has been considered using
semiactive absorber.[8] In this system, dampers, spring,
dynamic dampers and control system with sensors areincorporated.
In our system considered in this study, just two motors
and control system with sensors are used for the bed. In
other word, the control system can be installed on eachbed. So, we can control every bed on the specified
control objectives respectively. Above all, the goodadvantages of this system are the facts followed from
simple idea and usefulness. Of course the structuralmodifications are needed. Considering actual sea states,
a new apparatus for our objectives is necessary, such that
we design a control system and verify the usefulness ofdeveloped system from the experimental study.

I.

Introduction

New developments in naval architecture have been
focused on fast ships for passenger and freighttransportation. During 1970-1990 most new passenger
ferries were built with speeds in the range 35-40kn andwere considered to be fast. Since 1990, the speed of
newly developed fast ferries has been increased about
70kn. Further more, several container vessels will beable to cross the Atlantic between North America andEurope with 40kn ship speed. New technologies are©ISAROB 2006

Kwon Soon Lee
Division of Electrical, Electronics &Computer Engineering
Dong-A University840-1, Hadan-Dong, Saha-Gu,
Busan 604-714, Korea
kslee@daunet.donga.ac.kr

II. Problem Statement
In this paper, we consider the main aspects of the
problem, namely seasickness, sea states and ship motions.
Studies on seasickness published in reference [11], it
concludes that seasickness is a cumulative effect related
to vertical eleration at certain frequencies. There is

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Let us describe the controlled system :a. Roll dynamics of the bed
b. Pitch dynamics of the bed

frequency band around 1[rad/s]. In this area, theseasickness is most inducable. In the related studies, amathmatical model of motion sickness incidence (MSI),
an index defined as the percentage of the subjects who
became ill after two hours dew to motion, is given as
follws:⎡⎞⎤⎛ ± log ( a / g ) m µ⎜
10v
MSI ⎟ ⎥
MSI = 100 ⎢0.5 ± erf ⎜
(1)
⎟⎟⎥
⎢
0.4
⎜
⎠⎥
⎝
⎢
where erf is the error function;av

For the convenience, we express the each dynamics
with 2nd order system as following :(2)
I xθ&&x + Dxθ&x + K xθ x = Txd + Tx

I yθ&&y + Dyθ&y + K yθ y = Tyd + Ty

(3)

where, equation (1) denotes the roll dynamics and (2) is
pitch dynamics.
And,θ x , θ y : angle for each dynamics

is the verticalacceleration, averaged over a half-motion cycle, in achosen place; andI x , I y : inertia momentsµ MSI = −0.819 + 2.32(log10 ϖ e ) 2

D x , D y : damping coefficientswhere ϖ e is the dominant frequency[rad/s] of thevertical acceleration.
Ocean waves are generated by winds and those aredescribed by terms of wave energy spectra. In The World
Meteorological Organization(WMO), the sea states are
distingushed by ten grades.
In this paper, our research intrest is focused on
oscillated vibration induced by roll and pitch motion ofship regardless of the classified sea states. Ultimately, wesupress this type of vibration and get the riding comfort.

K x , K y : spring coefficients
Txd , T yd : disturbances
Tx , T y : control forces
The parameters are calculated or estimated bysimulation and experiment.
It is assumed that there does not exist any couplingterms in the system description.

IV. Controller Design and ExperimentIII. Considered System and System Description

As illustrated in the previous illustrations, it is assumed
that there is no coupling term. Then the controller isdesigned for each system dynamics as shown in Fig. 2.

Here let us describe the considered system(plant)
which is illustrated in Fig. 1.
As shown Fig. 1, the bed is controlled by two motors
for suppressing the roll and pitch motion respectively.
Especially, the motor installed on the left side of bed isonly controlled to suppressing pitch motion or heave
motion. Based on this idea, some desirable riding
comfort and performance can be achieved, because theheave motion is mainly related to seasickness.
Let us explain the system properties in operational
point. If the undesirable motions(pitching, rolling andheaving) are oscillated and measured by sensors, then the
motor(for roll) is controlled to suppress the motion
oscillated by ship rolling and the motor(for pitch) is
controlled to suppress the motion induced by ship
pitching or heaving.

Fig. 2 Block diagram for system control

In control
design, forFig. 1 Schematic diagram of the prototype
of an systemanti-seasickness
bed the convenience, we©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

design two controllers based on the PID control scheme.
In the results, the controllers :dCi (t ) = K Pi e(t ) + K Ii ∫ e(t )dt + K Di e(t )dt
i = 1, 2.
where,C1 (t ) : roll motion controller,
C2 (t ) : pitch motion controller
and
K P1 = 2, K P 2 = 1,
K I 1 = 0.01, K I 2 = 0.01
K D1 = 0.03, K D 2 = 0.005 .
(a)roll motion

Let us introduce the experimental results with thedesigned controllers.
Where the frequency range of the disturbance input isspecified in 3[Hz] because of the constraint on thesensor specification used in this study.
In Fig. 3, the uncontrolled case is shown where (a)denotes roll motion and (b) is pitch motion when theplant is exposed to oscillated vibration induced by
disturbances. As shown in this figure, the bed is strongly
exposed to and affected by disturbance. So, any desirable
riding comfort can not be expected.
Fig. 4 shows the controlled case when the disturbance
exists. In this case, based on the proposed system design
scheme, the effect of oscillated vibration is actively
suppressed and good riding comfort is achieved. To
compare the controlled case with the uncontrolled case,we show the results in Fig. 5.

(b)pitch motion
Fig. 4 Controlled case, when oscillated vibration exits(a)roll motion(a)roll motion(b)pitch motion
Fig. 5 Experimental results for comparison, when the
disturbances exist(b)pitch motion
Fig. 3 Uncontrolled case, when oscillated vibration exits

V. Conclusions©ISAROB 2006

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In this paper, we proposed and introduced ananti-seasickness bed to achieve good riding comfort ofthe ship. In many researchs, the authors have proposed
effective control strategies. As introduced in the previous
section, fin-stabilizer, anti-rolling tanks and etc are used
for suppressing roll and pitch motions. These apparatus
are useful for obtaing ship stability and riding comfort ina sense. But ultimate performance evaluation may not be
achieved. So, in this article we tried to get more clarified
and specified control performance. However we expectthat the proposed apparatus may be useful for evaluating
the unsolved problems in previous apparatus and skills.
In the experimental results, we identified thosepossibilities.

Aerospace Med., Apr., 1974

Acknowledgement
This work was supported by grant No. R01-2005-000-10418-0 from Korea Science & Engineering Foundationin Ministry of Science & Technology.

References
Y. Koike, “Study and at-sea experiment on actively
controlled anti-rolling system”, Journal of the SNAJ,
Vol. 174, pp. 137-143, 1994
[2] Y. Koike, “Application of hybrid anti-rolling system
to actual ship”, Technical Review of IHI, Vol. 35,
No. 6, pp. 395-399, 1995
[3] K. Mibasi, “Vibration absorber for marine
machinery”, Mitsui Zosen Giho, No. 138, pp.
33-40 , 1989
[4] K. Otaka, “The research trends of ship vibration”,
Techno Marine, No. 764, pp. 127-130, 1993
[5] S. Yamaguchi and A. Shinkai, “On activating andoptimizing procedures of a fluid tank system forship stabilization”, Journal of the SNAJ, No. 171,
pp. 125-131, 1991
[6] K. Tanida, “Active vibration absorber”, Techno
Marine, No. 707, pp. 283-290, 1988
[7] K. Tanida, “The applications of the active vibration
control for the super structure”, Techno Marine, No.
777, pp. 62-67, 1994
[8] W. J. Hsueh, “Vibration reduction of main hulls
using semiactive absorbers”, J. of Marine Scienceand Technology, No. 3, pp. 50-60, 1998
[9] J. M. De la Cruz, J. Arandra, J. M. Giron-Sierra, F.
Velasco, S. Esteban, J. M. Diaz and B. deAndres-Toro, “Improving the comfort of a fast
ferry”, IEEE Control System Magazine, Vol. 24, No.
2, pp. 47-60, 2004[10] T. Ono and H. Inooka, “Development of arotation-type actively-controlled bed for an
ambulance; Control system design based on theprinciple of matching”, Vol. 41, No. 3, pp. 202-209,
2005[11] J. F. O’Hanlon and M. E. MacCawley, “Motion
seasickness incidence as a function of frequency
and acceleration of vertical sinusoidal motion”,[1]©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Modeling and Position Control based on Two-Mass system for Machine Stand
Vibration System
Sou Watanabe, Fujio Ohkawa, Ryuichi Oguro and Jun Kobayashi
Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka, 820-8502, JAPANE-mail : sou@maple.ces.kyutech.ac.jp
Keywords : Machine Stand Vibration, Two-Mass System, Position Control, Modeling

Abstract

phenomenon is called MSVS[1]. The Machine Stand
Vibration greatly influences high-accurate positioningof the slider. To overcome this problem, many control methods have been proposed[2, 3]. That most
methods are based on a system model, which consisted of masses, springs and dampers[4]. The sliders are expressed the mass and the dynamics of themachine stand is expressed the mass, the springs andthe dampers. Therefore, control systems of MSVS become complicated especially in comparison with one
for 2MS. A 2MS model is a famous general one composed of masses, springs and dampers. Many position
control methods of the 2MS have been proposed since
the 80’s and are employed as one of benchmark problems of the robust control. If the dynamics of the
MSVS can be expressed with a 2MS, control methods
for the 2MS can be applied to MSVS. It can expectthat the control system becomes much more easy inthat case.
In this paper, we propose the modeling and position control method for the MSVS. The methods arederived by the similarity between the MSVS and the2MS. First; we analyze a dynamics of the MSVS and
2MS, and obtained the common mathematical modelof these systems in chapter 2 and 3. Then, a transfer function of the MSVS into the transfer functionof the 2MS is obtained in chapter 4. We realize theposition control of the MSVS with the control system
based on the 2MS model. The eﬀectiveness of the proposed method is validated by simulations. The control
method used here is the most general one based on
PD control with a disturbance observer (chapter 5).
Chapter 6 shows the conclusion of this paper.

This paper describes a modeling and position control for a linear slider with Machine Stand Vibration．We focus on the similarity between the MachineStand Vibration System (MSVS) model and the TwoMass System(2MS) model. Then the MSVS model
was transformed into the 2MS model．By this means,the conventional position control methods for the 2MS
are available for the MSV. It is shown that a position
control method of the MSVS is realized by using thecontroller based on 2MS by simulations.

1

Introduction

Recently, machining center requires high-speed positioning of a linear slider system to improve productivity. Most conventional linear slider systems consistof a ball screw slider. The ball screw slider has a motor, slider, ball screw, coupling and so on. When alength of the ball screw slider becomes longer and/or
a mass of a work mounted on the slider becomes heavier, an elasticity of the ball screw slider (ex. a coupling,ball nuts and so on) cannot be ignored to control thesystem. Namely, a vibration phenomenon may be occured, and it exerts a harmful influence on high-speed
and high-accurate positioning of the slider. The otherside, a direct drive system with a linear motor has
a little elasticity factor such as the ball screw slider.
However, in the direct drive system, a reaction force influences the machine stand directly because there is nogear element in the linear motor slider. Recently, therigidity of the machine stand becomes lower because
a weight of the machine stand reduces. Therefore, aquick motion of direct drive system by a linear motor
makes vibration phenomenon in machine stand due
to its elasticity. This phenomenon is called the machine stand vibration, and the system which has this©ISAROB 2006

2

General Model of MSVS

In this section, we describe a general model of
MSVS. Figure1 shows a physical model of a linear

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slider system of MSV. Table1 shows physical parameters of the system shown in Figure1. Eq(1) is a diﬀerential equation that descrebes the motion of the linear
slider system. A transfer function between f to x1 isderived from Eq.(1). This transfer function is being
used in many papers. Therefore, the Eq.(2) is defined
as a transfer function of MSVS based on the generalmodel.

Figure 2: General model of 2MS

Symbol
xM
xL
xs
MM
ML
fref
KC

Figure 1: General model of the MSVS

Symbol
x1
xabs1
M1
MB
f
KB
DB

Table 1: Parameters of Figure1
Meaning of the Symbols
Relative Position of Slider to Base
Absolute Position of Slider
Mass of Slider
Mass of Base
Force Reference for Slider
Spring ConstantDamper Constant

Unit
m
m
kg
kg
N
N/m
kg/s

½
M1 ẍ1 + M1 ẍB = f
M1 ẍ1 + (M1 + MB ) ẍB + DB ẋB + KB xB = 0(1)

1
X1 (s)
1
=
+
2
2
F (s)
M1 s
MB s + DB s + KB

(2)

3

DC

4

kg/s

Relationship between the MSVS andthe 2MS s2 +X1 (s)
M1 + MB=F (s)M1 MB In this section, we describe a general model of 2MS.
Figure2 shows a general model of 2MS. The side of themass which can give it direct input is called 1st side,and the other side is called 2nd side. Parameters in
Figure2 are shown in Table2.
A transfer function between fref to xM is derived asshown in Eq.(3). This equation is defined as a transfer
function of 2MS based on general model.

©ISAROB 2006

Unit
m
m
m
kg
kg
N
N/m

Next, we propose a method to transform the MSVS
into the 2MS. The method applies the similarity between the MSVS and the 2MS. Eq.(4) can be obtained
from Eq.(2).

General Model of 2MS

1
XM (s)=
Fref (s)MM

Table 2: Parameters of Figure2
Meaning of the Symbols
Position of 1st side
Position of 2nd side
diﬀerence between xM and xL
Mass of 1st side
Mass of 2nd side
Force Reference for 1st side
Spring Constant between
1st side and 2nd side
Damper Constant between
1st side and 2nd side


DB
KB


s+

M
+
M
M
+
M
B
1 ¶ B
µ1

DB
KB


s2 s2 +s+MB
MB

Moreover, new parameters are defined as follows.

(DC s + KC )
ML
µ
¶ (3)
(D
s
+
K
) (DC s + KC )
C
C
2
2
s s +
+
MM
ML
s2 +

218


(M1 + MB ) MB


Mp =



M1

Mt = M1 + MB

M1 + MB


 α=
M1

(5)

(4)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

It can be expressed with Eq.(6) when Eq.(4) is collected by using these parameters.
X1 (s)
1
=
F (s)
Mp

(DB s + KB )Mtµ
¶ α2 (6)
s
+
K
)
s
+
K
)
(D
(D
B
B
B
B
2
s2 s +
+
Mp
Mt
s2 +

The Eq.(6) is the same form as the Eq.(3) consideringthat Mp is a mass of 1st side，Mt is a mass of 2nd sideand the Eq.(3) is multiplied by constant value. Figure3 shows a block diagram of Eq.(6). Then, xm andFigure 4: Block diagram of the controller based on
2MS

The purpose of this controller is to transform characteristic equations of 1st side and 2nd side. The
characteristic equations after transforming is shownin Eq.(7). This equations of 1st and 2nd side are same
form after transforming.
µ¶¢Kc ¡ 22s + βs +s + Kv s + Kv Kp(7)
Mt

Figure 3: Block diagram of MSVS based on 2MS
xl are positions of 1st side and 2nd side respectively，
Kc and Dc are spring and damping constant between
1st side and 2nd side in Figure3. Table3 shows a relationship between the MSVS(variables : x1 ，xB ，f ，parameters : KB ，DB ) and the 2MS(variables : xm ，
xs ，f1 ，parameters : Kc ，Dc ). Consequently, the dynamics of the MSVS can be expressed with a 2MS,
control methods for the 2MS can be applied to the
MSVS. It can be expected that designing the control
system becomes easy in that case.

A torque reference to realize this transform is Eq.(8).
Frc is used for the cancellation of reaction force for 1stside, Fpd is used for position control for 1st side andFsc is suppression control for 2nd side.
Torque reference
Fref =

1(Frc + Fpd + Fsc )α2(8)• Cancellation of reaction for 1st side
Table 3: Relationship between MSVS and 2MS
MSV
x1
xB
f
KB
DB

2MS
xm
xs
f1
Kc
Dc

Frc = Kc xs

Conversion Coeﬃcient1• Position control for 1st side (PD feedback)M1 +MBM1
1α2

Fpd = Mp Kv {Kp (xref − xm ) − ẋm }1
1

Simulations

We realize the position control of the MSVS withthe control system based on the 2MS model by simulation. Many position control methods of the 2MS
have been proposed. The control method used in thispaper is the most general one based on PD control
with a disturbance observer[5]. Figure4 shows a block
diagram of this controller.

©ISAROB 2006(10)

• Suppression control for 2nd side
Fsc = −β

5

(9)

Mp
(Kc ẋs + Kv Kc xs
Kc
Z+ Kv Kp Kc xs dt)(11)

The parameters used for the simulations are shownin the Table4. The parameters shown in Table4 aretransformed in the relations of Eq.(5). Figure5 shows
simulation result of PD feedback control as conventional method. Figure6 shows simulation results ofposition control and vibration suppression based on

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2MS. In Figure5, the slider position has overshoot phenomenon for reference trajectory. Additionally, thebase has vibration phenomenon in 15Hz as the character of the spring. By this influence, accurate positioning of the slider cannot be realized. In Figure6,
the slider position have no overshoot phenomenon forreference trajectory. Moreover, the base vibration issuppressed by the controller based on 2MS. It is considered that the position control and vibration suppression can be realized using the controller based on
2MS.

Table 4: Parameters for simulations
Unit
Symbol Value
M1
3.22
kg
MB
42.0
kg
505324 N/m
KB
DB
0.0
kg/s
Kp
40
1/s
160
1/s
Kv
β
1

6

Conclusion

This paper describes a modeling and position control for a linear slider with Machine Stand Vibration.
The methods are derived by the similarity between the
MSVS and the 2MS. It is shown that control methods
using in 2MS can be applied to MSVS by simulations.
Almost all cases of 2MS, the mass of 1st side is smaller
than the mass of 2nd side. However, the method discussed here, the mass of 1st side becomes bigger thanthe other one. The investigation of this point is now
in progress.

References[1] H.Honda, et al, “A Position Control for Serial
Twin Sliders with Linear Motor Drive”, IEEJ, Vol.
124, No. 9 pp. 901-908, 2004. (in Japanese)

Figure 5: Simulation results of slider position control
and vibration suppression based on conventional PD
control[2] H.Zidan, et al, “Feedforward Control Design for
Vibration of Machine Stand”,IEEJ, Vol. 120, No.
3 pp. 404-409, 2000. (in Japanese)[3] K.Sato, et al, “Adaptive PI Control Method
for Positioning Control using Linear Slider Feedforward Control Approach-”, Proceedings ofthe IEEE International Conference on Control Applications, pp.588—593, 2002.
[4] K.Ito, et al, “GA-Based Autonomous Design ofRobust Fast and Precise Positioning Considering
Machine Stand Vibration Suppression”, IEEJ, Vol.
124, No. 6 pp. 607-615, 2004. (in Japanese)[5] H.Tsurumori, et al, “The Vibration control of 2 inertia which made PD control a base”, Proceedingsof the 22nd SICE Kyushu Branch Annual Conference, pp.101—104, 2004. (in Japanese)

Figure 6: Simulation results of slider position control
and vibration suppression based on 2MS©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Reinforcement learning of switching multiple controllers to control a real robot
Yoichi Tokita†

Yutaka Nakamura†
Junichiro Yoshimoto‡Shin Ishii†† Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan‡ Initial Research Project, Okinawa Institute of Science and Technology
Promotion Corporation
to switch incomplete controllers optimally is theoretically unsolved, however, a human designer must tune
the switching way based on trials and errors. Furthermore, precise system parameters, such as the friction coe–cient of each joint and the inertia momentof each link, must be known to design the incomplete
controllers.
In our previous work [3], the parameters of thetarget system were estimated by a system identiflcation method, and then incomplete controllers were
designed based on the identifled system. We also devised a framework to adaptively switch the incomplete
controllers based on RL, then succeeded in desirable
control of a real acrobot. However, its successful rate
was not so high and the robustness was not su–cient.
This degeneration of the performance might be dueto the inaccurate system identiflcation. Since each
incomplete controller was designed based on the approximated system, it could be incompatible with thecontrol of the real robot, if the approximation error
was not negligible. In this study, therefore, we propose a learning method for improving each incomplete
controller by RL, to enhance the compatibility withthe real robot system.

Abstract
Reinforcement learning (RL) for automatic controlof real robots is a challenging issue, because they usually have non-linear dynamics with a large number ofdegrees of freedom. In this study, we propose an RL
method that switches controllers which have been developed in the fleld of control theory. We apply our
method to an automatic control problem of an acrobot
simulator. The results show that our method is able
to obtain a good controller autonomously and that therobustness against the error in system identiflcation is
improved.

1

Introduction

Reinforcement learning (RL) methods have beensuccessfully applied to automatic control problems ofvarious dynamical systems, such as balancing control
of a cart-pole [1]. In RL, a control rule can be obtained autonomously without any knowledge of thetarget system. Since most real robots have non-linear
dynamics with a large number of degrees of freedom,and the action space would also be large, however, todevelop an RL method to deal with such situations isa challenging issue. This di–culty is called “curse ofdimensionality” in the context of RL, and we formerly
proposed an RL method to overcome the di–culty, inwhich the action space was restricted by utilizing theknowledge about the robot and incorporating in partthe existing control theory.
In this study, we in particular focus on the control ofan acrobot, one of the simplest under-actuated robots,
as an instance of dynamical systems. In the fleld ofcontrol theory, swinging-up and stabilizing this robot
can be realized by combining multiple controllers, eachof which tries to achieve a local subtask making up
the global control task [2] (we call such a local controller an “incomplete controller”). Since the criterion©ISAROB 2006

2

Acrobot control by switching controller

2.1

Acrobot

The acrobot is an under-actuated robot consistingof two links and two joints, as shown in Fig. 1. This
robot is regarded as a simple model of a gymnast
swinging on a high bar. The torque can be applied
to the second joint (corresponding to the gymnast’s
waist), but not to the flrst joint (corresponding to thegymnast’s hand on the bar). A state of the acrobot
is represented by a four-dimensional vector: angles of221The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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switch ICs being based on the identifled system. The
switching rule was then modifled to be suited to thereal robot, but ICs, which were suited to the identifled system, were not trained by using the real robot.
This may cause the performance degeneration of theobtained controller.

2nd link

q2

actuator
q1
1st link

3(a)MethodIn our previous work [3], the global control rule
(GC) for the real acrobot was obtained by the following procedure.

(b)

1. System identiflcation

Figure 1: Acrobot

2. Design of ICs based on the identifled system
the two joints (denoted by q1 and q2 in Fig. 1), andtheir angular velocities. The objective of the control
task is to swing up and balance the acrobot at the
upright position (q1 , q2 , q̇1 , q̇2 ) = (0, 0, 0, 0) from thehanging position (q1 , q2 , q̇1 , q̇2 ) = (−…, 0, 0, 0).
2.2

3. Acquisition of SC by RL using the computer simulation
4. Tuning of SC by additional RL using the realrobot
As a result, we obtained a GC that realizes swingingup and stabilizing the real robot, but the successfulrate remained around 50%.
Because the estimation of system equation from observed trajectories of the target system has instability
due to the sampling bias, the identifled system may bedifierent from the true one. Therefore, ICs, designed
for such an identifled system, may not necessarily suitto the target system. To overcome this problem, inthis study, ICs are designed to have internal parameters so that they are adjusted by RL. Because theinternal parameters are trained through interactions
with the target system, ICs are expected to become asuitable one to the target system. The learning of theswitching rule (SC) and that of IC parameters are alternately performed in our new method. Our method
can be applied, especially when the accuracy of system
identiflcation is not su–cient.

Switching control

Spong realized swinging-up and stabilizing controlof the acrobot by designing several incomplete controllers and a rule to switch them [2], where each incomplete controller is a controller for a local subtask(e.g., stabilizing at the upright position or swingingup) and used in a particular situation. In this control
scheme, the global controller (GC) is composed of aswitching controller (SC) and a set of incomplete controllers (ICs), in which the SC observes the state of therobot and selects a single IC, and then the selected IC
generates a control torque.
Although the dynamics of the acrobot are essential
for designing ICs, it is di–cult to directly make useof it in an actual situation. Therefore, it is often required to perform system identiflcation by operating
the robot reasonably. Then, ICs are designed basedon the identifled system equation. In our study’s case,
we employ the following flve ICs: an LQR controller
for stabilizing at the upright position, Σ1 and Σ2 controllers for swinging-up the acrobot from the hanging
position, a brake controller for avoiding large angularvelocities, and a τ0 controller for yielding no torque(See [2][3]).
By appropriately switching these ICs, the swingingup and stabilizing control of the acrobot can be realized [2] but the way to design the optimal SC theoretically has been unknown. We formerly proposed an RL
method to obtain an appropriate SC to combine and©ISAROB 2006

3.1

Training of SC

The output of the SC is an index of ICs, denoted bya, and the action is selected according to action-valuefunction:a(t) = arg max Q(x(t), a).
a(1)

The action-value function Q(x, a) indicates the utility,
expected future rewards, if the robot is controlled by
an IC a at a robot’s state x, and is trained based on
the SARSA(λ) algorithm [5].

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3.2

denoted by W1 ) and then performed RL of SC on
system Ψ1 . After a good SC was obtained, we performed additional learning of ICs on system Ψ2 andsubsequently RL of SC on system Ψ2 .
The GC observes the acrobot’s state with a flxed
time interval of 0.01 sec, and outputs a control torqueaccording to the IC selected at the state. The control torque was limited within the range between−4.5(N m) and 4.5(N m). At each time step, the GC(and SC) receives an immediate reward deflned as

Training of IC

In this learning phase, we assume that action a isdeterministically selected by the SC. Under this assumption, the control torque u can be specifled forthe robot’s state asu = f (x; W),(2)

where W denotes a set of parameters of ICs, and is
updated by RL . We employ a policy gradient method[4] for the training of this parameter vector, and weassume that the control torque is generated by equation (2) plus white Gaussian noise; the distributionof control torques follows a normal distribution whosemean is given by equation (2).
3.3

R(x) = 0.1 exp(−16 kxk),

Learning procedure

Our learning procedure is summarized as follows.
³¶

1. System identiflcation(a) Observe time-series of states and control
signals by operating the real robot(b) Design ICs for the identifled system
2. Learning on the identifled system(a) Learning of the SC
3. Additional learning on the target system(a) Learning of the ICsµ(b) Learning of the SC´(3)

where k·k denotes the Euclidean norm. This reward
becomes the largest at the goal state x = (0, 0, 0, 0);
the return is expected to be maximum when the robot
is stabilized at the upright position. At the beginningof each episode, the robot was set to an initial state,
and each episode was terminated after 60 sec elapsed.
We employed a CMAC to approximate the actionvalue function [6] for the SC, which consisted of 20
maps with difierent ofisets. In the learning phase ofICs, we employed a radial basis function (RBF) network to approximate the state-value function used by
the policy gradient method. The center of each RBF
was located at a lattice point in the state space.
Learning of SC for system Ψ1 Fig. 2 shows thelearning curve in the SC learning phase performed on
system Ψ1 . In this case, the parameters of ICs were
flxed at W1 . After about 10,000 learning episodes,
a good controller that can stabilize the robot at the
upright position was obtained. Let Q1 (x, a) be theaction-value function obtained in this phase.

4

Successful Rate

1

ExperimentFirst, we performed the system identiflcation. Twodatasets X1 and X2 were obtained from system trajectories by operating the real robot, and two systemparameters Θ̂1 and Θ̂2 were calculated from the twodatasets. Since these datasets were difierent from eachother due to the sampling bias, system parameters Θ̂1and Θ̂2 were not necessarily the same. We then denote the difierently identifled systems by Ψ1 and Ψ2 ,respectively. It is not guaranteed whether a good controller for system Ψ1 works well for system Ψ2 as well.
In this experiment, we assumed that system Ψ2 was
the real target system while system Ψ1 was the identifled and hence approximated system. We flrst designed ICs for system Ψ1 (parameter of these ICs was©ISAROB 2006

0.8
0.6
0.4
0.2
0
0

5000

10000
Episode

15000

20000

Figure 2: Efiectiveness of SC-learning on Ψ1

Learning of ICs for system Ψ2 RL of ICs was performed on system Ψ2 . In this phase, the action-valuefunction of SC was flxed at Q1 (x, a). Fig. 3 shows thelearning curve. Although the controller might be incompatible with this control task, the controller grewto achieve the task with the success rate of about 50%

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after 5,000 learning episodes. Let W2 be the parameters of ICs obtained in this phase.

have been improved but it is still di–cult to be applied
to real robots, because there are many factors to beconsidered, for example, sensor noise and time-delayof signals. Although our proposed method is based on
the control theory and the system identiflcation, it canbe applied to situations where the system identiflcation is not very reliable.

1

successful rate

0.8
0.6
0.4
0.2
0
0

500

1000

1500

2000

3000
2500
episode

3500

4000

4500

5

5000

Conclusion

In this study, we proposed an RL method for training of a controller consisting of several incomplete controllers, where not only the switching controller but
also incomplete controllers were trained. We applied
our method to the swinging-up and stabilization control problem of the acrobot simulator, and the results
showed that a good controller can be obtained by our
RL method.
On the other hand, a large number of training
episodes was necessary for obtaining a good controller.
This is a critical issue in implementing our scheme ona real robot, because the real robot would be broken
by iterating a large number of episodes. To improve
the learning speed is our urgent future work.

Figure 3: Efiectiveness of ICs-learning on Ψ2

Learning of SC on Ψ2 In this phase, RL was performed to train the SC by using system Ψ2 , while theparameters of ICs were flxed at W2 . Fig. 4 shows thelearning curve. After 2,000 learning episodes, a good
controller was obtained. Let Q2 (x, a) be the actionvalue function obtained in this phase. Fig. 5 showsa typical control sequence using the control rule (W2and Q2 (x, a)) after this learning phase.
1

successful rate

0.8
0.6

References

0.4
0.2
0
0

500

1000

1500

2000

3000
2500
episode

3500

4000

4500

[1] K. Doya, “Reinforcement learning in continuous timeand space,” Neural Computation, 12, pp.219-245, 2000.

5000[2] M. W. Spong, “The swing up control problem for theacrobot,” IEEE Control System Magazine, 15, pp.4955, 1995.

Figure 4: Efiectiveness of SC-learning on Ψ2

Height[3] M. Nishimura, J. Yoshimoto, Y. Tokita, Y. Nakamura and S. Ishii, “Control of real acrobot by learning the switching rule of multiple controllers,” The IEICE Transaction on Fundamentals of Electronics andComputer Science (JAPANESE EDITION), J88-A(5),pp.646-657, 2005.
[4] H. Kimura and S. Kobayashi, “An analysis of actor/critic algorithms using eligibility traces: reinforcement learning with imperfect value function,” 15th International Conference on Machine Learning, pp.278286, 1998.

Time [sec]Figure 5: Example of controlling system Ψ2[5] R. S. Sutton, “Learning to predict by the methods of
temporal differences,” Machine Learning, 3, pp.9-44,
1988.

Discussion For comparison, we also performed an
additional RL of the SC on system Ψ2 where the parameters of ICs were flxed at W1 , but no good controller was obtained in this case. This result showedthat ICs should be trained at least in our experimental setting. Recently, system identiflcation techniques©ISAROB 2006

[6] R. S. Sutton, “Generalization in reinforcement learning: Successful examples using sparse coarse coding,”
Advance in Neural Information Processing Systems, 8,
pp.1038-1034, 1996.

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Real Time Control of Humanoid-Robot Cooperative Motion
Using a Genetic Algorithm and Neural Network
Kook-Jin Choi

Yoon-Kwon Hwang

Dae Sun Hong

Dept. of Mechanical Design and Manufacturing
Changwon National University
Changwon, Gyeongnam, 641-773, Korea
choi@changwon.ac.kr

Dept. of Mechanical Design and Manufacturing
Changwon National University
Changwon, Gyeongnam, 641-773, Korea
ykhwang @sarim.changwon.ac.kr

Dept. of Mechanical Design and Manufacturing
Changwon National University
Changwon, Gyeongnam, 641-773, Korea
dshong @changwon.ac.kr

so as to increase working capabilities such as
enlargement of working space and increase of overall
working force.
Until now, a number of research works on
optimization of humanoid-robot motion have beenreported: optimization of postures for generation large
force using nonlinear equations[1][2], optimization of
joint torque ratio using a steepest descent method[3],generating large Cartesian force with manipulator
combination[4], and optimization of the locomotion
trajectory and velocity using genetic algorithms[5][6].
The nonlinear-equation-based method and the steepest
descent method can find analytic solutions. However,they not only might not find a valid solution but also
takes too much time to find solutions when the degree of
freedom of motion becomes larger or the robot motion
becomes more complex. The works on optimization of
trajectory and velocity[5][6] are restricted to used forstabilizing robot locomotion under no external force.
Thus, when humanoid robots are required to work withexternal environment, new efficient approaches tooptimization of robot motion in consideration of external
forces are required.
For such purpose, this paper proposes a method of
optimization of humanoid-robot cooperative motion withrespect to external force using a genetic algorithm andneural network. This study adopts an eighteen degree-offreedom humanoid robot, and a motion pushing an
object is considered. When the robot takes a motion topush an object, the palm and the sole are assumed to be
fixed at the object and the ground, respectively, andsense the reaction force from the object and the ground.
The reaction force results in change of torques of thejoints. This study finds an optimized posture using a
genetic algorithm such that torques are evenly
distributed over the all joints. Then, a number ofdifferent optimized postures are generated from varying
the reaction forces at the palm and the sole. The data is
to be used as training patterns of MLP neural networkwith BP learning algorithm. Using the trained neuralnetwork, humanoid robot can find the optimal posture atdifferent reaction forces in real time. To verify theeffectiveness of the proposed method, a number ofsimulations are carried out. The simulation result showsthat the proposed method can be used for real time
posture control of humanoid robots.

Abstract
This study proposes a method to achieve real time
posture control of humanoid robots using a neuralnetwork and genetic algorithm. Here, a humanoid-robot
motion pushing an object is considered. When the robot
takes a motion to push an object, the palm and the sole
are assumed to be fixed at the object and the ground,respectively, and sense the reaction force from the object
and the ground. The reaction force results in change of
torques of the joints. This study finds an optimized
posture using a genetic algorithm such that torques areevenly distributed over the all joints. Then, a number ofdifferent optimized postures are generated from varying
the reaction forces at the palm and the sole. The data is
to be used as training patterns of MLP(Multi-Layer
Perceptron) neural network with BP(Back Propagation)
learning algorithm. Using the trained neural network,
humanoid robot can find the optimal posture at different
reaction forces in real time. To verify the effectivenessof the proposed method, a number of simulations arecarried out. The simulation result shows that the
proposed method can be used for real time posturecontrol of humanoid robots.
Keywords : Humanoid Robot, Optimization, Genetic
Algorithm, Neural Network, Cooperative motion.

1. Introduction
A number of research works on the motion controlof humanoid robots have been reported on the basis of
accurate kinematics and dynamic analysis. However, it
has been known that human being takes a motion or
posture by learning and experience. Also, human
involuntarily takes a stable motion or posture against theinside or outside environment. This study proposes amethod to achieve real time posture control of humanoid
robots using a neural network and genetic algorithm.
Humanoid robots have a number of joints,
essential components for robots' motion, and each joint
usually consists of pair of an actuator and a reducer.
When a humanoid robot performs additional work as
well as walking, torques of the joint actuators along its
motion need to be adequately controlled, i.e. optimized,©ISAROB 2006

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⎡ 60 JνLeg ⎤ ⎡ 0 Rκ ×( 0P − 0P )01e
⎥ = ⎢1
6 J Leg = ⎢ 00Rκ⎢⎣ 6 JϖLeg ⎥⎦ ⎢⎣1

2. Modeling and analysis of humanoid robot
2.1 Degree of freedom and coordinate system

L
L

0
0
0
⎤
6 Rκ ×( e P − 6 P )
⎥
0
⎥⎦
6 Rκ

(2)

This study considers a humanoid robot witheighteen degrees of motion: two legs each of whichhaving six degrees and two arms each of which having
three degrees of freedom. Fig. 1 shows the degree of
freedom of motion and the coordinate system. Usually,
coordinate system for robots follows D-H parametersystem. However, this study takes a point fixed to
ground as the origin, and follows a joint coordinate
system for clarity.

⎡ 30 JνArm ⎤ ⎡ 10 Rκ ×(e0P −10P )0⎥=⎢
3 J Arm = ⎢ 0
0
1 Rκ⎣⎢ 3 JϖArm ⎦⎥ ⎣⎢

L
L

0
0
0
⎤
3 Rκ ×( e P − 3 P )
⎥
0
3 Rκ
⎦⎥

(3)
The torques can be found from the Jacobianmatrix. Since a work can be represented by the innerproduct of a force vector and a displacement vector, thetorques are derived by the following equations:F T ⋅ δx = τ T ⋅ δΘ
δx = J ⋅ δΘ
F T ⋅ J ⋅ δΘ = τ T ⋅ δΘ
F T ⋅ J = τT
τ = JT ⋅F(4)

The initial posture considered in this study isshown in Fig. 2. The figure shows that palms of both
hands are ready to push an object while the left leg takesa step forward. Under such initial posture, forces applied
to the palms of hands and the soles of feet are assumed
to be constant, then the torques are calculated from theconstant forces.

Fig. 1. Degree of freedom and coordinate system.

2.2 Jacobian analysis and torqueForward kinematics of a robot manipulator
expresses the relation of the joint rotation and the endeffector displacement. Based upon the forward
kinematics, the end-effector velocity is determined fromthe angular velocity of joint and the Jacobian. That is,
the Jacobian J plays a role of transforming the angularvelocities ( θ&i ) the joints to the velocities of the end
effector ( 0 d& ) in Cartesian space, the relation isFig. 2. Initial posture of the humanoid robot.
The maximum torque of the leg is generated at
joint 9 corresponding to the left knee, while themaximum one of the hand is at joint 13 and 16
corresponding to the shoulder joints.

nexpressed by
X& = Jθ&3. Optimization using a Genetic Algorithm(1)

where θ is a vector consisting of the joint angles, while
X is a vector consisting of the displacements of the end
effector. The Jacobian matrix is expressed by a 6×n (n:the number of joints) matrix: upper three rows for linear
velocity and lower three rows for angular velocity. The
Jacobian matrix deriving from the forward kinematics is
express by the following equations:©ISAROB 2006

3.1 Parameters and objective functions
Genetic algorithm is a search algorithm which
simulates evolution process of survival of the fittest withcrossover and mutation in ecology system, and can beapplied to find solution in various complex optimization
problems. This study adopts a genetic algorithm with

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roulette wheel selection, simple crossover and mutation.
In addition, elitist strategy, the strongest individual will
be survived in the next generation, is used for enhancing
performance of the algorithm. Table 1 shows parameters
used in this algorithm.

being has. The torque ratio at the hip joints has been
greatly reduced, while the ratio at the right knee joint has
been increased. As a result, the sum of torque ratios forall joints has been reduced. Fig. 4 shows the results for
Case2. Sum of generating torque ratios is reduced by
29.95%: 101.05 at the initial posture to 71.10 at the final
posture. It should be noted that the arms have been
moved near the singular point for minimizing the torqueof the arms; This result is coincide with the objective ofCase2. The arm motion seems to be identical to humanaction that he stretches out his hand when he feels strong
force at the palms. Also Fig. 4 shows that the surplus
torque of the arm joint at the final posture is 57.47Nm.
Actually, the maximum torque of the motor is known to
be 57.8Nm, thus the generating torque is shown to be
only 0.33Nm. It can be seen from this result that the
toque ratio at the arm joint is remarkably reduced, but
torque ratios at both the hip joints and the right knee
joint are slightly changed.
The simulation results show that surplus torquehas been increased for both cases, that is, acting force
has been increased at the final posture. Thus, it isconcluded that the proposed method can be extended tothe control of torques for humanoid robots cooperativewith external environment.

Table 1. Parameters of the genetic algorithm.
Parameters

Value

Maximum Generation
Population Size
Number of Variables
Probability of Crossover, Pc
Probability of Mutation, Pm
Range of Px (COG of robot)
Range of Pz (COG of robot)
Range of Ry (COG of robot)

300
100
3
0.8
0.01
0 ≤ Px ≤ 0.15 (m)0.6 ≤ Pz ≤ 0.8 (m)-15 ≤ Ry ≤ 15 (°)

In this study, two kinds of objective functions are
considered, as shown in Table 2. In the table, Case1 aims
to maximize the overall surplus torque ratio forincreasing overall acting force, while Case2 aims tomaximize the surplus torque of the actuator having thesmallest one.
Table 2. Objective functions for optimization.
No

Name

Objective function

Purpose4. Neural network for real time control

2⎞⎛
Maximize the⎜1 − τ i⎟ surplus torqueF ( x) =⎜⎟τ max ii =` ⎝⎠ ratio
n

1

2

Case1

Case2

∑

F ( x ) = ∧(τ max i − τ i )4.1 Multi-layer perceptron

Maximize theminimum
surplus torqueTo achieve real time control of humanoid-robot
cooperative motion, a neural network with MLP(MultiLayer Perceptron) is used in this study. Fig. 5 shows
MLP for real time control which is composed of threelayers: input, hidden and output layer. Here, BP(Back
Propagation) learning algorithm is used for training.

3.2 Simulation and results
Based upon the objective functions in Table 2, aseries of simulations are carried out, and the results areshown in Fig. 3 and Fig. 4.

Fig. 3. Result for Case1.

Fig. 4. Result for Case2.

Fig. 3 shows the simulation results for Case1. Sumof the torque ratios is reduced by 25.56%: 101.75 at theinitial posture to 75.22 at the final posture. This resultmeans that the acting force can be increased as much as
the torque ratio has been reduced. Fig. 3 shows that the
robot posture is rotated by -8° around the Y-axis. The
apparent posture seems to be different from that ofnormal human. This result comes from the fact that thehumanoid robot does not have the backbone that human©ISAROB 2006

Fig. 5. MLP structure for real time control.

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Reaction forces at the palm and sole are used as
inputs, and the output is optimal posture.

neural network. An eighteen degree-of-freedom
humanoid robot is adopted, and a motion pushing an
object is considered. Then, an optimized posture is foundby a genetic algorithm such that torques are evenly
distributed over the all joints. To achieve real time
control for humanoid robots, this study adopts an MLP
neural network with BP learning algorithm in which
optimized postures obtained from the above GA
algorithm and used as the training patterns. To verify theeffectiveness of the proposed method, a number ofsimulations were carried out by the trained neuralnetwork. The result shows that humanoid robot can findoptimal postures at different reaction forces in real time.
It can be seen that the proposed method can be used forreal time posture control of humanoid robots. In further
research, more complex and various tasks are consideredto improve the proposed method.

Table 3. Parameters of the neural network.
Parameters

Value

Number of total node
Learning rate
Momentum rate
Training step

28
0.1
0.9
500,000

A number of different optimized postures obtained
from the above GA algorithm are used as training patternof MLP. Table 3 shows the parameters used in thisstudy.

4.2 Result of real time control

Acknowledgment
This work was supported by the Korea Scienceand Engineering Foundation (KOSEF) through the
Machine Tool Research Center at Changwon National
University.

References[1] H. Yoshida, K. Inoue, T. Arai, and T. Mae, "Mobile
Manipulation of Humanoid Robots Optimal Posturefor Generating Large Force Based on Statics"
International Conference on Robotics & Automation,
pp. 2271-2276, Washington D.C., May, 2002.
[2] Y. Yokokohji, S. Nomoto, and T. Yoshikawa, "Static
Evaluation of Humanoid Robot Postures Constrained
to the Surrounding Environment through theirLimbs" International Conference on Robotics &Automation, pp. 1856-1863, Washington D.C., May,
2002.
[3] C.B. Kim and H.S. Cho, “A Study on the Trajectory
Optimization Planning of Biped Walking Machine"(in Korean) Journal of the Korean Society ofPrecision Engineering Vol. 15, No. 3, pp. 157-167,
March, 1998.
[4] Y.F. Zheng, and Q. Yin, “Coordinating multilimbed
robots for generating large Cartesian force”
Proceedings 1990 IEEE International Conference on
Robotics and Automation, vol.3, pp.1653-8, 1990.
[5] L. Rodrigues, M. Prado, P. Tavares, K. Da Silva, and
A. Rosa, “simulation and control of bipedlocomotion ga optimization” Proceedings of 1996
IEEE International Conference on Evolutionary
Computation, pp.390-5, 1996
[6] J.S. Gong, K.S. Han and J.G. Kim, “A Study on
Genetic Algorithm-based Biped Robot System”(inKorean) Journal of the Korean Society of Precision
Engineering Vol. 20, No, 8, pp. 135-143, August,2003.

Fig. 6. Result from the neural network.
In Fig. 6, blue circles are training data, green crossmarks are interpolated training data, and red line is theoutput from the trained neural network. It can be seenfrom the graphs that the neural network outputs are
successfully generalized to the result of the GA. It
means, trained neural network can

5. Conclusion
This paper proposed a method of real time control
for humanoid-robots using a genetic algorithm and©ISAROB 2006

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Navigation system for an autonomous robot
using an ocellus camera in indoor environment
Takuma Umeno
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, Ocellus camera

Abstract
We are attempting to develop an autonomous
personal robot that has the ability to perform practicaltasks in ahuman living environment by using
information derived from sensors and a knowledge
database. When a robot is made to adjust to a humanenvironment, the issue of safety must be considered inregard to its autonomous movement. Thus, robots
absolutely require systems that can recognize theexternal world and perform correct driving control. We
have thus developed a navigation system for anautonomous robot. The system requires only image
data captured by an ocellus CCD camera. Thisnavigation system has two functions: One is todetermine whether or not the robot is driving itself
accurately down a corridor. The other is to allow the
robot to search for obstacles present on the floor. We
applied this system to a robot in an indoor
environment and in a discussion of our experimental
results considered the system’s advantages andproblems.

Fig. 1

robot’s drive mechanism, and position control andspeed control are achieved by the drive mechanism’s
control system. The robot also has two arms and hands
and sensors; these devices enable the robot to respondto various demands. An installed wireless LAN canprovide a remote control for humans. All devices arecontrolled by a PC, and lead batteries supply electric
power.
The navigation system uses only an ocellus CCD
camera and processes the image information displayedby that camera. In order to detect the robot’s distanceerrors in a corridor, the system uses the sides of thecorridor as feature parameters and extracts them using
the Hough transformation algorism. Thus, this systemallows the robot to advance in a straight line without
distance errors, because the changing amounts offeature elements extracted from actual images arecompared with the ones calculated from theknowledge database. In order to detect obstacles, thesystem extracts the pixel group whose pixel groups aremuch different from the main background pixels andinterprets them as obstacles. The system can predictthe distances to the obstacles and also the obstacle’s
height and width by using the extracted information
and attached drive encoders.

1. Introduction
Currently, autonomous self-driving robots are
expected to provide various services within humans’
living environments. Such robotic technology isalready seeing practical use in industry. But so far the
robots for industry simply follow a given motion byhumans. Therefore, we are developing an autonomous
personal robot with the ability to perform practicaltasks in a human living environment by using
information derived from sensors and a knowledge
database.
Our robot has a drive mechanism consisting of two
front wheels and one back wheel. The front wheels areattached to a motor that operates the wheels on eitherside independently while the back wheel is a passive
castor wheel. This method has the advantage that a far
smaller turn can be negotiated than, for instance, thatusing a steering system that steers the wheel of apassenger car. DC servo motors are used for the©ISAROB 2006

Robot appearance

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2. Visual feedback system for autonomous driving
2.1 Outline
We developed a visual feedback system for robots
that can detect distance errors using image information
captured by an ocelus CCD camera. First of all, as an
initial step in developing the overall system, we havestarted to develop a system that enables the robot toadvance in a straight line in corridors. This visualfeedback system detects the errors in distancecalculation from the accurate driving course when the
robot is advancing.
Fig2. Patterns of straight lines based on robot’s position

2.2 Method for estimating distance error

2.3 Experiment
We let the system work continuously while therobot was advancing down a corridor. We evaluate theavailability of system by considering whether or not
the system can estimate the distance error accurately.
Figure 3 shows the straight lines extracted by thesystem in the case of that the robot advances straight.
Figure 4 shows that the robot experiences a distanceerror to left. The results of the experiment show thatthe system was able to detect distance errors at an
accuracy of +-5cm.

We have considered that the most significant
feature elements in an image are the lines on the sides
of a corridor. We applied the Hough transformation
algorism to the system for image processing in order toextract lines on an image as feature elements. The
processing flow is as follows.
Ⅰ. Image Acquisition
The image obtained by the CCD camera is read
into a PC in the robot. This visual processing system
uses only an 8-bit gray-scale image.
Ⅱ. Edge processing
The system uses Sobel edge enhancement.
.
Ⅲ. Segmentation processing
To limit the extracted data that are required forform extraction, the system performs Segmentation
processing.
Ⅳ. Thinning processing
The edge extracted by the edge processing has line
width. In order to decrease the amount of calculationnecessary and to stabilize the Hough transformation,
the system performs thinning processing.

Fig3. A result when the robot drives itself correctly

Ⅴ. Hough transformation and Characteristic point
The Hough transformation can extract the straightlines contained in an image.
For detecting the robot's distance errors, thesystem should evaluate the extracted straight lines. So,
the system uses the pattern database. This database canshow the predicted straight lines changed byadvancement situation of robot. Figure 2 shows thepattern database. The system compares the extracted
straight lines in an image with the lines given by thepattern database. As a result, the system can estimate
the distance errors.

©ISAROB 2006

Fig4. A result when the robot generates a driving error

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3. The visual processing system

The chain code is used to make one set of eachgroup of image pixels extracted from image. The
group of image pixels that leads to the detection of an
obstacle is distinguished by this process.

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 for avoiding obstacles is needed.
We have developed a system of recognizing obstaclethat uses only image data captured by an ocellus CCD
camera. The purpose of this system is to enable therobot to detect obstacles and roughly recognize an
obstacle's size and position.

3.2 Method of extracting obstacles

Fig.7 Chain code.

First of all, the system converts 24bit RGB image
data into HSV data. The system samples a group ofimage pixels in a rectangular region at the bottom
center of an image. The system uses the group ofimage data inside this region as its sample image data.
The system uses the deflection calculated by the
sample data. The system extracts the floor region interms of the difference of all pixels in the image.
Figure 5 shows an extracted obstacle.

Next, the system analyzes the object that has been
determined to be an obstacle. We define the lower sideof a group of image pixels as the distance to 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 group ofimage pixels as the height or a depth of the object. The
robot runs on the floor side, and presumes the height ofthe obstacle based on the position of the change in theimage pixel group, the camera position, and the angle
between the two images.

Fig.5 Extracted obstacle.
The conditions of a real environment shift due tochanges in light and shadows. Therefore, it isconsidered that this system is a source of some errors.
We added the edge enhancement processing to thesystem so that the system can extract objects moreaccurately.

Fig.8 Estimation about obstacle’s data
The data identified by the system as an obstacle isreflected in the decision processing part of the robot.
As a result, the robot can roughly recognize the
obstacle. Figure 9 presents an image showing the
obstacle in the system’s virtual space.

Fig.6 Obstacle extracted using edge data.

©ISAROB 2006

Fig.9 Obstacle in virtual space

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References

The entire flow of the obstacle extraction process
is shown below.

[1] Funakubo, Process and recognition of the vision
pattern, Keigakusyuppan, 1990.
[2] Shinoda, Self-driven robot on the grand, Journal of
the Robotics Society of Japan, Vol. 18, No.7,
pp.928-932. 2000.
[3] 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[4] 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.10 System flow.

[5] 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

4. Conclusions
We have proposed a system that enables anautonomous robot to navigate an indoor environment
using only an ocellus camera.
The developed system could produce visualfeedback and successfully recognize objects when the
robot drives down a corridor. The system could also
obtain available information necessary for the robot’s
safety using a visual approach.
Our next subject of study is to develop a system foraction planning.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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 9 ::'    $      
5  ' 7         $   9  9 =::::  9 =::: =:::    9 =:::5  , 7 3       $                5  '     9
 9 ':::::  9  =::: =:::   9 =:::5  . 7 0       #  $               5  '    9  9 =::::  9 .::: .:::    9 .:::

x10

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-2

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Input

Input

Input

-4

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0-4

Orientation Position error Position error Desirederror [rad](y) [m](x) [m] velocity [m/s]

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5 Time [s]1015  . %   5 ' ,   .
 )   7C      9   C C   B   C C  C    
''

239

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006&=( F    % %  I; 4   4  ;  5  %#   %   L    M            J !  "# N D  /' K /=
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''      /   =            ''     #   #      $               
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Initial state
-4
-8

0

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4

X [m]

Orientation Position error Position error Desirederror [rad](y) [m](x) [m] velocity [m/s]  /     3     

Input

Input

Input

&'( F G    H          H ;  3  'DD.
&,( F     #$   % ; I   % #  $ 5   %     J       
    ,@A K ,D> 'D@D&.( F  % %    L H I4  5   %   L M            J            N >A 2 >=/  /.> K //,,::'&/( F  % %    L H I4  5    %   L M            ,  L  ; M ) ;    1   N # #  5  0 J            N >A 2 >>.  .=/: K .=/A ,::'

-1

3 x10
0

-2

2 x10

Case 1

1

Proposed

0
-2

2 x10
1
0

-4

4 x10
0
-4
4 x10
0
-4
4 x10
0
-4
2 x10
0
-2
0

τ 2[Nm] τ 1[Nm]!        $                    *       ©ISAROB 2006

Robot

0

τ 3[Nm]!

TargetActual path of point of interest

5 Time [s]1015  = %   3   

240

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Optimal Parameter Tuning in Proportional Control for the Unicycle Mobile
Robot: An Experimental Study
Desa Hazry1 and Masanori Sugisaka2
1

Graduate School of Engineering, Department of Electrical and Electronics Engineering2

Department of Electrical and Electronic Engineering

The University of Oita, 700 Oaza Dannoharu, Oita, 970-1192, Japan
Tel: +81-97-554-7831, Fax: +81-97-554-7841
E-mail:hazry@cc.oita-u.ac.jp

Abstract

positive semi-definiteness of the state covariance. Thisfinding will assist in executing a more accurate testingin future experiments.
The paper is organized as follows. Section 2
explains the overview of the mobile robot. In section 3,
operations of DC motor controlled by pulse-widthmodulation are introduced. Section 4, proportional
control rules are discussed. In section 5, tuning
method for the optimal coefficient parameter ispresented and finally, conclusions are drawn in thesection 6.

Proportional-integral-derivative (PID) control is acontrol strategy that has been successfully used overmany years. Simplicity, robustness, a wide range ofapplicability and near-optimal performance are some of
the reasons that have made PID control are so popular in
the academic and industry sectors. This paper introduces
a method for parameter estimation in mobile robot as afirst step of the research in realization of theautonomous unicycle mobile robot. Motion is achievedby using two independent DC electric motors that drive
one wheel each. Differential drive is used to steer the
robot. Two caster wheels are used to keep the robot
horizontally. The velocity of each wheel is controlled
indirectly by applying voltages to the motors.
Experiment condition is stated as mobile robot on thefloor and running with a constant velocity. The desired
velocity is 80 (pulse/sec) in order to carry out a wheel
rotation as one revolution in one second. We use
proportional controller in order to get the constant andstabilized speed-movement of mobile robot. Coefficientparameter for each wheel is decided by confirmation of
the minimal means square error of deviations wheel
rotation of each wheel.

2

3

Introduction

Pulse-Width-Modulation
Motor Wheel Operations

(PWM)

DC

PWM DC motor controls the wheel motor speed bydriving the motor with short pulses. These pulses varyin duration to change the speed of the motor.
Pulse-width modulation control works by switching thepower supplied to the motor on and off very rapidly.
The longer the pulses, the faster the motor turns, andvice versa. The DC voltage is converted to asquare-wave signal, alternating between fully on andzero, giving the motor a series of power. DC motors arecontrolled by relays which switch the power on/off andchange the polarity to give forward/reverse of wheel
rotation. These relays are switched by bits zero to threeof output port 0x281. The speed of the motors at any
time is set by a byte. The right wheel speed byte is theoutput of port 0x282 and the left wheel speed byte is theoutput of port 0x283. A byte value of 0 will run a motor
at full speed, while a byte value of 255 will stop the
motor (see Table 1). The actual speed of the wheel

At the moment, there exist many different methods
to find suitable controller parameters [1]. The methods
differ in complexity, flexibility, and in the amount ofprocess knowledge used. Depending on the application,
there is a need to have several types of tuning method.
There are simple, easy to use methods which require
little information, e.g. the method described in [2] aswell as more sophisticated methods which require more
information and more computations. In previous
research [3], [4], [5] we use the proportional controller
but the confirmation is done by computer simulation. In
order to provide consistent, reliable and stabilized
movement during running course, the experiment was
performed in the real environment. This paper
introduces the mean square root for parameter
estimation. In addition, the mean square root forms havethe added benefit of numerical stability and guaranteed©ISAROB 2006

Overview of the Robot Model

It is assumed that the plane of each wheel isperpendicular to the ground. The contact between thewheels and the ground is pure rolling and non-slipping.
The velocity of the center of the mass of the robot is
orthogonal to the wheel axis. The mobile robot has two
independent motors, thus providing both drive andsteering (differential steering system). At the back andrear, two passive castors are provided for support. The
passive support does not considered in obtaining thekinematics and dynamics model. The encoder is used tomeasure rotation with accuracy of 4.5 degree with equal
to 80 intervals per revolution. The robot uses 10cm
diameter wheel.

Key words: PID controller, optimal parameter tuning,mobile robot.

1

msugi@cc.oita-u.ac.jp241The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

rotation DC motor may be estimated by counting theencoder pulse in a fixed 0.1 seconds of period time. The
counter card can be configured to automatically countthe pulse received in a periodic interval.

5.1 The mean square error (MSE)MSE is an old, proven measure of control andquality [6]. MSE equals the mean of the squares of thedeviations from the target, i.e.,

Table 1: Byte-speed setting control wheel rotation
number1 m
MSE =∑ ( xi − T )m i =12(3)where,

xi = ith value of a group of m values,
T = Target of intended values.

4

MSE should be zero. Usually, the model is not perfect.
The smaller MSE, the better performance of the model.

Proportional Control

At this stage, we run the mobile robot only by
proportional control because by computer simulation inprevious research [3], [4], [5] we found that the
numbers of wheel rotations reach to the target value inproportional control. We control the speed of the mobile
robot by proportional control using the number of pulse
obtained from the encoders. The control rules are givenby the following equation:r _ ope = r _ ope + c(t )l _ ope = l _ ope + c(t )5.2 Determining
parameter

proportional

control

Preliminary experimental has been conducted withthe proportional parameter value between the ranges of
0.1 to 1.8 for the right and left wheel. The result isshown as below:(1)

where,c(t ) = Kpr ,l x e(t )(2)r & l _ ope is the right and left wheel desired wheel
rotation pulse number. c(t ) is the correction factor to be
applied to the system. Kpr ,l is the adjustment coefficientparameter right and left wheel for the observed error.
e(t ) is the error function or error of the wheel rotation
number, where e(t ) is equal to the differences between
desired rotation pulse number and current rotation pulse
number.

5

Fig. 1: Preliminary experiment to confirm Kp parameter
The lowest mean square error of the wheel rotation
number is the best coefficient parameter. From the result
we found that the lowest Kp coefficient parameter for
right and left wheel is 1.1. From this result, we use toperform the fine tuning experiment. In fine tuning thetest has been perform in the range of 1.0 to 1.2. The
results are shown in Fig. 2 and Fig. 3.

Experimental Results

In order to prove the effectiveness of the proposed
control method in a practical application, a laboratory
experimental setup has been employed. Experimentcondition is stated as the mobile robot on the floor withstraight running. The desired pulse rotation number foreach wheel is 80(pulse/sec) in order to carry out a wheel
rotation as one revolution in one second. The minimum
mean squares error of wheel rotation deviation fromtarget rotation is taken as a coefficient control
parameter.

Fig. 2: Right wheel mean square error- fine tuning©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

controller but their confirmation of coefficient
proportional parameter is done by computer simulation.
The purpose of this study is to find out the bestparameter for the proportional controller for each left
and right wheel on real environment which is actual
running on the laboratory floor. We found that themobile robot is stabilized, means rotation of each wheel
reached at the desired value and adhere to that value by
using coefficient parameter of Kp=1.14 for the left
wheel and Kp=1.16 for the right wheel. To make wheel
rotation reach at the desire value of wheel rotation we
use pulse-width modulation. Pulse-width modulation
can be use to control the speed of mobile robot byswitching ON nd OFF very quickly the power supply(voltage) to the DC motor in order to make a variationof speed. This finding will assist in executing a moreaccurate testing in future experiments.
The authors are currently working, in making
stable-target tracking research. The coefficientparameter with the proportional controller in this paperand other several controllers will be used. Experiment
data will be collected in order to verify the result
presented in this paper.

Fig. 3: Left wheel mean square error – fine tuning
From Fig. 2 and Fig. 3, the minimal wheel rotationsquare error selected to be the Kp coefficient parameterfor the right and left wheel. The selected coefficientparameter is 1.14 for the left wheel and 1.16 for theright wheel.

References[1] K. Z. Tang, S. N. Huang K. K. Tan and T. H. Lee, “Combined

Fig. 4: Right wheel rotation number

PID and Adaptive Nonlinear Control for Servo Mechanical
Systems,” Article of Elsevier Mechatronics, Vol.14, Issue 6, pp.
701- 714, 2004.
[2] Ziegler, J. G and Nicholas, N. B, “Optimum Settings for
Automatics Controllers,” Trans. ASME, pp.759-768, 1942.
[3] M. Sugisaka and T. Hashizume, “Run Control of The Mobile
Robot Using Visual Information,” Proc. of The Int. Symposium
on Artificial Life and Robotics, pp. 667-670, 2005.
[4] Toshio Maeyama and Masanori Sugisaka, “The obstacle

Fig. 5: Left wheel rotation number
Avoidance Run in The Mobile Robot using The CCD Camera”,

From the result shown above (Fig. 4 and Fig. 5), we
found the target number of wheel rotation is stabilized
but there is some amplitude are slightly out. We assume
that, this is because of odometry non-systematic errors
which may be cause by wheel-slippage or irregularitiesof the floor. We also found that the robot will reachtarget speed approximately after 0.7sec after thecommand is send due to load of mobile robot andresistance of the wheel.

6

Thesis of Master Studies, Faculty of Engineering, Oita
University Graduate School, 2002.
[5] Masanori Sugisaka and Toshio Maeyama, “Obstacle Avoidancein the Mobile Robot Using the CCD Camera”, Proc. of The Int.
Symposium on Artificial Life and Robotics, pp.238-241, 2002.
[6] G. J. Battaglia, “Mean Square Error”, AMP Journal ofTechnology, Vol. 5, pp.31-36, June 1996.

Conclusions

In previous research [3], [4], [5] the movement ofmobile robot is stabilized with the proportional©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development of an autonomous personal robot“System of recognition for work”
Hiroshi Mizoguchi, Yuki Matsumoto, and 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
E-mail: haya@mse.kyutech.ac.jp
Key Words: Robot, Recognition, autonomous, Roots, Map
experi ment to evaluate the wo r k sear ch syste m,
and d iscuss its results.

Abstract
Due to the pro fusio n o f resear ch in robo tics
in r ecent years, new kind s o f ro bots, includ ing
do mestic robo ts and rescue r obo ts, have b een
develop ed. Ho wever, r egar dless o f thesead vances, the develop ment o f a r obo t to
rep lace human health wo rker s in the ho me andin the ho spital has pro ved pro blematic.
Desp ite the challenges inher ent in this ar ea,
our labo rator y is develo ping an auto no mo us
per so nal robo t that can wor k in the abo ve
settings and is easy to co mmand .
I n the p er fo r mance o f its tasks, the r obo t
mo ves to the po sitio n o f a wo r k object sho wn
on a map . As a r esult, it is po ssib le to mo ve tothe app ro ximate area in fro nt o f the wo r k
object. B ut b ecause o f exter nal factor s such as
the margin o f mo vement error and so o n, when
the r obo t mo ves, the po sitio ns o f the ro bot andthe wor k object can var y. T his syste midentifies an object b y searching for theobject‘s i mage in the r obot’s database andco mp ar ing it to a CCD camer a image based o npixel infor matio n.

2. Composition of robot
The robo t d evelo ped based o n this resear ch
is co mpo sed o f a r eco gnitio n syste m that
under stand s p er ip heral infor matio n and adecisio n syste m that d eter mines the rob ot‘s
actio ns. Figur e 1 sho ws the syste m and theprocessing co mpo sitio n.

2.1. Recognition system
This s ystem acq uires per ip heral
info r matio n fro m var io us sensor s and uses it todevelop the robo t’s und erstanding o f its
sur ro und ings.

2.2. Movement determination system
This s ystem d eter mines the ro bot’s d ir ectio nof mo ve ment and sp ee d a nd so o n b a se d o n afeed -for ward inp ut fro m the finite sp ace map
and feedb ack inp ut fr o m the r eco gnitio nsyste m.
The rob ot d ecides its next actio n b ased o n
inp ut fro m the r eco gnitio n system and thedecisio n system, and then send s instr uctio ns to
the dr ive co ntrol system.

1. Introduction
Recently, the chances o f seeing a wor kingrobot in o ur surro undings have increased.
Rob ots with var io us functio ns have beendevelop ed b ased o n r ecent techno lo gicalad vances.
Ho wever, a rob ot capable o f
helping us auto no mo usly has not yet appear ed.
Thus, o ur lab orator y is d evelop ing an
auto no mo us per so nal ro bot that can wor k in
the ho me and the ho sp ital while being easy toco mmand .
In ord er to allo w the robo t to wor k
auto no mo usly, it is necessar y to cr eate a
pro gr am for the r obo t that allo ws it var io us
actio ns.
In this p aper we exp lain an algor ith m o f
ro ute sear ching and a method o f i mageprocessing used with a r eco gnitio n s ystem. I n
add itio n, we descr ibe the r esults o f an

©ISAROB 2006

Recognition System
Supersonic

CCD Camera

wave Sensor

Distance
Measuring Sensor

D e c i s i o n S ys t e m
Processor for Autonomic drive
M a p o f t h e F i n i t e -S p a c e

Database of Action pattern

D r i v e C o n t r o l S ys t e m
Motor Actuator

Fig.1 p rocessing system fo r per so nal r obo t

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sho uld make an evasive mo ve ment. T he robo t
is ab le to detect and evade unkno wn obj ects,
but is also ab le to deter mine a mo re efficient
alter nate ro ute.
Ther efor e, when establishing a ro ute to thetarget point, t wo or mo re ro utes can be
deter mined.
The robo t d er ives the r oute fr o m the li mited
sp ace map, and decides the o ne with the
sho rtest distance fro m two o r mo re ro utes inthe beginning. This flo w chart is sho wn in
Fig3.

3．
． Finite space map
The robo t assu mes a limited sp ace exists
within the ho me, o ffice, sickroo m, etc. T hus,
the r obo t’s r ange o f action is defined b y theli mited sp ace o n each flo or.
I t is necessar y to calculate the necessar ycorr ectio ns and pro cess the image data inorder to d etect and evade obstacles, as well as
to maintain a sufficient error margin. I t can besaid it is inefficient when the ob stacle isdetected in ever y case when mo ving to thesa me space man y ti mes.
Ther efo re, the robo t is co nstr ucted to b e
cap ab le o f processing in fo r matio n efficiently
using a “finite sp ace map ” which co ntains
info r matio n regard ing the o bject’s var io us
aspects.

Mak e object groupNo
S e a r c h t h e o b j e c t s ( To t a r g e t

Recognize
Ta k e t h e m iodbdjleec t s
Noo bj e c ts

S e a r c h t h e o b j e c t s ( To m i d d l e

3.1. Concept of finite space map

R ec o g ni ze o b j e c ts
Ta k e t h e n e w m i d d l e p o i n t

The li mited space map is given to the robo t
as t wo - d imensio nal info r matio n. I nfor matio nin this map includ es the size o f the object, thepositio n o f the obj ect, an d the size o f the roo m.
This ar ranged object is assumed to be within
the li mited range o f a r ectangle or q uad rangle.
When the ro bot mo ves b ased o n thisinfo r matio n, feed -for war d operatio n isachieved .
This map sho ws the rob ot’s initialcoord inates o n the map gr id, as well as the
positio n o f the target and the arr ange ment o f
objects in the roo m. Figur e 2 sho ws thisli mited sp ace map.

Did the midd le po ints of constant nu mber is taken?

Ye s
The route is dismissed.

Next middle point

Move to the middle point.
Te r g e t .

End

Fig.3 Flo wchar t
Next, it is exp lained ho w a middle po int isdeter mined. W hen the ar rangement o f objects
is assu med as sho wn in Figure 4a, the p resenceof an ob stacle is d eter mined.
To evade the ob stacle, the candid ate in themidd le po int is taken a way in a po int left fro m
the cor ner o f the ob stacle at a d iago nal o f 45
degr ees and a length o f 5 0cm.
A midd le po int that can actually be reachedis cho sen fro m a mo ng the cand id ates in themidd le ar ea ( Fig.4b). I n this case, fo ur midd lepoints P1, P2, P3 , and P 4 can b e ob tained.
When the r obo t takes two midd le points
P3 and P4, the ro bot j ud ges o bject fro m P3 or
P4. Then it jud ges the same obj ect. W hen thecand id ate in the midd le point is taken awa y,
the system takes this point. T hese ar e the last
midd le po ints.
A co mp ar iso n o f the length o f the P0 -P1
ro ute with the length o f the P0 -P3 -P1 ro ute

Fig.2 Finite space map

3.2. System for route searching
When the r obo t’s initial r o ute isinter r up ted b y an unkno wn o bject, the robo t©ISAROB 2006

No

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

sho ws that the latter is ob vio usly lo nger.
Ther efo re, P1 is exclud ed fro m the middle
point P3 . P2 and P4 are exclud ed for thereaso ns sa me as P1.
Then, all the ro utes are d eleted fro m P3.
This ro ute is d ismissed because it beco mes aro ute as sho wn in d and it is not capab le o freaching the target po int. It is si milar in thecase o f P4.

ＳＴＡＲＴ①Re adi ng o f i ma ge f or wo rk②C a me ra Im a ge Ac q ui si ti o n③Binarization of camera④Search Processing⑤Tra ns f or m ati o n o f i m a ge f or w or k

P1

Ｎｏ
P3
P0

P0

P4

Larger than

Ｙｅ ｓ

P2

Discovery of work

a

b

Ｎｏ
⑥Robot head drive

Ｙｅ ｓ
ＥＮＤ

P1

F i g.5 S y s t e m f l o w

P3
P0

P2

c

P0

P4

4.2. System processingdFig.4 Method o f taking the mid dle po int

We explain the pr ocessing o f the system
flo w sho wn in Figure 5 . Fir st, data r egarding
the wor k object is r ead . This data is taken fro m
the r obo t’s kno wled ge data base. The data
regard ing the object co ntains var io us
info r matio n such as object image, the size, thepositio n, etc.
Next, the CCD ca mer a image is acq uir ed.
In add itio n, the obj ect‘s shape are extr acted b yprocessing the ed ge extr actio n to theacq uisitio n image. T he acq uir ed image is
co nverted into b inar y d ata. This transfor ms
the image data into mo no chro me data, and thus
pixel infor matio n can be simp lified .
Sear ch pro cessing seeks the obj ect‘s
image fro m a mo ng the acq uired i mages. The
object is searched fo r b y seeking pixel
info r matio n shared b y the acq uired image andthe object i mage.
The object image chang es accord ing to
the d istance bet ween the camer a and the object
or the angle via which the ca mer a sees theobject. Then, the object’s d istance and theangle to it is p rocessed b y the geo metr ic
transfor matio n calculation and the p rojectio n
co nver sio n. The d istance and the angle o f theimage when the wor k object is disco vered
beco me the r esults o f a p ositio nal d ecisio n o f
the wo r k o bject o f the system.
A CCD ca mer a is po sitio ned in the ro bot‘shead which ro tates as the robo t sear ches forthe object. T he ro tatio nal range facing fo r ward
is 60 degrees

4．
． System of recognition for work
4.1. Purpose of system
When the robo t wo r ks, it mo ves to ward
the app ro ximate fro nt of the wor k object
sho wn in the map. B ut because o f exter nal
factor s, including the movement err or marginand so o n, when the robo t mo ves, the po sitio nsof the r obo t and the wo r k object ar e notco nstant.
Thus, the reco gnitio n s ys te m d isco vers the
wo r k object fro m a mo ng the i mages o btainedb y the CCD camer a, and estab lishes the
positio n o f the wor k obj ect.
Mor eo ver, the p o sitio n o f the robo t and theobject specified in this syste m is used also forthe robo t’s self-po sitio nal co rrectio n. For
exa mple, when an obj ect in a cer tain fixedplace, such as elevato r b utto ns and lights witches in a roo m, is d isco vered , the robo t’s
positio n and tho se o f the fixed objects can b e
co nfir med. As a result, the er ror margin
bet ween the robo t‘s po sitio n within the li mited
sp ace map and the rob ot’s po sitio n in real
sp ace d ur ing rep eated mo vements can b ecorr ected. Figure 5 sho ws the flo w o f thesyste m.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

5. Experiment
An exp er imental evaluation o f the syste m
was per for med. I n this experi ment the r obo t
mo ves to the fro nt o f the ro o m to face theelevator and sear ches for the elevator b utto n.
The exp er iment invo lved the r obo tsear ching fo r the elevato r b utto n fro m var io us
distances and angles, and deter mining thecorr ect po sitio n fo r p ushing the b utto n. Tables
1-4 sho w the r esults regard ing distance andangle.
Wa l lEl e v a t o r b u t t o nαＬ

7. Conclusion
In this stud y, a syste m d esigned to sear ch
fo r an object was d esign ed, and exp er imental
results sho w that the object co uld b e
disco ver ed with a high d egree o f pr obab ility.
Mor eo ver, the d istance to the object co uld
be esti mated with great accuracy. Ho wever,estimatio n o f the angle remains prob le matic,
and the robo t has not co me to wor k for object.
I mpro vement o f the accur ac y o f po sitio nestimatio n is also needed. Estimatio n o f theangle to the object is no t a transfor matio n
co nd itio n o f the object i mage, and sho uld
analyze the entir e camer a i mage as fo r
co nsider atio n as descr ib ed.

Ro b o t

Fig.6． Situatio n o f exp eriment
Tabl e 1. R e sult o f e xp er i me nt （ Ca s e1 L=300 m m）
An g l e α [ °] R e s u lt o f d i s t a n c e[ ｍ ｍ ] R e s u lt o f a n g l e[ °]
０

３００

１１

１５
３０

３００
３００

１５
２７

Tabl e 2. R e sult o f e xp er i me nt （ Ca s e2 L=400 m m）
An g l e α [ °] R e s u lt o f d i s t a n c e[ ｍ ｍ ] R e s u lt o f a n g l e[ °]
０
４００
９
１５
３０

３８０
３８０

be easily ob tained when the ro bot was too
clo se to the object. Mor eover, the b utto n co uld
no t b e disco vered when the d istance b etween
the robo t and the object was too great. We cansa y that the sho rter the distance b etween the
robot and the obj ect, the mor e accurate isdistance esti matio n. The shor ter the distanceto the object, the larger the image r eceived b y
the camer a, and thus mo re d etails o f theobject’s shap e can be captur ed.
We can say that the deter minatio n o f theangle bet ween the r obo t and the object isdifficult o nly in the p ixel match. We co nsider
that this syste m sho uld ad op t o nl y thedeter minatio n o f d istance b y the p ixel match,and analyze the angle b ased o n the per spectivethat app ear s in the camer a image.

２０
２６

REFERENCES

Tabl e 3. R e sult o f e xp er i me nt （ Ca s e3 L=500 m m）
An g l e α [ °] R e s u lt o f d i s t a n c e[ ｍ ｍ ] R e s u lt o f a n g l e[ °]
０

４９０

２

１５

４７０

２０

３０

４４０

３０

[1]Takuma U meno, Eiji Hayashi, Develop mentof an auto no mo us p er so nal r obo t “T he visual
processing system fo r auto no mo us dr iving”,
proceed ings o f the 10th inter natio nal
symp o siu m o n ar tificial life and r obo tics,
Vo l.10 th, pp .4, Bepp u, Oita, Jap an, 2005.

Tabl e 4. R e sult o f e xp er i me nt （ Ca s e4 L=600 m m）
An g l e α [ °] R e s u lt o f d i s t a n c e[ ｍ ｍ ] R e s u lt o f a n g l e[ °]
０
６００
５
１５

５３０

２６

３０

５５０

１９

[2]Eiji Hayashi, Keisuke I keda, Develop mentof an s ystem for self-dr iving b y auto no mo us
robot, p roceed ings o f the 10 th inter natio nal
symp o siu m o n ar tificial life and r obo tics,
Vo l.10 th, pp .44, Bep p u, Oita, J apan, 20 05.

6. Consideration
We can say as fo llo ws co nsideratio ns fr o mthis exper iment r esult.
Fir st, an o bject the size o f an elevator
butto n can b e d isco ver ed when the d istance
bet ween the robo t and the object is 3 00 to
600 mm. Ho wever, the obj ect’s shap e co uld not©ISAROB 2006[3]Tor u Azu ma, Yo shihiro Takenaga, Eiji,
Hayashi, Develop ment o f a self-dr iven
per so nal
robo t,Ar tificalLifeandRob otics( AROB 8th’03) , pp.1 -3, Oita, J apan,
2003.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Model of Mckibben Muscle Actuator Based on Experiment
Huailin Zhao*,** (zhao_huailin@yahoo.com)
Masanori Sugisaka**
Datai Yu**
School of Information Engineering, University of Science and Technology Beijing**
Department of Electrical and Electronic Engineering, Oita University, Japangas is input. But b will keep constant. Based on theassumption that the actuator is a conservative system, anideal static model can be gotten as [2]F=Pgb2(3L2/b2-1)/ (4πn2)(1)

Abstract: Mckibben muscles are a kind of pneumatic
artificial muscles. It can be used as actuators to actuate
robots. Aiming at the force-length model, the paper
compared the ideal model with experiments and revised
the ideal model. Then it analyzed the reason leading to
the hysteresis phenomena, and established the hysteresis
model. The experiments showed that the hysteresis
model was consistent and robust to different length
Mckibben muscle actuators.
Keyword: Mckibben muscle actuator, force-length
model, hysteresis model

1.

(a)

Introduction(b)
Fig.1 Structure of Mckibben muscles

There has been some ink used for Mckibben muscles[1-3]. It is a kind of pneumatic artificial muscles. It cangenerate enough force and maintain proper compliance atthe same time-a little like real muscles. So it can be used
as an actuator to actuate the robot. A robot actuated by
Mckibben muscles probably will behave like an animal.
But it is not easy to find a model in literatures. And
different Mckibben muscle products are different. So wehave to establish the special model of the kind used in
our project. What we are interested about the model is its
force characteristic, because it is used as an actuator.

Fig.2 Geometry model

2. The ideal static model
Mckibben muscles used in our project are like Fig.1. It
has a double-layer structure like Fig.1 (b). It consists of arubber tube covered in tough braided fiber mesh, which
shortens in length when inflated with compressed gas.
Gas can be input in from its one end, and the other end isclosed. L and D are the nominal length and the nominal
diameter. Another important structure parameter is θ-the mesh weave angle. L and D will change when θchanges. So L and D must have close relationships withθ.
If the actuator is supposed as a regular cylinder, ageometry model can be established (see Fig.2). Except L,
D and θ, there are structure parameters n-the number oftunes that the fiber winds and b-the length of the fiber.
θ will increase and L will decrease when compressed©ISAROB 2006

In which, F is the pulling force generated by the actuator,
Pg is the relative gas pressure between the outerenvironment and the inner rubber, which can be readfrom the pressure gage. L is the current length. b and n
are the structure parameters.
Based on the ideal model (1), the force output is relatedwith the gage pressure, current length, and the structure
parameters. Fig.3 is the simulation of force-lengthcharacteristic for a Mckibben muscle actuator that its
free length is 450mm. It is easy to be seen that the
maximal contraction ratios for different gage pressures
are same based on this model.

3.

Revising of the force-length model
Analyzing the ideal model and observing the248

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

To the same current length and the samepressure, the model force is larger than that of
the experiment.
The maximal contraction ratios related withdifferent pressures are different in theexperiment.
And all the contraction ratios in experiment areless than the corresponding ones of the model.
What’s the reason leading to those differences? After
analyzing, we know that the ideal model is based onsome suppositions [2]. One of the suppositions is that all
the absorbed energy can be output, in another word - noenergy loss. But actually this is not possible. Anothersupposition is that Mckibben muscle actuator is thought
as an ideal cylinder and the inner rubber is very thin andcontacts the mesh very closely. But this is not the fact
too.

simulation figure, we can easily find something that is
not consistent with the fact. First, it is not real that the
maximal contraction ratios are same to different gagepressures (see Fig.3). Basic experiments have shown thatthe maximal contraction ratios are related with thepressures very much. The contraction ratio will bedifferent if the gage pressure changes. Second, from
Fig.3, the maximal contraction ratio can be calculated:(450-300)/450*%=33.3%. Based on the experiments, themaximal contraction ratio of the actuators used in our
project is less than 30%. Any way, the force-length
model must be revised.

Fig.3 Simulation of force-length model (450mm)
Now let’s do some experiments to measure the related
data. Fig.4 is the experiment system. The experiment
data is about the force–length relationship. In theexperiment, to every gage pressure (0.1Mpa, 0.2Mpa,
0.3Mpa, 0.4Mpa, 0.5Mpa), the length is changed from L0(free length) to the minimal length and then returned to
the free length again. Doing the experiment this way is
because Mckibben muscle actuator has hysteresis
characteristic [4]. Here the gage pressure is limited
within [0.1, 0.5]Mpa. Fig.5 is the experiment data.

Fig.5 Experiment data (450mm)

Fig.6 Comparison between the experiment and the model(450mm)Now let’s revise the model. Based on the above
comparison, the following should be done:
Reduce the force.
Revise the contraction ratio and make it has
relationship with the gage pressure.
Reduce the contraction ratio.
Synthesizing the above three points, we revise theFig.4 Experiment system
Comparing the ideal model and the experiment (seeFig.6), it is found there are some differences as thefollowing:©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(see Fig.8).
From Fig.8, it is found that there is difference between
the revised model and the experiment data. But thedifference is not large. So we can conclude that therevised model is somewhat robust to the length.

model into:
F= c1b2/(4πn2) Pg{3[(1-c2Rc)L/b]2-1}
In which, Rc is the definition of the contraction ratio(Rc=(L0-L)/L0)). Parameter c1<1, is used to reduce theforce output. c2 is related with the gage pressure. So c2Rc
is related with gage pressure too and used to change thecontraction ratio. c2Rc can be called the new contraction
ratio. Be sure that (1-c2Rc)<1.
Based on the experiment data and the new model, c1
and c2 can be got as the following:
c1 =0.90
c2 =1.11e-9Pg+0.34
So the revised model is
F=0.9b2/(4πn2) Pg{3[(1-(1.11e-9Pg+0.34)Rc)L/b]2-1} (2)Comparing the revised model with the experiment, wegot Fig.7. Now the model and the experiment areconsistent with each other.

4.

Fig.7 Comparison between the revised model and theexperiment (450mm)

Fig.9 The relative movement of the fibers whencontracting(a)Fig.8 Comparison between the revised model and theexperiment (225mm)(b)

Fig.10 Friction model

The revised model is based on the data with free
length 450mm. Does the model fit to actuators withdifferent lengths? In another word, is the model robust tolength? Now let’s do the same experiment with anotherMckibben muscle that its length is 225mm, right just halfof the former one and compare with the revised model©ISAROB 2006

The hysteresis model

From the above experiments, we find that thehysteresis characteristic is clear and can’t be ignored.
The difference is large between the force outputs when
the pressure increasing and that when the pressure
decreasing (see Fig.7 and Fig.8). So the hysteresis model
must be established too.
Analyzing the hysteresis phenomenon, we think it ismainly due to the frictions between the inner rubber andthe outer mesh as well as between the fibers in the mesh.
They should be Coulomb friction generated by the
relative movements when Mckibben muscle contracts.
Any way it is not easy to calculate it, because the relative
movements between the two layers and between thefibers are very complicated (see Fig.9).

Based on the analyzing, force output can be divided
into two parts as the following:
F=Fs±Fd
In which, Fs is the basic static force and can be calculateby (2), and Fd is the friction force and can be expressed

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show that the models are consistent with the experiments
and are robust to other ones with different lengths.

as
Fd=sgn(dx/dt)μFN
In which, μ is the symphetical friction coefficientand FN is the corresponding positive pressure. We will
establish Fd by experiments because it is not easy tovalue μ and FN.
From the experiment data, it is easy to find that Fd isrelated with the gage pressure and the contraction ratio.
Based on the experiment, a linear model of Fd can be got
as following:
Fd=2Fdm(L-Lmin)/(L0-Lmin)(3)
In which,
Fdm=33.91Pg+2.13
Lmin={-1+c2+[(1-c2)2+4bc2/(31/2L0)]1/2}L0/(2c2)（c2=1.11e-9Pg+0.34）
So we can denote the hysteresis model as following:
F=Fs±Fd
In which, Fs is calculated by (2) and Fd calculated by (3).
Fig.11 is the comparison between experiment and themodel for a 450mm actuator. They are consistent witheach other.
Now there is a similar question: is the hysteresis
model robust to other actuators with different lengths?
By the similar way, model Fd for 225mm can be got, andthe comparison can be done too. The comparison showsthat there is some difference. But the difference is notlarge. There is data showing that the maximal differenceof their Fd is less than 1N.
Now let’s rewrite the hysteresis model again as
following:
F=Fs±Fd=0.9b2/(4πn2) Pg{3[(1-(1.11e-9Pg+0.34)Rc)L/b]2-1}±2Fdm(L-Lmin)/(L0-Lmin)
In which,
Fdm=32.291Pg+2.83
Lmin={-1+c2+[(1-c2)2+4bc2/(31/2L0)]1/2}L0/(2c2)（c2=1.11e-9Pg+0.34）
And note:
Lmin ≤L≤ L0
0.1Mpa≤Pg≤0.5Mpa5.

(a) Pg=0.1Mpa,0.3Mpa,0.5Mpa(b) Pg=0.2Mpa,0.4Mpa
Fig.11 Comparison between the experiment and themodel (450mm)

Reference[1] Masanori Sugisaka, Huailin Zhao, Norrima Mokhtar,
The artificial muscle and its application in robot,
Proceeding of 9th international artificial life androbotics symposium, Jan.2003, Japan[2] Huailin Zhao, Masanori Sugisaka, Modeling of
Pneumatic Artificial Muscle Actuator, Proceeding of
10th international artificial life and robotics
symposium, Feb.2004, Japan[3] John Iovine, “Artificial pneumatic muscle”,
Poptronics, Nov.2000, p49-65;[4] Frank Daerden, Dirk Lefeber, Pneumatic Artificial
Muscles: actuators for robotics and automation,
European Journal of Mechanical and Environmental
Engineering,47(1):10-21,2002

Conclusion

Mckibben muscles are a special kind of actuators. The
robot actuated by it will behave a little like animals.
Based on the ideal force-length model and theexperiments, an actual model is established. At last ahysteresis model is designed too. All the comparisons©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Offensive Strategy of a Billiard Robot
Jr-Syu Yang(corresponding author)
Department of Mechanicaland Electro-MechanicalEngineering
Tamkang University
No.151, Yingzhuan Rd.,
Danshui Town, Taipei
County 251, Taiwan, R.O.C
096034@mail.tku.edu.tw

Je-Ting Li
Department of Mechanicaland Electro-MechanicalEngineering
Tamkang University
No.151, Yingzhuan Rd.,
Danshui Town, Taipei County
251, Taiwan, R.O.C

Abstract

compensator for a billiard robot by the neural and fuzzy
theory [5]. He also developed a pocket selection
algorithm for the billiard robot by the grey theory [6].

Billiard is one of the most complex game to play forhuman beings in the real world. The objective of thispaper is to develop an offensive strategy to make aposition play for a billiard robot in a nine ball game. The
robot is composed of a machine vision, decision-making,control and actuating subsystems in a miniature pooltable. The experimental results indicate that the robot isable to make a position play to pocket the object balls
from number one to four sequentially and successfully.

2

There are a CCD camera with an image capture
card and a PC in this subsystem. The CCD is installedon the position of 2.7 meters high above the pool table(Fig. 1). First, the images of balls and pockets arecaptured to identify locations of the cue ball, eachnumbers of object balls (color balls), and pockets. Then,the centers of these balls and pockets are calculated by
the developed image processing software in a PC. The
geometrical data of this specific case will be sent to thedecision-making subsystem for the further calculation(Fig. 2).

Introduction

The billiard robot is designed to imitate the learning
ability of human beings to play billiard. The billiard is
one of the amusing activities for years. The game ofbilliard is played on a rectangular table (billiard table).
One ball (cue ball) is struck with the end of a cue stick,causing it to bounce into other balls and reflect off theside of the table. A player needs to have capability of thegeometrical concept and offensive strategy to win thegame. A good billiard player needs to visualize thegeometrical relations between cue ball, object balls andpockets, and to control the strength of shot to strike theobject balls into designate pockets one by one
sequentially.
There are several related researches that simulate
the billiard game in virtual environments. Koo applied
the concept of fuzzy logic to develop the decisionalgorithm on computer generated pool environments [1].
Koo demonstrated the balls identification and calibration
for a pool robot by the image processing technique [2].
Nakama developed a shooting mechanism for a billiard
robot by a precise position mechanism [3]. A wearable
computer and augmented reality were demonstrated tohelp players to enhance the game of billiards [4]. All ofthem are lack of the integration both of strategy part andmechronics part. Yang demonstrated a shooting angle©ISAROB 2006

Experimental setup(1) Machine vision subsystem：

Keywords： billiard robot, position play,decision- making

1Chan-Yun Yang
Department of MechanicalEngineering
Northern Taiwan Institute of
Science and Technology
No. 2, Xue Yuan Rd., Peitou,
Taipei Country 112, Taiwan,
R.O.C

Fig. 1. The billiard robot

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

(3) Mechanical position subsystem:

CCD
Pneumatic
cylinderImagecapturecard

xyz
platform

Two-axes
rotatingmechanism

There are a x-y-z position platform and a two-axes
rotating mechanism to align the actuating mechanism
for the robot. The motion commands are determined by
the center controller (PC) and sent to the corresponding
motor controllers to drive the pneumatic cylinder to thedesired position to strike the cue ball.
(4) Actuating mechanism subsystem:

pneumatic
controlvalve

PC & VB
software5 axes controller andstepping motors

There are a pneumatic pressure control valve and acylinder in this subsystem. The first one is applied tocontrol the strength of shot of this billiard robot. The
second one is used to strike the cue ball to imitate theshooting behavior of human beings to play billiard with acue stick.

Fig. 2. Flow chart of the experimental setup

3

Offensive strategy(2) Decision-making subsystem：
The objective of the developed offensive strategy ofthe billiard robot is to win the nine ball game. The nineball game is played with nine object balls numbered one
through nine and a cue ball. On each shot, the first ballthat the cue ball contacts must be the lowest number ofobject balls on the table. But, the balls need not bepocketed in order. If a player pockets any ball on a legal
shot, he/she remains at the table for another shot andcontinues until he misses, fouls, or wins the game bypocketing the nine ball [8]. In the research, it is arranged
to have a cue ball and 4 object balls (number one to four)on the table for simplification.

The decision-making algorithm is developed by thefollowing four factors of the extension theory [7]. The
factors are (i) the shooting angle ( θ ), (ii) the distance(d1) between the centers of the object ball and thecorresponding pocket (Fig. 3), (iii) distance (d2) between
the centers of the cue ball and object ball, and (iv) the
perpendicular distance (db) from the center of block ballto the line (Y) (Fig. 4). The offensive strategy isdeveloped based on these four factors. It will make up
the shooting priority step by step automatically.

The offensive strategy is determined by the above
four factors of the extension theory [7]. The matterelement is assigned as

Θ
d1

⎡N
⎢
R = (N , C ,V ) = ⎢⎢⎢⎢⎣Object ballVisualhitting point⎡ selection
C1 V1 ⎤ ⎢⎥ ⎢ factor
C 2 V2 ⎥ ⎢=C 3 V3 ⎥ ⎢⎥ ⎢C 4 V4 ⎥⎦ ⎢
⎣

d1
d2
db

d2
The relational function can be assigned as⎛ −1 ,
θ > 90⎜K1 (θ ) = ⎜ 90− | θ |, θ < 90⎜
⎝ 90
⎛ d1 + 300, d1 ≤ 0⎜
K 2 (d1 ) = ⎜ 300⎜ 300 − d1 , d ≥ 0
⎜
1
⎝ 300

Cue ballFig. 3. The positions of cue ball,object ball and pocket©ISAROB 2006⎤
0°,90° ⎥⎥0,300 ⎥⎥
85,215 ⎥− 1,1 ⎥
⎦

θ

253

(1)

(2)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006⎛
d 2 − 150 − 65⎜
, d 2 ∉, < 85,215 >K 3 (d 2 ) = ⎜ d 2 − 165 − d 2 − 150 − 70⎜
d 2 ∈< 82,215 >65 − d 2 − 150 ,⎝⎛ − 1 , 0 ≤ d b ≤ 32
K 4 (d b ) = ⎜⎜d b > 32
⎝1 ,Pocket1(3)

P3,4

Pocket2

3
2(4)

Pocket3

P2,3

Cue ball

The shoot angle ( θ ) between the direction of thecue ball to the hitting point and the direction of thehitting point to the pocket is a very important factor to let
the object ball run into the pocket after collision (Fig. 3).
If the angle ( θ ) is equal to zero, that means the cue ball,object ball, and the pocket are on the straight line. It is
the easiest way to pocket the object ball. If the absolutevalue of angle ( θ ) is getting larger ( θ ≥ 90°, on either
right or left side), it is more difficult to pocket the object
ball successfully (eqn. (1)). If the distance d1 is small,that means the object ball is close to the pocket. It iseasier to pocket the object ball. The half length of thepool table is 300 pixel (1 cm = 8 pixel) (eqn. 2). The
distance d2 is between 85 and 215 pixel from many
striking experiments for pocketing the object ball into
designed pocket successfully (eqn. (3)). The diameter ofthe ball is 4 cm (32 pixel). If the distance db is small than
32 pixel, there is a block ball on the moving direction
from the cue ball to the object ball (Fig. 4). It will cause
a foul during the game. In this case, the value of k4(db) isequal to –1. By contrast with this block ball, the value ofk4(db) is equal to 1 (eqn. (4)).

4

P1,2

Pocket4

1

Pocket5

Pocket6

Fig. 5. Specific example of a position play

4

ResultFigure 6 shows the experimental results of a cueball and 4 object balls on the table in 9 pictures taken bya digital camera continuously. The picture (1) shows thatthe actuating mechanism is in the stand by situation
before a shot. Picture (2) shows that the cue ball is
striking and running into the object ball 1. Then, theobject ball 1 one is moved and pocketed into the rightbottom pocket (picture (3)). And the cue ball is bounded
and stop at a good position for the next shot (picture (4)).
After that, the object ball 2 is pocketed into the left side
pocket (picture (5)). And the cue ball is bounded andcontrolled to stop on a good position for the ball 3(picture (6)). Similarly, the object ball 3 and 4 arepocketed into the left bottom pocket and right bottom
pocket respectively (picture (7)~(9)). The experimental
results, indicate that the robot is able to make a positionplay to pocket the object balls from number 1 to 4
sequentially and successfully.

object ballblock ball5dbConclusion

hitting point

In this paper, we presents an intelligent billiard
robot with the offensive strategy to strike object balls
sequentially. The robot shows its ability to “read” thetable and make its own decision to sink the object balls
into the pockets in the nine ball game automatically andsuccessfully.

Y = m‧X + b

cue ballFig. 4. The position of block ballIn the ball game, it is necessary to make a positionplay for the next shot. The predicted position of the cueball and other object balls after collision are calculated tomake a position play in this offensive strategy in order to
win the ball game. For example, the white ball is the cueball (Fig. 5). First, the ball 1 is pocketed into pocket 4
and the cue ball is controlled to stop at the position P1, 2
for the next shot. And the ball 2 is pocketed into pocket 2
and the cue ball is controlled to stop at the position P2,3
for the next shot. Then, the ball 3 is pocketed into pocket
1 and the cue ball is controlled to stop at the position
P3, 4 for the next shot. Finally, the ball 4 is pocketed into
pocket 4.

©ISAROB 2006

Acknowledgements
The National Science Council of Taiwan, ROC, isgratefully acknowledged for financial support under
grant of NSC91-2213-E-032-006.

References[1] S. C. Chua, E. K. Wong, Alan W. C. Tan, and V. C.
Koo, 2002, “Decision Algorithm for Pool Using
Fuzzy System, ” Proceedings of the International
Conference on Artificial Intelligence in Engineering& Technology (ICAIET2002), 17-18 June 2002
Kota Kinabalu, Malaysia, pp. 370-375.

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Sensing and Control (ICNSC’04), March, 21-23,
2004, Taipei, Taiwan, CD-R.
[6] J. S. Yang, Z. M. Lin and C. Y. Yang, 2004, “Grey
Decision-Making for a Billiard Robot,” IEEE
International Conference on Systems, Man &Cybernetics (SMC’04), Oct, 10-13, 2004, Hague,
Netherland, CD-R.
[7] W.Cai, 1983, ”The Extension Set and Incompatible
Problem,” J. of Scientific Exploration, 1, p.81-93.
[8] Billiard : The official rules & records book, Billiard
Congracess of America, 1990.

[2] S. C. Chua, E. K. Wong and V. C. Koo, 2003, “PoolBalls Identification and Calibration for a Pool
Robot,” Proceedings of the International
Conference on Robotics, Vision, Information and
Signal Processing (ROVISP 2003), January 2003,
Penang, Malaysia, pp. 312-315.
[3] H. Nakama, I. Takaesu and H. Tokashiki, 2001,“Basic Study on Development of Shooting
Mechanism for Billiard Robot”, JSME, Robotic
Workshop, 1A1F8.
[4] T. Jebara, C. Eyster, J. Weaver, T. Starner and A.
Pentland, 1997 “Stochastics: Augmenting theBilliard Experience with Probabilistic Vision andWearable Computers,” In Proceedings of theInternational Symposium on Wearable Computers,
Cambridge, Massachusetts. pp. 138-145.
[5] B. R. Cheng and J. S. Yang, 2004, “Design of The
Neural-Fuzzy Compensator for a Billiard Robot,”IEEE International Conference on Networking,

cue
2

1

cue

2

1

3

3
4

3

cue
cue

4

cue

3
4

cue
4

4

Fig. 6. Experimental result©ISAROB 2006

4

2

cue

cue

3

4

2
3

2

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

VeriSync: A Verification System for Firing Squad Synchronization
Protocols on One-Dimensional Cellular Automata
Kouhei Matsumoto and Hiroshi Umeo
Univ. of Osaka Electro-Communication
Neyagawa-shi, Hatsu-cho 18-8, Osaka, 572-8530, Japan
Abstract

however, verification techniques for local rules of cellular automata are very few. We develop a verification system VeriSync that enables us to verify the correctness of the synchronization protocols, focusing on
the class of optimum-time two-sided firing squad synchronization algorithms. We give a genealogy from
Waksmann’s algorithms [7] to Balzer [1], ending with
Gerken [2]. A formal design technique is also proposedthat can ensure the correctness of synchronization processes.

In this paper, we develop a verification system
VeriSync that enables us to verify the correctness ofthe synchronization protocols, focusing on the class of
optimum-time two-sided firing squad synchronization
algorithms. We give a genealogy from Waksman’s algorithms [7] to Balzer [1], ending with Gerken [2]. A
formal design technique is also proposed that can ensure the correctness of synchronization processes.

1

C1

Introduction

C3

Cn

C4

...

Cellular automata are considered to be a simple
model for complex systems in which an infinite onedimensional array of finite state machines (cells) updates itself in a synchronous manner according to a
uniform local rule. Synchronizing a large scale of cellular automata has been known as the firing squadsynchronization problem since its development, andthe problem was originally proposed by J. Myhill inorder to synchronize all parts of self-reproducing cellular automata [4]. The firing squad synchronization
problem has been studied extensively for more thanforty years [1-7].
Since the problem was defined by Myhill in 1957,
a rich verity of synchronization protocols have beendeveloped. A class of optimum-time two-sided firing squad synchronization algorithms is interestingfrom mathematical point of view and important in
the design of other class of synchronization protocols. Waksman [7] presented a 16-state optimumtime synchronization algorithm. Afterward, Balzer [1]and Gerken [2] developed an eight-state and a sevenstate synchronization algorithm, respectively, thus decreasing the number of states required for the synchronization. All of the algorithms are referred to as
optimum-time two-sided firing squad synchronization
algorithms. Global synchronization is a fundamental
computing primitives in parallel and distributed systems, thus the correctness of the synchronization protocols is very important from practical applications,©ISAROB 2006

C2

GenaralSoldiers

Figure 1: One-dimensional cellular automaton.

2

Two-Sided Firing Squad Synchronization Algorithm

The firing squad synchronization problem is formalized in terms of the model of cellular automata. Figure
1 shows a finite one-dimensional cellular array consisting of n cells, denoted by Ci , where 1 ≤ i ≤ n. All
cells (except the end cells) are identical finite state
automata. The array operates in lock-step mode suchthat the next state of each cell (except the end cells)
is determined by both its own present state and thepresent states of its right and left neighbors. All cells(soldiers), except the left end cell, are initially in thequiescent state at time t = 0 and have the property
whereby the next state of a quiescent cell having quiescent neighbors is the quiescent state. At time t = 0 theleft end cell (general ) is in the fire-when-ready state,
which is an initiation signal to the array. The firingsquad synchronization problem is stated as follows.
Given an array of n identical cellular automata, including a general on the left end which is activatedat time t = 0, we want to give the description (state
set and next-state function) of the automata so that,at some future time, all of the cells will simultaneously
and, for the first time, enter a special firing state. The

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set of states must be independent of n. Without lossof generality, we assume n ≥ 2. The tricky part of the
problem is that the same kind of soldier having a fixed
number of states must be synchronized, regardless of
the length n of the array.

t = 0 G0
time

n

cell

n

t = 0 G0 celltime

G1 t = n-1

t = n-1

Firing squad synchronization algorithms have been
designed on the basis of parallel divide-and-conquer
strategy that calls itself recursively in parallel. Those
recursive calls are implemented by generating many
Generals that are responsible for synchronizing divided small areas in the cellular space. Initially aGeneral G0 located at the left end is responsible forsynchronizing the whole cellular space consisting of ncells. In Fig. 2 (left), the General Gi , i = 2, 3, ..., is responsible for synchronizing the cellular space between
Gi and Gi−1 , respectively. G1 synchronizes the subspace between G1 and the right end of the array. Thus,
all of the Generals generated by G0 are located at theleft end of the divided cellular spaces to be synchronized by them independently. On the other hand, inFig. 2 (right), the General G0 generates General Gi ,i = 1, 2, 3, ...,. Each Gi , i = 1, 2, 3, ..., synchronizes
the divided space between Gi and Gi+1 , respectively.
In addition, Gi , i = 2, 3, ..., does the same operations
as G0 . Thus, in Fig. 2 (right), we find Generals located at either end of the subspace for which they are
responsible.

G1

G3

G3
t = 2n-2

t = 2n-2

Figure 2: Optimum-time one-sided (left) and twosided (right) firing squad synchronization scheme.

3
3.1

VeriSync: Overview of Verification
System
Optimization and Numbering

Gerken [2] reduced the number of states realizing
Balzer’s algorithm and constructed a seven-state, 118rule synchronization protocol. In our computer examination, no errors were found, however, 13 rules were
found to be redundant. In Table 1, we give a list of thetransition rules for Gerken’s algorithm. The symbols“>”, “/”, “...” and “#” represent the general, quiescent, firing and boundary states, respectively. The
system VeriSync optimizes a transition rule set bysynchronization-test on n cells where 2 ≤ n ≤ 10000.
The transition rule set which was designed by Gerken[2] can be optimized resulting in 105 rules from 118
onrs. Table 1 shows a complete list of transition rules
in which each rule consists of a unique rule number,left-state, center-state, right-state and next-state.

If all of the recursive calls for the synchronization
are issued by Generals located at one (both two)end(s) of partitioned cellular spaces for which the
General is responsible, the synchronization algorithm
is said to have one-sided (two-sided ) recursive property. We call the synchronization algorithm with onesided (two-sided) recursive property as one-sided (twosided) recursive synchronization algorithm. Figure 2
illustrates a time-space diagram for one-sided (Fig. 2(left)) and two-sided (Fig. 2 (right)) recursive synchronization algorithms both operating in optimum 2n − 2steps.

[Theorem 1] The transition Table 1 can synchronize any n cells in optimum 2n − 2 steps.

3.2

Rule Classification

The system VeriSync classifies synchronization
rules to verify the correctness of the rule set. We focus our attention to the subset of Gerken’s [2] transition rules which can synchronize any n cells such thatn = 2k (k ≥ 1). In our verification process, the rule
set is mainly divided into two categories in which αor β-areas in Figure 3.
Stage 1, it makes the transition rule set used in αarea has 39 rules denoted by R1 ( See Table 2 ). Stage2, it makes the transition rule set used in β-area has
38 rules denoted by R2 which includes the part of R1( See Table 3). And Stage 3, it creates the transition

It is noted that optimum-time synchronization algorithms developed by Balzer [1], Gerken [2] and Waksman [7] are two-sided ones and an algorithm proposedby Mazoyer [3] is an only synchronization algorithmwith the one-sided recursive property.
[Observation 1] Optimum-time synchronization algorithms developed by Balzer [1], Gerken [2] andWaksman [7] are two-sided ones. The algorithm proposed by Mazoyer [3] is a one-sided one.

©ISAROB 2006

G2

G2

257

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Table 1: Gerken’s 7-state synchronization protocol R
consisting of 105 rules.


No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53



LS
/
/
/
/
/
>
>
>
>
]
]
]
]
ˆ
ˆ
ˆ
ˆ
ˆ
<
<
<
[
[
/
/
/
/
]
]
]
]
ˆ
ˆ
ˆ
<
<
<
<



>
>
>
>
ˆ
ˆ
ˆ
ˆ
[
[
[
[

CS
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
]
]
]
]
]
]
]
]
]
]
]
]

RS
/
ˆ
<
[

/
ˆ
[

/
ˆ
<

/
>
ˆ
<
[
/
ˆ
<
/
[
/
]
ˆ
<
/
]
ˆ
<
]
ˆ
<
/
]
<

/
]
<
/
>
ˆ
[
/
>
ˆ
[
/
>
ˆ
[

NS
→ /
→ /
→ /
→ /
→ /
→ ]
→ ]
→ ]
→ <
→ >
→ >
→ >
→ [
→ˆ
→ˆ
→ˆ
→ [
→ <
→ /
→ /
→ /
→ /
→ /
→ˆ
→ˆ
→ >
→ [
→ˆ
→ˆ
→ >
→ [
→ˆ
→ >
→ [
→ >
→ >
→ ...
→ ...
→ >
→ >
→ ...
→ ]
→ ]
→ ]
→ <
→ /
→ /
→ ]
→ <
→ ]
→ ]
→ ]
→ <

54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105

/
/
/
/
/
>
>
>
>
]
]
]
]
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
<
/
/
/
>
>
>
>
>
ˆ
ˆ
ˆ
ˆ
[
[
[
[
[
/
/
/
/
]
]
]
]
ˆ
ˆ
ˆ
<
<
<
<

ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
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ˆ
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ˆ
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ˆ
ˆ
ˆ
ˆ
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<
<
<
<
<
<
<
<
<
<
<
<
<
<
<
<
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[

/
>
ˆ
<
[
/
>
ˆ
[
/
]
ˆ
<
/
>
]
ˆ
<
[
/
/
ˆ
[
/
>
ˆ
[

>
ˆ
[

/
>
ˆ
[

/
]
<

/
]
<

/
]
<
/
]
<


Tα (n, i) = n − 1 +(2)[Theorem 2] R1 ∪ R2 ∪ R3 is necessary to synchronize cells, where n = 2k (k ≥ 1) for optimum-time twosided synchronization algorithm developed by Gerken.

→ /
→ˆ
→ˆ
→ [
→ <
→ ]
→ˆ
→ˆ
→ <
→ >
→ˆ
→ˆ
→ [
→ /
→ˆ
→ˆ
→ˆ
→ [
→ <
→ /
→ <
→ <
→ <
→ ]
→ ...
→ ]
→ ]
→ ...
→ <
→ /
→ /
→ <
→ /
→ <
→ /
→ /
→ <
→ [
→ [
→ [
→ [
→ >
→ >
→ >
→ >
→ˆ
→ [
→ [
→ˆ
→ [
→ [
→ [

Cellular Space

G1
Step

n

ǩ

t=n-1

Ǫ
Ǫ ǩ
Ǫǩ
t=2n-2

Figure 3: Computation for α- and β-area in the timespace diagram.



Table 2: State transition table R1 consisting of 39
rules for 7-state optimum-time synchronization algorithm.


1
5
6
9
10
15
16
24
26
28
29
30
32
33
39
40
42
43
44
45

We show verification of optimum-time two-sided
synchronization algorithm developed by Gerken. Gi
is generated with collision point where 1/1-wave and1/2i −1-wave. So, the generation position of Gi can beformulated for the synchronization algorithm. Figure
5 illustrates a general generation position P, a generalgeneration time T in α-area. And, the generated Gi
can be synchronize cellular-space below. The transition rule set R3 is necessary to generates Gi and tostay it in the place.

©ISAROB 2006

n/2j

j=1

rule set has 8 rules denoted by R3 which generates a
new general state in the boundary of α-area and βarea and keeps a general state ( See Table 4 ). Figure
4 shows that it synchronizes by including R1 , R2 for
optimum-time two-sided synchronization algorithm oncells n = 2k .

Pα (n, i) = n/2i + 1

i




4

/
/
>
>
]
ˆ
ˆ
/
/
]
]
]
ˆ
ˆ


>
>
>
>

/
/
/
/
/
/
/
>
>
>
>
>
>
>
>
>
]
]
]
]

/

/

/
>
ˆ
/
ˆ
/
]
ˆ
]
ˆ
/
]
/
>
ˆ
[

→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→

/
/
]
<
>
ˆ
ˆ
ˆ
>
ˆ
ˆ
>
ˆ
>
>
>
]
]
]
<

46
47
48
49
55
56
59
60
61
63
64
65
66
67
68
69
70
71
72

ˆ
ˆ
ˆ
ˆ
/
/
>
>
>
]
]
]
]
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ

]
]
]
]
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ

/
>
ˆ
[
>
ˆ
/
>
ˆ
/
]
ˆ
<
/
>
]
ˆ
<
[

→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→

/
/
]
<
ˆ
ˆ
]
ˆ
ˆ
>
ˆ
ˆ
[
/
ˆ
ˆ
ˆ
[
<



Conclusions

We have developed a verification system for
optimum-time two-sided synchronization firing squadsynchronization protocols. Also, I verified the algorithm developed by Gerken [2]. We must proceed withthe proof of the firing squad synchronization algorithm
more.

(1)

258

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Table 3: State transition table R2 consisting of 38
rules for 7-state optimum-time synchronization algorithm.


1
2
3
4
6
8
9
10
14
17
18
19
20
21
22
23
54
66
71

/
/
/
/
>
>
>
]
ˆ
ˆ
ˆ
<
<
<
[
[
/
]
ˆ

/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
ˆ
ˆ
ˆ

/
ˆ
<
[
/
[

/
/
<
[
/
ˆ
<
/
[
/
<
<

→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→

/
/
/
/
]
]
<
>
ˆ
[
<
/
/
/
/
/
/
[
[

72
73
74
75
76
83
84
85
86
89
90
91
93
95
97
99
101
102
104

ˆ
<
/
/
/
ˆ
ˆ
ˆ
[
[
[
/
/
]
]
ˆ
ˆ
<
<

ˆ
ˆ
<
<
<
<
<
<
<
<
<
[
[
[
[
[
[
[
[

[
/
/
ˆ
[
ˆ
[

/
[

/
<
/
<
/
<
/
<

→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→
→

<
/
<
<
<
/
/
<
/
/
<
[
[
>
>
ˆ
[
ˆ
[





Table 4: State transition table R3 consisting of 8 rulesfor 7-state optimum-time synchronization algorithm.


35
36
37
41

<
<
<


>
>
>
>

/
]
<
<

→
→
→
→

>
>
...
...

78
81
82
87

>
>
ˆ
[

<
<
<
<

>

>
>

→
→
→
→

...
...
<
<





1

2

0

>

/

1

>

]

2

>

]

3

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>

8

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9

3

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>

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15

>

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>

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15

>

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>

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16

>

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>

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>

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16

>

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62

Figure 4: Configurations of R1 (left) and R1 ∪ R2
(right) implementation of optimum-time two-sided
synchronization algorithm on cells n = 2k (k ≥ 1).

References[1] R. Balzer: An 8-state minimal time solution to the firingsquad synchronization problem, Information and Control,
10, pp.22-42, (1967).

Cellular Space

k

n=2

1

G

[2] Hans-D., Gerken: Über Synchronisations-Probleme bei
Zellularautomaten. Diplomarbeit, Institut fur Theoretische Informatik, Technische Universitat Braunschweig, pp.
50 (1987).

Step[3] J. Mazoyer: A six-state minimal time solution to the firing squad synchronization problem, Theoretical Computer
Science, 50, pp.183-238, (1987).
w1[4] E. F. Moore. (1964): The firing squad synchronization
problem. in Sequential Machines, Selected Papers (E. F.
Moore, ed.), Addison-Wesley, Reading MA., pp. 213-214.
[5] H. Umeo, T. Sogabe and Y. Nomura: Correction, optimization and verification of transition rule set for Waksman’s firing squad synchronization algorithm. Proc. of the
Fourth Intern. Conference on Cellular Automata for Research and Industry, Springer, pp. 152-160 (2000).

wi-1
3n
-1
t=
2

[6] H. Umeo, M. Hisaoka and T. Sogabe: A Survey on
Optimum-Time Firing Squad Synchronization Algorithms
for One-Dimensional Cellular Automata. Intern. J. of Unconventional Computing, Vol. 1, pp. 403-426 (2005).

t=

wi

G1

7n
-1
4
i

t=n-1+ ∑ n/2

G2

j

Gi-1
Gi

j=1

t=2n-2[7] A. Waksman: An optimum solution to the firing squadsynchronization problem. Information and Control, vol.
9, pp. 66-78 (1966).

©ISAROB 2006

w2

t=n-1

n
8

n
8

n
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n
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Figure 5: Time-space diagram for α-space on n cells,
where n = 2k (k ≥ 1).

259

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

State-Eﬃcient Synchronization Protocols
for Communication-restricted Cellular Automata
Takashi Yanagihara, Masaru Kanazawa 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

inputs from its left- and right-neighbor cells. A 1-bit
CA model, where k = 1, can be thought of as being
one of the simplest CAs to have a low computational
complexity. On the k-bit CA we consider the ﬁring
squad synchronization problem that has been studied
extensively on the conventional CA model and propose several time-optimum ﬁring squad synchronization protocols together with its implementation on acomputer. Although many researchers have examinedvarious aspects of the conventional cellular automata[1-5, 7, 8, 11-14, 20-23], studies focusing on the amountof bit-information exchanged in inter-cell communications are few. Mazoyer [6] ﬁrst studied this model
under the name of CAs with channels and proposed atime-optimum ﬁring squad synchronization algorithm
in which only one-bit information is exchanged. Umeo
[15] and Umeo et al. [17, 19] have studied algorithmic
design techniques for sequence generation and connectivity recognition problems on CA1−bit . In addition, Umeo and Kamikawa [16, 18] showed that inﬁnite non-regular sequences such as {2n | n = 1, 2, 3, ..},
{n2 | n = 1, 2, 3, ..}, Fibonacci and the prime sequences
can be generated in real-time by CA1−bit . Worsch [24]established a computational hierarchy between oneway 1-bit CAs.
First, in Section 2, we introduce a class of bitcommunication-restricted cellular automaton having
k-bit inter-cell communication (CAk−bit ) and deﬁne

In this paper, we propose several state-eﬃcient
bit-transfer-based synchronization protocols for onedimensional cellular automata. It is shown that there
exists a 1-state CA5−bit that can synchronize any ncells in 2n − 2 optimum-step. The result is interesting,since we know that there exists no 4-state synchronization algorithm on conventional cellular automata
with transition functions such that δ : Q3 → Q, where
Q is a set of internal states.

1

Introduction

Cellular automata (CA) are considered to be a goodmodel of complex systems in which an inﬁnite onedimensional array of ﬁnite state machines (cells) updates itself in a synchronous manner according to a
uniform local rule. In the long history of the study ofCA, generally speaking, the number of internal statesof each cell is ﬁnite and the local state transition
rule is deﬁned in a such way that the state of eachcell depends on the previous states of itself and its
neighboring cells. Thus, in the ﬁnite state description of the CA, the number of communication bits
exchanged in one step between neighboring cells is assumed to be O(1) bits. However, such inter-cell bitinformation is hidden under the deﬁnition of the conventional automata-theoretic ﬁnite state description.
In the present paper, we study a ﬁring squad synchronization problem on a very restricted model of cellular automata, CAk−bit , for which inter-cell communication at one step is restricted to k-bit, where k isany positive integer such that k ≥ 1. We hereinafterrefer to the model as k-bit CA. The number of internal states of CAk−bit is assumed to be ﬁnite in theusual sense. The next state of each cell is determinedby the present state of the cell and two binary k-bit©ISAROB 2006

C1

C2

C3

C4

Cn

Figure 1: One-dimensional cellular automaton having
k-bit inter-cell communication links.

260

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the ﬁring squad synchronization problem on CAk−bit .
In Section 3, we propose several state-eﬃcient bittransfer-based synchronization protocols for CAk−bit .
Due to space constraints, we do not present the detailed proofs of the theorems discussed herein.

2

Cellular automaton having
inter-cell communicationthat the total information exchanged per one step between neighboring cells consists of O(1) bits. By encoding each state in Q with a binary sequence of lengthlog2 |Q|, sending the sequences sequentially bit by
bit in each direction via each one-way communication link, receiving the sequences bit-by-bit again, andthen decoding the sequences into their corresponding
states in Q, the CA1−bit can simulate one step of N in
log2 |Q| steps. This observation yields the following
computational relation between the normal CA andCA1−bit .

k-bit

A one-dimensional k-bit inter-cell communication
cellular automaton consists of an inﬁnite array of identical ﬁnite state automata, each located at a positive
integer point (See Fig. 1). Each automaton is referred to as a cell. A cell at point i is denoted by Ci ,where i ≥ 1. Each Ci , except for C1 , is connected toits left- and right-neighbor cells via a left or right oneway communication link. These communication links
are indicated by right- and left-pointing arrows in Fig.
1, respectively. Each one-way communication link cantransmit k bits at each step in each direction. A cellular automaton with k-bit inter-cell communication(abbreviated by CAk−bit ) consists of an inﬁnite arrayof ﬁnite state automata A = (Q, δ), where[Lemma 1] Let N be any normal cellular automaton
having time complexity T (n). Then, there exists a
CA1−bit which can simulate N in kT (n) steps, where
k is a positive constant integer such that k = log2 |Q|and Q is the set of internal states of N .
In addition, the next lemma can be stated in thecase where each cell can transfer k-bits at each step.
[Lemma 2] Let N be any s-state normal cellular automaton. Then, there exists an s-state CAk−bit which
can simulate N in real time, where k is a positive integer such that k = log2 s.

1. Q is a ﬁnite set of internal states.
2. δ is a function, deﬁning the next stateof any cell and its binary outputs to its
left- and right-neighbor cells, such that δ:
Q × {0, 1}k × {0, 1}k → Q × {0, 1}k × {0, 1}k ,where
δ(p, x1 , x2 , ..., xk , y1 , y2 , ..., yk )=(q, x1 , x2 , ..., xk , y1 , y2 , ..., yk ), p, q∈
Q,xi , xi , yi , yi ∈ {0, 1}, 1 ≤ i ≤ k, has the following meaning. We assume that at step t thecell Ci is in state p and is receiving k binary
inputs xi and yi , 1 ≤ i ≤ k, from its left andright communication links, respectively. Then,at the next step, t +1, Ci assumes state q
and outputs xi and yi , 1 ≤ i ≤ k, to its left
and right communication links, respectively.
Note that k binary inputs to Ci at step t arealso outputs of Ci−1 and Ci+1 at step t. A
quiescent state q ∈ Q has a property such that
k

k

k

3

In this section, we study a famous ﬁring squadsynchronization problem on the newly introduced
CAk−bit model for which solution gives a ﬁnite-state
protocol for synchronizing a large scale of cellular automata. The problem was originally proposed by J.
Myhill to synchronize all parts of self-reproducing cellular automata [8]. The ﬁring squad synchronization
problem has been studied extensively in more than 40
years [1-14, 17, 19-23].
The ﬁring squad synchronization problem is formalized in terms of the model of cellular automata. All
cells (soldiers), except the left end cell, are initially inthe quiescent state at time t = 0 and have the property
whereby the next state of a quiescent cell having quiescent neighbors is the quiescent state. At time t = 0 theleft end cell (general) is in the fire-when-ready state,
which is an initiation signal to the array. The ﬁring
squad synchronization problem is stated as follows.
Given an array of n identical cellular automata, including a general on the left end which is activatedat time t = 0, we want to give the description (state
set and next-state function) of the automata so that,k          δ(q, 0, 0, ..., 0, 0, 0, ..., 0) = (q, 0, 0, ..., 0, 0, 0, ..., 0).
Thus, the CAk−bit is a special subclass of normal(i.e., conventional) cellular automata. Let N be any
normal cellular automaton having a set of states Q
eand a transition function δ : Q3 → Q. The stateof each cell on N depends on the previous states of
the cell and its nearest neighbor cells. This means©ISAROB 2006

Firing Squad Synchronization Problem on CAk−bit

261

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006n

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Figure 2: A time-space diagram for Mazoyer’s
optimum-time synchronization scheme.

35 G C G Q C Q C A A Q A C B Q Q Q G C G G Q Q Q Q
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at some future time, all of the cells will simultaneously
and, for the first time, enter a special firing state. The
set of states must be independent of n. Without lossof generality, we assume n ≥ 2. The diﬃcult part ofthe problem is that the same types of soldier having aﬁxed number of states must be synchronized, regardless of the length n of the array.
All of the implementations given below are basedon Mazoyer’s 6-state synchronization scheme shownin Fig. 2.
[Theorem 1][5] There exists a 6-state normal cellular
automaton that can synchronize any n cells in 2n − 2
optimum steps.
[Theorem 2] There exists a 54-state CA1−bit thatcan synchronize any n cells in 2n − 1 steps.
Figure 3 (below) illustrates the snapshots of ouron
54-state (2n − 1)-step synchronization protocol
CA1−bit . The small black triangles  and  indicate
a 1-bit signal transfer in the right or left direction, respectively, between neighboring cells. A symbol in acell shows internal state of the cell. For ease of understanding of the synchronization processes and forthe reference, we provide snapshots of the Mazoyer’s
6-state synchronization processes of the same size on
conventional cellular automata. See Fig. 3.
[Theorem 3] There exists a 6-state CA2−bit that cansynchronize any n cells in 2n − 2 optimum-step.
[Theorem 4] [1,2,11] There is no four-state CA thatcan synchronize any n cells.
The question that remains is: “What is the minimum number of states for an optimum-time solution of©ISAROB 2006

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Figure 3: Snapshots for Mazoyer’s synchronization
processes (above) and those for our 54-state (2n − 1)step protocol (below) on 24 cells.

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the problem?”. At present, that number is five or six.
On the CA3−bit model, we can establish the following
theorem.
[Theorem 5] There exists a 4-state CA3−bit that cansynchronize any n cells in 2n − 2 optimum-step.
[Theorem 6] There exists a 3-state CA4−bit that cansynchronize any n cells in 2n − 2 optimum-step.
[Theorem 7] There exists a 2-state CA4−bit that cansynchronize any n cells in 2n − 2 optimum-step.
[Theorem 8] There exists a 1-state CA5−bit that cansynchronize any n cells in 2n − 2 optimum-step.

4[8] E. F. Moore: The ﬁring squad synchronization problem. in
Sequential Machines, Selected Papers (E. F. Moore ed.),Addison-Wesley, Reading MA., (1964), pp. 213-214.
[9] J. Nishimura, T. Sogabe and H. Umeo. (2003): A design of
optimum-time ﬁring squad synchronization algorithm on 1-bit
cellular automaton. Proc. of the 8th International Symposium
on Artificial Life and Robotics, Vol.2, pp. 381-386.
[10] J. Nishimura and H. Umeo: An Optimum-Time Synchronization Protocol for 1-Bit-Communication Cellular Automata.
Proc. of The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, (2005).
[11] P. Sanders. (1994): Massively parallel search for transitiontables of polyautomata. In Proc. of the VI International Workshop on Parallel Processing by Cellular Automata and Arrays, (C. Jesshope, V. Jossifov and W. Wilhelmi (editors)),
Akademie, 99-108.

Conclusions[12] A. Settle and J. Simon. (1998): Improved bounds for the ﬁring synchronization problem. In SIROCCO 5: Proc. of the
5th International Colloquium on Structural Information andCommunication Complexity, Carleton Scientiﬁc, 66-81.

We have made an investigation into a trade-oﬀ between internal states and communication bits in ﬁring
squad synchronization protocols for communicationrestricted cellular automata and proposed several
time-optimum state-eﬃcient bit-transfer-based synchronization protocols. It has been shown that there
exists a 1-state CA5−bit that can synchronize any ncells in 2n − 2 optimum-step. The result is interesting,since we know that there exists no 4-state synchronization algorithm on conventional O(1)-bit communication cellular automata. The CAk−bit is conﬁrmed to
be an interesting computational subclass of CAs thatmerits further study. We conclude with the following
question for further investigations.

[13] A. Settle and J. Simon. (2002): Smaller solutions for the ﬁring
squad. Theoretical Computer Science, 276, 83-109.
[14] S. L. Torre, M. Napoli and M. Parente: A compositional approach to synchronize two dimensional networks of processors.
Theoretical Informatics and Applications, 34 (2000) pp. 549564.
[15] H. Umeo: Linear-time recognition of connectivity of binary
images on 1-bit inter-cell communication cellular automaton.
Parallel Computing, 27, pp. 587-599, (2001).
[16] H. Umeo and N. Kamikawa: A design of real-time non-regularsequence generation algorithms and their implementations on
cellular automata with 1-bit inter-cell communications. Fundamenta Informaticae, 52 (2002) 255-275.
[17] H. Umeo, K. Michisaka and N. Kamikawa: A synchronization
problem on 1-bit-communication cellular automata. Proc. ofInternational Conference on Computational Science-ICCS2003,
LNCS 2657, pp.492-500, (2003).

• What is the minimum number of states for protocols on each CAk−bit , k = 1, 2, 3, 4, 5, ..?

[18] H. Umeo and N. Kamikawa: Real-time Generation of Primes
by a 1-Bit-Communication Cellular Automaton. Fundamenta
Informaticae, 58(2003) 421-435.
[19] H. Umeo, M. Kanazawa, K. Michisaka and N. Kamikawa:State-Eﬃcient 1-Bit-Communication Solutions for Some Classical Cellular Automata Problems. Proc. of the International
Workshop on Tilings and Cellular Automata (Ed. M. Margenstern), pp.1-12, (2004).

References[1] R. Balzer: An 8-state minimal time solution to the ﬁring squadsynchronization problem. Information and Control, vol. 10(1967), pp. 22-42.

[20] H. Umeo, M. Hisaoka and T. Sogabe: A Survey on Firing
Squad Synchronization Algorithms for One-Dimensional Cellular Automata. International Journal of Unconventional Computing, Vol.1, pp.403-426, (2005).

[2] A. Berthiaume, T. Bittner, L. Perković, A. Settle and J. Simon.
(2004): Bounding the ﬁring synchronization problem on a ring.
Theoretical Computer Science, 320, pp. 213-228.

[21] R. Vollmar. (1981): On Cellular Automata with a Finite Number of State Change. Computing, Supplementum, vol. 3(1981),
pp. 181-191.

[3] Hans-D., Gerken. (1987): Über Synchronisations - Probleme
bei Zellularautomaten. Diplomarbeit, Institut für Theoretische
Informatik, Technische Universität Braunschweig, pp. 50.

[22] R. Vollmar. (1982): Some remarks about the “Eﬃciency” ofpolyautomata. International Journal of Theoretical Physics,
vol. 21, no. 12, pp. 1007-1015.

[4] E. Goto: A minimal time solution of the ﬁring squad problem.
Dittoed course notes for Applied Mathematics 298, Harvard
University, (1962), pp. 52-59.

[23] A. Waksman: An optimum solution to the ﬁring squad synchronization problem. Information and Control, vol. 9 (1966),
pp. 66-78.

[5] J. Mazoyer: A six-state minimal time solution to the ﬁring
squad synchronization problem. Theoretical Computer Science, vol. 50 (1987), pp. 183-238.

[24] T. Worsch:[6] J. Mazoyer: On optimal solutions to the ﬁring squad synchronization problem. Theoretical Computer Science, vol.
168(1996), pp. 367-404.

2000:Theoretical Informatics (Eds. G. H. Gonnet, D. Panario
and A. Viola), LNCS 1776, pp.417-426, (2000).

[7] M. Minsky: Computation: Finite and infinite machines. Prentice Hall, (1967), pp. 28-29.

©ISAROB 2006

Linear time language recognition on cellular

automata with restricted communication, Proc. of LATIN

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Analysis and Simulation of Group Dynamics based on Interaction
between Decision Making and Heider’s POX Systems
Tatsuya Nomura
Department of Media Informatics
Ryukoku University
1–5, Yokotani, Seta–ohe–cho, Otsu, Shiga 520–2194, Japan
Abstract

structures actually appear in large groups as a macro
structure of group dynamics based on micro behaviorsof the original POX systems in individual persons. Asan approach to this problem in the field of artificial societies, Wang and Thonegate [5] proposed a simulation
model of group dynamics based on POX systems, consisting of full connected graphs. However, this study
focuses on non–digraphs, that is, cases where all the
dyad relations are symmetric. We proposed a formalization of group dynamics based on POX systems asa finite Markov chain with a state space consisting ofsigns on all the edges in digraphs, characterized theconcept of balance as absorbing states of this Markov
chain, and executed computer simulations of the groupdynamics based on the Markov chain [6].
However, the model by Wang and Thonegate andour model focus on group dynamics only based on
POX systems, and they lack interaction between POX
systems and other psychological processes. To construct more realistic models of group dynamics, thispaper proposes a model of interaction between the social network dynamics based on POX systems and decision making process under the human network.

Heider’s balance theory is one of theories on micro
characteristics of triad relations in social psychology.
However, it has not sufficiently been discussed what
relations there are between group dynamics, this micro characteristic, and other psychological processes.
This paper proposes a model of interaction between
the social network dynamics based on POX systems
and decision making process under the human network, while comparing the proposed model with theprevious group dynamics model based on only POX
systems.

1

Introduction

As one of theories on micro characteristics of individuals in social psychology, balance theory proposedby F. Heider [1] states a psychological stability of anindividual included in a triad relation. In this theory,a person (P ), another person (O), an object or thethird person (X), and relations from P to O, from O
to X, and from P to X construct a system (called POX
system). These relations have either + or − value corresponding to the fact that the person likes or dislikes
the object respectively. Heider’s theory argues that a
POX system is balanced if and only if the product of
the signs on these three relations is +, and if the system is not balanced P changes one of the relations to
O and X so that the POX system becomes balanced.
As shown in Figure 1, if the system is not balanced,
then P inverts either the sign of P → O or that of
P → O to balance the POX system.
Although the original balance theory is limited totriad relations, its extension to groups consisting of
more than three persons have been proposed [2, 3, 4].
These studies of balance in social networks focus on
network structures of balanced situations based ongraph theory.
However, it has not sufficiently discussed what

©ISAROB 2006

2

Group Dynamics as a Finite Markov
Chain

In the same way as the previous work [6], we assume
that there are N persons and relations between them
are represented as a fully connected digraph in whichthese relations have + or − value, where + and −mean that the person likes and dislikes the other person, respectively. In addition, it is assumed that these
individuals make decisions for a subject by agreement(+) or disagreement (-).
This social network can be represented as asigned digraph G = (P, A), P = {p1 , p2 , . . . , pN , s},
A = {(a12 , s12 ), (a13 , s13 ), . . . , (a21 , s21 ), (a23 , s23 ), . . . ,(a1 , s1 ), . . . , (aN , sN )}. P is the set of (N + 1) vertices

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
Imbalanced
Situations

X

X

P

O

P

X

X

O

P

O

P

O

Modification by
a Psychologica Force
Balanced
Situations

P

O

P

X

X

X

X

O

P

+ relation

O

P

O

- relation

Figure 1: Balanced and Imbalanced Situations of POX Systems in Heider’s Balance Theory.
others having the same opinions to the individual anddisliked by the individual. Thus, reversion of the sign
toward the subjects means reversion of these numbers,
and the procedure represents decision making for the
subject based on minimization of the imbalanced POX
systems including the subject.
The change of signs from individuals to others in
the above procedure 1 is stochastic and dependent
only on the current signs, although the change ofsigns of individuals toward the subject is deterministic. Thus, group dynamics based on these procedures
equals to a finite Markov chain with the state space
S = {(s12 , s13 , . . . , s21 , s23 , . . . , s1 , . . . , sN )}, in which2
the total number of states is 2N . Figure 2 shows anexample of this finite Markov chain with 3 persons.

in the digraph, corresponding to the N persons andthe subject s, and A is the set of pairs of edges aij
from the i–th person to the j–th person and signs onthem sij , and pairs of edges ai from the i–th person
to the subject s and signs on them si .
In this model, each individual synchronously does
the following actions:
1. It selects one of the POX systems including itself(these POX systems include the triads consistingof the edge to the subject). If the selected POX
system is imbalanced, it balances the POX systemby randomly selecting any of the edges toward theothers and reversing the sign on the selected edge
(if the selected POX system includes the subject,
the sign of the edge toward the other is changed
and that toward the subject is not changed).

Relations between Absorbing States and Balanced Situations
Absorbing states in the above finite Markov chain
as group dynamics are situations where all the POX
systems are balanced. It is shown that these situations
coincide with the following situations of the group: (1)the group is partitioned into two subgroups (including
the case that one of the subgroups is empty), (2) all the
relations between individuals in the same subgroup are
positive, (3) all the relations between individuals in the
different subgroups are negative, (4) the individuals inthe same subgroup have the same attitude toward thesubject, and those in the different subgroup have theopposite attitude.
It is trivial that in the situations satisfying theabove four conditions all the POX systems are balanced. The converse is proved as follows.
First, all the POX systems consisting of N –persons
are balanced. In the previous work [6], it was provedthat this condition is a necessary and sufficient con-

2. Among the POX systems including the subject, if
the number of the imbalanced ones is larger thanthat of the balanced ones, the sign of the edge
toward the subject is reversed.
The above procedure 1 represents balance of the
POX systems in each individual. The above procedure
2 represents decision making of the individual basedon subordination to a majority and balance of POX
systems. The number of the balanced POX systems
including the subject is the sum of the total of thenumber of the others having the same opinion as theindividuals and liked by the individual, and the number of the others having the opposite opinions to theindividual and disliked by the individual. Moreover,the number of the imbalanced POX systems including
the subject is the sum of the total of the number of theothers having the opposite opinion to the individuals
and liked by the individual, and the number of the©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

P1

P2

S

P3

P1

P2

P1

P2

S

P3

S

P3

+ relation

absorbing states corresponding to situations where thegroup is polarized into two subgroups. However, thisanalysis does not clarify whether this finite Markov
chain has cyclic states. In other words, there is a possibility of the existence of cyclic states where modification of some POX systems and that of other POX
systems are repeated one another.
In order to investigate the above problem, we executed some computer simulations. The simulations
were executed for configurations of N = 4–8. For eachconfiguration, 300 trials with different random seeds
were executed. For each trial, the probability of selection of each POX system in the procedure 1 mentioned
1
in section 2 were fixed as follows: 2(N −1)(N−2) for the
POX system not including the subject, and 2(N1−1) for
the POX systems including the subject (note that the
total number of POX systems including the subject
and that not including it for a person are N − 1 and(N − 1)(N − 2) respectively).
Table 1 shows types of grouping in absorbing states
and the numbers of the corresponding states, the numbers of trials that converged to the corresponding
states, and mean number of iteration for convergence
to each grouping. The state converged to one of absorbing states shown in the previous section in all thetrials for all the configurations, and any cyclic state
was not observed. Moreover, no trend existed that
there is a specific absorbing state to which convergenceis faster than the other states.

- relation

State Transition

Figure 2: An Example of the Finite Markov Chain of
the Group Dynamics (3 Persons)dition for the above conditions (1)–(3). In fact, when
all the POX systems consisting of N –persons are balanced, it is proved that the relation between persons
defpi ∼ pj = {pi = pj or the edge pi → pj is +} is anequivalence relation. For this equivalence relation ∼,persons in the same equivalence class are connectedeach other by edges with + and vertices in the different classes are connected each other by edges with −.
Since balance of all the POX systems does not permit
the existence of more than three classes, this situation
equals to the above conditions 1–3.
Then, if persons in the same subgroup have the different attitudes toward the subject, the POX systems
consisting of these persons and the subject are imbalanced since the relations between these persons are
positive. Moreover, if persons in the different subgroups have the same attitude toward the subject, thePOX systems consisting of these persons and the subject are imbalanced since the relations between these
persons are negative. Thus, the above condition (4)must be satisfied.

3

Comparison with the Previous ModelIn the previous work [6], we proposed the groupdynamics model based on only POX systems, without the decision making process. This previous model
showed the analysis and simulation results similar to
the model proposed in section 2. However, there aresome phenomena different between the previous andproposed model.
The absorbing states of the previous model are alsosituations where the group is polarized into two subgroups, including the case that one of them is empty,
and the simulation results showed that the state converged to one of absorbing states in all the trials. However, the speed of convergence to the absorbing statesin the proposed model was much faster than that in
the previous model. Figure 3 shows means of iteration numbers spent until convergence for each N in
the proposed and previous models. For increase of thenumber of persons, the convergence speed of the proposed model increased linearly, although that of theprevious model increased exponentially.
Furthermore, there was a bias on which absorbing
state the group converged to, although this bias wasSimulation

As shown in the previous section, the group dynamics based on individual POX systems and decision
making is represented as a finite Markov chain having©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

N
Types of Grouping(#. Corresponding States)#. Convergence
Mean #. Iteration
N
Types of Grouping(#. Corresponding States)#. Convergence
Mean #. Iteration

Table 1: Results of Simulations
4:0
(1)
82
14.2

4
3:1
(4)
149
12.8

0:7
(1)
18
81.7

1:6
(7)
67
65.5

2:2
(3)
69
13.7

5:0
(1)
57
37.6

5
4:1
(5)
116
29.4

3:2
(10)
127
24.2

2:5
(21)
112
63.1

3:4
(35)
103
63.1

0:8
(1)
17
111.9

1:7
(8)
61
123.7

7

6
6:0
(1)
43
41.3
8
2:6
(28)
114
102.8

5:1
(6)
92
54.2

4:2
(15)
110
47.5

3:5
(56)
71
98.0

4:4
(35)
37
97.1

3:3
(20)
55
46.1

As future problems, we should investigate a causeof observed acceleration and bias in convergence ofthe proposed model in comparison with the previous
model while exploring the corresponding psychological phenomena. In addition, we need to extend thefull–connected graph structure of the model to generalgraphs including 2–D cellular structures.













Acknowledgments
Figure 3: Means of Iteration Numbers Spent Until Convergence (Solid Line: The Proposed Model,
Dashed Line: The Previous Model without Decision
Making Process)This study was supported by the Japan Society for
the Promotion of Science, Grants–in–Aid for Scientific
Research No. 15330133.

References

not observed in the previous model. For example in
N = 4, the situation where all the persons like eachother appeared 82 times among 300 trials, although
the polarized group situations appeared about 20–40
times in average. In the same simulations of the previous model, the situation where all the persons like eachother appeared 29, 16, 7, 5, 2 times among 300 trials
for N = 4, 5, 6, 7, 8 respectively. For each N , a χ2 –test revealed that there was a statistically significant
difference with 0.001 or 0.01 level on these numbers ofconvergence between the proposed model and previous
model.
The above facts suggest that the decision making
process has an effect of acceleration and bias in convergence of the group dynamics based on micro behaviors
of POX systems.

4[1] Heider, F. (1958), The Psychology of Interpersonal
Relations. John Wiley & Sons. (Japanese Edition:
M. Ohashi, Seishin, 1978).
[2] Cartwright, D., Harary, F. (1956), Structural balance: A generalization of Heider’s theory. Psychological Review, 63: 277–293.
[3] Harary, F., Norman, R. Z., Cartwright, D. (1965),
Structural Models: An Introduction to the Theory
of Directed Graphs. John Wiley & Sons.
[4] Flament, C. (1963), Applications of Graph Theory to Group Structure, Printice–Hall. (Japaneseedition: K. Yamamoto, Kinokuniya, 1974).
[5] Wang, Z., Thorngate, W. (2003), Sentimentand social mitosis: Implications of Heider’s balance theory. Journal of Artificial Societies andSocial Simulations, 6(3). (electric publication:
http://jasss.soc.surrey.ac.uk/6/3/2.html).

SummaryThis paper proposed a model of interaction between
the social network dynamics based on POX systems
and decision making process under the human network, while comparing the proposed model with theprevious group dynamics model based on only POX
systems.

©ISAROB 2006

[6] Nomura, T. (2004), Analysis and simulation ofgroup dynamics based on Heider’s balance theory
and a finite Markov chain. In Proc. 2nd International Conference of the European Social Simulation Association. (CD–ROM Proceedings).

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 

268

population of US cities in 2000

number of cities vs total population

0.1

(a)

(b)

-1.29

y = 537 + 3.92e-05 x
1990

number of cities

cumulative distribution

1 Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
The Eleventh International
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
8000

6000

0.01

4000

0.001

1900
1880

2000

0.0001

10000

1e+05

0

1e+06

0

1e+08

5e+07

population

1.5e+08

2e+08

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©ISAROB 2006

271

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Visualization of an Invisible Space Based on the MR Technique
Sunao Hashimoto
Joo Kooi Tan
Seiji Ishikawa
Department of Mechanical & Control Engineering
Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japan
hasimoto@ss10.cntl.kyutech.ac.jp, {etheltan, ishikawa}@cntl.kyutech.ac.jp

transparent objects, but the former cannot realize real-time
operation, whereas the latter doesn’t give exact views
when an observer changes his/her viewing direction.
In this paper, we propose a method of making aninvisible space visible by the employment of mixed reality
technique and a simple 3-D recovery technique. By theemployment of the simple 3-D recovery, the proposed
technique realizes real-time operation. Moreover thetechnique offers exact views to an observer, irrespective of
his/her viewing point, since it makes a 3-D virtual modelof the real world observed. Overview of the proposed
method is given followed by the description ofcomputational strategies for realizing a transparent
environment. Experimental results are shown withdiscussion.

Abstract
This paper describes a novel technique for realizing atransparent space employing a mixed reality techniqueand a three-dimensional (3-D) modeling technique.
Accidents often happen when a person, a car, etc., comesout suddenly from a blind corner. If the visibility of sucha blind corner is improved, many of the accidents will notoccur. The idea of this study is to devise a technique forachieving better visibility at various places of bad
visibility by use of a mixed reality technique. Mixed
reality was born from virtual reality study. It provides atechnique for superposing virtual objects in a real space.
As the initial step of realizing the above idea, in thepresent paper, a technique is described for giving a 3-Dview of a space beyond a thick wall employing acomputer vision technique and a see-through vision. The
technique provides an observer with a view of a 3-D
dynamic scene of the adjacent space partitioned by a wallin real-time without going into it.
Future application of the preset technique may include
its installation to various corners in the building and in
the intersections on the road with bad visibility. This will
surely contribute to reducing traffic accidents in future.

2

Configuration of the proposed system is shown in Fig.1.
A camera is set in the space that is separated by a wall ora partition and invisible from an observer. The space isdenoted as an invisible region. The scene in the invisible
region is taken images by the camera. The taken scene
recovers in a 3-D way and is described in the virtual space
simulating the invisible region. An observer puts on a seethrough type head mounted display (HMD), which isequipped with a position and orientation sensor.
Employing the information from the sensor, the PC
computes the view of the invisible space that could beseen from the observer and displays it on the HMD. Thenthe observer sees the wall superposed by the computed
view of the invisible space, thus realizing a transparent
view of an invisible space.

Keywords: 3-D recovery, mixed reality, virtual reality.

1

Introduction

Mixed reality is a technique for unifying virtual entities
and a real world using some instruments for see-throughvision. Many applications of the mixed reality studies
have already been reported [1].
Employing the mixed reality technique, useful
information can be added to a real world. This can be
regarded as visualization of invisible information. An
example of the invisible information in our daily life isblind or occluded spots. Many traffic accidents happen
when a person, a motorcycle, a car, etc., suddenly comeout of a blind corner. If the corner has good visibility,
most of the accidents may not occur. The idea of this study
is to devise a technique for achieving better visibility at
various places of bad visibility by use of a mixed reality
technique.
There are some researches [2,3] with respect to making©ISAROB 2006

Overview of the system

3

Computational strategies

In order to create a 3-D virtual space, which is a modelof an invisible space, camera calibration is performed in
the first place. The camera parameters are then calculated
and finally 2-D projective transformation is defined
between the floor in the 3-D space and the image plane of
the camera.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

PC
( X g 2 , Y g 2 ,0 )Wall/Partition

Optical axis
( X c 1 , Y c1 , Z c1 )

HMD
Head
sensorY

Camera( X c 2 , Yc 2 , Z c 2 )CameraLayer 1

X

Invisible region

Layer 2

( X g 1 , Y g 1 ,0 )Z

ObserverFig. 2 Location of objects and layers.

Fig. 1 Configuration of the system.

3.1

Acquiring the depth information

In the proposed technique, a single camera is used toobserve an invisible region. The depth information is
acquired by the employment of 2-D projective
transformation between the floor, the XY plane of theworld coordinate system, and the image plane. Let us
denote the object’s position on the XY plane in the world
coordinate system by (Xg,Yg,0) and its projection on theimage plane by (xg,yg). Then the 2-D projective
transformation between the planes are given by⎛ X g ⎞ ⎛ h0
⎟ ⎜⎜⎜ Yg ⎟ = ⎜ h3
⎜ 1 ⎟ ⎜h
⎠ ⎝ 6
⎝
4

h1
h4
h7

h2 ⎞⎛ x g ⎞⎟⎜ ⎟h5 ⎟⎜ y g ⎟1 ⎟⎠⎜⎝ 1 ⎟⎠Experimental results

5.1

Produced 3-D virtual space

The first experiment was conducted to create a virtual
space from the images of two persons walking around in aroom. The environment of the experiment is shown in
Fig.3a where two persons are taken images of their
walking motions by the settled camera. Figure.3b
illustrates the input images. The created virtual space isshown in Fig.3c, in which two persons’ images arecorrectly arranged in the 3-D virtual space. These images
move around according to the two persons’ movement in
the real room.

(1)

Flow of the procedure

5.2

The proposed system takes images of an invisible
region every moment and performs backgroundsubtraction. If some newly appeared objects are found in acertain frame, after some noise reduction procedures, it
separates each object by labeling, calculates their feet’s
position, and computes their locations in the world
coordinate system employing Eq.(1). Once thecoordinates (Xg,Yg,0) is obtained with the detected object,a perpendicular line is drawn from the point (Xg,Yg,0) to
the optical axis, whose foot is the point P(Xc,Yc,Zc)
discussed in the former section. Then the plane passing
through (Xg,Yg,0) and perpendicular to the optical axis isspecified. This plane is referred to as a layer on which theobject is projected.
In Fig.2, two objects located at (Xg1,Yg1,0) and(Xg2,Yg2,0) define the layers 1 and 2 that are perpendicular
to the optical axis of the camera. The objects are projectedon the respective layers in the virtual space.

©ISAROB 2006

5

Produced transparent space

In the second experiment, transparent space is realized
and demonstrated. In Fig.4a, the experimental
environment includes a walking person as Human, an
observer as User, and a partition as Wall that separates the
Human and the User. The User cannot see the Human
directly as the Wall obstructs the User’s sight, thesituation of which is given in Fig.4b. The transparent
space is realized in Fig.4c, where the Human’s model inthe 3-D virtual space is superposed onto the real image ofthe Wall. The human model walks around the same place
where the real person walks. Therefore, if the wall isremoved as shown in Fig,4d, the Human model and thereal person are situated at the same place. All of theperformance is realized almost real-time.

273

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

6

Discussion and conclusions

In this paper, we have proposed a novel idea ofmaking an invisible place visible by the employment of amixed reality technique. We have shown a system fortaking images of a dynamical scene of a space hidden by apartition, creating a 3-D virtual space describing the
dynamical scene, and displaying the virtual space alongwith the real space to an observer. In the performedexperiment, an observer could watch a person or two
persons walking around in a space hidden by an opaquepartition through the partition.
The performance was satisfactory. In fact, the systemachieved almost real-time processing for displaying thescene beyond the opaque partition. The displacement
between the displayed virtual person and the real person
was small enough to judge his location exactly. It is notedthat the created virtual space is a 3-D space. Although wehave employed a single camera for observing the invisible
space, we assume that the persons walk on the floor andobjects are placed on the floor. In this way, we obtain
depth information of the objects. If a person jumps up in
the air, the system looses his depth information. But when
he lands on the floor again, the system well recognizes his
exact location.
As was mentioned above, the created virtual space is a
3-D space. This allows an observer change his/her
observing position. His/her view is computed every timehe/she changes his/her observing location and orientation.
The sensor mounted on the HMD detects the information.
However the appearance of the virtual human becomesnot very natural, if the observer’s position change
becomes larger. We need more number of cameras torealize a natural virtual human model.
The proposed technique may have various application
fields. Especially it can be employed for a variety ofsafety purposes including children or aged people
watching, pedestrians, motorcycles, or cars sensing at
blind corners, and it would be very nice if the system
could give alarm to those people nearby.

(a)

(b)

References[1] Y. Ohta, H. Tamura (1999), Mixed Reality, Ohmsha[2] J. Hoshino (2001), See-Through Representation ofVideo, Proceedings of ISMR 2001, pp.155-156(c)[3] M. Inami, N. Kawakami, D. Sekiguchi, Y. Yanagida,
T. Maeda and S. Tachi (2000), Visuo-Haptic Display
Using Head-Mounted Projector, Proceedings of IEEE
Virtual Reality 2000, pp.233-240

Fig. 3 Experiment 1: Producing a 3-D virtual space.
(a) Environment; (b) Input images; (c) A 3-D virtual
space containing two appropriately arranged persons.

©ISAROB 2006

274

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

User

Human

Wall
(a)

(b)

(c)

(d)
Fig. 4 Experiment 2: Producing a transparent space.
(a) Environment; (b) User’s observing scene;(c) Transparent space; (d) Removal of the wall.

©ISAROB 2006

275

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

FACE EMOTION RECOGNITION – A SURVEY
M. Karthigayan

Mohd Rizon
Sazali Yaacob
School of Mechatronics
Northern Malaysia University College of Engineering
01000 Kangar, Perlis, Malaysia.
Email:karthigayan@ieee.org

ABSTRACTThe facial emotion is playing a vital role in visualcommunication system. The vision system is being
integrated to the robot to recognize the facial gestures or
emotion. The integration of the vision system and EMG
sensors enables designing advanced emotion recognition
package. The main objective of the paper is to discuss
the various types of feature extraction and recognition
system for face emotion and its application.

classify the six basic emotions. The statistical techniqueand hidden markov model were applied in this process ofrecognition [6, 7]. The correlation dimensions for theelectroencephalogram (EEG) in positive and negative
emotional states are estimated. The results show that thecorrelation exponent for the emotional state is higher thanthat for the rest state even if the variation between
subjects is considered. Discrimination is created between
yes and no for communication via EEG, particularly forseverely disabled persons [8]. Welfare support system isalso developed by robot vision. Robot vision isconstructed using a model of a knowledge, emotion andintention, from which welfare oriented active vision isrealized [9, 10, 11]. Human interface of an eating assist
robot has been developed for helping the disabled patients.
A spoon and CCD camera are fixed on the tip of therobotic arm. Here, A Galvanic Skin Reflex (GSR) sensor
is also implemented to evaluate the emotion. Such a
spoon-feeding robot assisting a disabled patient withcervical vertebrae damage has been successfully
demonstrated [12]. Physiological signals are used foremotion recognition. A support vector machine wasintroduced as the physiological signal pattern classifier.
The support vector machine overcomes the difficulty oflarge overlap among clusters and large variance withincluster. The advantage of this system is that it requires
shorter signal monitoring time than pervious ones, andthus better suited for practical use [13].

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 [2]. Human being possesses anability of communication through emotion in day to day
interactions with others. Some emotions attracted most of
the interest in human computer interaction environments.
The categories of emotion, as applied in human computerinteraction are: Sadness, Anger, Joy, Fear, Disgust or
Dislike and Surprise. In this paper, a survey on faceemotion recognition from the last decade is presented.
Image acquisition, feature extraction and classification
are also discussed as connected areas of face recognition.

Welfare robot has been developed to support the lives ofsenior citizen. The general measurement of emotion forbasic research is still being carried out. Rule of emotion
measurement has been discussed and new rules foremotion measurement have been derived and applied.
The results have been found to be satisfactory [14]. The
face emotion can be also classified based EMG, visionsystem and speech. The recognition of emotions has been
applied in various applications. The applications ofemotions can be robot, web, online gaming, biometricsecurity system etc [15, 16]. A robot, named as IFBOL,
understands a partner’s feeling [17]. IFBOL, a Sensibility
Technology Communication Robot, can detect emotion,
recognize conversation, go by affection of conversation
partner and speak with sentiment. Mental health is very
important to lead a safe and secured life. This IFBOL
robot is able to communicate with people upon their
feeling and stress. This robot expresses one’s feelings
with eyelid and eye, besides learning the habit andpersonality of the conversing partner, so as to respond to
the partner. Robot-Assisted Activity (RAA) to provide
physical and mental support and the emotion driven
model for RAA have been proposed [18]. Japan

FACE EMOTION
Face emotion presents an abstract means of description of
facial expressions by utilizing concepts included in the
MPEG-4 standard. It has been used in online-gaming [3].
Facial animation is a great means of improving HCI
applications and especially in computer games, since it
provides a powerful and universal means of expression
and interaction. A simple method of synthesizing realistic
facial expressions using lightweight representations has
been realized. The recognition of emotions has been
applied in various applications. Human face may acts asvisual interface to interact with a computer forcommunication and recognizing emotions. An humanarm motion model is recreated by a CRT display and by
an industry robot [4]. Another effort is to build a human
computer facial gesture and expression for use in robot
application. It has the ability to track facial features inreal time (30Hz). Kalman filter has been used in thisrecognition of facial gestures [5].
Both audio and video information are combined to getbetter recognition of emotions. These methods can©ISAROB 2006

R.Nagarajan

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address user emotions derived from those signals are also
described [30]. A novel method of a spatio-temporalapproach has been proposed to classify 6 universal
expressions from visual data and use them to derive thelevel of interest using psychological evidences. The
average recognition rate of the proposed facial expression
classifier is 90.9% without classifier fusion and 91.2%
with fusion using a five fold cross validation scheme on a
database of 488 video sequence that include 97 subjects[31]. Temporal template method has been proposed todetect automatic face action recognition. In this classifier,the first stage consists of a general K-nearest neighbor(KNN) classification scheme and the second stage uses
domain specific knowledge in a rule-based system. It hasreached a recognition rate of 70.6% for lower faceautomatic recognition and recognition rate of 81.8% for
upper face automatic recognition [32]. Visual data also
play a vital role in recognition of emotion of audio-visualmodel. A triple hidden markov model is introduced toperform recognition. This is the first time emotion
recognition is carried out from both visual and audio fromvideo clips [33, 34]. Two studies are carried out for thisemotion measures; they are first and second study with 16
and 15 participants respectively. Support vector machines
are applied to recognize emotion from multi-modal bio–
potential signals. It shows 41.7% and 66.7% recognition
rate for 5 and 3 emotions respectively from theexperiments results. The 5 emotions are joy, anger,sadness, happiness and relax. The 3 emotions are joy,anger and relax [35]. In the study of Affective Computing
for Behavior- Based UI Adaption, the video-cameras and
EMG sensors are employed for monitoring emotions [36].
The ongoing emotional state of the computer users ismeasured. To provide feedback in the software design
process on the emotional state, the facial EMG ismeasured for both verbal and performance (Joey andRichard Hazlett, 2005).

community feels that robots can give more support forsenior citizens. More researchers are concentrating on
welfare robot for aged people in many countries. The
robot has been proposed for the aged people to feel theiremotion and to support them. The construction of robot
model uses hierarchical knowledge. The robot canperform complex actions based on the robot’s internaland external environmental information. Many robots are
involved to support physically and mentally for the seniorcitizens. These robots work on the basis of face emotion
control. Many previous works were carried out on six
universal facial expressions or emotions. They are happy,
sad, fear, angry, surprise and disgust. Instead of universal
facial expressions, many people show ‘personalized’ or‘individualized’ facial expressions typically. A method
has been proposed to construct a personalized classifier
based on novel selection method [19]. Robust facial
expression recognition system using the result of facedetection by a convolution neural network and rule based
processing has been described [20]. The algorithm which
has been proposed to demonstrate the ability todiscriminate smiling from talking is based on the saliency
score. The sensitive system that incorporates “affects
recognition” into computer based learning has been
discussed [21]. Some problems of artificial emotions inmental therapy had been discussed from the perspectivesof sociology of emotions, clinical sociology and thesociology of health and illness [22]. The physiological
data of skin conductance and EMG interpret the emotion
and the user’s affective states in the form of empathic
feedback. Facial EMG has been proposed to measureuser’s emotional state continuously [23]. Double
structured neural network has been applied in themethods of face detection and emotional extraction. In
this two methods are proposed and carried out; they are
lip detection neural network and skin distinction neuralnetwork [24]. Jaw muscle mediation plays a vital role in
the recognition of emotion. In this, the results of awavelet analysis performed on electro-myography signals
are recorded from the cat masseter (jaw) muscle duringhypothalamic stimulation [25]. The effects of facial color
on virtual facial image synthesis for dynamic facial colorand expression of smile by using an average facial imageof 68 females are analyzed [26]. An autonomous robot isequipped with emotion recognition to evaluate thechanges of both the environment and its internal state.
Fuzzy emotion model has been created for theautonomous robot [27]. Several autonomous robots arecontrolled based on emotions. Facial expression wasmeasured using convergent measures, including facial
EMG, automatic feature-point tracking and manual Facial
Action Coding System (FACS). FACS unit conveys theinformation that can inform interpretation of
psychological states [28, 29]. This finding indicates thatdysphoric persons have impaired interpersonal reactivity
that is specific to happy facial displays. Empathic
Companion, an animated interface agent that
accompanies the user in the setting of a virtual job
interview is explained. A character based interface thatuses physiological signals of the user in real-time and©ISAROB 2006

CONCLUSION
In this paper, a brief survey of face emotion recognition
has been presented. The features of eye, lips, cheeks, eyebrows, mouth and skin contraction are extracted toclassify the face emotion. The feature extraction methods
such as wavelet transform, eigenface, projection profileand gabor transformation have been considered byresearchers. If IFBOL and RAA are implemented as faceemotion recognition system to understand human feelings.
Several application of robotics on the basis of faceemotion has been recorded. A real-time face emotion
change from one category of emotion to another has notbeen mentioned in any research. Emotion recognition canalso be achieved be measuring brain wave, vision, heartrate, EMG, speech and pulse. However, vision and EMG
were utilized in several papers. Several changes in blood
sugar content, blood pressure, temperature and Ph value
can occur with respect to emotion. These are yet to beincorporated in emotion recognition system.

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12) Yoshihiko Takahashi, Naoya Hasegawa, Katsumi
Takahashi and Takuro Hatakeyama, “Human
Interface Using PC Display with Head Pointing
Device for Eating Assist Robot and Emotional
Evaluation by GSR Sensor”, Proc. of the 2001 IEEE
International Conference on Robotics andAutomation, pp. 3674-3679, 21-26 May 2001.
13) Kyung Hwan Kim, Seok Won Bang and Sang Ryong
Kim, “Development of Person-Independent Emotion
Recognition System Based on Multiple PhysiologicalSignals”, Proc. of the Second Joint EMBS/BMESInternational Conference of Biomedical Engineering
Society, pp 50-51, 23-26 October 2002.
14) Fuji Ren, Matsumoto k, Mitsuyoshi S, Kuroiwa S
and Gai Lin, “Researches on the Emotion
Measurement
System”
IEEE
International
Conference on Systems, Man and Cybernetics, vol. 2,
pp.1666 – 1672, 5-8 Oct 2003.
15) Thomas Mc Kenna “Video Surveillance and Human
Activity Recognition for Anti Terrorism and Force
Protection”, Proceedings of the IEEE conference on
Advance Video and Signal based Surveillance, pp. 2,
21-22 July 2003.
16) Ka Leung Lee and Yangsheng Yu, “Real-Time
Estimation of facial Expression Intensity”,
Proceedings of the IEEE International conference on
Robotics and Automation, Taiwan. pp. 2567-2572,
14-19 Sept 2003.
17) Kenji Tamural, Shohei kat, Takanori Yamakita,
Kenji Kimurad and Hidenori Itoh. A System of
Dialogical Mental Health Care with Sensibility
Technology Communication Robot. International
Symposium on Micromechatronics and Human
Science, pp. 67-71, 2003.
18) Tomomi Hashimoto, Toshimitsu Hamada andToshiko Alolzawa. “Proposal of Emotion-driven
Control Model for Robot-Assisted Activity”,
Proceedings of IEEE International Symposium on
Computational Intelligence in Robotics andAutomation, Kobe, pp. 125-129, July 16-20 2003.
19) Dae-Jin Kim, Zeungnam Bien and Kwang-Hyun
Park, “Fuzzy Neural Network (FNN) – BasedApproach for Personalized facial Expression
Recognition with Novel Feature selection Method.”,
The 12th IEEE International Conference on Fuzzy
Systems, vol.2, pp. 908 – 913, 25-28 May 2003.
20) Matsugu M, Mori K, Mitari Y, Keneda Y, “Facial
Expression Recognition Combined with Robust Face
Detection in a Convolution”, Proceedings of theInternational Joint Conference on Neural Network,
Vol3, pp. 2243 – 2246, 20-24 July 2003.
21) Newman K.B., “Sensitive systems – incorporating
affect recognition into computer based learning”,
Proc. of the Sixth International Conference on
Computer Based Learning in Science (CBLIS), pp.
37-46, 5-10 July 2003.
22) Tatsuya Nomura, “Problems of Artificial Emotionsin Mental Therapy”, Proc. of the IEEE International
Symposium on Computational Intelligence in

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Robotics and Automation, pp. 567-570, 16-20 July
2003.
23) Richard L. Hazlett, “Measurement of User
Frustration: A biological Approach”, CHI 2003: New
Horizons, pp. 734-735, 5-10 April 2003.
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Fukumi and Norio Akamatsu, “Face Detection andEmotional Extraction System Using Double
Structure Neural Network”, Proc. of theInternational Joint Conference on Neural Networks,
vol. 2, pp. 1253-1257, 20-24 July 2003.
25) Petrock A and Reisman S, “Wavelet Analysis ofMasseter Muscle EMG during Emotional
Provocation”, Proc. of the Twenty Ninth IEEE
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22-23 March 2003.
26) Takashi Yamada and Tumio Watanabe, “Effects offacial Color on Virtual Facial Image Synthesis for
Dynamic Facial Color and Expression under
Laughing Emotion”, Proc. of the 2004 IEEE
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27) Changsheng Yu and Li Xu, “ An Emotion-BasedApproach to Decision Making and Self Learning inAutonomous Robot Control”, Proc. of fifth World
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2386-2390, 15-19 June 2004.
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http://research.nii.ac.jp/~prendinger/papers/helmutADS-04.pdf.
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37) Joey Benedek and Richard L. Hazlett, “Incorporating
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M.Karthigayan

MOMENTS IN IC CHIP CLASSIFICATION
R.Nagarajan
Sazali Yaacob Paulraj Pandian Mohd Rizon Mohd Rozailan+

School of Mechatronics, Northern Malaysia University College of Engineering, 02600 Jejawi, Perlis, Malaysia
Terengganu Advanced Technical Institute (TATI), Jalan Panchor, Teluk Kalong, 24000 Kemaman, Terengganu, Malaysia
Email: karthigayan@ieee.org
Abstracton the Thin Shrink Small Outline Packages (TSSOPIn this paper, an Industrial machine vision system
DGG) using neural network. Figure 1 illustrates various
incorporating Optical Character Recognition (OCR) is
marking errors that can occur during production where
employed to inspect the marking on the Integrated
as Figure 2 shows the error free marking.
Circuit (IC) Chips. This inspection is carried out while
the ICs are coming out from the manufacturing line. A
TSSOP-DGG type of IC package from Texas
Instrument is used in the investigation. The IC chips arelaser printed. This inspection system ensures whether
the laser printed marking on IC chips are proper. One of
the artificial intelligence components the neural network,
is used for this inspection. The inspections are carriedout to find the print errors such as illegible character,(a)Illegible
missing characters and up side down printing. The
vision inspection of the printed markings on the IC chip
are carried out in three phases namely image
preprocessing, feature extraction and classification.
MATLAB platform and its toolboxes are used fordesigning the inspection processing technique. Neuralnetwork is used as a classifier to detect the defectively
marked IC chips coming from the manufacturing line.
Moments are used as feature for this inspection.
(b) Upside down print
Keywords
machine vision, moments, backpropagation.
+

1. Introduction
Since 1950, Optical Character Recognition (OCR)
has been very active in the application of automatic
pattern recognition; now it is used to read the printed
characters at high speed [1, 2]. OCR is widely used inrecognition of hand written characters and printed
characters using Artificial intelligence techniques [1-4].
The OCR is applied for business card recognition [5]where the manual input is optional; however, byscanning, the OCR is able to create an easier database.
The document reading and analysis have reached an
important position in certain markets. The application of
OCR in the postal automation has followed into thebanks and industrial inspection processes [6, 7].

(c) Missing character
Fig. 1: IC chip marking errors

IC chips play a vital role in electronic industries.
Mass production of IC chips have brought down theprice of the electronic products. Texas Instrument is oneof the well-established IC chips manufacturing
companies in the world market. In the Texas group,
Texas Instrument, Malaysia TI(M), is one of the leading
producers of IC chips in. The IC chips undergo many
inspections and verifications to ensure a guaranteed
quality. Quality control of IC is performed by inspecting
the placement of die, inspecting lead dimension,
inspecting packaging and inspecting marking ofsymbols (IC number, year of manufacture and batch
code etc). In this research work OCR is employed tocheck the markings of the IC chips especially, themarking©ISAROB 2006

Fig. 2: An error free IC chip
2. Industrial setup
A digital video camera is used to capture the IC
marking images of chips coming out of manufacturing
line one by one. These images are zoomed about 20 to
30 times in size. Figure 3 shows the dimensions of IC
chip. The zooming index depends on the size of the IC
chip. Inspection module of the earlier commerciallyavailable industrial version checks about 7300 to 7500
IC chips per hour. IC chips undergo on fly inspection asshown in Figure 4 where OCR checks any defects in the

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markings on the ICs. If there is any defect in the IC chipmarking, the marking inspection will check and informthe production inspector the classification of defect; theproduction inspector then inspects the IC chip manuallyfor taking further decision. A monitor displays thecurrent image of the IC chip.

Image
capturingfrommoving IC

If good unit,
accept; else
reject/classify

Preprocessing

Selectingregion ofinterest,
cropping
generalized

Classi-fier

Featureextraction

Fig. 5: Image processing sequence
3.2 Moments Feature Extraction
Feature extraction is carried out to the processed andcropped image. Feature extraction processing time formoments is 0.2610 -0.2710 sec. This has been found by
experimental results. Moments have been used inrecognizing the printed and hand written characters andare also widely used in pattern recognition. There aredifferent types of moments used for recognition ofcharacters; here Central moments of binary image foreach column of the image orders are obtained. The
image orders can be 2 or 3. In the order 1, moments
values are zero. On the other hand, orders more than 3
produces smaller and smaller moments values thatcannot be generally used for feature extraction.

Fig. 3: Dimension of IC chips

Fig. 4: IC chips in the conveyor

Let f(x,y) be an image. Then, the 2D continuousfunction of
the moment of order (p+q), M pq , is defined as [6].

3. Marking Inspection Process
After final packaging, the IC chips are lined up in aconveyor for marking inspection. The marking on the
IC chips are captured as a movie clip by a Charged
Couple Device (CCD) non-standard camera. Images of
the IC chips are extracted from the Moving Picture
Expert Group (MPEG) format. The extracted images
undergo certain image processing techniques namely
preprocessing [1, 6, 7], feature extraction [7,8] andclassification using AI technique [9-12] These
processing stages are shown in Figure 5.

∞

M pq = ∫∫ x y f ( x, y)dxdy
p

q

(1)

−∞ −∞

The central moment , µ pq , of f(x,y) is defined as is [6].
∞ ∞µ pq = ∫ ∫ ( x − x) p ( y − y ) q f ( x, y )dxdy(2)− ∞− ∞

3.1 Preprocessing
The inspection begins with the extraction of single
image from the moving picture. The extracted color
image is converted into a 256 gray scale image. Then,the gray scale image is binarized with a threshold value.
The binarization converts the image pixels into ‘0’(black) and ‘1’ (white). The threshold value
differentiates the foreground and background of thegiven image. Region of Interest (ROI) as specified by
the production inspector is extracted for feature
extraction. If the ROI is not specified, a search for the
ROI from the extracted full image has to be made but it
is time consuming.

©ISAROB 2006

∞

M 10
M 01
and y =
.
M 00
M 00

where x =If f(x,y) is a digital image then equation (2) becomesµ pq = ∑ ∑ ( x − x) p ( y − y ) q f ( x, y )
x

(3)

y

where p and q are nonnegative integer values.
The moment values are considered for extracting thefeature. This method takes the central moments value ofeach column of the binary image. Di, i =1,..,k is thecentral moment value of each column from 0 to k of themarking. Di can be computed using Equation (3). In thiswork k is taken as 25.

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4. Marking Classification Using Neural Network
Recently there has been a high level of interest in
applying artificial neural network architecture forsolving many problems [9-12]. The application ofneural network gives easier solution to complex
problems such as character recognition. Here, neuralnetwork is employed to classify the character by the
extracted features (moments). A feed forward neuralnetwork is proposed to identify the various types ofillegible marking of symbols is shown in Figure 6. The
moments value for two different orders such order 2 andorder 3 are considered for training. The extracted
features are taken as input to the network model.
The Training is carried out for 10 times for moments.
Table 1 and Table 2 indicate duration of convergence inseconds. The networks with each of the above
mentioned data are trained using a backpropagation
training algorithm. The learning parameters for
moments are chosen as given in Table 1 and Table 2:
The cumulative errors versus epoch characteristics ofthe training for moments (order2 and order 3) are shownin Figure and Figure 8. The time and epoch details for
moments is given in Table1 and Table 2.

Fig. 7: Cumulative error versus epoch
plot for moments second order

Accept/Reject

D1
D2

:
:
D24
D25

NeuralNetwork
Classifier

Illegible
Marking

Fig. 8: Cumulative error versus epoch
plot for moments third order

Upside down
printing

References

Missingcharacter1) Mantas J (1986), An Overview of Character
Recognition Methodologies, Pattern Recognition,
V0l19(6), pp.425-430.
2) Shunji Mori, Ching Y Suen and Kazuhiko
Yamamoto (1992), Historical Review of OCR
Research and Development, Proceedings of the IEEE,
vol80 (7).
3) Adnan Amin, Humoud Al-Sadoun and Stephen
Fischer (1996), Hand-Printed Arabic Character
Recognition System Using An Neural Network,
Pattern Recognition, vol.29(4), pp.663-675.
4) Nagarajan, R., Sazali Yaacob, Paulraj Pandian,
Karthigayan, M. Shamsudin Hj Amin and Marzuki
Khalid (2003), A Neural Network Application for
Inspecting the Marking of Symbols on Integrated
Circuit Chips. The 7th Triennial AEESEAP
Conference Enhancing Engineering Education andTraining, Malaysia, 8-9 August 2003.
5) Wumo Pan, Jianming Jin, Guangshun Shi and Wang,
Q.R (2001), A System for Automatic ChineseBusiness Card Recognition. Proceedings of the Sixth
International on Document Analysis and recognition,
IEEE Computer society, pp. 577 – 581.

Fig. 6: Neural network model for moments

Conclusion
AI techniques such as neural network are applied tomarking inspection of IC chips. The preprocessing andfeature extraction method have been suggested for thisinspection. A feed forward neural network has beendeveloped for training and testing the samples ofmarking. Trainings are carried out for moments order 2
and moments order 3 using backpropagation algorithm.
Comparing moments order 2 and order 3, order 3 isfaster for training.
Acknowledgement
The authors wish to acknowledge the funding of
UMS IRPA grant No: 04-02-10-0024-PR0001 throughUTM in carrying out this research.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Table 1. Data Trained for Second Order of Moments for Neural Network
qh = q0 = 1.0
Input neuron: 25
Output neurons: 6
Activation function: (1/ (1+e-x))
Momentum factor: 0.87
No. of samples: 664
No. of trained samples: 400
Maximum no. of epoch: 19000
Testing tolerance: 0.05
No.
Epoch
Time in sec
1
11251
423
2
16567
624
3
8832
334
4
9390
352
5
10010
382
6
9320
373
7
8597
345
8
8473
340
9
8414
316
10
11000
420

Hidden neurons: 20
Learning rate: 0.20
Training tolerance: 0.01
% Misclassification
0
0
0
0
0
0
0
0
0
0

Table 2 Data Trained for Third order of Moments for Neural Network
qh = q0 = 1.0
Input neuron: 25
Output neurons: 6
Activation function: (1/ (1+e-x))
Learning rate: 0.2
No. of samples: 664
No. of trained samples: 400
Maximum no. of epoch: 14000
Testing tolerance: 0.05
No.
Epoch
Time in sec
1
7144
286
2
7874
315
3
7560
294
4
9649
376
5
9683
389
6
9086
364
7
10038
403
8
6787
272
9
9114
355
10
6325
246

6) Rafael C Gonzalez and Richard E Woods (1993),.
Digital Image processing. Addison-Wesley Longman,Inc.
7) Mauritius Seeger and Christopher Dance (2001),
Binarising Camera Images for OCR, Proceedings of
Sixth International Conference on Document
Analysis and Recognition, pp.54 – 58.
8) Oivind Due Trier , Anil K.Jain and Torfinn Taxt
(1996), Feature Extraction Methods for Character
Recognition- a Survey”, Pattern Recognition,
Vol.29(4), pp.641-662.

©ISAROB 2006

Hidden neurons: 20
Momentum factor: 0.87
Training tolerance: 0.01
% Misclassification
0
0
0
0
0
0
0
0
0
0

9) Sivanandam, S.N, and Paulraj, M (2003),
Introduction to Artificial Neural Network. Vikas
Publishing House.
10) Laurene Fausett (1994), Fundamentals of Neural
Networks. Prentice Hall Publishing..
11) Clifford Lau (1992), Neural Networks-Theoretical
Foundation and analysis, IEEE press, New York,
1992.
12) Bernard Widrow and Michael, A Lehr (1990), 30
years of Adaptive Neural Network Preceptron,
Madeline and Backpropagation, Proceedings of IEEE,
pp.1415-1442.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

DIMENSIONS REDUCTION FOR FACE RECOGNITION USING PRINCIPALCOMPONENT ANALYSIS
Muhammad Firdaus Hashim1, Mohamed Rizon2, Puteh Saad1, M. Rozailan Mamat3,
M.Karthigayan2, Shahrul Yaakob1 and Haniza Yazid1
1

Artificial Intelligence and Software Engineering Research Lab
School of Computer and Communication Engineering, Kolej Kejuruteraan Utara Malaysia (KUKUM),
Blok A, Kompleks Pusat Pengajian KUKUM, Jalan Kangar- Arau, 02600 Jejawi Perlis.
2
School of Mechatronic Engineering, Kolej Kejuruteraan Utara Malaysia (KUKUM),
Blok A, Kompleks Pusat Pengajian KUKUM, Jalan Kangar- Arau, 02600 Jejawi Perlis.
3
Terengganu Advanced Technical Institute (TATI)
Jalan Panchor, Teluk Kalong, 24000 Kemaman, Terengganu, Malaysia
E-mail:rizon@kukum.edu.my
T.Phiasai [1] integrates PCA and moment invariant forvaried-pose face recognition. They implement PCA for
global feature extraction to determine the error. M.A.
Turk and Alex P.Pentland [2] developed the eigenfaces
further to recognize human faces and obtained someparticular advantages over previous work. The
advantages of this approach are that, it is easy to
implement using neural network architecture.

Abstract
In this paper, a technique has been developed to identify
the face of an unknown person’s by applying eigenfaces
and neural network. The original image has been
extracted and the face segmentation is implemented todetect irises location of both eyes. The face segment has
been considered in this work because the processing time
is faster instead of going for the whole image. Eigenfaces
has been applied to extract relevant information andreduced some dimension from the face segment(grayscale). The eigenfaces values are considered as an
input to the neural network. The backpropagation neuralnetwork is applied for face recognition. The database forthis investigation consists of eight people with various 68
face images had been used for the learning. PCA
including implemented Jacobi’s method for eigenvalues
and eigenvectors has been performed.

The extraction of face segmentation from anintensity image of human frontal face is to represent new
data set instead using the whole image. The irises
detection proposed by Kawaguchi et. al. [3] is applied to
face segmentation.
PCA is applied to find important aspects of facesegment. Furthermore, the representation the facesegment with less data as input for recognition phase.
The dimensions of eigenfaces have been reduced tosynchronize the neural network inputs.

Keywords: Face Recognition, Feature Extraction,
Neural Network, Principal Component Analysis.
1

The recognition process begins with neural network.
It has been used to determine the unknown face throughlearning classification of data calculated by PCA. Figure
1 shows the block diagram of face recognition system.

Introduction

Machine recognition of human faces is a challenging
problem due to the changes in the face identity andvariation between images of the same face due toillumination and viewing direction. The issues are how
the features are adopted to represent a face underenvironmental changes and how the classification has
been done to a new face image based on the chosen
representation.

Preprocessing

FeatureExtraction

Input
Data
Normalization

PCA

Neural Network

Automatic face recognition systems have been
applied in many applications such as security system,
mug shot matching and model-based video coding.

©ISAROB 2006

Face segmentation

OutputFigure 1. Face Recognition System

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Preprocessing

In the preprocessing step the algorithm of [3, 4] isused to detect the irises of both eyes from a human facein an intensity images. Here, it has reduce space used
intensity variation to select potential area of irises
location. Next, each pair of prospect area computed acost using separability filter and Hough Transform tomeasure the fit of the pair of circles to the image. The
proposed algorithm select a pair of circles with smallest
cost as the eye location of both eyes. The implementationof the method take the center of the both irises (radius, r )and expands the coordinate to extract the face segment asshown in Figure 2.

Figure 3. Covariance matrix
Figure 3 shows the covariance matrix where
the cov(a, b ) = cov(b, a ) , the matrix is symmetricalabout the main diagonal and square, m × m . The
covariance matrix is then used to calculate theeigenvalues and eigenvectors [6, 7, 8 and 9].

n
m

3.1

Figure 2. Face Segmentation

3

Eigenvectors Selection

The eigenvectors determined useful features for theimage ordered by highest eigenvalues. The asset of theeigenvectors is the eigenvalues where each of themassociated with it. The extracted eigenvectors represent
the combinations of the set of facial image, which
capture most of the variability in the image and can beviewed as creating a lower dimensional subspace ofregular image space [10].

Principal Component Analysis (PCA)

PCA has been reviewed from the computational
aspects in [5, 7 and 8] including the identification of
eigenvalues and eigenvectors using Jacobi’s method.
Given the image with m × n . m be the dimension
and n the variables for each dimension. The original sizeof m × n changed to m × m by firstly subtracted eachvariables, n with the mean from each of the data
dimensions, m where the new data set mean is zero [5].

From figure 3, the eigenvalues decreases quickly as
their number increases. The eigenvectors with higher
eigenvalues provide more information. The all-off
diagonals are zero except main diagonal that stored
eigenvalues. Furthermore, the eigenvectors with lower
eigenvalues or specific eigenvectors can be reduced
because it only creates noise in the image [10, 11].

The next step to create the square matrix is applied
the covariance matrix. It measured between two
dimensions byn∑ ( x − x)( y − y)icov( x, y ) =

i

i =1

n −1

(1)

Thus, if the dimensions, m = x1 , x 2 , K , x m , thecovariance matrix with m × m :⎛ cov( x1 , x1 ) cov( x1 , x 2 ) K cov( x1 , x m ) ⎞⎟⎜⎜ cov( x 2 , x1 ) cov( x 2 , x 2 ) K cov( x 2 , x m ) ⎟C = ⎜⎜ cov( x 3 , x1 ) cov( x 3 , x 2 ) K cov( x 3 , x m ) ⎟⎟
⎟
⎜
M
M
M
M
⎟
⎜
x
x
x
x
x
x
K
cov(
,
)
cov(
,
)
cov(
,
)
m 1
m
m
m ⎠
2
⎝

©ISAROB 2006

(2)

Figure 4: Eigenvalues matrix

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

have been tested with 68 different impressions fromeight persons. Every image is an intensity image withplain background, the eyes of both eyes appear in theimage and the head region about y axis is approximately
within the interval (30,−30 ) . The eigenvalues andeigenvectors play a major role in PCA. The code for
eigenvalues and eigenvectors has been developed using
VC++. MATLAB has been used for verification purpose.
The PCA also tested with two dimensions data in [5].

The last eigenvectors with lower eigenvalues by theamount of variance found between images can be
reduced. Here, three variations has been proposed tochoose eigenvectors [10]. Firstly, the last 40% of theeigenvectors have been removed [11]. Then, the second
variation (equation 3) uses the minimum number of
eigenvectors to guarantee that energy e is greater thanthreshold (typically 0.9). It is ratio of the sum of all
eigenvalues up to and including i over the sum of all
eigenvalues:

The experiment defined that the energy, e selected
about 93% of eigenvalues and highest eigenvalues havebeen removed. However the stretch, s only choose about
13% started from highest eigenvalues and the lowest
have been removed. It is possible that the first
eigenvectors encode information that is not relevant to
identify image, such as lighting [11].

i

∑λ
ei =

j

j =1

(3)

k

∑λ

j

j =1

The last variation (equation 4) depends upon thestretching dimension. The stretch, s i for the

The image size is about 360 × 480 and the image
size has been reduced. Irises detection has been used forface segmentation. Each face segment has 120 pixels ofcolumn and the rows between 60 to 70 pixels of rows.
However, only 20 eigenvectors are selected to reduce
input series and synchronized the whole pattern. Thus,
each pattern has 20 × 120 of pixels.

i th eigenvectors is the ratio of that eigenvalues over thelargest eigenvalue (λ1 ) :
si =

λi
λ1

(4)

Table 1: Recognition Result

All eigenvectors with s i greater than a threshold (0.01)
are retained.

4.

Face
1
2
3
4
5
6
7
8

Face Recognition

The backpropagation neural network is used toachieve our objectives to identify unknown person’s face[12]. The strength of the classifier depends on the datathat it receives during training. A large number oftraining patterns would make the network stronger.
However, need be careful not to include all patterns in
the training set [13].

6.

0.000011 0.000011 0.000011
0.000194 0.000194 0.998024
0.000194 0.998024 0.000194
0.000000 0.999970 0.999970
0.998024 0.000194 0.000194
0.999850 0.000000 0.999850
0.999700 0.999700 0.000000
0.999800 0.999800 0.999800

Conclusion

In this paper, the PCA feature extraction method andneural network for recognition has been proposed. The
Jacobi’s method has been performed to calculate theeigenvalues and eigenvectors. This method is easy andreliable however the computation speeds is slow that
restricts its usefulness to special situations when theExperimental Result

PCA and neural network have been used for feature
extraction and recognition respectively. These features©ISAROB 2006

Network Output

In the recognition step, if mean squared error of
network output less than error (0.0001) than the nodes
trained are complete. 10 neurons have been used in
hidden layer in the network. Table 1 shows therecognition result through the network.

PCA value has been given as an input to the neuralnetwork. There are two groups (database) of images thatare tested by neural network. They are trained data anduntrained data. Here, three neurons has been used at
output layer for eight persons, thus it has been set thetarget for this approach made the number of weight
between hidden and output ( wkj ) less.

5.

ActualOutput
0 0 0
0 0 1
0 1 0
0 1 1
1 0 0
1 0 1
1 1 0
1 1 1

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

speed is the priority. Feature extraction and feature
selection are significant in image analysis applications.
PCA has proven itself to have the capability to select themost important features amongst the set of features
extracted.

[6]

J.H.Matheew, Eigen Values and Eigen Vectors, pp.
612-621, 2003.

[7]

M. Firdaus et al, “An approach of feature
extraction for face recognition”, sub’d toconference on intelligent systems and robotics(iCISAR2005), December 2005.

[8]

M. Firdaus et al, “Face recognition using neuralnetwork”, sub’d to international conference on
intelligent systems (ICIS), December 2005.

[9]

M.T Health,Introductory
191-200.

[10]

W. S. Yambor, B. A. Draper, and J. R. Beveridge,“Analyzing
PCA-based
face
recognition
algorithms: eigenvectors selection and distancemeasure”, In Empirical Evaluation Techniques in
Computer Vision, 2000.

[11]

H. Moon and J. Phillips, “Computational andperformance aspects of PCA-based facerecognition algorithm”, Perceptions, vol 30, pp.
303-321, 2001.

[12]

Marzuki Khalid, Multilayer Neural Network andCiting internet
Backpropagation Algorithm.
sources URL www.cairo.utm.my[13]

S.C. Debipersad, A.D. Broadhurst, Face
Recognition Using Neural Network, Proceedingof IEEE on Communication and Signal Processing(COMSIG), 1997, pp. 33-36.

Acknowledgments
The authors would like to acknowledge KUKUM forsupporting this research work.

References
[1]

[2]

[3]

[4]

[5]

T.Phiasai, S.Arunrungrusmi and K.Chamnongthai,Face Recognition System with PCA and Moment
Invariant Method, In Proc. IEEE Int. Symposium
Circuits Systems, vol. II, 2001, pp. 65 – 68.
M.A. Turk, A.L.Pentland, “Face recognition using
Eigenfaces”, Proceeding of IEEE Computer
Society
Conference on Computer Vision and
Pattern Recognition, 1991, pp. 586-591.
T.Kawaguchi,
D.Hidaka,
M.Rizon,”Robust
Extraction of Eyes from Face”, Proceeding of IEEE
on Pattern Recognition 5th International
Conference, 2000, pp. 1109-1114.
M. Firdaus et al, “A face recognition system using
template matching and neural network classifier”,
Proceeding of the 1st International workshop on
artificial life and robotics”, pp. 1-6, 2005.
Lindsay I Smith, A Tutorial on Principal
Components, 2002.

©ISAROB 2006

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Scientific Computing: An
Survey, McGraw-Hill, 2002, pp.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Automatic Motion Detection for Surveillance
Mokhtar Norrima , Sugisaka Masanori
Dept. of Electrical and Electronic Engineering,
Oita University, 700 Dannoharu,
Oita 870-1192, JAPANTEL +81-97-554-7841 FAX +81-97-554-7818
E-MAIL: norrima@miosh.net, msugi@cc.oita-u.ac.jp

setup have been conducted. The dynamic nature of 3 and 4 will be
the next step in this research.

Abstract
Automatic motion detection features is able to enhancesurveillance efficiency and quality. The aim of this research is toautomatically recognize and detect motion around the robot's
environment in order to equip the mobile robot with surveillancetask or ability. The required information exactly based on the input
obtained from the CCD (Charge Coupled Device) camera
mounted on the mobile robot. As the first step of achieving the goal,
the dynamic nature of the robot and world setup for this research isstationary mobile robot and moving objects. Experiments on
different setup environment such as different motions, size ofmoving objects and lighting conditions have been conducted. The‘adjacent pixels comparison’ is the proposed method to detectmotion in this experiment. The results have verified the motion
detection experiments operating as expected.

2. Mobile robot system description
The mobile robot used by in this experiment was manufactured
by DENKEN company in 2000. The robot is 130cm high, 45 cm
width and 50 cm depth. The whole figure is shown in Fig.1. It isconsists of 2 drive 2 caster (2DC2W) systems. Driving wheel isequipped on right and left while the auxiliary caster wheel support
for forward and backward movement. The driving wheel on eitherside is using DC motor and being equipped with the rotary encoder(80 Pulses Per Resolution). By counting the number of pulses, rightand left independence driving wheel can be controlled. The
difference of the rotation speed of right and left driving wheel
performs steering function. The CCD cameras (EVI-G20 : Sony)is mounted on top of the mobile robot at 55 degrees of
perpendicular head directions. However, in this research we only
use the left CCD camera. The image size used in the experiment is
120 X 120 pixels. The image size is made small to speed up image
processing time. Then, the image obtained from the CCD camera
is kept by the image-processing board (FDM-PCI3: FOTORON).

1. Intoroduction
Recent years, many researches have been carried out in order toincrease robots tasks and capabilities. Application on various fields
such manufacturing, surveillance, services, medical and evenentertainment have contributed to high demand on robotics
researches. Since surveillance has become important to ensure
security and safety, the research on automatic motion detection has
been conducted in order to equip our mobile robot withsurveillance capability.
The input to a dynamic scene analysis system used in thisexperiment is a sequence of image frames taken from a changing
environment. The CCD camera mounted on the mobile robot isused to acquire the image. Basically, the changes in a scene areconsidered when moving objects presents or lighting conditionchanges. There are four possibilities for the dynamic nature of themobile robot and world setup:
1. Stationary mobile robot, stationary objects.
2. Stationary mobile robot, moving objects.
3. Moving mobile robot, stationary objects.
4. Moving mobile robot, moving objects.
However, in this experiment, only stationary mobile robot,
stationary objects and stationary mobile robot, moving objects©ISAROB 2006

Figure 1: Mobile robot

3. Methodology
The method used is to detect changes between two frames bydirectly compare the corresponding pixels of two frames todetermine whether they are the same. This may be the simplest or

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very low level of image processing but from the conducted
experiments, the present of moving objects and no moving objects
manage to be recognized within 1 second response.
In the simplest form, difference picture DPjk(x,y) between
frames F(x,y,j) and F(x,y,k) is obtained by :1 if |F(x,y,j) – F(x,y,k)| > τ(1)

DPjk(x,y) =0 otherwise
where τ is a threshold. In a difference picture or frame, pixels
which have value 1 may be considered to be the result of object
motion or illumination changes. Picture or frame with value 0
indicates no moving objects in the current scene. By thisassumption the frames are properly registered.

Figure 3: Moving objects scene frame

5. Experimental Method and Results.
The experiment was conducted on various situations such as
following:•
No moving object•
Different size of moving objects•
Light is switch on (during day and night time)•
Light is switch off (during day time)

4. Image Processing
The image taken from CCD camera is an image with a total of
24 bits of 8 bits each of RGB. Then, the image is converted tomonochrome image in order to ease image processing in the next
step. Next, the edge filter and equation 1 is used to extract the line
in order to use ‘adjacent pixel comparison’ to recognize moving
objects and no moving objects situation in front of the mobile
robot.
The ‘adjacent pixels comparison’ is the proposed method in thisresearch. By this method, adjacent pixels value in the frame ischecked whether their values are within the same range. Then, if
the pixel values are within the same range, it will mask the area in
the frame to check whether all adjacent pixels have the value
within the range. If the number of adjacent pixel is less than thethreshold value it will be considered as noise. However, if thenumber of adjacent pixel is greater than the threshold value it will
be considered as moving objects present in front of the mobile
robot. Illustration is shown on figure 1 and figure 2 for easy
understanding.

The results of experiment are shown in figure 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, and 15.
18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

Number of pixels

Figure 4: No moving object scene
18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

Number of pixels

Figure 5: Moving object scene, in the present of human body
movement

Figure 2: No moving objects scene frame©ISAROB 2006

289

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

Number of pixels

Figure 6: Moving object scene, with arm movement
Figure 10: No moving object images.
18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

13000

14000

Number of pixels

Figure 7: Moving object scene, with object as small as handphonesize.

Figure 11: Moving human body images.

18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000 11000

12000 13000

14000

Number of pixels

Figure 12: Moving arm images.

Figure 8: No moving object scene, with light off.
18000000
16000000

Pixel value

14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000 12000

13000

14000

Number of pixels

Figure 13: Moving handphone images

Figure 9: No moving object scene, with light is on.

©ISAROB 2006

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show that the ability to determine that a scene change can bedetected by different size of moving objects and independent oflighting condition. Therefore, the goal to increase our mobile robot
functionality manages to become reality.
In addition, this technique can be used not only by mobile robot
but static CCD cameras or CCTVs in any security systems. In thisexperiment, the frame is captured twice to detect the changes.
However, in a systems where multiple CCD cameras or CCTVs, it
can be controlled by using master server in order to check on every
check points of CCD cameras and CCTVs. By using thistechnique, surveillance can be done in more efficient way, by only
focusing objects movements rather than monitoring all CCD
cameras or CCTVs. From my point on view, this can save a lot oftime, promote smart staff management and at the same efficient
surveillance can be done. Nonetheless, the results to date alreadyoffer potential benefits in today’s remote monitoring andsurveillance. Besides that, the technique used, can reduce false
alarm rate.

Figure 14: No moving object with light off images

References[1]Syohei Kuriyama , Masanori Sugisaka: Development of themobile robot run system using dead reckoning and vision
information，Oita University Graduate School Master Paper（2003）[2] John S.Boreczky and Lynn D.Wilcox: A Hidden Markov
Model Framework for Video Segmentation Using Audio andImage Features , Proceedings of the International Conference on
Acoustics, Speech, and Signal Processing , pp.3741-3744 (1998).
[3] S. Uchihashi and J. Foote: Summarizing video using ashot
importance measure and frame-packing algorithm, Proceedings of
ICASSP'99, pp. 3041-3044.
[4]Sofia Tsekeridou,Stellios Krinidis, Ioannis Pites: Scene change
detection based on Audio-visual analysis and interaction(1998).
[5] A. Hanjalic: Shot-boundary detection: Unraveled and resolved?,
IEEE Transactions on Circuits and Systems fo Video Technology,
vol. 12, no. 2, pp. 90–105 ( February 2002)[6]Chris Stauffer and W.E.L. Grimson.: Adaptive backgroundmixture models for realtime tracking, CVPR99, Fort Colins, CO,(June 1999).
[7] http://www.reindeergraphics.com/[8] http://homepage3.nifty.com/ishidate/vcpp.htm[9]http://www.cs.rochester.edu/u/nelson/research/motion/
motion.html[10] http://www.ee.washington.edu/research/isl/mingye/boeing/node15.html[11]http://www.opengl.org/resources/tutorials/advanced/advanced9
7/notes/node123.html

Figure 15: No moving object with light on images

6. Discussions
The technique was tested with various conditions as stated
before. From the graph shown in figure 4, it is understood thatwhen there is no moving object present, the pixel value rarelychanging. Graph shown in figure 5 indicates large moving objects(human body) as the pixel value is changing rapidly. Graph shownin figure 6, indicates small changes as only medium size of objects,
in this case human arm movement. While graph shown in figure 7,
indicates only small changes as very small objects which is mobile
phone movement.
In addition, lighting condition is also considered which have noeffect on the adjacent pixel comparison technique because no
significant change is detected. However the different between pixel
values is bigger because in the present of light, the pixel value ishigher while when the light is off the pixel value is low. This can beclearly seen by comparing graphs shown in figures 7 and 8.

7. Conclusions
Significant progress has been made toward the goal of
automatically detecting moving objects. The demonstrated results©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Surface Based Spatial Awareness for Mobile Robots
Kamen Kanev
Department of Computer Software, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
kanev@u-aizu.ac.jp
Shigeo Kimura
Department of Mechanical Systems Engineering, Kanazawa University, Kanazawa 920-1192, Japanskimura@t.kanazawa-u.ac.jp
saving human lives by showing emergency exits andproviding other essential guidance in the case oflarge-scale earthquake, fires, violent terrorist attacks
and other disasters.

Abstract
Human awareness of the spatial environment and theway humans fit into it is a well-known phenomenon,
which is still beyond the reach of current mobile robots
and related technologies. Global position and orientation
data, however helpful it might be, does not bring to a
direct solution of the spatial awareness problem [1].
Indeed humans do not need and in most cases do not relyon global positioning data to place themselves and to
become aware of the surrounding environment. Instead
clues derived from the sizes, shapes, colors, textures andother properties of surrounding objects and their visible
surfaces seem to play quite a significant role. Manyadvances in robotic vision have been achieved following
similar approaches but good practical implementations arestill to be seen.
In this work we discuss a method for enhancing
physical objects and their surfaces with digital data layersthat are barely visible to the naked human eye but could
be reliably read and decoded by mobile robot vision
optics. In such a way an easily accessible spatial data is
directly encoded and blended into the mobile robot
environment. Based on it, links to object properties andother parameters are established and could be used fordeveloping spatial awareness models for mobile robots.
Our method can be applied to automated storage
facilities and assembly line environments where robots
and mobile carts are commonly used for dispatch ofmaterials. The method is also suitable for educational
purposes, where robot models can be enhanced with
precise positioning capabilities by a simple wirelesscamera and a digitally encoded tabletop surface.
Concluding the work, a few possible applications inconnection with robotics, human-machine interface andpseudo-artificial intelligence to assist the human life will
be briefly discussed in order to demonstrate the ample
capabilities of the proposed method. One such example isa positioning method for mobile security robots andautomated floor cleaning robots in large office buildings
or hospitals. Another example is a human assisting
method based on cell phone cameras that helps people
locate themselves in large shopping complexes andunderground. Reasonable position awareness can beachieved using only local data, stored on a portable device,with additional context related information downloadablefrom the Internet. Such a system may prove to be vital in©ISAROB 2006

1.

Introduction

Nowadays GPS technologies are the method of
choice for securing reliable position and orientation
data for moving vehicles on a continuous basis.
However, when GPS satellite signals are not readily
accessible or if a higher precision and context
awareness is required other methods must be
considered [1]. In this work we will focus on local
methods that take advantage of the close surroundings
in order to derive position and orientation data. Suchmethods are well applicable to moving platforms androbotized equipment in buildings and undergroundwhere GPS is not generally available. A typicalexample is the system by Makino et al [2], which uses a
local position tracking system based on 1D barcodes
painted on the corridor walls. An intelligent wheelchairwith a video camera and a laptop computer can readthese barcodes and extract wheelchair position data.
When equipment is scaled down for experiments,
modeling or education, local methods are usually
preferred for their precision and independence. A goodpractical solution however may need specialized evencustom designed tracking equipment. For example
Rosenfeld et al [3] have designed a special table withposition sensitive detectors to investigate bi-directionaluser interfaces. Two-wheeled moving platforms areplaced on the tabletop and their positions are tracked
based on the modulated LED signals that are emitted.
A quick comparison between the wheelchair [2]and the tabletop [3] systems outlined above shows thatthe wheelchair system is self contained and uses only
static information from the surrounding environment. In
contrast the tabletop system is based on active exchange
of information between the moving platforms and thetabletop.
The methods that we will discuss in more detailhere will be close to the first example, it terms that wewill use only static information embedded in thesurrounding environment. However our approach will
be equally good for both medium scale motion devices

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consisting of a tabletop and a mobile car model placed
on. Our objective will be to track the position and theorientation of the car as it moves. A sample constructionof a model car with compact wireless camera placed in
the front right seat and facing down is shown in Fig.2.
When the model car moves, partial images of thetabletop under the camera can be obtained and sent overa wireless link to a PC for processing.
An experimental setup of a map with an embeddedcluster pattern and an enlarged view of the model car
placed on the map are shown in Fig.3. Car position andorientation in respect to the map is calculated bydecoding the digital cluster pattern layer embedded in
the map.

such as wheelchairs or robotized platforms as well as fordownsized applications on a tabletop.
2.

Surface encoding

The idea of painting barcodes on walls and using
them for guidance [2] has evolved rather significantly.
Large QR-codes [4] that can be read and decoded bycommon mobile phones are already posted in many places
in Japan (Fig.1).

Fig.3. A map with an embedded cluster
pattern and a detail of a model carwith a wireless camera on the map.

Fig.1. Large QR-codes posted on buildings in
Ginza and in Tokyo subway.

With a continuous video stream we have no control
over what will be seen by the camera at the moment a
specific frame is taken. Therefore if the tabletop werecovered with barcodes, there would be no guaranteethat a complete barcode could be seen unless a rather
large and thus inefficient camera view was used. Whenclusters of graphical objects are used however, large
components such as complete barcodes become
unnecessary and camera views can be kept as narrow as
possible. In Fig.4 a sample cluster pattern is shown andthe camera view, with a fully decodable set of graphicalobjects is denoted by a solid line rectangle.

While one can get an instant access to a link or someother information source through such a QR-code for
guidance, this is clearly not sufficient for precise positionand orientation. In fact providing position and orientation
feedback based on 1D and 2D barcodes has some
important limitations that are discussed in more detail in[5]. More specialized methods and in particular the cluster
pattern method invented and patented by the authors seemto be more appropriate for surface encoding.

Fig.2. A model car with a small wirelesscamera facing downwards.
Fig.4. A sample cluster pattern with camera views
enclosed in sold and dashed rectangles.

First let us consider a simple experimental setup©ISAROB 2006

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drivers seat can be shown. Further enhancements can beachieved by playing video clips or using augmented
reality techniques.
A simple, self-contained working model consistingof a notebook computer, USB image capture device andthe cluster pattern enhanced map from Fig.3 has been
implemented as a proof of concept. Our model is basedon driver software that installs in the task tray andmonitors the input image stream. First, incoming image
frames are analyzed and individual graphical objects
are identified, then clusters of graphical objects areformed and virtual blocks of clusters are constructed,
and finally position and orientation data is derived(Fig.5).

Now suppose the camera view moves slightly to theright as denoted by the dashed rectangle. The column ofclusters on the left of the dashed rectangle go out of theview while the column of clusters on the right of the solid
rectangle go into the view. Accordingly the clusters thatfall within the overlapping area of the solid and thedashed rectangles stay in view. For efficient decoding
such type of cluster carpet has to provide for:Decode x (view1) = x1
Decode x (view2) = x1 + ∆x
where ∆x denotes the minimum, digitally encoded
displacement in x direction.
Purely for illustration purposes let us assume that thebinary values 0,0,0,0,1 are assigned to the columns in
Fig.4 from left to right. Then we will have:

Decode x (view1) = 0000 2 = 0
Decode x (view2) = 00012 = 1
and the displacement ∆x is 1.
Our cluster pattern encoding scheme is based on theabove illustrated idea and handles two dimensional
encoding simultaneously in X and Y directions [6,7]. In
contrast to barcodes, our cluster-based approach requires
no markers and no physical blocks but instead virtual
blocks can be formed and used whenever necessary.
The cluster pattern approach ensures uniform area
coverage with no artifacts and visually disturbing
components. This feature is quite important when thedigitally encoded layer has to be integrated with thecurrent environment. In our tabletop example, the cluster
pattern carpet can be combined with a map as in Fig.3 oreven differently printed covers can be prepared for
different experiments.
It is important to minimize the area ink coveragewhen printing the digitally encoded carpet. Our cluster
pattern method works well with less than 1% area ink
coverage and can be printed on transparent media. In sucha case one transparent cluster pattern encoded sheet canbe prepared and used with table covers that have different
printed content.
3.

Fig.5. The cluster pattern processing sequence.
Obtained position and orientation data is directedto the standard output (Fig.6) and thus can be easily
piped to other applications.

Fig.6. Cluster pattern processing results as they
appear on the standard output.

Feedback and links

For our test implementation we have preparedsome code that takes a line of three real values: thex-coordinate, the y-coordinate and the rotation angle,
and searches in a reference table for a match. The
reference table itself consists of area definitions andangle ranges with associated links. An example of asquare area, defined by its center, width and height andfor angle ranges pointing in up, left, down and right
directions with their respective links is shown in Fig.7.
Line descriptions are included in the area definition as

Tracking information consisting of position andorientation feedback can be enhanced with additionalreferences. For example in our tabletop setup the cluster
pattern enabled map carpet can be linked to voice, image
and even video content. Voice guidance may consist of
standard announcements similar to those given by car
navigation systems and when the car model approaches a
street crossing, pictures of the real crossing taken from the©ISAROB 2006

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information becomes vital and may be even life saving.
Our suggestion is to use the cluster pattern encoding
already applied to the walls or embedded in thewallpaper for position and orientation recognition. Java
enabled mobile phones with built-in cameras are very
good candidates for such an application.

C-style comments. Areas of other shapes and withdifferent parameterization can also be represented in asimilar way.
define_rectangle {/*x y dx dy dummy*/241.8410 351.7782 13.3891 10.0592 0{/*begin_angle end_angle function parameter(s)*/{45 135 to_browser 1-Up.JPG}{135 225 to_browser 1-Right.JPG}{225 315 to_browser 1-Down.JPG}{315 45 to_browser 1-Left.JPG}}}

5.

We have experimented with image capture devices
that connect to an USB port of a PC directly or throughRF channels. In both cases all real-time image
processing, cluster pattern recognition and coordinate
decoding have been done on a Windows PC. Our code
has been developed entirely in C and is in process ofmodification for other platforms.
We are also considering porting our code to an
integral device consisting of a CMOS image sensor anda DSP for local image processing and decoding. Such a
device would directly generate a data stream similar tothe one shown in Fig.6 and could be connected througha simple serial protocol to cell phones, PDAs, audio andvideo players and other equipment.

Fig.7. A rectangular surface area definition example.
4.

Application areas

The tabletop example as described in the previous
chapter is fully functional and can be used for practicalapplications. Systems as the one described in [3] could bealmost directly implemented using our tabletop approach.
Our method however offers a distinct advantage when alarger tabletop is needed. In our case this will amount
only to printing a new, larger tabletop cover or may bejust printing and adding some patches. In contrast thephysical size of the system in [3] is predetermined by the
hardware specifications. More concrete tabletop
applications in education will be explored in the future.
Implementations for larger scale systems such as
wheelchairs are also almost direct. Instead of having
barcodes painted on walls as in [2] we can have thecluster pattern painted instead.
Given the special
properties of the cluster pattern that make it nearlyinvisible for the human eye and allow for smoothblending with other printed content entire walls can becovered with it. May be the most convenient way would
be to have the cluster pattern code embedded in thewallpaper. Since there will be no individual physical units
such as barcodes the system can achieve higher
granularity, better positioning and will be generally morereliable. Automated floor cleaning and security robots canalso be included in this line.
There are special circumstances where humans may
greatly benefit from direct position and orientation
feedback. In normal conditions humans can relatively
easy derive the necessary position related clues from thesurrounding environment. Usually there are well seen
inscriptions on the walls like room names and numbers,
signs indicating the entrances and exits, floor maps etc. In
case of emergency such as fire or an earthquake however,smoke or fallen objects may obscure those signs. In suchcases the ability to get directions based only on local©ISAROB 2006

Conclusion

References
1.

2.

3.

4.

5.

6.
7.

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Gellersen H., Schmidt A., Beigl M., Multi-Sensor
Context-Awareness in Mobile Devices and Smart
Artifacts, Mobile Networks and Applications 7,341-351, Kluwer Academic Publishers, 2002.
尾形利文，牧野秀夫，石井郁夫，中静真：非可視型バーコードを用いた視覚障害者用位置案
内装置の研究．電子情報通信学会論文誌 D-II，
J80-D-II，11，pp.3101-3107 (1997)
Rosenfeld D., Zawadzki M., Sudol J., Perlin K.,
Physical Objects as Bidirectional User Interface
Elements, IEEE Computer Graphics andApplications, January/February 2004, pp. 2-7.
Hara M. et al., Optically Readable Twodimensional Code and Method and Apparatus
Using the Same, US patent 5,726,435, Patent andTrademark Office, Washington, D.C., 1998.
K. Kanev, and S. Kimura, “Print Based Interfaces
with Direct Point and Click Functionality,” In
Proceedings of the Eighth Int. Conf. on Human andComputers HC’05, Aizu-Wakamatsu, Aug. 31Sept. 2, 2005, pp. 128-135.
カネフカメン，木村繁男：デジタル情報坦体，
特許登録第３６３５３７４号，日本国特許庁（ＪＰ）
Kanev K., Kimura S., Digital Information Carrier,
WO 2005/055129, International Patent Application
Published under PCT, WIPO.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Interactive musical editing system to support human errors andoffer personal preferences for an automatic piano- A system of inferring phrase 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
must spent enormous amounts of time working with thearrangement in order to simulate the expressions of an
actual performance.
Even for the skilled computer user, it becomesprohibitively burdensome to reproduce a score and add
expression simultaneously using the automatic piano anda computer music system. That is because the musicaldata cannot be rewritten all at once. By contrast, highly
skilled pianists can sight-read an unfamiliar piece ofmusic, even if the performance is not completely inaccord with a specific musical interpretation. The
computer system cannot sight-read a new piece of music;
it 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].
This time we devised a method for inferring aperformance from information on the score and aparticular user’s editing characteristic for similar phrases.
We evaluated the system by comparing performance
information inferred from individualized databases withthe 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.

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
elaborated for a particular piece of music. However, thissystem cannot sight-read a new piece of music, as in thesimulation of a human being’s expressive performance.
Therefore, we have developed a program that canmemorize and use knowledge databases and user
preferences concerning the interpretation of a piece ofmusic.
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 comes out.
Therefore, we developed a system of inferringphrase expression to infer the expression from thepatterns of the note. We evaluated the system bycomparing performance information inferred from thedatabases with the pianist’s actual performance.
Key words: automatic piano, knowledge database,music interface, user’s preference, computer music,
inference system

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 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©ISAROB 2006

Figure1. View of the automatic piano

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2. Musical Editing Support System

2.3 Editing Support Process with Database

2.1 System Architecture

The procedure of the editing by the system is shownin 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 the 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.

The structure of the system is shown in Figure 2. The
user edits music via the user’s interface on the computer
display. Also, the user can 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 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 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 is comprised of fourparameters: “Key (indicating the kind of pedal,“Damper” or “Shifting”)”, “Velo (the pedaling quantity)”,“Time (the duration of applying the pedal)”, and “Bar”.
Table 1.

The parameters of note information

Table 2.

The parameters of pedal information

Figure3. Structure of the editing system

2.4 Details of the Databases
2.4.1 Score Database
This database has symbols including notes and time
signature rests and so on in standard musical notation.
Symbols were pulled together in order of bars, and bar
symbols are arranged in time series.
This database is composed of three tables, the“Element table” (showing the position of the note and thecomposition 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).

Parameter
Key
Velo Gate Step Time BarUnit
m sec m sec m sec Reference 21～108 1～127

Parameter
Key
Velo Time Bar
Unit
m sec
Reference Damper or Shift 0～127©ISAROB 2006

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2.4.2 Musical Rules Database

Figure4. Bars 1～7 of “Moonlight Sonata”
VeloThis database has 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.

2.4.3 Preference Database

Figure5. The result of analysis

This database contains the expressions of the user’s
characteristic performance. The expressions show the
relationship between tempo and dynamic.
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.

3.2 Phrase Feature Database
In Figure 5, it is found that the pattern of “Velo”
exists in each beat. To express such a detailed
performance feature, we constructed a Phrase Feature
Database. Tables 3 to 5 show the composition of thedatabase.

3. System of Inferring phrase expression

Table 3. Phrase Feature Database; Tuplet Data Table

Parameter

As a result of the analysis, it has been found that thephrase in this pattern that exists in the same tune
becomes a performance of a similar expression mutually
by our research. Thus, we were able to develop a system
of inferring phrase expression to infer the expression
from the patterns of the notes.
In the following paragraph, we describe an
analytical result.

Index

Primary Key

BeatNum

Number of beats

BarNumS ummary

Number of bars

TupletRhythmNumber of notes included in tuplet
The number in which RhythmPtn is identified

TimeSig
Tempo

The number of rhythm sign in M usical Rules Database
The number of tempo mark in M usical Rules Database

Pattern

The number in which HighLowPtn is identified

Table 4. Phrase Feature Database; Tuplet Pattern Table

3.1 Analytical Result

Parameter
Index

We analyzed a pianist’s performance data in to clarify
his editorial tendency. Figure 4 shows the score of bars 1
to 7 of Beethoven's Moonlight Sonata. The graphs of theanalysis results are shown in Figure 5. The horizontal
axis of each graph is time (in milliseconds), and thevertical axis is velocity (1 to 127). As a result, we foundthat similar phrases like that of the third and the fourthbars create highly similar expressions as shown in Figure
5. In the following paragraph, we describe the method ofinference design based on an analytical result.

TupletRhy thm

Primary Key

S ummary

Number of notes included in tuplet
The number in which Rhy thmPtn is identified

Rhy thmPtn Composition of notes in tuplet
HighLow The number in which HighLowPtn is identified
HighLowPtn Composition of "Key " in tuplet

Table 5. Phrase Feature Database; Tuplet Rate Table

Parameter
Index

TupletPrimary Key

S ummary

Number of notes included in tuplet

Velo"Velo" of first note in tuplet
VeloRate1 The rate of “Velo” of second note to "Velo" of first note.

VeloRate2 The rate of “Velo” of third note to "Velo" of first note.
Step"Step" of first note in tuplet
StepRate1 The rate of “Step” of second note to "Step" of first note.

StepRate2 The rate of “Step” of third note to "Step" of first note.

©ISAROB 2006

Time

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Velo

3.3 Method of Inferring phrase expression
First, the vector is calculated at every beat in theoriginal phrase of the inference, and the ratio of the sizesof the vectors at other beats to the vector at the first beatis calculated. The components of the vector are Velo and
Step. (See Figure 6) Next, the expression of every beat in
the phrase is inferred. The user sets Velo and Step of thefirst beat of the phrase. Velo and Step of other beats arecalculated using the ratio of the vector of the first beat.
However, only Velo and Step of each beat can becalculated with this method. Then, the expression of theremainder of the phrase is inferred using the Phrase
Feature Database.

10761 Time

Velo

Figure7. Bar 1 of “Moonlight Sonata” by Gerhard Oppitz

Time

Figure8. Bar 4 of “Moonlight Sonata” inferredby our system

4. Inference Result
Velo

The phrase of the fourth bar was inferred from thefirst bar of Beethoven’s Moonlight Sonata, which
Gerhard Oppitz performed. And the phrase feature database used for the inference was made based on theperformance of Gerhard Oppitz. Figure 7 shows theperformance by Oppitz.
Figure 8 shows the performance of the fourth bar
inferred by the system. Figure 9 is an actual performanceof the fourth bar by Oppitz. When Figure 8 and Figure 9
are compared, their expression is shown to be very
similar. Therefore, it is thought that this system inferred
effectively.

Time

Figure9. Bar 4 of “Moonlight Sonata” by Gerhard Oppitz

References[1] Hayashi, E. et al, “Behavior of piano-action in a
grand piano.I”, Journal of acoustical Society ofAmerica, Vol.105, pp.3534-3544, 1999.

5. Conclusion

Size =

Velo 2 + Step 2

Angle = Tan

Velo

We developed a system of inferring phrase
expression to infer the phrase expression frominformation on the score and user’s performance
characteristics. Then, the system was evaluated bycomparing inferred phrase expression to the actual
performance of the pianist.
Moreover, a “Phrase Feature Database” that storesthe performance features of each pattern of tuplets wasconstructed. As a result, it became possible to express thefeatures of a detailed phrase.
In the future, it will be necessary to improve thesystem so that it can infer a phrase even if the Preference
Database and Phrase Feature Database are made fromperformances of other tunes.

− 1  Velo
 Step 

Figure6. Vector of a note

©ISAROB 2006

[2] Hayashi, E et al, “Interactive musical editing system
for supporting human errors and offering personal
preferences for an automatic piano”, Proc. of the 7thInternational Symposium on Artificial Life andRobotics, 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)

Size
Angle

Step

299

[4] Takamatsu, Y., Hayashi, E., et al, “Interactive musicalediting system for supporting human error andoffering personal preferences for an automatic piano-Preference
databaseforcrescendo
anddecrescendo-”, Proc. of the 10th International
Symposium on Artificial Life and Robotics, pp309-312, 2005.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

C:¥nobu¥ATR¥nobu_ATR¥paper¥AROB2005¥draft¥AROB11th_final_draft.doc

Minimal design of "in situ" communication agents
Nobuyoshi MATSUMOTO, Hiroyuki FUJII, Michio OKADA
Department of Ecological Communications,
ATR Network Informatics Laboratories
2-2-2 Hikaridai, "Keihanna Science City,"
Kyoto, 619-0288
JAPAN.
nobu@atr.jp

communication role, and this methodology should be
simple. We named this type of robot design methodology
the "minimal design.”
In this paper, we define a
communication robot as one that does not necessarily
communicate as a human does. Nor does it need toassume a human appearance. After discussing current
design of a robot, we explain our innovative approach todesigning communication robots. The most importantfeature for communication is possessing a physicalappearance that bonds a robot to a human. In this regard,
physical appearance is the essential characteristic of acommunication robot rather than elaborate internalmechanisms. Furthermore, we stress the importance of“robotness” as the essence of a non-human object thathas agency and life-likeness.
A robot that can anticipate cooperative workwith a human must be designed from the perspective ofcommunication ability. Human-robot communication is aquasi-interpersonal behavior, and we believe that acommunication robot need not imitate humans. Instead, aminimally designed robot should articulate messages
needed for human-robot communication. Many
researchers tend to develop all-round communication
robots that look much like mechanical humans, butsuch robots do not bring much satisfaction. As a result,they need to concentrate more on the ability to inspire thehuman imagination.
While we are still a long way from developing
perfect all-purpose communication robots, we can solve
problems in human-robot communication in other ways.
Communication can be performed by minimalrole-sharing robots, but the biggest problem inhuman-robot communication is the sustainability oflong-term communication. Maintaining long-term
communication is therefore an immediate problem forresearchers working in the field of communication robots.
In this paper, we discuss solutions to such problems andshow examples of our research. We also give ourperspective for the future.

Abstract
Communication
involves
exchanging
incomplete information with others. Communication
robots to date have been constructed to deal withproblems of how to resolve the incomplete nature of
information. Communication is not enriched by gaining
large quantities of information but by clarifying
intentions or messages that contain insufficient
information. Communication design should reflect thisfact. It is difficult to simulate communication, since there
is so much background information needed to realize
communication that cannot be controlled throughengineering [1,2,3].
In this paper, we discuss a standardized
method of designing robots capable of long-term
communication, which we call "minimal design." It is
unlike other industrial robot design methods. We havealready produced such communication robots, and here
we give explanations of them.
Furthermore, we discuss the importance of "insitu" robot design. The Latin "in situ" is a technical term
used in biological research. It means "in the place where
it occurs." In minimal design, a robot is designed inaccordance with a particular situation and the user's
preference: communication depends on the situation.
From this viewpoint, we discuss our recent
communication agents.
Finally, we illustrate by example a newconcept for designing robot faces that is very different
from conventional methods: it is based on a projected
face that can be easily produced by using computer
graphics. Some actuators and a spring-action equalizing
device are also used in our proposed system. This
combination of light projection, conventional actuators,
and equalizing device is based on recent the advances
made in robotics and screen agents.
Keywords: communication robot, minimal design,quasi-interpersonal behavior, in situ, robotness,
multi-bodies agents

2. Quasi-interpersonal nature ofcommunication

1. Introduction

When we consider the design of a robot, we
focus attention on the quasi-interpersonal nature ofcommunication. Humans generally realize that a robot is
not a living creature. Through our field observations, we

A communication robot does, however, needan independent design methodology that fulfills its
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neighbors of humans in the near future. "Robotness" hasgreater importance in robot design than designingmechanical human copies. In our sense, a robot must
behave like a robot, nor a human. Our goal ofcommunication robots is an equivalence ofcommunicative roles with humans rather than anequivalence of motion with humans. Eventually, a robot
must exist and perform as a robot.
Communication robots represent one conceptof a tangible form or realization of active media. In
existing mainstream robots, various functions ofrepresentations are recognized as important. However, we
stress the importance of embodied media attracting theaction of others. For example, most humanoid robots do
not prompt human touch although physical contact is agood way to communication needless to say, on-screen
agents are not capable of this. Furthermore, most of theserobots are covered with cold plastic skins, which do notinvite touch. Furthermore, What we call communication
robots do not appeal to the five human senses, except forvision and hearing. The other three senses are not dealt
with when engineers design a communication robot. But
vision and hearing can be handle with on-screen
communication agents. In developing an embodied
communication robot more attention should be given to
the other three senses in order to maximize its potential
advantages over on-screen agents.
A good communication robot cannot bedesigned by the present method of only enriching thefunctions of a robot. By applying such a method, theproblem of lost "life-likeness" mentioned above cannot
be overcome. In order to realize interaction with people
over a long period, it is also an important condition that a
robot's appearance be attractive to humans. Norman,
who presented various practical difficulties in his book
"The Psychology of Everyday Things[4]," classified theelements of design into instinctive design, behavioral
design, and introspective design in his recent work
"Emotional Design[5,6]." The ease of use discussed inNorman's previous work was intended only forbehavioral design. Norman stressed the importance ofbalance among instinctive design in connection withappearance, introspective design in connection with themeaning of appearance, and action design. Moreover, heclaimed that not only ease of use but also the attractive
features of a design could satisfy people. For a design
method of communication, it should be remembered that
attractive things are better than high-performance things.
In this paper, we describe our efforts to designa continuous-communication robot that is different
from the products of conventional robot research. Toward
this goal, we stress the importance of subtraction design
and minimal design, in which subtraction issystematically performed based on a certain intention.
Minimal design should not only minimize elements but
also clarify the interface in communication, that is, thedesign should be completed after identifying what isessential. Furthermore, we propose a minimal design thathave noticed that children also communicate with ourcommunication
robotsdifferently
from
their
communications with living organisms. Nowadays, many
humanoid robots appear on TV programs and innewspapers and exhibitions, and many people expect
humanoids to become supreme communication robots.
However, almost all humanoids are built to move as
humans do, not to communicate. In our opinion, ahumanoid is not an ideal embodiment of acommunicating agent. While body motion is an importantpart of communication, robots’ movements do notinteract with people but with physical environments,
whereas a human can even communicate with things thatare certainly not alive.
This
type
of
quasi-interpersonalcommunication can be found everywhere in the daily
lives of ordinary people, but such communication does
not continue for long. Designing good relationships
among humans and artifacts is also an importantconsideration when designing communication robots.
Although most robots are designed as independentmachines, we however emphasize the importance ofrelationships.
Our proposed communication robots arerole-sharing and multi-bodied. The robots described
below increase the transparency of the referent-agent
relationship. When a robot acts like a human, a humanmust search for the communication key from many
communication channels. A communication robot
developed with the minimal design approach is arealization of well-adapted information design. It seems
more rational to use simple role-sharing robots inresearch on human-robot communication than using asingle complex robot. Unfortunately, human interest incommunication robots ended a long time ago. Robot
researchers now need to return to developing methods ofcontinuous communication between robots and humans.
At this point, we would like to focus on theoriginal character of a robot. We confirmed theimportance of characterizing a robot through our fieldobservations of child-robot interaction. A robot's
character strongly influences communication, and our
distinctively designed robots have the property of
attracting children. However, we must also point out the
key observation that a fixed character on a robot limits
opportunities for continuous communication. The
stereotypes arising from mapping a robot's static
character have the effect of restricting possible
interpretations by subjects. Therefore, we need a robot as
an embodied medium that can deal with various
communication tasks.

3. Robotness
We have recognized the importance of"robotness" for a communication robot: Robots need not
be substitutes for carrying out human roles. They must
support humans, or no one would accept robots as
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Communication robot designers naturally assume that thesummation of efforts toward improved robot performance
results in good communication. Communication ability
does not complete the design of a robot: When we design
a communicative agent, we also need to take into accountthe relationship between humans and robots..

can also clarify aspects of the interfaces incommunication, such as the type of action or thesituational.

3.1. Differences between a robot
and a computer

4.2. Design by subtraction

According to the “Media Equation,” even a
non-living thing like a computer express a kind oflife-likeness[7]. However, there are differences between acomputer and a robot. First, an embodied robot has strong
agency that reminds one of a living thing, while acomputer has a minute-scale sense of agency. On theother hand, a computer stands for absolute accuracy andcorrectness in operation. Comparatively speaking, acommunication robot often makes mistakes. This makes
people realize that a robot is not always right and thus not
perfect.
In fact, well engineered computer systems do
not make an errors in their own procedures. Almost allerrors and faults are caused in such systems throughhuman intervention, such as using wrong programs or
trying to input unacceptable types of data. A computer
system is a symbol of infallible judgment, but a robot
makes many errors and slip-ups just as a human does.
The boundary line between a robot and its environment
has much more complicated elements than an ordinary
computer systems has. Consequently, the decisions that a
robot makes sometimes fail. These faults do notnecessarily indicate defects of the robot. Moreover, faults
often compose the character of a robot. Accordingly, thecharacter of a robot is often determined by human subject
and not by system designers.

A design based on function curtailment could
be called "subtraction design," as opposed to themethodology of function addition. As a robot designed byfunctional addition becomes more like a living thing that
actually exists, ironically the difference between the robot
and the living organism becomes even clearer. Such aproblem could be avoided by adopting a design peculiar
to the robot, not by copying the figure of a living thing. A
clear policy is needed to determine how far a design
should be reduced.

5. Minimal Design
We discussed design by subtraction in thepreceding section. In this section, we discuss minimal
design, which is what we call the directional
simplification of subtraction design. In order toemphasize the main point of communication, it is betterto omit non-essential design elements. The decision ofwhich design elements to omit is determined by goal
setting. An airplane has wings but it doesn't beat the air
with its wings. Accordingly, a rational idea does notalways copy living things. As mentioned above, aminimally designed robot is not complete in itself. It is
designed for communication with others and for initiating
human interest in communication with it. A minimal
design targets the principle design goals ofcommunication.
Ludwig Mies van der Rohe, an exemplar ofmodern architecture design, said “Less is more,” whichmeans a good design requires chip off non-essential
qualities. For our purposes, no one can fully pre-design
right answers for all communication. Every new result of
successful communication is simply put into a set of“right” answers. Moreover, “right” answers depend on
situations and circumstances. Incomplete design evokes
human interest, and, fundamentally, a robot should do thethings that only robots can do. Consequently, theunpredictable nature of the robot’s expressions inhuman-robot communication conveys a sense of
subjectivity. All subjective understandings that are
accepted by a person can be “right” answers. In this way
the answers are generated and shared among a person androbots. Minimal design is intended to facilitate a “tabularasa” that can evoke human imagination. Sometimes
unsatisfactory situations arise from reading unwritten
meanings that could hardly be expressed by robots, andthis effect is similar to optical illusions. You can see what
we do not draw.

3.2. Differences between a robot and amachine (without agency)
A machine that does not have agency iswell-suited to many aspects of daily life. Humans feel theeyes of a robot, as if they are observed by the robot. In
such cases, people feel put into a fishbowl without
privacy. But a machine without agency does not project
the impression of someone watching them. Humans need
not respond or give feedback to a system without agency.

4. Design Methodology
4.1. Design by addition
To illustrate by contrast the subtractive design
proposed in this paper, we call the main guiding principleof conventional robot design "additional design" here. In
the design of a conventional communication robot,
transfer equipment and a structure for voice dialog areadded to a fixed-position, industrial-type factory robot.
Although there are still robots that use wheels as a simple
method of transferring equipment, bipedal robots havealso received much research attention in recent years.
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itself and a particular user. Fitness to the circumstance or
the environment has an influence on the duration ofhuman-robot communication. We need to establish afundamental theory of adequate performance for a given
circumstance, which we call in situ robot design. The
shapes of living things are adapted to their circumstances.
The “bodyplan” of living things is highlysuggestive for in situ robot design. Thompson (1973)
wrote interesting ideas on the concept of bodyplan[8]. Hesuggested the similarity of geometric coordinatetransformation on fins. According to his idea, a globefish
and a rockfish can be interpreted through a geometriccoordinate transformation process. We divide thebodyplan of living things into static and dynamic aspectsof living things. "In situ" design implies use of artificial
and mechanical homologous organs for minimallydesigned robots.

5.1. Multi-bodies agents
Through our field observations, to deal withthe absence of an absolute communication robots, we
propose that human-robot communication should be
conducted by two or more robots at the same time. In thisway, each robot would have a different character.
Moreover, such a robot would reminds people of a uniquerobot. Such a robot would form an attachment to aparticular person. As a result, the robot could maintainlong-term communication with humans.

7. Proposed Design of a
Communication Robot
A robot with physical actuators has the
disadvantage of slow response time in comparison to CG
screen agents. Also, in robots with moving parts, theweight of those parts limits response time. At the sametime, the power-to-weight ratio of robots also limits theircommunication ability. Furthermore, unlike living
creatures, a robot cannot change its facial expressions
without changing prepared parts, which is certainly a"non-life-like" process. Moreover, living organisms areaffected by both the genetic and epigenetic environments.
These two problems can be solved in our proposed designby using a spherical screen and an LCD projector. A face
image forms the center of recognition of "life-likeness,"which can be seen in children's drawings[9,10]. Facial
expressions remain the greatest unsolved problem ofcommunication robots. Software processing limits the
responses of a robot, but it is also necessary to redesign
facial parts that incorporate sensor inputs and data
through networks.
We also plan to combine the projected facedescribed above with actuators used in conventional
robots. Furthermore, we plan to incorporate thespring-action equalizing device implemented in ourprevious communication robot Muu. This type of hybrid
robot doubles as a screen agent and a conventional robot.
Therefore, the above two problems can be solved byusing our proposed method. Blinking is a facile actionthat requires the least cost in term of bodily motion. It
can express acceptance with eye contact to a subject.
Unfortunately, our recent communication robot Muu was
unable to blink. When our robots reveal emotions without
saying any words, they need to move the entire body toreveal emotions. Also, blinking movements can be boundas symbols to short messages in communication. In
contrast, our newly designed robots reveal emotions
without difficulty by blinking.

Figure 1. Multi-bodies agents Muu

Figure 2. Example of human-multi-bodies agents
interaction

6. “in situ” design
In minimal design, a robot is designed inaccordance with a situation and the user's preference:
communication depends on the situation. The Latin "insitu" is a technical term used in biological research. Itmeans "in the place where it occurs." We stress theimportance of incorporating “in situ” robot design inminimal design. A message can have various meanings ina given situation; non-verbal expressions also affectcommunication. However, even if communication is
successful the first time in a controlled environment, suchas an experimental laboratory, there is no guarantee thatthe second time will be successful.
A robot must be designed not to fit into various
environments but to tune in to the relationship between
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elemental technology of robotics. On the contrary,technology can be used to make superior communication
robots in a minimally designed way. Through minimal
design, it is possible to develop a communication robotthat never bores its users. In addition, we introduced amethodology named "in situ design” to match the robots
with particular used and circumstances. We will continue
investigating robot identity and the integration of robots
in roles involving long-term communication.

Acknowledgements
The research reported here was supported inpart by a contract with the National Institute ofInformation and Communications Technology entitled,'Research on Human Communication'.

References
Figure 3. Projected face of a robot[1] N. Matsumoto, H. Fujii, M. Goan and M. Okada,"Minimal communication design of embodied interface,"
Proceedings of the 2005 International Conference on
Active Media Technology(AMT2005), pp225-230, May
2005.
[2] N. Matsumoto, H. Fujii, and M. Okada, Minimal
design for human-agent communication, Proceedings of
10th International Symposium on Artificial Life andRobotics(AROB 10th), pp214-217, 2005.
[3] N Matsumoto, H Fujii, M Goan and M Okada,
Minimal Design Strategy for Embodied Communication
Agents, IEEE International Workshop on Robots andHuman Interactive Communication; RO-MAN 2005(Aug.
2005), 335-340.
[4] Norman D. , “The Psychology of Everyday Things”,
Basic Book, New York, 2004.
[5] Norman D. "Emotional Design", Basic Book, New
York, 2004.
[6] Norman D., Emotion and design: Attractive things
work better. ACM Interactions July-August:36-42, 2002.
[7] B. Reeves and C. Nass, "The Media Equation," CSLI
Publications, Leland Stanford Junior University, 1998.
[8] Thompson D. W., On Growth and Form("Seibutsu no
Katachi" Japanese Translated Version), Tokyo Daigaku
Shuppan, 1973.
[9] P. Ekman and W.V.Friesen, "Unmasking the Face,"
Prentice-hall, 1975.
[10] M. V. Cox, “Children’s Drawing by,” Penguin books,
1992.

Summary
Until recent times, robots have merely been
tools for production. Now, however, people have certain
expectations of robots as partners in communication, andrecently many communication robots have beendeveloped. Many of these robots are designed on thesame principle as factory robots: the approach of design
by addition. Furthermore, many robots have been created
to imitate the shape, motion, and characteristics of living
things, as mechanical animals. Although such robots do
resemble living things at first glance, the differences soon
clearly stand out. In this paper, we proposed a techniquecalled "minimal design," the primary aim of which is tomaintain communication. Based on this method, we
stress the importance of the “quasi-interpersonal”
nature and “robotness” in the design of communication
robots. Also, we showed the significance of designing arobot based on the relationship between the user and thesituation. In minimal design, non-essential elements areomitted to emphasize the essential design elements.
Different needs demand different types of robots.
To clarify the design task, we presented thestrategy of minimal design as well as communication
robots based on minimal design. Although minimallydesigned communication robots are dissimilar to animals
or humans, they still remind us of animal characteristics
in some ways; however, since they are not physicallysimilar to animals, users do not expect too much. In
addition, we suggested a useful method for robots toexpress feelings in a similar way how to characters
express themselves in cartoons and children's drawings.
Such robots are well-suited to long-term communication
because of the uncertainty arising from their not giving asingle prefixed answer in human robot communication.
Minimal design does not imply a sharp
departure from exploiting the improvements made in the
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A Novel Human-Environment Interface for Conceptualizing Spatial Information in
Non-Speech Audio
Shigueo Nomura, Masayoshi Tsuchinaga, Yaichi Nojima,
Takayuki Shiose, Hiroshi Kawakami, Osamu Katai,
Dept. of Systems Science
Kyoto University
Kyoto, 606-8501, Japan
Abstract

ance in a room, the more space there is. So, this builtup association provides a natural conceptualization of
spatial information based on user experience and familiarization with everyday sounds, that is, provides
friendly information for users without the necessity ofhard cross-modal training.
The main purpose is a novel concept of interface
to support visually impaired persons and to provide
them with the chance to recover the freedom to conceptualize and interact with their environment without depending on such concepts as artificial patternrecognition or voice synthesizer systems. As an essential work background to achieve this main purpose,
we utilized our already conceived “sound visualization” [2] function and our already developed virtual
3D acoustic environment for training the “perceptionof crossability [3].”Section 2 presents apparatus and procedure for experiments. Experimental results are presented in Section 3. In Section 4, conceptualization performance of
subjects hearing different tones is discussed. Section 5
presents a conclusion of this work.

We propose a novel concept toward interfaces thatcan provide visually impaired persons with the chance
to recover the freedom to conceptualize their environment without depending on such conventional voicesynthesizer systems. Fourteen subjects participatedin ten experiments to provide results for evaluating
their performances to conceptualize spatial information based on cues in “artificial-sounding” (“AS”) and“natural-sounding” (“NS”) tones. The source of “AS”
tones was the digitized sound used by vOICe Learning
Edition, and the source of “NS” tones was a fan noisewith analogs in everyday world. Experimental results
revealed that the use of “NS” tones was essential to
improve conceptualization performances of subjects aseventual users of novel human-environment interfaces.

1

Introduction

Recently, interest and necessity have grown in using sounds more complex than ubiquitous interrupting beeps to provide information to computer users.
An actual example of interface is the evaluated vOICe
Learning Edition [1] that translates arbitrary video
images produced by an ordinary camera into sounds.
However, like vOICe, the problem is that dimensions(pitch, loudness, and duration) of sound themselves
have been used to represent the characteristics of thedata. This leads to the use of “artificial-sounding
tones” with no analogs in the everyday world and requires extensive trials before actually using the interface. We propose to break away from such typical useof artificial-sounding tones by designing novel interfaces based on “natural-sounding tones” that utilize
the same user skills employed in everyday tasks suchas crossing the street. An example of such a skill is
our “built-up association” of reverberation with empty
space. All other things being equal, the more reverber-©ISAROB 2006

Keiji Yamanaka
Faculty of Elect. Engrg.
Federal University of Uberlândia
Uberlândia, 38400-902, Brasil

2
2.1

MethodApparatus

To generate “artificial-sounding” (“AS”) and“natural-sounding” (“NS”) tones for investigating
conceptualization performances by subjects hearing
these tones, we used the virtual 3D acoustic environment system based on sound space processors as shownin Fig. 1.
The source of “AS” tones is the digitized soundused by vOICe Learning Edition with no analogs in
the everyday world. On the other hand, the source of“NS” tones is the real sound of a fan noise that was
saved by a precise microphone.

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ɂ

10 m

0.3 m

Generated sounds

Wall ("aural surface")dSubject

Traveling direction

4m

Figure 1: A front view of devices for generating nonspeech audio.

Figure 3: The upper view of a virtual course for experiments.

ComputerGenerated
Non-Speech
Audio

2. The distance d is calculated as d = v.t,
where v can be 4km/h or 12km/h, and t ∈{3.0, 3.5, 4.0, . . . , 6.0}s.

Digital Surround
Headphone

3. According to the value of angle θ, we set up thefollowing geometric shapes of virtual courses:

IBM-XeonUSB MIDI Interface

UM-880Sound Space
Processor 1

Audio Recorder 1

AR-3000

Sound Space
Processor 2

MDR-DS 8000

• Convergent for θ ∈ {3o , 5o , 30o }. The change
in “aural surface” is to become the course
narrow after traveling the distance d.
• Straight for θ = 0. There is no change in“aural surface” for the entire course.
• Divergent for θ ∈ {−3o , −5o , −30o }. Thechange in “aural surface” is to become thecourse wide after traveling the distance d.

Mixing Console
Sound Space
Processor 3

VM-C7100

Audio Recorder 2Control Line
Audio LineSound Space
Processor 4

Mixing Processor

VM-7200

RSS-10Figure 2: A schematic overview of apparatus for experiments.

Fourteen subjects participated in ten experiments
for evaluating two different tones (“AS” and “NS”)
used to perceive changes in “aural surface” of thesevirtual courses. All subjects had no audition problems.
No visual information was used in experiments.
Instructions for each subject before starting experiments are as follows:

Figure 2 shows a schematic overview of the apparatus used in our experiments. Each device identification
is followed by its model number.

2.2

Procedure

1. During a duration of time from 3s to 6s (eachduration is randomly defined) corresponding to
the distance d in Fig. 3, the subject travels acrossa straight course in the virtual acoustic environment.

Figure 3 shows the upper view of a course virtually created by using the 3D acoustic environment system. During experiments, subjects travel across thecourse and must perceive changes in “aural surface”
using their auditory system. Based on our previous
work [3], appropriate reverberation and reflection levels are adopted as −30dB in this virtual environment.
In Fig. 3, we have the following conditions for experiments:

2. After spending the above duration of time, a clock
starts to measure the time interval until pressing
a key that represents a perception response of thesubject. Each pressed key by the subject represents the following decisions:

1. A set of four generated sounds (“AS” or “NS”
tones) constitutes a piece of wall that represents“aural surface” of the virtual course.

©ISAROB 2006

• The key “←” must be pressed when the subject perceives that the traveled course is converging as shown in Fig. 4a.

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Table 1: Conceptualization performances of all subjects, where v1 = 4km/h and v2 = 12km/h.
Convergence(a)

No change(b)

Divergence(c)

ChangeFigure 4: Types of changes in virtual courses.

−30o

81%

o

53%

o

34%

o

49%

o

§60% ¦ ¨26% ¥ §53% ¦
47%§53% ¦ ¨50% ¥

3

37%

30%

5o

53%

31%

30o

84%

87%

−5

−3

0

• The key “↓” must be pressed when the subject perceives that there is no change on thetraveled course as shown in Fig. 4b.
• The key “→” must be pressed when the subject perceives that the traveled course is diverging as shown in Fig. 4c.

§¥¨51% ¦
71%
61%
¦
§
¥
¨
93%
97%
¦
§

39%

Table 2: Performance of subjects traveling at 12km/h;* indicates subjects with the best performances.

3. Each decision for pressing any key must be taken
as soon as possible but with belief.
Subjects performed tasks of the following two experimental stages during experiments:

SX1
SX2
SX3
SX4∗
SX5
SX6
SX7

• Training: Two steps were performed in this stage.
First step was to provide familiarization with existence of three types of virtual courses as shownin Fig. 4; these courses were presented to subjects
as converged by 30o , diverged by −30o , and unchanged; the traveling speed was set at 12km/h;
three courses for each type of tone (“AS” and“NS”) were virtually created. Second step was
to acquire abilities for capturing necessary cuesin each type of tone and classifying the virtual
course where the subject is traveling; in this step,
subjects were asked to press the key corresponding to the type of course in which they thought;
after each response, the correct answer was informed.

“AS”
54%
51%
51%
69%
51%
46%
77%

“NS”
SY 1
69%
SY 2∗
40%
¨63% ¥ SY 3
§89% ¦ SY 4
69%
SY 5
43%
SY 6
86%
SY 7

“AS”
57%
60%
49%
49%
60%
71%
71%

“NS”
¨60% ¥§86% ¦
60%
77%
77%
80%
80%

courses is randomly arranged to avoid the influence of learning process by subjects on performance evaluation. In the second experiment, theunique difference in relation to the first one is thatthe subject hears “NS” tone instead of “AS”.
Subjects were divided into two groups with sevenones: (X) composed by SX1, SX2, . . . , SX7, and (Y )composed by SY 1, SY 2, . . . , SY 7 that performed different sequences of experiments in each group.

• Testing: In this stage, each subject achieved ten
experiments (5 ones for each type of tone) to provide results for analysis of conceptualization performance based on different tones. We have two
types of experiments. In the first experiment, thesubject hears “AS” tone and travels across thevirtual course with speed at 4km/h or 12km/h;
the values of angle θ as shown in Fig. 3 can be−30o , −5o , −3o , 0o , 3o , 5o , or 30o ; for each speed
we have seven different virtual courses where thesubject travels across to classify them; a total of
fourteen different virtual acoustic courses basedon cues in “AS” tone must be classified by conceptualizing spatial information; the order of these©ISAROB 2006

“NS” tone
v2
v1
¥
¨
83%
§93% ¦ ¨90% ¥§¥¨80% ¦¨70% ¥ 36%“AS” tonev2
v1

3

Experimental Results

Results from the testing stages are presented in Tables 1, 2, and 3 as follows:• Table 1 presents results of conceptualization
process by all subjects. The average results are
categorized by type of tone (“AS” or “NS”),
course change (θ), and traveling speed (v1 or v2 ).

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subject SX4 was improved from 20% to 100% using“NS” tone in virtual course converged by 3o and from
60% to 100% in the case of 5o . Similar improvement
was reached by the subject SY 2 too.

Table 3: Best conceptualization performance fromeach group of subjects.
Change
−30o
−5o
−3o
0o
3o
5o
30o

Subject SY2
Subject SX4
“NS”
“AS”
“NS”
“AS”
100%
40%
100%
80%
100%
80%
100%
80%
60%
100%
100%
100%
40% ¨20% ¥ 20%¨60% ¥
20% §§80% ¦¥¥ 20% ¨¨100% ¦
60% §100% ¦ 60% §100% ¦
100%
100%
100%
100%

5

We evaluated “artificial-sounding” (“AS”) and“natural-sounding” (“NS”) tones as non-speech audio
means for eventual interface users to capture cues andperform natural conceptualization of spatial information.
Experimental results showed that “NS” tones weremore appropriate than “AS” ones to classify different
types of virtual courses by subjects traveling acrossthese courses and hearing those tones.
Furthermore, improved performances were verified
in tasks to perceive too small changes in “aural surface” when subjects heard “NS” tones even at hightraveling speed.
Finally, we concluded that the use of “NS” tones
with similar conditions to everyday world such as reverberation and reflection is an essential requirementfor designing novel friendly human-environment interfaces.

• Table 2 presents performances of subjects traveling at 12km/h. This traveling speed was selectedby considering results in Table 1. Each rate is theaverage of results corresponding to all changes in“aural surface.”• Table 3 presents detailed results for subjects SX4and SY 2 that reached the best performances intheir respective groups.
After the testing stages, we asked subjects about
the tone that they thought more preferable for conceptualizing spatial information. Among others, only
subjects SX2, SX7, and SY 3 chose “AS” tone. And,
we can verify that only the choice by subject SX2matched performance result as shown in Table 2.

4

Acknowledgements
We are very grateful to Prof. Kiyohide Ito from
Future University in Hakodate for his valuable contribution to this work supplying us with a fan noisesound source data used in our experiments.

Discussion

References

In Table 1, we verify that conceptualization performance by using “NS” tone was better than performance by using “AS” tone in the most of virtual
courses. An exception occurred for virtual course diverged by −3o in which conceptualization performancereached 60% using “AS” tone and setting the travelingspeed at 12km/h. Also, we can notice a considerable
improvement on performance results corresponding tochanges at −5o , −3o , 3o , and 5o when the travelingspeed was set at 12km/h using “NS” tone.
In Table 2, only subjects SX2 and SX6 suffereddecrease (respectively, 51% to 40%, and 46% to 43%)in their performances when they heard “NS” tones.
In Table 3, we can notice that both subjects improved their performances mainly in the case of virtual courses converged by 3o and 5o when they heard“NS” tone instead of “AS” one. The performance of©ISAROB 2006

Conclusion[1] W. D. Jones, “Sight for Sore Ears,” IEEE Spectrum, Vol. 41(2), pp. 13–14, 2004.
[2] S. Nomura, T. Shiose, H. Kawakami, O. Katai,
and K. Yamanaka, “A Novel “Sound Visualization” Process in Virtual 3D Space: the Human Auditory Perception Analysis by Ecological Psychology Approach,” Proc. of 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp.
137–149, Cairns, Dec. 2004.
[3] T. Shiose, K. Ito, and K. Mamada, “The Development of Virtual 3D Acoustic Environment forTraining ‘Perception of Crossability’,” Proc. of the
9th International Conference on Computers Helping People with Special Needs, pp. 476–483, Paris,
Jul. 2004.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Emerging Cell Array based on Reaction-Diﬀusion
Shuhei Miyashita
Department of Informatics
University of ZurichAndreasstrasse 15
CH8050 Zurich, Switzerland

Satoshi Murata
Tokyo Institute of Technology
4259 Nagatsuta, Midori
Yokohama, 226–8502 Japan

Abstract

gested a concept of positional information when hethought that all identical cells should recognize theirown position in order for the cells that had identicalgene [2]. C.G.Langton built a cellular automata modelof self-replication (SRloops) on a two dimensional cellarray [3]. As the model consists of discrete internal
state with ﬁnite state automata, it is not easy to relate this models to the biological cell. K.Tomita et al.
created graph automata, which enable description of
dynamic topological change of cell network, [4]. They
extended the concept of cellular automata to certain
graphs of cells. This model is able to describe morphological change such as cell division or cell death. But
little attention has been given to a relation between
morphogenetic process and reaction-diﬀusion mechanism.

This paper demonstrates self-replication and selforganization phenomenon based on reaction-diﬀusion
mechanism by computer simulation. The simulation
model consists of one dimensional cell array. Each cell
contains two kinds of chemical substances - activator u
and inhibitor v - that can generate reaction-diﬀusion
wave, which is a spatial concentration pattern. The
cells are supposed to be divided or be deleted depending on its concentrations of chemical substances.
As we tried several kinds of diﬀusion coeﬃcients tothe model, in some simulations, self-replication process and generating cell array with metabolic process
were observed. By applying division rule and apoptosis rule, cell arrays duplicate itself oscillating two
states, that is, self-replication process were observed.
And by applying division rule and annihilation rule, acell array that has stable length is generated changing
its cell components, that is, generating cell array withmetabolic process were observed. Surprisingly, thesetwo phenomena are realized independently of the initial number of cells.

1

2

Model

2.1

Reaction-diﬀusion

Introduction

Self-replication and self-organization abilities ofmulticellular organisms are one of the most fundamental characteristics that discriminate creatures from
non-creatures. As each cell contains the same genome,
this feat is realized in a distributed and autonomous
way with the absence of centralized control. However,no matter how big progress has been made in molecular biology over the past few decades, overall pictureof principles behind the spatiotemporal dynamics ofthese processes is still lacking.
In 1952, A.M.Turing advocated a reaction-diﬀusion
mechanism and suggested that the reaction-diﬀusion
mechanism, which is a kind of spatial concentration
pattern, plays a key role in forming a periodic shapeon a small creature called hydra [1]. L.Wolpert sug-

©ISAROB 2006

Figure 1: One dimensional cell array.
In this research, one dimensional cell array model
was chosen for simpliﬁcation. Fig.1 is a schema of
the model. These nodes represent cells containing two
kinds of chemical substances - activator u and inhibitorv - that can generate reaction-diﬀusion wave. These
two chemicals react each other and diﬀuse to neighboring cells according to its concentration gradient.

309

dur /dt= +5ur (t) − 6vr (t) + 1+ Du {ur−1 (t) + ur+1 (t) − 2ur (t)}(1)dvr /dt= +6ur (t) − 7vr + 1+ Dv {vr−1 (t) + vr+1 (t) − 2vr (t)} (2)The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The reaction-diﬀusion model that we use is thesame as Turing’s, which is expressed as partial diﬀerential equations (1), (2). Where, ur and vr represents
the concentration of activator and inhibitor, respectively with subscript r as an identiﬁcation number. Du
and Dv represent diﬀusion constants of activator andinhibitor, respectively (Du = 0.7, Dv = 1.2). To thesystem, Dirichlet boundary conditions were applied,which set the chemical concentration at the boundary
to zero. The initial concentrations are set as equilibrium point (u, v) = (1.0, 1.0).

phase. In this rule, the cell is deleted but the cell connections of both sides are kept connected. Apoptosis
rule (bottom) is similar to the annihilation rule, whichis activated when the concentration of activator becomes smaller than that of inhibitor in the same phase.
The system is divided into two separate groups by thecell deletion. Every diﬀerential equation is integrated
by the Runge-Kutta method (δt = 0.01, 1step=100δt,
1 interphase corresponds to 150 steps).

2.2

In the computer simulations, equations (1), (2) andcell diﬀerentiation rules are simulated synchronously
according to the cell cycle.

3

Cell cycle and cell diﬀerenciation

Simulations and Results

concentration of inhibitor
concentration of activator

division thresholdannihilation thresholdcell ID

Figure 2: Cell cycle.

cell array

Since cell cycle is determined by various factors,
in this paper, cell cycle is supposed to be generated
synchronously. Fig.2 represents the cell cycle of themodel. Every cell has interphase and mitotic phasein the cell cycle. In mitotic phase, reaction and diﬀusion of the chemical substances take place. In interphase, cells are diﬀerentiated depending on the concentrations (see below).

Figure 4: Simulation model.
Figure 4 shows the simulation model. Bars above
each cell represent the concentrations of activator andinhibitor. The concentrations of activator is displayed
front and that of inhibitor is displayed behind. Values of the concentrations of activator is presented oneach shoulder of bar. division threshold, annihilationthreshold and identiﬁcation numbers of cells are also
listed.

3.1

In this model, division rule and apoptosis rule areapplied in mitotic phase. Fig.5 shows self-replication
process when we settled one cell as an initial condition. In the ﬁrst stage, cell divisions are repeated twotimes in mitotic phase and the initial cell grows into
8 (#1 - #4). Then apoptosis rule is activated andfour cells positioned at the center (cell 8,13 at #4) aredeleted and four cells at the side (cell 9,10,11,12 at#4) are divided. Consequently, the system duplicatesitself oscillating between state #5 and #6.
Fig.6 shows the case that the number of initial cells
is larger than the oscillating number 10 (#7). The
phenomenon that ﬁnal state converges to the state #5
and #6 are observed.

Figure 3: Cell diﬀerenciation.
Fig.3 shows cell diﬀerentiation rules. Cell division(top) is took place when the concentration of activator becomes larger than speciﬁc threshold (divisionthreshold set as 1.5) in mitotic phase. The cell is
divided into two cells, and the concentration of eachchemical substance right before division is applied tothe concentration of the cell divided. Contrary, annihilation rule (middle) is activated when the concentration of activator becomes smaller than another speciﬁc
threshold (annihilation threshold set as 1.0) in mitotic©ISAROB 2006

Self-replication process

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3.2

Generating cell array with Metabolic
process

Division rule and annihilation rule are applied in
mitotic phase. Fig.7 shows that the number of cells
increases and the number ﬁnally becomes to 10 according to the cell division rule. In the mitotic phaseat state #6, four cells positioning at the center (cell20,7,14,17 at #6) are annihilated. And other four cells(21,22,15,16) at the sides are divided. At the end, thenumber of cells is kept stable to the ﬁxed number, 10.
The system perpetually produces new cells replacingsome old cells.

Figure 5: A self-replication process from one cell.

Figure 8: Generating cell array from 18 cells.
In order to investigate stability of the process, 18
cells are set for a initial state. Fig.8 shows the numberof cells ﬁnally converges to the same pattern (#6).
Figure 6: A self-replication process from 15 cells.

4Discussions
30

number of cells

25
20
15
10
5
0
0

Figure 7: A generating cell array with metabolic process from one cell.

2
4
6
8
10
steps (the number of M-phase)

Figure 9: The number of cells transition graph.
In addition to the simulation 3.2, we settled diﬀerent number of cells as a initial condition and showed©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

number of cells of which the concentrations
are above/below the threshold

number of cells

the results in Fig.9. The X-axis represents the number of passed mitotic phases and Y-axis represents thenumber of cells. The ﬁgure shows that the length of
all cell arrays which start from 1 cell to 30 cells ﬁnallyconverge to 10. This suggests not only that we cansettle variable numbers of cells for a initial condition(at least 1 to 30) but also we can cut any cell connections as we want which leads to generate two cellarrays consisting of same size, 10.

1.3
1.5 1.4

1.2 1.1

1.0 0.9

0.8 0.7

0.6

Du9divisionannihilation

8

Figure 11: The number of cells in each parameter sets.

7
6
5

5

4
2
1
0
0

5

10

15

Conclusion

In conclusion, two fundamental organic behaviors,that is, self-replication process and generating cell array with metabolic process were observed by applying
reaction-diﬀusion mechanism on distributed space. In
self-replicaion process, a phenomenon that cell arrays
duplicate itself oscillating two states were observed.
And in generating cell array with metabolic process, aphenomenon that a cell array which has stable lengthis generated changing its cell components were observed. This research shows posibilities that reactiondiﬀusion system plays some roles in controlling thesevital activities of living things.
Acknowledgment Many thanks to Peter Eggenberger Hotz for helpful suggestions. This research is
supported by the Swiss National Science Foundation,
project #200021-105634/1.

3

20

number of cells

Figure 10: Exceed threshold graph.

Since patterns of reaction-diﬀusion waves are seemto be unique to the length of cell arrays especially thelength is short, we counted the number of cells which
exceed division threshold and which are below annihilation threshold in the ﬁrst mitotic phase. The X-axis
in Fig.10 represents the number of cells, and Y-axis
represents the number of cells of which the concentrations are above (or below) the threshold. As theﬁgure indicates, When the number of cells is less than
9, the number of cells that the concentration exceed
division threshold is larger than that the concentration
are below annihilation threshold. That is, the length
becomes short in the next mitotic phase. And when
we settle the number of cells more than 11, the otherway round. This ﬁgure clearly shows that the lengthof the system converges to 10.

References[1] A.M.Turing: A reaction-diﬀusion model for development, The chemical basis of morphogenesis, Phil
Trans. Roy. Soc. of London, Series B; Biological
Sciences, 237, 5,PP.37-72 (1952)[2] L.Wolpert Positional Information and the Spatial Pattern of Cellular Diﬀerentiation, J. Theoret.
Biol., 25, PP.1-47 (1969)

Fig.11 represents the number of cells when mitotic
phase passed ten times in diﬀerent diﬀusion coeﬃcients. Initial number of cells were settled from 1
to 30 for each parameter sets. X and Y axis represent diﬀusion coeﬃcients Du and Dv, respectively andZ axis represents the number of cells. As it can beseen, the system doesn’t always converge to one state
and though the system shows its robustness for external disturbances in adequate diﬀusion coeﬃcients, theranges of these parameters are narrow.

©ISAROB 2006

30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
1.5
0
1.4
1.3
1.2
1.1
1.0
Dv 0.90.8
0.7
0.6[3] C.G.Langton: Self-reproduction in cellular automata, Physica D: Nonlinear Phenomena, 10, Issues
1-2, PP.135-144 (1984)[4] K.Tomita, H.Kurokawa, S. Murata: Graph Automata: Natural Expression of Self-reproduction,
Physica D: Nonlinear Phenomena, 171 No.4,
PP.197-210 (2002).

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Biomimetic Control of Mobile Robots
based on the Information Processing Model of P aramecium
Akira Hirano† , Toshio Tsuji† , Noboru Takiguchi†† , and Hisao Ohtake††††Graduate School of Engineering, Hiroshima Univ., Hiroshima 739-8527, Japan††Graduate School of Advanced Sciences of Matter, Hiroshima Univ., Hiroshima 739-8530, Japan†††Graduate School of Engineering, Osaka Univ., Osaka 565-0871, Japan

Abstractof unicellular organism is considered one of the effective approach for achieving robot control.
Some models that focus on the internal processing system of living organisms have been proposed.
The model called E-CELL [4] has been developed by
Tomita et al. to simulate the behavior of the entire
cell based on the chemical reaction, and another model
has been developed by Bray and Lay [2] and Hauri andRoss [3] to simulate part of the internal processing of
E. coli bacteria. Our research group has also developed
the E. coli model based on the chemical equation of
the internal processing system, and has applied it tomobile robot control [5, 6, 7].
At present, we are constructing a computationalmodel of Paramecium [8, 9], Virtual Paramecium, because its internal processing system is more advanced
than that of E. coli. Further, we have confirmed that
Virtual Paramecium can reproduce the chemotactic
response of a living Paramecium [9]. In this paper, we
report the results of our study on the control of a mobile robot by using Virtual Paramecium; in addition,
we confirm the effectiveness of the biomimetic control
that is based on the information-processing algorithmof living organisms.

In order to survive in complex natural environments, living organisms have been genetically acquiring various algorithms. Protozoans, for example, respond to various stimuli to search for a hospitable environment. Recently, the concept of Software Biology
has been proposed, in which the algorithms of livingorganisms are considered as a kind of software that
could be utilized for robot control.
Thus far, we have focused on the information processing algorithm of Paramecium, and proposed its
computer model, Virtual Paramecium, based on biological knowledge. Virtual Paramecium can approximately realize the chemotactic behavior of the actual Pramecium based on the information processing model. In this paper, we report the results obtained when a mobile robot is controlled using Virtual Paramecium, and confirm the effectiveness of thebiomimetic control based on the information processing algorithm of living organisms.

1

Introduction

In order to survive in complex natural environments, the genome of living organisms undergoes various changes, thereby forming various algorithms forsurvival. Recently, the concept of Software Biology
has been proposed; this concept proposes the use ofalgorithms of living organisms for control of machines,that can adapt to environmental changes [1]. In the research field of adaptive control, neural networks havebeen introduced to control robots, and their effects
have already been demonstrated. However, there isa possibility that the mechanism occurring in an actual living organism is not reflected in robot control
because the cellular characteristics that comprise theneural network are very simple in comparison with aliving cell. Hence, developing a computational model©ISAROB 2006

2

P aramecium

Paramecium can be assumed to be a discoid shapemeasuring approximately 250 µm in length and 50 µm
in width; it has a uniform cilia layer on the cell surface.
The direction of ciliary beat is modified by the stimuli from the environment. For instance, Paramecium
accelerates their motion toward areas of low K+ concentration by increasing their ciliary beat frequency.
On the other hand, Paramecium exhibits an escape
reaction from areas of high K+ concentration, thereby
accumulating in the areas of low K+ concentration.
This response behavior with regard to the chemical
environment is called chemotaxis.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

changes. It can be considered that a compulsory electrical change in membrane potential, similar to that in
the voltage-clamp experiments [10], is generated when
the ionic composition of the environment changes (seeFigure 1). For instance, the resting membrane potential rises when the K+ concentration in the environment increases, and then the potential difference
generated between the environment and the interiorof the cell decreases (see Fig. 1 A → B). An early current is generated as shown in Fig. 1B, when the cell of
Paramecium is considered as a capacitor. Then, thesensory unit determines the probability of the aperture rate of ion channels that are opened by changedin the environmental conditions.
First, in the sensory unit, the resting membrane potential Eleak , which depends on [Ca2+ ]o and [K+ ]o , iscalculated by using the Goldman-Hodgkin-Katz equation [10]:ACa2+outside
[mV]

K+

0

membrane

Eleak

-30
insideK+

B

Early current‫ޓ‬Ic(Local current)

Ca2+
outside
[mV]

K+

0
-10

membrane

Eleak
K+ K+
K+ K+ +
K
K+

insideFigure 1: Membrane potential change of Paramecium.
Paramecium processes information by electrophysiological phenomena. Ion channels and ion pumps
reside in the membrane, these proteins play an important role in maintaining the ionic connection between the interior and the exterior of the cell. In thecell, the Ca2+ concentration is maintained at a level
lower than that of the external environment, while theK+ concentration is maintained at a higher level. Ina standard salt solution, the membrane potential ismaintained at approximately 30 mV lower than thatof the external environment when Paramecium does
not receive the stimuli from the environment. When
Paramecium receives a stimulus, the ion channels open
and ionic flow occurs between the interior and the exterior of the cell. The membrane potential is thendepolarized or hyperpolarized by the ionic flow. We
consider that the depolarization of membrane potential occurs when Paramecium senses danger in the newenvironment; on the other hand, the hyperpolarization of membrane potential can be considered to occur
when Paramecium senses that the new environment issafe. The ciliary movement is determined based on theinformation processed by the cell.

3

Eleak =

Eion =

RT[ion]olncF[ion]i(ion ∈ {Ca2+ , K+ }), (2)where c is the ionic valency, F the Faraday constant,
R the gas constant, and T the absolute temperature.
Also, [ion]o is the ionic concentration of the environment, and [ion]i the intracellular ionic concentration.
Then, the early current Ic (t) is calculated by:
Ic (t) =1(Eleak (t) − V (t)),Rm(3)

where Rm is the input resistance of the membrane in
Paramecium, and V (t) the membrane potential. A
positive value of Ic (t) means outflow of current. In
this study, it is assumed that the early aperture rateof each ion channel is proportional to the size of Ic (t).
The early aperture ratios of the Ca2+ channel and theK+ channel are calculated as follows:½
bCa Ic(Ic ≥ Th1 )
(4)
OCa =
0
(Ic < Th1 ),½
0(Ic > Th2 )(5)
OK =−bK Ic(Ic ≤ Th2 ),V irtual P aramecium

Sensory unit [9]Figure 1 illustrates the relationship between thechange in membrane potential and environmental©ISAROB 2006

(1)

where αCa and αK are the existence ratios of the Ca2+
and K+ channels, respectively. Also, ECa and EK are
the equilibrium potentials which are generated by thedifference in the concentration of ions inside and outside the cell. These are calculated using the following
equetion:

Virtual Paramecium comprises three units, namely,
the sensory unit, information processing unit, andmotor control unit. In this section, we describe theoverview of each unit.

3.1

αCa ECa + αK EK,
αCa + αK

where bCa , bK , Th1 , and Th2 are constants. The early
aperture rate OCa of the Ca2+ channel increases when314

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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chemical treatment is utilized in order to formulate
the electrical characteristics of only the cell body. Thechanges in the membrane potential are given as:

the early current is larger than the threshold Th1 andflows outwards (Ic ≥ Th1 > 0). Conversely, the early
aperture rate OK of the K+ channel increases whenthe early current is smaller than the threshold Th2
and flows inwards (Ic ≤ Th2 < 0). Thus, the early
aperture rate of each ion channel is determined corresponding to the change in the environmental conditions.

3.2

V̇ (t)=

ICa(cell) = ḡCa(cell) m5(cell) {1 − (1 − h(cell) )5 }(V (t) − ECa ),In this unit, the change in membrane potential andthe Ca2+ concentration in cilia are calculated using
the inputs of the sensory unit.
First, the changes in the membrane potential of
Paramecium are modeled as follows:(14)

where ḡCa(cell) is the maximum value of the ion
conductance for the Ca2+ channel in the cell, andx(cell) (x ∈ {m, h}) is the activation probability in only
the cell body defined by:1[Ic (t)−ICa (t, V )−IK (t, V )−Ileak (t, V ), (6)Cmẋ(cell) (t, V, OCa ) =αx(cell) (V, OCa )·(1 − x(cell) (t, V, OCa ))−βx(cell) (V ) · x(cell) (t, V, OCa ).(15)where V (t) is the membrane potential, Ic (t) is theearly current, and Cm is the membrane capacity. The
Ca2+ current ICa (t, V ), the K+ current IK (t, V ), andthe leakage current Ileak (t, V ) are given by the following equations [8]:By using both ICa(cell) and the Ca2+ current ICa
in the whole Paramecium, the Ca2+ current in cilia
ICa(cilia) is expressed as:ICa (t, V ) = ḡCa m5 {1 − (1 − h)5 }(V (t) − ECa ),(7)IK (t, V ) = ḡK n(V (t) − EK ),(8)
Ileak (t, V ) = gleak (V (t) − Eleak ),(9)

ICa(cilia) = ICa − ICa(cell) .

(16)

Thus, the ionic flow of Ca2+ in the cell body and thatin cilia can be separated. Finally, the Ca2+ concentration in cilia is calculated as follows :£¤¤d Ca2+ in
1 £= −ICa(cilia) + (Ip )Ca , (17)dt
2F(J¯p )Ca(Ip )Ca = 2F
(18)
µ
¶3 ,
K
m
1 + Ca2+[]in

where ḡCa , ḡK , and gleak are the maximum valuesof the ion conductance for Ca2+ , K+ , and leakage
ion channels, respectively. Equilibrium potentials forCa2+ , K+ , and leakage ions are expressed as ECa , EK ,and Eleak , respectively. Also, m, h, and n are the activation probabilities of each ion channel. Activation
probabilities, x ∈ {m, h, n}, of each channel are calculated based on the Hodgkin-Huxley equations [11] asfollows:

where [Ca2+ ]in is the Ca2+ concentration in cilia, F
the Faraday constant, (J¯p )Ca the maximum active
Ca2+ extrusion, and Km the [Ca2+ ]in at which theactive Ca2+ extrusion is at half its maximum value.
Also, (Ip )Ca is the current produced by the Ca2+ pumpwhich discharges Ca2+ to the exterior of the cell, and
it is assumed that (Ip )Ca is included in Ileak calculated
by Eqs. (6) and (13) for simplicity.

ṁ(t, V, OCa ) = αm (V, OCa ) · (1 − m(t, V, OCa ))−βm (V ) · m(t, V, OCa ),(10)ḣ(t, V, OCa ) = αh (V, OCa ) · (1 − h(t, V, OCa ))−βh (V ) · h(t, V, OCa ),(11)ṅ(t, V, OK ) = αn (V, OK ) · (1 − n(t, V, OK ))−βn (V ) · n(t, V, OK ).
(12)

3.3

OCa and OK are the early open ratios of the Ca2+
channel and the K+ channel that are calculated by
the sensory unit. The complicated changes in depolarization are realized by the above mechanism.
Next, the Ca2+ concentration in cilia is calculated.
Deciliated Paramecium whose cilia are removed by©ISAROB 2006(13)

where ICa(cell) is the current in the cell of the deciliatedParamecium, and is defined by the following equation:

Infomation processing unit [8]V̇ (t)=

1[Ic (t) − ICa(cell) (t, V )Cm−IK (t, V ) − Ileak (t, V )],Motor control unit

During its movements through water, Paramecium
follows a spiral path while rotating around its long axis[12]. This is one of the effective mechanisms that canbe employed to escape from the dangerous elements inan environment. The proposed Virtual Paramecium

315

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006¡¡

going backward
12
11

turninground
φ ([Ca ]in)
3
2+

8

4
7

5
6

f (V)

going forward
x
(a)
vf

φ

Ff
(b)

vd

Fd

ωd

b

Fs

f (V, [Ca2+ ]in ) = f (V ) − f ([Ca2+ ]in ),f (V ) = f0 + Af 1 (|Af req − V (t)|)Af 2 ,

F
(c)

(d)

µ

Figure 2: Driving forces generated by ciliary movements.

2+f ([Cafmax]in ) =
1 + exp(Af 3 log10 (Af 4 [Ca2+ ]in )

(20)
(21)
¶
, (22)

where Af i (i = 1, 2, 3, 4) are constants, f0 the steadystate value of ciliary beat frequency, fmax the maximal
value, and Af req the membrane potential value during
the resting beat frequency. The driving force F iscalculated as follows:[9] reproduces the behavior of an actual Paramecium.
Here, we describe the motor control unit and the planar movements of Virtual Paramecium in order to apply it to control of mobile robots.
The swimming condition of Paramecium depends
on the frequency and direction of the ciliary beat. It
is easy to understand the direction of the ciliary beatof Paramecium by considering the front of Paramecium as the direction corresponding to 12 o’clock on
an analog clock, as shown in Figure 2(a) [10]. As
Paramecium senses stimuli, the direction of the ciliary beat changes between half past six and 12 o’clock,while it is half past four in the normal condition (seeFigure 2(a)). The swimming velocity increases when
the ciliary beat direction approaches 6 o’clock. Also,
Paramecium moves backward as the ciliary beat direction approaches 12 o’clock. The driving force F canbe resolved into the driving power Fd and the rotational power Fs (see Figure 2(b)). In addition, it ispossible to determine the spiral movement in combination with Fd , which is generated by the movementof peculiar cilia around the peristome [12].
The ciliary beat direction is regulated by the Ca2+
concentration in cilia [10, 13]. First, the ciliary beatdirection φ([Ca2+ ]in ) is modeled as the following functions of [Ca2+ ]in :µ¶1φ([Ca2+ ]in ) = π−
0.5
Aφ log10 ([Ca2+ ]in )©ISAROB 2006

(19)

where Aφ1 and Aφ2 are constants that determine thedirection of the ciliary beat, and Cφ is the concentration value of [Ca2+ ]in when the direction of the ciliary
beat approaches 3 o’clock.
The ciliary beat frequency is regulated by the membrane potential [14]. Although the steady-state frequency of the ciliary beat is 10 – 20 Hz, it is increased
to approximately 50 Hz corresponding to the change
in the membrane potential. In addition, the ciliary
beat frequency decreases when the Ca2+ concentration increases to more than 102 µM. Therefore, theciliary beat frequency, f (V, [Ca2+ ]in ), is modeled as
the following equations of the membrane potential:

2

9

y

Aφ = Aφ2¢([Ca2+ ]in < Cφ )¢([Ca2+ ]in ≥Cφ ) ,

1

10

z

Aφ = Aφ1

F = a0 f (V, [Ca2+ ]in ),(23)

where a0 is the coefficient which transforms the ciliary
beat frequency into the driving force F in Paramecium.
By using Eqs. (19), (20), · · ·, (23), the movementsof Paramecium in two-dimensional space can be calculated. The velocity of Paramecium is calculated by:
vf = −a1 F sin(φ),(24)

where vf is the velocity in the longitudinal directionof body, and a1 is the coefficient which transforms thedriving force into vf (see Figure 2(b)). The velocity
vd , which is perpendicular to the longitudinal axis, andthe turning angle velocity ωd by vd are given by thefollowing equations:
vd = −a2 fd ,

(25)

vd
,
(26)
b
where a2 is a coefficient that transforms fd into vd andb is the distance between the fulcrum of rotation and
ωd =

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can be given by the following equation:· ¸ · 1
¸·
¸
1
v
vL
2
2
= 1
,
ω
− 2c1rvR
2cr

Robot position

CCD

ControllerPC

where 2cr is the length of the axle; and vL and vR are
the speeds of the rear wheels. In these experiments,
each rear wheel was controlled according to the following equation:
¸−1 ·
·
¸
¸
· 1
1
vf
vL
2
,
(28)
= γ 21− 2c1rωdvR
2cr

Control signal

Maker
Infrared
Mobile robot
Infrared transmitter

Figure 3: Mobile robot control system.

where γ is a coefficient that transforms the movement
velocity of Virtual Paramecium into the mobile robot,
in consideration of each size. The values of vf and ωdare calculated using the motor control model (24) and(26).

the point of application of fd (see Figure 2(c), (d)).
The orbital period of spiral movement in Paramecium
is defined as Tf , and the sign of Fd is inverted frompositive to negative, or vice versa, in every Tf /2 period. Thus, it is possible to reproduce the serpentine
movement that the spiral movement of Paramecium isprojected on the two-dimensional plane.

4

Biomimetic control experiments

4.1

Control system configuration

4.3

Experimental results

The biomimetic control of a mobile robot was executed using Virtual Paramecium. The parameters included in Virtual Paramecium were determined basedon previous studies [9, 10].
In this paper, reactions of the mobile robot toward
the area where the K+ concentration was different
form that of the standard salt solution were examined. The results obtained when the K+ concentrations of the stimulation solution were set to 16 mM
and 0.25 mM are shown in Figure 4(a) and (b), respectively. The left side of Figure 4 shows the trajectoriesof the mobile robot, while the right side shows the timecourse of the swimming velocities of Virtual Paramecium, which the negative velocity implies backward
movement.
Figure 4(a) shows the escape reaction of the mobile robot from the area where the K+ concentration
is higher than that of the standard salt solution. Further, the mobile robot exhibited accelerated movement
toward the area where the K+ concentration was lower
than the standard salt solution (see Figure 4(b)). Additionally, in Figure 4(b), the escape reaction to thearea of standard salt solution is shown after cruisingin the environment with low K+ concentration for ashort duration. On the basis of above results, we confirmed the effectiveness of the biomimetic control thatwas based on the information processing algorithm ofliving organisms.

Figure 3 illustrates the control system for a mobile
robot. The biomimetic control experiments of a mobile robot were carried out according to the following
procedure. DQ-04 (TAKARA), whose length is approximately 5 cm, was adopted as the mobile robot.
This robot has four wheels in the front and rear, andonly two rear wheels on the right and left can be controlled independently. In these experiments, an image
processing system was adopted for environmental measurement because it was difficult to mount any sensors
on the robots. The measurement area was square (2.5m × 2.5 m), and the two-dimensional position of amarker on the head of the robot was measured using a
CCD camera suspended from the ceiling. The virtual
chemical environment was set as a function of the position in the horizontal plane beforehand. The chemical
concentration was input to the sensory unit of Virtual
Paramecium every 40 millisecond. Virtual Paramecium processes the input information and determines
the control input to the robot. Then, the control signal was sent to the robot to realize the comparable
behavior to the actual organisms calculated by Virtual Paramecium.

4.2

(27)

Cruise control of the mobile robot

5©ISAROB 2006

Summary

In this paper, the results of our study on the control
of a mobile robot by using Virtual Paramecium have

The relationship between the rotation speed of thetwo rear wheels in the mobile robot and its movement

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Membranepotential, V [mV]

a

y [m]

2

a

b

[4] M. Tomita, K. Hashimoto, K. Takahashi, T. S.
Shimizu, Y. Matsuzaki, F. Miyoshi, K. Saito, S.
Tanida, K. Yugi, J. C. Venter, C. A. Hutchison III(1999) : “E-CELL: software environment for whole-cellsimulation,” Bioinfomatics, Vol. 15, No. 1, pp. 72–84.

80
0
-80

1

[K]o = 4mMbInitialSwimming
velocity [mm/s][K]o = 16mM

0
0

1

x [m]

1
0

1

2

3

4

5

6

-1

2
Time, t [s](a) Response to the area of [K+]o = 16mM
ab2[K]o = 4mM[K]o = 0.25mM

y [m]

a

Membranepotential, V [mV]

Initialcb[K]o = 4mM

0
0

1

x [m]

Swimming
velocity [mm/s]

1[6] T. Tsuji, K. Hashigami, M Kaneko, H. Ohtake (2002) :“Emerging chemotaxis of virtual bacteria using genetic
algorithum,” Transaction of IEE of Japan, Vol. 122-C,
pp. 201–207. (in Japanese)

c

80
0
-80
2
0
-2

10

2

20

30

Time, t [s](b) Response to the area of [K+]o = 0.25mM[7] T. Tsuji, A. Sakane, O. Fukuda, M. Kaneko, H. Ohtake(2002) : “Bio-mimetic control of mobile robots basedon a model of bacterial chemotaxis”, Trans. of theJapan Society of Mechanical Engineers, Vol. 68, No.
673, pp. 171–178. (in Japanese)[8] A. Hirano, T. Tsuji, N. Takiguchi, H. Ohtake (2005): “Modeling of the Membrane Potential Change ofParamecium for Mechanical Stimuli”, SICE Symposium on Decentralized Autonomous Systems, Vol. 41,
No. 4, pp.351–357. (in Japanese)

Figure 4: Responses of a mobile robot to the virtual
chemical environments.
been reported. We confirmed that the escape reaction
increased in the area where the K+ concentration was
higher than that of the standard salt solution, andthe mobile robot attempts to continue to reside in thearea with a low concentration of K+ . On the basisof above results, we confirmed the effectiveness of thebiomimetic control that was based on the information
processing algorithm of living organisms.
In future research, our experimental results will becompared to the control results by using the information processing algorithm of E. coli bacteria [7].

[9] A. Hirano (2005) : “A Model of Chemotactic Responseof Paramecium Based on Electrophysiology,” Dissertation for the degree of Master of Engineering, Graduate
School of Engineering, Hiroshima University.
[10] Y. Naitoh (1990) : “Behavior of unicellular animals,”
University of Tokyo Press. (in Japanese)[11] A.L.Hodgkin and A.F.Huxley (1952) : “A Quantitative Description of Current and Its Application to
Conduction and Excitation in Nerve,” The Journal of
Physiology, Vol. 117, pp. 500–544.
[12] K. Sugino and Y. Naitoh (1988) : “The method formeasuring a helical trajectory of Paramecium ,” Medical science, Igaku-Shoin Ltd., Vol. 39, No. 5, pp.485–
490. (in Japanese)

Acknowledgements
This work is partially supported by the 21st Century COE Program of JSPS (Japan Society for the
Promotion of Science) on Hyper Human Technology
toward the 21st Century Industrial Revolution.

[13] Y. Naitoh and H. Kaneko (1972) : “ReactivatedTriton-Extracted Models of Paramecium: Modificationof Ciliary Movement by Calcium Ions,” Science, Vol.
176, pp. 523–524.

References[1] H. Ohtake (1998) : “Software of living organisms,” KYORITSU SHUPPAN CO., LTD.. (in Japanese)[14] P. Brehm and R. Echkert (1978) : “AN ELECTROPHYSIOLOGICAL STUDY OF THE REGULATION OF CILIARY BEATING FREQUENCY IN
PARAMECIUM,” J. Physiol., 283, pp. 557–568.

[2] D.Bray, and S.Lay (1994) : “Computer Simulated Evolution of a Network of Cell-Signaling Molecules”, Biophysical Journal, Vol. 66, pp. 972–977.
[3] D. C. Hauri and J. Ross (1995) : “A Model of Excitation and Adaptation in Bacterial Chemotaxis”, Biophysical Journal, Vol. 68, pp. 708–722.

©ISAROB 2006

[5] T. Morohoshi, T. Tsuji, H. Ohtake (1998) : “A Modelof Bacterial Motor Control and Computer Simulationof the Chemotaxis,” Journal of the Society of Instrument and Control Engineers, Vol. 34, No. 11, pp. 1731–
1738. (in Japanese)

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Evolutionary simulation of an agent based mobility systemusing indirect communication
Francesco Zanlungo
Department of Physics
University of Bologna
Bologna, via Irnerio 46 40126

Takaya Arita
Graduate School of Information Science
Nagoya University
Chikusa-ku, Nagoya, Aichi, 464-8601 Japan

Abstract

systems.
It can also be seen as an attempt to understand if
some kind of pheromone-like communication could be
used to improve the efficiency of a real mobility system
and avoid the formation of traffic jams.

We study the evolutionary dynamics of a mobility
system in which agents are able to explore the environment and to communicate between them in order toincrease the efficiency of the system, i.e. to minimize
the average time needed to reach their goal, choosing
the quickest path and avoiding the formation of traffic
jams. The agents use an evolvable pheromone-like indirect communication system, of which we also give aninterpretation as the average of different kinds of directcommunication.
Key words: Indirect communication, evolution ofneural networks, mobility systems, multi-agent system

1

2
2.1

The mobility system

The agents are located on the nodes of a manhattan 2D network (“a town”). At each time step they
have a probability p ≤ 1 to perform just one of fivepossible actions, i.e. to move to one of the four neighbour sites (at manhattan distance d = 1, where thedistance between the points P1 and P2 is defined byd(P1 , P2 ) ≡ |x1 − x2 | + |y1 − y2 |) or to rest on thesite they are located in. We want our town to havewider roads, in which higher speed is possible, and narrower roads. We also want to have a traffic problem,
i.e. agents should be slowed down when a great number of them is located in the same site (when we talkabout the agent’s speed, we are actually talking about
its probability p to perform an action). To achieve thiswe define, identifying a site with its discrete coordinates (i, j) and the number of agents on that site withn(i, j), the probability to act as

Introduction

A town is a complex system in constant evolution.
Both the environment (the structure of the town, its dimensions, the road and public transportation system,
the location of the places of interest) and the behaviourof the inhabitants are constantly changing, and influencing each other. Focusing only on the mobility system, new roads, railways and subway lines are built according to the needs of the citizens, but the presence of
these new structures modifies the behaviour and habitsof the citizens, and so on.
In this paper we propose a simple system of agents
whose behaviour is regulated by evolving neural networks. These agents move on a 2D manhattan gridthat represents a town’s road system, with the connected traffic problems. The knowledge of the structure of this road system is obtained through the exploration of the system by the agents, and communicatedto other agents through a “pheromone-like” field.
It is our opinion that the model that we propose canbe a fruitful environment to study the evolutionary dynamics of a system of different interacting agents regulated by a simple behavioural mechanism, and eventually the emergence of some kind of “global behaviour”.
In particular, we are interested in studying the emergence and evolution of an indirect communication system. This model could also be useful to get some insight about the evolution of actual mobility and urban©ISAROB 2006

The modelp((i, j), n(i, j)) = w(i, j)t(i,j) (n(i, j))(1)where 0 ≤ w(i, j) ≤ 1 is the wideness of the site andt(i,j) is a (eventually) site dependent “traffic jam function”, which we ask to be (not necessarily strictly) decreasing and to have t(i,j) (1) = 1 ∀(i, j). In particular,
we are going to use t(n) = (1/n)γ , with γ ≥ 0.
Even if we talk about “wide and narrow road” it isclear from the definition that the wideness is related tothe site, and not to the roads, since no road is actuallypresent in this model. We order the wide sites in rows
inside the grid (see figure 1 for the basic structure of
the town) to simulate the presence of a road, but p has
no dependence on the direction of the motion so there
is no difference between an agent that is following a“road” and an agent that is just crossing it.

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Pe (i, j) that diffuses with coefficient cd [1]. Agents areable to perceive the gradient of this field, i.e. the difference with the neighbouring sites Pe (i, j) − Pe (neigh).
We could interpret this pheromone field as a “rumor” that stands for the “collective knowledge” of theurban structure, and spreads between agents. Actually in our model the rumor is spreading on the urban structure, but we can think that in this model arepresent immobile and mobile agents, and the rumor isspreading between the (not explicitly represented) first
ones. Adopting this point of view, the medium thatmobile agents are using to communicate are the immobile agents.

Figure 1: Basic structure of the town’s road system.
Thick means wide.

2.3
This is a very simple and abstract model that is,according to our opinion, feasible to study the generalfeatures of the interaction of a group of agents in a
mobility system. Nevertheless in building the modelwe were inspired by the actual car traffic systems. A
node in the network represents a crossroad (plus some
portions of the roads leading to it). If a large numberof cars is located in the same crossroad, a traffic jam
occurs. w stands for the maximum speed that the carcan have on that (cross)road, while t is the “tendency”
of the crossroad to the formation of traffic jams.
We can also think of this model as a system of pedestrians, in which the wide roads correspond to public
transportation, and t is the waiting time to use it.

2.2

The evolution of neural networks through genetic algorithms has shown to be an efficient way to develop
agents and robots able to move in complex environments [2].
We use a fully connected neural network with 6 inputs (the distance from the goal in x and y coordinates(gx − x) and (gy − y) plus the 4 “pheromone gradients”), a layer with h hidden neurons and 5 outputs
corresponding to the 5 possible actions. The action
with the highest output value is performed. The connections of the networks are kept fixed during the wholegeneration, i.e. there is no learning, just evolution ofconnection weights and of the number of the hidden
neurons (h).
We define the fitness function in the following way.
Each agent is created in a randomly selected starting point (sx , sy ). If τ is the time to reach its randomly selected goal point (gx , gy ) (a minimum distance between start and goal is imposed), the ratior = (|gx − sx | + |gy − sy |)/τ is measured. If the agent
does not reach the goal in a maximum time T , r is set
to zero. The operation is repeated R times and theaverage value of the ratio is the agent’s fitness f = r̄.
Since we did not know the value of h that could solve
our problem, we used the following evolution strategy,(inspired by [3, 4]) to optimize also this parameter. We
start using different networks divided in Ns species,
according to their structure (i.e. to the value of h),each one with an equal number of members (in eachexperiment we have networks with different structure
and weights interacting and competing at the sametime on the urban structure). Tournament selection
is performed inside a single species for gs generations,then the dimension (number of agents) of the species ischanged according to its fitness. The size of the tournament is changed according to the species’s new size,and evolution is performed again for gs generations inside the species’s boundary, and so on. Mutation of theconnection weights was performed adding, with probability pm = 0.05, a gaussian noise with mean zero .

Communication

The way the citizens of a town learn to use its mobility system is obviously quite complicated and differentiated (past experience, communication with otheragents, centralized communication like news from theradio, etc.). In our model we wanted to introduce
some kind of mechanism that could represent an average of all these processes, that could be regulatedby the agents themselves and that could be utilized
just through a local observation of the environment (toavoid a too complex sensory system or the introductionof memory).
We decided to use pheromone based communication
which has shown to be an efficient way to evolve communities of collaborating agents [1].
We introduce a pheromone field P (i, j), generated by the same agents which drop an amount q ofpheromone every time that they cannot perform an action, i.e. every time that “they have to wait” because
they are located in a narrow road or they are stuck in a
traffic jam. This means that if the indirect communication system emerges, pheromone should be interpreted
as “repulsive” and used to inform the other agents
about “bad places”. Pheromone evaporates according
to a coefficient ce and turns into evaporated pheromone©ISAROB 2006

The decision mechanism and its evolution

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4000

h=5
h=6
h=7
h=8
h=9
h=10
h=11
h=12
h=13
h=14

0.2
3000

0.15

fitness

2000

0.1
1000

0.05
0

200

400

600

800

1000

generation
0
0

200

400

600

800

1000

generation

Figure 3: Evolution of the h species’ size for the same
simulation shown in figure 2. Initially 10 species (corresponding to values of h between 5 and 14) are present,
then h = 9 (green) and h = 14 (red) invade the population.

Figure 2: Evolution of the average fitness value throughgenerations in the case of no traffic jams.

3

Experiments

3.2

We used 4000 agents and a 50x50 grid, averaging
over R = 20 runs. Wide roads were defined to have
w = W = 1 and narrow ones to have w = N = 0.05.
The values of the pheromone parameters were q = 0.1,
ce = 0.1, cd = 0.1 (we reserve for future works thetask of optimizing these parameters through evolution,and eventually also of evolving the pheromone dropping mechanism).

3.1

We then fixed w = W on the whole grid and used
γ > 0, i.e. the agents did not have to find any “bestpath” but just to avoid traffic jams.
Using γ = 1 agents going straight to the goal
performed with an average fitness f = 0.292, while
the pheromone dropping agents had an higher fitness(f = 0.377). We tested the performance of these agents
without allowing them to drop pheromone and we unexpectedly found that their fitness did not change atall. Actually the genetic algorithm had developed a
network that reached the goal moving always clockwise(in the specific case). Due to the nature of the manhattan metric this allowed them to reach the goal on aminimal distance path, reducing strongly the probability of a collision (through the introduction of a “circulation rule”, i.e. of some kind of collective behaviour,
as clock-wise or counter clock-wise circulation, see figure 4). The emergence of this rule has no relationshipwith communication, since it was based only on the“distance to the goal” inputs. Actually, for this low
value of γ, the circulation rule was enough effective to
avoid the formation of traffic jams, and it prevented
the emergence of communication.
In the case of γ = 2 the agents going straight to
the goal without using a circulation rule performed
with f = 0.024, while the pheromone dropping agents
reached a (4 times higher) value of f = 0.098. If
we made them run without pheromone, i.e. without
communication, they performed with f = 0.030. Thisvalue is quite lower than the one they had when weallowed them to communicate, but higher than the one
attained by agents going straight to the goal. Thisshows that both communication and a circulation rule
had emerged.
We tested on this experiment also the pheromone
dropping agents that we had evolved in the previous experiment without traffic jams. Despite the completely

No traffic jams

In a first class of experiments we used γ = 0 (i.e.
t(n) = 1 ∀n) on the whole grid. In this case no traffic
jam can be generated, and the only problem is to findthe quickest path to the goal. We first fixed w(i, j) = N∀(i, j) and checked that we were able to evolve networks
with the maximum possible fitness f = 0.05. Then,
without introducing pheromone, we used a structure ofwide roads according to figure 1. The networks evolved
to f = 0.064 which we assumed to be the maximum
fitness in absence of further information (the fitness
attained by agents going straight to the goal). We willconsider pheromone communication to be successful if
it is able to improve this value.
The agents that dropped pheromone evolved toreach a fitness roughly 3 times higher (f = 0.202),
i.e., indirect communication had emerged. In this experiment, pheromone communication is used to markthe narrow roads (wide roads correspond to the minima of the pheromone field). The agents developed anability to reach the wide roads following the decreasing
pheromone gradient when they were distant from theirgoal, to move on the the minima (follow the wide road)in order to approximate the goal, and then to leave theminima (despite the positive pheromone gradient) inorder to reach the goal when they were near to it. The
results of this experiment are shown in figures 2 and 3.

©ISAROB 2006

Traffic jams in a uniform grid

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while the one of agents using pheromone increased to
0.067.
We finally tried to evolve explicitly a network forthis problem. The maximum reached average fitness
was 0.092, but this fitness dropped to 0.060 when we
tried to extract a “best” uniform population.

A

4

B

We have shown that simple neural networks are able
to evolve a (pheromone based) indirect communication
system that is effective in solving both the problem offinding a “good path” and to avoid “traffic jams” in a
simple mobility system.
We have found that a network evolved in a timeconstant environment (no traffic jams) could be effective also in the other two experiments without further
evolution, showing the effectiveness of the pheromone
communication in both the tasks of avoiding problems(pheromone maxima) and following good paths (minima). The cases with traffic jams (i.e. the environment
is constantly changing according to the behaviour of
the agents) showed the emergence of global behaviours(“circulation rules”) and the presence of a not trivial
evolutionary dynamics (the presence of differentiatedneural networks, different in connection weights andstructure, seems to cause an higher value of the fitness), which should be further investigated in future
works.

Figure 4: An agent has to reach point B from A, andanother A from B. Black corresponds to a circulation
rule that avoids collision, while red corresponds to go
straightly to the goal. Notice that the two paths cover
the same distance in manhattan metric.

different nature of this experiment, they were able todouble the fitness of the agents going straight to thegoal (f = 0.051). This happens because the agents,
trained in the first experiment to follow the wide roads
as minima of the pheromone field, result also able to
avoid the traffic jams (maxima of the pheromone field)in this experiment.

3.3

Complete model

In real mobility systems, the greatest traffic problems occur when one of the main communication routes
is blocked due to a traffic jam (often caused by a traffic accident). To simulate this problem on our simple
geometry we used the basic structure of figure 1 witha traffic function that used γ = 1.75 for the sites with
w = W and γ = 0 where w = N (this can be interpreted as a rule that says that accidents, and thus traffic jams, are easier to occur where the speed is high).
In this way we created a dilemma about the use of wide
roads.
In this environment, the agents going straightly to
the goal performed with values that changed strongly
according to different repetitions of the experiment,
but that where always smaller than 0.05 (between 0.035
and 0.048 in the experiments that we have performed).
The crucial point is that these values are lower than0.05, i.e. the introduction of wide roads on which traffic jams are easy to occur is harmful if they are notused in a “intelligent” way.
We then tested with these conditions the network
evolved dropping pheromone in absence of traffic jams,
which performed with f = 0.063 (almost the samevalue on each test), i.e. it was able to use the wide roads
in a way good enough to improve over the value of 0.05.
Furthermore, if we added to the road system a further“ring” of wide roads, the fitness of the agents goingstraightly to the goal decreased again (0.033-0.037),©ISAROB 2006

Conclusions

References[1] Y. Nakamichi and T. Arita, “Effectiveness ofEmerged Pheromone Communication in an Ant
Foraging Model”, Proceedings of the Tenth International Symposium on Artificial Life andRobotics, 324-327, 2005.
[2] S. Nolfi and D. Floreano,
Robotics”, MIT Press, 2000.

“Evolutionary[3] K. O . Stanley and R. Miikkulainen, “Evolving Neural Networks through Augmenting Topologies”, Evolutionary Computation 10(2), 99-127,
2002.
[4] F. Gomez and R. Miikkulainen, “Solving NonMarkovian Control Tasks with Neuroevolution”,
Proceedings of the Sixteenth International Joint
Conference on Artificial Intelligence, pages 13561361, Morgan Kaufmann, San Francisco, California, 1999.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Evolution, Development and Learning in the Prisoner’s Dilemma Game
Yukimasa Ogawa
Takaya Arita
Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
E-mail: ogawa@create.human.nagoya-u.ac.jp, arita@nagoya-u.jp
Abstract

model in which environment was static and the optimal solution was ﬁxed, and focused on the evolution ofdevelopmental process by analyzing the feature of the
genomes arising in the process of the Baldwin eﬀect[1].
The purpose of our study is to investigate the relationships among evolution, learning and development,
especially in dynamic environments where there is no
explicit optimal solution through generations, and theﬁtness of an individual depends on the interactions in a
population. To do this, we have adopt a synthetic approach and construct a computational model using the
Iterated Prisoner’s Dilemma (IPD) game as dynamic
environments. In the model, evolution is achieved by agenetic algorithm and learning is achieved by a simple
improvement algorithm termed Meta-Pavlov. Development is a kind of mapping process from genotype
to phenotype, and is the least understood one of thethree. We focus on two computation-universal mechanisms for development: a tag system and a Turingmachine, and compare them with the results of theexperiments.

Evolution, learning and development are the threemain adaptive processes that enable living systems toadapt to environments on diﬀerent time scales. The
purpose of our study is to investigate the relationships among evolution, learning and development, especially in dynamic environments where there is no
explicit optimal solution through generations, and theﬁtness of an individual depends on the interactions
in a population. To do this, we construct a computational model using the iterated Prisoner’s Dilemma
game as dynamic environments. In the model, evolution and learning are achieved by a genetic algorithm and a Meta-Pavlov learning, respectively. Development is handled by two alternative computationuniversal mechanisms: a tag system and a Turing machine. The results showed that almost all experiments
we conducted ﬁnally established cooperation throughevolution, learning and development, while there werevarious scenarios in which cooperative relationships
were established, corresponding to the ﬂexibility in therespective roles of them.
Keywords: development, genetic algorithm, trilateral adptation, the iterated prisoner’s dilemma, artiﬁcial life.

1

2

We construct a computational model in which three
processes (evolution, learning and development) create the strategies for the Iterated Prisoner’s Dilemma(IPD) game (Figure 1). The IPD game is a simple
2-player non-zero-sum game, in each round of which
each player independently chooses an action from cooperate (C) or defect (D) without knowing the other’s
choice, and obtains the score according to the payoﬀ
matrix (Table 1). We adopt the framework proposedby Downing [1], while we incorporate a tag system [4]as an alternative mechanism for development, and interpret the developed phenotypes as strategies for the
IPD game. The genotype encodes both a mechanism
performing a developmental process, which is either a
tag system or a Turing machine, and an initial tape on
which the mechanism runs. The ﬁnal developed tape
as a phenotype is interpreted as a deterministic strat-

Introduction

Evolution, learning and development are the threemain adaptive processes that enable living systems toadapt to environments on diﬀerent time scales. These
processes do not occur in isolation, and interactions
among them are very complex and not clearly understood in both biology and engineering. Combinations of them have a long history in the ﬁelds ofartiﬁcial life, evolutionary computation, and engineering. Also, recently, interactions between evolution anddevelopment, so-called “evo-devo” have attracted researchers in both biology and artiﬁcial life. However,very few models combine all these three adaptive processes, among which Downing introduced a developmental process into Hinton and Nowlan’s very simple©ISAROB 2006

Model

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Figure 1: Evolution, development and learning in themodel.
Figure 2: Individuals in the space of learnability anddevelopability.
Table 1: Payoﬀ matrix for the Prisoner’s Dilemma
game.
XXX
XXopponent
cooperate defect
XXX
playerX
X
cooperate(3, 3)(0, 5)
defect(5, 0)(1, 1)(player’s score, opponent’s score)

2.1

Each genotype has genotype-length bits, and is composed of four ﬁelds: ratio, rule, intron, and tape. The
ratio ﬁeld have 3t bits, which encodes three integers:
r1 , r2 and r3 , each representing the ratio in length of
the rules, the introns and the tapes, respectively. The
intron ﬁeld just separates the rule and the tape ﬁeld,
and is not used at all in subsequent development andlearning. The tape ﬁeld is used as an initial tape on
which the developmental process runs.
In case of a tag system, the rule ﬁeld encodes thetransition rules, each of which speciﬁes the elements tobe removed from the beginning of a tape, and the elements to be appended onto the end of the tape. Eachtransition rule requires (Pts +Qts )×nts bits, where Pts
and Qts are the numbers of removed and appended elements, respectively, and nts is the number of bits used
to encode these elements. The tape symbol encodes
an integer between 0 and 2nts − 1. These integers areconverted to 0, 1 or 2 with nearly equal probability.
In case of a Turing machine, the rule ﬁeld encodestransition rules as 5-tuples of the form (s, x, s∗ , x∗ , a),in which s is the current state, x is the tape symbol
being read, s∗ is the next state, x∗ is the next symbol,
and a is the action, that is either overwriting x with x∗ ,or inserting x∗ to the immediate right of x on the tape.
Each transition rule requires 2mtm + 2ntm + 1 bits,
where ntm and mtm are the numbers of bits used to
encode a tape symbol and a state, respectively. Eachegy of the IPD game. The action of the plastic phenotype is changed via a simple improvement algorithm
termed “Meta-Pavlov” [3] during a game. The averagescore of each individual is regarded as a ﬁtness value,
new population is generated by the roulette wheel selection according to the scores, and then mutation is
performed on a bit-by-bit basis.
The essential point of the model is that the developmental process can determine not only the strategies but also the possible amount of improvement bylearning during game play, and at the same time theevolutionary process can determine this developmental
process (Figure 2). Individuals evolved in the model
are characterized in the space shown in this ﬁgure,of which the horizontal axis represents the possibleamount of improvement by learning and the verticalaxis represents the level of the degree of dependence
on development. For example, evolution can generate
the individual (at the coordinate origin) that neverundergoes a developmental process, which means the
genotype-phenotype mapping is 1 to 1, and has nolearning ability, which means the phenotype has noplasticity.

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Figure 3: An example of development in case of a tag
system.
Figure 4: An example of development in case of aTuring machine.
element is expressed with 0, 1 or 2 as in the case of atag system.

2.3
2.2

Development

The ﬁnal developed tape as a phenotype is interpreted as a deterministic strategy for the IPD game,and it deﬁnes the next action according to the history of actions of both players, which is the same as in
Lindgren’s model [2] but is introduced plasticity as in
Suzuki s model [3]. Each strategy is represented as astring of 0’s (defect), 1’s (cooperate) and 2’s (plastic
action). “x” is used to express this plastic phenotype
in this paper.
A strategy of memory m has an action history hm
which is a m_length binary strings as follows:

The phenotype is generated from both the transition rules for a tag system or a Turing system and an
initial tape into which developmental process decodes
the genotype.
Figure 3 shows an example of development in caseof a tag system. In general, the transition rules fora tag system specify that a ﬁxed number of elements
should be removed from the beginning of the sequence,and depending on these elements, several number of
elements should be appended onto the end of the sequence. In the current model, both the number of theelements removed and the number of the elements appended are ﬁxed, and are 1 and 2 respectively. In theexample shown in Figure 3, ﬁrst, 0 is removed fromthe beginning of the tape, and then the rule corresponding to 0 is retrieved from the transition rules.
Since 0 → 02 is included in the transition rules, 02 isappended onto the end of the tape in this case. This
type of replacement process continues until the tapesize reaches L.
Figure 4 shows an example of development in caseof a Turing machine, which is the same as the one
adopted in [1]. The head always begins the developmental process in state 0 at the left edge of the cell on
the tape. In this ﬁgure, the current state and the tapesymbol are s0 and 0, respectively, and then the rule
corresponding to (s0 , 0) is retrieved from the transition rules. As (s0 , 0) → (s0 , 2, Insert) is included in
the transition rules, 2 is inserted to the immediateright of 0 on the tape, and the head moves right one
cell. If the head reaches the right edge of the cell on
the tape, it is again set to the left edge of the cellon the tape. This type of movement and replacement
process continues until either the tape size reaches Lor the number of the steps reaches max -devp-stepstm .

©ISAROB 2006

Phenotypehm = (am−1 , · · · , a1 , a0 )2 ,(1)

where a0 is the opponent’s previous action (0 and 1),
a1 is the previous player’s action. a2 is the opponent’s
next to previous action, and so on. S for a strategy ofmemory m can be expressed by associating an action
Ak (0, 1 or 2) with each history k as follows:
S = [A0 A1 · · · An−1 ] (n = 2m ).

2.4

(2)

Learning and Evolution

A plastic phenotype can be changed by learning
based on Meta-Pavlov during a game. Each agent
changes its phenotypes according to the result of eachround by referring to the Meta-Pavlov learning matrix(Table 2). It does not express a strategy but the way
to change its own strategy (phenotype) according to
the result of the current round, though this matrix isthe same as that of the Pavlov strategy. The learning
process is described as follows: At the beginning of thegame, the plastic phenotype is set randomly to either
0 or 1. If the phenotype used in the round is plastic,
the phenotype is changed to the corresponding value
in this matrix based on the result of the round. The

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Table 2: Strategy matrix for Meta-Pavlov learning.
XXX
XXopponent
cooperate defect
XXX
playerX
X
cooperateCD
defectD
C

Table 3: Classiﬁcation of the results by dependence on
development or learning.
ClassiﬁcationTag
systemTuringmachineL-independent XX
L-dependent XL
stableL-independent DX
D-dependent
L-dependent DL
USunstable

0
1
2
15
2

3
7
9
1
0

D-independent

agent uses the new strategy speciﬁed by the changed
phenotype from the next round on.
We shall consider a population of N individuals interacting according to the IPD. Each bit of gene is
set randomly in the initial population. The roundrobin tournament is conducted among strategies under the condition in which the performed actions could
be changed by noise with probability pn . Each plastic
phenotype is set randomly at the beginning of games.
Rounds are repeated with the probability pf , which
is decided at the end of each round. The tournamentis “ecological”: The average score of each individual
is regarded as a ﬁtness value, a new population of individuals is generated by the roulette wheel selection
according to the scores, and mutation is performed ona bit-by-bit basis with the probability pm .

3

(L-dependent) was decided in case that the average
ratio of plastic phenotypes chosen during game play
was more than 0.5, in the last generation. The average score was kept around 2.4 in the last generationin almost all trials in L-dependent case (XL and DL),
while DX trials varied in the score from trial to trial,
and achieved the highest score, 2.6.
From this table, we see that 15 trials of 20 trials
were DL in case of the tag system. This means that the
tag system tended to work eﬃciently to help evolution
explore the cooperative roles of learning and development in dynamic environments, in other words, three
adaptive processes evolved the individuals depending
on both learning and development, which could resultin the smooth establishment of cooperative relationships.
Figure 5 shows an evolutionary transition of theaverage score, dependency on learning and dependency on development in a typical trial of DL. Dependency on learning and dependency on development were calculated as the average ratio of “2”s inall of the developed expressions (which is diﬀerent
from the deﬁnition of L-dependent) and the numberof times of applying rules (in development) divided by
50, respectively. The dashed line shows the threshold that separates D-dependent and D-independent.
The evolutionary scenario in this ﬁgure is summarized as follows: Defect strategy spread until aroundthe 50th generation, which decreased the score (which
is diﬃcult to see from this ﬁgure). Simultaneously,
partially plastic strategies (e.g. [0xxx0x0x0x0x010x])
spread in the population. Then, fully plastic strategies (e.g. [xxxxxx0xxxxxxxxx]) occupied the population, which increased the plasticity and the averagescore. Around the 1300th generation, some strategies(e.g. [xx0x000x0x0000xx]) spread, which decreased
the plasticity, but around the 1600th generation, theplasticity increased again. Finally, some robust strategies (e.g. [x00x000x000x000x]) occupied the population. The number of times of applying developmental
rules was around 8 throughout the generations.

Evolutionary Experiments

We conducted 20 trials for 10000 generations ineach of the experiment with the tag system and thatwith the Turing machine, focusing on the strategies ofmemory 4 (m = 4). The other parameters were as follows: genotype-length = 300, N = 1000, pm = 1/5000,
pn = 1/25, pf = 0.995, t = 5, Pts = 1, Qts = 2, nts =5, L = 16, mtm = 3, ntm = 5, max -devp-steptm = 100.
First of all, we have found that cooperation was
ﬁnally established through evolution, learning and development in most of the trials we conducted, while
there were various scenarios in which cooperative relationships were established, corresponding to the ﬂexibility in the respective roles of them. The last generation in each trial was classiﬁed into the following ﬁve
types: XX, XL, DX, DL and US as shown in Table
3, in which D represents strong dependency on development, L represents strong dependency on learning,
X represents weak dependency and US represents thegray type owing to instability in the boundary areas.
For example, XX represents that the average individual depended neither on development nor on learning, and DL represents that the average individual depended both on development and on learning. Dependency on development (D-dependent) was decided incase that the average times of rule application in development was more than 5, and dependency on learning©ISAROB 2006

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1[xxxxxx0xxxxxxxxx][xxxx0x0x0xxx0xxx]

0.8[x00x000x000x000x]

Average score

0.6[xx0x000x0x0000xx]

0.4

Dependency

Average score

Dependency

Dependency on learning

3

1

2.5

0.8

2

0

2

[110x000000000001]

1.5
1[0000000100000000]

0.501000 2000 3000 4000 5000 6000 7000 8000 9000 100000Dependency on learning
0

Generation

Figure 5: An experimental result (tag system).

0.5

01000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Generation

Figure 6: An experimental result (Turing machine).

4

Conclusion

We have constructed a computational model using
the iterated Prisoner’s Dilemma game as dynamic environments in order to investigate the relationships
among evolution, learning and development. Development is handled by two alternative computationuniversal mechanisms: a tag system and a Turingmachine in the model. The evolutionary experiments
have shown that when adopting a tag system, each individual tended to depend on both learning and development, which often resulted in the smooth establishment of cooperative relationships. While, when adopting a Turing machine, either development or learning
worked exclusively and built cooperative relationships.
These results show the ﬂexibility in the roles of learning and development in establishment of cooperation.
Diﬀerences between the results of the two developmental mechanisms could possibly be due to the fact thatthe tag system could work more eﬃciently to help evolution explore cooperative roles of learning and development in dynamic environments.

11 trials of 15 DL trials showed the similar evolutionary scenario to the above described one, while
4 trials had other scenarios like the one, for example, in which dependency on learning did not reachsuch a high value and some robust strategies like
[x00x0xx00xx0x00x] evolved to occupy the population
while keeping the average score high.
As for the cases of the Turing machine, we see fromthis table that 16 trials of 20 trials were classiﬁed into
XL or DX, which means that either learning or development tended to work exclusively to build cooperative relationships. The possible cause of this is the
ﬂexibility in the complementary roles of learning anddevelopment in evolution of cooperation. Just 2 trials of the 9 DX were the special case in which cooperative relationships could not be established since
defect-oriented strategies continued to occupy the population.
A typical case of XL is summarized as follows:
D-dependent defect strategies spread in the population in initial phase. However, dependency onlearning did not increase and the strategies like
[x00x00x00x00001x] ﬁnally evolved with the averagescore converging to about 2.4 and with dependency
on learning hovering around 0.4. Figure 6 showsa transition in a typical trial of DX. Firstly, Ddependent defect strategies (e.g. [0000000100000000])grew in the population, which decreased the average score until around the 1300th generation.
But then, “Tit-for-Tat”-like non-plastic strategies(e.g. [0101010101010101]) spread in the population,
which increased the average score sharply. Finally,some cooperative strategies without plasticity (e.g.
[110x000000000001]) occupied the population, so that
average score converged to about 2.6.

©ISAROB 2006

Dependency on development
0.6

0.2

3
2.5

1.5
1

0

Average score

0.4

Dependency on development

0.2

Average score[0101010101010101]

References[1] Downing, K. L.: Development and the Baldwin
Eﬀect, Artiﬁcial Life, Vol. 10, No. 1, pp. 39–63,
2004.
[2] Lindgren, K.: Evolutionary Phenomena in Simple
Dynamics, Artiﬁcial Life II, pp. 295–311, 1991.
[3] Suzuki, R. and Arita, T.: Interactions between
Learning and Evolution: The Outstanding Strategy Generated by the Baldwin Eﬀect, Biosystems,
Vol. 77, Issues 1–3, pp. 57-71, 2004.
[4] Wolfram, S.: A New Kind of Science, Wolfram
Media, pp. 93–96, 894–895, 2002.

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Describing Metabolic Pathways Using an Artiﬁcial Chemistry
Based on Pattern Matching and Recombination
Yoshikazu Suzuki
Kazuto Tominaga
School of Computer Science
Tokyo University of Technology
Katakura, Hachioji 192–0982 Japan
Abstract

paper, we attempt to model a system in natural organisms — the metabolic pathway of the oxidation of
fatty acids — to explore possibilities of using the artiﬁcial chemistry as a tool to model such a system.

In this paper, we apply our artiﬁcial chemistry todescribing a real chemical system. The subject of description is a metabolic pathway: the oxidation of fatty
acids. We modelled the pathway using straightforward
design of molecules and reaction rules in the artiﬁcial
chemistry. The description was executed by a simulator and correct behavior was observed. The qualitativemodelling and simulation are a promising applicationof artiﬁcial chemistries.

2

An Artiﬁcial Chemistry Based on
Pattern Matching and Recombination

Keywords: artiﬁcial chemistry, metabolic pathways,
qualitative modelling, qualitative simulation.

This section brieﬂy explains a simple artiﬁcial
chemistry based on pattern matching and recombination of stacks of strings [2], which we are going to use
in the following discussion.

1

2.1

Introduction

An element is denoted by an upper-case character,or a capitalized sequence of alphabetical characters.
For example, A, B and Coa are elements. An element
corresponds to an atom or a group of atoms in nature.
An object (corresponds to a molecule or a compound of molecules) is a character string or a stackof strings, such as those depicted below.

Artiﬁcial chemistry is becoming one of the mostactive research areas in artiﬁcial life ﬁeld, and various artiﬁcial chemistry systems have been built [1].
An artiﬁcial chemistry is an abstract chemical model.
It is usually used to construct virtual systems in order to study behavior of various living systems and/or
necessary conditions for virtual systems to show such
behavior. We proposed an artiﬁcial chemistry basedon string pattern matching and recombination [2] andhave constructed several virtual systems including an
imaginary cell with cell cycle [3].
Recently, artiﬁcial chemistries are becoming applied
to natural chemical systems. For example, Benkö et al.
used an artiﬁcial chemistry based on graphs to produce
chemically expected reaction pathways [4]. We applied our artiﬁcial chemistry to modelling DNA computing [5]. The capability of modelling natural chemical or biochemical systems will be a good property for
artiﬁcial chemistries to broaden their applicable area.
Although DNA computing uses natural molecules(i.e., DNA) and their properties, modelling it does notcontribute directly to understanding living systems,
for it is performed in test tubes in laboratory. In this©ISAROB 2006

Elements and Objects

A B C

A B A B
C D

A B C
Coa

These objects are denoted by the string notations
0#ABC/, 0#ABAB/3#CD/, 0#ABC/1#Coa/ respectively;
the numbers represent displacements of the line (innumbers of elements) relative to the ﬁrst line. A displacement can be positive, zero or negative.

2.2

Patterns

A pattern matches (or does not match) an object,and it can utilize two kinds of wildcards. An element
wildcard, which is denoted by a number such as 1,
matches any element. A sequence wildcard, denoted bya number and an asterisk such as 2* and *2, matches

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any sequence of zero or more elements; the positionof an asterisk represents the direction in which thesequence can extend.
A pattern is denoted in a similar way to an object.
For example, the pattern 0#A1C/ matches the object
0#ABC/; the pattern 0#*1AB/1#CD/ matches the object 0#ABAB/3#CD/. Note that the displacements aremeaningful and that the length of a sequence wildcard
is treated as zero in the notation for patterns.

2.3

Modelling a metabolic pathway

In this section, we are going to describe a metabolic
pathway in natural organisms using the artiﬁcial
chemistry explained in the previous section. The pathway to describe is the oxidation of fatty acids. Fat provides energy for organisms that do not perform photosynthesis. Fat is decomposed to glycerol and fatty
acids, and fatty acids are degraded to acetyl CoA in
mitochondria [6, Chapter 2].

Recombination Rules
3.1

A recombination rule transforms a group of objects
into a group of objects, conserving elements just likea chemical reaction does. It is expressed in a manner
similar to chemical formulae, but in terms of patterns.
An example rule is

The oxidation of fatty acyl CoA

The oxidation pathway of fatty acids to acetyl CoA
is depicted in Figure 1 [6, Chapter 2]. Shown at the topof the ﬁgure is a molecule of fatty acyl CoA, which isactivated form of fatty acid. It has a long hydrocarbon
chain; R in the ﬁgure stands for the tail of it.

0#*1AB/1#CD/ + 0#AB2*/ → 0#*1ABAB2*/1#CD/

which is illustrated as follows.
A B
C D

A B

R
acetyl CoA-A B A BC D

H

If this rule is applied to the objects 0#ABAB/3#CD/ and0#ABC/, the object 0#ABABABC/3#CD/ is produced andthe reactants disappear.

CoA

Sources and Drains

C

H

S

H

O

C

C

C

H

H

H

S

CoA

fatty acyl CoA

CoA

R

H

O

C

C

H

S

FAD

CoA
(1)

(4)

H

O

H

O

C

C

C

C

H

S

H

NADH

R
CoA

H

(3)

R
NAD+

H

H

H

O

C

C

C

C

H

H+

S

CoA

(2)

H

O

H

O

C

C

C

C

H

H

H

S

H2O

CoA

Figure 1: The oxidation cycle of fatty acids.
The pathway is cyclic. One cycle oxidizes the fatty
acyl CoA and produces one molecule of acetyl CoA(at the top left of the ﬁgure). The acyl CoA loses two
carbon atoms from its chain in one cycle and ﬁnally it
becomes acetyl CoA itself.
The cycle comprises four steps of reactions ((1) to(4) in the ﬁgure); let us look at each step.

Dynamics

A system is a construct hΣ, S, D, R, P0 i where Σ isa set of elements, S is a multiset of sources, D is amultiset of drains, R is a set of recombination rules,
and P0 is the initial working multiset, which speciﬁes
objects in the working multiset at the initial state. The
system is interpreted nondeterministically as follows.

† Step 1: Acyl CoA reacts with FAD (ﬂavin adenine dinucleotide; activated carrier). FAD gets
two hydrogen atoms from the acyl CoA and becomes FADH2 .

1. Initialize the working multiset to be P0 .
2. Do one of the following operations.

† Step 2: Hydration. The product of Step 1 is hydrated.

† Apply one recombination rule to a collectionof objects.
† Operate one source.
† Operate one drain.

† Step 3: Dehydrogenation. NAD+ (nicotinamide
adenine dinucleotide; activated carrier) removes
two hydrogen atoms from the product of the previous step, making NADH and a proton (H+ ).

3. Go to Step 2.

©ISAROB 2006

C

H

C

FADH2

Objects are kept in the working multiset. A source
is deﬁned as an object, and it supplies objects of thespeciﬁed form to the multiset one at a time without
limit. A drain is deﬁned as a pattern, and it eliminates
objects matched by the pattern, one at a time, fromthe multiset.

2.5

O

SH

R

2.4

H

H

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† Step 4: Thiolysis. The product of Step 3 reacts
with CoA-SH and becomes acyl CoA with shorter
chain, producing one acetyl CoA. If no tail is left
(i.e., R is H), two acetyl CoA are produced in thisstep, and the oxidation completes.

H O
H C C
H S CoACoA S

3.2

H H H OH C C C C
H H H S Coa*1 H O*2 C C*3 H S CoA

Fad
(1)

(4)

H Fad H
*1 H O H O*2 C C C C*3 H X H S CoA

Nad H

(3)

H Po
Nad Po*1 H H H O*2 C C C C*3 H X X S CoA

H*1 H O H O*2 C C C C*3 H H H S CoA

(2)

H O H

Figure 3: The oxidation cycle described by the artiﬁcial chemistry.
† Fatty acyl CoA [10]: below is an example object:
0#HHHHHHHHHO/-1#HCCCCCCCCCC/0#HHHHHHHHHSCoa/† FAD [100]: objects of the form 0#Fad/.
† NAD+ [100]: objects 0#NadPo/.
† CoA-SH [100]: objects 0:#CoaSH/.
† H2 O [100]: objects 0#HOH/.

H H H OH C C C C
H X X S Coa

4

Execution

Recombination rules
We developed a simulator for our artiﬁcial chemistry.
The current simulator is implemented in
Objective-C and runs on the Cocoa framework of Mac
OS X. Figure 4 shows the window of the simulator
running the description given in the previous section.
The table shows molecular species and their numbers,
and the molecule window (bottom right) shows theform of speciﬁed species (by mouse click). In this ﬁgure, the product of the oxidation cycle, acetyl CoA, isshown. The execution of the above description consumed all the given acyl CoA molecules and produced
acetyl CoA molecules, which is the correct behavior.

The designed recombination rules are shown in Figure 2. The numbers given to the rules correspond to
the numbers of steps in Figure 1.
The hydrocarbon chain of the acyl CoA changes
its length during the process. In the rules, this partis represented by wildcards so that they can match a
chain of any length.
Because we introduced the dummy element X torepresent a vacancy, the rules do not conserve elementsin the artiﬁcial chemistry (i.e., we relaxed a restriction
imposed by the deﬁnition of our artiﬁcial chemistry
given in Section 2.3); however, they are designed toconserve atoms in real reactions.
The oxidation cycle described in terms of our artiﬁcial chemistry is shown in Figure 3.

3.4

H

fatty acyl CoA

Atoms and Molecules

We design elements in our artiﬁcial chemistry as
follows. For the atoms C, H, O and S, we provide
corresponding elements C, H, O and S. The activatedcarriers CoA, FAD and NAD are represented by the elements Coa, Fad and Nad, respectively. Using theseelements, NADH is represented by an object 0#NadH/,for example. We use the element Po to denote positive charge so that NAD+ and H+ are expressed as
0#NadPo/ and 0#HPo/. Furthermore, we introduce adummy element, X, to represent vacancy.
Depicted below are example objects: fatty acyl CoA
CH3 -CH2 -CH2 -CO-S-CoA (left) and a product of Step 1
CH3 -CH=CH-CO-S-CoA (right) assuming R is H in Figure 1. Because the latter has a double bond, twohydrogen atoms are missing compared to the former.
The vacancies are denoted by the element X.

3.3*1 H H H O*2 C C C C*3 H H H S CoA

acetyl CoA

5

As Figures 1 and 3 show, this artiﬁcial chemistrymodelled the pathway in a straightforward manner.
The elements correspond atoms and molecules naturally, and the recombination rules can be designed intuitively from the chemical formulae and the pathwaygraph. Describing chemical pathways using the artiﬁcial chemistry has signiﬁcant beneﬁts. First, a pathway is expressed as a mathematical system, so there is

Initial working multiset

We give the following objects in the initial working multiset. A number in brackets is the numberof objects of the form, determined empirically so thatthe simulator (discussed in the next section) eﬀectively
shows the process.

©ISAROB 2006

Discussion

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0#*1HHHO/0#*2CCCC/0#*3HHHSCoa/ + 0#Fad/ → 0#*1HHHO/0#*2CCCC/0#*3HXXSCoa/ + 0#HFadH/(1)0#*1HHHO/0#*2CCCC/0#*3HXXSCoa/ + 0#HOH/ → 0#H/-1#*1HOHO/-1#*2CCCC/-1#*3HHHSCoa/(2)0#H/-1#*1HOHO/-1#*2CCCC/-1#*3HHHSCoa/ + 0#NadPo/ → 0#*1HOHO/0#*2CCCC/0#*3HXHSCoa/ + 0#NadH/ + 0#HPo/(3)

0#*1HOHO/0#*2CCCC/0#*3HXHSCoa/ + 0#CoaSH/ → 0#HO/-1#HCC/0#HSCoa/ + 0#*1HO/0#*2CC/0#*3HSCoa/(4)

Figure 2: Recombination rules for the oxidation pathway.
and simulation of chemical pathways. While there isroom for improvement in the expressive power of theartiﬁcial chemistry, we think this is a promising application of artiﬁcial chemistries in general.

References[1] Peter Dittrich, Jens Ziegler, and Wolfgang
Banzhaf. Artiﬁcial chemistries — a review. Artiﬁcial Life, 7:225–275, 2001.
[2] Kazuto Tominaga. A formal model based onaﬃnity among elements for describing behavior ofcomplex systems. Technical Report UIUCDCS-R2004-2413, Department of Computer Science, University of Illinois at Urbana-Champaign, March
2004.

Figure 4: The simulator window.
no ambiguity. Second, it is executable on a simulator.
Although the current simulator does not deal with concentrations or energy, it still can perform qualitative
simulation. Furthermore, one can choose an arbitrarylevel of abstraction: for example, CoA can be represented by an element Coa like in the description givenin this paper, or can be treated as composed of many
atoms of C, H, O, etc.; the designer of the system canchoose any level.
Other than the oxidation of fatty acids, we havedescribed the following metabolisms: glycolysis, thecitric acid cycle, the catabolism of amino acids, andthe urea cycle. One more useful property of the artiﬁcial chemistry is that when the union of all the systems
above (i.e., the union of the initial working multisetsand the recombination rules) is given to the simulator,
the execution shows the behavior of the composite system.

6[3] Kazuto Tominaga. Describing protein synthesis
and a cell cycle of an imaginary cell using a simple
artiﬁcial chemistry. In Proceedings of the Workshop on Artiﬁcial Chemistry and Its Applications,part of the 8th European Conference on Artiﬁcial
Life (ECAL) 2005, 2005.
[4] Gil Benkö, Christoph Flamm, and Peter F.
Stadler. Explicit collision simulation of chemical
reactions in a graph based artiﬁcial chemistry. In
Advances in Artiﬁcial Life: Proceedings of the 8th
European Conference on Artiﬁcial Life (ECAL)
2005, pages 725–733. Springer, 2005.
[5] Kazuto Tominaga. Modelling DNA computation
by an artiﬁcial chemistry based on pattern matching and recombination. In Proceedings of theWorkshop on Artiﬁcial Chemistry and Its Applications, part of the 9th International Conference
on the Simulation and Synthesis of Living Systems(ALIFE9), 2004.

Concluding remarks[6] Bruce Alberts, Alexander Johnson, Julian Lewis,
Martin Raﬀ, Keith Roberts, and Peter Walter.
Molecular Biology of the Cell. Garland Science,
New York, NY, 4th edition, 2002.

In this paper, we presented a description of ametabolic pathway described in terms of an artiﬁcial
chemistry, and thereby showed possibility for the artiﬁcial chemistry to be applied to qualitative modelling©ISAROB 2006

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Morphogenesis of 3D Sheets Exploiting a Spatial Condition
Shuhei Miyashita
Artificial Intelligence Laboratory
Department of Informatics, University of ZurichAndreasstrasse 15, CH8050 Zurich, Switzerland
Abstract

solids [3]. There exist many approaches for morphogenesis that can be divided into several types: Lindenmayer grammars [4], cellular automata [5], [9], concentration gradient[2], mechanical approaches [6], recurrent diagram networks to express the bodies of simulated creatures [7], and extended grid space into graphmodel [8]. However, little attention has been given to
the characteristics of form - the topology of the cellular
network.

This paper reports on the morphogenesis of threedimensional folding sheets in computer simulation. In
order to exploit the topology of these cellular sheets,
we introduced cell connection map, which can prescript cell connections regardless of changing the number of cells. We show that morphogenetic patterns
such as exponential growth, self-replication process
and annihilation process can be easily realized just byobserving the number of neighbors of each cell. Thatmeans this feat is achieved in a distributed and autonomous way.

1

2

In our model, we choose the cell as the level ofabstraction. System consists of cells connecting eachother.

Introduction

Cells diﬀerentiate depending on the number ofneighbors.
Cells divide and die (cell diﬀerentiation) dependingon the number of neighbors. This is according to thefact that one of the possible biological mechanism assumed to code the behavior of morphogenesis would
be the concentration of chemical substances that diffuse into neighboring cells through channels. In otherwords, its concentration could reﬂect the number ofneighbors.

Multicellular organisms usually consist of large
numbers of cells, which are able to shape an organism
by an intricate web of cell-cell interactions, a process
called morphogenesis. As each cell contains the same
genome, morphogenesis relies on autonomous and distributed processes with no centralized control. Although the elucidation of the molecular details of morphogenesis has made big progress in biology, an overall
picture is still lacking. We hypothesize that morphogenesis depends on the following two conditions:
1. Morphogenesis is an autonomous, distributed process without any centralized control for all cells.

Diﬀerentiation rules are applied synchronously.
These cell behavior rules are applied synchronously
in speciﬁc order. After speciﬁc time passes (100 steps),
all cells count its neighbors and take actions. (The definition of step is prescripted below.) Once cell division
is took place, the cell is divided into four cells. This
is in order to sustain the symmetry of the cell network. In cell deletion, the cell is deleted by cutting
connection to its neighbors.

2. In essence, morphogenesis of living things is basically understood as expanding and folding sheets.
We used these two conditions as guidelines to screen
the existing literature of morphogenetic models. Alan
Turing’s reaction-diﬀusion model [1] uses two chemical
substances that are able to produce spatial patterns inspace. The point of this mechanism is that in essence,
reaction-diﬀusion mechanisms are means of breaking the symmetry among homogeneous cells in autonomous and distributed way. Focusing on the formof gastrointestinal tract, H.Honda advocates that ingeneral the form of multi cellular system is realized as
two-dimensional sheets rather than three-dimensional©ISAROB 2006

Model

Cell-cell mechanical interaction
Cells are expressed as mass points. Links between
them are represented as mechanical connections. The
mechanical interactions are expressed as spring anddamper model. Although it takes time to converge tothe form, the form of cell network topology is unique

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006a)to each sequence. We show the parameters of the system in Table.1. The equation of motion for the cell iis expressed in eq.1. Where subscript i, j is an identiﬁcation number of the cell.
Table 1: Sets of parameters.
Symbol Deﬁnition
Valuekspring coeﬃcient50lspring natural length 50
mass
10
m
c
damper coeﬃcient30ggravity50
cross product
2000
a

?
e)

d)

cell connection map

Figure 1: Cell reconnection.

(q̇i − q̇j ) + k
1−

introduced ﬁtness value (F), -kind of entropy- which
is prescribed as the follow equation (2), where Si denotes the area of each cell in cell connection map with
subscript i as an identiﬁcation number of the cell.
F =−The position of the cell qi is deﬁned as a vector.
The cell that exists in neighbor of cell i is denoted asj. Gravity is added to the system for z-axis direction
and cross-product force is also added in order to swell
the form of the sheet. The diﬀerential equation is integrated by the Euler method (δt = 0.01, 1step=30δt).

Si
1 
log
N i
ST

(2)

We set the area of the whole map 1.0. ST represents
sum of all areas of the cell. We generalized the value
by dividing a number of cells, N. The characteristicsof this ﬁtness value is as follows:
1. The ﬁtness value gets larger when the distributionof area sizes gets larger.
2. If the sum of areas are same, the larger the number of cells is, the bigger the value becomes.
3. If cell distribution is same, it doesn’t depend on
its scale. That means that the value doesn’t depend on order of morphogenesis.

Cell connection map is introduced to constrain
the form in ”a sheet”
Since cell reconnection after cell division sustaining
adequate topology is tricky, we introduced cell connection map, which prescripts relation of cell connection.
Fig.1 shows the example of cell connection map. Whencell is divided, the square corresponding to the cell isalso divided into four small squares. (”a)” and ”c)”corresponds to ”d)” and ”e)”, respectively). Links areconnected if squares touches other squares through theedge. As we are interested in how the two-dimensional
sheets expand, the most external cells, which exist atedge of the connection map, are ﬁxed in the same position. Due to this settling, the system grows like anexpanding balloon. Although many parameters are
decided arbitrarily, the most important thing here isthat once the feature of the model is decided, the form
converges to unique form.

3

Simulations and Results

By applying several parameter sets, some fundamental morphogenetic process were observed.
Exponential growth
Figure 2 shows the examples of exponential growthof the system. X-Z side view, X-Y top view, Cell connection map in left to right order. (The magniﬁer ischanged in each view.) In the left model(seq.A), we
set rule that if the number of neighbor cells is 0,2,4,6,
or 8, the cell is divided, and if the number is more than10, the cell is deleted. And these rules are applied one
after the other starting with division rule. The ﬁgure shows by only counting the neighbors, the system
can generate bended ”two dimensional” morphologicalform from one single cell. In the right model(seq.B),
if the number of neighbor cells is 0,2,4,6, or 8, the cell
is divided, and if the number is 1,3,5,7, or 9, the cell isdeleted. This time division rule is applied twice thenForm can be evaluated using cell connectionmap
Form of living things always relates to its function,and it plays an important role in the evolutionary process. But evaluating form is quite diﬃcult and sometimes tends to be arbitrary. However, if we evaluate
the form by analyzing cell connection map, the wholecell relations can be detected and estimated easily. We©ISAROB 2006

c)
?

cell


l
mq̈i + c(qi − qj )|qi − qj |
j
j

 
j (qj − qi ) × (qj+1 − qi )
 + gqi = 0 (1)+a  
| j (qj − qi ) × (qj+1 − qi ) |b)division

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cell deletion rule is applied once starting with division
rule. Judging from the X-Y and X-Z view, the form
generated by the rule seems completely diﬀerent fromthat of seq.A.
side view

top view

cell mapside view

top view

*4

*4

*16

*16

cell map

seq.A

Figure 3: Self-replication.

seq.B
M
M

A1

B1

A2

B2

A3

A3
A3

C1

D2

C2

B3

C3
C3

B3
B3
C3

D1

Figure 4: Detail of exponential growth sequences B.

4

Figure 4 represents the magniﬁcation of the partof cell connection map in Figure 2 (as marked ”M”).
After 16 cells, which are arrayed in square grid appears(A1 and B1), this part gets rounded (C1). Once thisform is created, all internal cells have four-neighbors
thus keep dividing. And ”big” eight cells surrounding
the cells have more than at least 10cells. Therefore
these cells won’t be divided any more. This is a kindof ”expanding bag”. And this bag can be seen at otherpart of the body (A2,B2,C2, and A3,B3,C3 and so on).
This shows that the system is growing creating many
expanding bags around the body.
Figure 5 represents the same part of cell connection
map under the diﬀerent condition of cell division andcell deletion. This time, cell is divided if the number of neighbor cells is 0,2,6, or 8. Cell is deleted if
it is 1,3,5,7, or 9. Although most of the condition
are the same between seq.A and seq.B, the morphogenetic processes are essentially diﬀerent. See the partmarked ”A”. Once this shape is created, the shapedoesn’t change any more. That means that the number of cells included in this part doesn’t change. (This

Figure 2: Exponential growth sequences A(left) andB(two left blocks and right).
Self-replication, Annihilation, Stop GrowthFig.3 shows self-replication process. In this model,
if the number of neighbor cells is 0,2,5,7, or 9, the cell
is divided. And if the number of neighbors is 1,3,4,6,
or 8, the cell is deleted. Each group keeps changing
the number of cells which consist its network one andfour generating new groups. Several model in otherparameters showed annihilation and stop growth behaviors. The simplest model of annihilation behavior
can be observed when we set the number of neighbors
0 for cell division and 2 for cell deletion applying division rule and deletion rule one after another. And thesimplest growth saturation model can be observed bysettling 0 for cell division and any numbers except for
2 for cell deletion.

©ISAROB 2006

Discussions

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

1. Several types of morphogenetic behaviors of
three-dimensional sheets can be realized in autonomous and distributed way - just by counting
the number of neighbors.
2. The form can be quantiﬁed easily by evaluatingcell connection map.
Some fundamental morphogenetic behaviors are observed; two types of exponential growth, selfreplication, stop growth and annihilation process.
Those models were sensitive to the cell diﬀerentiation
rules, to put it another way, sensitive to the topologyof cell connections. What we intended to show in thispaper is the abundant power of morphogenetic expression supported by the condition of spatial constraint,
and the possibility that we can evaluate complicate
forms by mapping it into other method, cell connection map.

B

A

A

B

B
C
A

Figure 5: Detail of exponential growth sequence C.

Acknowledgements
Many thanks to Peter Eggenberger Hotz for helpful
suggestions. This research is supported by Swiss National Science Foundation, project #200021-105634/1.

is seen in other parts of sequences B and C and soon.) This system keeps generating many ”gnarls” indiﬀerent positions. The X-Y view is shown in the same
ﬁgure. It can be seen that many gnarls are created in
the form. Sizes of each gnarl are the same.
100000[1] A.M.Turing: A reaction-diﬀusion model for development, The chemical basis of morphogenesis, Phil
Trans. Roy. Soc. of London, Series B; Biological
Sciences, 237, 5,PP.37-72 (1952)[2] L.Wolpert Positional Information and the Spatial Pattern of Cellular Diﬀerentiation, J. Theoret.
Biol., 25, PP.1-47 (1969)[3] H.Honda: The mathematical and physical analysis
of morphogenesis, Kyoritsu-shuppan (2000)[4] A. Lindenmayer: Mathematical Models for Cellular Interaction in Development, Journal of theoretical Biology, 18, PP.280-300 (1968)[5] C.G.Langton: Self-reproduction in cellular automata, Physica D: Nonlinear Phenomena, 10, Issues
1-2, PP.135-144 (1984)[6] M.Odell, G.Oster, P.Alberch: The Mechanical Basis of Morphogenesis, Developmental Biology 85,
PP.446-462 (1981)[7] K.Sims: Evolving 3d morphology and behavior bycompetition. Artiﬁcial Life, 1, Issue 4, PP.353-372(1995)[8] K.Tomita, H.Kurokawa, S. Murata: Graph Automata: Natural Expression of Self-reproduction,
Physica D: Nonlinear Phenomena, 171 No.4,
PP.197-210 (2002)[9] Martin Gardner: Mathematical games - the fantastic combinations of john conway’s new solitairegame, life. Scientiﬁc American, PP.120-123(1970)

12
seq.A
seq.B

seq.A
seq.B
10

10000

8
1000
fitness

number of cells

References

6

100
4
10

2

1

0
0

200

400

600

800
step

1000

1200

1400

0

200

400

600

800

1000

1200

1400

step

Figure 6: Left: Fitness value transition graph. Right:
Number of cells transition graph.
We show the number of cells and ﬁtness value transition graph in Fig.6 in order to quantify the diﬀerenceof the characteristics of these forms between sequence
B and C. In Fig.6 left, the X-axis and Y-axis represents
steps and the number of cells, respectively, comparing
sequence B and C. As the ﬁgure shows, the ﬁtness inseq. B is smaller than that in seq. C although thenumber of cells in seq. B is larger than that of seq.
C in each time. This means uniformity of the wholesystem of seq.C par cell is larger than that of seq.B.
Hence, it suggests that many kinds of diﬀerentiation
rules are not needed in order to get complicated forms.
In other words, sustaining an adequate cell diﬀerentiation rule is necessary for the morphogenesis of themodel.

5

Conclusion
These results lead to the following conclusion.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Sexual Reproduction in Digital Organisms
Ai

Ban Xiaojuan,Yin Yixin,Tu Xuyan

Dongmei

School of Applied Science

School of Information Engineering

University of Science and Technology Beijing

University of Science and Technology Beijing

Beijing , 100083

Beijing , 100083

Abstract

cell in computer multiplies, mutates, andevolves. They all keep to survival of thefittest in Charles Darwin’s Evolutionism andfight for the resource in computer. Theseso-called cells characterized by organisms
features are in fact programs. Therefore
these cells are also called digital organisms.
Afterwards, inspired by the ideas in Tierra,some other artificial life platforms aredeveloped [4], among which Avida is themost influential one.
In Avida system [5], digital organisms is
asexual reproductive. They self-replicate
and mutate in the process of reproduction soas to form organisms with different genes.
They keep to survival of the fittest; fight forthe memory and the resource of CPU in thesame environment. But whether among
animals or plants in nature, sexual
reproduction is the most common way.
Biologically, asexual reproduction is aprimitive reproduction. Sexual reproduction,on the other hand, synthesizes both theadvantages and disadvantages from bothparents, so that it could partly conceal thehereditary defects. Compared to theorganisms produced by asexual reproduction,organisms from sexual reproduction is more
adaptable and has stronger vitality [6].
Based on sexual reproductive, SEBRED isdeveloped. It brings sexual reproduction andcross breeding mechanism into digitalorganisms. This system aims at improving
the fitness of populations, and further

Artificial life platform is an emulational
system which uses computer software to
study the behaviors characteristics such as
reproduction, evolution, mutation, andemergence of digital organisms. After
studying the parameter and functioning
capability of Avida, the artificial life
platform, aiming at the asexual reproduction
in digital organisms, We designed the sexual
reproduction
mechanismofdigitalorganisms by utilizing the genetic
programming principle, introduced the crossbreeding mechanism of natural organisms
into digital organisms, and designed thecross breeding mechanism of digitalorganisms, so as to avoid the disadvantage
of inbreeding reproduction. Our results
demonstrate that sex is advantageous for
population survival under some conditions,the mean fitness of surviving sexual
populations was greater than in asexual
population.

1

Introduction

Artificial life platform is a system using
computer as tool and medium, utilizing
software to emulate organisms features [1].
Tierra, developed by Thomas S. Ray, is therepresentative of this field. He introduced
the concept of organisms evolution in
biology into computer science [2][3]. Every
Supported by NSFC(No:60374032,60503024)©ISAROB 2006

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improving the organisms features of digitalorganisms.

evolutional models—digitally set fitness andnatural selection, mutation, and selfreplicate. These organisms have experienced
250 generations, so their fitness has
improved; their organisms structure
becomes more complicated compared totheir ancestors. This is the evolutional
process of primitive digital organisms. We
regard this biological species as thepreliminary species of advanced digitalorganisms, and those organisms after 250
generations as the advanced digitalorganisms. Biologic experiments turn out inbiology that sexual cross is not beneficial tosimple organisms but beneficial to thecomplicated organisms developed from
simple organisms. Digital organisms before
the 250th generation have never experienced
sexual reproduction (cross or exchange), sothey have the same way of reproduction as
in Avida. But the difference is that in
SEBRED, although organisms before 250th
generation have no sexual reproduction,
when each organism comes into being, its
sex property randomly has two sex
divisions—female and male.

2.The Genetic Operate Design ofDigital Organisms in SEBRED
There are various reproduction methods
to raise up seed in nature organisms.
Primitive monad usually raises up seed byasexual reproduction. But, most of plants
and animals and human beings raises up
seed by sexual reproduction. In sexual
reproduction, the creature individual is
divided into male and female. The offspring
reproduced by sexual reproduction havemore mutations. The offspring from sexual
reproduction become more fit theenvironment than those offspring from
asexual reproduction. In natural selection,
sexual reproduction is beneficial mutations
and fit environment.
2.1 The Reproduction Model of Primitive
Digital Organisms in SEBREDIn SEBRED, digital organisms is divided
into primitive digital organisms andadvanced digital organisms. First of all, anancestor of digital organisms begins to
self-replicate and many offspring come into
being. In the process of reproduction, themutations of gene result in the different
genes of the offspring. The offspring also
reproduce a large number of offspring as
their ancestors do. Of course different
digital organisms has different fitness thateven changes in different periods. Those
digital organisms with strong fitness
reproduce more offspring, and those withlow fitness reproduce less.
In the reproduction process of theseprimitive digital organisms, improved
genetic algorithm is used. In this process
digital organisms has experienced three©ISAROB 2006

2.2The Sexual Reproduction Algorithm ofAdvanced Digital Organisms Based on
Genetic Programming

In SEBRED, after running 250
generations, there will be almost 3600
organisms. They have very different
genotypes and chromosomes, so theprograms (or organisms) length are also
different. In this system, the hereditary
individuals have two sex divisions: female
and male. Some sexual organisms havecome into being at random in this system.
After the 250 generations, the reproduction
mechanism has become sexual reproduction.
So in SEBRED, the intercrossed digitalorganisms have different sizes (programs

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

with different length). Moreover, during theprocess of evolution, program (organisms)
will change it’s size according to theenvironment [7]. But the string with set
length which frequently used by genetic
algorithm to illustrate questions has
restrained the application of genetic
algorithm.
Lackingofdynamic
changeability. String with set length ofgenetic algorithm has no dynamicchangeability, so once the length of string isset, it is very hard to dynamically express
the change of the state or the behavior.
So the copulation mechanism of digitalorganisms in SEBRED adopts genetic
programming to make two organisms
exchange.
In SEBRED, the advanced digitalorganisms structure adopts linear structure.
Every digital organisms performs the
program of its own chromosome during theprocess of evolution. When two digitalorganisms copulate, the length of theirchromosomes is different. With theevolution of the species, the length of digitalorganisms’ chromosomes will change. The
following part is about the principles andmethods of digital organisms’ copulation:
1.Organisms with same sex cannot mate up;
the female individual can only mate up withthe male individual randomly(the maleindividual can only mate up with the female
individual randomly).
2.Each individual mates up with four
different individuals around with opposite
sex., there will be eight filial individuals.
Then choose two best ones as the filial
individuals and add them into theenvironment, and this is sitting combination.
If two or more qualified individuals
participate in the copulation, the individual
should choose the one with the highest
fitness. But if they even have the same
fitness, the individual should choose the©ISAROB 2006

younger one.

3. The Cross Breeding Mechanism of
Digital Organisms in SEBRED
In every natural population, many
diploidize individuals’ genomes keep highproportion of heterozygosity. The hereditary
diversities usually are recessive instead of
dominant, so it is beneficial to the survivalof the species and makes it more adaptable
to the environment. Just like the biologicalsystem of nature, inbreeding also produces
bad offspring. Therefore the program of
judging the qualifications of both parents isvery essential to the selection process [8].
In SEBRED, the digital organisms
involving the sexual copulation have to becompared. Tracing back 4 generations ofeach side, if they have the same father ormother in the four generations, then thiscopulation has to be abandoned and selectother mating objects once again. But if theselection fails more than 10 times, selectanother digital organisms around this pairwith force and conduct the copulation, as
shown in Figure1. Triangle show female,diamond show male, the graphics of shading
are the same pedigree.

Ⅰ
Ⅱ
Ⅲ
Ⅳ
Figure.1

Four-Generation Pedigree

4. Fitness of Digital Organisms in
SEBRED System

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improves the life features of digital life.
Experiments turn out that the fitness ofdigital life after sexual reproduction ishigher than that of asexual reproduction.

The factors affecting the fitness of the
SEBRED system are different fromthose affecting Avida. So the fitness ofdigital organisms in SEBRED system
must be reconsidered.
The factors which affect the fitness ofdigital organisms include merit,gestation time, the number of the“inst_David” instructions contained in
the offspring genotype.
Fitness is given by the K multiply M,
divide by the gestation time G.
Fitness=K*M/G

References[1] Chen Hongjuan.: The Research of
Theory and Method on SelfReproduction of Digital Fish. The
Doctorial Thesis of USTB, 2002
[2] Wu Jianbing,Yang Jie,Wu Yuehua.:
Digital Life and Digital Intelligence.
Pattern Recognition and Digital
Intelligence (1998) 274-279[3] Ray,T.S.: An evolutionary approach tosynthetic biology: Zen and the art ofcreating life. In Digital Life Journal,
The MIT Press (1994) 179~209[4] A.N.Pargellis.In: Digital Life Behavior
in the Amoeba
world.ArtificalLife Ⅶ .Cambridge, MA:MIT Press(2001) 63~75[5] Adami,C. : Introduction to Digital Life.
Springer, New York (1998)[6] Liu Zudong: Genetics. The Higher
Education Press (1990)[7] Wang zhengzhi, Bo Tao.: Evolutionary
Computation. Changsha: National
University of Defense Technology
Press (2000)[8] Lin Yuan,He Lin,Xu JinLin.: Sex
Evolution. Origin and
Maintenance.
Life Science (2002) 197-200(1)

1. M(Merit)is a value indicating the CPU
time a particular organism has earned,
taking into account its length and the tasks
that it has successfully completed.
2. G（Gestation time）is the number of
instructions an organism must execute toproduce a single offspring. This is typically
proportional to the length of the genome.
3. K is a coefficient generated byconsidering both the number of
instructions contained in the offspring
genotype and Merit.
1
4

K = 1 − (1 − i )e−0.01
(i −1)2(2)

i is the number of offspring
instructions which contain “inst Divide”
instruction.
SummaryThis thesis is based on the digital life
platform Avida; using genetic algorithm andgenetic programming principles, combining
the sexual reproduction of advanced life inbiology, builds up the sexual reproduction
mechanism of digital life; enriches and©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development of NC Tool Path Generation System
by Absolute and Incremental Type Individual
H. Yamamoto
Faculty of Engineering,
Gifu University,
1-1 Yanagido, Gifu, 501-1193 Japan
E-mail: yam-h@cc.gifu-u.ac.jp

Rizauddin Bin Ramli
Graduate School of Engineering,
Faculty of Engineering, Gifu University,
1-1 Yanagido, Gifu, 501-1193 Japan
E-mail: k3812205@guedu.cc.gifu-u.ac.jp

Abstract
Lately, in order to improve the operating efficiency of
automatic production systems such as Flexible
Manufacturing Systems (FMS) and Flexible Transfer
Lines (FTL), many Numerical Control (NC) lathe
machine tools have been used and a few automatedprogramming methodologies of NC machine tools havebeen researched. The purpose of this research is todevelop a system that automatically generates the tool
path of the NC lathe machine tools in FTL. The GA
methodology of our system adopts the moving points ofthe tool path as an individual. In the paper, we introduce
two original individual expressions. One is an Absolute
Expression (AE) and the other is the Incremental
Expression (IE). After some simulations, a bettermachining efficiency for the cylindrical type of workpiece obtained in this machining process has beenacquired. It has been ascertained that our system isuseful.

time are found in an NC program using a simulator.
In the FTL systems that consist of many NC lathe
machine tools, where various types of raw materials areloaded, the development of software for an optimal
cutting method of the NC lathe machine tools is essential.
Because of that, the generation of the tool path and thedecrease of machining time of the NC lathe machine
tools are required by the machinist in order to increase
the efficiency of the system.
In this research, we propose an NC lathe machine
tool path generation system by using the incremental andabsolute style of the individual while applying GA when
the cycle time and the product shape are given. The
effectiveness of the system is verified by a simulation ofboth methods.

2. Genetic Information of AE and IE
A schematic pathway of the system that wedeveloped is illustrated in Figure 1. The system isdivided to 3 segments which consist of an input part, theGA simulation and NC lathe machine tools. In this paper,
we will describe the methodology of the GA as our main
research. The purpose of the system is to help a
production engineer quickly to decide the necessary NC
machine tools number under the condition that the target
cycle time and product shape is given. In other words,
our system is to generate the cutting path from a raw
material to a complete work piece just by inputting the
product shape and its target cycle time. The informationof the product shape is used to code the genetic
information as individual or genes in the chromosome.
In our system, we only assume the genetic
information in 2 dimensions of x, y axis where theindividual is expressed as the tool path in x, y direction.
We assume that the origin of the tool to be as (x0 , y0 )and the arbitrary point within the tool path as (xn , y n ) .
According to this assumption, the individual can be
expressed as,

1. Introduction
Several studies of the tool path of NC machine tools
have already been done. Akturk et al. [1] proposed a new
solution to solve the tool allocation and machining
conditions selection problems simultaneously to find theminimum production cost. However, the tool pathgeneration is not considered. Omirou [2] proposed a
locus tracing concept which defines a geometric property
to generate the tool path. Furthermore, research has been
done on an adaptive tool path generation algorithm for
precision surface machining [3]. Using this method,
however, a high performance CPU processor is required
for updating the tool positioning data because the nextreference point of the path had to be predicted. In
addition, Ko and Kim [4] proposed an iterative learning
method and cutting parameters of the process model
optimized through GA for the turning process. In spite ofusing a tool dynamometer for measuring the signal of the
cutting force, it is undesirable to adopt this method
because it cannot control the cutting depth. Shirase et al.
[5][6] developed an expert system to improve theproductivity in NC lathe tuning where the machining
information about cutting parameters, tools and operating©ISAROB 2006

Ind = { ( x 0 , y 0 ), ( x1 , y1 ),L , ( x n , y n ),L , ( x 0 , y 0 ) } (1)340The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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Inp ut part・ P rod uct shape
In fo r m a tio n

・ A b s o lu te ly

・ C y c le T im e

G e n e tic I n fo r m a tio n

・ I n c r e m e n ta lly

G A s im u la t i o n
T o o l p a th g e n e ra tio n s y s te m

N C L a t h e M a c h in e
T o o ls

F T L s y s te m

Fig. 1 Schematic pathway of system
In other words, the tool moves from its origin andgenerates a tool path, return back to its origin as one
cycle. In our research, we propose that the tool paths are
expressed by two type of individual. First, the tools’
origin is considered as the coordinates’ origin. In thismethod, the point on the tool path (xn , yn ) is consideredas the point of x, y coordinates as the tool origin is (0, 0).
For example, as shown in Figure 2, the tool path isexpressed when it moves f mm in x direction from point
A (x n , y n ) to point B (x n + f , y n ) . We express theindividual as an element of tool path which represents by
Equation (1) as the Absolute Expression (AE).
Second, the tool path point is expressed as the
distances that the tool path moves. In other words, for
any arbitrary point A (xn , yn ) , the next tool path point
B (xn +1 , yn +1 ) is considered as the distances of x and y
direction while point A still as the origin. For example, as
shown in Figure 3, the tool moves f mm in the x direction
from point A (x n , y n ) to point B ( f ,0) . We express theindividual as an element of tool path which represents by
Equation (1) as the Incremental Expression (IE). Eachindividual in the population is evaluated by a defined

fitness function that is given as,
Fitness= Cycle time − Total cut time(2)

3. Crossover of Row and Column
In our research, it is difficult to adopt the conventional
method of crossover for our IE and AE. Thus, in theoperation of crossover, we adopt a peculiar method ofcrossover. First, as shown in Figure 4, two individuals
are randomly selected from the rows of the population.
Also the point of the crossover is selected randomly.
Parts of rows of the selected individuals are interchanged.
We define this type of crossover as the rows crossover.
Second, as shown in Figure 5, two individuals are
randomly selected from the columns of the population.
Also the point of the crossover is selected randomly.
Parts of each column of the selected individuals areinterchanged. We define this type of crossover as thecolumns crossover. In the operation of mutation, the genein the chromosome that mutates is randomly selected.

Orig in(x 0 , y 0 ）Path distance of
X directionxyf
B(x n +f, y n )A(x n ,y n )B ( f, 0)fFig. 3 Tool path generation of IE

Fig. 2 Tool path generation of AE©ISAROB 2006

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A (xn, yn)

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Individual 1

x1n-1

x1n

Individual 2

Individual n

x23

x22

x21

xn1

xnn

xnn-1

xn3

xn2

xn1

x12

x11

x1n

x1n-1 x13

x12

x11

x2n-1

x22

x21

x2n-1

x22

x21

xn3

xn2

xn1

xn-1

xn2

xn1

x21

xnn

xn-1

xn2

x1n-1 x13

xnn-1

xnn

x11

x2n-1

x22

x2n

Individual 2

x12

x1n-1

x2n

x23

x1n

Individual 1

x1n

x11

x2n-1

x2n

Individual n

x12

Crossover line

x2n
Crossover line

xnn

After crossover

Before crossover
Fig. 4 Row Crossover

4. Simulation Applications
The GA methodology that we developed is thenadopted to create the tool path generation system. An
example of manufacturing a simple product from acylinder is shown. We use the system to simulate themachining process of a cylinder with its length is 157mm
and diameter is 38 to a demanding product shape asshown in Figure 6. The NC lathe machine will cut thework piece from step A, B, C, D, E, F and G. The
distance of step A and D is selected randomly, and whenthe machine finish cut step A, B and C, the material isreversed and the machine will began to cut D, E, F andG.
The evolution parameters of GA as the simulation of
the system runs were used as population size of 100,

probability of crossover 0.9 and the probability ofmutation 0.05.The tournament selection of next
generation with tournament size 5 is used. From oursimulation, we found that the cycle time needed tocomplete the machining process by IE is 22 cycle.
On the other hand, the cycle time needed to complete themachining process by AE is 20 cycle. From thecomparison of these results, it is assumed that in IE, thegenetic information of each step of individual is highly
changed and when the crossover was done to the row, an
individual with extremely large number of cycles has
been generated. In Figure 7, it is shown that the
maximum fitness of AE and IE is 311.01 and 647.92
respectively. The results shows that the AE had a bettermachining efficiency compare to IE.

Crossover line

Crossover line

Individual 1

Δ x 1 n -1

Δ x 11

00

Δ x 2 n Δ x 2 n -1 Δ x 2 2

Δ x21

0

Δ x n n Δ x n -1 Δ x n 20

Δ x nnΔ x n -1 Δ x n 2

0

Δ x 1 n Δ x 1 n -1 Δ x 1 2 Δ x 1 1

0

Δ x 110

Individual 2 Δ x 2 n Δ x 2 n -1 Δ x 2 2 Δ x 2 1
Individual nIndividual 1

Δ x 1nΔ x 1 n -1

Δ x 210

Individual n Δ x n n Δ x n n -1 Δ x Δ x
n3
n2

0

Individual 2

Δ x 2 n Δ x 2 n -1

Δ x1n

Δ x 1 n Δ x 1 n -1 Δ x 1 2

Δ x 11

0

Δ x 2 n -1

Δ x 210

Δ x n n Δ x n n -1 Δ x n 3

Δ x n20

Δ x 2nAfter crossover

Before crossover
Fig. 5 Column Crossover©ISAROB 2006

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Tool

step A

step D

step E

step B

step F
step C

step G

escapepath

return pathfeeding path
cutting path

Fig. 6 Example of tool path cutting view

[4] T. J. Ko and H. S. Kim, Autonomous cutting
parameter regulation using adaptive modeling andgenetic algorithms, Precision Engineering, Vol. 22, pp.
243-251 (1998).
[6] K. Shirase, M. Hirao, T. Yasui et al., Improving
Productivity in NC Lathe Turning Based on NC Program
Diagnosis System(1st Report)-Survey and Improvementon the Productivity in Turning Operation done in the
Manufacturing Industries by Using NC Program
Simulator, Journal of The Japan Society for Precision
Engineering, Vol. 60, No.2, pp. 235-239 (1994). (inJapanese).
[7] H. Kawabata, K.Shirase, K. Sasayama et al.,Improving Productivity in NC Lathe Turning Based on
NC Program Diagnosis System(2nd Report)-Turning
Processes Identification Using Syntax Analysis Approach,
Journal of The Japan Society for Precision Engineering,Vol. 60, No. 4, pp. 576-580 (1994). (in Japanese).

5. Conclusions
In this research, we considered the NC lathe machine
tool path generated by the absolute and incremental
methods. By comparing the results of each method, we
found that the tool path generated by the AE had a small
cycle time compare to the IE. This implies to thedecreasing of machine quantity which means that only asmall space of factory is needed. Then, since that thecycle time is small, the machining time could be reduced,
and improve the operating efficiency in the FTL system
which leads to low cost production.
Moreover, by just changing the product dimension
using our developed GA systems, the machining
efficiency of various shapes of product can be realized
and it is effective with this system to generate the tool
path of the NC lathe machine tools.

6. References

1500[1] M. S. Akturk, S. Avci, Tool Allocation and Machining
Conditions Optimization of CNC Machines, European
Journal of Operational Research, Vol. 94, pp. 335-348(1996).
[2] S. L. Omirou , A locus tracing algorithm for cutter
offsetting in CNC machining, Robotic andComputer-Integrated Manufacturing, Vol. 20, pp. 49-55(2004).
[3] Y. J. Lin, T. S. Lee, An adaptive tool path generation
algorithm for precision surface machining, Computer
Aided Design, Vol. 31, pp. 237-347 (1999).

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Fitness

1000

IE
500

AE
0
0

100

200

300

400

Generations

Fig. 7 Maximum fitness of AE and IE

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A Study of Selecting Optimal Parameters for Genetic Algorithm
So-Youn Park and Shanshan Jin 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
arashi@odyssey.kaist.ac.kr, shanshan@odyssey.kaist.ac.kr, jjlee@ee.kaist.ac.kr
Abstract

diversity - generally. Accuracy can be obtained from
fitness value of the best individual and convergence
speed can be measured by calculating slope of fitness
value vs. generation.
Population diversity is related to search space ofGA and premature convergence. Premature convergence occurs when GA reaches suboptimal point andcannot escape from that point. Several methods havebeen proposed to measure population diversity. First
one is calculating the occupancy rate of ’1’ at every
chromosome, passing it through a specific threshold
function, and adding up all [1]. In this theory maximum population density and chromosome length areexactly same. Second method is getting sum of hamming distance between individuals and dividing it bychromosome length [2]. Third one is calculating ratio
between mean and maximum values of fitness function [3]. At each generation, genetic diversity (gdm)lies between 0 and 1 and when gdm approaches 1, thisindicates many individuals have same genetic code andreaching its own optimal point (This does not guarantee the solution of problem). We choose third method
to measure population density.
In this paper we try to investigate the performanceof GA according to three aspects mentioned above
with varying values of population size, crossover rate,
and mutation rate. In session 2, we define the conditions of GA and explain test functions conducted inthis experiment. In session 3, we describe experimental results and discuss them. Finally we summarize
and conclude the work and suggest the future work.

Genetic Algorithms (GA’s) are very powerfultool to solve many optimization problems which are
difficult for other methodology. But when using it,users have to make a choice of its values of parameterand operations to get a good performance. Many
researches investigated the influence of different
selection strategies, genetic operators on schema
dynamics and developed various GA models, butthere exists little information about hierarchicalinfluence of parameter on GA performance. In thispaper we will try to take a deeper look on theinfluence of parameter values on the performance ofGA and relationship between them. In experiment,
two different types of functions are tested and wefind that increasing population size helps GA to findoptimal solution most and mutation rate is more
effective than crossover rate.

Keywords :tion diversity

1

genetic algorithm, parameter, popula-

Introduction

During several decades, optimization problems arerising as one of the important issues, and GA is powerful tool to solve those problems. It mimics biological evolution to solve problems. Different from hill
climbing or simulated annealing, GA uses the current
best solution, preserves it, and also travels other possibilities [5],[6],[7]. When using GA, users have to decide genetic operators and values of parameters. These
values affect performance of GA and many researches
have suggested various strategies to get good a result,but few on hierarchical relationship between these parameters and performance.
The performance of GA can be divided into three
aspects - accuracy, convergence speed, and population©ISAROB 2006

2

Methodology

To obtain reasonable values, we use GA in following
manner.
In this paper we focus on population size, crossover
rate, and mutation rate. Therefore except these parameters, all conditions for GA are fixed. We use Sim-

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3

ple Genetic Algorithm (SGA) with Roulette well selection, 1-point crossover, bit flip mutation and Elitism.
The ranges of parameters are following: Population
size is 10 ∼ 100, pc=0.1 ∼ 0.9, pm=0.01 ∼ 0.4. EachGA with specified parameter values is executed 20
times for each function.
Two analytical functions with different characteristics are chosen.

Results

Through the experimental results there exist somecommon features.

a. Rastrigin function
f (x) = 10 · n −X(x2 − 10 · cos(2πx))(1)

The number of variables for Rastrigin function is
2 and the variable bounds for this function is [-5.12,
5.12] each. This function has global minimum valueof 0 at (0,0) and many suboptimal(minimal) points.
b. Dejong function f2
f (x, y) = 100 ∗ (x2 − y)2 + (1 − x)2(2)

The variable bounds for this function is [-2.024,
2.024] each. This function has global minimum valueof 0 at (1,1).

Figure 3: Fitness value of Rastrigin function vscrossover rate vs mutation rate with different population size(10,30,60,100)

Figure 1: Rastrigin function

Figure 4: Fitness value of Dejong function vscrossover rate vs mutation rate with different population size(10,20,50,90)
At first, the increase in the population size or mutation rate enhances GA accuracy. When the population size is small, fitness values are smaller than one
with large population size and at the same time at
the same population size GA with high mutation rate
is more accurate than GA with low mutation rate.
This is because of the premature convergence. But

Figure 2: Dejong function f2
Fig. 1 and 2 show 3 dimensional shape of Rastriginfunction and Dejong function f2.

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too much high mutation rate decline GA accuracy.
Roughly speaking crossover corresponds to the neararea search and Mutation to the random area search.
Therefore even though with small population size, if
mutation rate is relatively high, GA has a chance toescape from sub optimal point. But when it increases
too much GA travels search space freely and also has
little chance to converge in certain point (This should
not be the solution). It’s very sensitive to the solutions
that large population size and high crossover rate cannot overcome (Fig. 3 and 4 show the result).

Figure 7: GDM of Dejong function with different population size(from top to bottom 10,30,50,90)

Figure 8: GDM of Dejong function with different mutation rate(from top to bottom 0.01,0.03,0.05,0.15,0.4)
Figure 5: GDM of Rastrigin function with different
population size(from top to bottom 10,20,50,90)

iment we use GA with mutation and elitism, therefore there is little gap between convergence speedwith large population size and one with small population size and we cannot deduce general tendencywith crossover rate and mutation rate.

4
Figure 6:
GDM of Rastrigin function withdifferent mutation rate(from top to bottom
0.01,0.02,0.03,0.04,0.2)

By the results, generally population size contributeto find the solution most and increase of mutation rateincreases accuracy and population diversity. But toohigh mutation rate disturbs finding the solution.
We checked the functions with different characteristics and there is some difference between test functions. Rastrigin function has many sub optimal points
and needs larger search space, therefore it’s more sensitive to change in the value of parameters. On theother hand, Dejong function f2 needs smaller search
space than Rastrigin function, so it’s less sensitive tochange in the value of parameters.
We showed that population size affects GA performance most and mutation rate is more effective thancrossover rate. If we can consider the function complexity, we can increase GA performance more.

Secondly, mutation rate and population size increase population density. Mutation rate increases theaverage population diversity and also population diversity increases as the generation grows. On the other
hand, population size increases the average population
diversity, but population diversity does not increase as
the generation grows. This is because when the population size is large or the mutation rate is high, GA has
more chances to have the variety of individuals. Besides population size cannot change individuals, while
mutation can modify the chromosomes of individuals.
Therefore the gdm of GA with high mutation rate decreases as generation grows(Fig. 5, 6, 7, and 8 show
the result). Crossover rate does not affect population
diversity very much.
In [4], Yuri shows that convergence speed decreases
as population size grows, but he does not include mutation and elitism in the experiment. In our exper-©ISAROB 2006

Conclusion

References[1] Yee Leung, Yong Gao, and Zong-Ben Xu, “Degree of population diversity - a perspective on pre-346

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mature convergence in genetic algorithms and its
Markov chain analysis,” IEEE Trans. Neural Network,Vol. 8, pp. 1165-1176, 1997.
[2] Chen Tanggong, Wang Youhua, “Adaptive population disappear of genetic algorithm and its application,” Proc. Computational Electromagnetics
and Its Applications, pp. 145-148, 2004[3] J.A. Vasconcelos, J.A. Ramirez, R.H.C. Takahashi,
R.R. Saldanha, “Improvements in genetic algorithms,” IEEE Trans. Magnetic, Vol. 37, pp. 34143417, 2004
[4] Y.R. Tsoy, “The influence of population size andsearch time limit on genetic algorithm,” Proc. KORUS., pp. 181-187, 2003
[5] D.E. Goldberg, Genetic Algorithms in Search,
Optimization, and Machine Learning, New York:
Addison-Wesely, 1989
[6] Zbigniew Michalewicz, Genetic Algorithms +Data Structures = Evolution Programs, SpringerVerlag, 1999
[7] Melanie Mitchell, An introduction to genetic algorithms, MIT Press, 1996

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Colour quantisation using Simulated Annealing
Gerald Schaefer, Lars Nolle
School of Computing and Informatics
Nottingham Trent University
Nottingham, U.K.
Gerald.Schaefer@ntu.ac.uk
Lars.Nolle@ntu.ac.uk

Tomoharu Nakashima
School of Computer Sciences
and Intelligent Systems
Osaka Prefecture University
Osaka, Japan
nakashi@cs.osakafu-u.ac.jp

Abstract

In this paper we apply a variant of Simulated Annealing (SA) as a standard black-box optimisation algorithm to the colour quantisation problem. The main
advantage of black-box optimisation algorithms is thatthey do not require any domain specific knowledge yet
are able to provide a near optimal solution. We evaluate the effectiveness of our approach by comparing its
performance to the results obtained by several purpose
built colour quantisation algorithms [4, 3, 2]. The results obtained show that even without any domain specific knowledge our SA based algorithm is able to outperform standard quantisation algorithms and hence
to provide palettised images with superior image quality.
The rest of the paper is organised as follows: Section 2 provides the background for optimisation basedon Simulated Annealing. Section 3 explains our application of SWASA, a modified SA algorithm, to thecolour quantisation problem. Section 4 provides experimental results based on a set of standard test images
while 5 concludes the paper.

Colour quantisation tries to find entries of a colour
palette for true colour images to be displayed on hardware with limited colour support while maintaining
the best possible image quality. The mapping fromtrue colour entries to the entries in the palette represents an np-complete problem so that colour quantisation algorithms apply various heuristics in orderto derive the palette. In this paper we apply a variant of Simulated Annealing (SA) as a generic optimisation algorithm to the colour quantisation problem.
Experiments on a series of standard test images showthat even without any domain specific knowledge ourSA based algorithm is able to outperform conventional
quantisation algorithms and hence to provide images
with superior image quality.

1

Introduction

Colour quantisation is a common image processing
technique that allows the representation of true colour
images using only a small number of colours and isuseful for displaying images on limited hardware suchas mobile devices, for image compression, and for otherapplications such as image retrieval [11]. True colour
images typically use 24 bits per pixel which results
in an overall gamut of 224 i.e. more than 16.8 million
different colours. Colour quantisation uses a colour
palette that contains only a small number of colours(usually between 8 and 256) and pixel data are thenstored as indices to this palette. Clearly the choiceof the colours that make up the palette has a crucial
influence on the image quality of the quantised image
However, the selection of the optimal colour paletteis known to be an np-hard problem [4]. In the image
processing literature many different algorithms havebeen introduced that aim to find a palette that allows
for good image quality of the quantised image [4, 3, 2].

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Simulated annealing

Simulated annealing (SA) was first introduced as a
general optimisation method by Kirkpatrick et al. [6],based on the work of Metropolis et al. [7]. It simulates
the annealing of metal, in which the metal is heatedup to a temperature near its melting point and thenslowly cooled down. This allows the particles to movetowards a minimum energy state, with a more uniform
crystalline structure. The process therefore permits
some control over the microstructure.
Simulated annealing is a variation of the hillclimbing algorithm. Both start from a randomly selected point within the search space of all the possible solutions. Each point in the search space has ameasurable error value, E, associated with it, which

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where smax (n) is the maximum step width at iterationn, s0 is the initial maximum step width, nmax the
maximum number of iterations and β is an adaptation
constant.

indicates the quality of the solution. From the current
point in search space, new trial solutions are selected
for testing from the neighborhood of the current solution. This is usually done by moving a small stepin a random direction. In this application, small andequally distributed random numbers from the interval [−smax , smax ] are added to each component of thecurrent solution vector, where smax is called the ’maximum step width’. The values for smax need to be chosen from the interval between 0 and the upper limitof the search space dimension. The decrease in error
values is ∆E. If ∆E is negative, i.e. the error of a trial
solution is less than the error of the current one, thetrial solution is accepted as the current solution.
Unlike hill-climbing SA does not automatically reject a new candidate solution if ∆E is positive. Instead
it becomes the current solution with probability p(T )which is usually determined usingp(T ) = e−∆E/T

3

(1)

1
error(C, I) = Pk

li
k X
X

j=1 lj i=1 j=1

colour||Ci − Ij || + p(C, I) (4)withp(C, I) =

k
X
i=1

½
δai ,

ai =

1 if li = 00 otherwise(5)

where li is the number of pixels Ij represented bycolour Ci of the palette, ||.|| is the Euclidean distancein RGB space, and δ is a constant (δ = 10 in our experiments). The objective function error(C, I) used ishence a combination of the mean Euclidean distanceand a penalty function. The penalty function p(C, I)
was integrated in order to avoid unused palette colours
by adding a constant penalty value to the error foreach entry in the codebook that is not used in the
resulting picture.
As can be seen from Equation 4 the objective function is highly non-linear, i.e. it has a high degree of
epistasis [1]. Past experience [9] has shown that forthis kind of optimisation problems simulated annealing outperforms other generic optimisation algorithms
like genetic algorithms [5].
For our colour quantisation algorithm we employ
a population based version of the SWASA algorithmwith a population size of 10. The start temperature
was chosen to be 100 and the cooling coefficient was
set to 0.9. The temperature was kept constant over 20
iterations and the maximum number of iterations was
set to 10000.

(2)

where Tn is the temperature at step n and α is thecooling coefficient (usually between 0.8 and 0.99).
During each step the temperature must be held constant for an appropriate number of iterations in order
to allow the algorithm to settle into a ’thermal equilibrium’ i.e. a balanced state. If the number of iterations
is too small the algorithm is likely to converge to a
local minimum.
Step width adapting simulated annealing(SWASA) [8] overcomes the problems associatedwith constant values for smax by using a scaling
function [10] to adapt the maximum step width tothe current iteration by
2s0(3)smax (n) =1 + eβn /nmax©ISAROB 2006

forIn this paper we apply the SWASA algorithm described in Section 2 as a black box optimisation algorithm to the colour quantisation problem. For colour
quantisation the objective is to minimise the total
error introduced through the application of a colour
palette. The colour palette C for an image I, a codebook of k colour vectors, should then be chosen so as
to minimise the error function

where T is referred to as ’temperature’, an abstract
control parameter for the cooling schedule. For a given
temperature and positive values of ∆E the probability function shown in Equation 1 has a defined upper
limit of one, and tends towards zero for large positive
values of ∆E. That means, in a practical computer
application, the probability p(T ) has to be calculated
for each candidate solution and to be compared withan equally distributed random number from the interval [0, 1]. If the probability p(T ) is greater than the
random number the candidate solution is accepted as
the current solution, otherwise it is rejected.
The algorithm starts with a high temperature i.e.
with a high transition probability. The temperature isthen reduced towards zero, usually in steps, according
to a cooling schedule such as
Tn+1 = αTn

Simulated Annealing
quantisation

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Popularity alg.
22.24
18.56
18.00
8.73
15.91
19.87
17.22

Image
Lenna
Peppers
Mandrill
Sailboat
Airplane
Pool
mean

Octree
27.45
25.80
24.21
26.04
28.77
29.39
26.94

Median cut
23.79
24.10
21.52
22.01
24.32
24.57
23.39

Neuquant
27.82
26.04
24.59
26.81
28.24
27.08
26.73

Sim. Annealing
27.79
26.16
24.46
26.69
29.43
29.84
27.40

Table 1: Quantisation results, given in terms of PSNR [dB].

4

Experimental results

where R(i, j), G(i, j), and B(i, j) are the red, green,
and blue pixel values at location (i, j) and n and m
are the dimensions of the images.
From Table 1 we can see our Simulated Annealing
approach to colour quantisation obtains the best results for three of the six images and comes close 2-nd
for the other three. Overall a mean PSNR of 27.40 dB
is achieved which is significantly better than the 26.94
and 26.73 dB obtained by Octree and Neuquant, thetwo next best algorithms.
An example of the performance of the different algorithms is provided in Figure 1 which shows part of
the Pool image together with the same part extracted
from the images colour quantised by all algorithms. It
is clear that the popularity algorithm performs poorly
on this image and assigns virtually all of the colours in
the palette to green and achromatic colours. Mediancut is better but still provides fairly poor colour reproduction; most of the colours in the quantised image
are fairly different from the original. The same holds
true for the images produced by Neuquant. Here themost obvious artefact is the absence of an appropriate
red colour in the colour palette. A far better result isachieved by the Octree algorithm, although here also
the red is not very accurate and the colour of the cue
is greenish instead of brown. Clearly the best image
quality is maintained by applying our Simulated annealing technique. Although the colour palette has
only 16 entries all colours of the original image are
accurately presented including the red ball and thecolour of the billiard cue.

In order to evaluate our new method for colour
quantisation we have taken a set of six standard images commonly used in the colour quantisation literature (Lenna, Peppers, Mandrill, Sailboat, Airplane,
and Pool) and applied our optimisation scheme to generate quantised images with a palette of 16 colours.
To put the results we obtain into context we havealso implemented four popular colour quantisation algorithms to generate corresponding quantised images
with palette size 16. The algorithms we have tested
were:• Popularity algorithm [4]: Following a uniform
quantisation to 5 bits per channel the n colours
that are represented most often form the colour
palette.
• Median cut quantisation [4]: An iterative algorithm that repeatedly splits (by a plane throughthe median point) colour cells into sub-cells..
• Octree quantisation [3]: The colour space is represented as an octree where sub-branches are successively merged to form the palette.
• Neuquant [2]: A one-dimensional self-organising
Kohonen neural network is applied to generate
the colour map.
For all algorithms, pixels in the quantised images wereassigned to their nearest neighbours in the colour
palette to provide the best possible image quality.
The results are listed in Table 1, expressed in termsof peak-signal-to-noise-ratio (PSNR) defined as
PSNR(I1 , I2 ) = 10 log102552
M SE(I1 , I2 )5

(6)with MSE (the mean-squared error) given asn

MSE(I1 , I2 ) =m

1 XX[(R1 (i, j) − R2 (i, j))2 + (7)3nm i=1 j=1(G1 (i, j) − G2 (i, j))2 + (B1 (i, j) − B2 (i, j))2 ]©ISAROB 2006

358

Conclusions

In this work we have applied a variant of Simulated Annealing as a standard black-box optimisation
algorithm to the colour quantisation problem. Experimental results obtained on a set of standard test images have demonstrated that this type of optimisation
techniques cannot only be effectively employed but isThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 1: Part of original Pool image (top-left) and corresponding images quantised with (from left to right, top tobottom): Popularity algorithm, Median cut, Octree quantisation, Neuquant, and our Simulated annealing approach.
even able to outperform standard purpose built colour
quantisation algorithms.

[7] A. Metropolis, W. Rosenbluth, M.N. Rosenbluth,
H. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal ofChemical Physics, 21(6):1087–1092, 1953.

References[8] L. Nolle. On the effect of step width selection
schemes on the performance of stochastic local
search strategies. In 18th European Simulation
Multi-Conference, pages 149–153, 2004.

[1] Y. Davidor. Epistasis variance: Suitability of arepresentation to genetic algorithms. Complex
Systems, 4:369–383, 1990.

[9] L. Nolle, D.A. Armstrong, A.A. Hopgood, and
J.A. Ware. Simulated annealing and genetic algorithms applied to finishing mill optimisation for
hot rolling of wide steel strip. International Journal of Knowledge-Based Intelligent Engineering
Systems, 6(2):104–111, 2002.

[2] A.H. Dekker. Kohonen neural networks for optimal colour quantization. Network: Computationin Neural Systems, 5:351–367, 1994.
[3] M. Gervautz and W. Purgathofer. A simple
method for color quantization: Octree quantization. In A.S. Glassner, editor, Graphics Gems,
pages 287–293. 1990.

[10] L. Nolle, A. Goodyear, A.A. Hopgood, P.D. Picton, and N. Braithwaite. On step width adaptation in simulated annealing for continuous parameter optimisation. In Computational Intelligence- Theory and Applications, volume 2206 of Lecture Notes in Computer Science, pages 589–598.
Springer, 2001.

[4] P. S. Heckbert. Color image quantization forframe buffer display. ACM Computer Graphics(ACM SIGGRAPH ’82 Proceedings), 16(3):297–
307, 1982.
[5] J.H. Holland. Adaptation in Natural and Artificial Systems. University of Mitchigan Press, 1975.

[11] G. Schaefer, G. Qiu, and G. Finlayson. Retrievalof palettised colour images. In Storage and Retrieval for Image and Video Databases VIII, volume 3972 of Proceedings of SPIE, pages 483–493,
2000.

[6] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi.
Optimization by simulated annealing. Science,
220(4598):671–680, May 1983.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Automatic generation of B-spline curve using the evolution technique
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

However, a lot of experiences are needed for it. Thus, it
is difficult to operate the number and the position of thecontrol-points [1].
The purpose in this study is to arrange thecontrol-points of B-spline automatically for the faithfulrepresentation of an arbitrary curve, and to reduce thecontrol-points as less as possible. For them, the conceptof the bug-type of artificial life is introduced [2]. This is
one of the extension-models of genetic programming [3].
Chapter 2 explains the method to generate B-spline
curve automatically, and chapter 3 shows the results.
Some hand-written curves are used to generate theB-spline curve. As the result, B-spline curve similar tohand-written curve is automatically reproduced, and it
uses a less control-points. Chapter 4 mentions theconclusion.

The B-spline curve is often used as a method todraw an arbitrary curve like a hand-written figure.
However, it is difficult to operate the position of thecontrol-point for the faithful drawing. In addition, toarrange the control-points by hand, a lot of experiences
needs.
The purpose in this study is to reproduce
automatically an arbitrary curve faithfully by using a lesscontrol-points. The concept of evolutional method is
utilized for the generation of the B-spline curve. The
detail process is as follows. The several points on the
curve are regarded as the chromosome. The crossover ispracticed between two chromosomes. In this study, theindividual with the same size of chromosome is regarded
as homogeneity, but the one with the different size isdefined as heterogeneity. The crossover between
homogeneities is called “sexual reproduction”. The
crossover is not practiced between heterogeneities. The
individual without the same size reproduce two its copies.
This is called “asexual reproduction”. By the strategy,
various size of chromosome evolves. For the validity,some hand-written curves are used to generate theB-spline curve. As the result, B-spline curve similar tohand-written curve is automatically reproduced, and it
uses a less control-points.
Keywords: hand-written figure, B-spline, genetic
algorithm

2. Method for the generation of B-spline
curve
Genetic programming (GP) has been proposed
recently[3]. Some unknown functions are discovered by
the use of GP. The discovered functions, however, areoften very complicated and quite different from thosewhich are discovered by human. For the improvement, abug type of artificial life, which is called S-system, wasproposed [2], [4]. The concept of a sexual / asexual
reproduction is introduced. This is very effective forfunction-search. A part of chromosome changes by
mutation. According as the generation proceeds, the bugswith the function adapting to the observation data
survive selectively, and find out the function
corresponding with the data.
The summary of S-system and the method for theapplication to generate B-spline curve are mentionedhere.
2.1 Design of chromosome
The arbitrary curve is represented by the B-Spline curve.
Some points [P1, P2, …] on the hand written-curve are
extracted, and they are called passing points. The

1. Introduction
The figure is often drawn by hand at the stage of theidea, although it is possible to draw precisely with acomputer. A hand-written figure gives man the relief, so
there are a lot of hand-written fonts in theword-processor. The B-spline curve is often used as oneof the methods to express a hand-written figure. The
B-spline curve is generated based on the control-point.
When the control-points are arranged automatically withthe computer, the number of them becomes large. For the
reduction of them, it is possible to arrange them by hand.

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Control-point

Chromosome 1
P1

Handwritten
curve

homogeneity
Chromosome 2
P5

Passing
point

P6 P7

Fig. 1 Relationship among curve, passing points andcontrol-points.
P2

heterogeneity

P3 P4

Passing points

dP1 dP2 dP3 dP4Displacement ofpassing points

P2 P3

Chromosome 3
P4

P5

P6

Fig. 3 The difference between homogeneity andheterogeneity.
dP1 dP2 dP3 dP4dP5 dP6 dP7 dP8Fig. 4 Crossover using bug type GA strategy.

Fig. 2 Definition of chromosome.

homogeneity exists, the chromosome with the highest
fitness is selected as the partner. The chromosome and its
partner reproduce two chromosomes by crossover. As far
as sexual reproduction is concerned, the crossover
operator is equivalent to that of real GA[2]. This strategy
uses displacement dPi of passing point Pi for a crossover.
It means that the target of crossover is the displacement.
The example is displayed in Fig. 4.
2.3 Limit of the number of homogeneities
According as generation progresses, the number ofhomogeneities may be almost all. This is because an
excellent chromosome survives selectively. In that case,
it is hard for the other heterogeneity to appear even if it
is very excellent. That is, the search is trapped in a local
optimum. For the search being as successful as possible,
we limit the number of homogeneities. By this strategy,
different many species are in existence at the same time,and each species evolves by sexual reproduction.
2.4 Calculation of fitness
Fitness is the value that shows how the B-Spline
curve expressed by the chromosome is similar to original
curve. The fitness is high when the B-Spline curve isagreement with the original curve, and the number ofpassing points is a little. For them, we introduce thedegree map. The outline is shown in Fig. 5. Fig.5(a) is
the handwritten curve, and Fig. 5(b) represents its degreemap. The image size of Fig. 5(a) is the same as Fig. 5(b).
In Fig. 5(b), the evaluation value (degree) of each pixel
is defined as follow.
(1)evalulation value = 255 - shift ´ gain

control-points [Q1, Q2, …] are obtained by reverse
conversion of passing points. The relation is shown in
Fig. 1. From the control-points, the B-spline curve canbe drawn. In a word, the B-spline curve can be drawn
from the passing points.
We define the passing points [P1, P2, …] as
chromosome as shown in Fig. 2. Each point has thedisplacement. The target of crossover is the displacement.
This is the same way of bug type of GA [5]. The numberof passing points corresponds to the size of chromosome.
In this system, the size of chromosome is not fixed. Thisis set at random at initial generation, and changes atmutation.
2.2 Sexual reproduction and asexual reproduction
Sexual reproduction and asexual one are introducedin this study. These are important strategy. In the
beginning, we have to explain the concepts ofhomogeneity and heterogeneity. The difference between
them is shown in Fig. 3. We define the chromosome withthe same size as homogeneity, while the one withdifferent size as heterogeneity. In Fig. 3, chromosome 1
has the same size as chromosome 2, so chromosome 1
and chromosome 2 are homogeneity. The size ofchromosome 3 is different from the others, so we callchromosome 3 is heterogeneity against the others.
Sexual reproduction is practiced between homogeneities.
Heterogeneity cannot reproduce sexually.
The procedure of sexual reproduction is asfollows. A chromosome is selected by tournament
strategy. It looks for the homogeneity. In case that

©ISAROB 2006

P8

heterogeneity
P1

P1

P2 P3 P4

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Start
Extraction of curve
Generation of initial bugs
Generation g = 0
Bug p = 1
Movement(a) a handwritten curve(b) degree map of (a)Calculation of fitness

Fig. 5 A handwritten curve and its degree map.
where, shift is the distance-gap from the handwritten
curve, gain is the constant. As understood from Fig. 5(b),the evaluation value of the pixel on the handwritten
curve is 255. The value decreases as parting from thecurve. When the value of gain increases, the evaluation
value rapidly decreases as parting from the curve.
The calculation of fitness is as follows. (i) The
B-spline curve is drawn in this map. (ii) The total ofevaluation value of each pixel along the curve iscalculated. (iii) The average value fitm per a pixel isobtained. Based on the following definition, the fitness
fit is obtained.

YN
p=Pop ?

N
g=g+1

Y
g=GeneMAX ?

N

Descendant
generation routine

Y

End

Fig. 6 The flowchart for generating B-spline curve.
Table 1. The value of parameters for simple curve.

fit = kfit × fit m / 255 + (1 - kfit ) × fit n(2)
where, fitn is the normalized number of passing points,
and kfit is a constant. This is same concept of MDL[6].
2.5 Algorithm of automatic generation of B-spline
curve
The flowchart for the generation of B-spline curve isshown in Fig. 6. The summary is as follows.
Extraction of curve: The handwritten curve is extracted
from the image (BITMAP format), in which a
handwritten curve is drawn.
Generation of initial individuals: Numerous
chromosomes with the arbitrary passing points are
generated at random.
Movement: By the movement, the passing point in thechromosome changes slightly.
That is to say, the values of Pk are replaced by
Pk + dPk or Pk - dPk , where Pk and dPk are
the chromosome in Fig. 2, and k=2, 3, …, n-1, n is
the number of passing points. This concept is basedon Ref. [5].
Calculation of fitness: Base on the chapter 2.4, thefitness of each chromosome is calculated.
Descendant-generation-routine: At this routine, newchromosomes are generated. For it, generation-gap,©ISAROB 2006

p = p+1

Find theB-spline?

Number of passing points

kfit
Gain
Number of chromosomes
Maximum generation
Range of dPk
Generation gap
Crossover rate
Tournament size
Limitation of the number ofhomogeneities
Mutation rate of passing point
Mutation rate of dPk

3, 4, …, 15
0.8
3
300
100
0, 1, 2
0.5
0.5
3
30
0.5
0.5

selection, crossover, and mutation are practiced.
Generation-gap: The chromosomes of the number
of Par are selected and they are passed down to thenext generation. The elite strategy is adopted for it.
Selection: An individual is selected by thetournament strategy.
Crossover: The selected one is judged whether it has
the ability to sexually reproduce. In the case that the
selected one has the ability of sexual reproduction,

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(a) simple handwritten curve(b) B-spline in case of 5 passing
points(c) B-spline in case of 6 passing
pointsFig. 7 B-spline curve generated from simple curve.

(a) complex handwritten curve(b) B-spline in case of 14 passing
points(c) B-spline in case of 16 passing
pointsFig. 8 B-spline curve generated from complex curve.
examples are shown in Fig. 7. Fig. 7(a) is thehandwritten curve. In Fig. 7(b) and (c), the generated
B-spline curves are drawn. In these figures, both
handwritten curve and B-spline one are drawn. As
understood from them, B-Spline curve is agreement withhandwritten one. In addition, the number of passing
points is a little. From the comparison of Fig. 7(b) with
Fig. 7(c), the B-spline curve drawn with 6 passing points
more agrees with the handwritten curve than that of 5
passing points.
3.2 Complex curve
Since the complex curve such as font is used in reality,
we tried to generate the B-spline curve for the complex
curve. We tried the generation of B-spline curve 100
times. The number of passing points=3,..,31 and kfit=0.72.
The conditions are the same as Table 1 except them. The
main breakdown of the number of passing points isshown in Fig. 9. The examples are shown in Fig. 8. Fig.
8(a) is the handwritten curve. In Fig. 8(b) and (c), thegenerated B-spline curves are drawn. As understoodsexual reproduction is performed. In the other case,asexual reproduction is carried out. This is mentioned
in chapter 2.2.
Mutation: A part of the chromosome is changed bymutation at a certain rate. In mutation, passing point
is added, deleted, or replaced in the chromosome. As
far as displacement of passing points dPk , the value
is replaced by the other value by mutation.

3. Experimental results
For the validity of this algorithm, we practiced theB-spline generation in two cases of curves.
3.1 Simple curve
We tried the generation of B-spline curve 100 times
for simple curve. The conditions are listed in Table 1. At
one trial, the B-spline curve with the highest fitness isobtained. Because of 100 trials, 100 B-spline curves are
gotten. The main breakdown of the number of passing
points is as follows. Passing points 5: 15 times, passing
points 6: 66 times, Passing points 7: 19 times. The©ISAROB 2006

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“Evolutionary Learning Strategy using Bug-Based
Search”, Proc. of the 13 Int. Joint Conf. on Artificial
Intelligence (IJCAI-93), vol.2, pp.960-966 (1993).
[ 6 ] Iba, H., Genetic Programming, Tokyo-Denki-Daigaku shuppan, p.141, p.204 (1996).

18
16

generated number

14
12
10
8
6
4
2
0
4

6

8

10

12

14

16

18

20

22

24

26

28

30

passing points

Fig. 9 The distribution of passing points forcomplex curve.
from them, B-Spline curve is agreement withhandwritten one. In addition, the number of passing
points is a little for the complex curve. From thecomparison of Fig. 8(b) with Fig. 8(c), the B-spline
curve drawn with 16 passing points more agrees with the
handwritten curve than 14 passing points.

4. Conclusions
The purpose in this study is to arrange
automatically the control-points of B-spline for thefaithful representation of an arbitrary curve, and to
reduce the control-points as less as possible. For them,the concept of the bug-type of artificial life is introduced.
As the result, B-spline curve similar to hand-written
curve is automatically reproduced, and it uses a lesscontrol-points.
In the future, we should investigate the method forthe further reduction of control-points, and apply topractical use.

References[1] Kanemoto, T., Numeric graphics processing for CAE,
Kyoritsu-shuppan, p.87, p.96 (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] J. Koza, Genetic Programming Ⅱ, Auto Discovery
of Reusable Subprograms, MIT Press, p.109 (1994).
[4] S. Serikawa and T. Shimomura,“Improvement of the
Search Ability of S-System (A Function-Discovery
System)”, Trans. IEE, Vol.120-C, 8, in Press, (2000).
[5] Iba, H., Higuchi T., de Garis, H, and Sato, T.,©ISAROB 2006

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A Genetic Approach to the Design of Autonomous Agentsfor Futures Trading
H. Kitano, T. Nakashima, H. Ishibuchi
Graduate School of Engineering
Osaka Prefecture University
Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan{kitano@ci., nakashi@, hisaoi@}cs.osakafu-u.ac.jp
Abstract

project is one of such virtual markets where multiple
trader agents including human traders simultaneously
participate in a futures market (Fig. 1).

This paper proposes a genetic algorithm-based
method for designing an autonomous trader agent.
The task of the proposed method is to find an optimal set of fuzzy if-then rules that best represents the
behavior of a target trader agent. A highly profitabletrader agent is used as the target trader agent in the
proposed genetic algorithm. A trading history of thetarget agent is obtained from a series of futures trading. In the antecedent part of fuzzy if-then rules timeseries data of spot prices are considered while the consequent part indicates the order of trade (Buy, Sell, orNo action) with its degree of certainty. The proposed
method determines the antecedent part of fuzzy if-thenrules. The consequent part of fuzzy if-then rules is automatically determined from the trading history of thetarget trader agent. The autonomous trader agent designed by the proposed genetic algorithm consists of a
fixed number of fuzzy if-then rules. The decision of theautonomous trader agent is made by fuzzy inference
from the time series data of spot prices.

1

U-MART Server

External information( S, F )U-MART Client
Internal information( Pos, Cash, t )Figure 1: The general view of U-Mart.

In futures markets, the price is determined basedon orders made by all trader agents that participate
in the market. Thus, it is important for a trader agent
to predict the orders of the other trader agents in order to make an optimal trade decision. The aim of thispaper is to propose a genetic algorithm-based method
for finding an optimal set of fuzzy if-then rules thatbest represents the behavior of a target trader agent.
In the proposed genetic algorithm-based method, an
individual represents an autonomous trader agent. A
pre-specified number of fuzzy if-then rules are encoded
into a string of binary and integer bits. First, an initial population is generated by randomly determining
each bit of strings. Then genetic operations such as
selection, crossover, and mutation are performed inorder to generate new strings. The new strings areused to form the next population together with best

Introduction

Fuzzy rule-based systems have been successfully applied to various problems such as control [1], function
approximation, and pattern classification [2, 3]. The
advantage of a fuzzy rule-based system is its interpretability. Since fuzzy if-then rules in a fuzzy rulebased system are written using linguistic values, human users can linguistically understand the meaningof the fuzzy if-then rules.
Recently, virtual economic markets have attracted
a great deal of attention for analyzing economic systems and developing autonomous trader agents. UMart (Unreal Market as Artificial Research Test-bed)©ISAROB 2006

Trade order( BS, P, Q)

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2.2

strings in the current population. The fitness value ofeach strategy is evaluated through the rate of matchedtrading decision during the futures trades. That is, astring has a high fitness value if the autonomous trader
agent generated from the string makes decision similar to that of the target trader agent. This procedure
is iterated until a pre-specified termination criterion
is satisfied. In a series of computational experiments
we examine the performance of the proposed method
using a time series data of spot prices. It will beshown that the autonomous trader agent designed by
the proposed method has a similar behavior to the target trader agent. For example, the autonomous trader
agent makes the same decision as the target trader
agent, and the profit of the autonomous trader agent
is similar to that of the target trader agent if the experimental settings are the same.

2

We use the following type of fuzzy if-then rules for
futures trading:
Rj : If x1 is Aj1 and . . . and x10 is Aj10
then action is Bj with CFj , j = 1, . . . , N,

Handling Trading History

The trading history of the target agent can be converted into a set of training patterns in pattern classification problems. Let us assume that m + 10 trading actions are included in a trading history data set.
That is, the trading history data set consists of twotime series data, spot prices and trading actions asfollows:

Membership
1.0
N
0.0

T H = {(st , at ), t = 1, . . . , m + 10},

(1)

MP

-40 -20

0

20

P
40

DC
0.0

Figure 2: Membership functions for antecedent fuzzysets.
The consequent part of fuzzy if-then rules canbe automatically determined by using a fuzzy rulegeneration method in [4] once the antecedent part is
fixed.

(3)

Each attribute in xp , p = 1, . . . , m is calculated as follows:

2.3

(4)

Decision Making on Trading Action

Let us assume that we have N fuzzy if-then rulesthat are obtained by our genetic algorithm-based
method. The fuzzy rule-based system is used to determine the trading action such as Buy, Sell, No action.

The class of the p-th training pattern xp is determinedas the trading action of the target agent at the (p+10)th time step.

©ISAROB 2006

Z

1.0

where

xpi = sp+10 − sp+10−i ,i = 1, . . . , n, p = 1, . . . , m.

MN

Membershipwhere T H is the set of trading history, st is the spot
price at the t-th time step, and at is the trading actionof the target agent at the t-th time step. We convert
the trading history T H into the training data T D as
follows:
T D = {xp , p = 1, . . . , m},(2)

xp = (xp1 , xp2 , . . . , xpn ), p = 1, . . . , m.

(5)where Aj1 , . . . , Aj10 are antecedent fuzzy sets of fuzzy
if-then rule Rj , Bj is the consequent action, CFj isthe grade of certainty, and N is the number of fuzzy
if-then rules in a fuzzy rule-based system.
Each attribute in the antecedent part of fuzzy ifthen rules shows the differences between the spot
prices at the latest time and the corresponding time
step. For example, x5 represents the difference of spot
prices between the latest and five time steps before.
Although we consider only ten attributes in this paper, it is possible to extend the number of attributes.
However, since the main focus of this paper is to findan optimal set of fuzzy if-then rules, not to find optimal fuzzy if-then rules, we fix the number of attributes
as ten. A modified version with more attributes is left
for future research.
As the membership functions for antecedent fuzzy
sets Aj1 , . . . , Ajn , we use five fuzzy sets and don’t careshown in Fig. 2.

Fuzzy Rule-Based Systems for Futures Trading

2.1

Fuzzy If-Then Rule

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

N
An individual

R1

R2

R3

R1

…

µĵ = max µj (x) · CFj ,(6)µj (x) = µj1 (x1 ) · µj2 (x2 ) · . . . · µjn (xn ),(7)

j=1,...,N

Fuzzy if-then rule

0

4

2

3

5

1

3

5

2

4

Inclusion of attributes

1

0

0

0

1

1

0

1

1

1

Figure 4: Encoding of a fuzzy if-then rule in the string.
fuzzy if-then rule, whether an attribute is used or not
depends on fuzzy if-then rules. The second part shows
the antecedent part of fuzzy if-then rules.
Note that the consequent part of fuzzy if-then rules
is determined automatically from the trading historyof the target agent after determining the antecedent
part of fuzzy if-then rules.

where

3.2

Fitness Function

The task of the proposed genetic algorithm-based
method is to find the rule set of fuzzy if-then rules thatbest represents the behavior of the target trader agent.
In the proposed genetic algorithm-based method, we
define the fitness of a fuzzy rule-based system S as
follows:nca(S)
f itness(S) =,(8)mwhere nca(S) is the number of correct actions in the
training data set T D, m is the number of training
data. The fuzzy rule-based systems with higher fitness value best represents the behavior of the target
trader agent, thus the fuzzy rule-based systems with ahigh fitness value are favored in the proposed genetic
algorithm-based method.

and µji (xi ), i = 1, . . . , n is the membership function of
fuzzy sets Aji , i = 1, . . . , n in the antecedent part of
the fuzzy if-then rule Rj .

Genetic Algorithm for Finding Fuzzy
If-Then Rules

In this paper, we use a genetic algorithm-based
method for obtaining fuzzy if-then rules. The taskof the genetic algorithm is to find an optimal set offuzzy if-then rules that best represents the behavior of
the target trader agent.

Encoding

3.3

An individual represents a set of fuzzy if-then rulesin the proposed method. Fuzzy if-then rules in a fuzzy
rule-based system are concatenated to form a single
string as in Fig. 3. It should be noted that the orderof the fuzzy if-then rules in the string does not haveany effect on its performance.
Each of fuzzy if-then rules in the string is divided
into two parts (see Fig. 4). The first part consistsof binary bits that show the inclusion of attributes.
That is, the value of 1 in a binary bit means that thecorresponding attribute is used in the fuzzy if-thenrule. On the other hand, the attribute is not used in
the fuzzy if-then rule if the value of the binary bit is 0.
It should be noted that whether an attribute is used infuzzy if-then rules is not common over the set of fuzzy
if-then rules. Since the binary bits are used for each

©ISAROB 2006

RN

10

We also assume that an input pattern x = (x1 , . . . , xn )to the fuzzy rule-based system is obtained from thetime series of spot prices. The trading action is determined by using a single winner rule where the consequent part of the fuzzy if-then rule Rĵ with the maximum product of the compatibility and the degree ofcertainty is employed:

3.1

…

R3

RN

Figure 3: A string for a set of fuzzy if-then rules.

3

R2

Overall Procedure

The procedure of the proposed genetic algorithmbased method can be described as follows:
Step 1: Initialization.
A pre-specified number ofstrings are randomly generated to form an initial population of size Npop .
Step 2: Fitness evaluation. Each string in the population is evaluated by using the fitness functionin (8).
Step 3: Genetic operations. We generate new strings
through genetic operations such as selection,
crossover, and mutation. For the crossover operation, we use rule-wise uniform crossover operation where cut-points occur only between

372

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0.7

substrings that represent fuzzy if-then rules.
In order to select a pair of strings for one
crossover operation, we use binary tournament selection where first two strings are selected randomly and then the better stringwith the higher fitness value in (8) is selected
as a parent in the crossover operation. For
mutation operation, we use bit-change operation where the value of randomly selected
bits is replaced with randomly selected one.
It should be noted that only 0 and 1 are possible for binary bits whereas 0 ∼ 5 can be used
for the other bits. We generate Npop − 1 new
strings by the above genetic operations.

Fitness

0.68

0.6

0

1000 2000 3000 4000 5000

Generation

Figure 5: Experimental results.

by the best string in the population (i.e., f itness(S))
increases over generations.

5

Conclusions

In this paper, we proposed a genetic algorithmbased method. The task of the proposed method is
to find a fuzzy rule-based system that best represents
the behavior of a target agent in a futures market.
Each fuzzy rule-based system is encoded into a stringwith binary and integer bits. Binary bits show theinclusion or exclusion of the corresponding attributesin each fuzzy if-then rule and the fuzzy sets in theantecedent part of fuzzy if-then rules are specified byinteger bits. Through the computational experiments
we showed that the proposed genetic algorithm-based
method successfully found fuzzy rule-based systems
that are consistent with the behavior of the target
agent.

Step 5: Termination test. If a pre-specified termination criterion is satisfied, terminate the procedure of the genetic algorithm-based method.
Otherwise go to Step 2.

Computational Experiments

This section shows the experimental results of the
proposed genetic algorithm-based method for obtaining fuzzy rule-based systems that best represent the
behavior of the target agent.

4.1

0.64
0.62

Step 4: Generation update. We employ so-called generational version of genetic algorithm with elitist strategy. That is, the new strings generated in the previous step and the best string in
the current population are combined to form
the next population.

4

0.66

Experimental Settings

The following experimental settings were used in
the computational experiments in this paper:

References

Target agent: ZCrossover
Population size: 50
Crossover rate: 1.0
Mutation rate: 1/6
The number of fuzzy if-then rules in a string: 50
Termination criterion: 5,000 generations[1] M. Sugeno, “An Introductory Survey of Fuzzy
Control”, Information Science, Vol. 30, No.
1/2:59-83.

As the target agent in the experiments we use
ZCrossover that is an autonomous trader agent developed in Ritsumeikan University.

[3] C.T. Leondes, “Fuzzy Theory Systems”, Techniques and Applications, Academic Press, San
Diego, Vol.1-4, 1999.

4.2[4] H. Ishibuchi, K. Nozaki, H. Tanaka, “Distributed
Representation of Ruzzy Rules and its Application
to Pattern Classification”, Fuzzy Sets and Systems,
Vol. 52, No. 1:21-32, 1992.

[2] C.C. Lee, “Fuzzy Logic in Control Systems: Fuzzy
Logic Controller Part I and Part II”, IEEE Trans.
Syst., Man, Cybernetics, Vol. 20:404-435, 1990.

Experimental Results

We show the results of the experiments in Fig. 5.
From Fig. 5, we can see that the rate of correct actions©ISAROB 2006

373

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Comparison between Self-organization with Sampling and Genetic Algorithms in
multi-modal function
Takayuki HigoTokyo Institute of Technology
4259, Nagatuta-cho, Midori-ku
Yokohama-city, Kanagawa, Japanhigo@cas.dis.titech.ac.jp

Keiki Takadama
Tokyo Institute of Technology
4259, Nagatuta-cho, Midori-ku
Yokohama-city, Kanagawa, Japankeiki@dis.titech.ac.jp

Mitujirou Katuhara
National Maritime Research Institute6-38-1, Sinkawa
Mitaka-city, Tokyo, Japan
kat@nmri.go.jp

Takahiro Majima
National Maritime Research Institute6-38-1, Sinkawa
Mitaka-city, Tokyo, Japanmajy@nmri.go.jp

Abstract

to search points which are near from other parents.
Recently Estimation of Distribution Algorithms(EDAs)[6] was proposed as a model of mathematically interpreted GAs. In EDAs, we represent the
search points as a probabilistic distribution. And weuse sampling according to the estimated probabilistic distribution instead of crossover. So EDAs can bethought more general framework. Self-organization
with Sampling (SOS) is one of the EDA-based optimization method. In SOS, the cost function is estimated from samples drawn according to Boltzmann
distribution whose energy is estimated cost function.
Using Boltzmann distribution, we can control variety
of search points as entropy. So SOS can be thought
appropriate method for multimodal function optimization. But SOS has one problem that it is too hard
to construct step function approximating cost function in high dimension. We therefore try to propose
computable SOS by extending ANS by using simplex
neighbor which is used in SOS for ﬁnding neighborsearch points.
In section 2, we explain ANS. In section 3, we explain SOS. In section 4, we propose a combinatorial
method of ANS and SOS. Section 5 is experiments
using benchmark problems. Section 6 concludes thispaper.

In this paper, we propose a novel method for multimodal function optimization. This method can bethought a extension of Adaptive Neighbor Search(ANS) by using simplex neighbor which is a techniqueof Self-Organization with Sampling. The diﬀerence
between ANS and the proposed method is the way ofdeﬁning the neighbor. ANS uses Euclidian distancebased neighbor and the proposed method uses spatial relation based neighbor. Through intensive experiments, we conﬁrm the robustness for scaling search
space.

1

Introduction

Real-coded Genetic Algorithms (RCGAs) draw
attention as general optimization methods for nonlinear continuous function. In RCGAs Unimodal Normal Distribution Crossover (UNDX)[8][5] and Simplex
Crossover (SPX)[4] are widely used for crossover , andMinimal Generation Gap (MGG)[9] is used for generation alternation model. Simply using these methods,
we encounter the diﬃculties of multimodality. Especially fast convergence of search points is a serious
problem. To over come this problem, island model
Genetic Algorithms are proposed. In island model Genetic Algorithms, search points are divided into somegroups, and the crossover parents are limited to individuals in same groups. One of the most eﬃcient island model Genetic Algorithms is Adaptive Neighbor
Search (ANS)[10]. ANS limits the crossover parents©ISAROB 2006

2

Adaptive Neighbor Search

Adaptive Neighbor Search (ANS) is one of the island model GAs. A feature of ANS is to limit the374

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

crossover parents to their neighbor search points. We
can see this limitation as adaptive clustering basedon distance between search points. This mechanism
prevent fast convergence and is one way to overcome
multimodality.
Figure 1 shows the brief procedure of ANS. The features of ANS can be seen in step 2 of the optimization
phase. In step 2, we compute neighbor search points
deﬁned as nearest search points in Euclidean distance.
Then we apply crossover to neighbor search points anda search point selected in step 1. In crossover we let
the search point selected in step 1 be a center of Gaussian (UNDX) or Simplex (SPX).

”pseudo search points” because they don’t move. After putting search points and pseudo search points, we
decompose the feasible region into simplexes whose
vertexes are the search points or the pseudo search
points by Delaunay triangulation[7].
Features of SOS are step 2 and 3. In step 2, SOS
generate samples around a search point selected in step
1. This is ensemble of SPX. In step 3, we locally makea sum of probabilistic density in each search point almost equal. In fact, this is not easy. One approximation way is explained in [1]• Initialization:
1. Put pseudo search points on the verticesof convex polyhedron deﬁned by linear constraints• Initialization:
1. Put search points in feasible region

2. Put search points in the convex polyhedronrandomly• Optimization:
1. choose a search point x0 randomly

3. Apply Delaunay triangulation to search
space and generate neighbor simplex set Nxi .

2. Find neighbor search points x1 , . . . , xM
3. Operate crossover to x0 , x1 , . . . , xM and generate sampling data set D• Optimization:
1. choose a search point x0 randomly

4. Alternate generation (exchange x0 for best
sample in D)

2. Draw K samples from each simplex si ∈ Nx0
according to uniform distribution

5. Jump to 1.

3. move x0 to make the sum of probabilistic
density in each simplex almost equal

Figure 1: ANS procedure

4. Goto 1.
Figure 2: SOS procedure3

Self-Organization with Sampling
3.1

Self-Organization with Sampling (SOS) is EDAbased method. SOS estimates the cost function anddraws samples according to Boltzmann distribution.
Boltzmann distribution is deﬁned as follows:
p(x)

=

Z

=

exp(−f (x)β),
Z∫
exp(−f (x)β)dx,

Computation time of Delaunay triangulation,
which is used in SOS is exponential in the numberof the dimension. So we cannot apply SOS to real
problems. In this section we show a polynomial timealgorithm which is similar to Delaunay triangulation.
This method is introduced in [3] and called simplex
neighbor.
Before showing simplex neighbor, we have to know
the Delaunay triangulation using a geometric transformation. A geometric transformation which transforma point p in d dimensional space into point p̃ in d + 1
dimensional space is deﬁned as follows:

(1)
(2)

where f (x) is energy function, β is inverse temperature and Z is partition function. Controlling the inverse temperature, we can control the variety of search
points and generating samples.
Figure 1 shows the brief procedure of SOS. In SOS,
we assume that the feasible region is convex polyhedron. This is equivalent to assume the bounded linear
constraints. So we put the search points on the verticesof the convex polyhedron. We call these search points©ISAROB 2006

Simplex Neighborp̃ = (p, f (p)),
f (x) =

d
∑
i=1

375

x2i .

(3)
(4)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

We can get a d + 1 dimensional point set S̃ by applying the geometric transformation to a d dimensional
point set S. The convex hull conv(S̃) of S̃ become theDelaunay triangulation. But a computation of convex
hull needs exponential time in the number of the dimension. So we try to use another relationship of S
and S̃ . We focus on a point xp in S and a point set
S − which is S without xp . We think of the Delaunay simplexes (triangles) of S − . If there is a simplex
which contains xp in Delaunay Simplexes of S − , wecan compute the simplex by solving a following linear
programming:∑
αi < x̃i · ed+1 >, (5)minf (α) =
s.t.

∑

reason is that there is no simplex contains the selected
search point. Another reason is that the arrangementof the search points is singular, for example, somepoints are on a line or on a face. We can detect thesefailure by solving the linear programming, because in
exceptional case the linear programming have no solution. Then if we detect the exception, we use nearest
neighbor instead of simplex neighbor.

5

In this section we compare between a nearest neighbor method and a simplex neighbor method. We use
two benchmark functions as follows:x̃i ∈S̃ −αi xi = xp ,xi ∈S −m∑αi = 1,(6)• sphere function: f (x) =
(7)
(8)

5.1

∑n

2
i=1 ixi ,Results

Figure 3, 4, 5 and 6 show relationship between thenumber of function evaluations and best value of costfunction. The eﬀect of scaling can be seen in the nearest neighbor method but cannot be seen in simplex
method. In scaled function the performance of thenearest neighbor method is worse. But the simplex
neighbor method stop convergencehalf way. Applying the simplex neighbor method to 2-dimensional
sphere function, we can see the search points splitting
up into some clusters. And this is caused by the exception of nonexistence of the simplex.

Combination of ANS and SOS

We propose a combinatorial method ANS and SOS.
In island model GA, how to limit crossover parents
is important problem. ANS uses Euclidian distancebased limitation. But this limitation is not good because it is depend on the scale of search space. We
therefore propose a ANS-based method which uses
simplex neighbor of SOS instead of nearest neighborof ANS. So in this paper we call this method simplex neighbor method and call ANS nearest neighbormethod.
We can assume a case of failure in computing simplex neighbor. There are two reasons to fail. One©ISAROB 2006

2
i=1 xi ,where n = 5 in this experiment.
We apply a nearest neighbor and a simplex neighbor to these function. We use SPX for crossover (sampling) and its expansion rate is recommendation valueof [4]. The number of search points is 100 and the
Number of samples generated in crossover is 100.

where ed+1 is the unit vector directed to d+1th dimension. A vertex of the found simplex are called simplex
neighbor.
We show the mean of this linear programming. Leta point set S̃ − be points which can get by applying
the geometric transformation to S − . If there is a simplex which contains xp , a line l = {x̃|x̃inRd+1 andx̃i =x˜p i f ori = 1, 2, · · · , d} cross at a face of the convex hullconv(S̃ − ) of S̃ − . Solving the linear programming, we
can ﬁnd the point at which the line and the face cross.
And it’s representation is the convex combination ofpoints in S − . Therefore we can compute the Delaunay
simplex contains xp by solving a linear programming.
And we will be able to use this method as approximation of Delaunay triangulation.

4∑n• scaled sphere function: f (x) =i=10 ≤ αi ,Experiments

6

Conclusion

We propose a novel method by combining ANS andsimplex neighbor of SOS. Through the experiments,
we conﬁrm that the proposed method has an advantage of robustness for scaling. But simplex neighborhas disadvantage that there are cases not to ﬁnd thesimplex. We can develop multimodal function optimization method based on proposed method.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Acknowledgments
cost function

100

The research reported here was supported in part by
a Grant-in-Aid for Scientiﬁc Research (B, No. 17360424)
from the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT).

10-5
10-10
10-15
10

References

-20

10

0

1

2

3

4

10 10 10 10 10
number of evaluation

5

10

6

[1] Higo, T., Takadama, K. (2004), Optimization method
based on adaptive search space decomposition: an approach towards multimodal function (in Japanexe).
The Society of Instrument and Control Engineers,
Symposium on System and Information 2004, pp. 95100[2] Higo, T., , Takadama, K. (2005), Self-OrganizedSampling as an Optimization Method: An Approch
towards Multimodal Functions (in Japanexe). The
Japanese Society for Artiﬁcial Intelligence, The 19th
Annual Conference of JSAI 2005
[3] Higo, T., Takadama, K. (2005) Optimization
method for high dimensional multimodal function (inJapanexe). The Society of Instrument and Control
Engineers, Symposium on System and Information
2005[4] Higuchi, T., Tsutsui, S., and Yamamura, M. (2000),
Theoretical analysis of simplex crossover for realcoded genetic algorithms, Parallel Problem Solvingfrom Nature(PPSN-VI), pp. 365-374[5] Kita, H., Ono, I. and Kobayashi, S. (1999), Multiparental extension of the unimodal normal distribution crossover for real-coded genetic algorithms, Proceedings of the 1999 Congress on Evolutionary Computation. pp. 1581-1587[6] Larranaga, P., Lozano.J.A. (2001), Estimation of distribution algorithms: a new tool for evolutionary optimization. Kluwer Academic Publishers, Boston[7] Okabe,A., Boots,B. and Sugihara, K. (2000), Spatial
Tessellation Concept and Applications of Voronoi Diagrams. John Wiley & Sons
[8] Ono, I., and Kobayashi, S. (1997), A A real-coded
genetic algorithm for function optimization using
unimodal normal distribution crossover. Proceedingsof the Seventh International Conference on Genetic
Algorithms(ICGA-7)), pp.246-253[9] Satoh, H., Yamamura, M., and Kobayashi, S. (1996),
Minimal generation gap model for GAs consideringboth exploration and exploitation. Proceedings of the
IIZUKA: Methodologies for the Conception, Design,and Application of Intelligent Systems, pp. 494-497[10] Takahashi, O. and Kobayashi, S. (2001), An adaptive neighboring search using Crossover-like Mutation
for multimodal function optimization. IEEE International Conference on Systems, Man and Cybernetics,
pp. 261-267

Figure 3: Nearest neighbor for non-scaled
function.

cost function

10

0

10-5
10-10
10-15
10-20 0123456
10 10 10 10 10 10 10
number of evaluation

Figure 4: Simplex neighbor for non-scaled
function.

cost function

10

0

10-5
10-10
10-15
10-20
0
1
2
3
4
5
6
10 10 10 10 10 10 10
number of evaluation

Figure 5: Nearest neighbor for scaled function.

cost function

10

0

10-5
10-10
10-15
10-20
0
1
2
3
4
5
6
10 10 10 10 10 10 10
number of evaluation

Figure 6:function.

©ISAROB 2006

Simplex neighbor for scaled

377

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A simple model of evolving e osystems
y

?

?

?

Takashi Shimada , Yosuke Murase , Satoshi Yukawa , Nobuyasu Ito , and Kazuyuki Aiharay?

Aihara Complexity Modelling Proje t, ERATO, JST

Department of Applied Physi s, S hool of Engineering, The University of Tokyoz

Institute of Industrial S ien e, The University of Tokyo

Abstra t

2 ModelEquations of time-evolution of our model are,We investigate the stru tural properties of the system emerged in the e osystem model whi h was proposed by the authors and show that the model and theeld data shares similar hara teristi s. In parti ular,
our model suggests that the universal s aling of the"spanning tree", whi h has been dis ussed using theeld data onsists only of less than 100 spe ies, an bevalid for larger s ale.

Xa x x\plants" :dx1= Gx1 (1x1 ) +\animals" :dxi= xi +

Xa xx

Xa x

aij <0aij >0

dt

dt

j

 1 
1j 1 j 1 +
ij i j

1  
xj
ij i

(aij = aji 2 ( 1; 1);  2 (0; 1=2) G > 0)(1)
where xi ; aij ; G denotes the \population" of the i-th
spe ies measured in the unit of energy, the preying intera tion oeÆ ient between the i-th and j-th spe ies,
and the growth rate of the sole produ er in the system
alled \plants", respe tively. The parameter  represents the saturation e e t of the preying rate due tothe behavioral adaptation of the animals.

1 introdu tion
E osystem, whi h shows an ine able balan e ofwide variety, is generally regarded as a result of the
Darwinian ompetition. Mathemati al des ription
will be one promising path to reveal its se ret. Manymodels were proposed to explain its diversity, stru ture, and temporal or evolutional behavior. However,theoreti al studies on su h omplex e osystem withmany spe ies have been fa ed with the diÆ ulty of
onstru ting a natural model whi h an yield a large
system. For this reason, properties of large e osystemsu h as stability, stru ture, and extin tion have beenargued separately.
We have reported that our simple populationdynami s-based model an solve this frustrated
situation[1℄. In the model, intera tion terms between
spe ies have nonlinear form but that is s ale invariant
against the population size. What should be notedis that the system shows spontaneous growth to diverse stru ture. Furthermore, the system emerged in
our model reprodu es the q-exponential distributionof the life span of spe ies evaluated from paleontologial data[2℄. The purpose of this study is to investigate
the stru tural properties of the food web in the model.

2.1 Transformation of Variables
The transformation of variables as fyi g = fx1i  ghanges the equations (1) into the partly-linearized
form as follows.

X\plants"
1
1 dy1
a1j y1
= Gy1 (1 y11  ) +1  dtj\animals"
1 2

1 dyi
= yi +
aij yj +aij yi 1  yj1 
1  dtaij <0aij >0(2)
Then one an lassify the property of these equations
by the parameter  to the following three di erent
types.

XX

1.  > 1=2 : The exponent of the power in eqs.(2)takes a negative value. It is beyond the range(0; 1) whi h above was assumed to guarantee theonvexity of the preying rate. We ex lude this takashiaihara.jst.go.jp©ISAROB 2006

yz

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

lass from our study sin e the negative value ofthe exponent of yi in preying term of yi meansthat the spe ies will never be ome extin t.

1800
1600
1400

Number of Species

2.  = 1=2 : Intera ting terms are linearized. The
growth rate of the plants remains nonlinear.
3.  < 1=2 : The exponent of the power stays in
the range (0; 1) so that the eqs.(2) maintain their
onvexity assumed before. Our study in this work
is fo used on this lass.

1000
800
600
400

2.2 Uniqueness of the Solution

200
0

It is worth stressing that we do not require arti ial thresholds for the extin tion rule in our model.
In the Lotka-Volterra model, populations of the unmat hed spe ies do not be ome zero in nite time but
they approa h zero exponentially in time, therefore athreshold is ne essary to ause extin tion. In ontrast,
our size-free intera tion auses the algebrai de ay of
the unmat hed spe ies, so the orbits of the unmat hed
spe ies de rease to zero in nite time. To hange thefeature of this intera tion term, with onserving thenite time extin tion property, additional parameters
are needed

0

1

2

7
6
5
4
3
Time ( x10^6 [unit time] )

8

9

10

Figure 1: A time development of the number of spe ies
in the system with m = 3,  = 0:4. After the initialtransient, the system size goes on u tuating. The
statisti al analysis of the web stru ture in this study
is performed for this parameter region.
s ales of their population, is the key to get su h diverse systems. Besides that, the statisti al hara teristi s su h as the temporal u tuation, the extin tion
size distribution and the distribution of the life-span of
spe ies shows good agreement with eld data. As an
approa h to getting better understanding of the modeland e osystems, we here fo us on the stru ture of theemerged webs.

2.3 Additional Rules for Extin tion and
Mutation

3.1 Degree Distribution and the Clustering CoeÆ ient

In addition to Eqs.(1) our model has the following
rules of extin tion and mutation:
Extin tion : If the energy of a spe ies i, xi be omes
zero, the ith degree of the freedom is eliminated. Isolated spe ies are eliminated instantaneously.
Mutation(Invasion) : New spe ies ome into thesystem randomly at time. Initial energy of the newomer is hosen to be very small: we use 10 8 as thetypi al example. The number of the intera tions is
assigned randomly in the range of (1; 2m), where m isa onstant and we take m = 5 here. The intera tion
oeÆ ients are also randomly assigned in ( 1; 1).

Sin e the food web generated in the present model
is not latti e-like, we rst examine the following measures to hara terize its topology: the distribution of
the degree (in this ase, number of intera tions of ea hspe ies) and the lustering oeÆ ient[3℄. The degree
distribution of the ordinary random graph is Gaussian around the mean value. Another important lassre ently re ognized, whi h is alled s ale-free, has apower-law tail in ontrast. It is shown that the growing and preferential atta hment of nodes(intera tions)
are the essential me hanism to evolve su h s ale-free
networks[4℄. In the present model, as in a real e osystems, the network (food web) is indeed onstru ted bysu essive growth. There is also the ompetition whi hmust re e t the preferen e of adding spe ies and intera tions. Therefore the degree distribution is expe ted
to indi ate the oarse-grained property of preferen ein the model.
It turns out that the distribution in our model does

3 Result: Stru ture of the Emerged
Food WebAs it has been mentioned, the present model yields
food webs having large number of spe ies with omplex stru tures( see [1, 2℄ and Fig.1 ). The s ale invariant intera tion term, whi h permits ea h spe ies
to appear and be ome extin t independently of bare

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not have power-law tails and is well tted by a Gaussian distribution. In this sense, the food web onstru ted in our model is rather a random net than as ale-free net. This may mean that there is no positive preferen e of atta hing intera tion or the larger
number of intera tion makes a survival harder. It may
be also the reason that the spe trum of eigenvalues ofs ale-free nets is thought to have broader tail so thatsu h net tends to break down. As to real food web,there are both arguments whi h support random net[?℄ and s ale-free nets [6℄ at this point.
The se ond measure we investigate is the lustering
oeÆ ients, whi h is de ned for the node (spe ies) i
has ki of edges (intera tions) as
Ci =

2Ti

ki (ki1);

The transportation property of the obtained tree
is evaluated from the exponent of the s aling relation B / A . This exponent  is 2 for hainlike trees(whi h means the original web has onedimensional stru ture) and is 1 for the star-like stru tures (whi h imply the original web is random graph).
Real networks embedded in the nite dimension has
the exponent between 1 and 2 (3=2 for river networks,
for example). It was shown that the exponent  of thereal food webs is in the range of 1:13 16[7℄. This
universal s aling is reported to hold both for the partial webs in the ertain food web and for the wholenetworks of the di erent food webs (B1 against the
total number of spe ies). Note that, sin e the empiri al data available are small and typi ally has only
three trophi level, the validity of this \universality"
is open to dis ussion[8℄.

(3)where Ei represents the number of triangles whi h inludes the node i [3℄. This quantity measures theorrelation between intera tions. In other words, it
dete ts lo al spatiality with nite dimension in the
random-graph-like graphs whi h an be embedded essentially only in the in nite dimensional spa e.
The average lustering oeÆ ient over the entire
self-organized web in the present model is di erent order of magnitude ompared with the one of randomgraph with same number of intera tions. However,
the average ex ept for the plants and the intera tions
related to them is slightly smaller than the one of therandom net. These results mean that the triangle
stru ture under ompetition in the present model isfragile though lustering emerges from the boundary
onditions of the energy sour e.

We evaluate the s aling relation for the webs
emerged in our model (Fig.2). We here hoose the parameter su h that the number of the spe ies is enoughlarge ( 1000) and the number of trophi levels is
about 7. The s aling exponent for the trees without
the plants, whi h we all animal trees, is 1.12. The
value is onsistent with the one estimated in the empiri al data. Sin e the animal trees still have up to 6
trophi levels, this result support the universality inpreying intera ting system. However, the s aling exponent for the whole web (B1 / A1 =1:064 ) is found tobe smaller. It means that the number of the spe ies in
the rst trophi level is larger than the one expe ted
from the s aling relation while ea h tree starting fromthose rst onsumers shares the same topology as theempiri al web. A possible reason why the model system has more spe ies in the rst trophi level is thatlarge growth rate of the plants in the model. In thepresent model, we adopt a large growth rate ( 50)
to prevent the system from the entire extin tion due
to the shortage of the energy in ow. Therefore theintera tions onne ting to the plants are free from theompetition among them and hen e it may not sharethe self-similar topology. Assuming that the s aling
relation in real food webs is valid, our result implies
that the ompetition towards the energy and resour eintake from produ ers and the environment is as severe as the ompetition at the higher trophi levels.

3.2 Transportation properties of theSpanning Tree
Here we fo us on the transportation property[7℄ ofthe dire ted web. To al ulate this quantity, one rst
has to map the web into spanning tree: a looplessand onne ted tree. Generally the spanning tree ofa food web is de ned as the olle tion of the shortest
paths from the plants to animals. This mapping is notunique when a spe ies has two or more possibilities for
shortest path. In this study we de ne the spanning
tree as the olle tion of the links from ea h animal
to its most important prey. Then we al ulate Ai ,the number of spe ies preying the spe ies i dire tly
or indire tly via the tree for all spe ies. Note that
we in lude the spe ies i itself into a ount: A of thespe ies in the top of the spanning tree is 1 and A1
is equal to the number of the spe ies in the system.

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We also al ulate the sum Bi = j Aj , where thesummation is taken over the tree whi h was used forthe al ulation of Ai .

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[6℄ R. V. Sole & J. M. Montoya, \Complexity andfragility in e ologi al networks" Pro . R. So .
Lond. B vol. 268, pp. 2039-45 2001.

average B

10000[7℄ D. Garlas helli, G. Caldarelli, & L. Pietronero,\Universal s alling relations in food webs" Nature,
vol. 423, pp. 165-68, 2003

1000

[8℄ J. Cama ho & A. Arenas, \Universal s alling infood-web stru ture?" Nature, vol. 435, pp. E3-E4,
2005

100

Whole Web
Animal Web
2.48189 A^1.06382
1.99255 A^1.12083

10

1

1

10

100
A

1000

Figure 2: Log-log plot of average C against A.

4 SummaryWe have investigated the statisti al hara teristi sof the food web stru ture of our model. As for the
degree distribution and the lustering oeÆ ient, themodel and the eld data shows good agreement. The
spanning trees in our model is also shown to share thesame topology with the one of real food webs. The
di eren e is found only in the intera tions around the
produ er spe ies, whi h implies the severe ompetitionat the rst trophi level in real e osystem.

Referen es[1℄ T. Shimada, S. Yukawa, & N. Ito, \Selforganization in an e osystem" Artif. Life Roboti s,Vol. 6, pp. 78-81, 2002.
[2℄ T. Shimada, S. Yukawa, & N. Ito, \Life-span offamilies in fossil data forms q-exponential distribution" Int. J. of Mod. Phys. C, Vol. 14, pp. 1267-71,
2003.
[3℄ R. Albert & A. L. Barabasi, \Statisti al me hani sof omplex networks" Rev. Mod. Phys., vol. 74, pp.
47-97 2002.
[4℄ A. L. Barabasi & R. Albert, \Emergen e of s aling
in random networks" S ien e, vol. 286, pp. 509-512
1999.
[5℄ J. Cama ho, R. Guimera, & L. A. N. Amaral, \Robust Patterns in Food Web Stru ture", Phys. Rev.
Lett vol. 88, 228102 2002

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The behavioral adaptation and the diversiﬁcation in ecosystem
Y.Murase, S.Yukawa, N.Ito
Dept. of Appl. Phys.
School of Eng., Univ. of Tokyo
Bunkyo-ku, Tokyo

T.Shimada
Aihara Complexity Modeling Project
ERATO, JST
Meguro-ku, Tokyo

Abstract

or nutrition transportation in food web structure.
The characteristic feature of this model is nonlinear preying rate. Preying rate per unit predator
is assumed to be a function of the ratio of the populations of prey and predator, which is described asxj
aij ( )λ ,(1)xi
where aij denotes the preying coeﬃcient of i-th andj-th species, xj is the population of prey, and xi is the
population of predator. λ is the exponent of preying
rate which is limited to the range of (0, 12 ].
Using (1), the interaction term between two species
is written as
aij xj λ xi 1−λ(2)

The eﬀect of behavioral adaptation on dynamics of diversity in ecosystems is studied using thesize-free model. The behavioral adaptation will beparametrized by value of exponent λ of the interaction in this model. Two phases are observed when
this adaptivity parameter, λ, is changed between 0 and
0.5: steady and diversifying phase. For 0 < λ < λc ,where λc denotes a critical value, the number of species
reaches to a stationary value and ﬂuctuates aroundit (steady phase). This stational value diverges as|λc − λ|−d . For λc < λ < 0.5, it grows monotonouslywith ﬂuctuation (diversifying phase). The value of λcdepends on interaction coeﬃcient.

1

Preying term is limited to be convex as a function of
prey and predator. This corresponds that preying rate
saturates under the condition when prey and predator
are abundant. These saturation is not unrealistic andwill be common in nature. [3]A possible explanation for nonlinearlity of preying
rate, which is represented by λ in this model, is the
behavioral adaptation of species.
Larger λ means larger dependence of the preying
rate on the population of the species. When λ is large,
predators are more likely to change the interaction
rate to eat the prey which has larger population morethan the prey which has smaller population. In otherwords, a predator can choose to eat abundant preyeven though preying coeﬃcient remains unchanged.
This change of interaction rate can be considered as
behavioral adaptation of predator.
For prey, even if the predator has larger population,
the preying rate is restricted not to be too large when
λ is large. This can also be considered as behavioral
adaption of prey to defend themselves from threat ofmore dangerous predators. [4]Hence λ is regarded as the parameter of behavioral
adaptation in ecological meaning. Species can change
their interaction rate, which can be regarded as phenotype, without changing their interaction coeﬃcient,which can be regarded as genotype, when λ is large.

Behavioral Adaptation Parameter

An essential feature of ecosystem is its tendency
to diversify as a result of the emergence and the extinction of species in the system, which in turn is attributed to the interaction among the species. Many
studies have been done to reveal that feature sinceDarwin’s era. Using mathematical models has been
one way to understand it.
The most popular model of ecosystem is LotkaVolterra model. It describe the diﬀerential equationof population density. Although this model has contributed to understanding the dynamics of population
density, there has been few work of studying dynamics
of diversifying of species. This is because of the difﬁculty of making simple models in which the systemself-organize into diverse structure.
Here we adopt size-free model, which Shimada.et.al
proposed.[1] This simple model is known to be able
to yield species-rich ecosystem. Furthermore the system shows the same statistical properties as in nature,such as the distribution of species’ life span and the
distribution of extinction size.[2]Size-free model assumes only prey-predator interaction as inter-species relationships and focus on energy©ISAROB 2006

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In this model, the system is organized by one plantand many animal species. Plant is sole energy source
and animals dissipate energy gained from plant. Only
plant has ability to survive alone and have logistic
growth rate. On the other hand, animals decrease exponentially without interaction among species. The
equation of time-evolution of the popualtion density
is described as follows.
Plants:∑(1−λ)
a0j xλ0 xj
x˙0 = Gx0 (1 − x0 ) +(3)

10

Number of Species

9
8
7
6
5
4
3
2
1

j

0

200

Animals:ẋi = −ci xi +∑aij <0aij xλi xj

(1−λ)

+

∑
aij >0(1−λ) λxj

aij xi

(4)

1000

1600

Number of Species

1400
1200
1000
800
600
400
200
0
0

1000

2000
3000
Times of Invasion

4000

5000

Figure 2: Typical behavior of number of species indiversifying phase is shown. λ is set to 0.5.
species don’t increase and ﬂuctuates without time dependency. The system remains to be simple structure.
We call this phase ’steady phase’.
The other phase is ’diversifying phase’. This phase
is found when λ is larger than λc . Typical dyanmics ofdiversity is showed in Fig.2. In this phase, the species
richness continue to increase with ﬂuctuation.
Between these two phases, when λ ≈ λc , the diversity of the system has neither trend to grow nor trend
to collapse and ﬂuctuate widely. Fig.3 shows typicaldynamics of species.
The system has tendency to have more species withlarger λ in both two phases. λ is considered to have theeﬀect of stabilizing the system. The average numberof species in steady phase are plotted as a function of

Steady and Diversifying Phases

We carried out numerical simulation and investigated the λ dependence on dynamics of diversity without changing the strength of interaction coeﬃcient.
The parameter is set to α = 1.0, m = 5.
As a result, two phases are observed as λ changes.
The ﬁrst phase is observed when λ is smaller thansome characteristic value λc . Typical dynamics of diversity is showed in Fig.1. In this phase, the number of©ISAROB 2006

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Figure 1: Typical behavior of number of species in
steady phase is shown. λ is set to 0.1.

In this paper, growth rate G was set to 100 andmetabolization rate ci was set to 1.
To see the dynamics of species richness, invasionand extinction are introduced in this model.
If the population of species i(xi ) becomes 0, i-th
specie is regarded as being extinct and eliminated fromthe system. And the animal which becomes to haveno prey is also extinct.
Invasion is applied when all the population of thesystem become stable. The initial population density
is chosen to be very weak (10−8 ). The number of interaction is selected randomly from uniform distribution
in the range of [1, m]. And the interaction coeﬃcient isalso selected randomly from uniform distribution ranging [−α, α]. m and α is the parameter of the strengthof interaction. Simulation starts from the situationthat there exist only plant in the system.
In this paper, λ is set to uniform in one simulation for any species for simplicity although λ can bevariable of species like aij and can be variable of time.
We studied the eﬀect of behavioral adaptation on
diversifying dynamics by investigating the λ dependence.

2

400
600
Times of Invasion

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0.5
0.4
250

Number of Species

c

0.3

200

0.2

150

0.1

100

0
0.4

0.5

0.6

0.7

0.8

0.9

1



50

Figure 5: λc as a function of parameter of interaction
strength, α, is shown. The parameter of number ofthe interaction, m, is set to be 5.

0
0

10000

20000
30000
Times of Invasion

40000

Figure 3: Typical behavior of number of species whenλ ≈ λc is shown. (λ = 0.4)

λ in Fig.4. The average number of species diverges as|λc − λ|−d . The exponent d is about 0.6.
In diversifying phase, increasing rate of species richness also increase with λ. Near λc , it is diﬀucult tomeasure quantitative amount because the system ﬂuctuate widely.
As R.May and others has insisted[5, 6, 7], stronger
interaction leads ecosystem to poorer-species structure. This is also true in this model. Larger m and α
make the system harder to be stable in diverse structure. The system shows diﬀerent phase with m, α,and λ. In other words λc is variable of m and α. Fig.5
shows λc as a function of α. This suggests λc monotonusly increase with α. This can be explained that
λc must increase to keep the system enough stable todiversify when m and α is larger.
Here it is considerable question that ”larger λmeans smaller interaction?”. In almost all the case,this is not true. For example, let’s consider simple foodchain with three species like inset of Fig.6. To investigate the eﬀect of λ on the amount of interaction, theinteraction coeﬃcients are both set to 0.5 and equilibrium population density is plotted in Fig.6. This suggests that the amount of enegy trasportation term (theamount of interaction) increase with λ monotnously.
This is because increase of dependence on prey’s population density which is larger than that of predator’sin many cases. So in this sense, enforcement of interaction derived from behavioral adaptation (increase ofλ) have an eﬀect of the system to being diverse structure. On the other hand, enforcement of interaction
derived from increase of interaction coeﬃcient like α
and m destabilize the system as May and others had

Average Number of Species

50
40
30
20
10
0
0

0.1

0.2

0.3

0.4

0.5

Figure 4: Average number of species in steady phaseis plotted as a function of λ. The parameter is α =1.0, m = 5. Estimated λc from simulation is 0.41.

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 

xi

 
 

 

 


 




 



 







)

therefore we can’t rigidly apply May’s argument. However we can suppose that the system have more probability of being stable when the variance of the valueof oﬀ-diagonal part is smaller.
When λ is increase, ratio of population density between prey and predator decrease. (Fig.6) Accordx∗
ingly factor xi∗ decrease and the absolute value of oﬀ-

x1
x2
x3

(
'
! "$#&%

jdiagonal part of Eq.(5) decrease. As a result, the system has more probability of being stable as λ increase.
Species’ getting behavioral adaptation is speculated
to be great importance to evolutionary process. The
eﬀect of behavioral adaptation on evolution is expected to be investigated in real ecosystem.

 
 


 


 

 

 

Figure 6: Population density of species as a function of
λ. Three species are connecting each other organizing
simple food chain like inset. Each interaction is set to0.5. xi is the population of i-th specie.

Referencessuggested.

[2] T.Shimada, S.Yukawa, N.Ito, ”Life-span of families in fossil data forms q-exponential distribution”,
Int.J.Mod.Phys.C, Vol. 14, pp.1267-1271, 2003.

3[1] T.Shimada, S.Yukawa, N.Ito, ”Self-Organizationin an ecosystem”, Artiﬁcial Life and Robotics., Vol.
6, pp.78-81,2002.

Summary and Discussions[3] P.A.Abrams, L.R.Ginzburg, ”The nature of predation: prey dependent, ratio dependent orneigher?”, Trends in Ecology and Evolution,
Vol.15, pp.337-341, 2000.

We have investigated the eﬀect of interaction rate
exponent λ. When λ is large, the dependency of theinteraction term on prey increase and the dependency
on predator decrease. This can be explained as theparameter of behavioral adaptation. The system tend
to be more stable and yield more species-rich system
when λ is larger. We found two phases depending on
λ. First phase is ’steady phase’ in which the system
don’t grow and remains poor-species community. It
appears when λ is smaller than critical value λc . The
other phase is ’diversifying phase’ in which the system
continue to grow into species-rich structure. It appears
when λ is larger than λc .
Large interaction coeﬃcient causes the interaction
term large and destabilize the system. On the other
hand, increase of λ causes the system to be stabilized
though it also causes enforcement of interaction at thesame time.
This can be understood using linear stability matrix. The oﬀ-diagonal element of Linear stability matrix in this model is as follows.
∗aij λ( xi∗ )1−λ(aij > 0)
xj
fij =
(5)
x∗
λ
i
aij (1 − λ)( x∗ )(aij < 0)[4] P.A.Abrams, ”Why predation rates should not be
proportional to predator density”, Ecology, Vol74,
pp726-733, 1993.
[5] R.M.May, ”Will a Large Complex System be Stable?”, Nature, Vol238, pp.413-414, 1972.
[6] A.Roberts, ”The stability of a feasible random
ecosystem”, Nature, Vol251, pp.607-608, 1974.
[7] M.E.Gilpin, ”Stability of feasible predator-preysystems”, Nature, Vol254, pp.137-139, 1975.

jElement of stability matrix is not totally random,©ISAROB 2006

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Stability of Equilibrium State of Multi-type SIS Modelon Network with Certain Property
Nobuaki Sugimine1 and Kazuyuki Aihara1,2
Aihara Complexity Modelling Project, ERATO, JST,45-18 Oyama-cho, Shibuya-ku, Tokyo 151-0065, Japan
E-mail address: sugimine@aihara.jst.go.jp
2
Institute of Industrial Science, University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
E-mail address: aihara@sat.t.u-tokyo.ac.jp

1

Abstract

essence since there are some well-studied simple epidemic models, such as SIS, SIR, and SEIR models,
to study infection diseases for a single homogeneousgroup. Here, S, I, R, and E mean the susceptible, theinfectious, the removal, and the exposed, respectively.
Further, difference of threshold forms for epidemics
caused by effects of networks is an important subject.
In this direction, R. Pastor-Satorras and A. Vespignani first succeeded [2]. They considered a version of
SIS models reflecting the scale-free property such thatvertex degree distributions in networks obey powerlaw distributions. This is one of two common aspects
that real networks have turned out to possess by last
decade studies of complex networks. In a passing remark, the other is the small-world property. They
showed that the epidemic threshold of infection rate
is zero if vertex degree distributions with finite mean
do not have the quadratic moment. In other words,
there is an epidemic equilibrium solution on such networks unless the infection rate is zero. Thus, an epidemic equilibrium state always exists on scale-free networks with indices between 2 and 3, which are typically observed in ever investigated real networks withpower-law vertex degree distributions. Moreover, several authors’ contributions [3, 4] show that epidemics
always occur in various models with same networks as
R. Pastor-Satorras and A. Vespignani considered.
We are also interested in the situation where themajority cannot sustain epidemics without the presence of the core, individuals who potentially become
infectious with the large infection rate [5]. For example, in prevalence of sexually transmitted diseases
the core would indicate individuals engaging in risky
sexual behavior, and in SARS the core would mean individuals called super-spreader [6]. Hence, we consider
a population with two types of individuals: the majority who potentially has the small infection rate and theWhether an epidemic equilibrium state is stable insome sense or not is an important research subject inmathematical epidemics modeling. In a homogeneous
population, the basic SIS model has the asymptoticalstability of its epidemic equilibrium solution since it
can be reduced to the logistic equation. Furthermore,even in the basic SIS model with a non-homogeneous
population, the asymptotic stability of its epidemic
equilibrium solution is proved [1]. However, this kindof analysis in other models with a non-homogeneous
population is not developed so much, even if it is wellknown in ones with a homogeneous population. In
addition, recent studies of complex networks have revealed that heterogeneity of the link number of vertices
drastically changes epidemic thresholds. For these reasons, figuring out roles of heterogeneity becomes major
topics in epidemics modeling. Thus, we have analyzed
the stability of an epidemic equilibrium solution in a
multi-type SIS model on heterogeneous networks.
Key words: multi-type SIS model, stability, scalefree network

1

Introduction

Epidemic processes get more and more complicate
both in terms of interactions and networks. In addition to that, heterogeneity in various levels, for example, characteristic of the pathogen, the infectious, andregion of outbreak of contagions, would prevent refined data analysis or mathematical modeling to clarify epidemiologically infection diseases for controllingthe spread of infection. To give a little help to this, weconsider a simple deterministic epidemic model given
by ordinary differential equation. For our aim, expressions of effects of networks in differential equation are©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006{(sk , uk , vk )}K
k=1 given bycore who potentially has the large one. To make calculation tractable, we only consider the susceptible andtwo types of the infectious with no immunes, and do
not take into account latent period, incubation period,and so on. We assume that infected people soon become infectious, and have the small infection rate withprobability (1 − p) or the large one with probability p.
In our talk, we introduce such the two-type SIS model
originally proposed for regular lattices [5]. We consider this model in networks, and we show a condition
for existence of an epidemic equilibrium state, which
is locally asymptotically stable. We also show that if
the condition does not hold, a disease free equilibrium
state is globally asymptotically stable.

dsk (t)= uk (t) + δvk (t) − ksk (t) U (t) + V (t) ,dt
duk (t)= −uk (t) + ksk (t) U (t) + V (t) (1 − p), (2)dtdvk (t)= −δvk (t) + ksk (t) U (t) + V (t) p,
dt
where δ > 0, p ∈ [0, 1],X
q(j)uj (t),U (t) = λ1jandV (t) = λ2

X

q(j)vj (t).

j2Although by nature we are supposed to replace U andV withXp(j | k)uj (t)Uk (t) = λ1

Model and Resultj

We introduce the following assumption on vertex
degree distributions in networks that is widely accepted in the complex network study [7].

andAssumption 1. We define p(k) to be probability thata vertex chosen uniformly at random has vertex degreek. The probability that a given vertex with degree k
is linked with a vertex with degree j is proportional tojp(j), which is independent of its own vertex degree
k. In other words, whenever p(k) > 0,

respectively, we can obtain the more simple expression(2) because of (1).
Let
for every 1 ≤ k ≤ K, xk , yk , zk ≥ 0 and.
Ω = {(xk , yk , zk )}K
k=1 :


xk + yk + zk = 1

jp(j)p(j | k) = X
j ′ p(j ′ )Vk (t) = λ2

p(j | k)vj (t),jTheorem 1. Ifp  hk2 i> 1,
λ1 (1 − p) + λ2
δ hki

(1)

j′

(3)

then there exists a unique equilibrium solution{(sk , uk , vk )}K
k=1 of (2) such that uk , vk > 0 for
all 1 ≤ k ≤ K.
For δ sufficiently close to1, {(sk , uk , vk )}Kislocally
asymptotically stable.
k=1
Moreover, {(sk , uk , vk )}Kisglobally
asymptotically
k=1
when
δ
=
1.
Unless (3)stable in Ω\{(1, 0, 0)}K
k=1
holds, a disease free equilibrium solution {(1, 0, 0)}K
k=1
is globally asymptotically stable.

holds independently of k.
Further, we need the following technical assumption
for mathematical analysis.
Assumption 2. There exists some positive integer K
such that p(k) = 0 for every k ≥ K + 1.
For simplicity of notations, we set p(k) > 0 for every
1 ≤ k ≤ K. We denote by hf i the expectation of fwith respect to {p(j)}K
k=1 . We write q(k) = kp(k)/hji
for every 1 ≤ k ≤ K.
Since we consider a population with two types ofindividuals such that the majority potentially has thesmall infection rate λ1 and the core potentially has thelarge one λ2 , we treat three relative densities sk , uk ,and vk of vertices with degree k; sk , uk , and vk are theones of the susceptible, the infectious among the majority, and the infectious among the core, respectively.
Along the line with [2], we consider the dynamics of©ISAROB 2006

X

3

Sketch of Proof

We first remark that sk (t) + uk (t) + vk (t) = 1 forevery 1 ≤ k ≤ K. We have that for any equilibrium
solutions {(sk , uk , vk )}K
k=1 of (2)
sk =

387

1
,
1 + kW (1 − (δ − 1)p∗ )uk = ksk W (1 − p),vk = ksk W p∗ ,(4)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006P∗
= p/δ.
where W =j q(j) (λ1 uj + λ2 vj ) and p
From (4), we have that


W
λ∗
,
(5)
k2
W =
1 + kW (1 − (δ − 1)p∗ )hki

and aj = λ∗ W jq(j).
We will show that real parts of all eigenvalues of
A are negative. Let cj = jsj aj . The characteristic
equation ΦA of A is as follows:ΦA (x)=x(x + 1 + W ) · · · (x + 1 + KW )+ c1 (x + 1 + 2W ) · · · (x + 1 + KW )+ c2 (x + 1 + W )(x + 1 + 3W ) · · · (x + 1 + KW )..
.
+ cK (x + 1 + W ) · · · (x + 1 + (K − 1)W ).
(8)

where λ∗ = λ1 (1 − p) + λ2 p∗ . Notice that the righthand side of (5) consists of the summation of increasing concave functions of W . Thus, (5) has a uniquepositive solution W if and only if
λ∗

hk2 i
> 1.
hki

Therefore, (2) has a unique equilibrium solution{(sk , uk , vk )}K
k=1 such that uk , vk > 0 for all 1 ≤ k ≤K if and only if (3) holds.
We consider the linearized dynamics of (2) at{(sk , uk , vk )}K
k=1 :The expression (8) of ΦA (x) shows thatΦA (0) > 0,
and the coefficient of degree K isdesk (t)
f (t) + W sek (t) ,=uek (t) + δevk (t) − k sk (t)W
dt


de
uk (t)
f (t) + W sek (t) ,= −euk (t) + k(1 − p) sk W
dt
(6)


de
vk (t)
f (t) + W sek (t) ,= −δevk (t) + kp sk W
dt

KX

f (t) =
Wjwhich implies that for every 2 ≤ j ≤ K there exists atleast one root −αj of ΦA (x) = 0 in (−1 − jW, −1 −(j − 1)W ). Then we obtain thatΦA (x) = (x + α2 ) · · · (x + αK )(x2 + βx + γ)

Notice that sek (t) + uek (t) + vek (t) = 0 for every 1 ≤ k ≤K.
From now on, we set δ = 1. Since we obtain from
(4) and (6) that!
f (t)λ∗ W X 2dW
k p(k)esk (t) ,=hki
dt

for some real numbers β and γ. In this expression, thecoefficient of degree K is
K
X

where





A=


©ISAROB 2006

seK (t)

0
−s1
..
.
−KsK

a1
−1 − W

···
..

aK

.
−1 − KW

αj + β.

(11)

j=2

k

seK (t)(10)

The expression (8) of ΦA (x) also shows that for every
2≤j≤K


ΦA −1 − jW ΦA −1 − (j − 1)W < 0,q(j)(λ1 uej (t) + λ2 vej (t)).

we consider the linear dynamics




f (t)
f (t)
W
W
 se1 (t) 

d 


 se1 (t) , = A

..
.
..
dt 



.

(1 + jW ).

j=1

where

X

(9)

From (10) and (11), we see thatKX

αj + β =

j=2

(7)

K
X

(1 + jW ),j=1which together with the fact that αj < 1 + jW implies
that
β=



K
X

(1 + jW ) −αj > 1 + W > 0.

(12)

j=2

j=2






K
X

When the equation
x2 + βx + γ = 0

388

(13)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

has no real-valued solutions, real parts −β/2 of the twosolutions are negative. Suppose that the equation (13)has two real-valued solutions. Notice that the equation(13) has never a zero solution because of (9). By thefact that β > 0, there is no possibility that both thetwo solutions are positive. If the equation (13) has
one negative solution and one positive solution, theequation ΦA (x) = 0 has only one positive solution.
This and (9) contradict that the maximal coefficientof ΦA (x) is equal to 1. Then both the two solutions
are negative. Therefore, we showed that real parts ofall eigenvalues of A are negative. In other words, thelinear dynamics (7) is asymptotically stable.
Recall that we consider the case where δ = 1 inthe linearized dynamics (6). The second and the third
equations of (6) together with the asymptotic stability
f (t), se1 (t), . . . , seK (t)) imply that the linearizedof (Wdynamics (6) is also asymptotically stable, which isequivalent to that real parts of all eigenvalues of thecorresponding matrix to (6) are negative. By continuity, this is still true for δ sufficiently close 1. Therefore, the dynamics (2) is local asymptotically stable at{(sk , uk , vk )}K
k=1 for δ sufficiently close 1.
To obtain the global asymptotic stability of{(sk , uk , vk )}K
k=1 for δ = 1, we need a slight modification to the argument in [1]. Key ingredients are thefollowing:

the core members at any time might be a required
strategy.

References[1] A. Lajmanovich and J.A. Yorke, ”A deterministic
model for gonorrhea in a nonhomogeneous population,” M ath. Biosci., Vol. 28, pp. 221-236, 1976.
[2] R. Pastor-Satorras and A. Vespignani, ”Epidemic spreading in scale-free networks,”
P hys. Rev. Lett., Vol. 86, pp. 3200-3203,
2001.
[3] J. Joo and J.L. Lebowitz, ”Behavior of susceptibleinfected-susceptible epidemics on heterogeneous
networks with saturation,” P hys. Rev. E, Vol. 69,
066105, 2004.
[4] J. Liu, J. Wu, and Z.R. Yang, ”The spread of infectious disease on complex networks with householdstructure,” P hysica A, Vol. 341, pp. 273-280, 2004.
[5] R.B. Schinazi, ”On the importance of risky behavior in the transmission of sexually transmitted diseases,” M ath. Biosci., Vol. 173, pp. 25-33, 2001.
[6] N. Masuda, N. Konno and K. Aihara, ”Transmission of severe acute respiratory syndrome in dynamical small-world networks,” Phys. Rev. E, Vol.
69, 031917, 2004.

puk (t) = (1 − p)vk (t) + zk e−t ,zk = puk (0) − (1 − p)vk (0),and[7] M.E.J. Newman, ”The structure and function ofcomplex networks,” SIAM Review, Vol. 45, pp.
167-256, 2003.

−t

psk (t) + vk (t) = p − zk e .
By using the argument in [1], we see that unless (3)holds, a disease free equilibrium solution is globally
asymptotically stable.

4

Conclusion

Since late 1990s, many real networks have been recognized to have the scale-free property that leads thedivergence of the quadratic moment hj 2 i of their vertex degree distributions often times. When hj 2 i is solarge, according to (3) (or [2, 3, 4]), epidemic thresholds become so small. Thus, even if epidemics do notstill occur, they are suppressed by a narrow margin.
For this reason, it is possible that a little rise of the
proportion of the core members causes epidemics. Especially, in the situation with δ = 1, this is inevitablesince the epidemic state is globally asymptotically stable. This suggests that decreasing the proportion of©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

On Chaos Suppression by Resonant Parametric Perturbation
M. Sekikawa
Aihara Complexity Modelling Project
ERATO, JSTTokyo, Japan

N. Inaba
K. Aihara
Faculty of Engineering Institute of Industrial Science
Utsunomiya University
The University of Tokyo
Tochigi, JapanTokyo, Japan
0.12

Abstract
Chaos suppression is a phenomenon in which achaotic attractor is suppressed and a periodic attractor is generated by a weak harmonic perturbation. We
assert that the phenomenon is caused by a slight shiftof a bifurcation curve. In this paper, we show that themechanism is applicable to the Duffing-Holmes equation with parametric perturbation in which the taming
chaos phenomenon was originally discovered.

1

0.1

0.08

0.06

λ 0.04Introduction

Chaos is sometimes suppressed and a periodic orbit
is generated by the second weak harmonic perturbation in nonautonomous differential equations. Thisphenomenon is called “chaos suppression” or “taming chaos,” and has been investigated intensively withgreat interest [1]–[6]. The taming chaos phenomenon
was originally discovered in the following DuffingHolmes equation:

0.02

0

0

1

1.5

2

2.5

3

3.5

4

Ω
Figure 1: Maximum Lyapunov exponent with β =4.0, δ = 0.154, γ = 0.088, ω = 1.1, and η = 0.03. The
dotted line refers to = 0.1017 in the unperturbedcase.

ẍ − x + β [1 + η cos(Ωt)] x3 = −δ ẋ + γ cos(ωt), (1)where parameters η and Ω denote the amplitude ofa parametric perturbation and its angular frequency,
respectively. We consider the case in which the parameter η satisfies the following condition: 0 ≤ η  1.
Parameters β, δ, γ and ω are chosen so as to generatechaos in Eq. (1) when η equals zero. Figure 1, whichis the re-creation of FIG. 4 in Ref. [1], depicts the
behavior of the maximum Lyapunov exponent with
η = 0.03 ( 1) when the parameter Ω is varied. We
describe the calculation method of the Lyapunov exponent in Chap. 2. The maximum Lyapunov exponent
vanishes and entrainments are generated when the angular frequency of the second weak perturbation Ω isan integral multiple of that of primary perturbation ω
as can be seen from Fig. 1.
The taming chaos phenomenon has been studied bymany scientists since 1990, and it is introduced as aneffective method of taming chaotic behavior without©ISAROB 2006

0.5

any feedback [2]–[6]. Contrary to this claim, we provide a hypothesis for the taming chaos phenomenon
caused by weak resonant perturbation [7]. According
to the hypothesis, the mechanism of the taming chaos
phenomenon is extremely simple, and taming chaos is
not exactly an effective method of controlling chaos.
The hypothesis based on our insights on the mechanism of taming chaos is illustrated as follows.
Figure 2 shows a schematic bifurcation diagram of
Eq. (1), where the abscissa denotes the angular frequency of the primary perturbation ω, and the ordinate denotes its amplitude γ. The bifurcation curves
denoted as G and I are the saddle-node bifurcation
curve with η = 0 and the period doubling bifurcation curve with η = 0, respectively. The region with390

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2γ

The maximum Lyapunov exponent
shown inFig. 1 is calculated using Shimada’s algorithm [8].
We transform Eq. (1) into the 6th-dimensional autonomous equation (2) in order to calculate the Lyapunov exponent, because Eq. (1) has two force terms.
ẋ = y ẏ = x − β (1 + ηθ) x3 − δy + γφ φ̇ = −ωΦ(2)Φ̇ = ωφθ̇ = −Ω

 ˙
= Ωθ.

Gη

I
Iη

G
A

Iη
I

G

Gη

The initial condition of the state variables (φ, Φ, θ, )is (1, 0, 1, 0) in Eq. (2).
We use the method presented in Ref. [9] to makea two-parameter bifurcation diagram. We summarize
this method here. If we note that both forces in Eq. (1)are periodic functions of 2π/ω, the phase plane at =0 and that at = 2π/ω can be considered to be thesame. The following Poincaré map Tλ is defined asωFigure 2: Schematic bifurcation diagram of Eq. (1).

the oblique lines corresponds to the parameter sets inwhich the chaotic attractors are observed. We consider
the case in which the parameter Ω satisfies the following condition: Ω = kω, where k is a natural number,because the taming chaos phenomenon occurs when
the weak resonant perturbation is in harmony with theprimary perturbation. What happens if the parameterη is changed from zero to 0 < η  1? For instance,
the saddle-node bifurcation curve G and the period
doubling bifurcation curve I move slightly to Gη andIη , respectively with k = 1 as shown in Fig. 2. What
happens if we choose the parameter k as two, three,four and so on? This is not a mathematical proof,
but intuitively, it is natural to imagine that a similar phenomenon will occur as in the case with k = 1,
since the weak resonant perturbation is in harmony
with the primary perturbation; that is, it is synchronized with the primary perturbation. Let us choose
an initial parameter set at the point marked with A
in Fig. 2. Chaos is generated for η = 0 since point A
is outside the entrainment with η = 0. Meanwhile, forsmall but nonzero η, the former chaos changes into theperiodic orbit since point A is inside the entrainment.
This is the trick of the taming chaos phenomenon [7].
In this paper, we make a two-parameter bifurcation diagram of Eq. (1) in order to verify the hypothesis. We show that the mechanism is applicable to
the Duffing-Holmes equation with parametric perturbation in which the taming chaos phenomenon was
originally discovered.

©ISAROB 2006

Analysis method

Tλ: IR2 → IR2
u 7→ Tλ (u) = ϕ(2π/ω, u, ),(3)

where ϕ( , u, ) is the solution of Eq. (1) for the initialvalue x = u.
If u is the m-th periodic point of Tλ , then u satisfies
the following equation:Tmλ (u) − u = 0.

(4)Tm
λ denotes the m composite map of Tλ . The properties of u are classified according to the root µ of thefollowing characteristic equation:d m
T (u) − µI = 0,
du λ

(5)

where I is the identity matrix.

3

Influence of weak parametric resonant perturbation

A two-parameter bifurcation diagram of theDuffing-Holmes equation with parametric perturbation (1) is analyzed in order to clarify the mechanismof the taming chaos phenomenon observed in Eq. (1)in this chapter. We fix parameters β and δ at 4.0 and
0.154, respectively. Figure 3 shows the two-parameterbifurcation diagram of Eq. (1), where the abscissa denotes ω, and the ordinate denotes γ. The bifurcation

391

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0.30

0.100

0.25

Gη (k=1)

0.0950

Gη

I

0.20

Gη (k=2)

G

0.15

0.090

Iη

γ 0.10

box1

I

Iη

γ

Gη (k=3)
Gη (k=4)

A
G

0.085
0.05

G

Gη

0.00

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

0.080
1.090

1.4

1.105

1.110

ω

Figure 3: Two-parameter bifurcation diagram with
β = 4.0 and δ = 0.154.

Figure 4: Enlargement of box1 in Fig. 3 (box1 ).

the entrainment (See Fig. 5(a)). The saddle-node bifurcation curve G moves to Gη(k=1) in the case of the
weak resonant parametric perturbation with η = 0.03
and Ω = 1.1. Then, the former chaos changes into theperiodic orbit as shown in Fig. 5(b) since point A is
inside the entrainment. The saddle-node bifurcation
curve G moves slightly and point A is inside the entrainment as in the case with k = 1 when we choose
the parameter k as two, three, or four. Therefore,
the phenomenon observed in Fig. 1 is realized by theslight shift of the saddle-node bifurcation curve when
the weak resonant perturbation is in harmony with theprimary perturbation.

curves denoted as G and I are the saddle-node bifurcation curve with η = 0 and the period doublingbifurcation curve with η = 0, respectively. The parameter values that generate the saddle-node bifurcation and period doubling bifurcation are determinedby simultaneously solving Eqs. (4) and (5) with µ = 1
and µ = −1, respectively. The simultaneous equation
is solved by Newton’s method. The dotted regions
where the maximum Lyapunov exponent is positive
are the regions where chaos occurs in the bifurcation
diagrams. We verify that the structure of the bifurcation diagram and the regions where chaos occurs are
consistent.
What happens if the weak resonant perturbation isadded to the Duffing-Holmes equation? We consider
the case in which the parameter Ω satisfies the following condition: Ω = kω, where k is a natural number,because the taming chaos phenomenon occurs when
the weak resonant perturbation is in harmony withthe primary perturbation. The bifurcation curves G
and I move slightly to Gη and Iη , respectively, in thecase of the weak resonant perturbation with η = 0.03
and Ω = 1.1(k = 1) as shown in Fig. 3. The behavior of the maximum Lyapunov exponent of Fig. 1 isobserved at point A in Fig. 4. The bifurcation curve
G is the saddle-node bifurcation curve without weak
resonant parametric perturbation (η = 0) in Fig. 4.
Chaos is generated for η = 0 since point A is outside©ISAROB 2006

1.100

1.095

ω

4

Conclusion

In this paper, we clarified that the taming chaos
phenomenon observed in the Duffing-Holmes equation
with parametric perturbation is caused by a slight shiftof the saddle-node bifurcation curves.

References[1] R. Lima and M. Pettini, “Suppression of chaos byresonant parametric perturbations,” Phys. Rev.
A, vol. 41, no. 2, pp. 726–733, Jan., 1990.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[2] Y. Braiman and I. Goldhirsch, “Taming chaotic
dynamics with weak periodic perturbations,”
Phys. Rev. Lett., vol. 66, no. 20, pp. 2545–2548,
May, 1991.

0.4[3] R. Chacón and J.D. Bejarano, “Routes to suppressing chaos by weak periodic perturbations,”
Phys. Rev. Lett., vol. 71, no. 19, pp. 3103–3106,
Nov., 1993.

0.3

0.2

0.1

[4] P. Colet and Y. Brainman, “Control of chaos in
multimode solid state lasers by the use of small
periodic perturbations,” Phys. Rev. E, vol. 53,
no. 1, pp. 200–206, Jan., 1996.

0.0.
x -0.1[5] R. Chacón, M. Sánchez, and J. A. Martı́nez, “Geometrical resonance analysis of chaos suppression
in the bichromatically driven van der Pol oscillator”, Phys. Rev. E, vol. 56, no. 2, pp. 1541–1549,
Aug., 1997.

-0.2

-0.3

-0.4
-0.8

[6] R. Chacón,“General results on chaos suppression
for biharmonically driven dissipative systems,”
Phys. Lett. A, vol. 257, no. 5-6, pp. 293–300,
July, 1999.

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

0.0

0.2

0.4

0.6

0.8

x
(a)
0.4

[7] N. Inaba, M. Sekikawa, T. Endo, and T. Tsubouchi, “Revealing the trick of taming chaos by
weak harmonic perturbations,” Int. J. Bifurc.
Chaos, vol. 13, no. 2, pp. 2905–2915, Oct., 2003.

0.3

0.2

[8] I. Shimada and T. Nagashima, “A numerical approach to ergodic problem of dissipative dynamical systems,” Prog. Theor. Phys., vol. 61, no. 6,
pp. 1605–1615, June, 1979.

0.1

0.0

[9] H. Kawakami, “Bifurcation of periodic responses
in forced dynamic nonlinear circuits: computation of bifurcation values of the system parameters,” IEEE Trans. Circuits Syst., vol. CAS-31,
no. 3, pp. 248–260, Mar., 1984.

.
x -0.1
-0.2

-0.3

-0.4
-0.8

-0.6

-0.4

-0.2

x
(b)
Figure 5: Attractors observed at point A in Fig. 4 with
β = 4.0, δ = 0.154, γ = 0.088 and ω = 1.1: (a) Chaotic
attractor with η = 0.0; (b) Periodic attractor with
η = 0.03 and Ω = 1.1.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

State Estimation with Finite Data Rates and Information Pattern
Hidetoshi Shimokawa∗Kazuyuki Aihara†∗∗†

Aihara Complexity Modelling Project
ERATO JST
Meguro-ku, Tokyo 151-0064, Japansimokawa@ieee.org

Institute of Industrial Science
The University of Tokyo
Meguro-ku, Tokyo 151-0064, Japan
aihara@sat.t.u-tokyo.ac.jp

Abstract

where xt is the state and ut is the input at time t ∈{0, 1, . . . }. We assume that we can directly observe
the state xt but the estimator get the information of
the state only via digital link with a finite data rate.

Many sensor networks carry quantized data thesedays and it is interesting problem to know how muchdata rate is necessary and how we can encode efficiently for estimating state of a linear system. Preceding study shows that if full information is available
at the encoder, we can estimate the state in arbitrary
precision asymptotically with finite data rate which isdetermined by instability of the plant. In this paper,
we propose a new quantization method which achieves
near-optimum data rate even if the encoder is memoryless and doesn’t know the input to the plant. This result is related to so-called side information problem inInformation Theory. We can consider that the input
to the plant is side information available only at the
decoder.

1• Encoder: The encoder at time t is a map
Et : (xt , σ t−1 , ut−1 ) → σt ,where codeword σt takes its value in a finite set.
• Decoder: the decoder at time t is a map
Dt : (σ t , ut−1 ) → x̂t ,where x̂t is the estimator of xt .
We denote (x0 , x1 , . . . , xt ) by xt and so on. We canconsider several configurations depending on which
information is available at the encoder. They arecalled the Information Pattern of the encoder([2],[4]).
Tatikonda and Mitter[2] discuss two classes of Information Pattern.

Introduction

Motivated by recent development of digital technology and communication, estimation and control in thepresence of information constrains have received considerable attention. This problem introduces the information theoretical view point and the question how
to choose the bits of information that would be mostuseful for estimation and control. In recent years, anumber of researches have analyzed various version ofthis problem ([1], [2], [3]).
In this paper, we focus on state estimation of onedimensional, unstable, deterministic, discrete time,
linear, time invariant system with a digital channel
connecting the sensor to the estimator. The system
may have bounded process noise. Our problem formulation follows that of Tatikonda and Mitter[2]. We
consider the following linear time-invariant system:• Encoder Class One:
Et : (xt , σ t−1 , ut−1 ) → σt .
This encoder has full access to information.
• Encoder Class Two (Figure 1):
E t : xt → σ t .
This encoder has no access to the input, moreover,no access to past observation and past encoded
symbols, that is, it is memory-less.
In both classes, the encoder and the decoder haveknowledge of the dynamics of the plant and knowledgeof the encoder and the decoder each other. Tatikonda
and Mitter[2] show that the state can be estimatedxt+1 = axt + ut ,©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

is always zero, but here, we allow some variable input
which is known only to the decoder and bounded process disturbance. Moreover, we show that the estimation error can vanish asymptotically with sufficiently
large encoding rate if disturbance is not present.
Let consider the following one-dimensional unstablelinear system with input ut and bounded disturbancewt .

with zero-error asymptotically for class one, but theystate, in Proposition 6.1, that it is impossible to estimate the state with bounded error for class two if
the encoding rate is finite. In this paper, we introduce a new quantization method and demonstrate that
we can achieve zero-error asymptotically even for class
two with a finite data rate. Moreover, we can boundestimation error even if the bounded process noise ispresent.
Plant

xt+1 = axt + ut + wt ,xt

Input ut

where |a| > 1 and |wt | ≤ D. We assume that theinitial value x0 is also bounded:

Encoder
Decoder(1)x0 ∈ [x̃0 , x̃0 + L0 ].

σt

(2)

We define system parameters K, Lt and δt to construct a encoder and a decoder as follows.

Estimator x̂t• Let K be an integer which satisfies that

Figure 1: Information Pattern

K > |a| + 1.

(3)

For example, we can choose K such that2

Simple Example

K = b|a|c + 2.

We construct a simple plant and a encoder/decoder
pair to illustrate that we can determine the system state with bounded error even if the encoder ismemory-less.

• For t ≥ 0, letδt =• Plant:
xt+1 = 2xt ,

Lt
.
K −1

(4)

• For t ≥ 1, let

x0 ∈ [0, 2).

Lt = |a|δt−1 + 2D.

• Encoder:
σt = bxt c mod 2

Note that these parameters doesn’t depend on observation and can be computed without memory. Now
we construct a encoder and a decoder.

• Decoder: Letx̂−1 = 0,x̂t = 2x̂t−1 + σt

(5)(t ≥ 0).

• Encoder:
Where bxc denotes the maximum integer which is lessthan or equal to x. Apparently, {σt }∞0 represent thebinary expansion of x0 and x̂t = bxt c. It follows thatσt = Et (xt ) = b

This shows that the error |x̂t − xt | is bounded even if
the encoder is memory-less. This is a counter example
of Proposition 6.1 ([2]).

• Decoder:x̃tx̂t = b c +
δt



x̃t
σt − b cδt

In this section, we state stronger result than the
counter example in the previous section. In the previous example, we assume that the input to the plant395



mod K ,

where, for t ≥ 1,(ax̂t−1 + ut−1 − Dx̃t =a(x̂t−1 + δt−1 ) + ut−1 − DMain Result©ISAROB 2006

(6)

Because σt ∈ {0, · · · , K − 1}, the encoding rate is
dlog2 Ke.

x̂ ≤ xt < x̂t + 1.

3xt
c mod K.
δt(7)

(a > 1),(a < −1).
(8)The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The following theorem states that the estimation errorof x̂t and x̃t is bounded as far as (3) is established.

Therefore,

Theorem 3.1. For t > 0,x̃t ≤ xt ≤ x̃t + Lt ,x̂t ≤ xt < x̂t + δt ,

and
(9)
(10)

δt = γ t L0 +1 − γ t 2D,
1−γ K −1(11)lim δt =2D,
K − 1 − |a|

(12)

(15)

Now, we prove the Theorem 3.1.
Proof. Because of definition of x̃0 and L0 , it is obvious that (9) is established for t = 0. We assume (9)is established for some t, and then show that (10) is
established for t and (9) is established for t + 1.
Substituting (7) by (6),




x̃t
xt
x̃t
x̂t = b c +b c mod K − b c mod K
δt
δt
δt



xt
x̃t
x̃t
b c − b c mod K .
=b c+
δt
δt
δt

and
t→∞

x̃
x
0 ≤ b c − b c < K,
δ
δ


x̃
x
x̃
x
b c − b c mod K = b c − b c.
δδδδwhere γ = |a|/(K − 1) < 1.
Corollary 3.1. If there is no disturbance, i.e. D = 0,then the estimation error vanishes asymptotically.
Tatikonda and Mitter[2] show that even if the encode has memory and knows the input, the encoding
rate must be greater than log2 |a| so that the error
vanishes. The encoding rate of the above memory-lessencoder is:
dlog2 (b|a|c + 2)e,(13)

It follows from (4), (9) and Lemma 3.1 thatx̂t = b

x̃t
xt
x̃t
xt
c + b c − b c = b c,
δt
δt
δt
δt

which implies (10).
For the case a > 1, it follows from (1) and (10) that

where dxe denotes the minimum integer which isgreater than or equal to x. For large |a|, the differenceof these two rates is relatively small.
To prove this theorem, we prove the following
lemma first.

ax̂t + ut − D ≤ xt+1 = axt + ut + wt
< a(x̂t + δt ) + ut + D.
and from (5) that

Lemma 3.1. Let x, x̃ and δ > 0 are real variables
and K ≥ 2 is a integer. Ifaδt = Lt+1 − 2D.

x̃ ≤ x ≤ x̃ + (K − 1)δ,Therefore, (9) follows for t + 1 by (8).
For the case a < 1, it follows from (1) and (10) that

then


x
x̃
b c−b c
δ
δ



x
x̃
mod K = b c − b c.
δδa(x̂t + δt ) + ut − D < xt+1 = axt + ut + wt
≤ ax̂t + ut + D(14)= a(x̂t + δt ) + ut − aδt + D.

Proof. Because x ≥ x̃,and from (5) thatxx̃b c − b c ≥ 0.
δδ−aδt = Lt+1 − 2D.

Using a basic inequality:

By (8), we havex − 1 < bxc ≤ x,x̃t+1 < xt+1 ≤ x̃t+1 + Lt+1 .

it follows that

Finally, it follows from (4) and (5) that

x
x̃
x x̃
b c−b c< − +1δδδδ
(K − 1)δ
≤+ 1 = K.
δ

©ISAROB 2006

δt+1 = γδt +which leads to (11) and (12).

396

2D
,
K −1

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4

Summary

References[1] Wing Shing Wong and Roger W. Brockett. Systems with finite communication bandwidth constraints – part I: State estimation problems. IEEE
Trans. Automat. Contr., 42(9):1294–1299, Sep.
1997.

In this paper, we show that we can construct a encoder and a decoder which can estimate the state withbounded error even if the encoder is memory-less anddoesn’t have access to the input of the plant. This isstronger result for encoders in class two than the result
by Tatikonda and Mitter[2]. As far as we know, thistype of quantization is new in this field and we believethat this method gives us new insight to understandwhich information bits are important for estimationand control.
Finally, we point out some relation between our result and studies in Information Theory. Encoders inclass two has two limitations. One is memory-less andthe other is unavailability of the input. Our result
suggests that we can construct a memory-less encoderat the expense of optimal rate, that is, we need aslightly higher rate (13) than log2 |a| bits. This is thesame situation in source coding that we can achieve
optimum compression rate with the long block length
which requires long memory. The latter limitation has
a strong connection with problem of side information.
We can consider the input to the plant as side information available only at the decoder. In lossless
source coding, it is shown that the optimum encoding
rate doesn’t change whether side information is available at the encoder or not ([5],[6],[7]). Our result is
consistent with this because we can achieve optimum
rate asymptotically if the encoder is allowed to havememory. The quantizer we proposed here is inspiredby bin-coding([8]). Though bin-coding is a random
coding and ours is deterministic, the idea that thesource symbols should be spread over the codewords
uniformly is the same. We should emphasize that our
result is not direct consequence of source coding withside information. It should be noted that for lossy
source coding([9]), the optimum coding rate can bedifferent whether the input is available at the encoder
or not. Actually, our method can not be applied tothe case when unbounded disturbance is present([3]).
We consider that extension to multi-dimensional
system is not a problem by means of real Jordan
canonical form as [3] and [2]. Application to a nonlinear plant([10],[11]) is a future work.

[2] Sekhar Tatikonda and Sanjoy Mitter. Control under communication constraints. IEEE Trans. Automat. Contr., 49(7):1056–1068, July 2004.
[3] Girish N. Nair and Robin J. Evans. Stabilizabilityof stochastic linear systems with finite feedback
data rates. SIAM J. Control Optim., 43(2):413–
436, 2004.
[4] Hans S. Witsenhausen. Separation of estimationand control for discrete time systems. Proceedingsof the IEEE, 59(11):1557–1566, Nov. 1971.
[5] D. Slepian and J. K. Wolf. Noiseless coding ofcorrelated information sources. IEEE Trans. Inform. Theory, 19:471–480, Jul. 1973.
[6] Aaron D. Wyner. On source coding with sideinformation at the decoder. IEEE Trans. Inform.
Theory, 21(3):294–300, May 1975.
[7] Rudolf Ahlswede and János Körner. Source coding with side information and a converse for degraded broadcast channels. IEEE Trans. Inform.
Theory, 21(6):629–637, Nov. 1975.
[8] T. M. Cover. A proof of the data compression
theorem of Slepian and Wolf for ergodic sources.
IEEE Trans. Inform. Theory, 21:226–228, Mar
1975.
[9] Aaron D. Wyner and Jacob Ziv. The ratedistortion function for source coding with side information at the decoder. IEEE Trans. Inform.
Theory, 22(1):1–10, Jan. 1976.
[10] Girish N. Nair, Robin J. Evans, Iven M. Y. Mareels, and William Moran. Topological feedback
entropy and nonlinear stabilization. IEEE Trans.
Automat. Contr., 49(9):1585–1597, Sep. 2004.
[11] Claudio De Persis and Alberto Isidori. Stabilizability by state feedback implies stabilizability byencoded state feedback. Systems & Control Letters, 53:249–258, 2004.

Acknowledgements
The authors would like to thank Prof. Tsumura for
his useful comments.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Modeling of Birdsong Learning with Chaotic Elman Network
M. Funabashi
Graduate School of Frontier Sciences
University of Tokyo

K. Aihara
Institute of Industrial Science
University of Tokyo

Abstract
Among passerines, Bengalese finches are known tosing extremely complex courtship songs, with 3 hierarchical structures: the element, the chunk, and the syntax. To elucidate the complex mechanism of the songof Bengalese finches, Okanoya and his colleagues havelaunched numerous studies based on Tinbergen’s fourquestions for ethologists.
To offer a more dynamic view on the development ofbirdsong learning, we first construct a model using the
Elman network with chaotic neurons, that successfully
learned the supervisor signal defined by a simple finitestate syntax.
Second, We focus on the process of individual-specificincreases in the complexity of song syntax. We propose
a new learning algorithm to produce the intrinsic diversification of song syntax without a supervisor based on
the itinerant dynamics of chaotic neural networks andthe Hebbian learning rule. The emergence of a novel
syntax modifying the acquired syntax is demonstrated.
The significance of this model is discussed.

1

Introduction

Courtship songs of passerines have been recently considered to be an evolutionary model of human language, if taken only in terms of syntactical structure.
Among passerines, Bengalese finches are known to singextremely complex songs compared with its ancestor
species. The courtship song can be expressed in the formof a finite-state syntax with three hierarchical structures:the element, the chunk, and the syntax, based on sonogram analysis and the variable N-gram model [5][6]. To
elucidate the mechanism of such an increase in the complexity of Bengalese finch song, Okanoya and his colleagues have launched numerous studies based on Tinbergen’s four questions for ethologists [8].
Besides these traditional approaches, there has recently been an insistence to construct an abstract mathematical model as an analogy that would be fruitful tounderstand the developmental dynamics of birdsong in
a comprehensive manner [4]. For this purpose, modeling©ISAROB 2006

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with a neural network is considered to be appropriate because of the possibility to implement several biologicalconcepts in the model structure and learning algorithm.

2

Modeling with chaotic Elman network

The following three conditions are naturally assumed
as the abstract conditions of neural network modeling.
First, we externally give the network the supervisor signal to learn, which coresponds to the song of a maleparent. Second, the network must have the capability togenerate song as a temporal sequence. Third, the network must also have the capability to express stochastic
finite-state automaton, which is the class of syntax tolearn. In addition, to implement the notion of chaos
as a learning catalyst[7], which is the driving force of
the intrinsic diversification of syntax, the use of chaotic
neurons is assumed to be the fourth condition.
Considering these four conditions, we first constructed a model of birdsong learning based on the Elman network [2]. The Elman network is a discrete timesystem that consists of four layers: Input, Hidden, Context, and Output. We used the Output layer consistingof eight analog neurons, each one representing a different alphabetical element that appears in the song, e.g.,
the first neuron represents element A , the 2nd neuron B , . . . , and the eighth neuron represents the end
signal = of the song. The analog neuron is defined
as follows:nXyi (t) = f (wij xj (t) + θiout ).
j=1

At time t, xj (t) is the output of the jth neuron of the
Hidden layer, yi (t) is the output of the ith neuron of the
Output layer, and wij is the connection weight between
the ith neuron of the Output layer and the jth neuronof the Hidden layer. n is the number of neurons in the
Hidden layer. θiout is the threshold of the ith Output
layer neuron, and the function, f (x), is defined as follows
with the increment parameter β:
f (x) =

1.
1 + exp(−βx)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

To express the stochastic bifurcation of song syntax,
as we run the network simulation to generate element
sequences after learning, only one neuron among eightof the Output layer was finally chosen to fire 1 which
represents one alphabetical element, and the rest were
set to remain 0. This winner-take-all selection is madewith a quasi-random variable and is proportional to theanalog output value, yi (t), of each Output layer neuron. The eight binary outputs from the Output layer
are then copied on a one-to-one basis to the eight outputs of the Input layer for the next time step. This
stochastic winner-take-all feedback allows the network
to express stochastic branches of finite-state syntax andto produce the time series of alphabetical elements. Thiscan be interpreted as the self-auditory feedback of Bengalase finches, which is necessary to maintain the song
syntax.
Each neuron of the Input and Context layer is connected to all neurons in the Hidden layer, and each neuron of the Hidden layer is connected to all neurons in the
Output layer. The output of the Context layer at time
t is identical to that of the Hidden layer at time t − 1.
For the Hidden layer, we used chaotic neurons thatcan be taken as an expansion of the analog neuron basedon the physiological property of the giant axon of asquid[1]. The chaotic neurons that we used in this model
were defined as follows:xi (t + 1) = f (

8
X
j=1

+

n
X

wij

j=1

t
X
d=0

υij

t
X

ked ιj (t − d)d=0

kfd xj (t − d) − αtX

krd xi (t − d) + θihidden ),d=0

where υij is the connection weight between the jth Input
layer neuron and the ith Hidden layer neuron, ιj (t) is
the output of the jth Input layer neuron at time t, andθihidden is the threshold of the ith Hidden layer neuron.
The parameters α and kr are set to 0 at the beginning,which makes the dynamics similar to that of the analog
neuron, but the influences of past inputs (outputs of the
Input layer) and outputs (outputs of the Context layer)
are controlled by ke and kf . By assuming the time scale
resolution 2k (k = 0, 1, 2, · · · ) of the past influence, we
distributed the values of ke and kf among the values set
by ked = kfd = 0.1, where d = 2k − 1 when k ≥ 1, andke = kf = 0 when k = 0. The number of neurons foreach value of ke and kf was set inversely proportional to
the time scale resolution.
We call this the chaotic Elman network model.
Using the back propagation method, the connection
weight and thresholds of the network were trained to
predict the next element of each supervisor song at
each time step, t. The previous element of each song

©ISAROB 2006

399

was used as the outputs from the Input layer for everyt. The initial outputs of the Input and Context layers at the beginning of a song were set to 0.5. Ten
thousand songs generated from the supervisor syntax
were used to train the network. As a result, the network successfully learned the supervisor signal, which
was defined by simple finite-state syntax, as seen in Fig.
2. For example, from the supervisor defined in Fig.
2, Sequences such as AABBDD=, AABBCCBBDD=,AABBCCBBCCBBDD=,· · · were generated randomly.
This stage could be regarded as the sensory-motor phaseof the birdsong development, which is considered to be
the process to acquire the song of a male parent. This
stage can be interpreted as the decrease of randomnessof the song compared to that of the male parent, as seenin Fig. 3. The song that the network acquired corresponded to the crystallized full song in the development.

3

Modeling of individual-specific diversification of syntax

Second, we focused on the process of individualspecific diversification of syntax that the song of Bengalese finches can go through even after the crystallization, about 90 days after hatching, which somewhat implies an internal mechanism of an increase in complexity
without the supervisor, as shown in Fig. 3. (Kawamura,T. and Okanoya, K. in preparation, and personal communication with Sasahara, K.).
As an analogy of such an intrinsic diversification ofsyntax, we proposed a new learning algorithm based onitinerant dynamics of chaotic neural networks and the
Hebbian learning rule. Setting the network in the retrieval mode, we increased the value of parameters α andkr , which represent the scale and the decay by the time ofrefractoriness, respectively. The increase of the refractoriness of chaotic neural networks can cause network
itinerant dynamics among the learned attractors[3]. In
this model, dynamics which seem to be caused by thischaotic wandering appeared at certain parameters, as
shown in Fig. 4. The syntax of itinerant dynamics implies that the network partially follows the orbit near theacquired attractor, but randomly slips off the learned
track because of refractoriness and returns to anotherpart of the attractor. During this chaotic retrieval process, we added the modification of the connection weightwij (between the Hidden and Context layer) and υij (between the Hidden and Input layer) according to the Hebbian learning rule. The Hebbian learning rule was defined as follows:wij (t + 1) = wij (t) + ²(2xi (t) − 1)(2xj (t − 1) − 1) andυij (t + 1) = υij (t) + ²(2xi (t) − 1)(2ιj (t) − 1),

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

where ² is the learning coefficient.
This algorithm can be taken as an application of theconcept of chaos as a catalyst of learning that was proposed in olfactory system[7]. There is already a neural network olfactory bulb model with multiplicative
noise[9], which exhibited itinerant dynamics among autoassociative memories. Additional learning in this stage
was also shown to recombine memorized attractors to
produce novel ones. Our modeling has an aspect thatcan be expanded to cross-associative memories.
As a result, based on the syntax acquired from asupervisor by back propagation, some new syntacticalstructures were obtained after additional Hebbian learning in the chaotic retrieval process, as shown in Fig.
5. Interestingly, the segmentation and convergence ofchunk sequences were observed. Forming novel branches
from/into the middle of supervisor’s chunks, chunks AA,
BB, and DD were segmented, and chunks BB and CC
were converged to form a novel chunk, BBCC. The
entropy measured as a Markov source increased from
1.162879 to 1.224444, which follows the notion of abstract dynamics of birdsong development, as shown in
Fig. 3. Syntactical structures vary depending on theamount of change of parameters α and kr , the learning coefficient ², and the Hebbian learning period. If
the Hebbian learning period is too long, the acquired
supervisor syntax attractors seemed to be completely
destroyed.

4neurons of the Hidden layer(n) does not guarantee theacquisition of a more complex syntax that often appears
in the song of Bengalese finches. In the cerebrum of Bengalese finch, there are three nuclei, NIF, HVc and RA,
that were found to control the structure of the syntax,
the chunk, and the element of their song, respectively[6].
For anatomical and physiological plausibility and the advancement of the ability of learning, this modularity ofthe brain which corresponds to three hierarchical structures of the song should be further implemented. For
this purpose, the definition or the distribution of parameters ke and kf , which represent the time scale ofthe influence from the past inputs, could be reconsidered.
For further research, more precise dynamics of thechaotic Elman network need to be analyzed. Expanding
the dynamics of chaotic neural networks to a structurewith nonlinear feedback (in this case stochastic neurons)
would be fruitful becausse the brain is a system composed of various types of neurons.
At the same time, the diversification of the song syntax in evolutionary context is also influenced by the interaction with females, so that process can be implemented in the network model. The expansion will enable
to connect the level of neuroscience and ethology in thismodel, aiming to find the actual degree of complexity ofthe song based on social interaction, which has not been
sufficiently modeled by entropy.

Discussion

In this model, we demonstrated that dynamics of thechaotic Elman network can serve as an abstract mechanism of individual-specific diversification of birdsong
syntax. Although two kinds of learning algorithms, backpropagation and Hebbian learning, are switched externally, the result provides the basis to construct a moreconsistent model of birdsong development. There is a
recent estimate that certain neurons in the HVc nucleus
of Bengalese finches refer to the error rate of their songcompared to that of their male parent, and the HVc nucleus is connected to the motor pathway that controls vocalization (personal communication with Nishikawa, J.).
Considering this possibility, by assuming the feedback
between the error rate of back propgation and learning coefficient ² of Hebbian learning, there might be apossibility to construct a consistent model that can autonomously reproduce several types of birdsong development, as shown in Fig. 3.
The convergence of learning to achieve a more complex syntax is a future task to be implemented in thismodel. The network seems to confront the local minima problem because a simple increase in the number of©ISAROB 2006

400

Figure1. Architecture of chaotic Elman network.

Figure 2. Definition of supervisor syntax and root
mean squared error rate of learning by back propagation.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The supervisor song consists of elements A, B, C, and D,
and chunks AA, BB, CC, and DD, with the end signal= . Cutting off branches of probability less than 0.05,
the network successfully acquired the supervisor syntax.
The entropy of syntax as a Markov source after learning
was 1.162879. The number of neurons in the Hidden
layer(n) was set to 20.

syntax was obtained after Hebbian learning of 120 itinerant songs at parameters ² = 0.00001, α = 0.25, andkr = 0.25. After the learning, parameters α and kr wereagain set to 0, and the network produced a novel syntax.
Branches of probability less than 0.05 are cut off. The
entropy of syntax as a Markov source after the additional
learning was 1.224444.

Acknowledgements
This study was partially supported by the Advanced
and Innovative Research program in Life Sciences from
MEXT of Japan.

References
Figure 3. Schematic diagram of birdsong development of Bengalese finches in terms of randomness andcomplexity. Several types of development are observed.
After the acquisition of syntax of the male parent until
about 90 days after hatching, the song, 1: maintains theacquired synax, 2: goes through the process of intrinsic diversification but returns to the syntax of the maleparent, 3: diversifies intrinsically and a more complex
syntax is produced. Considering the change of syntactical structure, the change of syntax entropy can be interpreted in two ways: as the decrease of randomness (redline) and the increase of complexity (blue line).

1. Aihara K., Takabe T. and Toyoda T. Chaotic neuralnetworks. Phys. Lett. A, 144, pp. 333-340, 1990.
2. Elman J. L. Finding Structure in Time. Cognitive
Science, 14, pp. 179-211, 1990.
3. Kitajima H. Itinerant memory dynamics and global
bifurcations in chaotic neural networks. CHAOS, Vol.
13, No. 3, pp. 1122-1132, 2003.
4. Okanoya K., Yoneda T. Phonetic Development ofAvian Species -Analysis by an Analogy with Neural
Networks-. (in Japanese)Comparative Physiology andBiochemistry (in Japanese), Vol. 12, No. 1, pp. 3-13,
1995.
5. Okanoya K. The Transition to Language, Oxford University Press, Oxford, 2002.
6. Okanoya K. From Birdsong to Human Language. (inJapanese), Iwanami, Tokyo, 2003.

Figure 4. Example of itinerant dynamics of chaotic
Elman network. After the succesful acquisition of thesupervisor syntax, the refractoriness parameters, α andkr , are both increased to 0.25. Branches of probability
less than 0.05 are cut off.

Figure 5. Example of novel syntax after additional
Hebbian learning during chaotic itinerancy. The novel©ISAROB 2006

401

7. Skarda C. A. and Freeman W. J. How brain ｓ makechaos in order to make sense of the world. Behavioral
and Brain Sciences, 10, pp. 161-195, 1987.
8. Tinbergen, N. On aims and methods of ethology.
Zeitschrift fur Tierpsychologie, 20, pp. 410-433, 1963.
9. Tsuda I. Dynamic Link of Memory -Chaotic Memory Map in Nonequilibrium Neural Networks. Neural
Networks, 5, pp. 313-326, 1992.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Classiﬁcation of the spike sequences by discriminating
their sources of time correlation
Kantaro Fujiwara† , Kazuyuki Aihara†‡†Department of Mathematical Informatics, Graduate School of Information Scienceand Technology, The University of Tokyo‡Institute of Industrial Science, The University of Tokyo, and ERATO, JST, Japan
4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505 Japan
Abstract

sequences since the number of incoming spikes arelarge and the ﬂuctuation in the accumuated potential is expected to be small. However, cortical neurons
do not actually generate regular spike sequences, although motoneurons do. Until recently, the biologicalmeaning of model parameters had not been throughly
examined, for example why neurons spike irregularly.
Spike production can be considered as a two-stepprocess. First, synaptic inputs are integrated by anextensive and complex dendritic trees resulting in a
total synaptic current. Second, the cell emits spikes in
response to this synaptic current. A numerous number of single neuron models that can reproduce some
aspects of spiking statistics of biological neurons, suchas the probability density of interspike intervals (ISIs)
have been produced, and most of them have largely
focused on the latter.
Many biological data exhibit non-zero value of correlation. What is the origin of producing temporalcorrelation? There are two possibilities. First, incoming synaptic inputs may cause the output temporalcorrelation. In most of the previous studies, temporal
properties of inputs were taken into account. Second,
neuronal integration mechanism may cause the output
temporal correlation. We now deﬁne the terms we use
in this study.
⋄ If the dynamics is entirely independent of theevent of spike production, we identify this dynamics
as the “input”. Incoming synaptic input is the typicalexample.
⋄ If the dynamics entirely depends on the eventof spike production, we identify this dynamics as the“neuronal integration mechanism”. In this case, entirely dependent means that the dynamics is reset by
every spike event. Membrane potential is the typicalexample.
⋄ If the dynamics partly depends on the event of
spike production, we identify this dynamics as the

Irregular spike sequences of cerebral cortex in vivo
are observed in numerous number of previous studies. Those spike sequences generally diﬀer from anentirely random sequence, and exhibit temporal correlations. There are two possible origins in producing
the temporal correlation: (1) temporal correlations of
the incoming synaptic inputs; (2) neuronal integration
mechanism. The temporal correlation of the neuronal
output is produced by (1), (2), or the mixture of them.
In this paper, we propose an algorithm that discriminates the origin of the temporal correlations of
spike sequences. Statistical characteristics of the spikesequences plays a key role in this algorithm. This algorithm helps classify the spike sequences by discriminating their sources of time correlation.

1

Introduction

Neurons are speciallized cells that enable speciﬁc
information processing mechanisms in the brain. Neurons can receive signals from many other neurons, typically on the order of 10,000 neurons. There are examples of cells that receive more inputs such as pyramidalcells in the hippocampus, which have been estimatedto receive on the order of 50,000 inputs. The sending neurons contact the receiving neuron at either at
the cell body or the dendrites. It is known that various mechanisms are utilized to transfer information
between the presynaptic neuron (the neuron sending
the signal) and the postsynaptic neuron (the neuron
receiving the signal). The general information processing feature of synapses is that they enable signals froma presynaptic neuron to alter the state of a postsynaptic neuron, and eventually to trigger the generation ofan electric pulse in the postsynaptic neuron.
One may think that neurons generate regular spike©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

“mixture”. This is the mixture of the input and theneuronal integration mechanism. Dynamics as Ca2+
dependent channels which are partly dependent on
voltage dynamics is also included in this term.
We suggest an algorithm to discriminate the threesources of temporal correlations in this study. By using this algorithm, we can classify the spike sequences
and the neurons that produces them by diﬀerences of
the neuronal spiking mechanism.

1.1

2.1

Auto correlation is deﬁned as a function of lag l.
〈λ(t)λ(t + l)〉t − f 2
f2(1)

λ(t) is the ﬁring rate deﬁned as the probability of a
spike event per inﬁnitesimal unit time t, f is the meanﬁring rate, and the notation 〈···〉 represents a temporal
averaging operation. The value of the auto correlation
determines the correlation of the spike sequences, positive or negative.
k-th moment of auto correlation is deﬁned as∫ ∞Ak ≡lk A(l)dl(2)

Property 1
If the ISI distribution is constant for T̄ modulation
and Ak+1 /Ak ∝ T̄ , time correlation is produced by
the neuronal integration mechanism.

0

2.2

If all of the Ak are equal to zero, the sequence has
no correlation. If each Ak has a positive value, theratio Ak+1 /Ak characterizes the sustaining time scaleof positive correlation.

1.2

Non-renewal process

From property 1, we can recognize the neuronal integration mechanism when the ISI distribution is constant under rate modulation. Therefore, in the caseof the ISI distribution constant, but Ak+1 /Ak does
not have a positive correlation with T̄ , there are two
possible sources of time correlation, the input or the
mixture. How can we discriminate them?
The diﬀerence of the correlation from the mixture
and the input is, whether it aﬀects the ﬁring rate ornot. Therefore, the ratio of the moment of auto correlation Ak+1 /Ak is constant when there is only the input correlation[1]. If there exists a term which aﬀects
the ﬁring rate, Ak+1 /Ak ﬂuctuates. This property is
to be true in any case, even if the ISI distribution ﬂuctuates with rate modulation.

Spiking irregularity CV

Coeﬃcient of variation CV indicates the spiking irregularity.
√∑n12i=1 (Ti − T )
n−1
CV ≡
(3)
T
Ti is the duration ∑of the ith ISI, n is the total numbernof ISIs, and T = n1 i=1 Ti is the mean ISI. CV take 1
for purely Poisson process, and 0 for perfectly regularsequences.

©ISAROB 2006

Renewal process

If the input is temporally uncorrelated, temporalcorrelation in a spike sequence is generated by only
the neuronal integration mechanism. The integrated
quantities in the neuron are reset at a spike event,so an event after a spike is uncorrelated to any event
before the spike, so called a renewal process. If it isa renewal process, there is one-to-one correspondence
between the ISI distribution and the spike auto correlation. In this case, the time scale of the auto correlation must have a positive correlation to the meanintervals, as long as the shapes of ISI are similar. We
can determine whether the shape of the ISI distribution has changed or not by measuring CV and theother higher dimensional statistical values. Thus, thefollowing property is to be true.

Spike auto correlation

A(l) ≡

Discriminating the sources of timecorrelation

Property 2
If Ak+1 /Ak is constant for T̄ modulation, time correlation is produced by the input.

403

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2.3

Inconstant auto correlation

56#46

01

If Ak+1 /Ak ﬂuctuates with T̄ modulation, there are
two possible sources of time correlation, the neuronal
integration mechanism or the mixture. How can we
discriminate them?

#M #MKUEQPUVCPVHQTOQFWNCVKQP
6

The diﬀerence of the correlation from the neuronal
integration mechanism and the mixture is, whether it
resets its dynamics after the spike event or not. It is
known that the positive correlation between CV and T̄
is not produced only by the neuronal resetting mechanism [2].

KPRWV
+5+FKUVTKDWVKQPKUEQPUVCPVHQTOQFWNCVKQP
6

However, the case that there is no positive correlation between CV and T̄ even in the presence of thetemporal correlation by the mixture still exists. In
this case, comparison of T̄ and Ak+1 /Ak make sense.
If Ak+1 /Ak ≫ T̄ , the temporal correlation is producedby the mixture. Temporal correlation produced by theneuronal integration mechanism lasts for the spike interval T̄ , since neuronal resetting mechanism vanishes
the correlation between spikes. Thus, the following
property is to be true.

#M #M ∝ 6

PGWTQPCNKPVGITCVKQPOKZVWTG

Figure 1: Flow chart of the algorithm to discriminate
the sources of temporal correlation. Arbitrary spikesequence is classiﬁed into 3 groups.

4

Algorithm for the classiﬁcation

Application to the experimental data

We applied the algorithm to the experimental data.
The ISI data is recorded from the gustatory cortex ofrats during taste-aversion learning experiment. The
data we analyzed was the spike sequence that exhibits
bursting patterns.
For the ﬁrst step, Ak+1 /Ak was inconstant withmodulating T̄ . Furthermore, the ISI distribution isinconsistent under T̄ modulation since CV had a positive correlation with T̄ (ﬁgure 2).
These results show that the temporal correlation ofthis spike sequence from gustatory cortex is producedby the mixture according to this discriminating algorithm.
Several slow inward currents have been implicated in the generation of somatic bursts in cortical neurons, including voltage-sensitive calcium[3] andsodium currents[4], and a calcium-activated cationiccurrent[5]. Therefore, the result from the algorithm
that the temporal correlation is produced by the corrrelation of the mixture is a natural result. The mixture partly depends on the event of spike production,

From the properties 1, 2 and 3, we can obtain thealgorithm to discriminate the sources of time correlation for arbitrary spike sequences. Figure 1 shows thealgorithm.
The algorithm begins from the determination of thecorrelation between Ak+1 /Ak and T̄ . If there exists
only the input correlation, Ak+1 /Ak is constant underrate modulation, since the input correlation doesn’t
aﬀect ﬁring rate (ref. property 2).
The second determination is that whether the ISI
distribution is constant under rate modulation or not.
If the ISI distribution is constant under rate modulation, one can determine whether the spike sequence isa renewal process or not by the existence of the correlation between Ak+1 /Ak and T̄ . Renewal process isonly produced by the neuronal integration mechanism(ref. property 1). If the ISI distribution is inconstant,the measure of CV and the value of the Ak+1 /Ak makesense as in the property 3.

©ISAROB 2006

%X ∝ 6QT#M #M 6OGEJCPKUO

Property 3
If CV ∝ T̄ or Ak+1 /Ak ≫ T̄ , time correlation is produced by the mixture.

3

;'5

404

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[2] K. Fujiwara, K. Aihara, “A Simple Neuron Modelwith Memory of Firing,” Proceedings of 2004 international symposium on nonlinear theory and its
applications (NOLTA2004), pp497–500, 2004.

㪈㪅㪏
㪈㪅㪎
㪈㪅㪍
㪈㪅㪌
㪈㪅㪋
㪈㪅㪊
㪈㪅㪉
㪈㪅㪈
㪈
㪇㪅㪐
㪇㪅㪏

[3] R. K. Wong, D. A. Prince, “Afterpotential generation in hippocampal pyramidal cells,” Journal of
Neurophysiology, vol.45, pp86–97, 1981.

㪚㪭

㪇

㪇㪅㪇㪌
㪓㪫㪕㩷㩿㫊㪼㪺㪀[4] R. Azouz, M. S. Jensen, Y. Yaari, “Ionic basis of
spike afterdepolarization and burst generation in
adult rat hippocampal CA1 pyramidal cells,” Journal of Physiology, vol.492, pp211–223, 1996.

㪇㪅㪈[5] Y. Kang, T. Okada, H. Ohmori, “A phenytoinsensitive cationic current participates in generating
the afterdepolarization and burst afterdischarge in
rat neocortical pyramidal cells,” European Journalof Neuroscience, vol.10, pp1363–1375, 1998.

Figure 2: Cv under T̄ modulation

including the slow dynamics that is not reset but inﬂuenced by every spike event.

5

Conclusion

We proposed an algorithm that discriminates theorigin of the temporal correlations of spike sequences.
We conﬁrmed its usefulness by applying to the experimental data.
In the experimental studies, it is diﬃcult to obtain
the information of the input. This algorithm helpsclassify the spike sequences by discriminating theirsources of time correlation even in those cases. It isinteresting to develop this brief algorithm more precisely.

Acknowledgements
We are thankful to Hiroki Fujiwara and Minoru
Tsukada for providing me the precious experimental data. This study is partially supported by theSuperrobust Computation Project in 21st Century
COE Program on Information Science and Technology
Strategic Core and by the Advanced and Innovational
Research program in Life Science from the Ministry of
Education, Culture, Sports, Science, and Technology,
the Japanese Goverment.

References[1] Y. Sakai, “Neuronal integration mechanisms havelittle eﬀect on spike auro-correlations of corticalneurons,” Neural Networks,Vol. 14, pp. 1145-1152,
2001.

©ISAROB 2006

405

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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406

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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409

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Dynamic Switching of Neural Coding Schemes
in a Network with Gap Junctions
Yuichi Katori1 , Naoki Masuda1,2 and Kazuyuki Aihara1
1
Aihara Complexity Modelling Project, ERATO,JST,
3-23-5 Uehara, Shibuya-ku, Tokyo 151-0064, Japan
2
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute,
2-1, Hirosawa, Wako, Saitama 351-0198, Japan
Abstracttion. One of the most plausible coding schemes is population rate coding, which represents information as afiring rate averaged over hundreds of neurons. This
makes it possible to transmit a signal within a shorttime and to have robustness against spike failure. Another plausible scheme is temporal coding, which indicates that information is coded by precise temporal patterns of spikes, such as in synchronous firing.
Synchronous firing with millisecond precision has been
observed in experiments as in multi-channel recording[3]. Many theoretical works have discussed what structures induce this synchronous firing and how these activities are relevant to neural coding. Furthermore,
recent studies have found the possibility of the coexistence of these two coding schemes which is controlled
by noise intensity [1][2].
Furthermore, recent experiments have shown amassive number of gap junctions. These gap junctions
are specialized areas of the cell membranes connecting
neighbor cells; they induce synchronous firing [4]. In
addition to the synchronous activity, theoretical studies suggest that gap junctions induce chaotic activities[6] and transitions between synchronous and desynchronous states. However, little is known about how
this transient activity is associated with neural coding.
We have used numerical simulations and information
theory to investigate how the transitions between synchronous and desynchronous states is connected withneural coding.

How neurons code information in our brain is one of
the most important themes in neuroscience. Population rate coding and temporal coding are two common
neural coding schemes. Recent studies have pointed
out that these two coding schemes may coexist [1][2].
Experiments have revealed a massive number of gap
junctions in various regions of the brain. Neurons connected with gap junctions induce synchronous spiking.
Moreover, theoretical works indicate that gap junction
induce not only synchronous activity but also a switching behavior between synchronous states and desynchronous states [5]. We used information theory andcomputer simulations to investigate how this kind of
switching behavior relates to the neural coding. First,
we demonstrated that even under time-varying inputs,
neurons coupled with gap junctions induced switching behavior between synchronous and desynchronous
states. Then, we used three types of mutual information to separate the contribution of each coding
scheme from the information carried on a network. We
measured these mutual information carried in sepatate periods of synchronous and desynchronous states.
Our results show that the population rate coding has
an advantage in periods of desynchronous states andthe temporal coding has an advantage in periods ofsynchronous states. We found that these two typesof coding schemes emerge alternately with switching
between synchronous and desynchronous states.
Key words: Neural coding, gap junction, chaotic
itinerancy, population rate coding, temporal coding,mutual information.

1

2

We investigated a neural network coupled with gap
junctions. We used a simple two-variable neuronmodel called a µ-model, which is more plausible thana one-variable neuron model like the integrate-and-firemodel [5]. The model is composed of two variables xi
and yi , where the first variable represents the mem-

Introduction

Many studies in the past few decades have investigated how neurons code information. However, noestablished theory fully addresses to answer this ques-©ISAROB 2006

Analysis410

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

brane potential and the second variable represents activation of some ion channels. Here we chose µ = 1.65.
dxi
dt
dyi
dt

=

3
−yi − µx2i (xi − ) + I(t) + J(t)
2

(1)

=

−yi + µx2i(2)Second was the spike count information which corresponds to the information carried by the firing rate of
the neural population. The spike count information
is based on the number of spikes in the population in
an arbitrarily chosen time window. Third was pure
temporal information. This is given as a subtractionof the spike count information from the full temporal
information.
Consider a time duration T , which corresponds toa period to apply the input to the network. Let us denote each input time series over the duration as si (τ ).
Each time-varying input is generated by a different
random seed and indexed by i(i = 1, ..., M ). Here, we
set M = 50. We ran 100 simulations for different initial states of x and y for each signal and record spiketrain {ti } generated by the population of neurons. Simultaneously, we calculated a trial average of spiketrain density of the population under the input timeseries s as
X δt,tk,(4)r(τ ; s) =∆t

(i = 1, · · · , N ),where J(t) is a current induced by gap junction
coupling with neighbor neurons:
(i = 1)
G(xi+1 − xi)J(t) = G(xi+1 + xi−1 − 2xi ) (i = 2, · · · , N − 1) (3)G(xi−1 − xi )(i = N ).
In this simulation, we used a chain network of 40
neurons, where, G is the coupling constant of the gap
junctions, assumed to be identical for all connectionsin the network. This kind of network shows an alteration of synchronous and desynchronous states evenwith a constant input [5]. In the field of nonlinear science, several studies have examined the mechanismof this phenomenon. However, our interest is how
this phenomenon is associated with the neural coding. We used mutual information as a measure ofefficiency of information transmission between inputs
and output spike trains. To investigate this, we applied time-varying input to this neural network andmeasured how neurons carry information. We chosethe input I(t) to be the Ornstein-Uhlenbeck (OU)process generated by the stochastic differential equation: dI = −mIdt + σdW , where dW denotes the
Wiener process. We set the amplitude of inputs smallenough not to break alteration of synchronous anddesynchronous states. We set m = 0.05 and σ = 0.005.
Figure 1 shows the simulation results of the network with the coupling constant G = 0.1 and the inputs specified above applied. Figure 1(a) shows thetime series of the input generated by the OU-process.
Figure 1(b) shows a superimposed time series of themembrane potentials of the 40 model neurons. The
time series shows the alteration of synchronous anddesynchronous states even with the time-varying input. Figure 1(c) shows a time series of a moving average of standard deviation of the 40 neurons’s membrane potentials D(t) as a degree of a desynchrony ofmembrane potentials.
To analyze how the information is transmitted on
the network, we used three types of mutual information to determine the contribution of the two coding
schemes. The first type of information was the full
temporal information, which is associated with all possible patterns of spike occurrence in the population.

©ISAROB 2006kwhere δt1 ,t2 is the Kronecker delta function (1 if t1 andt2 label the same time bin and zero otherwise). The
index k indicates spike number and ∆t is small timebin (we chose ∆t = 0.1). We considered a time windowof width W to measure the mutual information. The
full temporal information is given by integrating the
instantaneous information in this time window: [7]Z W r(t; s),(5)dt r(t; s) log2
If ull (W ) =hr(t; s′ )is′ s0where the brackets his indicate the average acrossstimuli s. The spike count information was calculatedusing a spike count in the time window asR(t; s)
Irate (W ) = R(t; s) log2,(6)hR(t; s′ )is′ swhere R(t; s) is the integration of number of spikes inthe time window W :Z Wr(t; s)dt.
(7)
R(t; s) =0Here, If ull (W ) and Irate (W ) satisfy If ull (W ) ≥Irate (W ). The pure temporal information is given as
the subtraction of the spike count information fromthe full temporal information:
Itemporal (W ) = If ull (W ) − Irate (W ).

(8)

To investigate the difference between the coding
schemes in the synchronous and desynchronous states,

411

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Input
0.1

(a) 0.0-0.1

Membrane potential

(b)

1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4

(c)

0.4
0.3
0.2
0.1
0.0

SD of membrane potential

0

2000

4000

6000

time [msec]

Figure 1: A typical response of 40 model neurons coupled with gap junctions when applying the input generated by
Ornstein-Uhlenbeck (OU)-process. (a) Times series of input applied for this neural network. (b) Superimposed plotof membrane potential of 40 neurons. (c) Time series of standard deviation of membrane potential of 40 neurons.

we needed to categorize these spikes by whether they
were generated in synchronous or in desynchronous
states. To do this, we defined each state using thestandard deviation of membrane potential D(t). We
defined the synchronous state as D(t) < 0.1 and thedesynchronous state as D(t) ≥ 0.1. Then we calculated the three types of mutual information using the
spike trains generated in the separate periods of synchronous and desynchronous states.
As a result of this calculation, we obtained the spikecount information, revealing the efficiency of the population rate coding, and the pure temporal information
revealing the efficiency of the temporal coding. Figures 2 (a) and (b) show how these quantities dependon the coupling constant of the gap junction. Figure2(c) shows degrees of synchrony of the network. The(+) sign indicates the temporal average of the standard deviation of membrane potentials. The (x) signshows the synchrony of the spikes. This quantity isdefined as the ratio of spikes that occurred in a shorttime window around each spike to the number of neurons in the network. The square sign shows the ratioof duration of synchronous states to duration of desynchronous states.
In the synchronous states, the temporal coding wasshown to have an advantage, the information carriedby the temporal coding increased as the coupling constant of the gap junctions increased. In contrast, the©ISAROB 2006

spike count information did not increase as much as
the pure temporal information. This was caused by
an improvement of the reproducibility of precise spiketiming, which depends on the synchrony of the neurons; the synchrony increased as the coupling constantof gap junctions strengthen.
In the desynchronous states, the spike count information had an advantage. In population rate coding,the amplitude of the input sequence is reproduced in
the firing rate of the output spike train. Information
carried by population rate coding increased in the region of G < 0.3. However, in the region of G ≥ 0.3,
each spike tended to synchronize and could not maintain the information about the amplitude. This isbecause, the spike count information decrease in thisregion. These results indicated that the two coding
schemes emerge alternately with the switching of thesynchronous and desynchronous states.

3

Summary

We have shown that a neural network coupled with
gap junctions induces the alteration of synchronousand desynchronous states even under time-varying inputs and that two coding schemes, population rate
coding and temporal coding emerge alternately alongwith the changing of these states. Our findings about

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Synchronous state(a) 1.6
1.5
Mutual information [bit]the functions examined here may contribute to information processing in the cortical network. In corticalneurons, ceaseless transitions of state take place as signals are received from sensory neurons and other partsof the brain. These states may represent memory patterns. This memory pattern becomes other pattern.
The population rate coding could be used efficiently
in the process of collecting signals from various partsof the brain. Then, the temporal coding could be used
to recall a memory in the synchronous state. After recalling the memory, the state could desynchronize tocollect new signals again. The states-switching behavior with gap junctions may contributes to such brain
functions.

1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

pure temporal information

spike counts information

0.0

1.0

2.0

3.0

Coupling constant of gap junctions

Desynchronous state

0.7

(b)
0.6

References
Mutual information [bit]

0.5

0.4

[1] N.Masuda and K.Aihara (2002), “Bridging rate
coding and temporal spike coding by effect ofnoise”, Rhysical Review Letters,88,248101

spike counts information

0.3

0.2

0.1

[2] N.Masuda and K.Aihara (2003), ”Duality of rate
coding and temporal coding in multilayerd feedforward networks”,Neural Computation,15,103-125.

pure temporal information

0.0
0.0

1.0

2.0

3.0

Coupling constant of gap junctions[3] A.K.Engel, P.Fries and W.Singer (2001),”Dynamic predictions:oscillations and synchrony in top-down processing”Nature Reviews
Neuroscience,2,880-888.

1.0(c) 0.9
0.8
0.7
0.6

[4] M.Galarreta and S.Hestrin (1999),”A network of
fast-spiking cells in the neocortex connected byelectrical synapses” Nature,402,72-75.

0.5
0.4
0.3
0.2

[5] 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.

0.1
0.0
0.0

1.0

2.0

3.0

Coupling constant of gap junctions

Figure 2: Mutual information and synchrony as a function of coupling constant of gap junctions. (a),(b)Spike count information as a measure of population
coding efficiency (bold line) and pure temporal information as a measure of temporal coding efficiency(thin line). (a) Information carried in the periods ofsynchronous states. (b) Information carried in the
periods of desynchronous states. (c) Degree of synchrony. Temporal average of standard deviation of
membrane potentials of these neurons (+). Spike synchrony (x). Ratio of duration of synchronous states toduration of desynchronous states (square).

©ISAROB 2006

[6] N.Schweighofer, K.Doya, H.Fukai, et al.(2004),“Chaos may enhance information transmission in
the inferior olive”, PNAS, Vol.101, no.13, 46554660.
[7] S.Panzeri and S.R.Schults (2001), “A unified approach to the study of temporal, correlational, andrate coding”, Neural computation, 13,1311-1349.

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MOSFET Implementation of Two-dimensional Neuron Model
Takashi Takemoto∗ , Takashi Kohno† Shigeki Tsuji† and Kazuyuki Aihara† ‡∗ Complexity Science and Engineering Frontier Science, The University of Tokyo.
† Aihara Complexity Modeling Project, ERATO, JST.
‡ Institute of Industrial Science, The University of Tokyo.

Abstract
We propose a two-dimensional neuron model using
metal-oxide-semiconductor field-effect-transistors (MOSFET). Two-dimensional models are tractable mathematically because the temporal evolution of the variables canbe visualized in the phase plane. Our circuitry were designed according to the mathematical structure determinedusing phase plane analysis and bifurcation theory. Hence,
our circuitry is compact and biologically plausible. Ourcircuitry can achieve class I mode without narrow channel,
class II, and I* mode when a few circuit parameters arechanged. Class I* neurons are mainly characterized by aphase plane structure with a narrow channel and exhibit
spatio-temporal chaos when coupled with gap junctions.
We analyzed the responses to singlet pulse, and demonstrated the threshold mechanism in class I, II, and I* modes.
Moreover, we showed that class II and I* neurons not only
exhibit periodic but also chaotic responses to periodic pulse
stimuli.

phase planes. It is well known that saddle-node on invariant circle bifurcation produces class I excitability, and thesubcritical Hopf bifurcation class II. These bifurcations canbe generated in two-dimensional systems.
The responses to periodic pulses of squid giant axons
and the Hodgkin-Huxley (HH) model have been intensively studied. Class II models such as the HH model
are well known to exhibit not only periodic but also aperiodic responses to periodic stimuli depending on their amplitudes and frequencies. Complex responses to simple inputs may play important roles in neural codings.
Our silicon neuron can achieve class I, II, and I* neurons
when a few circuit parameters are changed. The essential
nonlinearity of class I* is mainly characterized by a phaseplane structure with a narrow channel [2]. In the following section, we describe how our circuitry was designed.
In Sec. 3, we present its responses to singlet and periodic
pulse stimuli. Finally, in Sec. 4, we make concluding remarks.

1 Introduction

2 Modeling the Electronic Neuron Model

Various silicon neurons that reproduce the propertiesof biological neurons have been proposed to create somenew possibilities in neuroscience, computer science andelectronics. Conventionally, silicon neurons have been designed according to the two major principles: phenomenological and conductance-based designs. Recently, Kohno
and Aihara proposed mathematical-model-based designthat is based on mathematical structures revealed using
phase plane analysis and bifurcation theory [1]. It allows
us to implement simple and biologically realistic siliconneurons. Our circuitry is designed according to this designprinciple.
Neurons are classified into two classes according to their
responses to sustained current stimuli: class I and II. The
firing frequency of class I neurons at the onset is asymptotically zero, whereas that of class II neurons is nonzero.
Two-dimensional neuron models are tractable mathematically because their dynamic behaviors can be visualized in©ISAROB 2006

Our circuitry mainly consists of three blocks: the V-I
converter, the inverted-N-shaped nonlinear characteristics
circuitry, and the U-shaped nonlinear characteristics circuitry (see Fig. 1). We designed these blocks using enhanced MOSFETs, resistors, and capacitors.

Fig. 1: Block diagram of electronic neuron model.

The circuit equations are as follows:

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VDD

where



 CV dV
dt
du

 Cµ
dt

= f (V ) − E µ + Istim= g(V ) − K µ

M3

M4

VconN

M10

M5

M14 Mstim

M13

M11

Vstim

IsN

,

IdiffN

VIN

(1)

M7
M6

M1

Istim

IconN
M9

M12

M2

M8

IbN

IoffsetN

RsN

RconN

IN

VSS

Fig. 3: Schematic of inverted-N-shaped circuit.

Bκnf (V ) = Aexp
−C V + D,
1 + exp(−κnV /UT )
UT


F
κn
g(v) =+ Hexp −I V + J.
1 + exp(−κnV /UT + G)
UTcurrent, IconU , is applied to MOSFET M26 via resistor RsU
and converted exponentially. The slope of output current,
ICSU , can be altered by RsU and the current source, IsU .
The output current of this circuit represents the µ -nullcline
in the silicon neuron.

We can adjust the parameters, A to K, in these equations
by adjusting the resistances, the voltages, and the current
sources in the three main blocks in our circuitry.
A schematic of the V-I converter is shown in Fig. 2.
The source-follower, M29-M30, shifts the input voltage ac0 = V +V
cording to VININ
DD −VconV I , where VIN is the input
voltage and VconVI is a constant bias voltage that we cancontrol. The feedback structure composed of M32-M33
suppresses the nonlinearity of the input/output (IO) characteristics intrinsic to MOSFETs. The IO characteristics ofthis circuit are very linear.

VDD
M17

M18

VconU

M24

M19

M25

M27

IsU
M21

VIN

IconU

M20
M15

M16

IbU

M28
IoffsetU

M23

M26

VdiffU

IdiffU

IU

M22
RconU

RsU
VSS

Fig. 4: Schematic of U-shaped circuit.

VDD
VconVI

’
VIN

VIN

M34

M29

M35

A plot of the nullclines of our electronic neuron modelobtained using the circuit equations is shown in Fig. 5. Ourcircuitry can achieve three neuron modes, namely, class I
without a narrow channel and class II and I*. These neuron modes have a common inverted N-shaped V-nullcline
and different µ -nullclines depending on only two circuit
parameters, IsU and Vdi f fU .

M31

M30

M33

M32

IconVI

IoffsetVI

RconVI

VSS

Fig. 2: Schematic of V-I converter.
A schematic of the inverted-N-shaped nonlinear characteristics circuitry is shown in Fig. 3. It mainly consistsof a differential pair, M1-M2, V-I converter, M5-M9, andcommon-source (CS) amplifier. The combined output current of M1-M2 and M5-M9 has flatter N-shaped I-V characteristics. These currents are applied to MOSFET M12 as
the input voltage via resistor RsN and converted exponentially. This makes the ratio of the N-shaped I-V characteristics increase more in regions with small input voltages,
VIN . The ratio of the increase depends on the resistance,
RsN , and the current source, IsN . The output current of thiscircuit represents the V-nullcline in our silicon neuron.
A schematic of the U-shaped nonlinear characteristic
circuitry is shown in Fig. 4. It mainly consists of a differential pair, M15-M18, and a scalable CS amplifier, M19M26. The latter consists of a V-I converter to convert thescale of the input voltage. The input voltage is applied tothe differential pair and the scalable CS amplifier. One applied to the latter is scaled by the V-I converter. Its output©ISAROB 2006

Fig. 5: Phase plane structure of electronic neuron model.
Dashed line is V-nullcline and solid lines are µ -nullclines.

3 Circuit Operation and Characteristics
In this section, we give the responses of our circuitry tosinglet and periodic pulse stimuli. We focused on the three415

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neuron modes: class I, II, and I*. We used the fourth-order
Runge-Kutta method for numerical simulations.

3.1

Responses to singlet pulse stimuli

Plots of the responses to singlet pulse stimuli are shownin Fig. 6. We stimulated our circuitry with singlet pulses ofvarious strengths. When the pulses were weak, the system
immediately returned directly to the resting state. Whenthe strength was over the threshold, the membrane potential
rapidly increased and then plunged below its resting state
before returning to it. The time interval between the onsetof the action potential and the peak is obviously shorter
than that between the peak and the desinence.

Fig. 7: Phase plane dynamics in class I mode.

Fig. 8: Phase plane dynamics in class II mode.
(a) Class I
mode.

(b) Class IImode.

(c) Class I*
mode.

in Fig. 9 . As is the case for the class I mode, a stablemanifold defines a separatrix. However, when the stimulus shifts the system state beyond the ascending limb of theV-nullcline, the membrane potential overshoots even if it isinside of the unstable manifold. This excitation mechanism
is similar to class II mode, and the threshold is graded inthis region.

Fig. 6: Responses to singlet pulse stimuli.

The phase plane structure of class I mode is shownin Fig. 7. When the system is near a saddle-node on
the invariant circle bifurcation, the nullclines of this mode
have three intersections: (S) is a stable equilibrium (restingstate), (T) is a saddle, and (U) is an unstable equilibrium.
In this case, the system has a pair of incoming trajectories: stable and unstable manifolds of the saddle. The stable manifold defines a separatrix curve (threshold), and it
results in all-or-none behaviors. When the input pulse isstrong enough to pitch the system state over the separatrix,
an action potential is generated. Its trajectory follows along
the unstable manifold and eventually returns to the restingstate. The peak voltages of action potentials are approximately constant regardless of the stimulus voltage.
The phase plane structure of class II mode is shown in
Fig. 8 . The two nullclines have an intersection: (S) is astable equilibrium (resting state). The ascending limb of
the V-nullcline plays a role as a threshold. However, themembrane potential does not always overshoot if the stimulus voltage is over it. Phenomenologically, Class II neurons are well known to not have a well-defined threshold,but a graded one [3]. The peak voltage of the action potential largely depends on the ratio between the two time
constants, CV and Cµ .
The phase plane structure of class I* mode is shown©ISAROB 2006

Fig. 9: Phase plane dynamics in class I* mode.

3.2

Responses to periodic pulse stimuli

The responses to periodic pulses of squid giant axons
and the HH equations have been studied in detail. ClassII models such as the HH model are well known to not
only exhibit periodic but also aperiodic responses to periodic pulse stimuli depending on their amplitudes and interstimulus intervals, Tstim . In this section, we demonstratethat our circuitry exhibits chaotic responses to applied periodic pulses in several parameter regions.

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We fixed the pulse width, W , to 0.5 msec and Tstim to 7.0
msec in class II mode and 11.0 msec in class I* mode. The
stimulus strength, Vstim , was varied. When Vstim was low,
our circuit did not fire. As Vstim was slightly increased,
nonperiodic responses occurred. A typical nonperiodic response in class II mode is shown in Fig. 10. The trajectoryis not a closed curve but a complex pattern.
(a) Class II mode.

(b) Class I* mode.

Fig. 12: Return map of Vdelay .

4 Concluding Remarks(a) Time series.

We developed a two-dimensional electronic neuronmodel using MOSFETs that can achieve the three significant neuron modes: class I, II and I*. We reported numerical simulation results for responses to singlet and periodic pulse stimuli. We showed that class II and I* modes
have graded threshold mechanisms, and that they exhibit
chaotic responses to periodic pulse stimuli. Our circuitry
is compatible with standard CMOS processes. Hence, it
can be easily implemented in an analog very-large-scale
integrated circuit (VLSI), and large-scale network can beconstructed.

(b) Trajectory in µ V phase plane.

Fig. 10: Nonperiodic responses to periodic pulse stimuli(class II mode).
As we can see from Fig. 11, class I* mode also generated nonperiodic responses. We examined the responses
to periodic pulse stimuli in class I mode. However, we
did not find any parameter sets that caused nonperiodic
responses. The generation of nonperiodic responses mayneed the threshold to be graded as in the case of class II
neurons. Class I* mode has this kind of threshold as noted
earlier.

Acknowledgments
This study was partially supported by the Superrobust
Computation Project in 21st Century COE Program on Information Science and Technology Strategic Core and by
the Advanced and Innovational Research program in Life
Science from the Ministry of Education, Culture, Sports,
Science, and Technology, the Japanese Government.

References(a) Time series.

(b) Trajectory in µ V phase plane.

[1] Takashi Kohno and Kazuyuki Aihara, “A MOSFETbased model of a Class 2 Nerve membrane.”, IEEE
Trans. Neural Networks, Vol. 16(3), pp. 754-773,
2004.

Fig. 11: Nonperiodic responses to periodic pulse stimuli(class I* mode).

[2] 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, SpringerVerlag, 2004.

We focused on the membrane potential at 0.5 msec after the falling edge of stimulus current. We call this voltage Vdelay . Figure 12 shows return maps for Vdelay corresponding to Figs. 10 and 11, and Vdelay (n) is plotted versus
Vdelay (n + 1). Both modes represent single-valued maps.
The Lyapunov exponents we estimated for the class II andI* modes were 1.833±0.00246 and 1.691±0.00013. Notethat both are positive, which indicates that these nonperiodic responses are chaotic with orbital instability.

©ISAROB 2006

[3] Rinzel J. and Ermentrout G. B., “Analysis of Neural Excitability and Oscillation.”, In Koch C., Segev
I.(eds) Method in Neuronal Modeling, The MIT Press,
Camblige Mass., 1989.

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A Stepping motor control for Electric Vehicle
Masanori Sugisaka , Khairunizam Ahmad
Department of Electric and Electronic Engineering
Oita University
700 Oaza Dannoharu, Oita, Japan
Tel: +81-97-554-7831
Fax: +81-97-554-7841
Email: msugi@cc.oita-u.ac.jp, hinagata96@hotmail.com

Abstract

that are used to control the stepping motors, which isplayed an important role that must be understand and tosuit with a lateral control. The third section will show acharacteristic of the steeping motors used in our EV,
which is the result of the experiment. The forth section
will be the conclusion and a further research related tothe complete EV system will also be introduced.

This paper is discussing about a stepping
motor, which is used to control acceleration and brake’s
paddle of an electric vehicle. The moving angle of the
stepping motor will decide a position of the paddles andalso the electric vehicle itself. As a conventional electricvehicle, the user controls the position of the acceleration
and brake paddle manually by pushing or releasing it toa desire position. In this project I used the electricvehicle called MINI-SWAY, which is restructured to be
fully controlled by computer mode that play the samerole as a user in a conventional electric vehicle.

2. Devices
Sensor technology will have a critical role inintelligent EV operation and safety. Sensors will beneeded for lateral position, speed, and brake etc forvehicle control. As in lateral control, discrete markers [4]were introduced that shows a great promise on control
sensing of EV. For a better image of my projectequipments, lets introduce all of the important devices ingeneral. Electric vehicle in our laboratory is composedof 6 ultrasonic sensor E4B-LS704 having 70cm asdetection distance. The sensor can automatically slow
down the speed when detecting unexpected thing. The
host computer CPU II Celeron 466MHz does the control
of all motors and devices connected to motor driving
card PCI-7208 and input output port IBX-2726C as aninterface to the controller. The NEC computer is used forimage processing having a Liquid Crystal Display touch
panel. These PCs are connected to hardware via
controller card. The hardware uses are stepper motors, aservomotor [5]; drive frame grabber and a serial port for
camera control. In addition 40x of chip ROM and 256
Mbytes of RAM and 10/100m of LAN are used. A 24V
lead acid is used as the main power supply for all motors
and devices on the vehicle. According to this DC supply
24V is directly used for motors, sensors and LED while
the computer responsible for processing the image
receives an AC 100V from AC/DC inverter. In our
MINI-SWAY EV the steeping motor is used to drive a
DC brush less motor to rotate a rear tire before moving.
As a feedback to us, a rotary encoder E6C2-CWZ6C
from OMRON is used. The rotation numbers of the tireis determined depending on the output pulse from rotary
encoder to a controller board PCI-7208. The encoder isused as a sensor to detect the direction and the numbersof rotation of the rotating tire. It has a specification 100
Pulse Per Revolution. It is means that for a complete one
revolution of the rotary encoder will produce 100 pulses

1. Introduction
The twenty first century will be theenvironmentally century. One of the most pressing needsof our life is an alternative clean, efficient, intelligent
and environmentally friendly for urban transport system.
Electric vehicle (EV) offer a solution for improving air
quality, reducing the reliance on fossil fuel, andenhancing energy efficiency. Furthermore, electricvehicle equipped with artificial intelligent, intelligent
navigation, advanced control and communication,
positioning system can improve traffic safety and road
utilization. The main important reasons why we arethinking about automatics or intelligent vehicle are toprevent incident happen on road and also to avoid traffic
control such in a big city [1]. The long-term goal of the
EV is the implementation of the Automated Highway
System (AHS), which will include completely“hand-off” driving in which vehicles are fully
automatically controlled once they enter the system.
Achieving this goal requires significant research insystem modeling, lateral (steering) control [2][3], speed
control, and combined lateral and speed control of thevehicles. This paper is the initial step in solving the
problem of combining a lateral control and a speed
control of the electric vehicles. As mentioning in the titleof this paper, a detail research on the steeping motor
must be done in term of the stability of the system. This
paper describes a characteristic of the stepping motors,
which are used as a speed and brake control for EV tomove a long a target line using a lateral control
algorithm. The second section will introduce the devices©ISAROB 2006

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as an input to a motor controller board. Figure 1 shows aconstruction of the encoder basis, which is used in our
experimental equipment.

In combining the lateral control and the speed control for
getting a single complete intelligent EV system dwSpeed(Hz) and nStep (Number of Pulse) play an important rolethat must be taken onto account. The characteristic of the
stepping motor that drive a DC Brush less motor should
be studied in detail to suit the condition of the lateral(steering) control. In my research, the main target is toarchive a perfect steering control and at the same time
another control strategies will be added such as brake,
speed, signal etc in term of stability and safety of thesystem [7]. The next is a third section that will discuss theexperimental method and the experimental result.

Fig. 1 Rotary Encoder

3. Experimental method and result

There are two stepping motors used in our EV system. It
is needed for controlling a DC Brush less motor for
rotating a rear tire and also for pulling the brake pedal up
and down. In this paper, the experiment is done on thesteeping motor, which is control, the DC Brush lessmotor only. Let see the parameters of the signal
commands (Table 1) that we set to create an input pulse
from a controller board to stepping motor driver.

In the explanation as above, the purpose ofthis paper is to find the characteristic of the steeping
motor that drive a DC Brush less motor and running therear tire of the EV. Also, that characteristic must fulfill
the requirement or suit with the lateral algorithm, which
is becoming the main objective of the project. To archive
this, giving a certain pulse to a steeping motor driver wasdone in the experiment. As a feedback to us, thealgorithm has also been studied on how to obtain thepresent situation of the rear tire after giving a certain
pulse to a steeping motor. Later, in the next project theresult will be an important factor in determining thestability of the EV by combining a lateral control and a
speed control. The rotational speed of the rear axle canbe deduced by taking definite difference of the rear
encoder position, and normalizing for the number ofpulse per revolution n as below,

Table 1 Signal Parameters of the commands

ω=The signal parameter as describes in Table 1 is very
important because the changes of it will cause a hugechanging of the tire rotation. As in the lateral control fora machine vision [6], the same parameters were used forcontrolling a steering angle while running on line. For adetail understanding, Figure 2 shows a graph related tothe signal parameters.

1 p a − p a −1
n t a − t a −1

(1)

For a successive reading of the axle encoder p a −1 andp a at time t a −1 and t a . The Rotation Per Minutes of
the tire can be derive as below,

RPM =1 ∆Pulsen ∆Time(2)∆Pulse = Pt − Pt −1∆Time = t − (t − 1)

(3)

n = EncoderSlot × RatioValue(4)
Equation from eq.1-eq.4 is used to determine the rotationof the tire in RPM. The result of the experiment is shown

Fig. 2 The Relation Between Signal Parameters©ISAROB 2006

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to control because it move step by step of angle. The
problem is DC Brush less motor. It will be discussed in
the next paper.

by plotting a graph of RPM versus Time after giving acertain pulse to a steeping motor. Ten times experiment
was done with a different output pulse from a controller
board, but here I will shows one of the results with
output pulse is 600 Pulse.

References[1] T.A. Lasky, B. Ravani (1993), A Review of
Research To Automated Highway System, AHMCTResearch Report, pp. 5-7[2] Hessburg. T and Tomizuka. M (1991), A fuzzy
rule-based controller for automotive vehicle
guidance. Technical Report UCB-ITS-PRR-91-8[3] S. Tsugawa, I. Mori, S. Kato (1998), A Lateral
Control Algorithm for Vision-Based Vehicle with aMoving Target in the Field of View, IEEE
International Conference on Intelligent Vehicles, pp.
41-45

Fig. 3 RPM Vs Time
From the experiment the relation between RPM and
PULSE was also obtained by plotting the graph RPM
versus PULSE as shown Figure 4.

[4] Zhang W.B (1991), A Roadway information systemfor vehicle guidance/control, Vehicles Navigation &Information System Conference Proceedings, pp.
1111-1116[5] M. Sugisaka, M. Zacharie (2002), The Control of
the EV Steering Wheel using an AC Servomotor,
The 21st SICE, pp.39-42

RPM Vs Pulse
70

RPM

60
50
40

[6] T. Tokuda (2005), A Machine Vision for Electric
Vehicle, Masters thesis Oita Uni.

30
20
10
0
590

600

610

620

630

640

650

660

[7] Novak, R. J (1992), Diagnosis of System Fault,
Vehicle Electronic Meeting Society’s Need: Energy,
Environment, Safety, pp.163-170

Pulse

Fig. 4 RPM Vs Pulse

4. Conclusion
The characteristic of the steeping motor,
which is driven a DC Brush less motor has been studied
and give a good response for combining with a lateralcontrol in the next project. The feedback of the running
tire is obtained by using a sensor, rotary encoder. But a
detail studies should be done on mechanical devices,which is connected from DC Brush less motor to a reartire. Even a small failure happens on it, will cause a
precision problem while comparing a desire position ofprocess and a present process. Before combining with asteering control, which is the main objective on myproject, a single motor control is also important and theexperiment is planned after this. The use of control
methodology in motor control has been discussed in a
numbers of papers. A PID or Fuzzy control methodology
may be applicable for controlling motor in EV. The main
problem may be happen because the steeping motor is
not directly control the rear tire. A steeping motor is easy

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Reinforcement Learning with Self-Instruction by using dual Q-tables
Osamu NISHIMURA1) Hirokazu MATSUI2) Chieko HIOKI3) Yoshihiko NOMURA2)
1) Graduate School of Engineering, Mie University. 2) Faculty of Engineering, Mie Univ.
3)Toyota Communication Systems Co., Ltd.
Address Kurima-machiya 1577, Z, 514-8507, Japan
voice: +81-59-231-9692; E-Mail: nosamu@robot.mach.mie-u.ac.jp
Abstract

learning, that has two Q-tables for knowledge andfor experience, and updates two Q-tables at the sametime, so that the trial frequency tries to be decreased.

In this paper, we propose the Q-learning methodthat selects proper actions of robot in unknown environment by using the Self-Instruction based on theexperience in known environment. Concretely, it has
two Q-tables, that are updated at the same time. Oneis smaller, based on a partial space of the environment. The other is lager, based on the whole space ofthe environment. The smaller is used for the knowledge storing as self-instructing. The larger is used forthe experiment storing.
We simulated and compared the proposed methodwith the simple method under conditions with several
rates of proper actions by self-instruction. As a result of the simulation, we showed that the proposed
method is more effective than the ordinaries.

1

Principle

2.1

Concept

Here, we describe the proposed reinforcement learning method with Self-Instruction. The proposed methods make two Q-tables update at the same time, one
is larger for experience storing, the other is smaller forknowledge storing. We think the following, an experience is one example in the environment, so we set the
Q-table for the experience to the whole space of theenvironment, and knowledge is compressed the experience in the environment, so we set the Q-table for theknowledge to a partial space of the environment, The
smaller Q-table (for the knowledge storing) makes thelearning finish earlier. The larger Q-table (for the experience storing) makes the learning be even in moredetail.
This research aims at the method that makes belearned the environment earlier and in more detailby using these two Q-tables at the same time whileswitching by information entropy by each Q-table.

Introduction

In this paper, we propose an efficient learning algorithm using a Self-Instruction for an autonomous
robot. When human being encounters a known environment, he acts based on his experience. When heencounters an unknown environment, he acts based on
his knowledge, that is compressed his experience. In
many cases, his knowledge is effective, and he can acquire his target. Even if not effective, he stacks theexperience for the next chance. We think that human being has two kinds of tables for his effective actions, which are for knowledge storing and for experience storing. We aim at making an efficient learningalgorithm by adding the human inference [Hioki:01].
The reduction on the trial frequency is important for
reinforcement learning under an actual environment.
In the researches such as [Takahashi:03] [Uchibe:04],
learning modules are arranged hierarchically. In the
researches such as [Katayama:04], the learning is gradually changing based from on prior knowledge to on
profit sharing. We propose the method based on Q-©ISAROB 2006

2

2.2

Materialization

Here, we materialize the concept in the above paragraph to the algorithm of the proposed method, as
shown in Fig. 1.
(1) Set Environment: The learning environment isprepared for a learning agent. In the environment,
the proper action is defined. The agent can know
indirectly the environment by getting rewards.
(2) Init Q-tables: The agent initializes the two Qtables to set each Q-value to an init value.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3
Set Environment (1)Init Q-tables (2)We compare the proposed method with the ordinary method by simulation in the three environment.
We simulate the proposed method with the two Qtables, one is smaller, based on the partial space of theenvironment, the other is larger, based on the whole
space of the environment, and simulate the ordinary
method with the Q-table for the partial space, andthe ordinary method with the Q-table for the whole
space. Here, the Q-table for the whole space can represent perfectly for the three environments, but the
Q-table for the partial space can represent perfectlyfor one environment (case 1), half for another (case2), not at all for the other(case 3). We verify that the
proposed method is effective in the above three cases.

Episod Loop
Select Q-table (3)PartialWhole

Select Action byPartial Q-table(4p)Select Action by
Whole Q-table(4w)Updatepartial Q-table(5p)Update whole Q-table(5w)

Until getting reward

Fig. 1 NS chart of the proposed method

3.1

Xp(a | s) log2a∈A1p(a | s)(1)

: Learning agent

where p(a | s) is a probability of selecting action
a at the state s, that is defined by the following
Eq. 2. We think that it means Q-table is effective,that the entropy of Q-table is low.

Fig. 2 Experimental Environment at certain time(4) Select Action (for both Q-tables): The agent decides the action by the Boltzmann selection usedgenerally on Q-learning. The selection probabilityof action a is shown by Eq. 2.

An agent exists on a cell. The agent can move from1 step to 5 steps forward at a time. Each cell has
1 out of 6 colors (Red, Yellow, Green, Cyan, Blue,
Magenta), the color is changed by each unit time. The
agent gets reward, in the case that continuous 3 times,
agent’s actions are matched with the actions that are
pre-decided by the current cell and the current color of
the cell. An agent starts at a random cell. We define
an episode to be time until getting reward, and define
one trial to be 100 episodes.
The proposed method has two Q-tables, as shownin Fig. 3. and the ordinary method has each Q-table.
We define Q-tables for the whole space and for the partial space to be the following. Q-table for the whole
space: It has 3 axes, one is positions(20), another, colors(6), the other, actions(5). Q-table for the partial
space: It has 2 axes, one is positions(20), the other,actions(5). We simulate in the three types of the environments:kkp(a | s ) = P

exp( Q(sT ,a) )a0 ∈A exp(

Q(sk ,a0 )
)
T

(2)

where p(a|sk ) is probability of selecting action aon state sk , k is times, T is temperature.
(5) Update Q-table (for both Q-tables): Q-value isupdated by Eq. 3. The set of equations is used
standard with updating Q-value.
Q(sk , a) ← (1 − α)Q(sk , a) + α(r + γV (sk+1 ))V (sk+1 ) = maxa∈A Q(sk , a)(3)where sk is current state, sk+1 is the next state, ais selected action, r is reward, α is learning rate,
γ is discount rate.

©ISAROB 2006

Experiment condition

We simulate the methods in the environment of 20
cells arranged like a ring, as shown in Fig. 2.

(3) Select Q-table: The agent selects the Q-table withlower information entropyH(s), out of the partial and the whole Q-table, that is calculated by
Eq. 1.
H(s) =

Experiment

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3.2

Here, we simulated the proposed method with the
Q-table for the partial space and the Q-table for thewhole space(PM), the ordinary method with the Qtable for the partial space(OMP), and the ordinary
method with the Q-table for the whole space(OMW)
in each case from 1st episode to 100th. Each graph isthe average of 1000 trials.

ActionActionPosition

Position

Color

Whole space

Fig. 3space

Partial space

Q-tables for the partial space and the whole[steps until reward]
40

Case 1 : The environment is enough represented by the
Q-table for the partial space. Concretely, in thisenvironment, the agent can decide the optimal
action only with information of position, without
information of colors. In other words, all the optimal actions are decided only by the Q-table forthe partial space in the environment. We thinkthat Self-Instruction is perfect effective.

30

The Ordinary method withthe partical Q-table

The Proposed method withthe whole and the partial Q-tables

20
10

0

Case 2 : The environment is half represented by the Qtable for the partial space. Concretely, in thisenvironment, the agent can decide the optimal
action at 10 out of 20 positions on the ring, onlywith information of positions, and cannot at therest. In other words, half actions are decided by
the Q-table for the partial space, but the otherhalf actions can be decided only by the Q-table forthe whole space. We think that Self-Instructionis half effective. In this case, we expect that the
proposed method is more effective than the ordinary.

The Ordinary method withthe whole Q-table

50

100[episode]Fig. 4 Case1: Self-Instruction is perfect effective.

The result of the case 1 is shown in Fig. 4. In thecase 1, the agents with all the three methods, PM,
OMP and OMW, acquired the optimal value respectively. The agent with OMP converged earlier than theagents with PM and OMW, but the agent with PW
converged almost as early as the agent with OMP. The
agent with OMP learned the environment the mostefficiently, since the partial space was optimal for theenvironment. The agent with OMW learned it not efficiently, since the whole space was redundant. The
agent with PM learned it almost efficiently, since the
Q-table for the partial space is almost used, the Qtable for the whole space is a few used. In the case
1, PM was almost as effective as OMP, more effective
than OMW.
The result of the case 2 is shown in Fig. 5. In theCase 2, the agents with PM and OMW acquired the
optimal value respectively. But the agent with OMP
acquired not optimal value, since the agent with OMP
was able to decide only half actions and had to selectthe rest half actions at randam. The agent with PM
learned earlier than the agent with OMW, since at firstthe agent with PM depended on the Q-table for thepartial space. The agent with PM learned the optimal

Case 3 : The environment is not represented at all by
Q-table for the partial space. Concretely, in thiscase, the agent cannot decide the optimal action
only with information of positions, and it needs
information of the color at that time more. In
other words, no action is decided only by the Qtable for the partial space. All the actions are
decided only by the Q-table for the whole space.
In short, Self-Instruction is not effective at all.
The Q-values are initialized to 0.0, the rewardr is
set to 1.0, the learning rateα is set to 0.9, the discountrateγ is set to 0.005, The threshold of the learning
degree of each space is set to 0.1. The temperature T
of Boltzmann selection is set to 0.1.

©ISAROB 2006

Results and discussion

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We verified that PM is more effective in any case,by simulating in the above three cases, that represent
any cases.

[steps until reward]
40

The Ordinay method withthe whole Q-table

30

The Proposed method withthe whole and the partial Q-tables

20

The Ordinary method withthe partial Q-table

4

In this paper, we proposed the Reinforcement
Learning Algorithm with Self-Instruction by using
dual Q-tables for a partial space and for a whole space.
and verified that the proposed method is more effective than the ordinaries. In the future, we will study amethod that finds an effective partial space on learning. We will expand the relation between a partial
space and a whole space to multilayer.

10

0

50

Conclusion

100[episode]Fig. 5 Case2: Self-Instruction is half effective.

References
value since at last the agent with PM depended on the
Q-table for the whole space. In the case 2, PM wasmore effective than the ordinary methods.

[1] Chieko HIOKI, Hirokazu MATSUI, Yoshihiko NOMURA, “Reinforcement Learning by Using a SelfInstruction for Actions in Unknown Environment,”
The 44th technical committee of Special Interest
Group on Fundation of Artificial Intelligence(SIGFAI),2001.

[steps until reward][2] Yasutake Takahashi, Minoru Asada, ‘State-Action
Space Construction for Multi-Layered Learning
System,” Journal of the Robotics Society of Japan,
Vol. 21 No. 2, pp.164–171, 2003.

40
30
20
10

0

The Ordinary method withthe partial Q-table
The Proposed method withthe whole and the partial Q-tables[3] Eiji Uchibe, Kenji Doya, “Hierarchical Reinforcement Learning for Multiple Reward Functions,”
Journal of the Robotics Society of Japan, Vol. 22
No. 1, pp.120–129, 2004.

The Ordinary method withthe whole Q-table

50[4] K. Katayama, T. Koshiishi, H. Narihisa, “Reinforcement Learning Agents with Analystic Hierarchy Process,” Transactions of the Japaneses Society for Articicial Inttelligence,Vol. 19, No. 4,
pp.279–291, 2004.

100[episode]Fig. 6 Case 3: Self-Instruction is not effective at all.
The result of the case 3 is shown in Fig. 6. In theCase 3, the agents with PM and OMW acquired the
optimal value respectively. But the agent with OMP
did not learned it at all, since the agent with OMP wasable to decide no actions or had to select any action at
randam. The agent with PM learned the optimal value
since the agent with PM learned the environment withusing the Q-table for the whole space without using
the unavailable Q-table for the partial space. In thecase 3, PM was almost as effective as OMW, more
effective than OMP.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Position Estimation of a Mobile Robot Using U-SAT (Ultrasonic Satellites)
Su Yong Kim, Chang Sup
Kim

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

Sang Min Kim

Department of Interdisciplinary
Program in Mechatronics
Pusan National University,
Busan, 609-735, KOREA

Abstract

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 toproblems such as low performance and limitationsin terms of its application in an outdoor
environment [3]. The GPS can give accurate
information to identify locations. By a differential
GPS, the accuracy of positioning is also improved.
However, this type of sensor usually provides
measurements at 1-10 Hz. This sampling rate may
not be sufficient if the dynamics of a positioning
object changes relatively fast. Besides, it does notoperate well in the place where no GPS satellite
signal is available or where there are less than four
visible satellites [4]. In addition, pseudo-satellites forindoor or outdoor places have been re-searched.
Ultrasonic ranging systems, which are similar to thebasic 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 [5]. 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 [6]. 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
Experiment was performed in the case that themobile robot moves to the target point using
U-SAT. The performance of U-SAT is estimatedfrom the result of the experiment.

In this paper, we propose a new method to findan absolute position by using ultrasonic sensors. In
order to evaluate the performance, U-SAT(Ultrasonic Satellites) is compared with the RTK(Real Time Kinematics)-DGPS (Differential Global
Positioning System). As a result, the possibility ofusing U-SAT can be discussed as pseudolites or
pseudo-satellites in the place where GPS is notavailable. Experiment was performed in the casethat the mobile robot moves to the target point
using U-SAT, that is, absolute positioning method.
The performance of U-SAT is estimated from theresult of the experiment.

1 Introduction
The process of finding robot in environment is amajor concern in mobile robot navigation. To
measure the position of a mobile robot, a variety ofstudies are going on and robot positioning has been
done in two basic methods. They are not only
absolute and relative 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 robot. 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].
Other methods use sensors such as a rate-gyro anda magnetic compass. A rate-gyro accumulates errors
continuously with the passage of time and amagnetic compass does not function well at theplace where the magnetic fields vary from position
to position. For these reasons, it is hard to findaccurately the location of the robot moving far
away only by using relative positioning.
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©ISAROB 2006

Man Hyung Lee

School of MechanicalEngineering,
Pusan National University,
Busan, 609-735, KOREA

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 is425

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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
c = 331.5 + 0.60714 T(2)

Fig.2 System description(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 [6].
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 2006

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 the426The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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.

5

Fig.6 Experimental results of the static position(GPS: +, U-SAT: o)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 2006

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.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

moves slowly or stops, the position information
guarantees very stable performance. In Addition,
U-SAT can be used as a proper system in the place
where GPS is not available, such as indoor area.
Finally Experiments were performed in the case thatthe mobile robot moves to the target point using
U-SAT. The performance of U-SAT is estimatedfrom the results of the experiment. The mobile
robot can move accurately along the reference pathusing U-SAT. As a result of experiment, thismethod using U-SAT shows good performance andis suitable. Absolute positioning method using
U-SAT can be applied to any indoor system thatneed absolute position information. 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.

Fig.8 Configuration of the mobile robot
Moving Path
Reference Path
Limits of Error

1200
1000

Y Axis (mm)

800
600

References

400
200

[1]

Jae Hwan Kim and Poong Hyun Seong,"Experiments on orientation recovery andsteering of autonomous mobile robot using
encoded magnetic 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] Y. Arai and M. Sekiai, "Absolute position
measurement system for mobile robot based onincident angle detection of infrared light," 2003.
(IROS 2003). Proceedings. 2003 IEEE/RSJ
International Conference on, Intelligent Robots
and Systems, 2003, pp. 986~991.
[4] 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.
[5] 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.
[6] 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.
[7] Y. Kanayama, Y. Kimura, F. Miyazaki, T.
Noguchi, "A stable tracking control method foran autonomous mobile robot," Robotics andAutomation, 1990. Proceedings., 1990 IEEE
International Conference on, Vol. 1, 1990,
3863661, pp. 384~389.

Start Point

0

Destination Point
-200
0

500

1000

1500

2000

X Axis (mm)Fig.9 Position estimation result of the mobile robot

6

Position Estimation of a Mobile Robot

Fig.8 depicts the configuration of the mobile
robot. Two ultrasonic receivers are mounted on themobile robot so that they can obtain the position
information and orientation. The localization
information is sent to PC via bluetooth. And PC
executes its trajectory tracking control. So themobile robot moves along the reference path in acounterclockwise direction. As seen in Fig.9, thereference path is situated on the four points of therectangular whose length and width are 2m and 1m
respectively [7].
Experiment was conducted in order to verify thatthe mobile robot can move accurately along thereference path using U-SAT. Fig.9 shows theexperimental result. A maximum translational
velocity of 0.1m/s was used for this experiment,
resulting in a maximum error of 3cm for this run.
Experimental result using U-SAT shows that theposition estimation of the mobile robot using
U-SAT is closed for closed reference path.
However, 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
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 mobile robot which©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Robotics Safety Issues For Human Welfare in an Automated Manufacturing System
Dr.Bhuvenesh Rajamony
Program Chairman
School of Manufacturing Engineering
Northern Malaysia University College of Engineering
01000, Kangar, Perlis, Malaysia
Email: bhuvenesh@kukum.edu.my

Dr.Mohd Rizon Juhari
Associate Professor
School of Mechatronic Engineering
Northern Malaysia University College of Engineering
01000, Kangar, Perlis, Malaysia
E-mail: rizon@kukum.edu.my

Abstract

control, including material and machine scheduling, andfactory automation, including materials processing,inspection, assembly, and materials handling. The
application of robotics in companies implementing CIM
requires that robot system become a part of the total CIM
concept. Implementing a CIM system enterprise-wide
requires extensive planning, many months and hard work,and a substantial investment in people and their welfare,
hardware and software.

The high degree of automation, that until recently was
reserved for mass production only, is applied now, withthe aid of robots and computer, also to small batches.
This requires a change from hard automation in the
production line to a flexible manufacturing system which
can be more readily rearranged to handle new market
requirements. Safety is an important factor in design for
robots working in this manufacturing environment. The
issue of robot safety in an automated manufacturingsystem deals with the design of a reliable control system
to prevent malfunctions and operator’s safety. Industrial
robots have a wide range of potential applications in
manufacturing systems because they are flexible andprogrammable themselves. Different robot configuration
generates different characteristic working envelop
shapes .The design of the work station layout andtraining of plant personnel are important. Robot
application designers need to give more careful thought
to persons who work within the confines of the robot
workspace. The worker and the operator safety is themost important concern. The discussion follows thepractical sequence in which one must think when
designing a piece of automatically acting equipment.
This paper highlights the safety issues of robots forhuman welfare in an automated manufacturing system.

2. ROBOT ELEMENTS FOR SAFETYCONSIDERATION
An industrial robot is essentially a device that canmove materials, parts, or tools from one point to another
under programmed control without human intervention.
It can also be easily taught to perform simple tasks suchas pick-and-place or spot welding or painting. The typesof servomechanisms used to control the motion alongeach axis are similar in each case. There are three basic
components of an industrial robot for safety
consideration: Manipulator, Controller, and Tooling.
(i) Manipulator: The manipulator consists of thebase and arm of the robot, including the power
supply, which may be electrical, hydraulic, or
pneumatic. The manipulator is the device thatprovides movement in any number of degrees of
freedom. The movement of the manipulator can be
described in relation to its coordinate system, which
may be cylindrical, spherical, anthropomorphic, or
Cartesian. Depending on the controller, movement
can be point-to-point motion or continuous-pathmotion.

Keywords:
Automated, Robotic design, Manufacturing, Plant
personnel, Work safety

1. INTRODUCTION
The high degree of automation, that until recently was
reserved for mass production only, is applied now, withthe aid of robots and computer, also to small batches.
This requires a change from hard automation in the
production line to a flexible manufacturing system which
can be more readily rearranged to handle new market
requirements. The supervision of this factory will be
performed by computer-integrated manufacturing (CIM)
system, in which the production flow, from theconceptual design through the finished product, will beentirely under computer control and management. This
involves the steps of design, including analysis andsimulation, documentation, manufacturing planning and©ISAROB 2006(ii) Controller: The versatility of a robot arises fromits multi-axis mechanical configuration and therobot controller. The ability to reprogramme therobot controller gives the flexibility to the robot to
perform a wide range of actions. The controller
contains various interfaces with both command
devices and sensing units. The controller has todefine the trajectory of the robot gripper with time
and transform this trajectory, which is in Cartesian,into its base-frame coordinate system and finally
into joint movements. Many of these tasks are to be
performed in real time.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(iii) Tooling: Tooling is what enables the robot to do aparticular job. Tooling is sometimes usedsynonymously with end effectors, although the latterhas a more restricted meaning to apply to end-ofarm fixturing to grasp, lift, or turn. Tooling, on theother hand, has a broader context which can apply topower tools for drilling and grinding, as well as forpainting and welding guns. Typical end effectors
include electromagnets, hooks, vacuum cups,
adhesive fingers, and bayonet sockets. Different
robot configuration generates different characteristic
working envelop shapes. The work volume refers to
the space within the robot can operate.

3. APPLICATIONSMANUFACTURINGOF

ROBOTS

IN

Industrial robots have a wide range of potential
applications in manufacturing systems because they are
flexible and programmable themselves. The use of
sensors allows the robots to see, hear, and smell theenvironment. The robot controllers can be generally
integrated easily into the manufacturing system
environment and are capable of communicating withother programmable controllers. In hostile environments,
such as foundries or forges, robots are often used forhigh-temperature handling, particularly in stamping
operations. Feeding and unloading of machine tools is anarea in which robots are used, especially with automatic
lathes.

Automated assembly operations using industrial robots and circular andlinear transfer lines

Welding is an unpleasant and repetitive task, and one
for which robots are already widely used, and are likely
to be used more extensively, due to the progress made in
the development of sensors. The technique of spot
welding is well developed, and is used frequently,
especially in the motor industry. Continuous arc welding
is a far more delicate operation, the main problems being
to follow the joint, and find a sensor which can function
properly near a welding torch. The market for robots incontinuous welding is optimistic. Some manufacturing
offer robots specifically designed for this purpose.

©ISAROB 2006.

Robots are also widely used for assembly, either ofprinted circuit boards, into which components areautomatically installed, or of small systems. The scale ofuse of robot in painting operations has not been
spectacular, even though the task is unpleasant, andpotentially dangerous. There are however several robots
available which have been designed solely for thisfunction.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4. EMPHASIS FOR HUMAN SAFETY
Strong emphasis is given to the following laws:
1. A robot must not harm a human being, nor throughinaction allow one to come to harm.
2. A robot must always obey human beings, unless thatis in conflict with the first law.
3. A robot must protect itself from harm, unless that is in
conflict with the first and second laws.
It is, therefore, the responsibility of the designers toconsider measures to diminish the possibility ofaccidents to both hardware and humans. A robotics
installation is still a new occurrence in most factories,
therefore, that some type of protective barrier be placedaround the work envelope to prevent unauthorized
workers from getting too close to the robot work cell andto restrict traffic flow in the area. Both physical barriersand electronic detectors can be employed. Some
examples are:• Painted lines on the floor• Chains and guard posts• Safety rails• Wire mesh fencing• Presence –sensing device, with light beams forming
a curtain across the zone of operation.
• Breaking the curtain of light beams by operator’spresence sets the robot off.
In addition to physical barriers, detectors such as
electronic curtains, motion detectors, and pressure sensitive floor pads can also provide protection. A
typical robotic installation might include a combinationof barriers and protection devices. Another approachstates that only robots themselves are able to detect theapproach of humans. Therefore, the solution to the safety
problem is to provide sensor systems, which can detect
intruders that enter the robot area while it is operating.

5. CONCLUSION AND FUTURE TREND
Safety is an important factor in robotic design,from the beginning of the design to the implementationof the process. Specific areas of safety awareness in arobotic installation include types of barriers for worker
protection, general personnel safety, and operator andmaintenance personnel safety. The uses of robotics in the
manufacturing no doubts will bring a lot of benefits asmentioned above. But the introduction of the robot into
the work place may have a significant impact on theworkers, management, and the organizational unit. To
effectively use this new technology, managers need to
identify the critical points of impact and develop
mechanisms to effectively adjust to this new technology.

BIBLIOGRAPHY.
1.

James A.Rehg, Henry W. Kraebber, “Computer
Integrated Manufacturing”, Prentice Hall,2001..

©ISAROB 2006

431

2.

Mikell P.Groover ,“Automation, Production
Systems, and Computer-Integrated Manufacturing,
Prentice Hall International, Inc.2001

3.

E.Paul Degarmo, J T.Black,Ronald
A.Kohser(2003):Materials and Processes in
Manufacturing, John Wiley & Sons. Inc.

4.

Bhuvenesh Rajamony, “Safety Issues of Robotics inan Automated Manufacturing System, Proceedingsof the 1st International Workshop on Artificial Lifeand Robotics Vol 1,pp. 113-116, 2005.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Asymmetry Analysis of Human Motions for Examining Rehabilitation Training
Keiko NODA*, Joo Kooi TAN*, Seiji ISHIKAWA*
Nobuhiro KITO**, Koji OKUMURA***Department of Control Engineering, Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japan
E-mail: {noda, etheltan, ishikawa}@ss10.cntl.kyutech.ac.jp**Kawashima Orthopedic Hospital
Miyao 14-1, Nakatsu, Oita 871-0012, Japan
in various human motions related fields. For example, it
can be used to evaluate the effect of the rehabilitation
training in the medical field.
In medical rehabilitation training, it is requested toestablish the method of evaluating training effectquantitatively employing a motion capture technique. Itis, however, note very easy in a small hospital tointroduce motion capture systems commerciallyavailable, because they are extremely expensive andneed a large space like a drama studio to take images bymultiple cameras. Operations of such motion capturesystems are also not very easy for those who are
unfamiliar with this field, since they include camera
calibration. A cheap motion capture system with simple
operation is expected in the communities that want toanalyze human motions.
We have developed a novel motion capture system
based on the factorization method [1]. It allows simple
manipulation in video image taking, as it does not haveto perform camera calibration before image capture. We
apply this motion capture system to a patient’s motion
recovery and analysis in rehabilitation training. They all
suffer from coxalgia The motion is analyzed with respect
to the variances of the right-hand side as well as theleft-hand side joint angles of some human portions.
Their symmetry/asymmetry is examined to understandthe degree of asymmetry with each patient. By theinspection of this asymmetry degree with a patient
periodically, we expect that the effectiveness ofrehabilitation training is evaluated properly.

Abstract
Rehabilitation is to make a recovery from theimpediment as much as possible with the people whose
normal life is difficult with the natural disabilities or thesequelae due to some disease or injury, and to help themgaining independent life as much as possible. In medical
rehabilitation training, it is requested to establish themethod of evaluating training effect quantitatively
employing the motion capture technique.
This paper proposes a method of examining a walking
motion by asymmetry analysis. In the experiment,
three-dimensional recovery is performed of threepatients’ walking motions. We analyze the walking
motions using the data obtained by the 3-D recovery. A
normal walking motion is assumed to have equalvariances between the angles of the right-hand side andthe left-hand side of a human body. Hence the motion issymmetric. Since one of the major purposes of therehabilitation training is regarded as to recoversymmetry in human motions, examination of the degreeof asymmetry employing the proposed technique is
expected to contribute to evaluating the training effects
and therefore the training strategies in rehabilitation
properly.
Keywords: asymmetry analysis, rehabilitation, 3-D
recovery, factorization

1. Introduction
2. Technique of 3-D Recovery
Three-dimensional recovery of human motions isapplicable to the analysis and the examination of humanmotions and the technique is therefore requested for use©ISAROB 2006

The developed 3-D motion recovery technique based

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on uncalibrated cameras is briefly described in thissection. F ( 3 ) video cameras are fixed around an
object with the feature points and obtain the images of an
object.
We
extract
thefeaturepointsPt (t 1, 2, L , T ) that are observed among F video
cameraimagesandevery
sampletimet (t 1, 2, L , T ) . Let the 2-D coordinates of thefeature point p on the image be denoted by( x fp (t ), y fp (t )) . Then the matrix W (t ) at sample
time t is defined as

x11 (t ) L
MO

x1Pt (t )M

x F 1 (t ) L x FPt (t )W (t )y11 (t ) LMOwalking motion of the three patients suffering fromcoxarthrosis and receiving rehabilitation training. The
number of markers attached to these patients is 23. They
are instructed to walk on a rectangle mat of about 5
meters long in a straight and natural way. The video
images of the walking motion of the patients are
obtained by three video cameras that are placed fixedand distant around the patient. The frame rate is 60 [fps],
and the measurement time is 5 seconds. The walking
cycles within the measurement time are about fourcycles. The images are successively taken into a PC, and~the matrix W of Eq.(3) is defined from the marker~
locations on the images. The matrix W then receives
decomposition as shown in Eq.(4). Figure 1 shows thethree-dimensional recovery result of the walking motionof Patient A.
We analyze the walking motions using the dataobtained by the 3-D recovery. The employed parameters
are the angle of a knee joint on each side, the angle of a
hip joint on each side, the angle of an elbow on eachside, the angle of an upper arm on each side (See Fig.2a),the rotation angle around the medial axis of the body(See Fig.2b), and the slope of the medial axis of the
body against the vertical line (See Fig.2c).
We analyze two walking cycles in order to obtainstable walking data. The data are examined using the
average and the standard deviation of these parameters.
We analyze whether or not the average of eachparameter values distribution is mutually different
between the right-hand side and the left-hand side of the
body statistically. The significant difference equation isdefined by

y1Pt (t )

,

(1)

M

y F 1 (t ) L y FPt (t )where t 1, 2, L , T .
When the matrix W (t ) is arranged from t=1 to T, it
is expressed byW

W (1) W (2) L W (T ) .

(2)

Subtracting the mean quantity of each line from each~element of the matrix W, the matrix W is defined by

~
W

1
WE
Q
~
~
~
W (1) W (2) L W (T ) ,W(3)

where Q is the number of all the feature points, and E is
a Q Q matrix each element of which is unity.
Equation (3) results in the decomposition of the matrix~
W into two matrices such as

~
W

MS .

u

(4)

L

.

R

L

m

n

(5)

The quantities
R and
L are the average of theparameter values distribution on the respective side of
the body. The values
R and
L are the standard
deviation of the parameters on the respective side of the
body. The number of frames (the number of data) aredenoted by m and n on the respective side. In Eq.(5),under 5[%] level of significance, there is significant
difference between the right-hand side and the left-handside, if u1.96 holds. Otherwise, i.e., in case ofu 1.96 , there is no significant difference between
them.

The matrix M, an orientation matrix, contains theinformation on the directions of the light axes of
employed video cameras, whereas the matrix S, a shapematrix, gives the 3-D coordinates of all the feature points~
registered in the matrix W .

3. Recovery and Analysis of Walking Motion
Three-dimensional recovery is performed of the©ISAROB 2006

R

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Fig. 1 3-D recovery result of the walking motion with respect to Patient A.

(a)

(b)

(c)

Fig. 2 The analyzed parameters.

(a)

(b)

Fig. 3 (a) The angle of knee joints, and (b) the angle of upper arms with respect to Patient A.

©ISAROB 2006

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Table 1 Analysis of the angles of knee joints, the angles of crotch joints, the angles of elbow joints and the angles ofupper arms with respect to Patient A.
Average[degree]
Knee joints
Crotch joint
Elbow joint
Upper arms

Right

151.04

11.10

Left

160.20

11.36

Right

153.194.67

Left

170.47

4.97

Right

150.808.63

Left

157.92

9.22

Right

26.50

4.33

Left

29.98

7.59

Significant difference

○

○

○

○

establishes the optimal walking motion under the disease,when walking.
A normal walking motion is assumed to have equalvariances between the angles of the right-hand side andthe left-hand side of a human body. Hence the motion issymmetric. Since one of the major purposes of therehabilitation training can be regarded as to recoversymmetry in human motions, examination of the degreeof asymmetry employing the proposed technique is
expected to contribute to evaluating the training effects
and therefore the training strategies in rehabilitation
properly.
The developed motion capture system has anadvantage over others in that calibration-free cameras
can be employed in motion capture. By the refinement of
the proposed technique, it may possible to produce a
motion capture system of simpler manipulation. This
may therefore lead to wide spread of a motion capture
technique.

4. Experimental Results
Figure 3 shows the change of the angles of knee joints,
and the angles of upper arms of Patient A as time passes.
The blue line represents the right knee joint angle,
whereas the red line gives the left knee joint angle.
Obviously the left knee joint angle is larger than the rightin average.
Table 1, on the other hand, gives the average and thestandard deviation of the angles; angle of a knee joint on
both sides, the angle of a crotch on both sides, the angleof an elbow on both sides, and the angle of an upper arm
on both sides. We have analyzed statistically thesignificant difference of these angles between theright-hand side and the left-hand side. As a result, it has
been proved that there is the significant difference
between them. Therefore, we judge that the walking
motion of the patient A is asymmetric.

5. Discussion and Conclusions

Acknowledgement

Patient A is suffering from a disease in the left crotch
joint. From Table 1, in regard to Patient A’s walking
motion, it was found that the left knee joint is not bent
enough in comparison with the right knee joint. And it
was found in regard to the upper arms that the left arm isshaken more largely than the right arm. These facts
indicate that Patient A tries to balance the walking byshaking the left arm wide, since the right knee doesn’t
bend much. In this way, the patient unconsciously©ISAROB 2006

Standard deviation

We wish to express our sincere gratitude to thepatients who cooperated as subjects in the experiment.

Reference[1] Tan, J. K., Ishikawa, S.: Human motion recovery by
the factorization based on a spatiotemporal measurement
matrix , Computer Vision and Image Understanding,vol.82, no.2, pp.101-109, 2001

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Optimization of Coordinated Control Parameters of Traﬃc Signals Using ACO
Manami Shinohara
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

and it is diﬃcult to ﬁnd an optimal solution. Therefore, we have to solve the problem by some heuristic
method. In this work, we optimized the oﬀsets of coordinated control by ACO. ACO is a metaheuristics
for combinatorial optimization problem. Applyingit
to several combinatorial optimization problems, e.g.
traveling salesman problem and graph coloring problem, its availability is conﬁrmed. There are several
works of optimizing the control parameters of traﬃclights with algorithms imitating swarm behavior suchas ACO. However, it is diﬃcult to newly apply thealgorithms into the present signal systems of Japan,
because the scale of the systems that the algorithms
assume is large. The purpose of this research is to optimize the oﬀsets of local systems having several signals
on the assumption that such optimizations are introduced in existing systems in Japan.

In this paper, we focus attention on oﬀset that is
one of coordinated control parameters, optimize theoﬀsets by ant colony optimization(ACO). Finding an
optimal solution is carried out by repeating the above
procedure. Experimental results show the good coincidence with the solutions of traﬃc signal control
theories in oﬀset. And, the oﬀsets optimized by the
proposed method gave a better evaluation than a current oﬀsets under the road traﬃc condition of an areaof our city. In addition, as experimental comparison of
optimization methods, we applied a genetic algorithm(GA) to the problem of interest.

1

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 version of coordinated control. Especially, a large eﬀectof traﬃc light control can be expected in coordinatedcontrol and area traﬃc control if they go well. The
object of this study is to improve the performance incoordinated control. The parameters of coordinatedcontrol consist of common length, split, and oﬀset. We
focus attention on the oﬀset. Within the parameters of coordinated control, common cycle length, split,
and oﬀset, we focus attention on the oﬀset. The decision of oﬀsets is deﬁned as a combinatorial problem.
In deciding combination of oﬀsets, the total numberof combinations of intege oﬀsets becomes the possible
number of oﬀsets to the power of N-1, where N is thenumber of intersections. If the number of intersections
increases, the number of combinations becomes huge,©ISAROB 2006

2

Coordinated control

Two or more signals on a arterial road work in association with each other shown in the upper of ﬁgure 1
called coordinated control. Common cycle length andoﬀset are particular parameters of coordinated control.
Cycle length is a total time to complete one cycle. The
system needs a common cycle length. Oﬀset is the delay time to star the green phase between the reference
signal at the intersection 1 in ﬁgure 1 and the signal
concerned.
According to the manual of JSTE [1] , if round trip
time T of a link in overall speed is multiple integer ofcycle length C (T = nC), vehicle delay time can be
minimal.

3

ACO

ACO algorithm is shown in ﬁgure 2.
Pheromone τij (t) on route (i, j) from condition i to
j in time t evaporats at the rate of ρ, and increases toτij (t + 1) by each ant agent k(k = 1, · · · , m). Hence,
pheromone volume τij (t+1) in time (t+1) is calculated
by equation 1.

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by a microscopic traﬃc simulator CORSIM. From theresult of the simulation we calculate a cost index CI
based on Vt, Tt, Dt, and Sp asreference
intersection
offset=0

link
1

2

N-1

CI

n=1

Intersection
0
No.

1

0

0

0

0

0

1

1

1

1

2

2

2

2

C-1

C-1

C-1

C-1

2

N-2

where Vt is the number of vehicles that have beendischarged from the links, Tt is total time on the link
for all vehicles, Dt is the time that vehicles are delayed
if they cannot travel at the free ﬂow speed, Sp is theratio of the number of vehicles that have stopped atleast once on a link to the total link trips, and K isweight coeﬃcient of Sp. The CI is the weighted sumof delay and stop. So, the smaller the CI thebetterthe oﬀsets.
The CI is used to ﬁnd the best solution in thecolony at time t. The pheromone amount on the pathof best solution is additionally increased. Moreover,the pheromone amount on the paths neighboring thebest are also increased. Let τq be a pheromone amounton the path from the oﬀset p at n-th signal to the oﬀset
q at (n+1)-th in the best solution at time t. The additional increment for the pheromone amount on thepaths neighboring the best is expressed as

Figure 1: Coordinated control(upper) and the set ofoﬀsets(lower)

１． Initialize (initialized pheromone)
２． Loop until fulfill condition (repeat time of colony) to end
２．１ Loop until M agents end
ⅰ Selecting a route probabilisticallyⅱ Updating pheromone
２．２ Evaluating all solutions
２．３ Updating pheromone （to excellent solution of colony）
３． End.

x2

τq+x ← τq+x + 0.1e− σ2m∆τijk (t)

(1)

k=1

τmin ≤ τ ≤ τmax

Applying ACO to coordinated control[3]5In this paper, we optimized the oﬀsets of coordinated control along a road with N traﬃc signals. For coordinated control with N signals, N − 1 links as shownin the upper half of Figure 1, we restrict the oﬀset ofeach signal to integer. So, in the case where common
cycle length is the integer value C [s], the oﬀset canbe chosen is one of C integer values ranging from 0
to C − 1. The oﬀset of one of the N traﬃc signals is
ﬁxed to 0, and for the rest of the signals the numberof combination of their oﬀsets is C N −1 as shown in
the lower half of Figure 1. A combination of oﬀsets
generated by ACO as a feasible solution is evaluated©ISAROB 2006(3)

where x is the integer representing the displacement from the best oﬀset, and σ is the standard deviation of Gaussian function. In our experiments we
use σ=5.0. After the increments we limit all thepheromone amounts to the range between lower andupper limits preventing the ACO from concentrating
the pheromone into a speciﬁc path based on MAXMIN Ant System.

Figure 2: ACO algorithm

τij (t + 1) = (1 − ρ)τij (t) +
1
Sp × Vt
Dt
× Vt ×
+K×
(2)
Tt
Tt
Tt

N-1

The total number of
N −1
combinations of offsets＝ C

4

=

N
−1 


(4)

Applying GA to coordinated control

Under the same traﬃc condition as on the previous section, the oﬀsets are optimized by a simple GA
according to the research [5] . The chromosome for
the GA consists of 3(N-1) bits as shown in Figure 3,
where the oﬀset of each signal is represented 3-bit binary number.
In the case of 5 signals and 4 links in Figure 3, thelength of the chromosome becomes 12 bits. In order toevaluate a set of oﬀsets represented by a chromosome,
we deﬁne a ﬁtness function. The ﬁtness F is deﬁned
by the inverse of CI as

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Intersection 0
No.

Link No.
offset0reference
intersection
offset=0

1

1

4

2

2

N-2

7

0

N-1

N-1

Step 2

64

64-71

65

66

67

68

..

69

70

C-1

71

Figure 4: coarse-to-ﬁne
1

0

0

1

1

1

……

0

0

0

1

0

1

chromosome

which are the simpliﬁed versions of real traﬃc roads.
The optimal oﬀsets by the ACO and the oﬀsets derived from the theory are shown in Table 1 with their
CI.

1
(5)
CI
Before starting the GA, we must generate an initial population. Each individual is generated from uniform random bits. In GA, after the evaluation of
all individuals of current generation, the operations
based on the genetic rules are applied to them forthe construction of next generation. As the genetic
rules, we adopt elitism, and apply crossover and mutation to the individuals selected by roulette. Elitism
is a mechanism that protects the chromosomes of themost-highly-ﬁt population from the genetic operation.
In experiments, the top of the individuals become elite.
Crossover is the main mechanism of GA which expects the more-highly-ﬁt children than their parents. In
experiments, a parent is chosen from all individuals
based on the roulette selection at the probability proportional to the F value. From a pair of parents, theircopies are made as children, and one-point crossover isapplied for the children in the probability of 0.5. The
mutation inverts each(a one of) bit in the chromosome
according to the probability of 0.1. The set of oﬀsets
with most highly ﬁtness at 100th generation is madeto be the best solution. The case of 5 signals and 4
links as Figure 3, feasible solution can be expressed atbinary number 12 digits. The GA optimizes the oﬀset
of each signal in the resolution of 3 bits. So, we apply
coarse-to-ﬁne method. That is, in the ﬁrst stage wecoarsely obtain the upper 3 bits of each oﬀset using
the GA, and in the second stage we ﬁnd the optimal
lower 3 bits of the coarse oﬀsets obtained in the ﬁrst
stage using the same GA.
F =Table 1: comparison with theory solution
link length,cycle length

oﬀsets by ACO

CI

1000[ft],50[s]0 16 34 32 23

145.0

1300[ft],66[s]0 22 46 38 43

115.6

1650[ft],84[s]0 29 56 53 49

92.7

oﬀsets by theory

CI

1000[ft],50[s]0 25 0 25 0171.91300[ft],66[s]0 33 0 33 0

128.6

1650[ft],84[s]0 42 0 42 0

107.0

Next, we compare the CIs obtained from the ACO
with that from the GA under the same conditions of
the above in the case of link length of 1300[ft] and common cycle length of 66[s]. The ACO and the 2-stage
GA are performed 5 times with randomly generated
initial values. In the ACO, the number of ants is madeto be 10 and the number of ant-cycles to be 10. In the
2-stage GA, the number of individuals is made to be
10 and the number of generation to be 5. In the bothof the algorithms, the total number of evaluation by
the simulator CORSIM becomes 100 times. The best
oﬀsets by the ACO and the 2-stage GA of each execution are shown in Figure 5 and their CIs are shown in
Table 2.
Table 2: comparison with CI by GA solution

Experimental Results

First, we compare the oﬀsets derived from the trafﬁc engineering theory [1] with those optimized by the
ACO under the traﬃc condition with an undersaturated bottleneck intersection. Experiments are performedon the settings of 5 signals, 4 links equally spaced at 3
types of link length, and no turn vehicles at main road

©ISAROB 2006

0…

5

Figure 3: coding

6

Step 1

CI by ACO

CI by GA

117.05124.83117.80125.26118.06134.10118.80135.59119.95139.23

Thirdly, we compare the actual oﬀsets of the existing system with those optimized by the ACO for the438

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

65
60
55
50
]45
[st40
35
sfe30
fo
25
20
15
10
5
0

acceptable for the ACO. Therefore, the number of evaluations necessary to obtain a good solution for the
ACO is smaller than that for the GA. The result of
the last experiment shows the potential of the ACO
applied to an actual coordinated control, that is, thebest oﬀsets selected by the ACO is near to the actualones and the CI value of the best solution by the ACO
is smaller than that of the actual oﬀsets.

GA
ACO

1

2

3

4

8

5

intersection number

Figure 5: comparison with oﬀsets by GA

actual data of link length, cycle length and traﬃc ﬂow
taken from an arterial road in Utsunomiya city, Japan.
Figure 6 shows the graph of oﬀsets and CIs.

Acknowledgements

offset[s ]5 signals, C=120[s]Link length=206.7, 360.9, 1017.0, 623.4[ft]

120
100
80
60
40
20
0

We would like to thank Tochigi Prefectural Police
Traﬃc Control Center in Japan for oﬀering us the actual data set of a coordinated control.

offsets by ACO
actual offsets

ACO CI=224.3
actual offset CI=272.7

1

References[1] Japan Society of Traﬃc Engineers ed. (1994). Manual on Traﬃc Signal Control, Tokyo, Japan(inJapanese).
[2] Bonabeau, E., Dorigo, M. and Theraulaz, G., “Inspiration for Optimization from Social Insect Behaviour”, Nature, vol.406, pp.39-42(2000).
[3] Manami Shinohara, Kenji Shoji, Fubito Toyama,“Optimization of Coordinated Control Parameters of Traﬃc Signals Using ACO”, Forum on
Information Technology (FIT2004), vol.2, p259260(2004)(in Japanese).
[4] Oliveira,D., Ferreira,P., and Bazzan,A., “ A
Swarm-Based Approach for Selection of Signal
Plans in Urban Scenarios”, In Proceeding ofFourth International Workshop on Ant Colony
Optimization and Swarm Intelligence-ANTS 2004,
vol. 3172 of LNCS, pp.416-417, Springer-Verlag,
Berlin, Germany(2004).
[5] Masakazu Odawara, Mamoru Hisai, “A Study on
Common Cycle Length of Coordinated Signal System through Genetic Algorithm”, The Infrastructure Planning Committee, JSCE, vol.20(2), pp815818(1997)(in Japanese).

2345intersection number

Figure 6: comparison with actual oﬀsets of a coordinated control in a city of Japan

7

Discussion

We obtained lower CI values of solutions by the
ACO than those of the theoretical solutions in theﬁrst experiments. These results seem to be because
the evaluation function CI includes the item of starting delay which is one of the factors simulated by thesimulator CORSIM and which is out of scope in thetheoretical solutions. In the comparison of the ACO
with the 2-stage GA, the diﬀerence between the solutions of the ACO and those of the GA is clear. In
Figure 5 of the results of 5 trials of both methods,
we can see the relatively convergent oﬀsets by the ACO at every intersection, while the oﬀsets by the GA
are scattered. The value of oﬀset in each intersection
has been scattered in comparison with GA on ACO.
In addition we obtained lower CI values of solutions
by the ACO than those by the GA as shown in Table
2. From the above, it is considered that the numberof evaluations (100 times in the experiments) is notenough for the GA to search solutions and that it is©ISAROB 2006

Conclusion

In this paper, we focus attention on oﬀset that is
one of coordinated control parameters, optimized theoﬀsets by ACO, and examined the eﬃcacy of the ACO for the optimization of the coordinated control
parameters. As future works, we plan (1) to improve
the ACO program such that it can be applied to coordinated control under more realistic conditions, (2)to expand it such that it can optimize the other signal
parameters of split and cycle length, and (3) to apply
ACO to area traﬃc control.

439

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Evolution Strategies Based Gaussian Sum Particle Filter
for State Estimation of Nonlinear Stochastic Systems∗
Katsuji Uosaki
Toshiharu Hatanaka
Department of Information and Physical Sciences
Graduate School of Information Science and Technology, Osaka University
Suita, Osaka 565-0871, Japan
Abstract

the estimates are obtained based on corresponding importance weights. We propose here a novel particle filter,
which combines the ideas of Gaussian sum filter [3] andEvolution strategies based particle filter (ESP) [5], which
substitutes selection rule in Evolution strategies to resampling in the conventional particle filter to overcome the degeneracy phenomenon. Numerical simulation studies havebeen conducted to indicate the potential to create high performance filters for nonlinear state estimation.

Recently, particle filters have drawn much attentions for
optimal filtering of nonlinear systems. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy
phenomena in the importance weights deteriorate the filter
performance, and Evolution strategies based particle filter,
in which selection process in evolution strategies is applied,
has been proposed to overcome this difficulty. In this paper, we propose a novel particle filter, which combines theideas of Gaussian sum filter and Evolution strategies based
particle filter. Numerical simulation study exemplifies theapplicability of this approach to nonlinear filtering.

1

2

Consider the following nonlinear state space model.
xk+1
yk

Introduction= f (xk ) + vk ,= g(xk ) + wk ,

(1)
(2)

where xk , yk , vk and wk are the state variable, observation, system noise and observation noise sequences, respectively, and f and g are known possibly nonlinear functions. We assume vk and wk are independently identically
distributed (i.i.d.) and mutually independent. Problem tobe considered here is to find the best estimate of the statevariable xk in some sense based on the all available dataof observations y1:k = {y1 , y2 , . . . , yk }. We can solve theproblem by calculating the posterior pdf of the state variable xk of time instant k based on y1:k .
The posterior pdf p(xk |y1:k ) of xk based on the observation sequence y1:k satisfies the following recursions:

Optimal filtering for stochastic systems has a lot of applications in signal processing, control system sciences andrelated areas. It can be carried out by evaluating recursively the evolving posterior probability density function(pdf) with a prior pdf for the unknown state and a likelihood function relating them to the observations. However,the posterior pdf do not admit a closed-form expression except for simple models such as linear Gaussian state spacemodels where well-known Kalman filter [1] can be applied.
In many realistic problems, where state space models include nonlinear and non-Gaussian elements that preclude
a closed form of expression for the optimal filters, several approximations have been proposed. A class of filters
called Gaussian filters provide Gaussian approximations to
the filters including the extended Kalman filter (EKF) andits variation [2], and other filters such as Gaussian sum filters [3], where the posterior pdf’s are approximated by theGaussian mixtures, have been attempted. Recent massive
increase of the computational power allowed to the rebirthof Monte Carlo integration and its application of Bayesian
filtering, or “particle filtering" [4]. In this approach, pdf’s
are represented by a weighted sum based on the discrete
grid sequentially chosen by the importance sampling andTime update:Zp(xk |y1:k−1 ) =p(xk |xk−1 )p(xk−1 |y1:k−1 )dxk−1 , (3)Observation update:p(yk |xk )p(xk |y1:k−1 )p(xk |y1:k ) =p(yk |y1:k−1 )(4)with a prior pdf p(x0 |y0 ) ≡ p(x0 ) of the initial state variable x0 . Here normalizing constantZp(yk |y1:k−1 ) = p(yk |xk )p(xk |y1:k−1 )dxk

∗ This work is partially supported by the Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science(C)(2)14550447.

©ISAROB 2006

Gaussian Sum Filter

depends on the likelihood p(yk |xk ), which is determinedby the observation equation (2).

440

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Since a closed analytical form solution is not admitted except in very restrictive cases such as linear Gaussian state
space models, some approximations should be introduced.
The most common approximation approach is the extended
Kalman filter (EKF) [2], where a linearization techniquebased on a first order Taylor expansions of the nonlinear
system and observation equations about the current estimate is used. Another one is the Gaussian sum filter, where
the following Gaussian sum representation of the posterior
and the predictive pdf’s is used [3],p(xk |y1:k ) =

p
X

(i)

(i)

(i)

wk|k N (xk ; µk|k , Σk|k ),sum filter instead of grid approximation,p(xk |y1:k ) ≈p(xk+1 |y1:k ) =

(5)

(i)

(i)

(i,j)

from p(xk+1 |xk
puted by

(i)

(i)

(i)T

(i)

(i)

(i)

(i)

(i)T

Σk+1|k =+ Rk )−1 ,(C̃k Σk|k−1 C̃k

(11)

(i)

(i)

(i)

dg(x)
(i)
C̃k =
dx

(i)

,

x=µk|k−1(8)

df (x)
(i)
Ãk =
dx

(i)

,

x=µk|k

(i,j)

(i)
(i)
wk|k−1 βk(i),
wk|k = Pp(i)(i)
i=1 wk|k−1 βk(i)

On the other hand, observation update (4) with (5) is carried out by importance sampling. We first draw samples(i,j){xk , (j = 1, . . . , m)} from the importance function
q(xk |y1:k ), then compute the corresponding weights by(i)TΣk+1|k = Ãk+1 Σk|k Ãk+1 + Qk ,(i,j)

w̃k|k−1 =(i)(i)
wk+1|k = wk|k ,
(i)

(i)

(i)T

βk = N (yk ; g(µk|k−1 ), C̃k Σk|k−1 C̃k(i)µk|k =(i)Σk|k =(i,j))N (xk

(i,j)

q(xk

(i)

(i)

; µk|k−1 , Σk|k−1 )|y1:k ). (12)

m
X
j=1
m
X

(i,j)

(i,j)

wk|k−1 xk

,
(13)

(i,j)
(i,j)
(i)
(i,j)
(i)
wk|k−1 (xk − µk|k )(xk − µk|k )T ,

j=1
(i,j)

with the normalized weights wk|k−1 =Pp(i,j)/ i=1 w̃k|k−1 , and then update the weights by

Gaussian Sum Particle Filter

Pm
(i,j)
(i,j)
j=1 w̃k|k−1 wk−1|k−1(i).
wk|k = Pp Pm
(i,j)
(i,j)
w̃
w
i=1
j=1 k|k−1 k−1|k−1

Gaussian sum particle filter assumes the posterior andpredictive pdf’s by Gaussian sum as (5) and (6) in Gaussian©ISAROB 2006

p(yk |xk

We compute the estimates of the mean and covariance as).

In Gaussian sum filter, the mean and covariance evaluation(8) may be tedious and time-consuming, and divergence
may occur due to the linearizations in (8) when severe nonlinearities exist in the models. To resolve these difficulties,
Gaussian sum particle filter has been proposed [6], which
will be briefly explained in the following.

3

1 X (i,j)
(i)
(i,j)
(i)
(x
− µk+1|k )(xk+1|k − µk+1|k )T .
m j=1 k+1|k(i)µk+1|k = f (µk|k ),(i)

1 X (i,j)x,
m j=1 k+1|km

Σk|k = (I − Kk C̃k )Σk|k−1 ,Kk = Σk|k−1 C̃k). Mean and covariance are then com-

m

(i)

(i)

(i)

(10)

are estimated through the following Monte Carlo samplingin stead of mean and covariance update (8) in Gaussian sum
filter.
(i,j)
For each i = 1, . . . , p, we draw m samples {xk , (j =
(i)
(i)
(i,j)
1, . . . , m)} from N (xk ; µk|k , Σk|k ), and then draw xk+1|kµk+1|k =µk|k = µk|k−1 + Kk (yk − g(µk|k−1 )),(i)

N (xk ; µk+1|k , Σk+1|k ) corresponding to
Z
(i)
(i)
N (xk ; µk|k , Σk|k )p(xk+1 |xk )dxk

(i)

(i)

(i)

(i)

(i)
(i)
(i)
wk+1|k N (xk+1 ; µk+1|k , Σk+1|k ),(6)

(i)

(9)

the mean µk+1|k and covariance Σk+1|k of Gaussian pdf

where N (x; µ, Σ) is Gaussian pdf with expectation µ andcovariance Σ,


1
1
T −1
N (x; µ, Σ) =
exp
−
(x
−
µ)
Σ
(x
−
µ)
.
1
n
2
(2π) 2 |Σ| 2
(7)
(i)
(i)
The weight {wk|k−1 , wk|k , (i = 1, . . . , p)} are nonPp
Pp
(i)
(i)
negative and satisfy i=1 wk|k−1 = 1,i=1 wk|k = 1.
Then, the means and covariances are updated by
(i)

(i)

with Dirac’s delta function δ(·) such that δ(x) = 1 for
x = 0 and δ(x) = 0 otherwise, in the conventional parti(i)cle filter [4]. Here, the particles {xk , (i = 1, . . . , p)} are(i)generated and associated weights {wk , (i = 1, . . . , p)}
are chosen using the principle of “importance sampling.”(i)(i)
Under the assumption wk+1|k = wk|k , (i = 1, . . . , p)

i=1

(i)

(i)

wk δ(xk − xk )

i=1

i=1
p
X

p
X

441

(i,j)

w̃k|k−1(14)The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Since this filter approximates the posterior pdf by aweighted sum of continuous functions (Gaussian pdf’s)
while the conventional filter approximates it with a set of
grid points, this filter can approximate the pdf with smaller
number of summands p than the conventional filter, andit leads smaller computation burden. However, there may
exist the degeneracy phenomenon as in the conventional
particle filter [4], that is, after several updates, the posterior
pdf’s are approximated by a single Gaussian. It leads poor
approximation and a large computation effort is wasted
in updating Gaussians whose contributions to approximate
the posterior pdf p(xk |y1:k ) is negligible.

4

fitness from the union of parents and offspring or offspring
only to form the parents of the next generation in order to
evolve towards better search region. Two main selection
processes have been proposed, i.e., (µ + λ)-selection and(µ, λ)-selection. In these selection processes, λ offspringare created from µ parents and the µ best individuals are
selected out of the union of parents and offspring, and offspring only, respectively.
The deterministic selection rules in ES can make the fluctuations of computation time smaller, then they are substituted to resampling process in GSR to derive a novel filter, “Evolution strategies based Gaussian sum particle filter” (ESGSP). The filter implements the observation update(4) with (5) as follows:(i,j,`)For each i = 1, . . . , p, ` = 1, . . . , r, samples {xk,(j = 1, . . . , m)} are drawn from the importance function
q(xk |y1:k ), and the corresponding weights are computedbyEvolution Strategies Based Gaussian Sum
Particle Filter

In order to reduce the effects of degeneracy in Gaussian
sum particle filter, resampling process [7] can be applied asin the conventional particle filter.
The basic idea of resampling is to eliminate the factors withsmall weights and to concentrate on the factors with larger
weights. In conventional particle filter, a new set of par(i)ticles {xk , (i = 1, . . . , p)} are generated by resamplingfrom an approximate representation of p(xk |y1:k ) given by
(j)
(j)
(i)
(9) so that Pr(xk = xk ) = wk , and the weights are(i)reset as wk = 1/p. This filter is called as “Sequential
importance sampling resampling filter" (SIR) [7]. Resampling process in SIR can be applied to Gaussian sum particle filter as follows:(i)(i)
A new set of Gaussian pdf’s {N (xk ; µk|k , Σk|k , (i =1, . . . , p)} are generated by resampling from the set ofGaussian pdf’s generated by (12)–(14) in the approximate
representation of p(xk |y1:k ) (5) so that
(i)

(i)

(j)

(i)

(i,j,`)(i,j,`)w̃k|k−1 =(i,j,`))N (xk

(i,j)

q(xk

(i)

(i)

; µk|k−1 , Σk|k−1 )|y1:k )

(16)Pp
(i,j,`)(i,j,`)(i,j,`)and normalized as wk|k−1 = w̃k|k−1 / i=1 w̃k|k−1 . The
estimates of the mean and covariance are computed by
(i,`)

m
X

(i,`)

j=1
m
X

µk|k =
Σk|k =(i,j,`) (i,j,`)wk|k−1 xk(i,j,`),(i,j,`)

wk|k−1 (xk(i,`)(i,j,`)− µk|k )(xk(i,`)

− µk|k )T ,

j=1

(17)
The weights are then updated by(j)Pr(N (xk ; µk|k , Σk|k ) = N (xk ; µk|k , Σk|k )) = wk|k .
(15)(i)
The weights are reset as wk|k = 1/p. We can call this filter as “Gaussian sum resampling particle filter" (GSR) [6].
Fluctuation of the computation time in GSR may becomelarge since the choice of particles in GSR is probabilistic.
We propose here a novel particle filter combining the idea
of Gaussian sum filter and Evolution strategies (ES) by
Rechenberg and Schwefel [8] as in Evolutionary strategies
based particle filter (ESP) [5]. ES is one of the Evolutionary Computation (EC) approaches, computational models
simulating natural evolutionary processes to design and implement computer-based problem solving systems [9]. It
has been applied to continuous function optimization in
real-valued n-dimensional space via processes of selection
and perturbation such as recombination and mutation depending on the perceived performance (fitness) of the individual structures. Mutation process, which is realized byadditive process of random fluctuations, introduces innovation into the population. Then, selection process is carriedout by choosing deterministically the individuals of higher©ISAROB 2006

p(yk |xkPm(i,j,`) (i)j=1 w̃k|k−1 wk−1|k−1(i,`).
wk|k = Pp Pm(i,j,`) (i)i=1
j=1 w̃k|k−1 wk−1|k−1(18)

We sort the set of p × r pairs of Gaussian pdf’s and
(i,`)
(i,`)
(i,`)
corresponding weights, {(N (xk|k ; µk|k , Σk|k ), wk|k ,(i = 1, . . . , p, ` = 1, . . . , r)} according to the weights(i,`)
wk|k in descending order. We retain the first p pairs with
(i)

(i)

larger weights, and denote them {N (xk|k ; µk|k , Σk|k ),(i)w̃k|k , (i = 1, . . . , p)}. The weights are normalized as(i)(i) Pm(i)
wk|k = w̃k|k / i=1 w̃k|k . This process corresponds to(p, pr)-selection in ES.

5

Numerical Example

A numerical simulation is carried out to exemplify theapplicability of the proposed ESGSP filter. The following

442

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

20

10

15

10

0

10

x

20

y

x

20

5

-10

0

-10

0

-20

-20

0

20

60

40

80

0

100

20

40

k

60

80

100

0

20

40

k

(a) True state(b) Observation

20

20

10

10

60

80

100

k

(c) Estimate by SIS
40

30

x

x

x

20

0

0

10

0

-10

-10

-10

-20

-20

-20

0

20

40

60

80

100

0

20

40

k

60

80

100

k

(d) Estimate by GSP

0

20

40

60

80

100

k

(e) Estimate by ESGSP(f) Estimate by EKF

Figure 1: Sample behavior of state and observation processes, and the state estimates (solid line: estimate, dashed line: true
state)
nonlinear state space model with known parameters is considered.
xk−1
25xk−1xk =+ 8 cos (1.2k) + vk+
2
1 + x2k−1(19)= f (xk−1 ) + vk ,of other evolution operations and better choice of design
parameters will lead the particle filters with better performance.

Referencesx2
yk = k + wk ,20
where vk and wk are i.i.d. zero-mean normal random variables with variance 10 and 1, respectively. The normal dis(i)tribution with mean f (xk−1 ) and variance 10 is chosen as[1] Sorenson, H. W. (1985), Kalman Filtering: Theory
and Application, IEEE Press[2] Jazwinski, A. H. (1970), Stochastic Process and Filtering Theory, Academic Press[3] Alspach, D. L. and Sorenson, H. W. (1972), “Nonlinear Bayesian estimation using Gaussian sum approximation,” IEEE Trans. on Automatic Control, AC17 (4): 439–448[4] Doucet, A., de Freitas, N. and Gordon, N.
(Eds.) (2001), Sequential Monte Carlo Methods inPractice, Springer[5] Uosaki, K., Kimura, Y. and Hatanaka, T. (2004),“State estimation of nonlinear stochastic systems by
evolution strategies based particle filter,” Proc. 9thInt. Symposium on Artificial Life and Robotics: 406–
409
[6] Kotecha, J. H. and Djurić, P. M. (2003), “Gaussian
sum particle filtering,” IEEE Trans. on Signal Processing, SP–51 (10): 2602–2612[7] Liu, J. and Chen, R. (1998), “Sequential Monte Carlo
Methods for Dynamic Systems,” J. of American Statistical Association, 93: 1032–1044[8] Schwefel, H.-P. (1995) Evolution and Optimum Seeking, J. Wiley[9] Bäck, T. (1996), Evolutionary Computation, Oxford
Press

(i)

the importance density q(xk |xk−1 , y1:k ). Sample paths ofthe observation process and the state estimates by the particle filters (SIS (n = 50), GSP (p = 5, m = 10), andthe proposed ESGSP (p = 5, m = 5, p = 2)) are given
in Fig.1, and that of EKF as well for comparison. Particle
filters, especially GSP and proposed ESGSP filters, show
better behaviors in nonlinear state estimation, while the estimate by EKF cannot follow the true state.
The other choice of design parameters and evolution processes may improve the performance, and their better
choice will be pursued.

6

Conclusions

A novel filter, Evolution strategies based Gaussian sum
particle filter (ESGSP), has been proposed by combining
the ideas of Gaussian sum filter and Evolution strategies
based particle filter (ESP). Since this filter employs theGaussian sum approximation of the posterior probability
distribution with the mean and covariance evaluated byMonte Carlo sampling, it can be applied any type of nonlinear systems and non-Gaussian noise cases. Introducing©ISAROB 2006

443

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A New Algorithm for Obtaining Molecular Information
Based on TaqMan® Real Time PCR
Zuwairie Ibrahim1&2, Yusei Tsuboi2, Sasaki Shinya2, Yamaguchi Tomohiro2, and Osamu Ono2
1

Department of Mechatronics and Robotics
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
81310 UTM Skudai, Johor Darul Takzim
Malaysia
zuwairie@fke.utm.my

2

Institute of Applied DNA Computing
Meiji University
1-1-1 Higashi-mita, Tama-ku
Kawasaki-shi, Kanagawa-ken
214-8571 Japan(zuwairie, tsuboi, ee21008, ee21012, ono)@isc.meiji.ac.jp
between the start node and the end node, and also theorder of these nodes is commonly required during the invitro computation. Normally, in practice, twoconventional methods: DNA sequencing and graduated
PCR are employed in order to obtain the molecular
information. The need for an automated, simple, andinnovative approach for obtaining the molecular
information is the driving force of this research. Hence,
we propose the use of real time PCR and its associatealgorithm in DNA computing, especially for obtaining
molecular information. For real time PCR, several
detection methods have been designed and developed
such as hydrolysis or TaqMan probes, scorpions,hybridization probes, SYBR Green I, and molecularbeacon [8]. In this research, TaqMan probe is chosen due
to its capability to perform real time PCR with highspecificity.

Abstract
We investigated a novel algorithm for obtaining
molecular information based on TaqMan real timepolymerase chain reaction (PCR). Two kinds of doublestranded DNAs (dsDNAs) of 120 base-pairs (bp) and170-bp are chosen as the input of the algorithm, whichreflects the output of Hamiltonian Path Problem (HPP)and the shortest path problem, based on Adleman’s DNA
computing and direct-proportional length-based DNA
computing, respectively. This study explores that after a
combination of TaqMan real time PCR is performed, a
step-by-step analysis is important to analyze the result of
TaqMan real time PCR, which in turn, make extractionof molecular information possible.
Keywords: Real Time PCR, TaqMan, Hamiltonian Path
Problem, DNA Computing

2
1

TaqMan Chemistry

Introduction
A TaqMan DNA probe is a modified dual-labeled ofoligonucleotides. The 5’ and 3’ end of oligonucleotides
is attached with a reporter and quencher fluorescentmolecule, respectively [9] as shown in Figure 1.
Examples of common quencher fluorophores include
TAMRA, DABCYL, and BHQ, whereas FAM, VIC, andNED are frequently used as reporter fluorophores [10].

The innovation of the real-time polymerase chain
reaction (PCR) technique played a crucial role inmolecular medicine and clinical diagnostics [1].
Examples are the quantitation of relative gene expression[1], detection of minimal residual disease [2], cancer
diagnostics [3], pathogen detection [4], and quantitationof viral load [5]. Other applications include detection ofgenetically modified organisms in food samples [6], andallelic discrimination [7].
Even though the use of real time PCR in molecular
medicine and clinical diagnostics has gain its obvious
popularity, the application of real time PCR in an
unconventional DNA computing system is stillquestionable. In DNA computing point of view, aproblem of obtaining molecular information is frequently
occurs, especially in the case of molecular search, suchas Hamiltonian Path Problem (HPP), Traveling Salesman
Problem (TSP), and the shortest path problem. In thesecases, for an optimal path, obtaining molecular
information that is defined as the intermediate nodes©ISAROB 2006

Figure 1: The structure of TaqMan DNA probe.
3

Real Time PCR with TaqMan Probe

The presence of dual-labeled TaqMan DNA probeswith forward and reverse primers is important during realtime PCR. As PCR is a repeated cycles of three steps:
denaturation, annealing, and polymerization, TaqMan
DNA probe will anneal to a site of DNA template in

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

sequences in the DNA template. On the other hand,
TaqMan(a,b,c)=NO if no amplification is detected andthe condition of this case is graphically described in
Figure 5. Note that in this case, the DNA template does
not contain complement sequences b , which is required

between the forward and reverse primers during
annealing step, if the sequences of the site of DNA
template is complement to the sequences of DNA probe.
During polymerization, Thermus aquaticus (Taq) DNA
polymerase will extend the primers in 5’ to 3’ direction.
At the same time, the Taq polymerase acts as a “scissor”
to cleave the probe and thus, separating the reporter fromthe quencher. As a result, the separation allows thereporter to emit its fluorescent as shown in Figure 2 [11].
As a result of PCR, the number of DNA templateincreases exponentially and at the same time, theintensity of fluorescent emitted by TaqMan probes isincreases exponentially as well. The increasing of
amplified DNA template with respect to fluorescentintensity can be detected and visualized by a real time
PCR instrument such as Rosche LightCycler® 2.0.

for DNA probe to anneal at the specific location in the
DNA template.
5

The Proposed Algorithm based on TaqMan RealTime PCR for Hamiltonian Path Problem

Let say the output of the in vitro computation of
HPP is represented by a 120-mer dsDNA:
a20x20z20w20y20b20
where the Hamiltonian path begin at node a, end at nodeb, and the intermediate nodes are x, z, w, and y,consequently. But in practice, one only knows the startnode, the end node, and the intermediate nodes. However,
the order of these intermediate nodes is not known.
The algorithm based on TaqMan real time PCR for
this case consists of several reactions as follows. Notethat for every reaction, the amplification output, which isindicated by YES or NO, is also given.
z
z
z
z
z
z
z
z
z
z
z
z

Figure 2: Sequence specific amplification detection by
TaqMan real time PCR.
4

Notations

We introduce several notations in this paper. Firstly,
agbhcidjekfl denotes a dsDNA which contains
subsequences a, b, c, d, e, and f, consequently. The
subscripts g, h, i, j, k, and l indicate the length of each
respective subsequence. As such, a20 means the length ofsubsequences a is 20-mer. A dsDNA can also be
represented without indicating the length of eachsegment as abcdef.

= NO
= YES
= NO
= YES
= YES
= YES
= NO
= NO
= NO
= YES
= NO
= YES

Importantly, real-time PCR itself does not provide
direct molecular information as we need. In order toobtain molecular information based on this method, aseries of step-by-step analysis is done to compute theorder of the intermediate nodes, based on the results ofprevious reactions. During the step-by-step analysis, only
the reaction with the result YES is taken into account. By
referring to the expected output of the real time PCR,
step-by-step analysis is performed as below:

Reaction called TaqMan(a,b,c) means amplificationis performed using forward primer a, reverse primer c ,and TaqMan probe b. TaqMan(a,b,c)=YES if anamplification is detected and the amplification graph is
obtained. An example of amplification graph is shown in
Figure 3 [12]. The condition of TaqMan(a,b,c)=YES isgraphically explained in Figure 4. Note that the specificamplification is detected if the TaqMan probe, forward
and reverse primers anneal to their complement©ISAROB 2006

TaqMan(a,w,x)
TaqMan(a,w,y)
TaqMan(a,w,z)
TaqMan(a,x,w)
TaqMan(a,x,y)
TaqMan(a,x,z)
TaqMan(a,y,x)
TaqMan(a,y,w)
TaqMan(a,y,z)
TaqMan(a,z,w)
TaqMan(a,z,x)
TaqMan(a,z,y)

1. TaqMan(a,w,y)
2. TaqMan(a,x,w)
3. TaqMan(a,x,y)
4. TaqMan(a,x,z)
5. TaqMan(a,z,w)

445

= YES
= YES
= YES
= YES
= YES

awyb
axwyb
axwyb
(no change)ax(w/z)(y/z)ba(x/z)wyb

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 3: An example of amplification plot obtained using real time PCR.
6. TaqMan(a,z,y)= YESa(x/z)wyb (no change)
7. Refer again to TaqMan(a,x,z)= Yes axzwyb
8. Output of step-by-step analysis=
axzwyb6

The Proposed Algorithm based on TaqMan RealTime PCR for the Shortest Path Problem

Let say the output of the in vitro computation of theshortest path problem based on direct-proportional
length-based DNA computing [13] is represented by a
170-mer dsDNA:
a20m10x20n30w20o20y20p10b20
where the shortest path begin at node a, end at node b,
and the intermediate nodes are x, w, and y, consequently.
In this case the node z is not part of the intermediate
nodes. But in practice, one only knows the start node andthe end node only. Unlike HPP, information such as thenumber of the intermediate nodes, the presence orabsence of a node in between the start and end node, andthe order of the intermediate node are not known.

Figure 4: Graphical description of TaqMan(a,b,c)=YES.

The algorithm based on TaqMan real time PCR for
this case consists of several reactions as follows. Notethat for every reaction, the amplification output, which isindicated by YES or NO, is also given.
zzzz

Figure 5: Graphical description of TaqMan(a,b,c)=NO.

©ISAROB 2006

446

TaqMan(a,w,b)
TaqMan(a,x,b)
TaqMan(a,y,b)
TaqMan(a,z,b)

= YES
= YES
= YES
= NO

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

z
z
z
z

TaqMan(a,w,x)
TaqMan(a,w,y)
TaqMan(a,x,w)
TaqMan(a,x,y)

= NO
= YES
= YES
= YES

References[1] L. Overbergh, A. Giulietti, D. Valckx, B.
Decallonne, R. Bouillon and C. Mathieu, “The useof real-time reverse transcriptase PCR for thequantification of cytokine gene expression,” Journalof Biomolecular Techniques, Vol. 14, pp. 33-43,
2003.
[2] A. Li, E. Forestier, R. Rosenquist, and G. Roos,“Minimal residual disease quantification inchildhood acute lymphoblastic leukemia by realtime polymerase chain reaction using the SYBRGreen dye,” Experimental Hematolology, Vol. 30,
pp. 1170-1177, 2002.
[3] P. S. Bernard and C. T. Wittwer, “Real-time PCRtechnology for cancer diagnostics,” Clinical
Chemistry, Vol. 48, pp. 1178-1185, 2002.
[4] D. M. Norton, “Polymerase chain reaction-based
methods for detection of Listeria monocytogenes:
toward real-time screening for food andenvironmental samples,” Journal of AOAC
International, Vol. 85, pp. 505-515, 2002.
[5] H. G. Niesters, “Quantitation of viral load using
real-time amplification techniques,” Methods, Vol.
25, pp. 419-429, 2001.
[6] F. E. Ahmed, “Detection of genetically modified
organisms in foods,” Trends in Biotechnology, Vol.
20, pp. 215-223, 2002.
[7] J. S. Sevall, “Rapid allelic discrimination from realtime DNA amplification,” Methods, Vol. 25, pp.
452-455, 2001.
[8] A. Giulietti, L. Overbergh, D. Valckx, B.
Decallonne, R. Bouillon, and C. Mathieu, “Anoverview of real-time quantitative PCR: application
to quantify cytokine gene expression,” Methods, Vol.
25, pp. 386-401, 2001.
[9] N. J. Walker, “A technique whose time has come,”
Science, Vol. 296, pp. 557-559, 2002.
[10] M. A. Valasek and J. J. Repa, “The power of realtime PCR,” Advances in Physiology Education, Vol.
29, pp. 151-159, 2005.
[11] P. M. Holland, R. D. Abramson, R. Watson, and D.
H. Gelfand, “Detection of specific polymerase chain
reaction product by utilizing the 5’→3’ exonucleaseactivity of Termus aquaticus DNA polymerase,”
Proceedings of the National Academy of Sciences of
the United States of America, Vol. 88, pp. 72767280, 1991.
[12] http://pathmicro.med.sc.edu/pcr/realtime-home.htm[13] Z. Ibrahim, Y. Tsuboi, O. Ono, and M. Khalid,“Experimental
implementationof
directproportional length-based DNA computing fornumerical optimization of the shortest path problem”,
European Conference on Artificial Life 2005:
Workshop on Unconventional Computing, pp. 35-46,
2005.

Similar to the previous case, by referring to the
expected output of the real time PCR, step-by-stepanalysis is performed as below:
1. TaqMan(a,w,b)
2. TaqMan(a,x,b)
3. TaqMan(a,y,b)
4. TaqMan(a,z,b)

= YES
= YES
= YES
= NO
Thus, node z is not included.
5. TaqMan(a,w,y)= YES awyb
6. TaqMan(a,x,w)= YES axwyb
7. TaqMan(a,x,y)= YES axwyb (no change)
8. Output of step-by-step analysis
=
axwyb
7

Discussions and Concluding Remarks

Step-by-step analysis after the real time PCR is done
is crucial because real time PCR did not provide the
DNA information directly. It is shown that based on thisexample, the step-by-step analysis consists of 8 steps,
where theorder of subsequences, or molecular
information, is updated after each step. However, it isalso possible that the updated molecular information issimilar to the previous updated molecular information ofprevious step. According to the expected results of realtime PCR, after step 6 of step-by-step analysis, the finalorder of subsequences still can not be obtained. Thus,
additional steps are required to refer to the step 4 before
the order of subsequences can be finalized.
For the case of the shortest path problem, steps 1-4
are importance for confirmation whether a particular
node exist in between the start and end node. Hence,
based on the expected output of real time PCR, it can beconclude that the node z is not exist in between the startand end node and not part of the intermediate nodes.
After that, the steps 5-8 are performed to extract themolecular information of the shortest path.
A combination of TaqMan real time PCR could be
performed for extracting molecular information, insteadof using DNA sequencing method and graduated PCR.
Since the proposed algorithm appears more automatedand simple, this study strongly encourages the use ofreal-time PCR for obtaining molecular information in
DNA computing©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Automatic evolution of bipedal locomotion in a simulated humanoid
robot with many degrees of freedom
Malachy Eaton, T.J. Davitt
Department of Computer Science and Information Systems,
College of Informatics, University of Limerick, Irelandemail: malachy.eaton@ul.ie
A genetic algorithm using the crossover,mutation, and selective reproduction operators
provides the values for the individual
keyframes. The fitness function employed is a
simple function mainly based on the robot
remaining standing together with the distancetraveled by the robot in a forward direction.
Using this fitness function together with thegenetic algorithm the simulated humanoid
robot evolves sophisticated movements overtime for up to eighteen degrees of freedom andfour keyframes per degree of freedom.
In order to get the robot to walk a fitness
function based on the product of the length oftime the robot remains standing by the total
distance traveled by the robot was devised.
This was later modified to reward walking in a
forward (rather than backward or sideways
direction) and also to promote walking in amore upright position, by taking the robots
final height into account.
The current length of chromosome is 288 bits
comprising 4 bits determining the position of
the 18 motors for each of 4 keyframes.
Twenty strings are used per generation. The
genetic algorithm uses roulette wheel selection
with elitism; the top string being guaranteedsafe passage to the next generation, together
with standard crossover and mutation.
We consider these results obtained todemonstrate the utility and possible future
potential of this approach to the automaticdevelopment of bipedal locomotion together
with other complex control applications for
future humanoid robots.
For a good introduction to the general topic ofevolutionary robotics see [9]. For a discussion
on the general topic of the performance
evaluation of bio-inspired embodied andsimulated artifacts see [4]. For other work in
thisgeneralarea
seereferences[1,2,3,6,8,10,12] We consider one of theadvantages of our approach to be the relative
simplicity of application and also the potential
ease of extensibility to other control functions
and robot bodies.

Abstract:
This paper describes a methodology together
with an associated series of experiments
employing this methodology for the evolutionof walking behaviour in a simulated humanoid
robot with eighteen degrees of freedom. The
robots evolved in this study learn to walksmoothly in an upright or near-upright positionand demonstrate a variety of different
locomotive behaviours, including ‘skating’,‘limping’ and walking in a manner curiously
reminiscent of a mildly or heavily intoxicatedperson. A previous study [5] demonstrated thepossible potential utility of this approach while
evolving controllers based on simulated
humanoid robots with a restricted range ofmovements. Although walking behaviours
were developed, these were slow and relied on
the robot walking in an excessively stooped
position similar to the gait of a stooped elderly
person. This paper extends the previous work
to a robot with many degrees of freedom, up to
eighteen in total (arms, elbows, legs, hips,
knees, etc.) and demonstrates the automatic
evolution of fully upright bipedal locomotion
in a humanoid robot using an accurate physics
simulator.

Introduction
The humanoid robot is simulated using the
Webots mobile robot simulation package[7,11].
The humanoid robot is broadly
modeled on the Sony QRIO humanoid robot[8].
For each motor (or joint) a number of
keyframe values can be defined. These
keyframe values are passed to a separate
existing utility; the sequence manager. The
keyframes are values that must be reached at a
specific point in the movement. Theinterpolation function in the sequence manager
divides up the distance between keyframes into
the specified number of interpolation values.
Every keyframe must be passed through inturn; once the final frame is reached the cycle
repeats.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu,
Japan, January
23-25, 2006
TheOita,
WebotsWorld

We a mobile robot simulation package called
Webots. It allows you to create and modify
various robot worlds ranging from offices tofootball pitches. The package also provides
sample controllers for the robots which range
from two wheeled machines with no sensors tohighly sophisticated humanoid robots withseveral sensors, cameras etc. Using the
package you can create new robot worlds withrelative ease as well as modify the existing
ones. Similarly you can create and/or modify
robot controllers. using C, C++ or Java.
The provided worlds are of great importance inassisting you in your development of robot
controllers. Inside the robot world you canchange the angle of the image you are see ormove the robot to any point inside the world.
Figure 1 shows the robot in the simulated
world modelled for this work.
The world also contains a facility where by
you can try out various movements with therobot without having to actually program it.
While this can be slow and at times tedious it
is vital in understanding the robots
functionality. A controller menu is available
from which you can control all the robots
joints. The joints operate in a similar (butsimplified manner) to human joints. By this wemean that the knee only moves in one way i.e.
back/forward whereas the shoulder moves inthree ways: Up/Down, Left/Right and incircular motions.
The sequence manager is an existing utility
which gets sent an integer value corresponding
to the sequence to be performed. Once it has
this it reads in the servo positions from a fileand moves the joints to the desired position at
the desired speed. A key feature of thesequence manager is the ability to useinterpolation values. These greatly cut downon the amount of positions to be defined. It
does this by taking in a series of keyframes.
The keyframes are the values which must be
reached at a specific point in the movement.
The interpolation function in the sequence
manager divides up the distance between
keyframes into the specified number ofinterpolation values. Every keyframe must be
passed through.

©ISAROB 2006

Figure 1. The Robot walking with a limping
gait; this can be seen in the third frame inthis sequence, although overall forward
motion is quite good. Notice also the rightarm moving forward in a steadying motion
in frame 2.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
being mutatedis 0.04. These values werearrived at after some experimentation.

The Genetic Algorithm
A genetic algorithm was used to develop robot
walking sequences. The string length of thechromosomes is 288 bits. This encompasses 4
keyframes representing 18 motors. Four bits
are used to determine the position of each
motor. There are fifty strings per generation
and up to five hundred generations. The basic
GA algorithm is as follows.
• Firstly a random number generator isused to give random binary values toeach string.
• Minimum values and range values foreach joint to be included in thealgorithm are entered.
• The string is broken into values foreach four bit section giving a value of
between 1 and sixteen.
• The range of each motor is divided by
the number obtained in the previous
step and added onto the minimum
motor value to give a set motor
position.
• This process is continued until all
motors have assigned values.
• Once the motors have their assigned
values,asequencesfileisautomatically produced to be read by
the sequence manager.
• This sequence is then tested until therobot falls over, or a sufficientamount of time has passed
corresponding to approximately 30
seconds of ‘real’ time
• After the robot has reached one of theabove conditions it is reset to a
standing position and the fitness of
the walking attempt is calculated.This
is done for each string in the given
generation.
• Once all strings have been tested thefitness function is applied todetermine which strings will beallowed to reproduce, together withthe genetic operators of mutation andcrossover with specified probabilities.
This is carried out for eachgeneration.
Roulette wheel selection was used where theprobability of selection is proportional to thestrings’ fitness was used, with or without
elitism. Elitism is also used which means thatthe top string is guaranteed safe passage into
the next generation. These strings were alsoavailable for selection for crossover andmutation. Two point crossover is applied witha probability of 0.5 and the probability of a bit©ISAROB 2006

Fitness function and initial results
The original fitness function used was basedon the product the length of time the robot
stayed standing and the distance travelled. Thiswas determined by calculating the amount of
movement frames the robot had travelled andmultiplying it by the distance travelled. The
distance travelled was computed using theformula for measuring a straight line i.e. ¥((x2-x1)² + (y2-y1)²). The points used were therobots initial position and the position where it
eventually fell.
At this stage the robot was effectively mostly
learning to dance on the spot. Also sequencesthat walked backwards scored equally well as
those that went forward. To remedy this, aweight was used to increase the value of all
walks that go directly forward: the fitness was
multiplied by a factor (2.0) if it moved in the
direction it was facing to promote forward
walking as it was the main goal.
Average of 3 runs over 500
generations
1400
1200

Fitness

1000
800

Maximum

600

Average

400
200

497

373

435

249

311

125

187

1

63

0
Generation

Figure 2. Average and maximum fitness
The fitness function then was the product of
the number of timesteps completed up to a
maximium (800) by the distance moved fromthe starting point multiplied by two if the final
resting position of the robot was ahead ofwhere it started. In order to promote walking
in an upright position the robots height wasalso taken into account. This used the Y value
returned by the GPS sensor to determine theposture of the robot; the more upright the robot
was the higher it was rewarded.
Figure 2 shows the average and maximum
fitness values for the robot over 500
generations. These figures are averaged over 3
runs. Each run took several days of actual
computer simulation corresponding to about aweek of simulated time (the simulation runs at
about three times faster than an actual robot).
A value over about 100 corresponds to therobot at least staying standing for some period

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu,
Oita, Japan,
23-25, 2006 to a walk of
Robots and Agents” December 13-15, 2004,
of time,over January
500 corresponds

pp. 440-445.
[2] Boeing A, Braunl T (2002) “Evolving
Splines: An alternative locomotion controller
for a bipedal robot” in Proc. 7th International
Conference on Control, Automation, Robotics
and Vision (ICARCV’ 02), Dec. 2002, pp.898802.
[3] Busch J et al (2002) “Automatic
Generation of Control Programs for Walking
Robots using Genetic Programming” Proc 5th
EuropeanConferenceon
Genetic
Programming (EUROGP), LNCS 2278, J.A
Foster et al. (Eds.) pp. 258-267, 2002.
[4] 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.
[5] Eaton, M. and Davitt, T.J. (2005)"Evolution of walking behaviour in asimulated humanoid robot" in Proceedings of
the 9th International Conference on Cognitive
and Neural Systems, Boston University May
2005, page 26.
[6] Hornby, G.S., Fujita, M., Takamura, S.,
Yamamoto, T. and Hanagata, O. (1999).
“Autonomous evolution of gaits with the Sonyquadruped robot.” Proceedings of 1999
Genetic and Evolutionary Computation
Conference (GECCO). Banzhaf, Daida, Eiben,
Garzon, Honavar, Jakiela, Smith, eds., Morgan
Kauffmann, pp. 1297-1304.
[7] Michel O (2004), “Webots: Professional
Mobile Robot Simulation”, International
Journal of Advanced Robotic Systems, Vol. 1,
No. 1, pages 39-42.
[8] Mojon S (2003) “Realization of a Physic
Simulation for a Biped Robot” Semester
project,
SchoolofComputerandCommunication Sciences EPFL, Switzerland
2003.
[9] Nolfi S, Floreano D.(2000) “Evolutionary
Robotics: The Biology, Intelligence, andTechnology of Self-Organizing Machines”.
MIT press, 2000.
[10]Wolff K and Nordin P (2002)“Evolutionof Efficient Gait with an Autonomous Biped
Robot using Visual Feedback, Proceedings of
the 8th Mechatronics Forum International
Conference pp 504-513. 2002 .
[11] Webots. http://www.cyberbotics.com.
Commercial Mobile Robot Simulation
Software.
[12] 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.

some description. We see that for about 50
generations no walk develops, then there is anice steady increase in fitness up to about
generation 500, followed by an up and down
pattern with a gradual upward trend. Note that
although elitism was employed, because of theaccurate physics simulation with noise etc. thisallows for the maximum fitness to fall as wellas rise from generation to generation. Any
fitness above about 800 will generally
correspond to a reasonable walk in the forward
direction, 1200 or above corresponding toquite good, stable walks. The maximum
fitness generated was in generation 470 of thesecond test run with a value of 1860
corresponding to a fine forward walk with aslight limping gait (see Figure. 1).
Other patterns of locomotion included‘skating’ , with the robot keeping one footconstantly in contact with the ground andpushing along with the other, and walking in amanner reminiscent of an intoxicated person,with arms and legs akimbo, however stillwalking in a surprisingly effective manner.
The simulations, while running, have provokedcomments on the various human-like attributesof the walking patterns developed, especiallyin the later stages of evolution.

Summary and future work
Using a genetic algorithm a simulated robot
has been able to teach itself how to walk. The
section on the Genetic Algorithm explains how
the controller file was able to process binary
strings in order to develop walking sequences
through the manipulation of 18 of the 22
motors used in the robot.
The different evolutionary techniques usedsuch as roulette wheel and elitism are
explained. The development of the fitness
function used to determine the best walksequences has been described in a little detail.
To our knowledge this is one of the firstpublished papers to allow for the automatic
generation of walking behaviour in a robot
with such a large number of degrees of
freedom.
Work is continuing on thedevelopment of controllers for humanoid
robots, by extending again the number ofdegrees of freedom, and by the application of
the techniques described here to different
humanoid robot morphologies.

References:[1] Boeing A, Hanham S, Braunl T(2004)“Evolving autonomous biped control
from simulation to reality” in Proc. 2nd
International Conference on Autonomous©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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           !" # $#$      %&&#'%($
  )  #                  #   ! "                #   ( %         %&             
$" +1.! (         #     $"    ( %                  ! 2    3/0''               $"     +4.! '         ( %     )                     (              (                      !         
    !                     !   % %                       %          %                 %       !  &      ( %                 %           !
 ( %            %            %              !
'  (          ( %       %                               %               ! 0 ( % %                    ! *  (% %                          %  %                           !

           
                            ! "             #   ! $            %&             $" !     $"              %                     ! '  ( %                       )                    (              (               ! *    ( %                     !
!

   %           ) "                      +,( -.!                           ! /                          !                             !
           
                            ! '   (         % )    #  ! 0                  ( %      !!(©ISAROB 2006

452

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006"       

               
  !    ( %                 !

        

     %      
5  ½   (  5 , -    
%                            5 , - ! 2                             +6 ,.!  ( %    %            %                 +6 ,. !
2        %   )7   ) ' ½  ½   !!!     
 8   %  %           ( 5 ½          (     
  (        (        % !
2  ,4           
   *!, ! 2    9 : 9        ! 0                    &       +;. !
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              
 %          (%     %     )½ )               (¾ )         (¿ )                !!(         !
*    ( %     %                   "#           )"#,)   ½ (    ¾ ("#-)   ½ (    ¿ ("#1)   ½  (    ¾    ¿  !

 !   
$2               Æ                                 ) "#,( "#  "#1! "       % 
 #                            &     ! "             %&   3/0''      %)/  ,)                %        !
/  -)                %      ), /                      !
-                      !
1 0                         %        !

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        !!(      ! 2                  (     %   %                 %   !           =     %   !
2      %    =  
       (            !           %          %             +;.!
   =       %                      (       ©ISAROB 2006

"#

453

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4 7         %       %     !
/  1) 8                     !                      !
/  4) '            (    /  -! " %            ! '   ( %                  /  1       !
'   % ( %          ! $                         %              !!(          ! 2  ,;    !!( 6(,( !!!( >( ( ( ( (       :    ,4       *! ,! 0                            %                  !
2                     /  ,       ! * %                 (        !
3 %                                      ,4           !  (   
  %           %             !                  %   :            ! '   (          ! 0             !       ( %          !!(        !
' /  - ,       (                              #  &     %      3/0''   +4.!         ½   ¾ !
/                    %      / - - !                 %             +, ½ .   +, ¾ .(   !
(              !
'         ( %                        %        !
       46   !                           ! 2        (     %     %   ! $       %               %                   !
0             / - - (           %             %         /  - 1 )
1 ,) 0             !

1 -) 8           
!                          !
1 1)                             ?        !
1 4) 7   %        %         !
1 ;) 7            !

%                               1 -         ! *     ( %         %                  !                                     +1. !
2  %            %       % (           /  - 4 !                        ! 0                                       3/0''    +4.! 2          (                      !

¼

©ISAROB 2006

  

454

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2         %         !  (         ( % %                      !       %             %           %       ,68@ !  (     %   ,66  !
2    %             )            ) -6  (        : ) 6!A(    ) -66    (          ) 6!>(          ) 6!>(             ) ,(            ) ,(       ) ;666    !
* - %                    !     
BCA    %  A       -  !       "#,         %         ! 0  "#,(       "#-        %         !       "#1         %                  ! <%( %     %          ?      %      ;6    (                      ! * *!-( %                               !
* 1 %                   ! 2          %           %    "#1!                       !!(                                      !
&

'

24
22
20
18
2

3

4

5

6

7

8

MOP-1
MOP-2
MOP-3

26
24
22
20
18

9

0

5

Number of fuzzy rules

10

15

20

25

Total rule length

Error rate on test patterns (%)* -) $                  !
MOP-1
MOP-2
MOP-3

28
27
26
25
24
2

3

4

5

6

7

8

9

MOP-1
MOP-2
MOP-3

28
27
26
25
24
0

Number of fuzzy rules

5

10

15

20

25

Total rule length* 1) $                !
                     %    !
( +,. D! 8 ( "! 8 ( *! <( !   ! (     (/ (   ( -661!
+-. D! 8 ( "! 8 ( *! <( !    ! (         ( / (   ( -661!
+1. <! ' ( ! E   ( ! 3& ( <              (            ) #  1; - -66;
1;> 1C;!
+4. F! G ( 0! # ( /! 0% ( !  ( 0                )3/0''(         C - -66- ,A-,>B!
+;. <! ' ( ! 3& ( ! 3(   '  ( %                  
                      ! $    %                 %             ! " ©ISAROB 2006

MOP-1
MOP-2
MOP-3

26

Error rate on test patterns (%)$%

Error rate on training patterns (%)#

Error rate on training patterns (%)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006  !

   " 

 #  !      


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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006         

                        
   !!"#$#% &  !  .     *       *                        *    *  *                                     $/&                    *   *                                    
                                                                                                                                                !"       # !      $%&        '                                    
                           !   (                                                                                                )               !         *         + *                 , $-&   ©ISAROB 2006

       
                               )                0       
                $1& # !       2 3        4           4,              
!           )                                  
                                    5 
"  5             67                                   
 5       . 8 $9&   5       

456

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006*  +  B 4  $:&              
 .5"       )=C=" → =)% =C%" → %) % %C%" → %) > .5       +B   A %       "                                           
5                                 67                                                !   5  $: ; <&               %*      #  %"                )  ="      %"

       ! "                          D                                                      E                "                #                           "                                                   %   "                     A ="           #  -           








#  %) >         
    !           - ? %                                                                @                           #  9                                     "         A %                              *                                                                   !                                   *     0 #                    












START

Yes: Prec
recognition
Target Nodeis Normal

Target Nodeis Abnormal

No: 1-Prec
repair trial
No: 1-Pr
Yes: Pr

no action

repair#  -) .    D    D #  /                                                 ,                  >      







©ISAROB 2006

recognition trial

457

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006      "     E           "                      ,                 00

1. recognition trial : Prec006                         A = !" 6          !
  )                    A =  !"'                #  1       "  #  9     "                 
              $F&
                      G                   ! =1          "                   =11’. repair trial : (1-Prec) Pr

0

1

1

2’. repair failure : 1-Pra

no repair trial

0

1

1

#  /)  @!   D    D  
 7  %)      

Æ

Æ 
Ö
Ì
Æ
È
È

                  !   " !
    !  #$   %      &        &    

500

400
Number of normal nodes! "  # # #     
5                             " .      %  !     >   )                     


300

200

100
Prec = 0.6
Prec = 0.8
Prec = 1.0
0
0

0.1

0.2

0.3

0.4

0.5
Preca

0.6

0.7

0.8

0.9

1

#  1)  @   7  7 
7     '    A =  A =  !                 !       
    =F   =<          H   
       !              )     0              0                   ©ISAROB 2006

 $% &  

1

1

2. recognition success : Precn





458

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

$ #                        >        Æ                   !   G               
!                8     ,  D  H" %:/===:; -==1'    -% 5   5>@ .   J   G ,     I    @  5  ,  ,       2  

500

Number of normal nodes

400

300

200
Prec = 0.4
Prec = 0.5
Prec = 0.6
Prec = 0.7
Prec = 0.8
Prec = 0.9
Prec = 1.0

100

0
0

0.1

0.2

0.3

0.4

0.5
Preca

0.6

0.7

0.8

0.9

1

#  9)  @   7  7 
7     '    A %  A =  !#$%&  6H  6 )  7 ,   7   .  -==-
$-& G I     # K      5 )  D4   6  + D+   %F<=
$/& D@ 6    I 5 " ,       )LL  "  -===
$1&  M)   H ,)  +.   ,    8G -==1"$9& 8 .)D 5   ,>  4  2 , . %=/ -==%"  19 -:;$:& +  @   B 4 ) @               ，. D 6 9/ %F<1"  /%%$;& H  # H  7   .   .) .      .  5       ,  N) 
LF;=9%;% - %FF;"$<& K  8M .   G   G)           2 , . 19 %F<:"  <;9$F& M   5  .     , D 
7   I 5  ，6  7      67 /:<-" -==9" <: F- ' (        )       *                "              "         0 #  :                          $F&         ! =1      =1        "       #          "         "  














1
Pr = 0.0
Pr = 0.3
Pr = 0.5
Pr = 0.8
Pr = 1.0

0.8

Prec

0.6

0.4

0.2

0
0

0.1

0.2

0.3

0.4

0.5
Pra

0.6

0.7

0.8

0.9

1

#  :) I              ©ISAROB 2006

459

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006                 

                         
 ! " ! ##$%&%' (                                              2    "                   ,   3      "             4  5                           %6'          "      7              "                               "                  "                                 "                   %8" 9'                                                                                                !     "      "        "    #   $   "   #    "               "            %&'     %('     
 "                     )     *       + , "           -   "          +  ,    "                                  %.'"                            %/" 0' !                      
$        "          "                                     
        "         1 
            
                        

©ISAROB 2006      
                )*      :           )  " ;"  " * <     7 )=" " "* ) &*          (  > 2 2"        2 "                      "                           "  "         

460

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006 &

                   

step: 29998

 ?
;  ! ; = 
  

50

  #  4  %@" &A'         ,                                9     !   )  
*         "                              ,             ;   %&&'      ; )*   ; )*                )BA"&"… "9* + , " !; %;;;;;;;;;'" !  %' 2     "          ; "        @    0&(
,      2  ;  "                          ;                        # + , " /;  %;;;;;'    +   & !      (      +   (

20

40

30

10

0
0

D

C

C
five D units

C

D

D

Strategy Code (4C)

0 1 2 3 4 5 6 7 8
D D D D C C C C C+   & !     /;  2 !     

2           "                   "   "                                         )       * +  "         ©ISAROB 2006

30

40

50

                   -      "               -                  <            );*    )*          =                     !                         "        ,       ,                "           
2          "     λ             Æ         "                   #    $              )           *"           "                   
      3                   

DD

20

+   ( !      0A  0A (  

next stepC

10          ! ;                     ,    461

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006         "            A;    4 "              9;                      2 "  Æ )               *                                          +   /             )AAA(*        )AAA&* $   " +   / )*       9;
 A; ) &;*                          ( (          
 



     !"  !#$
 % & '  !"  "  !#(     )  )Æ*  )     )  * +#  ,$     #   *$      

2     " ;                     "           )       *    ;  "   "   ;     "           ;      +   .                                ;         ;    , C"             ; 

failure rate : 0.001
2500
2250
Number of Units

2000
1750
1500
1250
1000
750
500
250
0
0

5000

10000

15000

20000

15000

20000

failure rate : 0.002
750

2250
2000
Number of Units

Number of abnormal units

2500

500

250

1750
1500
1250
1000
750
500

0
0

5000

units with D

10000
Step

15000

250

20000

0
0
abnormal units

5000

10000

+   . <                     D   5 .A.A+   /  <    +    E            AAA&)* AAA() *





            9;         Æ            "   " 9;       +  

+   &.                    2     ©ISAROB 2006!   "  

462

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006           7                   

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)*  9;   ) *"    !             )      *    
#                                                                                                   "            ©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Sensor Data Mining System with Multiagent Approach for Metrological Data andPachinko System
Ayahiko Niimi
Department of Media Architecture
Future University-Hakodate
116-2 Kamedanakano-cho, Hakodate-shi, Hokkaido 041-8655, JAPAN
Abstract

and the data of various sensors can be obtained. The
feature of these data is continuous data in the timeseries.
The techniques which applied data mining fromdatabases are used for time series data, but some techniques were improved for time series data.
In this paper, we proposes the technique of the sensor data mining with multiagent approach. In the sensor data mining by multiagent approach, we define thesource to which sends sensor data as agent, and sensor data analyze system including sensors and mining
programs is defined as multiagent sensor data mining system. The advantage of multiagent system isscalavirity of the number of sensors. It becomes tobe able to correspond to the change in the number of
sensors easily by constructing the multiagent system.
We discuss proposed multiagent system which is applied to the meteorological observation data and thedata of the store system of pachinko. The meteorological observation data contains a large amount of
the numerical data. The meteorological observation
data also contains both of data only before and after
the data observed at the regular time and the time atwhich special event is occurred. The purpose of thedata analysis is defined as to extract the observation
data of the regular time when the relation is strong to
the event of thunder or lightning.
The pachinko is Japanese pinball. The pachinko
data composed the stream data of the number ofpachinko bolls used to play a game with each pachinko
machine and the number of balls at jackpot, etc. It is
used to investigate prevention of the user from illegal
using and a popular pachinko machine model.
The advantage of the proposal approach is that it
is easy to correspond to the change in the numberof data sources. The change of the system is not tohard even when the composition of the sensor changes.
Moreover, the data acquisition system from the sensor in the remote place can be easily constructed byconstructing the system on the Internet.

In this paper, we proposes the technique of the sensor data mining with multiagent approach. In the sensor data mining by multiagent approach, we define thesource to which sends sensor data as agent, and sensor data analyze system including sensors and mining
programs is defined as multiagent sensor data mining
system. We discuss proposed multiagent system which
is applied to the meteorological observation data andthe data of the store system of pachinko. The meteorological observation data contains a large amount of
the numerical data. The meteorological observation
data also contains both of data only before and after
the data observed at the regular time and the time atwhich special event is occurred. The purpose of thedata analysis is defined as to extract the observation
data of the regular time when the relation is strong to
the event of thunder or lightning. The pachinko data
composed the stream data of the number of pachinkobolls used to play a game with each pachinko machine
and the number of balls at jackpot, etc. It is used
to investigate prevention of the user from illegal using
and a popular pachinko machine model.

Keyword:Sensor Data Mining System, Multiagent Approach,
Metrological Data, Pachinko Data

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©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

Sensor Data Mining using Multiagent

2. The place of black board(BB) is set with Query
agent.
3. Query agent generates Mining agent, and theplace of BB is transmitted.

In KES2003, the multiagent technology is defined
as a technology that processed information by cooperatively operating two or more independent programs(agent). [1]Generally, multiagent technology is discuss with anautonomous control of an individual agent, but in thispaper, we do not discuss it mainly.
A communication between agents between one toone, one to multi, multi to multi. In this paper, we
use one to one communication by UNIX process communication, one to multi by Black board model.

2.1

4. Query agent generates Result agent, and the placeof BB is transmitted.
5. DB-access agent is generated, and Mining agent
is accessed to the database.
6. DB-access agent gets data from the database.
7. Mining agent receives data from DB-access agent,
and applies the data mining algorithm.

Agent Definitions

8. Mining agent writes the result of data mining on
BB.

The definition of agent which is used for data mining in this paper is defined as follows.

9. Result agent checks BB, and if all results are written, arranges the results and presents to the user.
10. All agents are eliminated.

Query agent: Query agent receives used the
database and the data mining algorithm from auser, and generates other agents. Query agent isgenerated at each demand of a user.

User

Mining agent: Mining agent generates DB-accessagent, acquires data from DB-access agent, andapplies data mining algorithm. Mining agent isgenerated of each applied mining algorithm.

Result agent

DB-access agent: DB-access agent acquires datafrom the database, and sends it to mining agent.
DB-access agent is generated of each database andof each mining agent.

Black board

DB-accessagent
DB-access
agent

DB

DB

Figure 1: Flowchart of Proposed System

2.3Black board(BB): Place where results from data
mining agent is written.

Feature of Proposed Method

The proposal method has the following features.
The result of data mining can be made for moremeaning result by building in the thesaurus agent as
Mining agent, and making it can access the thesaurus
database.
Query agent generates two or more Mining agent,
it becomes possible to execute data mining algorithms
in parallel. Moreover, it becomes possible that constructing the system and the access to the database
and the processing of data are divided by separating

Flow of System

A flow of proposed system is defined as follows.
(Fig. 1 shows flowchart of proposed system.)
1. A user generates Query agent, with setting theused database and the used data mining algorithm as its parameter.

©ISAROB 2006

Mining agent

Mining agent

Result agent: Result agent observes a movement ofmining agents, and obtains result from mining
agents. When result agent obtains all results, result agent arrangement/integrates, and shows it
to a user.

2.2

Query agent

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

does data mining collecting data on the network byusing the sensor network where the fixed point observation data has been disclosed on the Internet named
Teiten2000.[3, 4]
In Teiten2000, the information such as temperature, humidity, and the fixed point image are collectedwith each sensor unit, and sensor unit preserves data
in local database. In the Teiten2000 sensor network,
the data of each local database ties on the network.
The system by which the meteorological data analysis system that proposes it collects data from a local
database of each sensor unit, and does data mining
from this network. Data mining that uses the data of
Teiten2000 has already been evaluated.[4]The agent which collect data from the sensor unit
and the agent which chooses two or more sensor data
are being constructed now. It is scheduled that data
mining from the Teiten2000 network is done after theagents are constructed, and an effective output is evaluated.

DB-access agent accessed the database with Mining
agent that processes data.
It becomes possible that the processing of each data
mining algorithm and its arrangement/integration are
separately thought by setting the agent which arranges
the result. Moreover, it becomes easy to build arrangement/integration according to user’s purpose into thesystem.
The system user comes to be able to construct thesystem corresponding to the purpose by recycling DB
Agent and Mining Agent, and do tuning of Query
agent and Result agent.
In this paper, the black board model with the file
was handled with the interprocess communication on
UNIX, but it can be easily enhanced to the communication on TCP/IP. Then, it is possible to enhance
proposed approach to application to database that has
been distributed on Internet. The problem of proposed
approach is not using interprocess communication on
UNIX but using black board model. Writing in theblack board becomes a problem when the number of
databases and data mining algorithm used increase,then the entire operation is influenced from the operation of the slowest agent. Therefore, the access todatabase and the processing of the data mining algorithm can be run parallel, but processing stops whenchecking results in the blackboard. It is necessary toconsider that the maximum time is set to the blackboard writing check, and the system can show the result after each agent process.

2.4

4

We construct the data mining system of thepachinko(Japanese pinball) data as an example of applying the proposal technique, and verify the effectiveness of the proposal technique.
The data mining system of the pachinko data isa system that analyzes the movement of the ball of
the pachinko stand in the pachinko parlor by data
mining. The sensor that examines the opening andshutting door of pachinko stand and number of output balls, the big hit(jackpot) frequency, etc. is built
in and it is called stand computer(”DaiCom”) in eachpachinko stand. Data is collected by the computerthat is called island computer(”ShimaCom”) brings
several stand computer together. In addition, the computer that is called hall computer(”HallCom”) collects
the data of the pachinko stand of the parlor.
We build multiagent sensor mining system into thissystem, and construct the system that in real timemoves the number of ball in data mining. It aims todetect illegal play by the outlier analysis of the movement of the number of ball, to examine the model ofa popular pachinko stand, and to analyze how for thecustomer to play by using the constructed system.
While with the data mining system as a system thatcollects data from the pachinko stand by the network
the system that collects the data of several stand computer(”DaiCom”) directly is constructed under thepresent situation. It is scheduled that the network

Adapt for Sensor Data Mining

The proposed multiagent system was for databasesystem. Then, we expanded the proposed system forsensor data mining. In this expansion, sensor units
which are data sources treat as databases, and sensor
units have both sensor part which measurement system and send part which send measurement data. If
the sensor units can send data for the Internet, theexpanded multiagent system can easy to construct. A
lot of recent sensor units can make network and sendmeasurement data for the network. So, the proposed
multiagent sensor data mining system can build into
an existing sensor network system.

3

Metrological Data Mining System

We construct the meteorological data analysis system as one of the examples of applying the proposal
technique, and verify the effectiveness of the proposal
technique. We construct the data mining system that

©ISAROB 2006

Pachinko Data Mining System

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Proceedings, PartII, Lecture Notes in Artificial
Intelligence 2774, Springer:pp.1412–1418 (2003)

where island computer(”ShimaCom”) and hall computer(”HallCom”) were used for the pachinko standof an actual parlor to correspond is constructed, andthe multiagent sensor data mining system is built into
the system.

5[2] Niimi, A., Konishi, O.: Extension of Multiagent Data Mining for Distributed Databases.
KES’2004, Proceedings, PartIII, Lecture Notes in
Artificial Intelligence 3215, Springer:pp.780–787(2004)

Conclusion and Future Works[3] Teiten 2000 Project, http://www.teiten2000.jp/
In this paper, we proposes the technique of the sensor data mining with multiagent approach. In the sensor data mining by multiagent approach, we define thesource to which sends sensor data as agent, and sensor data analyze system including sensors and mining
programs is defined as multiagent sensor data mining system. The advantage of multiagent system isscalavirity of the number of sensors. It becomes tobe able to correspond to the change in the number of
sensors easily by constructing the multiagent system.
We discuss proposed multiagent system which is applied to the meteorological observation data and thedata of the store system of pachinko. The pachinko
data composed the stream data of the number ofpachinko bolls used to play a game with each pachinko
machine and the number of balls at jackpot, etc. It is
used to investigate prevention of the user from illegal
using and a popular pachinko machine model.
The advantage of the proposal approach is that it
is easy to correspond to the change in the numberof data sources. The change of the system is not tohard even when the composition of the sensor changes.
Moreover, the data acquisition system from the sensor in the remote place can be easily constructed byconstructing the system on the Internet.
Each system is being constructed now. The effectiveness of the system is scheduled to be verified byconstructing the multiagent sensor mining system, andusing actual data in the future.

[4] Toda, M., Osanai, A., Niimi, A., Akita, J.,
Kimura, K., Konishi, O.: Information Access
Method to Meteorological Data for Educational
Application, World Conference on Education
Multimedia, Hypermedia and Telecommunications (ED-MEDIA2005), Association for the Advancement of Computing in Education, Montre’al, Canada, Proceedings: pp.4404–4408, June2005.

Acknowledgements
We thanks Dr. Masashi Toda of Future UniversityHakodate for helpful discussion about sensor system
of Teiten 2000. We thanks Dr. Masaaki Wada and
Dr. Kei Ito of Future University-Hakodate for helpful
discussion about Pachinko data store system.

References[1] Niimi, A., Konishi, O.: Data Mining for Distributed Databases with Multiagents. KES’2003,©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Application of Graduated PCR in Concentration-Controlled Direct-Proportional
Length-Based DNA Computing
Zuwairie Ibrahim1&2, Yusei Tsuboi2, and Osamu Ono2
1

2

Department of Mechatronics and Robotics
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
81310 UTM Skudai, Johor Darul Takzim
Malaysia
zuwairie@fke.utm.my

Institute of Applied DNA Computing
Meiji University
1-1-1 Higashi-mita, Tama-ku
Kawasaki-shi, Kanagawa-ken
214-8571 Japan(zuwairie, tsuboi, ono)@isc.meiji.ac.jp

Abstract

visualize the computation output of Hamiltonian path
problem (HPP) [2].

Graduated polymerase chain reaction (PCR) is amethod to obtained molecular information in DNA
computing, especially in the case of molecular search forweighted graph problems based on length, concentration,and melting temperature. Graduated PCR is not new inthe area of DNA computing. In fact, graduated PCR has
been performed by Adleman, when he demonstrated a
DNA computer for solving an instance of 7-nodes
Hamiltonian path problem. We report here the use of
graduated PCR in concentration-controlled directproportional length-based DNA computing and theimportance of graduated PCR is discussed.

2

A new hybrid approach called concentrationcontrolled direct-proportional length-based DNA
computing (CCDPLB-DNAC) has been proposed [3].
The hybrid approach combines two characteristics:
length and concentration, for encoding and at the sametime, effectively control the degree of hybridization of
DNA. The encoding by length is realized whereby thecost of each path is encoded by the length of theoligonucleotides (oligos) in a proportional way. Thiskind of encoding is exactly the same as the previously
proposed
direct-proportional
length-based
DNA
computing (DPLB-DNAC). On the other hand, thecontrol of hybridization by concentration is done byvarying the amount of oligos, as the input of computation.
The main objective of this approach is the same as
DPLB-DNAC, which is to compute the shortest path of aweighted graph in vitro.

Keywords: DNA Computing, Molecular Information,
Graduated PCR
1Introduction

In the area of DNA computing, there are two kindsof methods that are useful to extract the information from
DNA: DNA sequencing and graduated PCR. DNA
sequencing is done by a machine called sequencer, which
is able to reveal every nucleotide in DNA. Lee et al. [1]have employed this method at the end of their algorithmof temperature gradient based DNA computing (TGDNAC). Nevertheless, it seems likely that it is quite hardto analyze the output of the sequencer. The second
method, which is graduated PCR, is less expensive thanDNA sequencing and not able to reveal every nucleotide
of DNA, but adequate to visualize the desired
information. Moreover, the interpretation is easier tounderstand compared to DNA sequencing. Therefore,
graduated PCR is more preferred as an extended
protocols in concentration-controlled direct-proportional
length-based DNA computing (CCDPLB-DNAC).
Graduated PCR consists of several protocols, such as
DNA extraction from a gel, polymerase chain reaction(PCR), and polyacrylamide gel electrophoresis (PAGE).
Graduated PCR, in fact, has been used by Adleman to©ISAROB 2006

Concentration-Controlled
Direct-Proportional
Length-Based DNA Computing

The origin of DNA computing by concentrationcontrolled can be traced back to the work by Oliver inwhich, concentration of DNA has been proposed for a
calculation of multiplication of Boolean matrix [4]. The
principle underlying the concentration-controlled DNA
computing for weighted graph problems is that during
the initial pool generation, the rate of biochemical
reactions depends heavily on the reaction rate constants
and reactant concentrations. Thus, as the concentration of
DNA strands increase, the paths including them can begenerated more frequently and the hopeful DNA paths
can be generated with high concentration.
The experimental protocol, in fact, is almost thesame with the previously proposed DPLB-DNAC,
however, the difference is that the amount of oligos
poured into the initial test tube is relatively different

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the gel. The gel slice was chopped and placed into thefilter cup of the Quantum Prep Freeze ‘N Squeeze DNA
Extraction Spin Column. Then, the filter cup was placed
into a dolphin tube. The Quantum Prep Freeze ‘N
Squeeze DNA Extraction Spin Column was placed in a 20ºC freezer for 5 minutes and the sample was spun at
13,000 x g for 3 minutes at room temperature. The
purified DNA was collected from the collection tube andready for PCR. A gel image of the purified DNA is
depicted in Figure 4. After the DNA extraction from thepolyacrylamide gel is done, four different PCR, namely
PCR12, PCR13, PCR14, and PCR15, were ran to thepurified solutions. The pair of primers used for every
PCR is listed in Table 1.

depending on the relative concentration, which is
calculated using Equation 1 [5], such that:2⎛ Min ⎞⎟
Dij = ⎜(1)⎜ C ⎟⎝ ij ⎠
where Min is the minimum weight of the graph, Cij is theweight of an edge, E(Vi,Vj), and Dij denotes relative
concentration of the corresponding edge. The relative
concentrations can then be translated into the actual
amount by the following equation:

Aij = Dij Aref(2)

where Aij is the actual amount of oligos for an edge
E(Vi,Vj) and Aref denotes the reference amount.
Based on CCDPLB-DNAC, if the input of theshortest path problem is shown in Figure 1, the output of
the in vitro computation is shown in Figure 2.
3

Output Visualization by Graduated PCR

For the sake of explanation, the DNA molecule
representing the answer of the shortest path V1 – V3 – V4 –V5 is taken again for an instance. For this purpose, it isimportant to extract the shortest band of DNA from thegel, and graduated PCR can be performed by running
four different PCR operations to the solution containing
DNA duplex V1 – V3 – V4 – V5 separately. The pair ofprimers used for every PCR reaction are V1 − V2 , V1 − V3 ,Figure 1: An example of the input of the shortest path
problem.

V1 − V4 , and V1 − V5 . It is expected that for the final

M

solution containing the strand V1 – V3 – V4 – V5 withlength of 100 base-pairs (bp), graduated PCR willproduce bands of x, 50 bp, 75 bp, and 100 bp in
successive lanes of a gel as depicted in Figure 3. The
symbol x denotes the absence of a band corresponding tothe omission of nodes V2 along the DNA duplex. Thismeans that there are intermediate nodes, V3 and V4 in
between the start node V1 and the end node V5. Therefore,
the shortest path of the graph can be readout as V1 → V3
→ V4 → V5.
4

1

2

Experimental Protocols and Results

Based on gel image of Figure 2, four DNA mixtures,
which are the product of PCR, were subjected to PAGE
for 40 minutes at 200V. After that, the gel was stained by
SYBR Gold (Molecular Probes, USA) and the bands of
DNA were viewed by a 312nm UV Transluminator.
Quantum PrepTM Freeze ‘N Squeeze DNA Gel
Extraction Spin Columns (Bio-Rad, Japan) was used forthe purpose of DNA extraction from the polyacrylamidegel. By using a clean razor blade, the band of interest,
which is the shortest band, was carefully excised from

©ISAROB 2006

Figure 2: Experimental results of gel electrophoresis on
10% PAGE in the case of DPLB-DNAC. Lane M
denotes 20-bp ladder, lane 1 is the product of POA, andlane 2 is the product of PCR.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 3: Example showing a brief protocol and the expected results of graduated PCR for CCDPLB-DNAC.
Each PCR was performed in a 25 µl solution
consisted of 2.5 µl of each primers, 1 µl template, 2.5 µl
dNTP (TOYOBO, Japan), 2.5 µl 10x KOD dash buffer(TOYOBO, Japan), 0.125 µl KOD dash (TOYOBO,
Japan), and 13.875 µl ddH20 (Maxim Biotech, Inc.,
Japan). The reaction consisted of 25 cycles and for eachcycles, the appropriate temperature were 94ºC for 30s,
55ºC for 30s, and 74ºC for 10s.

nodes. The information regarding all the nodes in the
shortest path and the order can be obtained by applying
graduated PCR. By referring to the experimental resultsin Figure 5, it is proved that graduated PCR is able toproduce bands of x, 50, 75, and 100 in successive lanesof a polyacrylamide gel. The symbol x denotes theabsence of a band corresponding to the omission ofnodes V2 along the DNA duplex. This means that there
are intermediate nodes, V3 and V4 in between the node V1
and V5, and the order of those intermediate nodes can be
known. Therefore, the shortest path of the graph can bereadout as V1 → V3 → V4 → V5.

Again, the product of graduated PCR was subjectedto PAGE for 40 minutes at 200V and the gel was stainedby SYBR Gold (Molecular Probes, USA). The band of
DNA was viewed by 300nm FOTO/Phoresis® UVTransilluminator (Fotodyne Inc., USA) and the resulted
gel image was captured. Figure 5 shows the gel image ofthe product of graduated PCR.
5

Two significant benefits of graduated PCR for
CCDPLB-DNAC are identified. The first is due to its
capability to show and visualize the detailed output of
the shortest path computation based on CCDPLB-DNAC.
The other benefit is that at the same time, the correctnessof the proposed CCDPLB-DNAC for the computation of
the shortest path problem can be proved. Hence, it isfound that graduated PCR should be essentiallyincorporated in the CCDPLB-DNAC in order to improve
the overall performance of CCDPLB-DNAC.

Discussions

Based on the shortest length dsDNAs, one only
knows that the shortest path begins from V1 and ends at
V5. However, the information does not contain the nodes
that pass through the shortest path and the order of those©ISAROB 2006

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1

Technology, Hokkaido University, Sapporo, Hokkaido,
Japan. The first author would like to thank Masahito
Yamamoto for discussions that led to improvements inthis work. Also, the first author is sincerely grateful toAtsushi Kameda, Satoshi Kashiwamura, and members of
DNA Computing Laboratory of Hokkaido University for
fruitful explanations and kind assistance during thepractice of biochemical experiments. Lastly, the firstauthor is very thankful to Universiti Teknologi Malaysia(UTM) for granting a study leave at Meiji University
under JPA-SLAB scholarship.

M

M

1

2

3

4

Figure 4: Experimental results of gel electrophoresis on
10% polyacrylamide gel. Lane M denotes 20-bp ladder,and lane 1 is the purified DNA of gel extraction.
Table 1: Four sets of primers used for the graduated PCR.
Solution
PCR12
PCR13
PCR14
PCR15
6

Forward Primers (F) and Reverse
Primers (R) (5’-3’)
F = AAAGCTCGTCGTTTAGGAGC
R = CGTGATCCCTAAACCTCCAA
F = AAAGCTCGTCGTTTAGGAGC
R = CGATACGGCATCATCTCGCT
F = AAAGCTCGTCGTTTAGGAGC
R = GCTATGGCTTGACTATTCGC
F = AAAGCTCGTCGTTTAGGAGC
R = GCACCCACCGAGACATTATC

Figure 5: Experimental results of graduated PCR. Lane
M denotes 20-bp molecular marker, lane 1, lane 2, lane 3,and lane 4 are the product of PCR on DNA mixtures
PCR12, PCR13, PCR14, and PCR15, respectively.
References

Concluding Remarks[1] J. Y. Lee, S. Y. Shin, T. H. Park, and B. T. Zhang,“Solving traveling salesman problems with DNA
molecules encoding numerical values,” BioSystems,
Vol. 78, Issue 1-3, pp. 39-47, 2004.
[2] L. Adleman, “Molecular computation of solutions to
combinatorial problems,” Science, Vol. 266, pp.
1021-1024, 1994.
[3] Z. Ibrahim, Y. Tsuboi, O. Ono, and M. Khalid,“Hybrid concentration-controlled direct-proportional
length-based DNA computing for numerical
optimization of the shortest path problem,” Second
International Workshop on Biologically Inspired
Approaches to Advanced Information (accepted)[4] J. Oliver, “Computation with DNA: matrix
multiplication,” DNA Based Computers 2: DIMACS
Series in Discrete Mathematics and TheoreticalComputer Science, Vol. 44, pp. 113-122, 1999.
[5] M. Yamamoto, A. Kameda, N. Matsuura, T. Shiba,
Y. Kawazoe, and A. Ahochi, “A separation method
for DNA computing based on concentration
control,” New Generation Computing, Vol. 20, pp.
251-262, 2002.

In this paper, we presented graduated PCR, as an
option to extend the protocol of CCDPLB-DNAC. Basedon the proposed approach, the product of CCDPLBDNAC is subjected to DNA extraction frompolyacrylamide gel, PCR, and PAGE. As supported by
the experimental results, a series of bio-molecular
reactions have been successfully carried out in laboratory.
Further, it is showed that graduated PCR is also able tovisualize detail additional information of the shortest
path, such as the intermediate vertices and the order ofthese vertices in the shortest path and at the same time,
proved the correctness of the CCDPLB-DNAC.
Acknowledgements
This research was supported partly by the IEEE
Computational Intelligence Society (IEEE-CIS) Walter J
Karplus Student Summer Research Grant 2004 for a
research visit in September 2004 at the DNA Computing
Laboratory, Graduate School of Information Science and©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Molecular Computing Approach for Constraint Assignment Problem
Zuwairie Ibrahim1&2, Yusei Tsuboi2, and Osamu Ono2
1

2

Department of Mechatronics and Robotics
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
81310 UTM Skudai, Johor Darul Takzim
Malaysia
zuwairie@fke.utm.my

Institute of Applied DNA Computing
Meiji University
1-1-1 Higashi-mita, Tama-ku
Kawasaki-shi, Kanagawa-ken
214-8571 Japan(zuwairie, tsuboi, ono)@isc.meiji.ac.jp

Abstract

computing called direct-proportional length-based DNA
computing is chosen and extended for the developmentof a DNA algorithm for the computation of a constraint
assignment problem.

The concept of direct-proportional length-based
DNA computing is considered in order to solve a
constraint assignment problem on an unconventional
molecular computer. Constraint assignment problem isan extended version of unconstraint assignment problem,
where a constraint that is the cost of an assignment istaken into account during the optimization. This paper
shows that the complexity of constraint assignment
problem could be reduced to a path-finding problem andan algorithm based on direct-proportional length-based
DNA computing is design for searching the optimal
assignment.

2

Modified Graph

A simple input of a constraint assignment problem is
depicted in Figure 1. This input consists of a set
employees, E = {a, b, c} and a set jobs, J = {d, e, f}. The
cost of an assignment is also shown. Based on the input,
the optimal assignment is a-d, b-e, and c-f, where theassignment cost is 5.

Keywords:
Constraint
Assignment
Problem,
Optimization, Direct-Proportional Length-Based DNA
Computing1

Introduction

In the previous paper [1-2], we proposed a DNAbased computing algorithm for solving unconstraint
assignment problem. The objective of the unconstraint
assignment problem is to establish a full one-to-one
correspondence between two set E (employees) and J(jobs), both of which have N elements. An assignment is
a one-to-one mapping, α: E → J [3]. The assignment
problem is so fundamental in operations research as wellas in engineering field. It is very useful because
assigning n tasks to n people is a basic primitive in many
applications [4].
Based on the constraint assignment problem, aconstraint is considered that is the cost of assigning anelement in E to an element in J, and thus, the objective of
the assignment now is to find one-to-one correspondence
between two set E and J, both of which have N elementswith the lowest cost.
Basically, from the DNA computing point of view,
there are several characteristic of DNA that could bemanipulated in order to solve a weighted graph problem.
Those are length [5], concentration [6], and melting
temperature [7]. However, a length-based DNA©ISAROB 2006

Figure 1: Input of a constraint assignment problem.
In order to reduce the constraint assignment problem
to a path-finding problem, the input graph in Figure 1 isvisualized slightly different into a modified graph asshown in Figure 2. According to the input, it is clear thatthe nodes a, b, and c are connected to nodes d, e, and f.
The modified graph is constructed in such as way thatthe nodes d, e, and f that belong to node a are connectedby connective edges to node b. Similarly, nodes d, e, andf that belong to node b are connected by connective
edges to node c. Lastly, nodes d, e, and f that belong tonode c are connected by connective edges to node end.
We introduce a node end as a stopper. The costs ofconnective edges are set to 0. According to the modified
graph, the constraint assignment problem can be solvedin vitro on a molecular computer by computing theshortest path problem employing direct-proportional
length-based DNA computing as shown in Figure 3.

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amplify exponentially generated double stranded DNAs
(dsDNAs) that begin from node a and end at node end.

Figure 2: A modified graph.
Figure 4: All possible solutions of the constraint
assignment problem.
In constraint assignment problem, an employee
should be assigned only to one machine, either d, e, or f.
In order to select only possible solutions that satisfy thisrule, magnetic bead separation can be applied three times,
particularly, for extraction of subsequences d, e, and f.
Magnetic bead separation is a biotechnology tool to
extract dsDNAs that contain certain subsequences from asolution. Possible solutions that survived after themagnetic bead separation are shown in Figure 5.

Figure 3: The shortest path as solution to a constraint
assignment problem.
3

DNA sequence design

The sequence design is exactly the same as directproportional length-based DNA computing as follows:a. If there is a connection between V1 to Vj, where j ≠ n,synthesize the oligo for edge asV1 (20) + W1j (ω- 30) + Vj (20)b. If there is a connection between Vi to Vj, where i ≠ 1,
j ≠ n, synthesize the oligo for edge as
Vi (20) + Wij (ω- 20) + Vj (20)c. If there is a connection between Vi to Vn, where i ≠ 1,
synthesize the oligo for edge as
Vi (20) + Win (ω- 30) + Vn (20)where V, W, and ‘+’ denote the DNA sequences fornodes, DNA sequences for weight, and ‘join’
respectively. The synthesized oligos consist of two nodesegments and an edge segment. ‘ω’ denotes the weightvalue for corresponding DNA sequences for weight Wij,
where Wij denotes the DNA sequences representing a
cost between node Vi and Vj. The value in parenthesis
indicates the number of DNA bases or nucleotides foreach segment. Table 1 lists all the oligos designed using
DNASequenceGenerator [8] for the in vitro computation.
4

Figure 5: Possible solutions survived after magnetic beadseparation of subsequence d, e, and f.
All possible solutions survived after the previous
reactions contain one-to-one assignment of employees
and jobs with various costs. The solution of theconstraint assignment problem is the one-to-one
assignment with the lowest cost. The selection of thatkind of assignment is possible by performing a method
called gel electrophoresis. Gel electrophoresis, in fact, isa method to separate and list dsDNAs according to theirlengths, in an agarose or polyacrylamide gel. The
separation by gel electrophoresis is shown conceptuallyin Figure 6.
After the gel electrophoresis is successfully done,
the shortest band appears in the gel represents thesolution of the constraint assignment problem. However,a method to extract the information represented by
dsDNAs in the shortest band is required. Fortunately, it
is possible to extract the information by using a method
called graduated PCR. Graduated PCR can be performedby extracting the dsDNAs of interest from the gel andapplying three different PCRs with three different set of

In vitro computation

In this research, POA is performed for initial pool
generation. After parallel overlap assembly is done, allpossible solutions of the constraint assignment problem
as shown in Figure 4 could be generated. The values inparenthesis indicate the cost of each assignment.
Polymerase chain reaction (PCR) is performed to©ISAROB 2006

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Table 1: DNA sequences required for the in vitro computation
Name
a
b
c
d
e
f
end
Name
a
b
c
d
e
f
end
Name
a-d
a-e
a-f
b-d
b-e
b-f
c-d
c-e
c-f
d-b
e-b
f-b
d-c
e-c
f-c
d-end
e-end
f-end

Nodes
DNA sequences (5’-3’)
CGTCTGTCTAGCGGACCTTATACACCGACTACCCATGTGCGATCCCTGAGAGGTGAATGGGTTCAACTGACAGGTGTGCCCTAAGTTGTGGTGAGTGGGCGAATATCCCGTCCTCTACGCTGGTCCCAGTGATACCAGTC
Complements of nodes
DNA sequences (5’-3’)
TAAGGTCCGCTAGACAGACGGCACATGGGTAGTCGGTGTA
CCATTCACCTCTCAGGGATCGGCACACCTGTCAGTTGAAC
GCCCACTCACCACAACTTAGGCGTAGAGGACGGGATATTCGACTGGTATCACTGGGACCA
Edges
DNA sequences (5’-3’)
CGTCTGTCTAGCGGACCTTAGTTCAACTGACAGGTCTCGG
CGTCTGTCTAGCGGACCTTAACGTGCTAAGTTGTGGTGAGTGGGC
CGTCTGTCTAGCGGACCTTACCGTCTTTTAGAATATCCCGTCCTCTACGCTACACCGACTACCCATGTGCAGGTCGTTCAACTGACAGGTGTGCCTACACCGACTACCCATGTGCCTAAGTTGTGGTGAGTGGGCTACACCGACTACCCATGTGCGCGTGCTCTCGAATATCCCGTCCTCTACGCGATCCCTGAGAGGTGAATGGACGTGTTTTGGTTCAACTGACAGGTGTGCCGATCCCTGAGAGGTGAATGGCTAAGTTGTGGTGAGTGGGCGATCCCTGAGAGGTGAATGGGCGTGGAATATCCCGTCCTCTACGAGTTCAACTGACAGGTGTGCCTACACCGACTACCCATGTGCCTAAGTTGTGGTGAGTGGGCTACACCGACTACCCATGTGCGAATATCCCGTCCTCTACGCTACACCGACTACCCATGTGCGTTCAACTGACAGGTGTGCCGATCCCTGAGAGGTGAATGG
CTAAGTTGTGGTGAGTGGGCGATCCCTGAGAGGTGAATGG
GAATATCCCGTCCTCTACGCGATCCCTGAGAGGTGAATGGGTTCAACTGACAGGTGTGCCTGGTCCCAGTGATACCAGTCCTAAGTTGTGGTGAGTGGGCTGGTCCCAGTGATACCAGTCGAATATCCCGTCCTCTACGCTGGTCCCAGTGATACCAGTC

Concluding Remarks

This research concerning with an extended version
of DNA-based algorithm based on direct-proportional
length-based DNA computing for solving an example of©ISAROB 2006

Length
20
20
20
20
20
20
20
Length
40
45
50
45
40
50
50
40
45
40
40
40
40
40
40
40
40
40

engineering problem, namely, constraint assignment
problem. A constraint that is the cost for each assignmentis taken into account in this paper. It has been shown thatthe complexity of the constraint assignment problem
could be reduced to path-finding problem and the directproportional length-based DNA computing could be
performed for the in vitro computation. For this purpose,
direct-proportional length-based DNA computing is
improved by additional reactions called magnetic beadseparation and graduated PCR. For the future works, theproposed algorithm will be implemented in laboratory
experiments.

forward and reverse primers. After that, gelelectrophoresis is applied again to the three different
products of PCRs and the resultant lengths are observed.
The conceptual description of graduated PCR is shownin Figure 7. If the expected product of gel electrophoresis
shown in Figure 7 is observed, one can conclude that thebest assignment is a-d, b-e, and c-f.
6

Length
20
20
20
20
20
20
20

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Acknowledgments
The first author is very thankful to Universiti Teknologi
Malaysia for granting a study leave in Meiji University
under JPA-SLAB scholarship.
References[1] Z. Ibrahim, Y. Tsuboi, and O. Ono, “Solving
unconstraint assignment problem by a molecularbased computing algorithm,” IEEE International
Symposium on Industrial Electronics 2004, pp.
1473-1478, 2004.
[2] Z. Ibrahim, Y. Tsuboi, O. Ono, and M. Khalid,“Unconstraint assignment problem: a molecular
computing approach,” International Arab Journalof Information Technology, Zarqa Private University,
Vol. 3, No. 2, pp. 183-188, 2006.
[3] G. Liu and R.M. Haralick, “Assignment problem inedge
detection
performance
evaluation,”
Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, Vol. 1, pp. 26-31,
2000.
[4] A. Arora, A. Frieze, and H. Kaplan, “A newrounding procedure for the assignment problem withapplications to dense graph arrangement problems,”
Proceedings of the 37th Annual Symposium on
Foundations of Computer Science, pp. 21-30, 1996.
[4] D. M. Norton, “Polymerase chain reaction-based
methods for detection of Listeria monocytogenes:
toward real-time screening for food andenvironmental samples,” Journal of AOAC
International, Vol. 85, pp. 505-515, 2002.
[5] Z. Ibrahim, Y. Tsuboi, O. Ono, and M. Khalid,“Experimental
implementationof
directproportional length-based DNA computing fornumerical optimization of the shortest path problem”,
European Conference on Artificial Life 2005:
Workshop on Unconventional Computing, pp. 35-46,
2005.
[6] M. Yamamoto, A. Kameda, N. Matsuura, T. Shiba,
Y. Kawazoe, and A. Ohuchi, “Local search byconcentration-controlledDNA
computing,”
International Journal of Computational Intelligenceand Applications, Vol. 2, No. 4, pp. 447-455, 2002.
[7] J.Y. Lee, S.Y. Shin, S.J. Augh, T.H. Park, B.T.
Zhang,“Temperature
gradient-based
DNA
computing for graph problems with weighted
edges,” Lecture Notes in Computer Science, Vol.
2568, pp. 73-84, 2003.
[8] F. Udo, S. Sam, B. Wolfgang, and R. Hilmar, “DNAsequence generator: a program for the constructionof DNA sequences,” Proceedings of the 7thInternational Workshop on DNA Based Computers,
pp. 23-32, 2001.

Figure 6: Separation by gel electrophoresis of possible
solutions after magnetic bead separation of subsequenced, e, and f.

Figure 7: Expected output of graduated PCR for
extraction of molecular information of the constraint
assignment problem.

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Matrix Multiplication by DNA Computing
Kasai Nobuyuki1, Zuwairie Ibrahim1&2, Yusei Tsuboi1, and Osamu Ono1
12

Institute of Applied DNA Computing
Meiji University
1-1-1 Higashi-mita, Tama-ku
Kawasaki-shi, Kanagawa-ken
214-8571 Japan(ce55024, zuwairie, tsuboi, ono)@isc.meiji.ac.jp

Department of Mechatronics and Robotics
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
81310 UTM Skudai, Johor Darul Takzim
Malaysia
zuwairie@fke.utm.myrestriction enzymes is proposed in this paper. We carriedout an experiment on the method.

Abstract
In this paper, a method to do binary multiplication
with DNA molecules based on DNA computing ispresented. The first proposal on matrix multiplication
with DNA has been presented by John S. Oliver. In thispaper, the scheme of Boolean matrix multiplication,
which has been proposed by Oliver is improved,
simplified, and realized in laboratory experiments.
During the in vitro implementation, parallel overlap
assembly is employed to construct every possible path in
the directed graph in massively parallel fashion. A test of
the reaction to identify the existence of a path is done bypolymerase chain reaction. After that the product ofpolymerase chain reaction is subjected to polyacrylamide
gel electrophoresis in order to visualize the result of thecomputation. By analyzing the existence of the bands ofgel electrophoresis, it is able to read the output of theBoolean matrix multiplication. The experimental results
prove the performance of a mathematical calculation
based on DNA computing.
1

The main strategy behind the in vitro
implementation is by representing the Boolean matrices
and their product by a directed graph, which consists of aset of vertices and edges. If an element in the input
matrix is the symbol '1', we can draw an edge that isconnecting two vertices in the directed graph and viceversa. Since it is possible to construct any consecutive
matrices and the multiplication product of those matrices,then it is certainly possible to do the Boolean matrix
multiplication based on DNA computing.
2

Two Boolean matrices and their product can be
represented by a directed graph (kim 1982; Robinson andFoulds 1980).For example, the product of two Boolean
matrices, X and Y and the directed graph representation
are shown in Figure 1.
In this graph, the initial vertices, “1” and “2”represent the row identifiers for the first matrix and the
product matrix. The terminal vertices “A” and “B”
represent the column identifiers for the second andproduct matrix. Based on the definition of matrix
multiplication, the number of columns in the first matrix
must equal the number of rows in the second matrix. Theintermediate vertices “a” and “b” represent the columnsof the first matrix and the rows of the second matrix.
Nonzero elements in the first or second matrix are
represented by a directed edge (vector connection) in thegraph. Connections between initial and intermediate
vertices represent nonzero elements in the first matrix.
Connections between intermediate and terminal vertices
represent nonzero elements in the second matrix.

Introduction

In 1994, L. Adleman’s [1] ground-breaking work
demonstrated the way to use DNA molecules forcomputational purposes. This experience also
contributed into a better understanding where to go with
DNA machines, namely, to try to develop memorymachines that are machines with very large memory that
implements rather simple search operations.
In 1997, a method to perform this calculation using
DNA to represent the edges, vertices, and paths in thegraph above has been proposed by John S. Oliver [2].
The method used restriction enzymes, which cut DNA
sequences which represented the edges, vertices, andpaths in the graph. To solve an 10×10 matrix, 100 typesof restriction enzymes are used. However, all types ofrestriction enzymes are approximately no more than 230
types. This method can not be used to solve a large
matrix. Thus a method to do the Boolean matrix
multiplication based on DNA computing without©ISAROB 2006

Boolean Matrix

Such as, in the first matrix, the element at theintersection of row “1” and column “b” is the symbol ‘1’.
Thus, we draw an edge proceeding from vertex “1” tovertex “b”. Again, in the first matrix, the element at theintersection of row “1” and column “a” is the symbol‘zero’. Thus, there is no edge proceeding vertex “1” and476

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and edges are represented by ssDNAs. Nonzero elementsin the product of two Boolean matrices, X and Y connect
initial, intermediate and terminal vertices with edges ingraph. Therefore these ssDNAs make a unique 60-mer
incomplete dsDNA.

vertex “a”. Similarly, we can construct edges between
vertex “a”, “b”, “A” and “B” to represent symbols of one
in the second matrix. Each of the constructed edge has its
own directionality indicated by an arrowhead.
The elements of the product matrix symbolize theexistence or nonexistence of paths made form continuous
linkages of edges between initial and terminal vertices.
Paths can only be constructed by proceeding along edges
in the direction indicated by the arrowhead associatedwith each edge. In essence, one is asking if it is possible
to form a path from each initial vertex to each of terminal
vertices. The problem of evaluating the elements in Z
can be reduced to determining a compete set of paths
from the initial vertices “1”, “2” to terminal vertices “A”,
“B”.

For example, three ssDNAs of vertices “1”, “b” and“A” are linked by two ssDNAs of edges “1b” and “bA”.
Their ssDNAs make a unique 60-mer dsDNA. So theelement in row “1” column “a” of the product matrix isnonzero. Two ssDNAs of vertices “1”, “a” aren’t linked
because of no edge. Similarly, other ssDNAs are linked.
3.2 Analysis
Parallel overlap assembly (POA) [3-5] is employed
to generate complete dsDNAs, which represent all thepossible paths of the graph. The reaction mixture iscalled template. The template is distributed into reaction
test tubes. The number of the reaction test tubes is thesame as the number of element in the product matrix.
Each reaction test tubes is represented each elements inthe product matrix. Two primers corresponding to theinitial and terminal vertices in the graph are added to oneof the reaction test tubes (Figure 2). A test of the reaction
to identify the existence of a path is done by polymerase
chain reaction (PCR). If the two primers corresponding
to the initial and terminal vertices of a path exist in thereaction test tube, the dsDNA represented the path isamplified by PCR. To amplify, the reaction test tube hasa path. Thus the element in the product matrix is symbol‘1’. For example, if a dsDNA represented a pass thatconnects vertex “1” to vertex “A” and two primers
corresponding to vertex “1” and vertex “A” exist in areaction test tube, the dsDNA are amplified in the tube
by PCR. The reaction test tube has a path. The element at
the intersection of row “1” and column “A” is the
symbol ‘1’. After that, the product of polymerase chain
reaction is subjected to polyacrylamide gelelectrophoresis in order to visualize the result of thecomputation. By analyzing the existence of the bands ofgel electrophoresis, we are able to read the output of theBoolean matrix multiplication.

Figure 1: Multiplication of matrix and the graphrepresented the matrix.
3

The Proposed Methodology and Experiments

3.1 DNA Sequence Design
In order to perform the calculation using DNA,
single-stranded DNA (ssDNA) of each vertex in theconstructed directed graph is assigned with a unique 20mer DNA sequences generated using DNA Sequence
Generator. As Adleman's experiment, DNA sequences of
ssDNA of each edge connecting two particular vertices
are represented by DNA sequences assigned those twovertices. For example, an edge connecting vertices “i”
and “j” is represented by complementary sequences used
the second half of the sequences assigned vertex “i” andthe first half of the sequences assigned vertex “j”.
Consequently, the ssDNA of the edge link these two
ssDNAs of vertex “i” and “j”, making an incomplete
double-stranded DNA (dsDNA). Similarly, all of vertices©ISAROB 2006

3.3 Experimental Protocols
We solved Boolean matrix in Figure 3. To generate
all the possible paths of the graph by POA was
performed in a 108μℓ solution containing 20μℓ oligos,
10μℓ dNTP (TOYOBO, Japan), 10μℓ 10x KOD dash
(TOYOBO, Japan), 0.5μℓ KOD dash (TOYOBO, Japan),and 67,5μℓ ddH2O(Maxim Biotech). The reaction
consisted of 25 cycles and for each cycles, theappropriate temperature were as follow:- 94℃ for 30s- 55℃ for 30s

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- 74℃ for 10s
The product of parallel overlap assembly is called
template.

Table 1: DNA sequence.
Name
a
b
c
d
e
f
g
h
i
j
Ead
Ebc
Edf
Ece
Efg
Eeh
Ehj
Egi
primer aprimer b
primer iprimer j

The number of reaction test tubes was four. In eachtube added two primers corresponding to the initial andterminal vertices of a path, DNA amplification was doneby PCR. The PCR was performed in a 22.5μℓ solution
consisted of 2.5μℓ for each primers, 1μℓ template, 2.5μℓ
dNTP (TOYOBO, Japan), 2.5μℓ 10x KOD dash buffer(TOYOBO, Japan), 0.125μℓ KOD dash (TOYOBO,
Japan), and 13.875μℓ ddH2O (Maxim Biotech). The
reaction consisted of 25 cycles and for each cycles, theappropriate temperature were as follow:- 94℃ for 30s- 55℃ for 30s- 74℃ for 10s
In order to visualize the result of the computation,
the product of PCR was subjected to polyacrylamide gelelectrophoresis for 40 minutes at 200V. After that, gelelectrophoresis, the gel was stained by SYBR Gold
(Molecular Probes) and the gel image was captured.

4

Results

In each tube added two primers corresponding to theinitial and terminal vertices of a path, DNA amplification
was done by PCR. The product of PCR was subjected topolyacrylamide gel electrophoresis. The gel image of theresult is showed Figure 4. Lane 1 was represented theelement at the intersection of row “a” and column “i” inthe product matrix. Similarly, other Lanes wererepresented the element in the product matrix. Lane M
denoted 20-bp ladder. The existence of the bands of gel
electrophoresis was observed at 100-bp on each Lane.
Each bands differed from the shade of color. Lane 1 andLane 4 were dark. The dsDNAs of these Lanes amplified.
Lane 2 and Lane 3 were light. The dsDNAs of theseLanes didn’t amplify. Consequently, in the productmatrix, the element at the intersection of row “a” andcolumn “i” was the symbol ‘1’. The element at theintersection of row “a” and column “j” was the symbol‘0’. Similarly, other elements were found.

Figure 2: Add two primers in each tubes.

5

Figure 3: Multiplication of matrix and the graphrepresented the matrix.

©ISAROB 2006

20-mer Sequences (5’-3’)
AAAGCTCGTCGTTTAAGGAAGAAGCCTACTGTACTCTGCG
TACCCAATCGAACTGATAAGCAGCCACGTAGTAGAGCTAG
GCCCAAGGTGCGTCAGTAATTCGGTCAACGGAGGGGTATATGGAATGGGTCAGAGAGCACGCACGAAGATAATCCGCAAC
ATGCCTGGCTAAAGTGAGACTAATAAATTCGTATCCACGG
TACGTGGCTGTTCCTTAAAC
CGATTGGGTACGCAGAGTAC
CGTTGACCGACTAGCTCTAC
CACCTTGGGCCTTATCAGTTACCCATTCCATATACCCCTC
ATCTTCGTGCATTACTGACGGAATTTATTAGTTGCGGATTAGCCAGGCATGTGCTCTCTG
AAAGCTCGTCGTTTAAGGAAGAAGCCTACTGTACTCTGCG
GTCTCACTTTAGCCAGGCATCCGTGGATACGAATTTATTA

Discussions

We proposed the method to do the Boolean matrix
multiplication based on DNA computing without
restriction enzymes and carried out the experiment. As aresult of the experiment, each bands of gel
electrophoresis at 100-bp differed from the shade of478

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

As a conclusion, we were able to calculate Boolean
matrix multiplication based on DNA computing without
the use of restriction enzymes. In future, the same
methodology will be used to perform a 10×10 matrix
multiplication on a molecular computer.

color. When POA was conducted, two dsDNAs
represented paths between “a” and “i” vertices andbetween “b” and “j” vertices were generated. PCR was
known that a dsDNA existing the two primers
corresponding to the initial and terminal vertices of apath in the reaction test tube, is amplified faster thanexisting the primer corresponding to the initial or
terminal vertices of a path in the reaction test tube. If
dsDNA amplifies, a band of gel electrophoresis is dark.
A band which is light doesn’t amplify dsDNA. Thus
each bands differed from the shade of color.
Consequently, we found, in the product matrix, theelement at the intersection of row “a” and column “i”
was the symbol ‘1’ and element at the intersection of
row “a” and column “j” was the symbol ‘0’. Similarly,
other elements were found.
M

1

2

3

References[1]L.Adleman, “Molecular computation of solutions to
combinatorial problems”, Science 266, pp.583-585,
1994.
[2]John S.Oliver, “Matrix Multiplication with DNA”, J
Mol Evol 45, pp161-167, 1997[3]Lee,J.Y., Lim, H.W., Yoo, S.I, Zhang, B.T., Park,
T.H., “Efficient Initial Pool Generation for Weighted
Graph Problems using Parallel Overlap Assembly,” inPreliminary Proceedings of the Tenth International
Meeting on DNA Based Computers, pp.357-364, 2004
[4]Ibrahim, Z., Tuboi, Y., Ono, O., and Khalid, M.,“Experimental Implementation of Direct-Proportional
Length-Based DNA Computing for Numerical
Optimization of the shortest path Problem”, Workshop
on Unconventional Computing, 18th European
Conference on Artificial Life, Luniver Press, pp. 3546,2005[5]Kaplan, P.D., Ouyang, Q., Thaler, D.S., Libchaber, A.,“Parallel Overlap Assembly for the Construction ofComputational DNA Libraries”, Journal of Theoretical
Biology, Vol. 188, Issue 3, pp.333-341, 1997

4100-bp→Figure 4: Experimental results of gel electrophoresis on
10% polyacrylamide gel. Lane M denotes 20-bp ladder.
Lane 1 was represented the element at the intersection of
row “a” and column “i” in the product matrix. Lane 2
was represented the element at the intersection of row“a” and column “j” in the product matrix. Lane 3 wasrepresented the element at the intersection of row “b”
and column “i” in the matrix. Lane 4 was represented theelement at the intersection of row “b” and column “j” inthe matrix.

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Independent Agents in a Globalized World Modelled by Tissue P Systems
Marion OSWALD
Faculty of Informatics
Vienna University of Technology
Vienna, Austria
Abstract

Gheorghe Păun in 1998 (for a detailed description see[6], for the actual state of research see [7]).
Here we consider agent tissue P systems (ATP systems for short) consisting of cells (independent agents)
with specific programs which allow for changing theobjects inside the agent or for exchanging objects frominside the agent with objects from the environment
through the cell membrane. The environment only
acts as a communication medium but cannot change
the objects it communicates.
The agents may carry different programs which may
be chosen to be run non-deterministically. In each evolution step of the whole system, an arbitrary numberof agents may run one of its programs which we callthe partially parallel or asynchronous derivation mode(see [1]). We may also consider the maximally parallel
derivation mode, often used in the area of P systems,
where in every cell (agent) one of the programs has tobe run, or the sequential mode where, in each evolution step, only one agent runs one of its programs.
A model of tissue P systems closely related to themodel considered in this paper is the model of P
colonies as it was first introduced in [4], where theprograms of the agents are very simple, just allowing
for exchanging two symbols between the agent and theenvironment and changing a second symbol inside theagent to another symbol at the same time.
In the generating case, even one agent with a suitable number of such programs turned out to be computationally complete (see [2]). But in contrast to theusual investigations in the area of P systems where thegenerative and accepting power of those systems is investigated we here are interested in the time development of our ATP systems. Special questions concern
the probability of events to happen in a given time interval. Theoretically these questions may turn out tobe quite difficult, as for example even the simple question whether a specific event may ever happen or not
may turn out to be undecidable at all. Hence, it is achallenging problem to find ATP systems with simple
programs in the agents that allow at least for deciding

For modelling the interactions of independent
agents (only interacting via a common environment,
but not depending on any direct physical interactions
with other agents) in the globalized world, we here
consider so-called agent tissue P systems (ATP systems for short). Based on the model of tissue P systems, such ATP systems consist of cells (independent
agents) with specific programs which allow for changing the objects inside the agent or for exchanging objects from inside the agent with objects from the environment through the cell membrane. We investigate
the computational power of specific variants of ATP
systems and also discuss the special decidability problem whether or not a given ATP system of specific
type from its initial contents can ever reach a configuration where all objects in the system are containedin a specific subset of the set of objects.

1

Introduction

In a globalized world, the actions of an individual(in the following called agent) may affect not only its
direct environment, but instead also other agents inan arbitrary (physical) distance. In this paper weonly investigate the part of interactions between theagents which does not rely on the physical distance,
but due to the modern communication facilities, likeemail, web, etc., allows for interactions independent
from the physical distance of the agents. (In what follows we shall refer to this part of the world we aregoing to model as “the globalized world”.)
For modelling the interactions of independent
agents (only interacting via the environment, but notdepending on any direct physical interactions withother agents) in the globalized world we use a specificmodel of tissue P systems.
Tissue P systems first were considered in [5]; they
are a variant of membrane systems as introduced by©ISAROB 2006

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some specific questions.
As an extension of the static variant described before, we may also consider a dynamic variant, where
agents may die or may be newly born. Moreover, as anormal feature of artificial intelligence, the programsof the agents also may undergo dynamic changes.

2
2.1

system in the environment. Throughout the computation, a configuration of ΠAT P is described by thecontents (state) of the environment and the contents(states) of the agents.
In the sequential derivation mode, a derivation stepof ΠAT P , i.e., passing from one configuration of ΠAT P
to another one, means that one agent carries out oneof its programs, non-deterministically chosen. In theasynchronous derivation mode, ΠAT P may run one
program in an arbitrary number of cells (agents). In
the maximally parallel derivation mode, every agent(cell) has to carry out one of its programs (if possible).

Definitions
Preliminaries

An alphabet V is a finite non-empty set of abstract
symbols. Given V , the free monoid generated by Vunder the operation of concatenation is denoted by V ∗ ;moreover, we define V + := V ∗ \ {λ} , where λ denotes
the empty word. A multiset over V is represented asa string over V (and any of its permutations). By | x |we denote the length of the word x over V as well as
the number of elements in the multiset x.
A deterministic finite automaton (DFA for short) isa quintuple M = (Q, T, δ, q0 , F ) where Q is the finite
set of states, T is the input alphabet, δ : Q × T → Q
is the state transition function, q0 ∈ Q is the startingstate and F ⊆ Q is the set of final states. The transition function δ can be extended in a natural way to afunction δ : Q × T ∗ → Q. The language accepted by
the DFA M is the set of all strings w ∈ T ∗ that are
accepted by M in such a way that δ (q0 , w) ∈ F .
For more notions from the theory of formal languages, the reader is referred to [3].

2.3

A Special Variant of ATP Systems P Colonies

As a special variant of ATP systems, we consider
the model of P colonies introduced in [4] and further
investigated in [2]. A P colony is a construct
ΠC = (V, e, T, B1 , ..., Bn )ΠAT P = (O, OT , E, B1 , ..., Bn )where V is an alphabet (its elements are called objects), e is a distinguished object of V (the environmental object), T ⊆ V − {e} is a set of final objects, and B1 , ..., Bn are agents; each agent Bi is a pair
Bi = (Oi , Pi ), where Oi is a multiset over V (the initialstate of the agent), and Pi is a finite set {pi,1 , ..., pi,ki }of programs; each program pi,j is either a non-checking
program of the form ha → b, c ↔ di, or a checking program of the form ha → b, c ↔ d/c0 ↔ d0 i. In the generating case, we always assume that each Oi consistsof two copies of e. A P colony can be written as the
ATP system
∗ΠAT P = V ∗ , T ∗ , {e} , B1 , ..., Bn .

where V is an alphabet (its elements are called objects), E is a set of environmental objects (i.e., theinitial contents – or initial state – of the environment),
OT ⊆ O is a set of final objects, and B1 , ..., Bn areagents; each agent Bi is a pair Bi = (Oi , Pi ), where
Oi is a multiset over O (the initial contents – or initialstate – of the agent), and Pi is a finite sequence ofevolution and/or communication rules. An evolution
rule from Pi changes one object from O inside the cell(the agent) to another one, whereas a communication
rule exchanges one object from the environment withan object from inside the cell.
A computation in the ATP system ΠAT P starts with
Oi in the corresponding agent and with E and eventually some additional multiset as the input of the

At the beginning of a computation performed bya given P colony, the environment contains arbitrarily many copies of e; moreover, each agent contains two copies of e. At each step of the computation, the contents of the environment and of theagents change in the following manner: In the maximally parallel derivation mode, each agent which
can use any of its programs should use one (nondeterministically chosen), whereas in the sequential
derivation mode, one agent uses one of its programs at
a time (non-deterministically chosen). By using a program ha → b, c ↔ di, an agent with objects ac insideand with d in the environment will get the objects bd
inside and c will now be placed into the environment.
Using a program ha → b, c ↔ d/c0 ↔ d0 i means to pass

2.2

ATP Systems

An agent tissue P system (ATP system) is a construct©ISAROB 2006

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from ac inside and d outside to bd inside and c outside, and this should be done whenever possible; if theinterchange c ↔ d cannot be done, then we pass fromac0 inside and d0 outside to ad0 inside and c0 outside.
Note that the first rule is always applied, and that either the first or the second communication rule has tobe applied, with priority for the first one.
Any copy of an object can be involved in only
one rule. Using the programs in this way, withall agents acting simultaneously or sequentially, nondeterministically choosing the program(s) to be applied, we can pass from one configuration of the system (represented by the contents of the agents and of
the environment) to another configuration. Formally,a configuration can be written as an (n + 1)-tuple(w1 , ..., wn ; wE eω ), with wi ∈ V 2 representing the ob∗
jects from agent i, 1 ≤ i ≤ n, and wE ∈ (V − {e}) ,representing the objects from the environment different from the “background” object e; eω is used as notation for the arbitrarily many copies of the object ethat are always contained in the environment; the initial configuration of a system is always (ee, ..., ee; eω ).
A sequence of transitions is called a computation. A
computation is said to be halting, if a configurationis reached where no program can be applied anymore. With a halting computation we associate aresult which is given as the number of copies of theobjects from T present in the environment in the halting configuration.
Because of the non-determinism in choosing the
programs, starting from the initial configuration we
obtain several computations, hence, with a P colony
we can associate a set of (vectors of) numbers, denotedby N (Π), computed by all possible halting computations of Π.
2.3.1

languages generated by matrix grammars without appearance checking.

3

The general variant of decidability problems weconsider here is the following: Given an ATP systemwith specific initial contents, is it decidable whether itever can reach a configuration where all objects in thesystem are contained in a specific subset of the set ofobjects?

3.1

Undecidability for P Colonies

Can a configuration fulfilling specific constraints
ever be reached (e.g., the objects occurring in the system all are included in a specific subset of the set of objects)? This question in general is not decidable. For
a given P colony, the question whether such a system
will ever produce anything, i.e., whether N (Π) = ∅,corresponds with the famous halting problem (e.g., see[3]), hence this question is undecidable.

3.2

Decidability for ATP Systems

We now consider a specific variant of ATP systems
that model finite automata, and in this framework weconsider some problems which are easily decidable.
Let M = ({q1 , ..., qn }, T, δ, q1 , F ) be a finite automaton. Then we construct the ATP system
ΠAT P
O
OT
Oi
Pi

Computational power of P colonies

In [2], P colonies working in the sequential mode withchecking programs of height at most 5 were shown tobe computationally complete, whereas when working
in the maximally parallel mode, P colonies using programs again with height 5 but without priorities on
the communication rules (i.e., without checking programs) achieve the same computational power, too.
Already one agent using checking programs isenough to obtain computational completeness in P
colonies working in the sequential mode, which is aquite surprising result optimal with respect to thenumber of agents. If only non-checking programs areused, even P colonies with only one agent as well as
P colonies with an arbitrary number of agents working in the sequential mode characterize the family of©ISAROB 2006

Undecidability / Decidability for
ATP Systems

=
=
=
=
=

(O, OT , ∅, B1 , ..., Bn ),Q̂ ∪ QT ∗ ,F ∪ Q̂,{q̂i }, 1 ≤ i ≤ n,{hq̂i ↔ qi ui , hqi aw → qj wi , hqj w ↔ q̂i i| δ (qi , a) = qj , 1 ≤ i, j ≤ n, u, w ∈ T ∗ } .

The programs in the agents consist of single communication rules (q̂i ↔ qi u and qj w ↔ q̂i ) and single
evolution rules (qi aw → qj w); as only the beginning of
the strings is changed, these rules could also be written
in the form q̂i ↔ qi and qj ↔ q̂i as well as qi a → qj
which shows that the infinite set of rules Pi can be
described in a finite way.
Observe that the information processing in ATP
systems (other than in finite automata), only takes
place via the environment without any direct communication between the cells (agents), which here can beseen as parallel processors working in an asynchronous(sequential) manner.

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We now want to know whether the system started
with the input q1 wk for some wk ∈ T ∗ , 1 ≤ k ≤ m,
m ≥ 1, can ever reach a configuration where all objects
in the system are contained in OT (indicating that
all the input string(s) w1 , ..., wm are accepted). This
problem is easily decidable in a reasonable amount oftime: after at most (|w1 | + ... + |wm |)∗3 steps we know
the answer. Moreover, in the affirmative case, we knowthat the input string(s) have been accepted, while in
the negative case, at least one input string has notbeen accepted. In fact, we even know exactly how
many input strings have been accepted by counting
the objects qi ∈ F. Hence, by just looking at the environment, we can figure out the desired result and thus,
after a period that can easily be computed, can decide
the given problem.

for a specific accepting variant of ATP systems someproblems were shown to be easily decidable. Using
suitable representations and systems, in general also
the question whether w ∈ L can be decided for any
recursive language L. One of the most challenging
questions for future research is to find variants of ATP
systems between these two extremes of decidable problems. Another question not yet investigated in detailin the area of P systems is concerned with permitted
and forbidden configurations (e.g., given by complementary subsets of O): Given an initial configuration,
can the systems always stay within the permitted configurations, or is it possible that it may reach a forbidden configuration?

Acknowledgements
4

Variants

We gratefully acknowledge interesting and fruitful
discussions with Rudolf Freund.
The work of Marion Oswald is supported by FWFproject T225-N04.

One variant of ATP systems is taking the environment not only as a communication medium, but alsoenabling it to change the objects it communicates.
Formally, this, for example, could be done by defining an additional agent (symbolizing the environment)with its own corresponding programs.
On the other hand, as an extension of the static
variants described above, we may also consider a dynamic variant, where agents may die or may be newly
born, i.e., we can consider cell deletion, cell divisionand cell generation. When a cell is deleted, the exchange of a specific element between the environment
and the cell or the evolution of an element inside thecell may cause the death of the cell (with variants
where all the elements inside the cell also die or elsethe contents of the deleted cell is added to the environment). By cell division, one cell can be reproduced with its whole contents and the corresponding
programs, whereas new cells can be generated with explicitly stated contents and new or modified programs.
Moreover, as a normal feature of artificial intelligence,the programs of the agents also could undergo dynamicchanges.

5

References[1] R. Freund, Asynchronous P systems, Proceedings
WMC5, 2004, 12–28.
[2] R. Freund, M. Oswald, P Colonies working in the
maximally parallel and in the sequential mode,
in: G. Ciobanu, Gh. Păun (Eds.), TAPS’05,
Timişoara, Romania, 2005, 49–56.
[3] J. Hopcroft, J. Ullman, Introduction to automata
theory, languages, and computation, AddisonWesley, 1979.
[4] J. Kelemen, A. Kelemenová, Gh. Păun, On the
power of a biochemically inspired simple computing model: P colonies, in: M. Bedan et al.
(Eds.), Workshop on Artificial Chemistry, ALIFE9, Boston, USA, 2005, 82–86.
[5] C. Martı́n-Vide, J. Pazos, Gh. Păun, A. RodriguezPaton, Tissue P systems, Theoretical Computer
Science 296 (2) (2003) 295–326.

Conclusion[6] Gh. Păun, Membrane computing - an introduction,
Springer-Verlag, Berlin, 2002.

We have introduced the computing model of agent
tissue P systems (ATP systems) and investigated somedecidability problems concerning questions whether a
specific event may ever happen or not in these systems.
As we have seen in the generating case of P colonies,
even simple questions may be highly undecidable, yet©ISAROB 2006

[7] The P systems webpage:
http://psystems.disco.unimib.it

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A Bioinformatics Method for Signaling Pathways in Cells
Jian-Qin Liu and Katsunori Shimohara
ATR Network Informatics Laboratories
2-2-2, Hikaridai, "Keihanna Science City,”
Kyoto 619-0288, Japane-mail: {jqliu, katsu}@atr.jp
Abstraction is not only a way to represent data
for theoretical study but also a necessary method
for simulation, especially in large-scale
bio-informatics systems. From experiments onmolecular computers, we can gain some insight.
For example, in DNA computers, the operators
are carried out in test tubes to accomplish
specific computing tasks so that the relationshipbetween input and output is exactly determined.
This process acts as a kind of bottom-up
wetware-level inference machine for synthesisof the biochemical reactions in vitro. Based on
the formalization of this physically feasible
computing process, the formal models, operators
and algorithms extracted will be beneficial toextending our knowledge of mechanism behindthe underlying biological objects verified fromexperimental data to the broader domain of
biological systems that include the underlying
molecules in the reactions. This knowledge
would act as a bridge toward top-downknowledge discovery.

Abstract
In this paper, a new bioinformatics method formodeling cellular pathways is proposed basedon graph rewriting and network reconstruction.
The simulation result shows that the graphrewriting operators and networking structure arebeneficial to quantitatively describe thesignalingmechanismof
GEF/GAP/kinase/phosphatasepathway
networks.
1. Introduction
The motivation of the research work presented
here is to study the mechanism of molecular
computers made from molecular biochemical
reaction and to design new bioinformatics
simulation methods based on this mechanism forapplicationtobioinformatics
analysis.
Bioinformatics simulation modeling andmethods are more difficult than one might
expect, even though many simulation models
and methods have recently been proposed. It isimperative to design new models and to usethem in discovering biological knowledge [1].
As a highlighted theme in the field of genomics
and proteomic bioinformatics, pathway modelingwith complex behaviors due to non-linear
interactions is one of the most important
remaining tasks [2∼7]. For example, “pathway
logic” [8] directly handles pathway modeling.
The “Maude system” [11] is software that canrealize pathway logic by adopting the functionof “rewriting”. The software is based on
rigorous theoretical computer science, and it is
helpful for discovering pathways. This work has
encouraged us to explore a formal modelingmethod for pathway analysis.
The rapid progresses in experimental molecularbiology are providing us much knowledge intheory and tools for biological cell experiments
where many biochemical reactions are carriedout with an enormous number of bio-molecules.

©ISAROB 2006

2. Structural Scheme for Bioinformatics
Toward the goal of analysis-based pathway
simulation, this paper proposes a formal systemdefined as a type of graph rewriting to unify
different jobs in pathway modeling, since graphs
can provide efficient representation and related
semantic explanation.
Here we focus on pathway simulation. In the
study of the signaling pathways in cells, it ismeaningful to work out a unified formal method
based on a molecular mechanism, where themechanism of modeling bio-pathways and thestructure of simulating pathways are consistent.
For this purpose, a graph-rewriting method forpathway simulation is discussed in terms ofcorresponding
formalcharacteristics,
representation schemes and simulation issues.
This is one of the efforts being made towardautomatic analysis for bioinformatics in which

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multidisciplinary fields can be integrated into
computer-aided systems for bioinformatics.
Great progresses have been made in the field of
bioinformatics.
However, much difficulty
would remain in integrating these fields in orderto make breakthrough in new knowledge
discovery based on theory and by software. In aword, we are trying to tackle this topic by aunified model -- graph rewriting -- that is
supported by computer science theory andprogramming technology. Furthermore, we
expect to extend this model to a powerful
technology.
On an abstract level, the corresponding formal
model can be constructed as a graph rewriting
system. Now, we define the graph rewriting in aformal way as:
Let G = < V, E>
where
A -- the alphabet set;
V -- the set of vertexes;
E -- the set of edges.
Consequently, from the above model, we canderive the following writing processes on graphs:

Fig. 1The kernel of the algorithm is the mechanism of
selection and variation by the estimation ofinteraction rating. The interaction algorithm oftwo pathways is outlined in a stochastic way by
the following steps:
Step 1: The probability Pp (x, t) is simulated bya logistics function (a = 4, X0 = 0.05).
Step 2: The two classes of the population are
estimated based on a uniform distribution
assumption such that the classes are given asclass1 ranges in [0,0.5]; class2 ranges in (0.5,1].
The mean m(t) and variance v(t) are the main
measures for the two classes. The element
numbers of the two classes are L1(t) and L2 (t).
Step 3: Two individuals are selected from theclass with the largest probability.
The two pathways (individuals) are selected tobe combined into a new pathway by theinteraction operation. The new pathway is thenplaced in the pathway set (pool). The probabilityof a new pathway is estimated by the weightsummation of the probability of the two
individual pathways, and its simulated
interacting-rate is assigned by this value.
Step 4: The martingale measure Mp for thetermination is defined as: Mp = INP – ANP,
where INP denotes the interaction rate of the
new pathway and ANP denotes the average ofthe interaction rating of the pathway set. In thisexample, the measure is estimated by weighted
summation of the probabilities of the twopathways.
Step 5: If this measure is supermartingale(increased), then goto step 2. If submartingale(decreased), goto next step.

G -> G’
where G and G’ refer to the graphs.
3. Simulation for Cellular Pathways
In order to elucidate this network’s behavior
quantitatively, we discuss an example ofsimulated pathways (Cf. Fig. 1) in which theatomic pathway set of Rho-MBS-MLC is
defined as the set of {pathway of Rho, pathwayof MBS, pathway of MLC}. In the normal case,the interaction-rating array (symmetric form) is
set in an empirical way, that is, the mutual links
are designed to reflect the reversibility ofphosphorylation/dephosphorylation of related
reactants in the pathways. The concentration
representation for signaling proteins of Rho
GTPase in the set of {MLC+, MLC-, MBS+,
MBS-} can be defined in different ways, such as
digital, continuous or symbolic values, where+ and – refer to the phosphorylation anddephosphorylation states, respectively. In thesimulation, initialized values of concentration
can be set in a random way. The activation ruleof cellular agents on quantified concentration isdefined in the following form: IF perception
THEN action. Here, the related threshold
function and fitness function are defined in an
empirical way.

©ISAROB 2006

Relations among the pathways

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states of pathways can be obtained.

Step 6: Stop and output the current pathway set
as the final result.
Because it is impossible to quantitatively
measure all of the coefficients of biochemical
reactions for pathways through biochemical
experiments, in the sense of local concentrationof the related signaling molecules, simulation isvery important to understanding the quantitative
effect of the interaction functions of pathways.
Considering the importance of the interaction
operation in the entire simulation work, let us
select one sample from the pathway pool of theinteracting processes. This is a snapshot of arandomly selected episode from the current
moment to next moment in a continuousinteracting process. Here we have 19 pathways
in the pathway set. They are clustered into twoclasses whose concentrations vary at (0, 0.5] and(0.5, 1.0). Here, ‘concentration’ refers to theestimated average concentration of the signaling
protein, such as MBS in the MBS pathway and
MLC in the MLC pathway, which act asfunction
effectorsofthephosphorylation/dephosphorylation
intheunderlying pathway. The pathway index of class
1 is 6, and that of class 2 is 13. Two pathways
with larger probability than the others in thepathway pool, indexed as 2 and 15, are selected
for combining to generate a new pathway.
The new pathway is assigned an average
concentration -- 0.493133 -- of the signaling
proteins to connect the signaling proteins in thetwo pathways. Then, this pathway is classified
as class 1 and its probability is estimated as
0.853140, according to the following formula:

100%
80%
60%

series 3
series 2
series 1

40%
20%
0%
1

3

5

7

9

11 13 15 17

Fig. 2 (a) Concentration vs. time curves that
correspond to the two states of the pathways
1.2
1
0.8
0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

-0.2

Fig. 2 (b) Size of the signaling molecules
Pr' = Pr1 * Pr1/(Pr1+Pr2) + Pr2 * Pr2/(Pr1+Pr2),
where Pr' refers to the probability of the newpathway. Pr1 and Pr2 refer to the probabilities ofthe original pathways indexed as 2 and 15,
respectively. We find that Pr' is bigger than the
average probability of class 1 -- 0.184589, whichmeans it is more plausible from the viewpoint ofinteraction-rating measurement.
The termination for the interacting process is
as follows:
If the new pathway has a higher probability
than the average value, the computing
process will be continued.
If the total allowed time or the newprobability is lower than the average
probability, the computing process will beterminated.
As Fig. 2 (a), (b), (c) and (d) shown, the two©ISAROB 2006

1
1

19

2

Through analysis
in 0.8
theory and3in simulation,
18
0.6

17

4

0.4
16

5

0.2
0

15

6

14

7
13

8
12

11

10

9

Fig. 2 (c ) Partial interaction network
reconstructed by estimation

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

(Main) References[1] Nir Friedman, Inferring Cellular Networks
Using Probabilistic Graphical Models, Science,Vol. 303, 6 February 2004; pp.799-805.
[2] O. Wolkenhauer, et al., Control andcoordination in biochemical networks, IEEE
Control Systems Magazine, 24 (4) Aug. 2004;
30-34.
[3] Saez-Rodriguez et al., Modular analysis of
signal transduction networks, IEEE Control
Systems Magazine, 24 (4) Aug. 2004; 35-52.
[4] S. Y. Shvartsman, et al., Epidermal growth
factor receptor signaling in tissues, IEEE
Control Systems Magazine, 24 (4) Aug. 2004;
53-61.
[5] M. Khammash and H. El-Samad, Systems
biology: from physiology to gene regulation,
IEEE Control Systems Magazine, 24 (4) Aug.
2004; 62-76.
[6] S. Paliwal, et al., Responding to directional
cues: a tale of two cells [biochemical signaling
pathways], IEEE Control Systems Magazine, 24(4) Aug. 2004; 77-90.
[7] H. Schmidt and E. W. Jacobsen, Identifying
feedback mechanisms behind complex cell
behavior, IEEE Control Systems Magazine, 24(4) Aug. 2004; 91-102.
[8] C. Talcott, S. Eker, M. Knapp, P. Lincoln, K.
Laderoute, Pathway Logic Modeling of Protein
Functional Domains in Signal Transduction,
PSB 2004.
[9] John S. McCaskill and Ulrich Niemann,
Graph Replacement Chemistry for DNA
Processing, in A. Condon (Ed.): DNA 2000,
LNCS 2054, pp.103-116, 2001. Springer-Verlag,
Berlin, Heidelberg, 2001.
[10] Carol Ezzell, Proteins rule, Scientific
American, 286 (4), April 2002; pp. 26-33.
[11] http://www.csl.sri.com/projects/maude/

1.2
1
0.8
0.6
0.4
0.2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Fig. 2 (d) Concentration vs. time under the
dynamical environment
In a word, the interaction operators based on
estimation can generate stable states of thesimulated pathways under certain conditions. As
a result, the proposed method is efficient.
4. Conclusion
The simulation method based on graph datastructure and related rewriting operators is
discussed. Through pathway simulation inbioinformatics, new knowledge may emerge andwill surely be beneficial to develop
computer-aided system for proteomics [10].
Acknowledgement
This research was conducted as part of“Research on Human Communication” withfunding from the National Institute ofInformation and Communications Technology,
Japan.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3-D Modeling of Remote Dynamic Situations
Using Mobile Stereo Cameras
Iku Yamaguchi, Joo Kooi Tan, and Seiji Ishikawa
Department of Control Engineering, Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japan
E-mail: {yamaguti, etheltan, ishikawa}@ss10.cntl.kyutech.ac.jp

Abstract

prerequisite to the modeling. Instead, the camera

This paper proposes a technique for recovering 3-D
shape of the objects in remote dynamic situations
employing mobile cameras. Since the proposed
technique allows independent move of the employedcameras, 3-D recovery under various environments suchas remote places can be realized. Our technique has anadvantage over others in that camera calibration before
taking images is not prerequisite to the recovery. A
system is described that captures images of remoteobjects by the proposed motion capture technique,transfers the images by analog airwaves, and recovers
3-D shape of the objects from the images. It is expectedthat this technique will improve the efficiency of image
information transfer. In the performed experiment, ahuman walking motion captured at a remote place
recovered its 3-D motion successfully at a laboratory by
image transfer.
Keywords— Motion capture, Mobile cameras, Imagetransfer, Factorization.

calibration is done by employing the 2-D coordinates ofthe feature points of rigid object in the images of a video
stream. This leads us to the modeling of any events andperformances that need immediate understanding of thesituation. In recent years, dynamic calibration techniques[2] that can calibrate the employed cameras speedier thanthe conventional techniques are employed in several
commercial motion capture systems. However they
actually need some preparation before starting of taking
images even if it is possible to calibrate cameras in ashort time. Therefore, if they are employed, a decisive
moment may be missed after fixation and calibration ofcameras.
In this paper, we propose a mobile motion capturesystem that recovers 3-D shape of remote objects in anarbitrary environment. Moreover, in order to perform anefficient 3-D recovery from image data obtained at a1. Introduction

remote environment, an image transfer system is also
described. Some experimental results are shown andThree-dimensional (abbr. as 3-D hereafter) modeling

discussion is given.

techniques of non-rigid objects have quite considerably
developed in recent years. On the other hand, it has
become possible to transmit images by using the Internet,

2. Proposed 3-D modeling techniqueas long as the image of a video camera can be converted
into digital data. Therefore it will be useful to develop a

In the first place, the 3-D coordinates of objects are

3-D modeling technique with video images acquired

necessary in order to realize 3-D modeling. This section

from mobile camera at remote dynamic situations.

shows how to obtain the 3-D coordinates of objects forThis paper presents a technique for 3-D modeling of

3-D modeling.

remote dynamic situations containing non-rigid as well

The technique proposed in this paper recovers the 3-D

as rigid objects employing mobile stereo cameras. We

coordinates of rigid objects and non-rigid objects, if

have developed this technique as a new motion capture

more than or equal to two mobile cameras are employedtechnique whose cameras can move around the objects

for motion capture. Figure 1 shows the outline of the

independently.

image capture system.

We have already proposed a shape recovery techniqueThis system adopts a method of taking images of rigidof 3-D non-rigid objects based on multiple uncalibrated

objects and non-rigid objects simultaneously, whilecameras [1]. This technique has an advantage over others

F ( F ³ 2) cameras move in l (l = 1,2, L , L) directions.

in that camera calibration before taking images is not©ISAROB 2006

Some feature points are put on the surface of each object.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Camera F

Camera 1: Rigid points: Non-rigid points

Camera 2

Fig.1 System overview.

()

Wl = Wl R

We call the feature points on rigid objects as rigid
points and those on non-rigid objects as non-rigid points

Wl N .

(2)

Since camera orientation changes from l=1 to L, thehereafter. Camera orientations are all different throughmeasurement matrix W at all the observation time isthe camera movement from l = 1 to l = L . Therefore

described as follows;

the cameras need calibration to obtain 3-D informationsuch tools for the camera calibration. Instead it employs

æW1R W1N
ö
ç
÷
R
N
ç
÷
W2 W2
W =ç
÷.
O
ç
÷
R
N÷
ç
W
W
L
L ø
è

rigid points observed in the captured images. Details of

It should be noted that the data don’t exist at blankthe procedure are described below. There are twoelements in the matrix W , except for Wl R and Wl N .

of the object. Although conventional 3-D recovery
techniques need camera calibration employing particular
tools, the proposed technique doesn’t have to employ(3)

Matrix Wl R is a measurement matrix of rigid points.

important phases in the procedure.

Since we assume that same rigid points are always
tracked and taken images during image capturing, even if

2.1 Recovery of Rigid Objects

the camera moves, this matrix with superscript R
The 2-D coordinates ( x, y) of the feature points incontains the coordinates of the same rigid points.

the image obtained from the camera whose orientation is

Therefore it is possible to transform Eq.(3) as follows

l are stored in the measurement matrix as follows;with respect to the coordinates of rigid points;

æ x1l L x Rln
Wl = ç lç y L ylR1nèx Rl n +1 L x Rl n + Nn ö÷
y Rl n +1 L y Rl n + Nn ÷ø

æW1R
ç
çW R
W =ç 2
ç M
çW R
è L

(1)

where the number of rigid points is denoted by
Rn ( Rn ³ 6) and the number of non-rigid points by N n .

(Here at least not less than six rigid points are required toºW

the calculation. Let the sub-matrix containing rigid

W

N

)

W2N

ö
÷
÷
÷
O
÷
WLN ÷ø
.

(4)

Applying the factorization method [3] to Eq.(4), we have

Wl R and the sub-matrix
containing non-rigid points be denoted by Wl N withrespect to the matrix of Eq.(1). Then Eq.(1) can be
rewritten as

points be denoted by©ISAROB 2006

R

W1N

W R = M ×SR .
Thus the 3-D coordinates of rigid points S
obtained.

489

(5)
R

are

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3.1 Transferring images to PC for capture

2.2 Recovery of non-rigid objects
In Eq.(5), matrix M is a camera orientation matrix

The analog image output of the video camera is

at all the observation time. When 3-D coordinates of thetransmitted to PC with a transmitter (RG-1000w) for

non-rigid points at position l are denoted by

SlN , theperforming 3-D modeling.
The signal is amplified and transferred by employing

following relation holds.

WlN= M l × SlN ,

Yagi

(6)

(CD-x1218)

that

is

one

of

the

high-directivity antennas. The signal of the camera ofwhere M l denotes a camera orientation matrix at

each channel is transmitted and received with an

position l . Therefore SlN is obtained as

SlN = M l+ × Wl N

antenna

individual antenna. The reception side of the antenna is(7)connected to a receiver (RG-1000w).

by using the pseudo-inverse matrix M l+ of M l . Thus

3-D coordinates of non-rigid objects are given by S N .

3.2 Capturing images to PC via a capture board

In this way, 3-D recovery of both feature points isachieved.

Images of the objects concerned are captured into PC
via a capture board. The maximum frame rate is 30 fps
and its resolution is 640 ´ 480 pixels. The captured

3. Transferring images from a remote place

images are transferred through Internet to a modeling PC
in a remote laboratory for recovery calculation and

The method of transferring images obtained by adisplay of the result.

mobile camera is important for 3-D recovery of theobjects at a remote place.

3.3 3-D recovery from obtained images

In recent years, it has become possible to transmit
images by using various ways such as the Internet, as

Applying the proposed technique to the obtained

long as the image of a video camera can be converted

images, we finally get recovered human motions. For

into digital data. This leads us to obtaining images from a

3-D expression with high reality, we create human

remote place immediately. Thus we construct an image

models employing OpenGL.

transfer system for a mobile motion capture system. The
protocol of image transfer includes analog airwave,
wireless LAN, and Internet. Alternatively there is of

4. Experimental results

course a method of storing images once into a video tapeand mailing them to another place, we choose a moreIn the experiment, a human moving motion captured at

effective way in this study. In order to use a larger spacea remote place was modeled its 3-D shape successfully.

efficiently for 3-D recovery outdoors, we construct an

Figure 3 and Figure 4 show the captured images and its

image transfer system which employs an analog airwave,

3-D modeled results, respectively. In both figures, time

although wireless LAN is also effective for some outdoor

proceeds as indicated by arrows.

experiments.
We construct the following system. Figure 2 shows
the overview of the image transfer system.
Mobile camera

5. Conclusions

Modeling PC Internet/LAN

This paper proposed a technique for 3-D modeling of
remote dynamic situations employing mobile stereo

Transmitter
Yagi antenna(CD-x1218)

cameras.

Capturing PC

One of the largest advantages of the proposed
technique is the degrees of freedom for the applicable

Receiver(RG-1000w)

situations. Some of its reasons are described as follows.

Fig.2 Image transfer system.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006i) In the proposed system, ordinary camera calibrationThis

is not necessary before taking images for the recovery.

Foundation of Information Science Advancement, which

Therefore, we can start the experiment immediately,is greatly acknowledged.

which may be applicable to broadcasting of an

References

accident at a remote place promptly in a 3-D way.

[1] J. K. Tan, S. Ishikawa, “Recovering 3-D motion of teamii) Even if the object concerned moves widely, we cansports employing uncalibrated video cameras”, IEICE Trans.

also track along its moving, because the system

on Information and Systems, E84-D, 12, 1728-1732, 2001.

doesn't have a limit with the range of motion capture,[2] Borghese N.A., Cerveri P. “Calibrating a video camera pairunlike the existing methods employed in a studio.

with a rigid bar”, Pattern Recognition, 33, 1, 81-95, 2000.

In this way, the proposed technique, 3-D modeling of[3] C. Tomasi, T. Kanade, “Shape and motion from image

remote dynamic situations, has finally been realized.

study

was

partlysupported

Kayamori

streams under orthography: A factorization method”, Int. J.
Computer Vision, 9, 2, 137-154, 1992.

Fig.3 Video images of a person in an action.

Fig.4 Recovered motion and the environment.

©ISAROB 2006

by

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Tracking an Object Using an Update Type Two-dimensional Histogram
Hirofumi UEMURA

J oo Kooi TAN

Seiji ISHIKAWA

Department of Mechanical & Control Engineering
Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japanuemura@ss10.cntl.kyutech.ac.jp, {etheltan, ishikawa}@cntl.kyutech.ac.jp
Abstract: In this paper, we propose a new technique fortracking an object under changeable illumination. In object
tracking that use color information, illumination change is aserious problem. The reason is that the color to be tracked
usually changes, when the illumination changes. The robustnessof the tracking performance can be improved to a large extent
by the employment of an update-type color histogram. The
weight imposed by the Gaussian distribution is used together
with the update of the histogram. The Gaussian distribution ismade from the initial color model of the object. The distribution
near the initial color model is added to the histogram with higher
weight. As a result, the color model can be updated with better
accuracy. The effectiveness of the proposed technique isconfirmed by experiments.

describes the shape of color distribution. Already reported
technique [4] employs the color histogram with normalized rg
expression. But it is not very easy to understand the distribution
intuitively, as it doesn’t match the human sense of color.
In this paper, we propose a new technique for color detection
employing the 2-D color histogram on the chrominance space.
Illumination change of a given environment is also taken into
account by the employment of an anisotropic Gaussian
distribution in the modeling of a 2-D color histogram. We
employ HSV system for color expression because of its
popularity among computer vision researchers and the similarity
to human sense.

Keywords: Human tracking, Two-dimensional color histogram,
Anisotropic Gaussian distribution, Robot vision.

Object tracking systems normally employ shape and texture
information in order to calculate correlation between successive
image frames. The reason why the employment of color
information is not very popular is that it is not robust toillumination change. The color information is, however,advantageous when a human is searched in an image, because
the skin color is an intrinsic nature to a human.
Illumination change occurs gradually in the time lapse of theday, but it occurs suddenly, if one turns on or turns off the lightin the room or goes into a shadow/shade spot from a san lightspot outdoors. Although both situations should be overcome inorder to employ color in the tracking, the former is taken into
consideration in this particular paper.
We have developed a color tracking system based on a 2-Dcolor histogram. It can track a specified color in a room withmoderate illumination change. It achieves it by acquiring thechanging color, and updating 2-D color histogram. Overview ofthe system is given in Fig.1.

2. A color t r a ckin g syst em

1. I n t r od uct ion
Color is an important cue as well as shape or texture infinding an object in a scene. A mother can identify her childeasily in a park among lots of children by the color of her child’s
upper or lower clothes. A computer vision system searches forskin color regions in an image to extract human faces or hands
in order to find out a human. Many studies have been performed
until now for the purpose of detecting or segmenting objects
from images using color information [1,2]. The difficulties of
employing color information in computer vision problems is,however, it is not very stable as a feature to be detected, since it
is normally influenced by illumination conditions caused byweather, time in a day, lights, shade, etc. A robust color detection
technique hasn’t been proposed yet in the computer vision field.
When a computer vision system performs color extraction, it
needs to make a color model. There have been many methods
for the color model making [3]. The employment of
two-dimensional (2-D) color histogram on the chrominancespace seems promising for color extraction, since it clearly©ISAROB 2006

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PC

DisplayS

RS-232C

128

Video

Pan-Tilt-Camera(a)Target Object

0

128
(b)

H

Fig.2 Target image (a) and its histogram (b).

Fig.1 Overview of the developed color tracking system.

2.1 A histogram model of color
In the proposed technique, we use a 2-D histogram model
based on parameter H and S among the HSV color space. Note
that, both H and S are sampled by the 8bit level. Histogram H Iof the object area is acquired from the input image by an oval
window. However, it adds it to the histogram only when S>30
holds. Moreover, the frequency value of H I is normalized sothat max(H t )=255. The example of the input image and thehistogram are shown in Fig.2.

(a)Fig.3 The extracted area (a) and the oval window (b).

2.3 Limitation of update area with the anisotropic
Gaussian distribution

2.2 Update of the histogram model
In the first place, we extract the object area using HI. At thistime, the density value of the extracted pixel is assumed to be afrequency value of HI. As a result, it will have the high-density
value with a pixel that represents the color of the object.
Afterwards, the area in the image obtained to update thehistogram is decided by the following procedure.
1) The centroid C of the extracted area is calculated.
2) The edge image is obtained from the input image.
3) If the extracted area exists in four top and bottom of
the centroid and right and left directions, it makes astarting point there and the extracted area is enhanced.
4) The width from the centroid of the enhanced area andthe width in the oval window used with a former
frame are compared. If it is long,,
is multiplied. If
it is short,
is multiplied.

The newly acquired HN(t) is added and the histogram is
updated. However, it is not preferable that the color area whichis quite different from the area of H I is added. Because there is apossibility that the color model of the background area is added.
It is likely that the color change takes place around the color
distribution of H I . Then, decentralization of each direction isrequested in addition based on the color distribution around thecentroid position of H I . Anisotropic Gaussian distribution H G
given by Eq.(1) is multiplied to HN(t) as the weight. The update
calculation of the histogram is defined by Eq.(2).
H 2 S2

H G (H , S)2e 2f ( )H U (t 1) H U (t ) H N (t ) H G

A new histogram HN(t) is acquired from the new oval
window area decided above. The image which piles up the
extracted area, the enhanced area and the centroid position to theedge image is shown in Fig.3(a) and the new oval area is shownin Fig.3(b).

©ISAROB 2006(b)

H U (0) H I(1)

H I (2)(3)

Here HU(t) is a histogram used to extract color at frame t. The
function fσ(θ) gives the variance dependent on the orientationon the 2-D histogram.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3. Experimental results

References[1] Russ, J. C.: The Image Processing Handbook, CRC Press(1999).
[2] Ishikawa, S., Tabusa, T., Kawaguchi, E: “Recognizing
clothes of a person in an image”, The 5th Int. Conf. on Control,
Automation, Robotics and Vision, pp.56-60 (Dec., 1998).
[3] Terrillon, J.C., Akamatsu, S.: “Comparative performance ofdifferent chrominance spaces for color segmentation anddetection of human faces in complex scene images”, Proc. 12th
Conf. on Vision Interface (VI ’99), Vol. 2, pp. 180-187 (1999).
[4] Kawato, S., Tetsunari, N.: “Skin-color model and face region
extraction”, Technical Report of IEICE. PRMU2001-59,
pp.143-148 (July, 2000).

The results of tracking experiments are shown in Fig.4.
The tracking object is human clothes. In the experiment, theillumination of the room has been made variable. The tracking
screen, the state of the histogram model and the appearance ofthe enhanced area in each frame are shown at each sample time.
Note that:
1.1,
0 .9
As a result, the proposed system was possible to track theobject without losing the sight of the object, even if theillumination of the room changed. The background area was not
added to the histogram model when the oval window area in the
tracking screen included it. This is an effect of the weight
imposed by the Gaussian distribution. Moreover, more effective
setting of the oval window has become possible, because itenhanced the color extraction area. Therefore, the update of thehistogram has been achieved with high accuracy.

4. Discussion and conclusions
We have proposed a color tracking system employing a
2-D color histogram under changeable illumination. It is
possible to update the histogram by adding a new color area thatchange according to the illumination. This suggests that the
proposed technique is able to track an object in an unknown
illumination environment. When the illumination change
becomes larger, an integrated 2-D histogram has a large region
on the HS plane and this may cause inclusion of various colors
around the specified color. Then the result of the specified color
extraction may become worse as it may contain undesirable
noise. In the proposed technique, the possibility of including theundesirable noise is reduced to a large extent by making
limitation by the Gaussian distribution around the initial color
distribution.
Applications of the proposed color tracking system may
include monitoring a particular person. We know parents dress
their small child with loud colors, e.g., a red sweater with blue
pants, so that they can easily find out their child among lots ofchildren in a children's park. The present system may be
employed in place of the parents to monitor their child while
they are busy chatting.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Oval window

Extract and enhanced area

t=275 (Room illumination is bright)

t=345(dark)

t=675 (dark)

t=725 (dark)

t=875 (bright)

t=905 (bright)(a) Tracking images

Gaussian distribution

t=275

Histogram model
t=345

t=675

t=725

(b) History of histogram

Fig.4 Experimental results.

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t=875

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Detecting Method of Coconuts Location using Circular Hough Transform
Mohamed Rizon1, Haniza Yazid2, Puteh Saad2, Ali Yeon Md Shakaff2, Abdul Rahman Saad2, Masanori Sugisaka3,4,
Sazali Yaacob1, M. Rozailan Mamat5, M. Karthigayan1, Desa Hazry4 and Muhammad Firdaus Hashim2
1
School of Mechatronic Engineering
2
School of Computer Engineering
Northern Malaysia University College of Engineering
01000 Kangar, Perlis, Malaysia
3
Advanced Research Institute for Science and Enginering, Waseda University
3-4-1 Okubo, Shinjuku, Tokyo, 169-8555 Japan
4
Department of Electrical and Electronics Engineering
Faculty of Engineering, Oita University
700 Dannoharu, Oita-shi, 870-1192 Japan
5
Terengganu Advanced Technical Institute (TATI)
Jalan Panchor, Teluk Kalong, 24000 Kemaman, Terengganu, Malaysia
Email: rizon@kukum.edu.myheight of 20-22m and an 80 year old palm may attain aheight of 35-40m. Due to the increasing usage of coconut,
new method had to be explored to assist the coconut
gripping process. In this research, automatic coconut
recognition has been done to assist the coconut gripping.
Two techniques had been considered to solve thedetection and recognition process, the separability filter
and the CHT. The separability filter was introduced by
Fukui [1] and had been used in iris detection. Meanwhile
the CHT is a kind of HT that can extract circular objects
from an image. The CHT had been used in several
researches in detecting fingertips position, automatic ballrecognition, and iris detection for face recognition [2-3].
The application of the two techniques for the coconut
detection is the main contribution of this research. Figure
1 shows the block diagram of the system.

Abstract
The goal of this research is to develop efficient coconuts
detection in an image. In this work, two main techniques
have been used for solving coconuts detection andrecognition. The first technique comes with theseparability filter which will obtain the most possible
points to be the coconut candidates. Next, Houghtransform (HT) has been applied to the extracted points by
the separability filter. This Hough transform has been
widely applied to extract analytical features such as circles,
lines and ellipses. The Hough transform method andseveral modified versions have been known as robust
techniques for curve detection. In this research themodified version of the HT is used to detect the circularregions. The Circular Hough Transform (CHT) is a
modified version of HT that will use the edge information
to minimize the computational requirement of the HT.

Input

Preprocessing

Keywords: separability filter, Circular Hough Transform,edge detection, mathematical morphology
Result1 Introduction©ISAROB 2006

Feature extraction
Circular Hough
Transform

Coconut is known in scientific as coco nucifera and is amember of Family Arecaceae (palm family). Coconut is
an important plant in the lives and economies of people
in South East Asia like Burma, Indonesia, Philippines
and Malaysia. In Malaysia, coconut is planted either for
personal use or commercialize. There are varieties ofcommercial coconut that had been planted in Malaysia
such as Malayan Tall (MT), Malayan yellow Dwarf(MYD), Malayan Green Dwarf (MGD), Rennel Tall,
Malayan Red Dwarf (MRD) and Pandan. Coconut
provides almost all necessities of life like food, drink, oil,
medicine, timber, thatch, mats, fuel and domestic utensils.
For a good reason, it has been called the “tree of heaven”
and “tree of life”. A 40 year old palm typically attains a

Image
segmentation

Separability
filterFigure 1: Coconut recognition system

2 Detecting method
Images used in this paper have 320 pixels wide and 240
pixels high. The input image is in gray scale using
portable gray map (PGM) file format. The image had to
be enhanced using histogram equalization. Histogram
equalization tends to increase the contrast of the image.
Histogram equalization produced a better result for

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The circle give the local maxima of the separability η isselected as the best possible coconut candidates.

region-based feature extraction. Then, the image
segmentation is done. In this segmentation step, the edge
detection and mathematical morphology had been
employed. The edge detection process is very importantas the edge information will be used by the CHTtechnique. There are lots of edge detectors that can beused. Canny edge detector has been employed to theimage and Canny can give thin edge compared to Sobel[4]. Beside that, the opening and closing morphology are
employed to minimize the points to apply the templatematching. The closing tends to close small gaps andremoves much of the white pixel noise, giving a fairly
clean image. Opening on the other hand tends to open
small gaps or spaces between touching objects in animage. The closing has been applied first then the
opening. The closing ‘close’ small gaps and make theregion is clearly seen but it still consist lots of
unnecessary information. Then, the opening took place.
The opening will eliminate some of the unwanted
information.

2.2 Circular Hough Transform
The HT and several modified versions have beenrecognized as robust techniques for curve detection. Thismethod can detect object even polluted by noise. The
CHT was sketched by Duda et al [5]. The CHT is one of
the modified versions of the HT. The CHT aims to findcircular patterns within an image. The CHT is used totransform a set of feature points in the image space into a
set of accumulated votes in a parameter space. Then, foreach feature point, votes are accumulated in an
accumulator array for all parameter combinations. The
array elements that contain the highest number of votes
indicate the presence of the shape. A circle pattern is
described by Equation 1(x − x ) + (y − y ) = r
2

After the segmentation process, both the proposed
techniques are applied. The separability filter, describe insection 2.1 had been applied to the image to obtain themost possible coconut coordinates.

p

0

2

p

0

2

(1)

where x0 and y0 are the coordinates of the center and r
is the radius of the circle. An example of conventional
CHT is shown in figure 3.

The CHT has explained in section 2.2, is applied to image
after the separability technique had been employed. Whenusing the CHT, the radius r is known in advanced becauseof the coconut’s shape is peculiar feature by each other, so
the radius r can be treated as known parameter. In thiswork, the radii for the coconut largeness have been set to30, 40 and 50 pixels.

2.1 Separability filter
The separability filter is a template-based method. The
proposed technique will place the template of figure 2 ateach point (xi,yi) and then compute the separability
between the two regions R1 and R2 in the template withsize r.
R1

Figure 3: The contribution of the edge points to theaccumulator space

1/4r

The black circles indicate a set edge points within theimage. Each edge point contributes a circle of radius R toan output accumulator space indicated by the grey circles.
The output accumulator space has a peak where thesecontributed circles overlap at the center of the original
circle. Modification to the CHT has been widely
implemented to either increase the detection rate or
reduce its computational complexity [6-8]. In this work,
the edge orientation information is used to increase theCHT performance. This method was fist proposed by
Kimme et al [6]. The use of the edge orientation
information limits the possible positions of the center foreach edge point. Using this method, only an arc needs tor
R2

Figure 2: circle template to detect coconut candidate©ISAROB 2006

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be plotted perpendicular to the edge orientation at a
distance R from the edge point.

3.0 Result
The best template moving is 20 pixels to the right and 20
pixels down. The graph below has summarized thedetection rate of 25 images that has been tested. The best
template moving is 20 pixels with the highest percentage
96 % is as shown in figure 4 and tabulated as in table 1.
Figure 6: coconut detection with maximum voting= 55

percent

100
95
90
85
80
75
70
10

15

20

25

30

pixel

Figure 4: the best pixel for template moving
Table 1: percentage of the tested template moving
Pixel

Percent

10

88

15

80

20

96

25

80

30

80

Figure 7: coconut detection with maximum voting=65

rate

To validate the both techniques, 30 images have beentested. For now, maximum voting to detect the presenceof circular shape for the CHT technique is selectedmanually for each image. Summarized result for thetested images as shown in figure 5.

Figure 8: coconut detection with maximum voting=71

100
80
60
40
20
0
true

fals ede te ction

Figure 9: coconut detection with maximum voting=68

Figure 5: the detection rate for 30 images

As discussed above, the coconut largeness had been
fixed from 30-50 pixels. If the image taken from far, thecoconut image tends to be small and difficult to be
detected as shown in figure 10.

It has been tested for 30 images and detected 86.67% ofcoconut location. The successful detections are shown infigure 6-9. The error occurred when the image that had
been captured is far from the coconut palm.

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References[1] K. Fukui, O. Yamaguchi, “Facial feature points
extraction method based on combination of shape
extraction and pattern matching”, Trans. IEICE Japan
J80-D-II. Vol 8 (1997) 2170-2177.
[2] T. D’Orazio, C.Guaragnella, M.Leo, A. Distante,”A
new algorithm for ball recognition using circle Houghtransform and neural classifier”, Pattern Recognition, vol37, (2004), 393-408.

Figure 10: undetected coconut location[3]
Tsuyoshi
Kawaguchi,
Daisuke
Hidaka,M.Rizon, ”Detection of eyes from human faces by
Hough transform and separability filter” , Proceeding ofIEEE ICRP, vol 1, (2000), 49-52.

4.0 Conclusion
The separability filter tends to detect the possible
coordinates of the coconut. Then, using the CHT, theobtained coordinates that are not considered as circle will
be eliminated. In this research the coconut location can bedetected but some constraint had been exposed. The main
constraint is the size of the coconut. Template is used to
get the radius of the coconut largeness. The radius is fixed
between 30 to 50 pixels. The problem arises if the coconut
image had been captured far away and the image tends tobe small. Beside that, some of the coconuts are in a bunchand it overlaps between each other. These make thedetection method difficult in carrying out the task. In the
future, automatic threshold will be applied to the CHT forthe voting process to detect the presence of circular shape.

[4] Haniza Yazid, Mohamed Rizon, Puteh Saad, Ali
Yeon Md Shakaff, Sazali Yaacob, Abdul Rahman Mohd.
Saad and Masanori Sugisaka, “An approach of coconuts
detection using edge information”, sub’d to Conferenceof Intelligence System and Robotic (iCISAR2005),
Universiti Putra Malaysia,2005[5] R.O Duda, P.E Hart, “Use of the houghtransformation to detect lines and curves in picture”,
Commun. ACM (1972)11-15.
[6]C. Kimme, D. Ballard, J. Sklansky, “Finding circles
by an array of accumulators”, Proc. ACM vol18(1975)120-122.

Acknowledgements
The authors wish to thank Northern Malaysia University
College of Engineering for their supports throughout thisresearch.

[7] D.Ioannou, W.Duda and F.Laine,”Circle recognition
through a 2D Hough Transform and radius
histogramming”,Image and Vision Computing, vol 17,(1999), 15-26.
[8] N Guil, E.L.Zapata, “Lower Order Circle and Ellipse
Hough Transform”, Pattern Recognition, vol 30, (1997),
1729-1744.

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Natural Intelligence: Noise-resistance of Neural Spike Communication
Noriyasu Homma
Facl. of Medicine
Tohoku University
Sendai, 980-8575

Koh Fuchigami
Toshiba Med. Sys. Corp.
3-26-5, Hongo, Bunkyo-ku
Tokyo, 113-8456

Abstract

Kenichi Abe
College of Eng.
Nihon University
Koriyama, 963-8642

neural information may not be always carried by the firing
frequency due to sensitivity to noisy inputs.

In this paper, we analyze neural spike dynamics of adouble feedback neural unit (DFNU). An essential emphasis of the DFNU is not only on its simple formulations thatcan provide quantitative analytic results, but also on physiological plausibility of the dynamics that is comparable
to that of Hodgkin-Huxley’s model. The results suggestthat a proportional coding of neural information on firing
frequency may not be always reasonable due to sensitivity
to noisy inputs especially for low-frequency neural firings.
On the other hand, high-frequency firings are relatively appropriate for a neural informational carrier due to the reliability and robustness to noisy inputs.

2 Double feedback neural unit
The DFNU is a model to generate pulse sequences,
based on a dynamic neural unit (DNU) [5], and represented
by the following difference equations with discrete timen = 1, 2, . . .
s(n + 1) = y(n) + αs(n)y(n) = f (u(n) − s(n))

(1)
(2)

where u(n) and y(n) represent the input and output of the
DFNU, respectively. s(n) denotes the internal feedback
state variable and α, 0 < α < 1, is a decreasing rate of the
feedback. f is the following nonlinear sigmoidal function
with a slant β

1 Introduction
A typical engineering neural network deals with nonspiking or analog signals as the medium of informational
communication. In the nervous system, neural spikes areused as the medium of communication instead of the analog signals. Neural spikes can therefore be the essential
element of the mechanism for communication in brain [1].
Thus, analyzing the dynamics of the neural network withpulse sequence is one of the approaches to investigate themechanism of neural signal transmission, and to construct
a biologically-plausible model of nervous systems.
Various models that include Hodgkin-Huxley’s (H-H)
model [2] and pulse-coupled neural networks [3] have been
designed to take advantage of neural spikes. Although behavior of these models are physiologically plausible, it isoften very hard to analyze the models’ dynamics due totheir structural and dynamical complexity. Thus, it is difficult to design and control the dynamics as well.
To overcome this problem, we have proposed a double feedback neural unit (DFNU) [4]. An advantage ofthe proposed DFNU is that the dynamics is not only sufficiently physiologically plausible, but also analyzable due
to its simple formulations. We then conduct qualitative andquantitative analysis of the firing dynamics of the DFNU inthis paper. The results suggest some new aspects that the©ISAROB 2006

Masao Sakai
Grad. Sch. of Inf. Sci.
Tohoku University
Sendai, 980-8576

f (x)

1
1 + exp(−βx)=

The block diagram of DFNU is illustrated in Fig. 1. In thispaper, 0 ≤ u < 1, α = 0.6, and β = 1.0 × 105 unlessotherwise specified. For such slant value, the sigmoidal
function is a step function approximately. In this case, from(2), the output y = 1 only when the input u is larger than+ PÄu (n )

s(n)

f (:)
+

z

Ä1

s(n+1) P

Dynamics

ã

y(n)

+

ãs(n)

Self-feedback

Figure 1: Block diagram of the DFNU given by (1) and (2).

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Outputs

3 Analysis of firing dynamics

A stable pattern with k årings

3.1 Formulation of DFNU’s internal state forconstant inputs
T1

ÅÅÅ

T2

Tk

T1

T2

ÅÅÅ

For simplicity, let us consider a simple firing pattern ofk = 1 with constant firing periods T ∈ {2, 3, . . .}. Notingthat the output y = 0 when the DFNU does not fire, thestate variable s(n+1) = αs(n) from (1). Since 0 < α < 1,s decrease every step in this case. In the stable state, thestate s thus takes a peak value spT at one-step after thefiring, and after that it decrease until the next firing. Thatis, letting n = 1 denote a discrete time at one-step after afiring, the state s(1) = spT . At the next step n = 2, thestate s(2) = y(1) + αs(1) = αs(1) since y(1) = 0 in (1)if the neuron does not fire. Similarly, the states s(T ) at thenext firing and s(T − 1) at one-step before the next firing
are given as

Steps

Figure 2: A stable pattern with k firings of intervals (T1 , T2 , . . . , Tk ) steps, Tj ∈ {T, T + 1}, j =1, 2, . . . , k, T ∈ {2, 3, . . .}.

ã = 0:6

Average firing interval Ta109s(T − 1) = y(T − 2) + αs(T − 2) =s(T ) = y(T − 1) + αs(T − 1) =8
7

αT −2 spT (3)αT −1 spT (4)6

In the stable state, the state at one-step after the next
firing is also the same peak value spT . Thus, s(T + 1) =spT . On the other hand, s(T + 1) = 1 + αT spT since theoutput at the next firing y(T ) = 1 in (1). Therefore, thepeak value of the state can be given by

5
4
3
2
1

0

0.2

0.4

0.6
Input u

0.8

1

spT =

Figure 3: The average firing interval Ta as a function of theconstant input u.

(5)

3.2 Design of firing patterns
Let us consider a constant input u for generating neuralspikes with a stable firing period T . For this purpose, thestate at a firing, s(T ) for example, must be less than the
input u, and the state at one-step before the firing, s(T −1),
must be larger than the input u. Therefore, from (3)–(5),the state variable s, otherwise y = 0. That is, the DFNU
fires only if u > s.

αT −2
αT −1
<u<
T
1−α
1 − αT

Previous work [6] showed that the firing dynamics of
DFNUs with constant inputs becomes stable after progressof enough time, namely, firing patterns of DFNUs converge. A generic stable firing intervals are combinationsof intervals T and (T + 1) steps with k firings as shownin Fig. 2. Here, the interval, T ∈ {2, 3, . . .}, and the number of firings within the stable pattern, k ∈ {1, 2, . . .}, arechanged depended on the input u and the parameter α in(1). Although the firing dynamics of DFNU is dominatedby the parameter α, a qualitative relation between the averk
age firing interval Ta = i=1 Ti /k and the input u is notchanged regardless of α. Figure 3 shows a typical relation
between Ta and u.

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1 − αT(6)

4 Noise resistance of neural spikes
In digital communication by using pulse signals, communication can be robust to distortion of the pulse shapesince the information may not be carried by the pulse
shape, but encoded on whether the pulse exists or not atthat moment. Physiological aspects of the signal transmission at synaptic connections also suggest that the neural
information is not carried by the pulse shape, but by thepulse frequency (period) [5].

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On the other hand, a relatively simple firing pattern requires the input with extremely great accuracy for biological information [4]. In order to generate more complex
firing patterns such as chaotic ones, the input with infinite
great accuracy may be needed. Even if the great accuracy
can be realized, such systems are too sensitive to noise [7].
In other words, if the pulse frequency by which the neuralsignal may be transmitted is sensitive to noise, the neuralcommunication system is not robust to noise even the neural communication through spike signals is robust to distortion of the pulse shape.

4.1

or lower frequency generated by a smaller input are sensitive to the input noise, while spikes of the smaller period(higher frequency) are relatively robust to the noise.
Since firing outputs are inputs to other neurons, the sensitivity to noise suggests that neural information uniformly
encoded on a wide band of the firing frequency from highto low is not always reasonable. This implies that a proportional expression of neural information by using the firing
frequency or period may not be appropriate for the modelof neural communication, although such proportional encoding has been widely used in many engineering neuralmodels. A manner of neural communication that is robust
to noisy environments seems to be more plausible as important control signals for life to survive.

Analysis of noise-resistance of neural spikesignals

Using the theory derived in the previous section, we will
now analyze robustness of spike signals to noisy inputs by
evaluating the input accuracy required for generating a desired firing pattern. For simplicity, let T be a constant period of the stable firing pattern with k = 1. In this case,from the condition in (6)1>uα2 (α−T − 1)(7)1α(α−T − 1)

(8)

and
u>

4.2 Plausibility of DFNU’s dynamics
The hypothesis that a proportional encoding of the neural information by using the firing frequency is not always
reasonable is derived from the analytic results of DFNU’sdynamics, instead of real neural dynamics. It is thus important to verify how much the DFNU’s dynamics is physiologically plausible. For this purpose, we will compare
the DFNU’s dynamics with that of H-H model [2] as themost familiar and physiologically plausible model of real
neurons.

Noting that 0 < α < 1 and thus a ≡ 1/(log α) < 0, thenfrom (7)T < a log α2 u − a log(1 + α2 u)

By conducting simple simulations, we can find that firing intervals of the H-H model become smaller as the input
to the model is larger [4]. This is summarized in Fig. 4
illustrating the average firing interval Ta as a function of
the input’s amplitude Ia . Note that comparing Fig. 4 with
Fig. 3, the qualitative relation between the average firing
interval and input amplitude is the same. That is, (i) the
relation is not in inverse proportion, namely, the firing frequency is not proportional to the input, and (ii) a larger interval or lower frequency of firings is relatively more sensitive to the input amplitude than a smaller interval or higher
frequency.

(9)Since −a > 0 and 0 < log(1 + α2 u) < log(1 + α2 ) for
0<u<1
T < a log u + 2 − a log(1 + α2 )(10)Similarly, from (8), by using the relation α−T − 1 < α−T
T > a log u + 1(11)

Since H-H model is a continuous time system while the
DFNU model is a discrete time system, we need a quantitative relation between these two models to compare themodels with each other. For simplicity, let us consider constant firing periods T as calculated in Section 3.1. In thiscase, the average firing period is T , and thus let T denotethe average period of the DFNU to discriminate between
T of the DFNU and Ta of H-H model. Supposing that
(Ia , Ta )  (30, 10) in Fig. 4 of H-H model corresponds to(u, T ) = (0.2, 5) in Fig. 3 of the DFNU model,

Finally, from (10) and (11)a log u + 1 < T < a log u + 2 − a log(1 + α2 )(12)Letting 1 < b < 2 − a log(1 + α2 ), the firing period T canbe given as the following logarithmic function of input u.
T = a log u + b(13)

From (13), dT /du = a/u. Note that the differential coefficient of the function is in inverse proportion to the input
u. In other words, neural spikes of a relatively larger period©ISAROB 2006

T = Ta /2,506

u = Ia /150

(14)

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frequency, the analytic results reported in this paper suggest different coding of the neural information that is moreplausible from the viewpoint of noise-resistance of neuralspikes as important control signals for life to survive. Using the results presented in this paper, we can develop anovel informational processing tool that is robust to noisy
environments.

Average firing interval Ta [msec]

16
14
12
10
8
6

Ta = Ä3:90 log Ia + 23:5

2
5

Acknowledgment

2

R = 0:984

4

10

15

20

25

30

35

40

45

This work was partially supported by Grant-in-Aid for
Scientific Research #15700119 and #17500083.

50
2

Input Ia [µA/cm ]Figure 4: The average firing interval Ta as a function of the
input amplitude Ia .

References[1] F. Delcomyn, Foundations of Neurobiology, W. H.
Freeman and Co., New York, 1997.

Substituting α = 0.6 and (14) into (12),−3.915 log Ia + 21.62 < Ta < −3.915 log Ia + 24.82(15)
On the other hand, an approximate equation derived from
Fig. 4 is as follows.

[2] A. L. Hodgkin, et al., “A Quantitative Description of
Membrane Current and Its Application to Conduction
and Excitation in Nerve,” J. Physiol., Vol. 117, pp. 500,
1952.

Ta = −3.90 log Ia + 23.5

[3] R. D. Broussard, S. K. Rogers, M. E. Oxley, and G.
L. Tarr, “Physiologically Motivated Image Fusion for
Object Detection using a Pulse-Coupled Neural Network,” IEEE Trans. Neural Networks, Vol. 10, No. 3,
pp. 554–563, 1999.

(16)

In spite of DFNU’s simple formulations that do not include detailed and explicit membrane dynamics consistingof several ionic channels represented in H-H model, both
relations between the average period and the input are almost equal.
From this point of view, the simple DFNU model, explaining the logarithmic relation between the average firing
period and the input of the H-H model, is physiologicallyplausible. We can therefore expect that the analytic resultsof the proposed model can estimate quantitative behaviorof the H-H model and real neuron’s dynamics. A new neuron model based on the results reported in this paper will
be presented at the conference.

[4] K. Fuchigami, N. Homma, M. Sakai, and K. Abe: “Is
Neural Firing Frequency an Informational Carrier in
Neural Systems?”, Proc. SICE 2005, pp. 2067–2072,
2005.
[5] M. M. Gupta, L. Jin, and N. Homma, Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, IEEE Press & Wiley, New York, 2003.
[6] K. Fuchigami, N. Homma, M. Sakai, and K. Abe,
“Analysis of Firing Dynamics by Using a Double
Feedback Neural Unit,” Bull. Coll. Med. Sci. Tohoku
Univ, Vol. 13, No. 1, pp. 33–41, 2004. (in Japanese)

5 Conclusions[7] J. K. Douglass, L. Wilkens, E. Pantazelou, and F.
Moss: “Noise Enhancement of Information Transfer in
Crayfish Mechanoreceptors by Stochastic Resonance,”
Nature, Vol. 365, pp. 337–340, 1993.

We have studied dynamics of the DFNU. Using the simple formulations of the DFNU, we have conducted quantitative analysis of the model. Comparing the DFNU’sdynamics to that of H-H model as the most familiar andphysiologically plausible model of real neurons, it has
been shown that dynamics of DFNU is also physiologicallyplausible.
Note that although most engineering analog neurons
assume a proportional relation between inputs and firing©ISAROB 2006

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Acquisition of Deterministic Exploration Behavior by Reinforcement Learning
Katsunari Shibata
Dept. of Electrical and Electronic Engineering, Oita University, 700 Dannoharu, Oita 870-1192
shibata@cc.oita-u.ac.jp

Abstract
Exploration is an important factor that influences the performance in reinforcement learning, and random factors areusually used to realize it. However, the exploration that real lives are doing does not seem just random actions, but
seems a kind of deterministic and intelligent actions using their knowledge and considering the context. In this paper,
the author tries to explain such explorations as a deterministic behavior and propounds a novel approach that effective
exploration is acquired by reinforcement learning. It is shown that an agent with a recurrent neural network trained
based on reinforcement learning becomes to explore effectively to some extent in some simple problems.
Keywords: exploration, reinforcement learning, recurrent neural network, context

1. Introduction
Reinforcement learning is an autonomous andpurposive learning based on reward and punishment bytrial and error. The trial and error is usually called“exploration” and it is an important factor that has alarge influence on the performance of reinforcement
learning. Usually, it is realized by stochastic action
selections using random numbers, such as -greedy or
Boltzmann selection[1]. However it is difficult todecide the ratio of the random factors in the action
selection due to the “exploration-exploitation dilemma”.
This research was triggered by the simple questions
as “Do we take stochastic actions?” and “Is there arandom number generator in our brain?” For example,
if we are put in an unknown maze, we try not to pass thesame way as we have already passed, and try to imagine
a two-dimensional map in our brain. In other words, we
explore the maze using the knowledge we have andconsidering the context. When we are at a branch, we
don’t move our fingers, jump, or go to the midway of
the two paths, and choose one of the two paths. From
this fact, our “exploration” is not just random actions,but seems very intelligent actions. When we don’t have
any idea about which way we should go, we sometimes
shoot a dice. However, even in such a situation, thedecision itself can be considered to be deterministic.
Against the above interpretation, the following
discussions can be accepted. One of them is that that is
the result of a stochastic action selection with a highprobability for the action with a high action value after
representing appropriate action values using knowledge
and/or context. Another is that the exploration is notdone at each actuator level, but is the result of thestochastic action selection on higher abstracted space.
Actually, abstraction of action space has been discussed
as temporal abstraction[2].
It is certainly difficult to deny the possibility of theexistence of stochastic action selection. However, there
seems no rational reason why stochastic action selection
must be employed except for “the concern that an agent
cannot explore all the unknown states without stochastic
factors” or “easiness of statistical analysis”. When our“exploration” mentioned above is considered, it isthought that exploration can be realized withoutstochastic factors.
If exploration is a result of©ISAROB 2006

deterministic decision making, it is considered as one of
the actions in a wide meaning. Then it is expected thatappropriate explorations can be learned throughexperiences as well as the other functions. The author
has been thinking that the reinforcement learning isuseful not only for the learning of actions but for thelearning of a variety of functions including recognition
and memory, and plays an important role in theemergence of our intelligence as living creatures[3].
According to the above discussion, in this paper,
based on the hypothesis that “exploration” by real lives
is not realized by stochastic action selections, but a kindof deterministic ones, it is propounded that the actions
that can be interpreted as exploration are acquired byreinforcement learning. However, since the learning
without using random number generator is anotherongoing research subject, this paper is focused only on
the acquisition of non-random exploration and random
numbers are used for learning. Since context is usefulto realize effective “exploration” as mentioned above, arecurrent neural network is used[4]. Two kinds of very
simple tasks are picked up, and the acquisition of
exploration behavior is examined. In one of them, thegoal cannot be known explicitly, but effective
exploration using knowledge and context is required.
The other task is mainly focused on the acquisition ofknowledge by explorations.

2. Learning
Here, Elman-type recurrent neural network whose
hidden outputs are fed back to the input layer at the nexttime step is employed as the most general recurrent
neural network. The present observation signals st are
the input of the network. The number of output neurons
is the same as the number of actions, and each output isused as Q value for the corresponding action. Afterforward computation of the output for the previous input
signals, the training signal Qs,a t 1 for the Q value of theprevious action Qa t 1 (st1 ) is generated autonomously
based on Sarsa algorithm[1] as

Qs,a t 1 = rt + Qa t (st )(1)where r is a given reward and  is a discount factor.
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3. Simulation
3.1 Exploration on branch situation
At first, the branch situation is simulated in which
only one of two marked states among many states is thereal goal, but there are no information about which is
the real one between the two marked states. As shownin Fig. 1, an agent is located at the center of the 5x5 gridworld, and two landmarks are put randomly on two
states on the four sides of the world. The real goal ischosen randomly from the two marked states, but theagent does not know that. The two points that should beobserved are whether the agent can go to one of themarked states by its intention and also whether the agent
can change its moving direction after reaching the stateand finding that it is not the real goal.
The agent can choose one of the four actions, moving
up, right, down, and left, and the state transition isdeterministic. If the agent hits against a wall, it stays at
the same state. The agent can observe whether the stateis marked or not for each of 9x9 grid world whose
center is fixed at the agent location. This enables theagent to catch the both landmarks wherever the agent
exists. The input signals to the network are 9x9=81
binary signals. The neural network has three layers, andthe number of hidden neurons is 20. The maximum
time steps traced back through time for BPTT is 30.
-greedy is used as stochastic action selection during
learning.  is fixed at 0.1 and discount factor  is 0.92.
The initial weight for each hidden-output connection is
0.0 and that for each non-feedback input-hidden
connection is chosen randomly from -0.5 to 0.5. For the
self-feedback connections, the initial weight is 4.0,

4while that for the other feedback connections is 0.0.
The learning constant for BPTT is 0.2.
At the early phase of the learning, even though theagent reached one marked state, it was difficult to go
toward the other marked state. Sometimes it took
several thousands of steps to reach the real goal, but the
number decreased as the learning progressed. Afterlearning of 100000 episodes, the agent still failed toreach the goal for 5 combinations of real and fake goal
locations, but it successfully reached the real goal 251
combinations among the total of 256 combinations.
Successful sample behaviors for 4 combinations areshown in Fig. 2. It is seen that the agent heads to one of
the marked states at first, and then after finding the state
is not the real goal, it changes its moving direction tothe other marked state even though one redundant action
is seen just after reaching the first marked state in thecase of Fig. 2 (a) (b). In these cases, the distance fromthe center to each marked state is the same with each

4

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0.8
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(2,4)

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(a)

(0,4)

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(2,4)

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real
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(b)

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G t

(b)
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(2,4)

(4,0)
(0,0)

(b) The output of one hidden neuron

0

1

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3

4

Fig. 1 The task in which there are two landmarks ofgoal, but no information about which is thereal goal.
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G
2 3 4
(b)

0
2 3 4
(a)

4

G: landmarkof goal

1 G

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hidden output

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2

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maximum Q-value

The training signal is given only to the corresponding
output, and the network is trained by BPTT (Back
Propagation Through Time)[5]. The output functionused in the hidden and output layer is the sigmoid
function whose value ranges from -0.5 to 0.5, so the
output of the network after adding 0.4 is used as Q value,
and the training signal is used after subtracting 0.4 fromthe signal generated by Eq. (1).
The learning is done independently for each episode(trial). Before each episode, all the hidden outputs arereset to 0.0, and all the Q values after reaching a goal
are 0.0. When the agent reaches the goal, it can get areward r = 0.8, and otherwise r = 0.0 always.

Fig.3 The change of the maximum Q value and one
hidden neuron’s output in one trial for the case of
Fig.2 (a) and (b). ‘G’ indicates the landmark ofgoal and circled one indicates the real goal.
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other, but when one of the marked state was moved
closer to the center, it visited to the closer marked placeat first as shown in Fig. 2 (c) (d) with some exceptions.
Fig. 3 shows the change of the maximum Q value
and the output of one hidden neuron in one trial for theboth cases of Fig. 2 (a) and (b). It is seen that the
maximum Q value increased as the agent approached tothe marked place, but once it found the marked state
was not the real goal, the Q value went down. However,it increased again as the agent approached to the othermarked state. In the case of Fig. 2 (a), it passed on thestate (2,4) twice, and the observation is completely thesame between before and after reaching the fake goal at
the state (4,4). Nevertheless, the Q value on the state(2,4) is 0.54 for the right move and 0.45 for the left
move before reaching the fake goal, while it changes to0.42 for the right move and 0.50 for the left move. Thismeans that the recurrent network extracted and kept the
information that the first marked state was the fake goal,
and the network reflected the information to the Q value.
The hidden neuron in Fig. 3 (b) took the value around
0.0 at first, but once it reached the fake goal, the value
went down around -0.4. For the other combination of
the marked states, the same tendency can be seen in thisneuron. It can be considered that the neuron isrepresenting the passing of the fake goal.
3.2 Exploration without information of goal location
A mouse in a maze explores effectively without any
information about food location. There appears to be a
problem whether such exploration behavior can beacquired by reinforcement learning. Then this section is
focused on the exploration without information of goal
location, and it is examined whether actions that can becalled as exploration can be acquired through learning.
As shown in Fig. 4, five 2x2 mazes are prepared.
Four of them have one wall inside, and the other has nowalls. One of the five is chosen randomly at every trial,
and the agent is located randomly at one of four states.
The agent can take one of the five actions each of which
is move to one of the four directions or stay at the samestate. The state transition is deterministic and if theagent chooses the action to move against a wall, it stays
at the same state. A total of 17 observation signals canbe divided into four kinds. The first four signals
indicate whether a wall exists in one of the four
directions, and other eight signals indicate whether thegoal exists at each of eight neighbor states. The other 5
signals indicate the previous action that is one of thefive actions. Each signal is binary signal and is put into
the recurrent network. At the beginning of a trial, all
the observation signals representing previous actions are
0. Action selection method is -greedy and  is 0.1 at
the beginning of learning, and is gradually decreased to
0.0 by a constant value until the end of the learning.
This means that the agent selects its action greedy at theend of the learning. The number of layers in the
network is four and each of which has 17 (except for the
feedback input), 20, 10, 5 neurons respectively from the
input layer to the output layer. All the outputs of the©ISAROB 2006

1

1

0

1

0
0
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0
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1

1

(b)

0
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(c)

0
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1

(d)

0

1

(e)

1

Fig. 4 Five 2x2 mazes used in the simulation

A: initial agent location
before target appearsA

staying at the same place
after target appears

A

A
(b)

(a)

(c)

A

A
(d)

(e)

Fig. 5 Acquired exploration behaviors before and after
the goal appears.

upper hidden neurons are also the input at the next time
step together with the 17 external input signals. The
network is trained by BPTT with the training signal as
Eq. (1). The number of time steps traced back to thepast is 10 here. The initial weight value is 0.0 for all theconnections from the upper hidden layer to the output
layer, and that for the other connections is chosenrandomly from -1.0 to 1.0. The discount factor  is 0.9
and learning rate for BPTT is 0.2.
The goal appears randomly one of the three states
where the agent does not exist after three statetransitions by the agent action. For example, if theagent is located initially at (0, 0) and no wall is detected,
it cannot identify whether the maze is (a), (b) or (e).
Accordingly, unless it moves, it cannot know whether itshould go up or right when the goal appears on the
diagonal state (1, 1). However, if the agent moves
beforehand and identifies the maze, it can go to thecorrect direction even though the goal appears on the
diagonal state. Furthermore, if the agent exists at thenext of the inner wall when the goal appears, it has tomove three times to reach the goal if the goal was put
behind the inner wall. Accordingly, it should be on astate that is not next to the inner wall just before theappearance of the goal and should remember where is
the inner wall.
The average time steps from the appearance of thegoal for 1000 trials after 100000 trials of learning is1.35 for the four-layer Elman network case, 1.55 forthree-layer Elman network case, and 1.57 for four-layer
regular network case. The optimal number of expected
steps is 1.33. The behavior of the agent using four-layer
Elman network is shown in Fig. 5. In this figure, theinitial agent location is (0, 0), and a thick arrow or point
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indicates the action before the appearance of the goal,
and a thin line indicates the action after the goal
appeared on the diagonal state of the agent. In all thecases, the agent existed on the state that is not next to
the inner wall after some actions before the appearanceof the goal, and when the goal appeared on the diagonalstate, it could go to the way that the inner wall did not
block. Comparing with (a) and (b), the agent went up to(0, 1) at first, and then returned to (0, 0) in the bothcases, but the third action is to go right in the case of (a),while to go up in the case of (b) even though theobservation signals are the same between them. Thismeans that the recurrent network memorized whether
the inner wall existed or not on the state (0, 1), and thememorized information was reflected to the third action.
Finally, in order to examine whether the state value
increases by knowing the maze shape and it promotes
the exploration behaviors, the maze shape is made to be
more complicated and the sight of the goal iscompletely deprived. Here, the shape of 3x3 maze suchas shown in Fig. 6 is decided randomly at every trial,
and the agent is put on the center of the maze. The goalis also located randomly, but the agent cannot know thelocation. The agent’s observation has eight signals.
Four of them represent the wall presence in each of 4
directions, and the other four represent the previous
action. The Elman network has three layers, and thenumber of hidden neurons is 30. The initial weightvalues are the same as in the previous simulation. The
learning rate for BPTT is 0.01, and the number of time
steps traced back to the past is truncated at 30 steps. in the -greedy is 0.1, and the discount factor  is 0.9.
Fig. 6 shows the learning result. Even though themaze shape changes randomly at every trial, theexploration behavior to go everywhere effectively canbe obtained as shown in the figure with some exceptions.
In this case, since there is certainly a goal in the maze, if
exploration progresses and the number of non-visitedstates becomes small, the possibility to reach the goal at
the next time step becomes large when it goes to a
non-visited state. Accordingly, it is expected that therecurrent network learns the fact, and the maximum Q
value that is the state value increases graduallyaccording to the progress of exploration. However, theincrease of the Q value could not be observed.
Then the goal location was limited at one of four
corners. The learning of exploration behavior could not
be obtained. For example, in the case of Fig. 6 (a), if
the goal is located on one random state on the four sides,
there is a possibility to reach the goal whatever direction
the agent goes to. However, if the goal is located only
on one of the four corners, there is no possibility toreach the goal at the next time step from the center ofthe maze. That might be the reason that the agent could
not learn an appropriate exploration behavior such thatthe agent goes to the left since it has been to the rightbefore.
If the recurrent network memorizes theinformation about all the visited states in the maze, aproper state value function might be obtained. The
recurrent network is difficult to learn the function of a
©ISAROB 2006

A

A
G

(a)

G
(b)

Fig. 6 Exploration behavior after learning in the case ofno sight of the goal.

counter or multiple state transitions. The problem might
be originated from the learning ability of the recurrent
network. However, hereafter, more detailed analysis isnecessary to know the cause correctly.

4. Conclusion
It is propounded that by not treating “exploration” inreinforcement learning as simple stochastic actions but
treating it as a kind of intended actions, effective“exploration” is acquired by reinforcement learning.
This idea was introduced in the task that the goal
information is not clear and also in the task in which“exploration” is required with no information about thegoal location, and effective “exploration” can beobtained to some extent by reinforcement learning.
It is thought that this idea is deeply relating to thetemporal and spatial “abstraction” and also to the“curiosity” that has a possibility to make actions emerge
without reward. It is desired to construct a system toexplain these items consistently.
Acknowledgement
This research was supported by JSPS Grant in Aid in
Scientific Research (B) #14350227and #15300064, andalso Overseas Advanced Education and Research
Application Assistance Program. I would like to say
thank to Prof. R. S. Sutton at Univ. of Alberta, Canada
for useful comments.
Reference[1] Sutton, R.S. and Barto, A.G., “Reinforcement
Learning: An Introduction”, MIT Press, Cambridge,
MA (1998)[2] Sutton, R.S., Precup, D., and Singh, S., “Between
MDPs and semi-MDPs: A Framework for Temporal
Abstraction in Reinforcement Learning”. Artificial
Intelligence 112:181-211 (1999).
[3] Shibata, S., “Reinforcement Learning andIntelligence of Robots“, Proc. of the 16th Annual
Conf. of JSAI, 2A1-05 (2002) (in Japanese)[4] Shibata, K. and Sugisaka, M., “Dynamics of a
Recurrent Neural Network Acquired through the
Learning of a Context-based Attention Task”,
Artificial Life and Robotics, Vol. 7, pp. 145-150,(2004)[5] Rumelhart, D.E, Hinton, G.E., and Williams, R.J.,“Learning Internal Representations by Error
Propagation”. Parallel Distributed Processing, The
MIT Press, pp. 318-362 (1986).
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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Hierarchical Q-learning in POMDP Environments
Hiroyuki Kamaya
Dept. Electrical Eng.
Hachinohe National College of Tech.
Tamonoki, Hachinohe 039-1192, Japan

Kenichi Abe
Dept. Computer Science
College of Eng. Nihon Univ.
Tamura-machi, Koriyama 983-8642, Japan

Abstract

To reduce the computational complexity, several
hierarchical approaches for RL have been proposed.
Wiering and Schmidhuber have presented the interesting method, called HQ-learning[3]. In HQ-learning, a
non-Markovian task is automatically decomposed into
simpler subtasks that can be solved by multiple Q
agents. Sun and Sessions have proposed a more general algorithm, called Self-Segmentation of Sequences(SSS) algorithm[4]. The SSS algorithm segments action sequences to reduce non-Markovian temporal dependencies and to create hierarchical structures basedon reinforcement received during task execution.
In this paper, we propose a new hierarchical RL
algorithm to speed up learning, to make it more robust to hidden states, and to handle non-deterministic
problems. This on-line RL algorithm is called SSS(λ),
which extended the SSS algorithm using eligibility
traces. Compared with HQ-learning, it is demonstrated that SSS(λ) can outperform in a partially observable navigation task.
The rest of this paper is organized as follows. Section 2 introduces the bases of reinforcement learning. Section 3 reviews the proposed SSS(λ) algorithm.
Section 4 describes the empirical results. Finally, section 5 concludes this paper.

This paper presents a new hierarchical reinforcement learning (RL) algorithm to speed up learning,to make it more robust to hidden states, and to handle non-deterministic problems. This on-line RL algorithm is called SSS(λ), which extended Sun and Sessions’s original Self-Segmentation of Sequences (SSS)
algorithm using eligibility traces. SSS(λ) is comparedwith HQ-learning, which is a hierarchical RL algorithm proposed by Wiering and Schmidhuber, for partially observable navigation tasks. The results of extensive simulations demonstrate that SSS(λ) is clearly
outperforming HQ-learning.

1

Introduction

Reinforcement learning (RL) is a general framework for describing learning problems in which an autonomous agent learns strategies for interacting with
its environment[1]. A key assumption underlying
many studies on RL is that the agent-environment interaction can be viewed as Markov decision processes(MDPs). In many domains, e.g., in mobile robotics,
in multi-agent and distributed control environments,
etc., however, it will not be possible for the agent tohave perfect and complete perception of the state of
the environment. This problem is also referred to as
the problem of incomplete perception, perceptual aliasing, or hidden state. And their problem can be formulated as partially observable Markov decision processes (POMDPs). In POMDPs, the mapping fromthe set of environmental states to a ﬁnite set of observations is not one-to-one. Therefore, diﬀerent environmental states may be in the same observation. Recent
research on hidden state RL problems has been concentrated on overcoming partial observability by using
memory of previous actions and observations to disambiguate the current state[2]. Such methods, however,require huge computational burden, and so result invery limited applicability.

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2

Reinforcement Learning

At each time step t(∈ 0, 1, 2, ...), the agent observes
its environmental state, st ∈ S, where S is the setof possible states, and selects an action, at ∈ A(st ),where A(st ) is the set of actions available in state st .
As a consequence of its action, the agent receives ascalar reinforcement signal, referred as reward, rt ∈ R.
One time step later, the agent observes a new state,st+1 .The aim of the agent, based on its experience
with the environment, is to ﬁnd a strategy or an optimal policy for choosing actions
that maximizes the∞
expected discounted reward E{ t=0 γ t rt }, where γ isa parameter, 0 ≤ γ ≤ 1, called the discount factor. Q-

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2-level SSS agent
abstract
actioncontinue

activemoduleBehavior of a 2-level SSS agent which has one
higher-level controller AQ and three pairs of Q/CQ
modules is demonstrated as an example (Fig.1). The
agent operates as follows: when the system ﬁrst starts,
AQ decides which Q/CQ module should be taken overnext from a starting state (Q1 /CQ1 is chosen in Fig.1);
at a given time, only one pair of Q/CQ can be active;
when the CQ selects continue, the active Q will selectan action with regard to the current state that will beaﬀected with the environment and thus receive reinforcement from the environment; conversely, when the
CQ selects end, the active Q relinquishes its control,
and the control is returned to AQ, which will decide
which Q/CQ module should be taken over next fromthe current point; this procedure then is repeated in
the same way.
In order to speed up learning, we extend the original SSS algorithm using eligibility traces. This newalgorithm is called SSS(λ)[5]. The 2-level SSS(λ) algorithm is listed below (we use k to denote the abstract action at the higher-level and the pair of active
controllers simultaneously).

AQ

end

CQ1

CQ2

CQ3

Q1

Q2

Q3

rewardstate
actionEnvironment

Figure 1: SSS agent with two level hierarchy.
learning, TD(λ), and Sarsa are widely known as the
RL algorithms.
The Sarsa(λ) algorithm is multi-step version of
the Sarsa. In Sarsa(λ), on experiencing transition< st , at , rt , st+1 , at+1 >, where at+1 is the selected action in st+1 , the following updates are performed inorder:
1,
for ai = atη(s, ai ) ←(1)0,
for all ai = at
δ = rt + γQ(st+1 , at+1 ) − Q(st , at ),- Initialize Q, CQ, and AQ- Repeat (for each trial)ηQ , ηCQ , and ηAQ are initialized to zero
Observe sk ← AQ (AQ selects Q/CQ from s), s̃ ← sa ← Qk (Qk selects an action from s)
Repeat (for each step of trial)
Take action a, observe r, sca ← CQk (CQk selects continue or end from s )Update CQk , ca ← ca
if ca is continue thena ← Qk (Qk selects an action from s )Update Qk
else (that is, ca is end)k ← AQ (AQ selects Q/CQ from s )Update Qk , CQk and AQ, s̃ ← s , k ← kηQ and ηCQ are reset to zeroa ← Qk (Qk selects an action from s )s ← s , a ← a
until s is the terminal(2)

for all si and ai
Q(si , ai ) ←Q(si , ai ) + αδη(si , ai ),(3)

η(si , ai ) ←γλη(si , ai ),(4)where α (0 < α ≤ 1) is the learning rate, η(s, a) isthe eligibility trace function, and λ (0 ≤ λ ≤ 1) is theeligibility factor.

3

SSS(λ) Algorithm

The SSS algorithm involves three types of learning
modules:• Individual action module Q: Each discovers a local control policy,

We can extend the two level hierarchy to more levels. In our previous paper, we implemented the 3level SSS algorithm[6]. In such a case, on the topof AQ0 (=Q)’s, CQ0 (=CQ)’s, and AQ1 (=AQ)’s, we
introduce AQ2 for selecting among AQ1 ’s as well as
CQ1 that determines whether the corresponding AQ1
should continue its control or give up the control. By
the same manner, we can extend the 3-level SSS(λ)
to more levels.

• Individual controller CQ: For each Q module,
there is a corresponding controller CQ. Each CQ
learns whether the Q module should continue its
control or give up the control,• Abstract controller AQ: It performs and learns
abstract control action, that is, selects which
Q module should be taken under given circumstances.

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4

Empirical Results

K

In order to evaluate the robustness and the ability
to handle hidden states problems, we test both of the
3-level SSS(λ) algorithm and the HQ algorithm fora partially observable maze navigation task. In thistask, the agent has to discover paths leading from thestarting location to the goal location, while the agent
has no information about maze environment.
Fig.2 shows a partially observable 26 × 23 maze[3].
Starting at “S”, the agent has to (1) fetch a key at
position “K”, (2) move towards the “door” (the grayarea) which normally behaves like a wall and will open
(disappear) only if the agent is in possession of thekey, and (3) proceed to goal “G”. There are only 11
diﬀerent, highly ambiguous inputs; the key (door) is
observed as a free ﬁeld (wall). The optimal path takes
83 steps.

S
G

Figure 2: ‘key and door’ problem.
steps
1000
800

The agent can only perceive if the four adjacentcells are free spaces or walls. Note that the agent
can’t perceive the true state such as agent’s global
position. Therefore, many locations, from which theagent should execute diﬀerent actions, may be perceived as identical.

600

In each location, there are four possible actions: goleft, go right, go up, and go down, which deterministically (or non-deterministically) cause the agent tomove one location in the corresponding direction on
the grid, except a movement taking the agent to awall. In that case, agent’s location is not changed.

0

400
200

optimal
0

5000

10000

15000

20000
trialsFigure 3: Estimated performance.

4.1

The reward is 500 if the agent reaches the goal location, and -0.1 otherwise.

Noiseless Environment

Fig.3 shows the average performance of SSS(λ) and
HQ, respectively, over 100 simulations. The X-axis
indicates the number of trials the agent has attemptedso far. The Y-axis indicates the number of steps to thegoal. In all cases, the better the performance becomes,
the lower the curve is. The SSS(λ) agent achievedmuch better results in term of the number of trials
than did the HQ agent.
Table 1 shows the performance after learning over
100 independent runs. The 2nd column is the numberof modules, the 3rd column is average path lengths
over simulations, the 4th column is the percentage toachieve the optimal steps, the 5th column is the percentage to reach the goal, and the 6th column shows
average path lengths of solutions. As listed in Table
1, SSS(λ) with 5 × 5 modules could ﬁnd the optimal
path at the rate of about 34%. On the contrary, HQwith 25 modules could ﬁnd the optimal path at therate of about 11%. It is obvious from the experimentsthat SSS(λ) could usually ﬁnd good solutions.

The parameter settings for 3-level SSS(λ) were following: the discount factor γ was 0.97, the learning
rates were αAQ0 = αCQ0 = 0.05，αAQ1 = αCQ1 =αAQ2 = 0.2 and the eligibility factor was λ = 0.9.
The AQ2 and AQ1 modules select one pair of Q/CQ
module according to the -greedy rule, where  was
initially set to 0.1 and then linearly decreased to 0.
Q/CQ’s action choice depends on the current observation. During learning, at time t, the active CQ1 ,AQ0 and CQ0 modules select an action according to
the Max-Boltzmann rule, where pmax was initially set
to 0.9 and then linearly increased to 1. The temperatures were τAQ0 = τCQ0 = 0.1, τCQ1 = 0.2.
The parameter settings for HQ were similar to [3].
Each simulation consists of 20, 000 trials. In eachtrial, the number of steps is limited up to 1, 000. The
trial is stopped either when agent’s goal is achieved,or after time runs out.

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HQ:module=9
HQ:module=25
SSS(λ):module=3x3
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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Table 1: Performance of HQ and SSS(λ) after learning.
Algorithms
HQ
HQ
SSS(λ)
SSS(λ)

Modules
9
25
3×3
5×5

Av. steps
97.7
99.9
111.5
88.3

(%)Opt.
13
11
19
34

(%)Goal
99
99
98
100

Av. sol.
88.6
90.8
93.4
88.3

Table 2: Performance of HQ and SSS(λ) after learning in the noisy case.
Algorithms
HQ
HQ
SSS(λ)
SSS(λ)

steps
1000

Modules
9
25
3×3
5×5

Av. steps
405.3
458.2
299.0
192.7

600
400
200

(%)Goal
88
83
86
96

Av. sol.
324.2
347.2
184.8
159.0

been tested on a partially observable navigation task.
Even in a noisy POMDP environment, the proposed
SSS(λ) algorithm can usually ﬁnd good solutions.
Furthermore, SSS(λ) can achieve much faster convergence property than HQ in both of noiseless and noisy
environment. As conﬁrmed by the results of our experiments, SSS(λ) can be clearly outperforming HQlearning.

HQ:module=9
HQ:module=25
SSS(λ):module=3x3
SSS(λ):module=5x5800(%)Opt.
00
00

optimalReferences

0
0

5000

10000

15000

20000
trials[1] R.S.Sutton and A.G.Barto, “Reinforcement Learning: An Introduction,” MIT Press, 1998.

Figure 4: Estimated performance in the noisy case.

4.2[2] L.P.Kaelbling, M.L.Littman and A.W.Moore, “Reinforcement Learning: A Survey,” Journal of Artiﬁcial Intelligence Research 4, pp. 237–285, 1996.

Noisy Environment

In addition to the hidden state problem describedabove, we also evaluate learning performance for thecase that the state transitions are noisy; random actions are executed at 10% probability. The parametersettings were similar with the noiseless case.
Fig.4 shows the average performance for the noisycase. Table 2 shows the performance after learning.
In many cases, long paths were found. A little noise
did decrease performance; however, the agent almost
reached the goal in the simulations. It is obvious fromthese experiments that SSS(λ) performs signiﬁcantly
much better than HQ.

5[3] M.Wiering and J.Schmidhuber, “HQ-learning,”
Adaptive Behavior, 6-2, pp. 219–246, 1997.
[4] R.Sun and C.Sessions, “Self-Segmentation of Sequences: Automatic Formation of Hierarchies ofSequential Behaviors,” IEEE Trans., SMC-B-30,
pp. 403–418, 2000.
[5] H.Kamaya and K.Abe, “Self-Segmentation ofSequences Algorithm with Eligibility Traces in
POMDPs,” Proc. of ASCC 2002, pp. 408–413,
2002.
[6] H.Kamaya and K.Abe, “Hierarchical SelfSegmentation Algorithms for Q-learning in
Non-Markovian Environments,” SNPD’01, pp.55–
62, 2001.

Summary

In this paper, we have presented a new hierarchical RL algorithm, called SSS(λ), to deal with nonMarkovian temporal dependencies. The algorithm has©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Improvement of Finishing Quality on Blow Mold’s Constriction Parts
by Using an Intelligent Finishing Robot
F. Nagata
Fukuoka Ind. Tech. Center
Kitakyushu, Fukuoka, Japan

T. Hase, M. Omoto, Z. Haga
K. Watanabe
Meiho Co. Ltd.
Saga University
Nohgata, Fukuoka, Japan
Saga, Japan

Abstract
Force sensor

We have already proposed a few types of mold polishing robots which have a high relationship with 3D
CAD/CAM and can regulate the feed rate due to asimple fuzzy reasoning. In this paper, in order to improve the ﬁnishing quality on blow mold’s constriction
parts, a compact spindle driven by an electric servo
motor is ﬁrst introduced at the tip of the robot arm.
The spindle can rotate with a low speed within from0 to 3000 rpm. We also consider a scheme that can
suppress the undesirable tool slide by applying a weakcoupling control to between position feedback control
loop and force feedback control loop. The eﬀectivenessand promise are shown through an experiment.

1

Servo motor
SpindleMold

Figure 1: Servo spindle attached to the tip of the robot
arm.

introduction

2

When conventional air-driven spindles are usedwith a speciﬁed rotational velocity about 20,000 rpm,
undesirable over-polishing tends to occur. On theother hand, enough driving torque can’t be performedin the case of under the speciﬁed velocity. Besides thedetection and control of the rotational velocity andtorque are also not easy. In order to solve such problems, it is eﬀective to drive the spindle with an electric
servo motor. Figure 1 shows a polishing spindle provided by Yaskawa Electric Corp., attached to the tipof the robot arm via a force sensor. The position of
the servo motor is designed parallel to the spindle totransfer the driving torque through a pulley and velt.
A ball-end abrasive tool is ﬁxed to the spindle and rotated within from 0 to 3000 rpm by the servo motor.
The spindle torque is also monitored with the servo
ampliﬁer.
A representative polishing image using the ball-end
abrasive tool is illustrated in Fig. 2. Position of theabrasive tool is controlled following a zigzag path generated from a 3D CAD/CAM, and concurrently theWe have already proposed a mold ﬁnishing robotthat has a high relationship with 3D CAD/CAM systems. The robot can also regulate the tip feed rate(tangential velocity) by means of a simple fuzzy reasoning. In this paper, in order to improve the surface
quality around blow mold’s constriction part, a compact spindle driven by an electric servo motor is ﬁrst
introduced at the tip of the robot arm. The spindle hasan ability of low speed rotation within from 0 to 3000
rpm. We also consider a hybrid position/force control
scheme that has a delicate cooperation (i.e., weak coupling between force feedback loop and position feedback loop) only in pick feed direction so that undesirable phenomena on tool slide can be suppressed. The
force feedback loop controls the polishing force whichis the resultant force consisting of tool contact force,
friction force due to translational motion and anotherfriction force due to rotational motion. The result
allows the ﬁnishing root to perform stable polishingforce control precisely following desired trajectory on
the blow mold’s constriction part.

©ISAROB 2006

Polishing Strategy

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

d
Fee

n
ctio
dire

Workpiece

Fd
od

Moving direction

Abrasive tool

Position
PositionFeedforward
Feedforward
Control
ControlLaw
Law
Using
UsingCL
CLDataDataandandFuzzy
FuzzyReasoning
Reasoning

Contact point
Rotational friction force

xd
Contact force

Friction force

polishing force acting between the tool and the workpiece is regulated to a desired value Fd . The polishingforce vector F = [Fx Fy Fz ]T is the resultant force
among of the contact force f = [fx fy fz ]T , friction
force F t = [Ftx Fty Ftz ]T yielded by translational motion and another friction force F r = [Frx Fry Frz ]Tgenerated by rotational motion, which is given by

where each component of the vectors is composed inbase coordinate system. It is assumed that the friction
force in Fig. 2 consists of Coulomb friction and viscous
friction forces, so that F t + F r is represented by

3
3.1

SF
x

2

+

SF
y

2

+

SF
z

2

(3)

where the superscript S denotes the value in sensor coordinate system, and the components SFi (i = x, y, z)
are obtained from the force sensor shown in Fig. 1.
Although there exist an attachment, spindle, tool axis
and ball-end abrasive tip between the force sensor and©ISAROB 2006

Σ

vp +
Sp

Cartesian-Based
Cartesian-Based
Servo
ServoController
Controller

Polishing
Polishing
Robot
Robot

xd : desired position
od : desired direction
x : current position
Sp : switch matrixxControl Strategy
Hybrid position/force control withweak coupling

In this section, a hybrid position/force control
method is proposed to realize the polishing strategy
described in previous section. Figure 3 shows the block
diagram of the proposed control system in Cartesian
coordinate space. xd and od are desired position anddirection vectors minutely obtained from cutter location (CL) data [1]. The CL data are calculated fromthe main-processor of CAM. x is the current positionat the tip of the abrasive tool obtained from the forward kinematics. F is the force sensor measurement
given by eq. (3).
The key idea is a weak coupling control between
the position feedback loop and force feedback loop.
Directions to perform such a weak coupling control
are selected by a switch matrix S p = diag(Sx , Sy , Sz ).
If the position feedback loop makes interfere with theforce feedback loop, then corresponding diagonal element of S p should be set to 1. The force feedback
loop regulates the polishing force. Normal velocity
v n = [vnx vny vnz ]T at the contact point is the manipulated variable from the force feedback control law [2].

(2)where µi and ηi (i = x, y, z) are the coeﬃcientsof Coulomb friction per unit contact force and viscous friction, respectively. v t = [vtx vty vtz ]T andv r = [vrx vry vrz ]T are the translational (tangential)
velocity and circumferential velocity at the contact
point.
As can be guessed, it is not easy to independently
measure f , F t or F r constructing the polishing force.
However, those resultant force norm, i.e., polishingforce norm, can be referred by






F  =+

F+

tool contact point, the force control system used inexperiment has a stiﬀ and linear property about 12
kgf/mm in case that the displacement in normal direction after a contact motion is under 1 mm. Therefore,
the system can be regarded as an almost rigid bodywhen the polishing force is given around 3 kgf. Whenthe robot runs, F  is controlled to Fd to obtain auniform surface quality.

(1)  v t
F t + F r = diag µx , µy , µz f  v t 

+diag ηx , ηy , ηz v t + diag ηx , ηy , ηz v r

Position
Position
Feedback
Feedback
Control
Control
Law
Law

vn
vt

Figure 3: Block diagram of the proposed hybrid position/force controller.

Figure 2: Polishing strategy considering friction forces.

F = f + Ft + Fr

Fd : desired polishing force

Force
Force
Feedback
Feedback
Control
Control
Law
Law

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Z-axis(５)(1)
CL dataFigure 4: Example of CL data composed of positionand normal direction components.

p(i -1)

(２)

(４)
(３)

The position control is carried out by applying boththe feedback control and feedforward control. The
feedforward control using CL data and a fuzzy reasoning is used to make the abrasive tool move with avariable velocity norm along desired trajectory, whose
manipulated variable is given by v t . Since v t is generated from the feedforward control law, the force feedback loop is not disturbed.
The manipulated variable v p = [vpx vpy vpz ]T inpick feed direction is generated for position feedback
control which has an important function to exactly
move the abrasive tool with a constant pitch. Consequently, high quality surfaces without irregularity canbe obtained. Finally, the summation v = [vx vy vz ]T of
three manipulated variables written by following equation is given to the reference of the robotic servo controller.
v = vn + vt + vp

3.2

Workpiece

X-axisB A

Figure 5: Relation between the tool’s center and CL
data.

lowing direction vector.
t(i) = p(i + 1) − p(i)(5)

In this case, it is very important to realize a smooth
feed motion that when the step number i should be
updated: how to decide the timing of reading the next
step i+1. After here, the following three conditions
are evaluated.
1) Detecting from the current position x(k) of tool tip2) Considering the force measurement in normal direction besides the condition 1).
3) Detecting from the sum of the manipulated variablev t (k)Concerning the condition 1), when the tool tip x(k)becomes out of [p(i), p(i + 1)], i.e., x(k) goes overp(i + 1), i is updated. However, it doesn’t work wellaround the situations (1) and (5) in Fig. 5. The reason
is that a constraint caused by a contact between thetool contour and workpiece prevents the tool tip fromgoing over p(i + 1). For example, even if the tool tip
approaches to the point A in situation (5) through (4),it can’t arrive at the point B. Consequently, the next
step is not read out.
In the condition 2) to solve the problem, theforce measurement is further considered in addition
to the condition 1). In other words, when a large xdirectional force such as |fx | > Fd is detected, then
the next step is immediately read in. In this case also,
however, an incorrect detection tends to occur. Because the force control system in the ﬁnishing robot(4)

Update timing of CL data

Here, we describe how to update the current step iof the CL data, i.e., when the next step i+1 should
be set to i, to realize a smooth feed motion. The
main-processor of the CAM generally produces the
CL data CL(i) = [pT (i) oT (i)]T as shown in Fig.
4 so as to machine a workpiece within a given tolerance to a designed model. p(i) = [px (i) py (i) pz (i)]Tand o(i) = [ox (i) oy (i) oz (i)]T (i = 1, 2, 3, . . . , l :l is the number of steps) are the position and normal
direction components, respectively. Figure 5 shows
an image of the proposed polishing strategy where thetool contour is eﬃciently utilized. One of the key
points is that the tool doesn’t go up over the situation (1) or (5) shown in Fig. 5 in order to avoid an
undesirable over polishing around the edge.
Actually, the tangential velocity of the ball end
abrasive tool is linearly calculated based on the fol-

©ISAROB 2006

p(i)p(i+1)

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

5

Conclusions

In this paper, a compact spindle with a function of
low speed regulation is ﬁrst introduced at the tip of
the robot arm to suppress undesirable over-polishing.
We also proposed a hybrid control scheme in which
the position feedback loop delicately contributed to
the force feedback loop only in the pick feed direction.
Consequently, fatal phenomenon concerning tool sliding down disappeared and the ﬁnishing quality aroundthe blow mold’s constriction part could be successfully
improved.

Constriction part

References
Figure 6: Finishing scene of blow mold’s constriction
part.

[1] F. Nagata, K. Watanabe and K. Izummi, “Furniture Polishing Robot Using a Trajectory Generator
Based on Cutter Location Data,” Procs. of IEEE
International Conference on Robotics and Automation, pp. 319–324, 2001.

is considerably high: its stiﬀness in the tool contact
direction is about 12 kgf/mm, so that an undesirable
spike over Fd sometimes breaks out.
Through the try and error mentioned above, thecondition 3) is now introduced. In the condition 3),
if the following equation is satisﬁed, then i is updated
to i + 1.
kv t (n) ≥ t(i)

[2] F. Nagata, K. Watanabe, Y. Kusumoto, et al.,
“ Generation of Normalized Tool Vector from 3Axis CL Data and Its Application to a Mold Polishing Robot,” Procs. of IEEE/RSJ International
Conference on Intelligent Robots and Systems, pp.
3971–3976, 2004.

(6)

n=ki

where ki is the discrete time when the last updating
was conducted. In eq. (6), when the sum of the manipulated variable in the tangential direction, which has
been given to the servo system after the last updating, goes beyond the current distance t(i) between
two adjacent steps, i is updated to i + 1. Due to thedetection of the update timing by using eq. (6), thefeed motion can be smoothly carried out even under
the constrained situations (1) and (5) shown in Fig. 5.

4

Experimental Results

In this section, an experiment was conducted using an aluminum blow mold with a constriction partin order to evaluate the eﬀectiveness of the proposedsystem. Figure 6 shows the ﬁnishing scene of the constriction part. It was observed from the result that the
tip of the abrasive tool could move along the desired
trajectory without sliding down the constriction part.
In this case, the pick feed in the CL data was given
0.2 mm along the constriction part.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Self-Organizing Neural Networks for Incremental Category Learning
Masao Sakai∗1 , Kazunori Takei∗1 , Noriyasu Homma∗1 , Yosuke Koyanaka∗2 , and Kenichi Abe ∗3∗1

Tohoku University,
Kawauchi, Aoba-ku,
Sendai,
980-8576, JAPAN∗2

Abstract

2
Introduction

2.1“Category” is defined as a group of people or things
with particular features in common [3]. Human autonomously forms the categories from acquired information, thus can recognize and divide various things
by using the self-formed categories. Therefore it is often said that the category formation is one of the mostimportant ability of human.
In this paper, we develop a neural model to carryout the category formation. The developed model isbased on Ward’s hierarchical and K-means clustering
algorithms. Ward’s method[1] is a hierarchical clustering algorithm for sequential inputs under an incremental and unsupervised learning environment; hence
the model can actually self-organize an appropriate setof clusters corresponded to human categories without
teach data such as an ideal classfication of the inputs.
In contrast, we have to consider individual variations in the classification formed without the teachdata. The classfication and recognition are depend on
a number of the formed categories; thus to manage
the number is important for the design of the idealclassfication. The teach data can be available to es-©ISAROB 2006

Nihon University,
1 Nakagawara, Tokkusada,
Tamura-machi, Koriyama,963-8642, JAPANtimate the ideal number; hence we improve on thedeveloped model based on the Ward’s method to takeadvantage of this. Furthermore, the K-means clustering algorithms is introduced into the developed model
to manage the drastic reclassification of formed clusters due to the discontinuous teach data.
In this paper, the category formation ability of thedeveloped model is demonstrated on a practical image
recognition task under an incremental and unsupervised learning environment. In addition, the adequacyof the formed category is evaluated by using principal
component analysis.

In this paper, we develop a neural model that forms
categories of inputs for some practical applications
such as pattern recognition, learning, image processing, and trend analysis.
The model is based on Ward’s hierarchical and Kmeans clustering algorithms[1, 2], thus carries out thecategory formation under an incremental and unsupervised learning environment. In addition, the model
can also make use of discontinuous teach data for forming ideal categories. Simulation results demonstrate
the usefulness of the model for a category formation
task.

1

∗3

FANUC LTD,
Oshino-mura,
Yamanashi,
401-0597, JAPANCategory Formation Model
Problem Definition

In this research, a vector representation x for athing is defined asx =[x1 , x2 , · · · , xn ]T ∈ <n ,(1)where xi , i = 1, 2, · · · , n, denote features of the vector
x, the superscript T means transpose, and n is thenumber of features. For simplicity, we consider normalized features value xi ∈ [0, 1].
The goal here is to make appropriate clusters corresponded to the proper categories from the set X(t) of
all input vectors {x(1), · · · , x(T ) }, where x(t) is a vector at time t = 1, 2, · · · , T . Clusters S ` , ` = 1, 2, · · · , L,
can be defined as sets of things that satisfy specificconditions depending on the set X
S`={x ∈ X | x satisfy a condition `},(2)

where L is the number of clusters.
The cluster S ` are memorized by a set of neuralparameters {w` , r` , N ` , T ` } stored in the `th neuron,
where w` , r` , N ` , and T ` are the center, scale, number

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

S

where α is a large positive constant. If the distance between the neural memory w` and the input x is small,then the neuron activates strongly, that is, the neural
output is large.
The recognition algorithm for the input x is explained as follow. We select a neuron c whose output
is the largest among the neural networks

Vectors in S 1

2

xk12 S 1
1

w2

r1

r2

w1

Cluster S 1

xk2 2 S 22wk`x` 2 SS``

c = arg max y ` (x).

l`

If there are some neurons c` , ` = 1, 2, · · · , that satisfy
Eq. (7), then the candidate neuron is selected randomly.
Here, we introduce a boundary value, τ , 0 < τ < 1,for evaluation of the neural output. If yc (x) > τ , thenthe input vector x is within the region of the candidate
category S c , otherwise is not within that. Therefore
we can quantitatively evaluate similarities of the input
x among the already-formed categories S ` . In this research, τ is defined by the following equation.

r``

Figure 1: Vectors x and clusters S l in a vector space.
of the constituent vectors, and proper category information such as name or number obtained from teachdata of the cluster S ` , and M is the number of neurons.
If the proper category information T ` is unknown, we
set T ` = 0. In general, M is equal to L.
For simplification, we consider only a sphere withcenter w` ∈ <n and radius r` as a shape of the cluster
S ` , as show in Figure 1. It generally seems reasonable
to suppose that all the vectors of a same category exist
in close proximity; hence the model must satisfy thatfor any vectors x ∈ X, x is in a cluster S `(x) and thememory w`(x) in the `(x)th neuron is the nearest one
among neural networks and kw`(x) − xk is less than
the radius r`(x) . That is,∀x ∈ X,∃

τ = exp (−αβ) ,min`∈{1,2,···,M }kw` − xk ≤ r`(x) .

2.3

`

w

=

N
X

xk`

±

N `,

(3)

(4)

1. Initialize time t = 0 and the number of neurons
M = 1.

k=1

r`where xk` , k
`

=

max

k∈{1,2,···,N ` }kw` − xk` k,(5)

2. Select a input vectors x(t) for a thing randomly, and store it with a set X(t) ={x(1), x(2), · · · , x(t)}.

`= 1, 2, · · · , N , are vectors belonging toclaster S .

2.2

3. Is the time t equal to 0 ? If so, go to step 4; if
otherwise, go to step 5.

Neural Structure

4. Create a new cluster by input vector x(t) as thefollowing: M ← M + 1, wM ← x(t), T M ←Q(x(t)), and go to Step 13.

We explains the neural structure and recognition
process for input x of the model. In this paper, the`th neural output for the input x is defined as¶µkw` − xk,(6)y ` (x) = exp −αr`©ISAROB 2006

Category Formation Algorithm

In this section, we explains the algorithm to form
suitable categories autonomously from sequential inputs. The algorithm is based on the Ward’s hierarchical and K-means clustering algorithms, and makeuse of discontinuous teach data for forming ideal categories.
The category formation algorithm will now be proposed as follow:

Therefore, the center and radius of the cluster S ` aredefined as`(8)

where β is an arbitrary positive constant．The output
vector Y of the model is defined as½ cw , (y c ≥ τ ),(9)
Y =~0, (y c < τ ).

`(x) ∈ {1, 2, · · · , M } such that x ∈ S `(x) ,kw`(x) − xk =(7)

5. Is the proper category number Q(x(t)) obtained
for the input x(t) ? If yes, go to Step 6; if no, go
to Step 7.

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6. Is there a candidate cluster S c satisfied T c =Q(x(t)) ? If yes, go to Step 11; if no, go to Step
7.

10. Select a candidate cluster S c satisfied the following equation

7. Whether or not at least one of the following conditions holds ?

`∈{1,2,···,M }c = argT c ← Q(x(t)).

`

wc

w `1
T `1

CASE 1: The influences F (t) adding x(t) tothe already-formed categories S ` are calculatedby the following equation.
N ` · kw` (t) − x(t)k,N` + 1F ` (t) =· · · (Q(x(t)), ·T ` = 0),


(10)

∞, · · · (otherwise).

3
3.1

(15)
(16)

Character Recognition
Preprocessing of Character Images

We applied the developed neural model to a character recognition task. We prepared images of threealphabet characters, “A”, “C”, “F”, for several fontstyles and regarded the images of the same character as patterns belonging to the same category. The
category formation was an incremental learning task,
and thus the model required to acquire categories withsequential input of the images and simultaneously torecognize the inputs based on already-formed underdeveloped categories S ` represented by a set of neuralmemories {w` , r` , N ` , T ` }.
In this task, we prepared 15 images with 32 × 32

G``12 (t) ?

If the former, go to Step 12; if the latter, go to
Step 10.

©ISAROB 2006

N `1 w`1 + N `2 w`2,
N `1 + N `2
← T `1 + T `2 ,←14. Increase time t ← t + 1 and return to Step 2.

9. Which is smaller;
min

(14)

13. Reclassify all the vectors x(1), x(2), · · · , x(T ) of
the set X(t) by the K-means clustering algorithmwith initial centers, and compute the centers andnumbers of the constituent vectors for the clusters.

N `1 · N `2 · kw`1 (t) − w`2 (t)k,N `1 + N `2
G``12 (t) =`1`2· · · (T · T = 0),


(11)

∞, · · · (otherwise).

`1 ∈{1,2,···,M −1}`2 ∈{`1 +1,`1 +2,···,M }N c wc + x(t),Nc + 1and let the input vector x(t) be the cluster S `2 as
the following: w`2 ← x(t) , T `2 ← Q(x(t)), andgo to Step 13.

CASE 2: Any two already-formed categories S `1and S `2 combine as a revised cluster S `1 , where`1 ∈ {1, 2, · · · , M −1}, `2 ∈ {`1 +1, `1 +2, · · · , M },the cluster S `2 is deleted, and a new clusters iscreated by x(t). In these case, the influencesG``12 (t) are calculated by the following equation.

or←

12. The combination of S `2 with S `1 revises the centerw`1 and proper category information T `1 of thecluster S `1 by the following equations.

`F ` (t)(13)

and go to Step 13.

8. Evaluate influences of the input vector x(t) on thealready-formed categories S ` , ` = {1, 2, · · · , M } atthe two following cases:

min

(12)

11. Revise the center of the candidate cluster S c as
the following:where ML is an arbitrary positive integer, η1 , η2 ,and η3 are arbitrary positive constants, `, `1 , and`2 denote ` ∈ {1, 2, · · · , M }, `1 ∈ {1, 2, · · · , M −1}, and `2 ∈ {`1 + 1, `1 + 2, · · · , M }. If yes, go to
Step 4; if no, go to Step 8.

`∈{1,2,···,M }F ` (t),and revise T c of the candidate cluster S c as thefollowing:(a)
M < ML ,(b) min kw` − x(t)k > min η1 · kw`1 − w`2 k,``1 ,`2
P `
` N > η2 · M,
and
(c)

 min kw` − x(t)k > max η3 · r` ,
`

min

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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EGPVGTUQHENWUVGTU S `+PRWVUQH#+PRWVUQH%+PRWVUQH(XGEVQTUQH
XGEVQTUQH
XGEVQTUQH

A

S
SC
SF

` 2 fA; B; C; F; F 0 ; F 00 gSFÅ XGEVQTUQH

CXGEVQTQH S F

0

00







wF

wF

0
ÅÅ
Å

Å

̂
̂

C

wF




D

Figure 2: (a)Images of three alphabet characters, “A”,
“C”, “F” for 5 font styles for learning. (b)Distributionof the first two principal components for all the 15 input images. The contributions of the two axes were
47% and 25% respectively, giving a cumulative contribution of 72%.

C

w



̂
̂

C





D

Figure 3: Distribution of the first two principal components for the constituent vectors x and center w offormed categories. (a) The success case. (b) A typicalfailure case.

4

Conclusion

In this paper, we have developed a new neuralmodel for the category formation. It has been shownthat the model can self-organize an appropriate setof clusters from character images with few teach data
and recognize the character images adequately by the
relation among an input vector and formed clusters.
However, we have to determine the key parameterssuch as ML , ηi , i ∈ {1, 2, 3}, and β by trial and error
for unsupervised formation of unknown complex categories. The development of the determination method
will be the future work of this study.

Simulation results

The goal of this simulation were to form clusters
corresponded to the proper categories. The order ofthe sequential inputs was decided randomly, and theparameters, ML = 1, η1 = 1, η2 = 3, η3 = 1, were set
experimentally.
Figure 3 showed results of the category formation
task. The model probably succeeded to form such suitable clusters corresponded to the proper categories asshown in Figure 3 (a) by using only one teach data foreach character, not to mention the perfect one. The
model, however, failed in the rare cases, for example,
the typical failure case shown in Figure 3 (b). In this
000case, two clusters, S F and S F , were formed as tothe character F by accident. The unsuitable numberof formed categories probably depended on values of
the above parameters ML , and ηi , i ∈ {1, 2, 3}.

©ISAROB 2006



wA



wC

̂
̂

pixels and gray-scale (8-bit) for 5 font styles as shown
Figure 2 (a). In order to reduce the computationalcost, the size of the images is compressed into 22 × 22
pixels by 2-dimensional discrete cosine transformation(2-D DCT) method, and then let the compressed data
be the input vectors x = [x1x1 , · · · , x22x22 ] ∈ <484 tothe model.
Figure 2 (b) shows the distribution of the first two
principal components for all the 15 inputs. Note that
inputs of same characters exist in close proximity. In
contrast, the clusters of each character separately exist. This implied that the inputs can be recognizedand divided fundamentally.

3.2

wA



00

References[1] Joe H. Ward (1963). Hierarchical Grouping to optimize an objective function. Journal of American
Statistical Association, 58(301), 236-244.
[2] J. B. MacQueen (1967): ”Some Methods forclassification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability”, Berkeley, University of California Press,
1:281-297[3] Oxford Advanced Learner’s Dictionary, Oxford
University Press 2000.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Simplified Geometric Models in Skill-Based Manipulation
for Practical Use
Akira Nakamura*, Kosei Kitagaki*, and Takashi Suehiro*
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, t.suehiro@aist.go.jp
various human tasks by using the concept of skill. Thisskill level control is placed between the task level control
and the servo level control in the hierarchy of manipulator
control, and a programmer is able to describe a task
program easily as a sequence of these skills, without
taking account of the servo level control.
Skill technique is also used to compensate for errors
both in the geometric model and in manipulator motion.
The move-to-touch skill can cancel model errors in the
direction of the transition. Similarly, the rotate-to-levelskill and the rotate-to-insert skill can cancel model errors
in the direction of rotation. Furthermore, we can derive
simplified geometric models by allowing for a maximum
error or margin in a task that is composed of skill motions.
We call such robust geometric models "false models" andhave shown their effectiveness as follows [5]. False
models are constructed using only the data necessary inplanning for manipulation and visual sensing for model
matching. Since a smaller number of data with respect toshapes, positions and orientations of objects are needed,visual sensing is easier.
The topics of this paper are false models in skill-based
manipulation for practical use. It is expected thatmaintenance robots for domestic appliances will greatly
take an active part in the future (Fig. 1). Therefore, we takeup maintenance robots that carry out repair, inspection andso on as practical use. The robots open and close theenclosure, and replace the parts (Fig. 2). Therefore, it isnecessary for the robots to perform tasks related to a screw

Abstract
Dexterous manipulation plays an important role inworking 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.
Skills are also used to compensate for errors both in thegeometric model and in manipulator motions. There aredispensable data in the shapes, positions and orientationsof objects when achieving skill motions in a task.
Therefore, we can simplify geometric models byconsidering the dispensable data in a skill motion. We callsuch simplified models "false models." This paper
describes our definition of false models in planning andvisual sensing and shows application to tasks performedby maintenance robots.
Key words: manipulation skill, geometric model, false
model, planning, visual sensing

1. Introduction
For manipulation robots to be useful in several fields, itis necessary for a robot to achieve various tasks by using
special techniques. By analyzing human motions in tasks
such as assembly and disassembly, movements were foundto consist of several significant motion primitives. We
called these "skills" and showed that most of the tasks ofthe manipulator can be composed of sequences of skills[1]-[4]. That is, we demonstrated that robots can perform

RepairInspection

Breakdown

Vision system(Hand-eye type)Examination
FailureManipulator

Passing

End

1. Opening the case (or the enclosure)
2. Investigating the cause of the trouble
3. Identifying broken-down parts

Workingenvironment

Visual sensor
Acoustic sensor
Sensing Vibration sensor
Data

4. Replacing the parts (or the unit)• Electric appliances• House furniture• Plumbing

5. Closing the case (or the enclosure)End

Fig. 1 Maintenance robot for domestic appliances©ISAROB 2006

Fig. 2 Procedure of maintenance524

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

and a bolt. In this paper, we will explain definition of false
models and show false models throughout tasks such as
loosening a screw and unfastening a bolt for practical use
of our technique. Then, degrees of difficulty of executing atask can be derived using complexity of skill sequence andfalse models.
The next section explains manipulation skills and thecomposition of skill sequences. The processes of visual
sensing, geometric modeling and execution of a task are
explained for task and skill levels in section 3. False
models in planning and visual sensing are defined in
section 4. Our proposed method is demonstrated using two
examples of tasks performed by maintenance robots in
section 5.

A specific task is composed of sequences of skill
primitives such as move-to-touch, rotate-to-level androtate-to-insert skills. The skill sequences can be decided
by several methods. We showed a method using variationsof the number of contact points in skill primitives [4].

3. Process of Sensing, Planning andExecution
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.

2. Manipulation Skills(Step 1) Task Level

This section explains our concept of skills. See
References [1]-[3] for more details.

At the task level, the skill sequence composing the task is
decided. First, visual sensing of the working environmentof the robot is performed using a vision system andmodeling is done. Next, planning follows, and skill
command sequences and the initial position andorientation of a grasped object are derived.

In assembly and disassembly tasks, skills in which thecontact states vary are particularly significant. In
References [4], [6], we considered three skills,"move-to-touch," "rotate-to-level" and "rotate-to-insert,"
which play an important part in such tasks.

(Step 2) Skill Level(1) Move-to-touch Skill

At the skill level, each skill in the command sequence{Skill1, Skill2, … } is executed successively. Before thissequence is performed, the transition of the grasped object
P to the initial state is done. We represent the transition as
PreSkill1.

The move-to-touch skill is defined as the transition of agrasped object P in a constant direction that continues untilcontact with another object Q occurs (Fig. 3).
(2) Rotate-to-level Skill
This skill is defined as rotation around either a contact
point or a contact edge to match the face of the grasped
object P with the face of another object Q (Fig. 4).

4. Concept of False Models
In planning for manipulation and visual sensing formodel matching, it is not necessary to use geometricmodels that express real objects completely. By
constructing geometric models using only the datanecessary for planning and visual sensing, it is possible tofacilitate these processes. We call such robust models"false models" (FM). False models in planning and visual
sensing are defined as follows.

(3) Rotate-to-insert Skill
This skill is this motion of rotating the object P obliquely
into the hole in another object Q to insert it accurately (Fig.
5).
θP(i) False models in planning (FMp) are geometric models
composed only of the necessary and minimum data for

P
z
OA

y
Q

Q

z
OA y

Task Level
1(b) Move-to-touchc skill(a) Move-to-touchf skill

Fig. 3 Move-to-touch skills

OA

z

P
OA

y

Q

Q

Q

Q

Fig. 4 Rotate-to-level skill

(a)

(b)

Sensing(P)8Sensing(S1)12Modeling(P)9Modeling(S1)13Modeling(S2)6Re-planning(P)10Confirmation(S1)14

Confirmation(S2)7

Execution(P)11

Execution(S1)15

Execution(S2)

4
5

PreSkill1(c)Fig. 5 Rotate-to-insert skill©ISAROB 2006

Planning(T)Skill Level

P

Q

Modeling(T)

3

P

P
P

Sensing(T)2Skill1

Fig. 6 Process flow

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ScrewdriverScrew
Tip ofscrewdriver

ScrewheadFig. 7 Rotate-to-bite skill
ScrewheadFig. 8 Rotate-to-loosen
skill

Screwdriver

Screwhead(a) Phillips(d) Hex-drive(b) Slotted(e) Clutch-drive(c) Robertson(f) Torx

Skill11

Skill22

Skill33

Skill44

Fig. 10 Skill sequence of loosening with a cross-head screwdriver

Screwdriver

((

))

((
))

Fig. 9 Screwdrivers

shape, position and orientation to perform skill-basedplanning.
Skill11(ii) False models in visual sensing (FMv) are geometricmodels composed only of the necessary and minimum
data for shape, position and orientation to perform model
matching. False models in planning (FMp) are constructedfrom the data of false models in visual sensing (FMv).

Skill22

Skill33

Skill44

Fig. 11 Skill sequence of loosening with a slotted screwdriver

examples.
We assume that the task of loosening a screw using ascrewdriver is composed of the following skills (Fig. 10
and Fig. 11); Skill1: Move-to-touch skill, Skill2:
Rotate-to-bite skill, Skill3: Rotate-to-(loosen ／ extract)skill (without interruption), Skill4: Move-to-remove skill.
False models are shown in Table 1; (a): case of across-head screw, (b): case of a slotted screw, (c): cases ofRobertson, hex-drive, clutch-drive, torx screws.

5. Examples of False Models
We will also consider several skills related to the tasks inthe examples mentioned later. In these skills, we use across-head screwdriver and screw according to tasks as
examples.

5.2. Unfastening a Bolt Using a Wrench
Let us consider the task of unfastening a bolt using awrench.
We assume that this task is composed of the following
skills (Fig. 12); Skill1: Move-to-touch skill, Skill2:
Rotate-to-insert skill, Skill3: Rotate-to-loosen skill, Skill4:
Rotate-to-extract skill, Skill5: Move-to-remove skill. False
models are shown in Table 2. (rv is (3 ln3・m)／π (= about
1.049 m), which is derived by the least mean-square
method.)(4) Rotate-to-bite Skill
This skill is a rotation around the axis of the screwdriverto 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 is performed by matching theaxes of rotation of a part and a tool. If these axes do notcorrespond, the tool is moved to the position before
rotation is executed. In this paper, we assume that this skill
also includes the transition to remove an error before
rotating.

A skill sequence has been considered in each example.
However, the following can be shown in general.
(i) The skill sequence that composes the task is notnecessarily uniquely decided.
(ii) False model FMp, FMv in each skill primitive is
not necessarily unique.
The degrees of difficulty of executing a task can bederived using complexity of skill sequence and false
models. It is important to select simple skill sequence andsimple false models for practical use.

(6) Rotate-to-extract Skill
This skill is defined as revolutions to pull the screw out.
The skill continues after rotate-to-loosen skill.
(7) Move-to-remove Skill
This skill is the transition to remove the screw.

5.1. Loosening a Screw Using a Screwdriver

6. Conclusions

We will consider the task of loosening a screw using ascrewdriver [6]. Although there are many kinds of screws
and screwdrivers as shown in Fig. 9 [7], we will consider
this task by unifying the skill sequences in the following

We have shown false models for planning skill-based
manipulation. It is possible to perform planning efficiently,
since the false models are composed of only indispensable
data. We also showed the application of the false models to©ISAROB 2006

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skill-based backprojection. Artificial Life and Robotics
2(3):134-137[7] Jackson A and Day D (1992), Tools and how to use them.
Wings books, New Jersey, p.221

tasks which are often performed by maintenance robots.
In the future, we will further study false models forvarious skill-based tasks and attempt to apply our method
to real systems.

Preskill1
Sensing(P)Acknowledgment

Modeling(P)Re-planning(P)Execution(P)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.

Skill1
Sensing(S1)Modeling(S1)Confirmation(S1)
Execution(S1)Skill2

References
Execution(S2)[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 theIEEE/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

Skill3

Execution(S3)Skill4

Execution(S4)RepeatSkill5
Execution(S5)Fig. 12 Procedure of unfastening

Table 1 False models in loosening a screw using a screwdriver
Skill1(Preskill1)
RO

Skill2Skill4

Skill3

FMp FMv FMp FMv FMp FMv

Ⅰ (a) PhillipsNotneededNotneededⅡ (b) SlottedNotneededNotneededⅢ (c) Robertson

Not
neededNotneededNotneeded

FMp

FMv

Not
neededRO : Real objects

orFMp : False models
for planningorNot
neededNotneededFMv : False models
for visual sensing
and model matchingor

Table 2 False models in unfastening a bolt using a wrench
Skill1 (Preskill1)

Bolt©ISAROB 2006

RO

FMp

M

M

m

m

FMv
rv

Skill2Skill3Skill4

FMp FMv FMp FMv FMp FMv

Not
Not
Not
Not
Not
Not
needed needed needed needed needed needed

527

Skill5FMp

RO : Real objects
FMv

FMp : False models
for planningrvorFMv : False models
for visual sensing
and model matchingThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Constructing technique of state space with low dependence
on sensor configurations for autonomous mobile robots
Takeshi SHIBUYA and Tomoki HAMAGAMI
Graduate School of Engineering
Yokohama National University
Yokohama, Japan
Abstract

mation through sensors and to construct state space.
However, domestic robots like the IWCs equipping
low-cost, simple, and discrete range sensors have difficulty of efficient learning of environment[2].
The first difficulty is a dilemma of sensor ordersin which how many sensor can be equipped and how
many dimension of state space is required. If the sensors are equipped without restriction, the rapidly increasing computational cost, so called curse of order,
will be raised by using high-order and precise environmental information. Otherwise, limited sensors willraise several aliasing state in an environment called
perceptual aliasing.
Another difficulty is that the deviation of sensor
configuration in each robot causes serious problems
in which each learning policy can not be commonizedamong them. This problem prevents reusing of thelearning results of a robot to other robots, and becomes a barrier of development of ubiquitous network
robot.
In order to solve the problems, this study has an
assumption that each robot has a different sensor configuration between them. Under this assumption, inorder to realize efficient learning through the cooperative behavior of each robot, the communizing ofinternal state between them is significant technique.
The primary objective of this paper is to propose
a simple and effective technique of partitioning state
space for efficient learning independent of sensor specification and configuration.

The goal of this study is to develop heterogeneous
mobile robots which realize cooperative actions to accomplish each task in an environment. An importantpart of achieving the goal is that the agent controlling the robots can realize suitable state space automatically. In particular, domestic robots equipped lowcost, discrete simple range sensors have difficulties of
efficient learning of environment because the deviation of sensor configuration in each robot causes serious problem in which the learning policies can not
be shared among them. To overcome this problem,a new technique of constructing effective state spacewith low dependence on sensor configurations is conducted. The technique consists of two parts: those arerectangle-model abstracting real environment and selfconstructing universal state environment with SOM
map. The simulation experiment results have shown
the technique gives an agent controlling an intelligent
wheelchair robot an ability of autonomous constructionof environment.

1

Introduction

For realising ubiquitous network robots and constructing their society,the novel technique for autonomous mobile robot and agent becomes increasingly significant. In this future environment with therobots, various type of robots can cooperate and collaborate each other, then will emerge a new service forour welfare.
As one of the application of the service, we havestudied an intelligent wheelchair (IWC)[1]. The IWC
is defined as a domestic mobile robot which can explore the environment autonomously, observe the state
space through sensors, and learn suitable behavior.
In order to realize the functions, an agent controllingon IWC robot needs to acquire environmental infor-©ISAROB 2006

2

Autonomous mobile robot platform:
Intelligent wheelchair

We have been developing prototype of IWC whichis named ACCoMo (intelligent wheelchair as autonomous, cooperative, and collaborative mobile
robot)[1]. Appearance of an ACCoMo is shown in

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 1. The IWC has several range sensors (PSD:
Position Sensitive Detector sensors) equipped aroundthe frame of the platform wheelchair and moves witha direct drive servo motor in each wheel. An agent
software observes environment as space of several dimensions with these sensors, and decide an action ineach step, then controls these motors. The rule set of
deciding an action from sensor inputs is called policy.
The goal of the learning of agent is acquire a suitable policy through exploring in the environment. In
our previous study, we have designed the ACCoMo as
an intelligent mobile robot acquiring intelligent behavior with the reinforcement learning (RL); the neuralnetwork (NN) evolved by the genetic algorithm (GA)and their decision tree evolved by the genetic programming.

Figure 1: Prototype of the Intelligent Wheelchair

However, we have encountered some problems
which have been caused by the deviation of sensor configuration (the number of sensors, the type of sensors)when the ACCoMo is applied in several environments.
One example of the problems is that each agent recognizes a state of real environment as a different statebecause the accuracy and attachment positions of sensors are different among each IWC even though the
IWCs have same sensors. The problem is a kind ofperceptual aliasing[3] which is caused by a lack of sensor precisions. Although many approaches have been
proposed to compensate for the problem, to realize
sufficient sensor precisions is difficult problem underreal environment. Another example is that the policy acquired by an agent through the experience withan IWC does not work in other IWCs which have different sensor configuration from it. These problems
cause a serious problem in which a policy is limited toa specific hardware, and inefficiency of learning.

Figure 2: IWC simulator

3
3.1

Rectangle model representing abstract environment

As shown in Figure 3 , i denotes range sensor number, ˆli denotes the output of sensor i which means themeasured distance between the sensor and sensed obstacle. ĥi denotes the distance between the obstacle
and the center of rotation of the robot. Each ĥi calledhazard vector which is recognized in the agent as abstract image of neighboring environment is calculated
by geometric operation with these parameters in eachstep. Moreover, each ĥi s are classed in valid or invalid
values with threshold. Where, the valid value meansthat the sensed obstacle is in the attention distance.
Then, collecting adjacent valid vectors and surrounding them by rectangle as few number, these vectors aremodeled by surrounding with suitable size rectangles.
Figure 4 shows how the vectors are surrounded by themodel. As shown in the figure, this model can work
well to represent abstraction image of obstacles. This
abstraction technique can enable an agent free from

One possible method to have the policy in common
among agents according to various IWC is that highaccuracy and enough sensors are attached to identify
the state of real environment. However, so many sensors can not be attached because IWC platform has
a limitation of attachment positions. Furthermore, somany sensors lead so many space dimensions whichcause fail of learning , so-called curse of dimensionality.
In order to solve these problems, following proposed
techniques are combined. The first part models the environment as rectangles to abstract obstacles layout,which rectangles can be synthesized directly from sensor outputs by using dimension free algorithm for one
thing.

©ISAROB 2006

Methodology

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3.3

hProposed technique provides mapping a environmental information to the agent’s state with low dependency on the sensor configuration. This enables
agent to decide behavior using common state spaceamong robots with different sensor configuration.
Then, this technique can represent the environment
with some simple figures in the model.
This advantage realizes robustness for noises when
the agent maps the environmental information to astate. Furthermore, state space constructed with our
proposed technique has a topological structure.
This structure will be useful for effective learning
because the group of states in which suitable action issimilar is neighbor in each other in the map, that isthe propagation of reinforcement value can be realizedon the map.

lrFigure 3: Parameters for geometric positioning between a robot and walls.

4

dependencies from various sensor configuration, howmany sensors are equiped or how much precise sensors
are required.

Constructing state space with self organization map

In order to construction state space with the rectangle model as mention above, this study applies imagebased self organization technique: self organization
map(SOM)[4].
First, a 2-D image which consists of rectangles generated by the model is synthesized. The image is alayout as if the agent looks down him/herself image
from the overhead. During exploring the environment
with acquiring various images, then clustering the image into some class by SOM, the agent can constructstate space autonomously. Furthermore, in order toconstruct state space effectively, each image needs tobe normalized for rotation. In particular, the central
moment is applied.

©ISAROB 2006

Simulation Experiments and Results

Two simulation experiments are conducted todemonstrate the effectiveness of our proposed technique.
The purpose of the first experiment is to verify
that the agent can construct state space through sensor outputs as the environmental information. Another experiment makes another agent evaluate theconstructed state space, the agent which controls another IWC with different sensor configuration.
As simulation environment, an simple aisle shownin Figure 2 is explored. An agent that controls thesimulated IWC behaves in the environment and constructs state space autonomously.
The agent acquires ĥi as environmental information
during random exploring. Then, the agent generates
rectangle images from vector ĥ. Figure 5 shows theexample of acquiring the environment information andmodeling it.
The image is classified into a class on SOM map,and the map is reorganized when 200 images are obtained. Figure 6 shows the SOM map constructedstate space in Figure 5. Each state shows typical obstacle alignment shown in Figure 5.
Figure 7 shows the mapping between environment
positions and the states, where (a) is a mapping constructed by the agent which has 16 range-sensors, (b)is a observed state by the agent which has 12 rangesensors.
The results show that the state space which hasconstructed 16 sensors can be reused for the agent

Figure 4: Methodology of making rectangle from hazard vector.

3.2

The State of the arts

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

with another sensor configuration. Consequently, theproposed technique enables the agent to constructed
generalized state space for autonomous learning.

Figure 6: The SOM map constructed state space inFigure 2

Figure 5: The example of acquiring the environmental
information.

5

Conclusion

A new constructing technique of state space withlow dependence on sensor configurations for autonomous mobile robots is proposed. The advantageof the technique is that the state space can be constructed from simple, low precise sensors on domestic
robots like IWC. It enables us to realize various application by using cooperative action within ubiquitous
network robot. In future plan, we plan to conduct thetechnique to adapt effective reinforcement learning inreal environment.

Figure 7: The mapping between environment positions
and the states by agents with different sensor configuration. Each coordinate is corresponded to the one of
the state of SOM map

References[4] T.Kohonen,Self-Organizing Maps, Springer Verlag,2000.

[1] T.Hamagami, H.Hirata, “Development of Intelligent Wheelchair Acquiring Autonomous, Cooperative, and Collaborative Behavior,” Proc of IEEE
International Conference on Systems, Man and Cybernetics, pp.3235-3530 (2004).
[2] T.Hamagami, H.Hirata, “State Space Partitioning and Clustering with Sensor Alignment for Autonomous Robots,” Proc of IEEE International
Conference on Systems, Man and Cybernetics,
CD-ROM (2005).
[3] L.P.Kaelbling,M.L.Littman,A.P.Moore,“Reinforcement Learning: A Survey,” Journal of
Artificial Intelligence Research, Vol.4, pp.237–285,
1996.

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Emergent Societies: An Advanced IT Support of
Crisis Relief Missions
Peter Sapaty 1 Masanori Sugisaka 2 Robert Finkelstein 3 Jose Delgado-Frias 4 Nikolay Mirenkov 5
1

Institute of Mathematical Machines and Systems, National Academy of Sciences,
Glushkova Ave 42, 03187 Kiev, Ukraine, +380-44-5265023, +380-44-5266457 (fax)sapaty@immsp.kiev.ua
2

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
34Robotic Technology Inc., 11424 Palatine Drive, Potomac, Maryland 20854
301-983-4194 Voice, 301-983-3921 Fax
RobertFinkelstein@compuserve.com

School of Electrical Engineering & Computer Science, Washington State University
Pullman, WA 99164-2752, Ph: (509)335-1156, Fax: (509)335-3818
jdelgado@eecs.wsu.edu
5

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 novel distributed control ideology and technology will
be described for management of advanced crisis relief
missions. The approach is based on the installation of auniversal “social” module in massively wearable electronic
devices, like laptops and mobile phones, which cancollectively interpret a spatial scenario language,
exchanging high-level program code (waves), data, andcontrol with other modules in parallel. This can dynamicallyintegrate any scattered post-disaster human and technicalresources into an operable distributed system capable ofsolving autonomously complex survivability, relief, andreconstruction problems.
Keywords: critical infrastructures, key resources,
emergency management, emergent societies, crisis relief,
information technologies, distributed control, WAVE-WP
model, wearable electronic devices, mobile robotics.

recent very sad examples. Earthquakes are anotherdisaster area, like the recent one in Pakistan, and thetsunami a year before. Manmade disasters caused byarmed conflicts and terrorist attacks are effectively
contributing to this black list too.
How to regain integrity, restore law and order, andassemble scattered resources for a collective survival?
How to rebuild the damaged territory, revive theprevious infrastructures or create new ones, and return tonormal life?
In Fig.1, a symbolic picture of the post-disaster area,
once representing an integral organism, is shown withthe wreckage of living quarters, organizations andinfrastructures, also separated and scattered individuals
and their emergent grouping.
Emergent grouping

Individuals
Casualcommunications

1 Introduction
1.1 The Grim Big Picture
Millions of people are on the move, traffic jams
everywhere. Houses destroyed, infrastructures gone, winds
hundreds kilometers per hour, flooding and fires. No
electricity, shortage of food and fuel, usual ties broken,
businesses vanished, jobs lost. No central authorities orservices, looting and lawlessness… This is becoming afamiliar picture throughout the world, especially due toglobal warming and climate change. Katrina and Rita are©ISAROB 2006

532

Wreckage of regions, organizations, and infrastructures

Figure 1. A grim picture of the disaster area.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Despite indiscriminate damages, the disaster area canstill hold key human, technical, and natural resources, andits parts can still be able to communicate with each other(for example, if cellular towers, at least some of them, arestill operating, and/or access to internet remains available).
Radio communications, local however, can be helpful too,and some units in the area may have satellite phones. The
electric grid power may still be available, and the same gridcan potentially be used for data communications (suchpromising projects already exist). So there may be sufficient
resources for self-survival and even self-recovery, especiallywith an external aid hurrying, but they are pretty muchscattered and disorganized. Restoring integrity andcoordination in the disaster area is becoming a primary goal.

1.2 Emergency Management
Emergency management [1], due to the increased world
dynamics, is one of the hottest topic today. The emergency
managers around the world are faced with new threats, newresponsibilities, and new opportunities. It is widely believed[2] that the combination of novel technologies and databases can allow law enforcement and intelligence
investigators to identify potential terrorist plots, use a
multitude of data bases that may contain hidden patterns of
information about transactions needed to execute plots, andthen mount preemptive strikes to stop their plans.
The National Response Plan [3] establishes acomprehensive all-hazards approach to enhance the ability
to manage domestic incidents. The plan incorporates bestpractices and procedures from different incident
management disciplines. Another prominent document, The
National Infrastructure Protection Plan (NIPP) [4] provides
a unifying structure for the integration of all criticalinfrastructures and key resources protection efforts into asingle national program.
However the existing efforts are actually offering yet
another infrastructures, to be built on the same principles as
the existing ones, i.e. consisting of specialized components
located in certain places and communicating with eachother, with a good deal of central control over them. Due tothis, they may inevitably be as (if not more) vulnerable toattacks and failures as other infrastructures, and may
become a burden rather than savior.
Even in the relatively modest Katrina case, local
infrastructures were indiscriminately fragmented and totally
inoperable (while malicious ones thriving), and federalbodies showed clumsiness and inefficiency. The new global
infrastructures outlined, like what we see in NIPP, may
result in a similar performance in case of major disasters
caused by hurricanes, earthquakes, or WMD attacks.

1.3 Towards
Approaches

New

Emergency

Management

We believe that the critical infrastructure protection,
recovery, and relief ideologies and technologies should bebased on quite different, revolutionary rather thanevolutionary, principles, and they should evolve and operate
in other time-space dimensions than the traditional
infrastructures and forces that can harm them, in order to be©ISAROB 2006

533

incomparably superior and unaffected themselves in caseof major crises.
A completely different approach is being developedof how to penetrate into any distributed and open
systems and establish an overwhelming power over them
(destruction of malicious infrastructures in these systems
being an option). Any global or local scenario we want toimplement over any area or system is formulated in a
special spatial control language, which is interpreted
cooperatively in a distributed system widely using smart
mobile program code covering the system or its parts inparallel. This provides spatial hologramlike algorithms,
which dynamically exist in between system components
rather than inside them, thus being totally unobservableand unreachable by conventional means.
Actually, this work follows a sort of an intelligent
supervirus ideology, with its potentially unlimited selfpenetration and self-recovery possibilities. The approach
can also setup, at runtime, any needed infrastructures
over scattered post-disaster human or technical resources,
and these infrastructures can evolve and freely migrate inboth physical and virtual worlds, self-recovering after
damages and preserving integrity and goal orientation.
This paper summarizes the technology called
WAVE-WP that serves these purposes, outlines its main
applications and possible implementation with the use of
massively wearable devices. Examples of spatial
programming of some exemplary crisis relief operations
will be demonstrated too.

2 WAVE-WP Model and Technology
The distributed computation and control WAVE-WP(or World Processing) model and technology [5-7] arebased on a higher-level language describing parallel
distributed solutions in computer networks as a single
seamless spatial process rather than traditional collection
and interaction of parts (agents), while shifting these andother routines to an efficient automatic implementation.
Communicating copies of the WAVE-WP language
interpreter (WI) should be installed in most sensitive
points of the system to be governed (like internet hosts,
robots, troop carriers, dismounted soldiers, laptops,
mobile phones, etc.).
Parallel spatial scenarios (or waves) written in thelanguage can start from any interpreter, covering the
network at runtime and cooperating with each other inthe distributed space. The approach often provides
hundreds of times application code reduction andsimplification, allowing us to concentrate on efficient
global solutions rather than implementation details.
Spreading via networked WIs, waves can create
dynamic knowledge infrastructures arbitrarily distributed
between the system components. Subsequently or
simultaneously navigated by same or other waves, theycan effectively support distributed databases, advanced
command and control, global situation awareness,parallel inference, and autonomous decisions. It isconvenient to operate in this seamless virtual world fully
ignoring its physical distribution, whereas virtual

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

networks can migrate (partially or as a whole) in physicalnetworks while being processed.

moving in space with control; and environmental,
accessing the navigated environment in points reached.

Installed in advance in different components that may
happen to be located in a disaster area, with possibilities of
their communication using any remaining channels, WIs canconvert the whole area (like the one shown in Fig. 1) into an
operable self-organized system. The latter will be able tosolve complex tasks on itself (see Fig.2), where high-level
relief scenarios in WAVE-WP can start and evolve from any
interpreter, covering the whole system or its needed parts atruntime, while establishing full control over it.

Rules being forward rules, coordinating spreading ofwaves or setting up special navigation contexts, or echo
rules detailing the fusion and return of (remote) statesand data. Acts classify as fusion acts producing newvalues from operands, flow acts moving data and control
in space, and application acts activating alien or native
procedures. Constants and variables may represent bothinformation and physical matter; they may also represent
program code to be created, processed or modified witha subsequent execution as waves, thus providing highprogramming flexibility in dynamic environments.

Continuous
processes

WI

Wave interpreters

3.2 Spatial Interpretation

WI
WI

What follows from this language definition, is theunwrapping and replication of the recursive formulae,rather than traditional reduction, as shown in Fig. 4. The
wave program matches, conquers, floods, covers the
distributed physical or virtual world in parallel,establishing full control over the space. Each act isperformed in the space positions reached on local data
there, or on what is obtained and returned by other wavesof the expression. The resultant value on any constructcomprises all values obtained in the points of spacereached by it. All constructs return control states, which
are merged and generalized at higher levels with the useof rules for making hierarchical spatial decisions.

Waves
WI

WI
Waves
WI
Spatial scenario

Knowledge network

Figure 2. Converting the disaster area into an operable
system.

Spatialcoordination
with rules

3 The WAVE-WP Language

advances
mo
ve

WI

WI

e
ov
m
e
ov
m
ve
mo

mo
ve

3.1 Language Basics
We are considering here only the top definition of theWAVE-WP language, shown in Fig. 3, with details in [5, 6].
wave
advancemove
unit
variablerule
act
constant→ { advance ; }→ { move , }→ { unit act }→ constant | variable | [ rule ] ( wave )→ nodal | frontal | environmental→ forward-rule | echo-rule→ fusion-act | flow-act | application→ information | physical-matter |program-code

e
mov

move

move

move

Start
Control & data echoing

Figure 4. Spatial evolution of waves.

Figure 3. WAVE-WP language syntax.
Starting from a certain position, the program, or wave,
navigates in physical or virtual space, with successive
advances starting from positions reached by the previous
advances. An advance may consist of moves, which candevelop in parallel. Moves may be data processing and/or
space propagation expressions of units separated by acts. A
unit can represent a value directly, as constant or variable,or may be an arbitrary wave in parentheses, optionallyprefixed by a rule. Variables may be: nodal, dynamicallyassociated with space positions and shared by waves; frontal,©ISAROB 2006

e
ov
m
move

Positionsreached inphysical or
virtual space534

A number of successful implementations of thisapproach have been made in different countries, withpublic domain available on the internet [5]. The new,advanced version is currently being re-implemented andpatented, with orientation on both software product anddirect “wave chip”.

4 Current and Prospective Applications
Let us mention some applications of this technologythat may be directly or indirectly helpful for theadvanced emergency management, with more details andother areas presented in [5-7].

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Distributed Robotic Brain. WAVE-WP interpreters put
on top of (and integrated with) the usual robotic
functionality may effectively convert any group of robots
into an integral spatial machine capable of doing complex
jobs simultaneously and cooperatively. Individual robots
may be of any type (from most advanced to primitive)
devices, some can even fail at runtime, but the group as awhole may remain functional and goal-oriented.
Future Combat Systems. Installed in main components
of FCS, WAVE-WP can provide global vision of theterritory and overall situation awareness, despite limitationsof individual sensors and control facilities. It optimizes
fusion and distribution of scattered targets, provides flexible
command and control, where the CC infrastructure may be
adaptable at runtime.
Tracing Physical Objects. The technology enables anefficient chase of (aerial or ground) physical objects by selfnavigating and replicating program agents following them
via computer networks. The multiple mobile intelligence,migrating in computer networks, provides a new level offlexibility for various management systems. It may guide,
for example, the destruction of unwanted objects after
individual investigation of their routes and behavior.
Global Fighting of Network Intrusions. WAVE-WP
interpreters installed in internet hosts and accessing usual
management tools, can form a higher layer converting large
volumes of raw data into the seamless distributed
knowledge for global network analysis and optimization.
The WI network can simultaneously discover and analyze
viruses throughout the whole world, blocking their spreadand inferring attack sources.
National and International Infrastructures. The
technology can effectively support vital infrastructures ofnational and international scale, solving complex problems
in them in parallel. These may relate to politics, economics,
demographics, weather prediction, environmental pollution,
postal service, transport, industrial goods flow, tracing
international criminals, and air and space defense, with
optimized solutions without central resources.

5 Using Massively Wearable Devices
Emergency management may be fundamentally assistedby the massively wearable individual data processing andcommunication devices, which are expected to remain withindividuals under any circumstances. WIs, installed in them(this can be done without problems, taking into account theexisting experience of implementation of the WAVE systemon different platforms and compactness of the language
interpreter) can make the whole societies, especiallyemergent ones in the disaster areas, to be programmable andcontrollable in the way required, despite the scattering ofresources and limited communications.
Mobile phones, the undisputed leader among wearables,
are expected to be the most common consumer electronics
device on the planet [8]. By the end of 2009, some 2.6
billion mobiles will be in regular use around the world.
Mobiles, for example, are catapulting rural Africa into the
21st century, making it the world's fastest-growing
cellphone market [9]. They are enabling millions of people©ISAROB 2006

535

to skip a technological generation and bound straightfrom letter-writing to instant messaging. Asia is the next
fastest-expanding market.
Many also use GPS, digital cameras, portable PCs
along with mobile phones, and there already exist
advanced products that combine all these and many otherfeatures within a single piece (including also Bluetooth,
WiFi, phone radio and TV [10,11]). 3G, or third
generation, brings broadband for mobiles too [12]. Also,
the recently unveiled sub-$100 laptop design [13], with
its internet wireless access and a hand crank for when
there is no power supply, is expected to contributedrastically to the unprecedented electronic integration of
the whole world.
We may assume that these devices (at least some ofthem) would remain able to communicate with eachother during and after the disasters via the wirelessnetworks, internet, radio or any other channels, and in
the worst case even via humans (who, using voice,handwriting or gestures, can pass manually the needed
code and data, with the return to an electronic WI level at
the reception end).

6 Emergency Management Code Examples
We will consider here only elementary examples ofspatial programming in WAVE-WP for some tasks that
may relate to emergency management, withsimplifications necessary for showing program code
within the limited paper space.

6.1 Spatial Counting of All Casualties
Let us consider a fully distributed and parallelcounting of the total number of casualties in the disasterarea, on all affected regions, assuming for simplicity that
only a single WI is available in each region or group ofindividuals (the interpreter-participant can be negotiatedlocally if more than one).
The following program can be applied from any
available WI as an entry one, which can be away fromthe disaster area (say, located in a federal center) or can
reside within the area, as a special or emergently selectedbody or individual. (Characters #, _, ?, and ! are serving,
respectively, as hop, aggregation, external procedure call,and halt acts; whereas grasp and repeat being
forward rules , and sum as an echo rule of the language.)
Farea = disasterarea;
USER = sum(direct # any _ Farea;
repeat(grasp(Nmark == nil; Nmark = 1);? casualtiesinfo ! done,
any # Farea))

Within the identified disasterarea, assigned toand subsequently carried in frontal variable Farea(names of frontal variables must begin with F) theThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

program first hops into any directly reachable units within
the area. Then from all these units it repeatedly (in parallel
to all reachable neighbors) navigates the area using any
available local channels between the interpreters (manual
communications are not excluded too). The program enters
nodes only once, marking each with an individual nodal
variable Nmark (which names should start with N in thelanguage), grasping nodal resources for performing an
indivisible check-assignment sequence.
This parallel spatial process forms a spanning tree
covering (if communications permit) the whole disaster area
via the reached WIs in it. In each WI reached by the waves,an external procedure casualtiesinfo (which can be
performed manually, by humans or groups of them sharing
the same WI) returns the number of detected casualties inthis region. These collected numbers are then summed up inparallel in the tree nodes and moved up the tree in the global
echo process, receiving finally the total number of casualties
on the whole area, output to the USER owning the entry WI.
As can be seen, the whole disaster area had been
dynamically converted into a parallel spatial machine
capable of solving the problem in a fully distributed manner,without central computational resources, as shown in Fig. 5.
31
77

31

WI

WI

108
Waves

96

WI

36

Merging
results
45

120

WI

204

WI

WI

45

Responding
nodes
128
WI

332

8

Start

Figure 5. Spatial counting of the total number of casualties.

6.2 Relief Delivery to the Most Affected Region
If to use echo rule max instead of sum of the previous
program, and also lift physical coordinates of the regions
navigated (via environmental variable WHERE associated,say, with GPS devices), it is possible to get the final resulton the disaster area as the following aggregated value. The
first part of it will give the number of casualties of the mostaffected region, and the second one providing physicalcoordinates of this region (as measured from the related WI).
The following program records this two-value result in the
nodal variable Nmax associated with the entry node.
Farea = disasterarea;
Nmax = max(
direct # any _ Farea;
repeat(grasp(Nmark == nil; Nmark = 1);
number ? casualties _ WHERE ! done,©ISAROB 2006

Using the obtained first value in Nmax, it is easy todescribe in WAVE-WP the assemblage of needed
number of relief packages (storing all physical packages
in frontal variable Fsupply). This can be succeeded bya direct movement into the physical location in space (bythe second value in Nmax), distributing afterwards therelief packages between the suffered individuals (using
external manned procedure distribute), as follows:
Fsupply = “reliefpackage” * Nmax : 1;
direct # Nmax : 2;
Fsupply ? distribute
In this program, : is an indexing act of the language,
being superior to multiplication *, the latter working on
physical matter at the left (“reliefpackage”) andinformation at the right (number of packages needed,same as number of casualties).

6.3

The Delivery to All Regions

It is easy to modify and merge into one the previous
two programs in order to find casualty numbers
separately on all affected regions, with corresponding
coordinates of these regions, and then assemble theneeded amount of goods for every region and forward allthese to the proper destinations, with a subsequent
manual distribution, as follows.

14
Returning
results
25
25

14

WI

any # Farea))

Farea = disasterarea;
Nall = (direct # any _ Farea;
repeat(grasp(Nmark == nil; Nmark = 1);
number ? casualties _ WHERE ! done,
any # Farea));
Nall; N = VALUE;
Fsupply = “reliefpackage” * N : 1;
direct # N : 2;
Fsupply ? distribute
The needed number of packages will be delivered to
all related destinations via the rotes available, which may
happen to be complex, taking into account the remaining
usable road infrastructures and peculiarities of the terrain.
This delivery may be performed by manned vehicles or
convoys, or by advanced unmanned mobile groundsystems, like those described in [14], with omission offurther details on this within this short paper. Any
detailed delivery, including path finding and avoidanceof obstacles, can be effectively represented in WAVEWP, with related examples discussed in [5-7]. The
general picture of parallel physical delivery to the needed
destinations may look like the one shown in Fig. 6.
In a similar way, it is easy to describe and executemuch more complex scenarios of self-organization ofaffected regions, including massive evacuation, forming

536

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

new emergency infrastructures, fighting malicious postdisaster infrastructures (like looting), movement andspreading of communicating relief teams, cooperating withthe self-organizing disaster area, and so on. As WAVE-WP
technology allows us to set up a formal description of what
was considered as predominantly human before, the relief
scenarios can be effectively performed by any combinationof manned and unmanned units, with humans and robots
sharing responsibility for the key decisions in different
space-time locations.
WI

WI

77

14

WI
WI

31

25

WI

36
WI

WI

45

96
WI

Start

8

Figure 6. Delivery of the needed goods to affected regions.

7

Conclusions

A ubiquitous, supervirus-like approach for emergency
management and critical infrastructure and key resources
protection has been proposed, based on the universal
distributed processing and control technology WAVE-WP.
Effectively operating in a different space-time continuum
than the existing systems or forces that can harm them, it
can dynamically set up an unlimited power over the
distributed worlds, being itself much less vulnerable todamages than other systems.
The compactness and simplicity of relief scenarios in theWAVE-WP language allow us to program them on the fly,reacting timely on rapidly changing situations. The
technology can effectively convert scattered human andtechnical resources into highly operable distributed systems
capable of organizing relief missions in other systems or
autonomously solving complex self-survivability and selfrecovery problems.
The implementation of the technology can be easily
done on any existing software or hardware platform, and thecorresponding “social” module, as a software or hardware
wave interpreter, can be readily installed in many wearable
devices, and first of all mobile phones and handheld andlaptop PCs.
Massive use of advanced mobile robotics in the future
relief missions, and its integration with manned components
under the unified command and control, can be drasticallysimplified by this flexible control model too.

©ISAROB 2006

537

References[1]

J. A. Bullock; G. D. Haddow, “The future of
emergency management”, Journal of Emergency
Management, Winter 2004, Volume 2, Number 1,
pages 19-24, http://www.pnpco.com/.
[2] S. Nunn, “Preventing the Next Terrorist Attack:The Theory and Practice of Homeland Security
Information Systems”, Journal of Homeland
Security and Emergency Management, ISSN
Iss.
3,
1547-7355
Vol.
2(2005),
http://www.bepress.com/jhsem/.
[3] National Response Plan, US Department ofHomeland Security, December 2004, 95p.,
http://www.dhs.gov/interweb/assetlibrary/NRPba
seplan.pdf[4] Draft National Infrastructure Protection Plan, US
Department of Homeland Security, Office of theAssistant Secretary for Infrastructure Protection,
November 2, 2005, 2005, 161p., www.dhs.gov(unpublished yet).
[5] P. S. Sapaty, Mobile Processing in Distributedand Open Environments, John Wiley & Sons,
ISBN: 0471195723, New York, February 1999,
436 p.
[6] P. S. Sapaty, Ruling Distributed Dynamic
Worlds, John Wiley & Sons, ISBN 0-471-655759, New York, May 2005, 256p.
[7] P. Sapaty, M. Sugisaka, N. Mirenkov, M. Osano,
R. Finkelstein, “Grasping the Distributed
Entirety”, Proc. Tenth International Symposium
on Artificial Life and Robotics (AROB 10th),
Beppu, Japan, February 4-6, 2005.
[8] “Mobiles Head for Sales Milestone”, BBC
NEWS,Technology,
19
July
2005,
http://news.bbc.co.uk/.
[9] S. LaFraniere, “Cellphones Catapult Rural Africa
to 21st Century”, The New York Times, August25, 2005, www.NYTimes.com.
[10] “Samsungi730”,
http://www.vzwshop.com/samsungi730info/.
[11] “GPSMobilePhone”,
http://www.easydevices.co.uk/pcp/Mobile_Phone.html.
[12] “3G Industry Optimistic for 2006”, BBC NEWS,Technology, Friday, 25 November 2005,
http://news.bbc.co.uk/.
[13] “Sub-$100 Laptop Design Unveiled”, BBC
NEWS, Technology, 29 September 2005,
http://news.bbc.co.uk/.
[1] Joint Robotics Program, Master Plan FY 2005,
US
Department
ofDefense,http://www.jointrobotics.com/activities_new/mas
terplan.shtml.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Cognitive Contour Detection for Negative Filtering
Zoltán Petres 1,2

Barna Reskó 1,2

1 Institute of Industrial Science

2 Computer and Automation Research Institute

The University of Tokyo

Hungarian Academy of Sciences

Abstractof Hubel, and later V1 models [3] suggest that cells in thevisual cortex are organized in a 3D structure, where a location on the visual field and an input stimulus preference(e.g. orientation preference) can be assigned to each cell.
A cognitive system is implemented in a biological neuralnetwork, where simple units of computation are connected
in a very complex structure. Our research goal is to turnthe cognitive information processing system into engineering models which can later be organized into a cognitive
psychology inspired model running on a biology related
computational architecture.

This paper presents a cognitive model based on the
mammalian visual cortex, which is able to perform image
contouring by the means of extracting contour line segments as abstract objects. The line segments are organizedby their orientation and length in a three-dimensional array,
the Visual Feature Array, which allows image processing
and transformations along new dimensions, such as orientation or length. Negative Filtering is the process when
the Visual Feature Array is used to reconstruct the original
contour image by redrawing the stored line segments, thus
removing noise from the image. The presented approach isstrongly based on cognitive psychology and neurobiology.
The processing model has a strictly parallel architecture inorder to mimic its biological inspirator, and to allow constant time processing on a parallel computational hardware.

1

This paper introduces a model strongly based on the
cognitive functions of the visual cortex for image contour
detection. The model was elaborated on the analogy of the
mammalian visual system. Each phase from the retina to
the visual cortex is represented in the model by imitating
the biological structures and cognitive functions in order toperform similar image transformations and operations. Inclassical image processing algorithms, such as edge detection using a sobel filter, both the input and the output are
matrices containing pixels. These algorithms thus represent a pixel-to-pixel transformation between two matrices.
Similarly to the neural networks in the cerebral cortex, themodel proposed in this paper implements a pixel-to-featuretransformation, where feature refers to a more abstract visual object, such as a line segment of a certain length andorientation, or a line crossing. The result of the transformation is thus a feature-level abstraction of the input image.
The abstract features can also be re-transformed into the
pixel level by a feature-to-pixel inverse transformation, allowing a visual representation of the feature-level abstraction. The re-transformation of features into pixels will exclude noise from the result, thus it can be used as a filtering technique, described later in this paper. The presented
model can be applied in many practical applications. Oneof them is the Intelligent Space, detailed in [4].

Introduction

In order to show why cognitive models can give the necessary boost in computation, consider the example where aperson has to decide whether there is a cat or somethingelse on an image. Such a task is impossible for a computer
to perform today, yet a human can do it reliably in half asecond or less. This result is very interesting when considering that the “processing time” of a typical neuron is in therange of milliseconds while that of a logic gate of a modern
silicon-based computer is in the range of nanoseconds. The
computational capacity of the brain thus has to lie in its special architecture and particular information representationand processing, rather than in the speed of its processing
elements. It is our belief that in order to step beyond theborders of today’s computer systems’ architectures the basic way of information representation and processing has
to be changed. For new ideas we turn to existing cognitive systems in biological architectures to study them, because they already bear the solutions that we are seeking
for. Hubel and Wiesel first described the visual system [1],and suggested that iso-orientation domains are packed inessentially linear parallel stripes, which Hubel [2] subsequently referred to as the “ice-cube” model. The model©ISAROB 2006

Hideki Hashimoto 1

The rest of the paper is organized as follows. Section 2
describes the proposed architecture of the model for highspeed image processing. Section 3 is devoted to the model
evaluation and experimental results. Finally, Section 4 concludes the paper.

538

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

Cognitive model of the visual pathway

receptive fields of the neurons in LGN are also circular likethose in the retina. This suggests that the LGN does not
add any extra image processing functionality to the visual
pathway. It rather has an important role in modulating the
input to the cortex by attention, but the exact functionality
is still a subject of research.
For the above reason we consider the retinal and LGNneurons as primary edge detectors, and their overall functionality in the aspects of image processing is covered by
the M1 matrix in the model. The input from the cells ofsuch receptive fields project into the visual cortex, where
further image processing takes place.
The image representation in the visual cortex is retinotopic, which means that neighboring regions of the visual
field are projected to neighboring regions in the cortex. The
neurons of such a region are tuned to respond to a variety of
input stimuli described by different receptive fields characteristics. This implies that a vast variety of receptive fields
belong to one small region of the visual cortex, and thus toa small region of the visual field. The variety of receptive
fields representing different visual features (e.g. line orientations) can be organized along new dimensions.
After an edge detection discussed above, an edge detected image is available in the matrix I where

A scene projected to the retina becomes a twodimensional image, which is transferred to the brain forfurther processing. Such an image is composed of image
features like regions of a certain color and texture, theirboundaries as segments of different orientation and length.
The image features make part of more abstract features like
simple shapes, curves, circles.
The main goal of the present model is to understand thebasic primitives of an image, on the analogy of the cerebral
cortex as a complex cognitive system. The understandingof a feature in biology is defined as the firing of a set ofneurons, which tend to fire when that particular feature ispresented on the input as a stimulus. In the proposed modela feature is represented by the activation of a single neuron
instead of a set, and it is considered understood when thecorresponding neuron fires. The neurons of the understoodfeatures can project their outputs to higher and lower levels in the neural hierarchy. Projecting the output further
up allows the neurons in higher levels to understand more
abstract features as the composition of lower level features.
On the other hand, a neuron that projects its output to lower
levels in the neural hierarchy can be considered as an expectation from above, and will help the low-level neurons
to understand the lower level features.
This paper concentrates on how primitive image features are understood, and how they can be used as an expectation in lower levels.
The proposed model in this paper receives an image on
its input, which is immediately subjected to an edge detection filter. This filter is based on the receptive field characteristics of the retinal ganglion cells. In the small region of the visual field which is centered around the position of the ganglion cell the afferent connections have arelatively high positive weight, while in the surrounding
regions the synapse weights are inhibitory. The receptive
field is modeled with a 3 × 3 matrix M1 with higher positive input weight values in the middle and small negativevalues in the surrounding regions.
1
 8
 1
M1 = 
 −8
 1
−
8


−

1
8
1
1
−
8

−


1
8 
1 
− 
8 
1 
−
8

I ∈ Rn×m ,n and m representing the image dimensions.
According to the visual cortex, several different features
can be extracted from the edge detected image I. The extraction of the features begins with those having the largest
number of pixels, i.e. the longest lines. When the first feature is extracted from the edge detected image I, the feature
pixels are removed from I, resulting a new matrix that werefer to as I (1) . After extracting and removing the kth feature from I (k−1) the matrix I (k) remains. Using this notation
the original edge detected image is denoted I (0) . This stepis necessary to ensure that only one of many possible similar features is extracted from the edge detected image I (0) .
The kth feature is removed from I (k−1) and added to a twodimensional matrix Fk , such that∀i, j, k : (Fk )i, j ∈ {0; 1},

−

(1)

(3)

and the value (Fk )i, j indicates if any pixel of the detectedfeature k is present in the edge detected image at the posi(k−1)
tion Ii, j .
It is important to note that the features to extract areordered by the number of pixels they contain in order toensure thatF k ⊇ F l , k < l,(4)

The output pattern of the cells with input weights of M1
will be an edge detected image of the original image. It
is to note that at this level of neural processing the image
features understood (or represented by neural activation)
are pixels of an edge detected image, edge elements.
Going further on the visual pathway we find that the©ISAROB 2006(2)

where F k is the set of pixels contained by the kth feature.
Since there are several image features to be extracted from539

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

from an edge detected image, for each feature k a mask matrix Rk obtained from a corresponding receptive field has tobe defined. In the proposed model the visual features are
extracted by a convolution of the edge detected image anda matrix Rk . In the present case the receptive fields aremodeled by binary matrices instead of matrices with realvalues. These matrices contain the sought feature as it may
appear on the binary edge detected image.
Consider a grouping transformation on the VFA, whichsimply groups all the layers into one final layer containing all the extracted features. This transformation equals
sending the output of the VFA neurons down in the neural
hierarchy, and can be used to reconstruct an image by redrawing the detected visual features. This reconstruction
will include only the features that were extracted from theoriginal image. This implies that the noise (pixels not considered as the part of any feature) will not be present in thereconstructed edge detected image.
We have introduced the notion of negative filtering as
the process of understanding image primitives and reconstructing the image from them. The notion arose from thefact that on contrary to a filtering process, the above defined process adds useful information to the image, insteadof subtracting it.

the image, there will be a matrix F for each of these features. We define the three-dimensional array with the F
matrices overlapped along a third dimension as follows:V ∈ Rn×m×r(5)

For the three-dimensional matrix V we introduce the notion of Visual Feature Array or VFA, where r represents thetotal number of visual features. By construction, the ele(k−1)
ment V i, j,k of the VFA represents if an edge pixel Ii, jbelongs to the kth visual feature.
In the VFA each element corresponds to the response ofa cortical neuron tuned to a certain feature in a certain location. In the VFA the features are organized along a third
dimension, orthogonal to the other two dimensions. Sucha system of visual features yields a 3-dimensional neuralarray model of the primary visual cortex.
In the visual cortex there are neurons tuned to a wholevariety of visual features. The present model includes theorientation selective cortical cells with end-inhibition characteristics. Each feature in the VFA can thus be describedby an orientation angle and an optimal length. The possible
orientations are equally distributed with a specified angularresolution. The angles represented in the VFA are definedwith the angle α and angular resolution θ, such thatα ∈ [0 . . . π], α = k · θ, k ∈ N,

(6)

3 Model evaluation, results

and thus the matrix elements (Fα=π/5 )i, j will be values of
1 where an edge line segment with an orientation close to
π/5 is found in the edge detected image at Ii, j .
The end-inhibition property of the neurons is also formalized in the model. An optimal length l of a neuron is alength to which it gives a maximal response. The different
lengths are distributed between the shortest length and thelongest length, and their number is h. Since the line lengths
are measured in pixels, the shortest possible line segment
is 3 pixel long. The maximal length can be chosen taking
the requirements of the input image and the available computational capacity into consideration. Normally this value
is between 20 and 30 pixels.
Given an angular resolution of θ and the number of different length values h, the number of possible visual features r can be assessed as follows:π(7)r = · h.
θ

The proposed model has two important advantages
compared to classical solutions. By virtue of the simple but
numerous computational units (neurons) that work parallelon the solution, the model can perform the proper activation of the VFA and the negative filtering in constant time.
This, however requires a parallel hardware implementationof the model.
In our case only a computer simulation of the model wasavailable, which allowed to evaluate the functionalities ofthe model, but not its performance.
The input test image used to evaluate the model is shownin Figure 1a. This image is subjected to a primary edge
detection according to the model. The result is a binary
image of edge elements, with white dots representing highcontrast points on the original image. This edge-detected
image is shown in Figure 1b.
The edge-detected image in our model corresponds tothe image that is projected to the visual cortex. In themodel, this image is used as the input to the neurons in theVFA. In the present implementation 5 different line lengths
were used with the possible orientations to calculate thevalues of the VFA. These lengths were 3, 5, 9, 17, and 33
pixels.
The VFA layers of two different orientations and the

A visual feature k is thus characterized by two values, anorientation α and length l. The matrix elements (Fk )i, j willthus have a value of 1 if the edge pixel on the edge detected
image Ii, j belongs a feature with the characteristics of k.
In the visual cortex there are receptive field characteristics that actually define the visual feature the particular neuron is responsive to. In order to extract the desired features©ISAROB 2006

540

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(a)tected image. The extracted contour pixels are clusteredinto visual features which are more abstract representationsof the visual information. The features are organized intoa three-dimensional orthogonal array (the VFA) according
to their properties. The extracted features are used in two
ways: further abstraction or top-down image reconstruction. This latest adds an augmented information space tothe original edge detected image, which we refer to as negative filtering.
The VFA containing different features can be submitted
to grouping transformations, that merge layers of the VFA
according to certain rules, such as similar line length ororientation. The grouping transformations are necessary
for further transformations, such as line crossing and vertex
detection.
The model and especially the VFA has been designed tooperate in a fully parallel manner. In the present system binary array values were used for the sake of easy hardware
implementation. An FPGA or other parallel implementation of the model yields a constant time contour detection
and visual feature extraction.

(b)

Figure 1: Original test image (a) and the result of the primary edge detection (b)

(a)

(b)

References[1] D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’svisual cortex. J. Physiology, 160:106–154, 1962.

(c)[2] D. Hubel. Eye, Brain and Vision. W.H. Freeman &Company, 1995.

(d)Figure 2: The segments of two different orientations (a)–(b) and the segments of two different line lengths (c)–(d).

[3] A. Grinvald, D. Malonek, A. Shmuel, D. Glaser,
I. Vanzetta, E. Shtoyerman, D. Shoham, and A. Arieli.
Imaging of Neuronal Activity. Cold Spring Harbor
Laboratory, 1999.

segments of two different line lengths are shown in Figure
2.
The union of the VFA layers yields the top-down reconstruction of the edge detected image from the detected line
segments. The reconstruction will exclude the edge elements detected as noise noise, which was not recognized
as a visual feature (a line segment of certain length and orientation).

[4] Zoltán Petres, Barna Reskó, Péter Baranyi, and Hideki
Hashimoto. Biology Inspired Intelligent Contouring
Vision Device in Intelligent Space. In Proceedings of
the 6th International Symposium on Advanced Intelligent Systems (ISIS 2005), pages 865–870, Yeosu, Korea, Sept 28–Oct 1 2005.

4

Conclusion

A model for intelligent contour detection was presented
in this paper. The basic structure and functionality of themodel is based on the mammalian primary visual cortex,
which can perform edge contour extraction on an edge de-©ISAROB 2006

541

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Multistable Perception Model by Multilayered Bidirectional Associative Memory
Kazuyuki Kitamura†, Teijiro Isokawa†, Haruhiko Nishimura‡,
Naotake Kamiura†, and Nobuyuki Matsui††Graduate School of Engineering, University of Hyogo,
2167 Shosha, Himeji, Hyogo, 671-2201, JAPAN‡Graduate School of Applied Informatics, University of Hyogo,
1-3-3, Chuo-ku, Kobe, 650-0044, JAPAN
Abstract

biguous patterns based on the multilayered bidirectional associative memory, aiming at the explanationof the above facts. This model has two main features,
one of which is a bottom-up and top down information(signal) ﬂow between the lower layers and higher ones
by expansion of the bidirectional associative memory
scheme[4]. The other is a checking process of the conformity of the bidirectional signals. According to the
input signal from the lower level, the higher level feeds
back a suitable candidate among the stored templates
to the lower level. If the lower area cannot get a good
match, the process starts over again and lasts until asuitable interpretation is found. We analyze temporal
behaviors of the system under this framework throughcomputer simulations.

Multistable perception phenomena in seeing ambiguous ﬁgures have been observed and their distribution curves of alternation durations are well-known as
the Gamma distribution through psychophysical experiments. It is important and interesting to investigate its describable model for clarifying brain functions. In this paper, we propose a model based on themultilayered bidirectional associative memories andreport good simulation results on the distribution ofalternation durations.

1

Introduction

Multistable perception is perception in which two(or more) interpretations of the same ambiguous image alternate spontaneously while an observer looks atthem. Three kinds of this phenomenon, ﬁgure-ground,perspective (depth), and semantic ambiguities are wellknown (As an overview, for example, see[1]). In thiscircumstance the external stimulus is kept constant,but perception undergoes involuntary and random-likechange. The measurements have been quantiﬁed in
psychophysical experiments and it has become evident
that the frequency of the time intervals spent on eachpercept is approximately Gamma distributed[2, 3].
Figure-ground reversal is an automatic process
which happens even if there is no premise knowledge
about the object form. However, the others are processes depending on conceptual knowledge. This indicates a possibility that each perceptual conﬂict happens similarly in the individual (diﬀerent) place within
the large region from the initial vision process to thehigher cognitive reasoning process. Therefore, existence of some neural mechanism common to all is suggested.
In this work, we propose a perception model of am-©ISAROB 2006

2

2.1

Multistable Perception and brain
mechanism
Multistable Perception

Multistable perception (or so-called perceptual alternation) is a phenomenon occurred in our brains inwhich there are plural candidates of interpretations
whereas retinal input is not changed, and only one
interpretation of them arises in our brains at a time.
Interpretations are autonomously exchanged over time
and the timing of exchanges is known to be random.
This phenomenon usually occurs when we see ambiguous ﬁgures, such as the Necker-cube in Fig. 1. We have
two interpretations on this cube. From psychophysicalexperiments, the distribution of the perceptual durations can be obtained. It is known that this distribution follows well the Gamma distribution deﬁned by
f (x) =

5421
xb−1 exp(−x/a)ab Γ(b)(1)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 2: The structure of the multilayered BAM
module for multistable perception
Figure 1: Frequency distribution of the duration of the
Necker cube for a subject

3

Bidirectional Associative Memory (BAM) network
is a kind of associative memory with capability of retrieving input patterns from target patterns, as well as
target patterns from input patterns[4]. It consists of
two layers of neurons, U-layer and V-layer, connected
through weights. The outputs of neurons in the Ulayer are input to those in the V-layer through weightw and the outputs in the V-layer make feedback to
the U-layer through wT , so the information ﬂows between these layers repeatedly. The states of network
converges to the input pattern and its corresponding
target pattern by updating the states of neurons.
In this paper, we introduce a multilayered BAM
module, which allows more layers in the BAM network. Figure 2 shows the structure of multiple BAM
module, the information ﬂows from lowest layer tohighest layer and vice versa. The lowest layer of the
network corresponds to visual receptor that accepts
ambiguous inputs from outside, and The highest layer
represents interpretations for the input signals.
The connection weight between the i-th neuron in
the k-th layer and the j-th neuron in the k+1-th layer,
wij , is deﬁned as

In Fig. 1, we also show a typical example of the distribution of the perceptual duration by seeing Neckercube and its corresponding Gamma distribution.

2.2

Brain Mechanisms for Multistable
Perception

The mechanism for multistable perceptions should
be functional in the wide range of cerebral cortex beyond primary visual cortex, since there exists several levels (kinds) of multistable perceptions, such as
ﬁgure-ground, perspective, and semantic ambiguities.
The cortico-cortical ﬁbers connect among areas on cortex and their circuitry is uniform over it, without theirfunctions or locations. Information processing in neocortex is performed by the interactions among areas
through ﬁbers.
In recognizing an object in a scenery, this type ofinteractions between higher-order and lower-order cortex areas could be also used. Higher-order cortex receives signals from lower cortex, based on these signals
it retrieves the pattern from stored templates that ismost feasible for them, and transmits this pattern asa feedback to lower cortex. An interpretation for theobject is achieved by making a match of informationfrom the higher and lower cortex. Ping-pong style ofmatching process based on interactions in the brain[5] is expected to serve as a candidate for the general(common) neural mechanism of multistable perception. The brain mechanism proposed in [6], in which
the top-down prediction from inferotemporal cortex
confronts with the bottom-up recognition from primary visual cortex, also corresponds to this matchingprocess.

©ISAROB 2006

Multilayered Bidirectional Associative Memory

w[k]ij =Lu[k]li · u[k + 1]lj .

(2)

l=1

where u[k]li is the state of the i-th neuron in the k-thlayer, given the l-th pattern.
The state of a neuron is updated by(with p = g(x[k]i ))f (x[k]i )(3)
u[k + 1]i =−f (x[k]i ) (with 1 − p)wheref (x[k]i )543= tanh(x[k]i /),The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

g(x[k]i ) =
x[k]i

=

1
,
1 + exp(−|x[k]i /T )m
1 
w[k]ji · u[k]j .
m j=1

We run multilayered BAM modules for acquiring 10,000 durations in interpreting the pattern #1(u[K]1 ). The input pattern F [1] at the lowest layer
is always set to the intermediate pattern between two
stored pattern, i.e., F [1] = {1, · · · , 1}, and initial interpretation at the highest layer of the network is determined by information from lower layers. The interpretation is changed to another one when the number ofdisagreements at the highest layer reaches the upperbound of disagreements, and at the same time we get
one duration of this interpretation.
We introduce Kolmogorov-Smirnov test with 0.05
of signiﬁcant level for determining whether these durations follow the Gamma distribution. The parametersof Gamma distribution, a and b, are calculated by

At the lowest layer and the highest layer of the network, the ambiguous input and the candidate of interpretations is fed respectively by these equations:u[1]i (t + 1) =u[K]i (t + 1) =αu[1]i (t) + (1 − α)F [1]iβu[K]i (t) + (1 − β)u[K]li

(4)
(5)

where α and β are called loop-input rates in the lowest
layer and the highest layer respectively. F [1] represents ambiguous input signal, which has intermediate
states between u[1]1 and u[1]2 . When the states of thehighest layer are changed by Eq.(5), u[K]1 or u[K]2
is chosen as a new interpretation u[K] according to
the current interpretation of the network. The initial interpretation (at the ﬁrst application of Eq.(5))
is set to the one that is more resemblant to u[K]1 oru[K]2 . The interpretation is not changed until thecounter exceeds a certain value (called upper-boundof disagreement, Lupper ). This counter incrementsby one whenever the interpretation and the states in
the highest layer are diﬀerent.
We prepare the patterns that contain the same
number of 1’s and -1’s and their inverse for the stored
templates for each layer. The template patterns forthe k-th layer is represented bya = σ/µ,where σ and µ are variance and average of the duration
data, respectively. The maximum diﬀerence between
the duration data and the Gamma distribution withthe parameters a and b is deﬁned as
Dn = max |F (x) − Sn (x)|xwhere F (x) and Sn (x) are cumulative frequency functions of Gamma distribution and the duration data,
respectively. The critical value at 0.05 of signiﬁcantlevel, Dn0.05 , is given by√
Dn0.05 = 1.358/ n.

1, · · · , 1−1, · · · , −1 },u[k]1 = {u[k]2 = { −1, · · · , −1
1, · · · , 1}.

When Dn < Dn0.05 , the duration data is regarded asfollowing a given Gamma distribution.

For the measurement of interpretation represented
at the highest layer, we introduce an overlap value
M (t) asN
1 
1M (t) =
u[K]i (t) · u[K]i
(6)
N i=1

4.2

4.1

Simulation Results
Experimental Setup

We explore the behaviors of our BAM module by
the computer simulations. An BAM module with 4
layers whose number of neurons in all the layers is 4,
i.e., 4-4-4-4 network, is applied. We employ  = 10−10
and T = 0.10 in Eq.(3) in the simulations.

©ISAROB 2006

Results

First we show an example of temporal behavior of
the network as the transition of M (t) at the highest
layer in Fig. 3. In this simulation, the upper boundof disagreement Lupper is 40, α = 0.1 and β = 0.2,
are used for the parameters. The durations for an interpretation are diﬀerent each other. From these durations, we get frequencies and a Gamma distributionthat is most ﬁtted to them, as shown in Fig. 4.
We perform K-S tests with respect to the resultsfor various β and Lupper parameters, in order tocheck whether the durations from our model follow theGamma and normal distribution. Table 1 shows thenumber of acceptance by K-S test, where the numberof trials is 10 for each parameter and α = 0.1. The
numbers without brackets in this table indicate thenumber of acceptance for Gamma distribution and thewhere N is the number of neurons in the highest(Kth) layer.

4b = µ/a,544

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

1

Table 1: The number of acceptance by K-S test for
Gamma (without brackets) and normal (with brackets) distributions

overlap

0.5
0duration−0.5

Lupper

−1
0

10000

20000

30000

40000

10
20
30
40
50
60
70
80
90
100
110
120

50000

time

Figure 3: Temporal behavior of the highest layer’s
state under ambiguous input
0.25

Frequency

0.2
0.15
0.1

0.1
0 (0)
1 (0)
1 (0)
3 (0)
3 (0)
5 (0)
10 (0)
8 (0)
9 (0)
8 (0)
10 (0)
9 (0)

β
0.2
0 (0)
2 (0)
3 (0)
4 (0)
9 (0)
4 (0)
10 (0)
8 (0)
9 (0)
9 (0)
6 (0)
9 (0)

0.3
0 (0)
0 (0)
2 (0)
4 (0)
7 (0)
7 (0)
8 (0)
5 (0)
10 (0)
10 (0)
8 (0)
9 (0)

0.05
0
0

2000

4000

6000

8000

References

10000 12000 14000

Duration[1] Attneave F (1971), “Multistability in perception,”
Scientific American, vol. 225, pp. 62–71.

Figure 4: Frequencies of duration data (bar) and a
Gamma distribution that is most ﬁtted (solid line)[2] Borsellino A, Marco AD, Allazatta A, Rinsei S,
Bartolini B (1972) “Reversal time distribution in
the perception of visual ambiguous stimuli”, Kybernetik, vol.10, pp.139–144.

number with brackets show those for normal distribution. It is found that the distribution of durations follow the Gamma distribution, rather than the normal
distribution.

5[3] Borsellino A, Carlini F, Riani M, Tuccio MT,
Marco AD, Penengo P, Trabucco A (1982), “Effects of visual angle on perspective reversal for ambiguous patterns”, Perception, vol. 11, pp. 263–
273.

Conclusion[4] Kosko B (1988), “Bidirectional Associative Memory”, IEEE Transaction on System, Man, and Cybernetics, vol. 18, No. 1, pp. 49–60.

We have proposed a model scheme for representing multistable perception in the visual system. Ourmodel can represent interactions between the lowerand higher cortical area in the brain by introducing a
multilayered associative memory and its information
ﬂow. From the simulation results, the durations fromour model well follows Gamma distributions that is
one of characteristics in multistable perception, thus
our model can represent the temporal structure formultistable perception.
In the future researches, we further explore theagreement with the cortical architecture and brain-likecomputation. Moreover, a new ﬁnding was recently
reported that a shape-deﬁning parameter of Gamma
distribution ﬁtted to time intervals data takes a quantal and natural number [7]. We are now proceeding toinvestigate our model for this result.

©ISAROB 2006

[5] Mumford D (1992), “On the computational architecture of the neocortex II.the role of corticocortical loops”, Biological Cybernetics, vol. 66, pp.
241–251.
[6] Koerner E, Matsumoto G (2002), “Cortical Architecture and Self-Referential Control for Brainlike Processing in Artiﬁcial Neural Systems”, IEEE
Engineering in Medicine and Biology Magazine,
vol. 21, No. 5, pp. 121–133.
[7] Murata T, Matsui N, Miyamoto S, Kakita Y,
Yanagida T (2003), “Discrete stochastic process
underlying perceptual rivalry”, NeuroReport, vol.
14, No. 10, pp. 1347–1352.

545

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Comparative Study on Fuzzy and Non-Fuzzy
Cost-Sensitive Classification Systems
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

G. Schaefer
School of Computing and Informatics
Nottingham Trent University
Nottingham, U.K.
Gerald.Schaefer@ntu.ac.uk

pattern classification problems in this paper is to minimize the cost of misclassification/rejection as well as
to minimize the number of misclassifed patterns. Let
wp , p = 1, 2, . . . , m be the cost of the p-th training pattern. We define the cost of misclassification/rejection
caused by a classification system S as follows:

In this paper we compare the performance of fuzzyand non-fuzzy classification problems. We particularly focus on the case where the cost of misclassification/rejection is considered as the performance measure of classification systems. Computational experiments show the comparative results of these classification systems on real-world pattern classification problems.

Cost(S) =m
X

cost(xp , S),

(1)

p=1

where

1

½

Introduction

cost(xp , S) =Various methods have been proposed for patternclassification systems [1]. In many cases the classification rate is considered as the performance measure ofclassification systems. In this paper, we consider thesituation where the cost of misclassification/rejection
is necessary. Through the computational experiments
we examine the performance of cost-sensitive fuzzyclassification systems proposed in [2] comparing with
non-fuzzy cost-sensitive classification systems.

2

if xp is correctly classified,otherwise.
(2)

3

Cost-Sensitive Fuzzy Classification
SystemThis section describes the generation of fuzzy ifthen rules from given training patterns. Our fuzzy
rule-based classification system consists of N fuzzy ifthen rules each of which has the following form:

Pattern Classification Problems with
Cost

Rj : If x1 is Aj1 and . . . and xn is Ajn
then Cj with CFj ,Let us assume that our pattern classification problem is an n-dimensional problem with C classes andm given training patterns xp , p = 1, 2, . . . , m. Without loss of generality each attribute of the given training patterns is normalized into a unit interval [0, 1].
That is, the pattern space is an n-dimensional unit hypercube [0, 1]n . In this paper we consider a situation
where a cost of misclassification/rejection is associatedwith each training pattern. That is, the task in our©ISAROB 2006

0,
wp ,

(3)

for j = 1, . . . , N where Ajn is an antecedent fuzzy set,
Cj is a consequent class, and CFj is the grade of certainty. There are two steps in the generation of fuzzy
if-then rules: specification of the antecedent part anddetermination of the consequent class Cj and the gradeof certainty CFj . The antecedent part of fuzzy if-thenrules is specified manually. Then the consequent part(i.e., consequent class and the grade of certainty) is
determined from the given training patterns.

546

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3.1

Fuzzy Rule Generation

Step 1: Calculate αClass h (x) for Class h, j
1, . . . , C, as=

Let us assume that m training patterns xp =(xp1 , . . . , xpn ), p = 1, . . . , m, are given for an ndimensional C-class pattern classification problem.
We also assume that a weight wp , p = 1, . . . , m, isassigned to each training pattern a priori. The consequent class Cj and the grade of certainty CFj of theif-then rule are determined in the following manner:

Step 2: Find Class h0 that has the maximum value ofαClass h (x):Step 1: Calculate βClass h (j) for Class h asXµj (xp ) · wp ,βClass h (j) =If two or more classes take the maximum value, thenthe classification of x is rejected (i.e. x is left as an
unclassifiable pattern), otherwise assign x to Class h0 .

αClass h (x) = max{µj (x) · CFj |Cj = h}, (9)αClass h0 (x) = max {αClass k (x)}.
1≤k≤C

(4)

(10)

xp ∈Class h

4

where
µj (xp ) = µj1 (xp1 ) · . . . · µjn (xpn ),(5)

We also examined the performance of non-fuzzyclassification methods such as neural network andnearest neighbor classification. Both the two classification methods are modified so that they can handle training patterns with misclassification/rejection
costs.

and µjn (·) is the membership function of the
fuzzy set Ajn .
Step 2: Find Class ĥ that has the maximum value ofβClass h (j):βClass ĥ (j) = max {βClass k (j)}.
1≤k≤C

(6)

4.1

We note that this fuzzy rule generation method canalso be applied to the standard pattern classification
problems where there are no pattern weights. In thiscase, the class and the grade of certainty are determined from training patterns by specifying a patternweight as wp = 1.0 for p = 1, . . . , m.
If two or more classes take the maximum value, theconsequent class Cj of the rule Rj cannot be determined uniquely. In this case, specify Cj as Cj = φ. If
a single class ĥ takes the maximum value, let Cj be
Class ĥ. The grade of certainty CFj is determined as
βClass ĥ (j) − β̄
CFj = Ph βClass h (j)
with

P
β̄ =

3.2

h6=ĥ βClass h (j)

C −1

.

Neural Network Classification

Neural network classification systems are one of themost well-known systems. We convert common neural network classification systems into cost-sensitive
ones. Let us assume that a neural network classification system has n input units, Nh hidden units,
and No output units. We also assume that an output vector o = (o1 , . . . , oNo ) is obtained by the neuralnetwork classification system from an input patternx = (x1 , . . . , xn ). The input pattern is classified as
Class î if and only if the following equation holds:
oî > ok , for ∀k, k 6= î,(11)

For the learning of the weights of neural networkclassification systems the error-backpropagation algorithm is normally employed in which the following error function is used to calculate the amount of weightadjustment:

(7)

(8)

N

Fuzzy Reasoning

Ep =

o
1X
(ok − tpk )2 ,
2

(12)

k=1

Using the rule generation procedure outlined above
we can generate N fuzzy if-then rules as in (3). After
both the consequent class Cj and the grade of certainty CFj are determined for all N rules, a new pattern x = (x1 , . . . , xn ) can be classified by the following
procedure:©ISAROB 2006

Cost-Sensitive Non-Fuzzy Classification Systems

where tp = (tp1 , . . . , tpNo ), p = 1, . . . , m is the target
signal for the p-th training pattern and is defined asfollows:½
1, if the class of xp is k,
tpk =
(13)
0, otherwise.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

We construct cost-sensitive neural network classification systems by modifying the error function as
follows:No
1 X(ok − tpk )2 ,(14)
Ep = wp2

Table 1: Data sets.
# of attributes # of classes
Data set2
9
Breast cancer
2
3
Haberman
2
60
Sonar
3
4
Balance scale
3
9
CMC
7
9
Glass
3
4
Hayes roth
3
4
Iris
3
13
Wine

k=1

where wp is the cost of misclassification/rejection forxp . The amount of weight adjustment is calculated byusing the modified error function in (14).

4.2

Nearest Neighbor Classification

We use 1-NN where an n-dimensional unknown pattern x is classified as the class of its nearest neighbortraining pattern xp̂ . Euclidean distance is used to determine the nearest neighbor training pattern as follows:d(x, xp̂ ) = min{d(x, xp )},
(15)
v
u n
uX
(16)
d(x, xp ) = t (xi − xpi )2 ,Since the original data sets do not have weights
associated with the training patterns, we temporarily assign them for our experiments. For two-classpattern classification problems (i.e., Breast cancer,
Haberman, Sonar), we consider two weight assignment schemes. The first scheme is called Class-1focused scheme where the cost of training pattern xp ,p = 1, . . . , m is specified as wp = 1.0 if xp belongs toClass 1 and wp = 0.5 otherwise. On the other hand,
we specify the cost as wp = 1.0 for Class 2 training
patterns and wp = 0.5 otherwise in the second weightassignment scheme called Class-2-focused scheme.
For pattern classification problems with more than
two classes, we assign the cost of misclassification/rejection of training patterns by the degree ofclass overlap between different classes. We specify theweight of training patterns as follows:i=1

where xp , p = 1, . . . , m, is the p-th training patternand d(x, xp ) is a distance measure between x and xp .
We also consider a modified version of the 1-NN where
the weight of training patterns is used in the determination of the nearest neighbor training patterns. In
the modified 1-NN we use a modified distance measure given by (15):d0 (x, xp ) =d(x, xp )
,
wp

(17)

where wp is the weight of the p-th training pattern.
From (17) we can see that those training patterns withlarge weight have smaller distance measure than thecase of Euclidean distance. On the other hand, themodified distance becomes larger than the Euclidean
distance when the weight of a training pattern is small(less than 1).

5

wp =

Computational Experiments

Data Sets

In the experiments in this paper we use nine data
sets to evaluate the performance of the three costsensitive classification systems. We show the data setsin Table 1.

©ISAROB 2006(18)

where Nsize is the number of selected neighboring patterns and Npsame is the number of training patterns
from the same class as xp in the Nsize selected patterns.
In the experiments the 10-fold cross validation technique is used to examine the performance of classification systems on test data. In 10-fold cross validation data sets are partitioned into ten disjoint subsets.
Nine subsets out of the ten were used as training data
and the other subset is used as test data. This procedure is iterated until all the ten subsets have seen
as test data. In the computer simulation in this subsection we performed the 10-fold cross validation 100
times. The average results (both classification results
and costs) over the 100 10-fold cross validations arethen calculated. We also performed one-side t-test to
examine the difference between every pair of classification methods.

We compare the performance of the three costsensitive classification methods: fuzzy rule-based systems, neural network classification systems, and nearest neighbor classification systems.

5.1

Npsame
,
Nsize

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

5.2

Experimental Results

Table 5: Results of t-test for multi-class problems.
Fuzzy Â Neural Fuzzy Â 1-NN
Data set××
Balance scale
°
°
CMC
°
×
Glass
Iris
°
×
Hayes roth
°
°
Wine
°
×

Tables 2 and 3 show the results of the experiments
for two-class and multiclass problems. The costs of
misclassification/rejection in (1) obtained by the threeclassification methods are shown in these tables.
Table 2: Results for two-class problems.
Class-1-focused
Fuzzy Neural 1-NN
Data set
1.74
8.01
2.17
Breast cancer
5.53
8.03
4.15
Haberman
4.54
4.85
4.44
Sonar
Class-2-focused
Fuzzy Neural 1-NN
Data set
1.94
1.44
1.76
Breast cancer
8.12
4.05
7.05
Haberman
4.15
5.55
4.68
Sonar

From Tables 4 and 5 we can see that the fuzzy classification systems are better than the neural networkclassifiation systems for almost all data sets. For thecomparison between the fuzzy and the 1-NN classification systems, we can say that the fuzzy classification
systems are better than the 1-NN classification systems for two-class problems while it is not the case formulti-class problems.

6
Table 3: Results for multi-class problems.
Fuzzy Neural 1-NN
Data set
0.99
2.01
4.09
Balance scale
25.08
39.24
22.86
CMC
1.64
5.82
1.98
Glass
0.23
9.31
0.61
Iris
2.31
2.40
1.72
Hayes roth
0.23
11.44
0.71
Wine

In this paper, we compare the performance of three
cost-sensitive classification systems: fuzzy classification systems, neural network classification systems,
and 1-NN classification systems. We particularly focused on the comparison between fuzzy and non-fuzzyclassification systems, that is, comparison between
fuzzy and neural network classification systems, andbetween fuzzy and 1-NN classification systems. The
results of the computational experiments showed thatthe fuzzy classification systems are better than theneural network classification systems. We also showedthat the fuzzy classification systems are better than
the 1-NN classification systems for two-class problems
while the 1-NN classification systems are better thanfuzzy classification systems for multi-class problems.
Further research is necessary to investigate the reasonof these results.

Then we performed one-side t-test for every pair of
the three classification methods. The results of the ttest are shown in Tables 4 and 5. We only show thecomparison between fuzzy and non-fuzzy classification
methods (i.e., fuzzy-neural and fuzzy-1-NN).
Table 4: Results of t-test for two-class problems.
Class-1-focused
Fuzzy Â Neural Fuzzy Â 1-NN
Data set°×
Breast cancer°°
Haberman–°
Sonar
Class-2-focused
Fuzzy Â Neural Fuzzy Â 1-NN
Data set–×
Breast cancer°
×Haberman×°
Sonar©ISAROB 2006

Conclusions

References[1] R. O. Duda, P. E. Hart, and D. G. Stork, Patternclassification, John Wiley & Sons, 2001.
[2] T. Nakashima, Y. Yokota, H. Ishibuchi, and
A. Bargiela, “Effect of Data Weighting Methods on the Performance of Fuzzy Classification
Systems,” Proc. of 2005 NAFIPS, pp. 216-221,
Michigan, USA, 2005.

549

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Emergent design of a control system for cooperation between robots
Koichi NAKAYAMA1) Hirokazu MATSUI2) Katsunori SHIMOHARA1)
1) ATR Network Informatics Lab. 2) Faculty of Engineering, Mie Univ.
Address 2-2-2 Hikari-Dai, Keihanna Science City, Kyoto 619-0288, Japan
voice: [+81](0774)95-1098; e·mail: knakayama@atr.jp
Abstract

List-structural Genetic Algorithm. In the sLs-GA, theagents act one by one in order on a list. As a result,the nearer agents on the list have a bigger infuluenceon each other. The sLs-GA automatically adjusts theinfluence so we don’t have to consider the range of theinfluence. We optimize not only the control parameters but also the structures of control system by sLsGA. And we show that a MACS applying the sLs-GA
is designed to work stably, even if the characteristicsof the controlled object are changed unexpectantly.

We describe sLs-GA (Serial and List-structural Genetic Algorithm) that designs control systems. Generally, the short term optimality of output is different
from long term optimality. In this paper, we describe
the characteristics of sLs-GA, which obtains not only
the optimal control parameters but also the optimal
structures of control system for the long term. We
verify experimentally that the sLs-GA is applicable,using a target tracing problem with the physical robot”Movable Intelligent Evolutional Computer (MieC)”.

2
1

Introduction

In this paper, we propose a Serial and Liststructural Genetic Algorithm (sLs-GA) that uses list
structures to represent the genes of agents for the design of robot control systems. The designed control
system is modeled as a Multi-Agent oriented Control
System (MACS).
In the conventional design of control systems, accurate modeling or analyzing of the controlled object isnecessary to design a control system. However, accurate modeling and analyzing are difficult because error
margins between the model and the actual controlled
object are unavoidable. We propose an adaptive designing algorithm for the control system without modeling and analyzing the controlled object. We aim at
the automatic design of an entire control system, including the controlled object.
In our previous research, we modeled as an agent,unknown parameters of a control system. We proposed a Serially and Dynamically Separating Genetic
Algorithm (sDS-GA)[1], and applied it to optimize theparameters of the robot control system[2]. The sDSGA obtained not short-term but long-term optimal
parameters under an unknown and dynamic environment. But the sDS-GA must have a pre-fixed colony
size which depends on the range of the agent’s timely
influence. In this paper, we propose the Serial and©ISAROB 2006

Serial and List-structural Genetic
Algorithm (sLs-GA)

In this section, we propose the List-structural Genetic Algorithm (sLs-GA) to design robot control systems.
The basic idea of sLs-GA is as follows. In sLs-GA,
control-agents are positioned as the list-structure. In acertain period, one control-agent is chosen, according
to the list order, and it controls the system. In thenext period, the next control-agent in order is chosenfrom the list, and it controls the system.
Each agent is evaluated each period. When an
agent has a high evaluation, the agent splits into two
agents, And when an agent has a low evaluation theagent is extinguished.
We show the main routine of the algorithm using
sLs-GA in Fig. 1.

Initialization (1)Agent loop (2)Action (3)Split and Extinction (4)Figure 1: Main routine of the sLs-GA shown by NS
chart.

550

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

The learning algorithm used by the sLs-GA is asfollows.
(1) Initialization: NA (t) agents at t = 0 are createdand positioned as the list-structure. The evaluation value of an agent a, EA (a, t), is initially
set to EA (a, 0) and its action determination gene,
GeneAct (a), is initially randomly chosen.

3.1

Experimental Model

3.1.1

Physical structure of MieC

We use MieC (Movable Intelligent Evolutional Computer) as the physical robot, that is a kind of autonomic vehicle. MieC has two motors as movable actuators, two encoders for the motors as internal sensors,
and a camera as an external sensor (Fig. 3), and hasbatteries as a power supply. The traced target is oneof three colored balls, red, green and blue. These areprojected on a screen. Each ball swings by each ten
seconds. The phase between two balls is 120 degrees(Fig. 4). It is not specified for MieC which coloredball should be traced. MieC learns this from the obtained reward.

(2) Agent Loop: Every agent takes charge of the control in order for robot control.
(3) Action (Robot control by agent): The agent acts
for robot control. Details are shown in section 3.
The agent changes its own evaluation value basedon the result of the action.
(4) Split and Extinction of Agents: An agent is split
into two agents when the evaluation value by anagent becomes more than twice the initial value(EA (a, 0)). One split agent stays in the same position on the list, and the other is inserted at thenext position (Fig. 2(A) ). The two agents inherit
half of the original agent’s evaluation value. An
action gene is mutated according to the mutation
probability Pmut .
An agent is extinguished when its evaluation
value becomes less than or equal to zero. The
current position of the agent is omitted on thelist, and the order(the previous, the current andthe next) is changed to the previous and the next
(Fig. 2(B) ).

3

2

1

3

2

1

Figure 3: Movable Intelligent Evolutional Computer(MieC)

3.1.2
2

1

2’

(A) Split of Agent

3

1

3

The flow of target tracing control is shown in Fig. 5.
This control flow is one of the simplest in this case.
(1) The current position of the target decided by
Switch SC ∈ {Red, Green, Blue} is solved by processing an image from the camera. (2) The position command of MieC is determined by the error between thetarget position and the current position. (3) The target rotation angles of the left and the right tracks arecalculated from the position command, considering theinverse kinematic of MieC. (4) The voltage commands
to motors are determined by the errors between thetarget positions and the value current positions, respectively. (5) The motors are rotated by the voltage(B) Extinction of Agent

Figure 2: Splitting and Extinction of the sLs-GA.

3

Experiment

In this section, we verify experimentally that thesLs-GA is applicable using the physical robot.

©ISAROB 2006

Control System for the Target Tracing
Problem

551

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

+ -+ target L

InverseKinematic targetR
+ (3)

GI

(2)

GM

Motor(L)

(4)

(5)

GM

Motor(R)R
θL (t)θR(t)Tracked
Vehicle

G

Sc

B

(1)
Pose(t)Figure 5: A block diagram showing the target tracking of the traced vehicle

1.2, · · ·, 5.4}. SC shows the color MieC traces. The
agents with gene GI and gene GM can call the cycle(1)-(3) and the cycle (4)-(5), respectively. However,
the agent cannot call a cycle before the same cycle iscompleted.
3.1.4

Agent Evaluation

Each agent is evaluated by how much MieC can trace
the target during the time in its duty. An agent is evaluated by the following equation at the time it finishes
the control.
Evaluation = 1.0 − (BP (X))2

where X is color {Red,Green,Blue}. BP (X) is thehorizontal position of the ball X. The value is -1 when
the ball X is at the left end, The value is 1 when theball X is at the right end,
The equation includes the effect of the previous
agents’ actions, because the current ball position is
influenced by the previous agents’ actions.
In this experiment, the number of initial agents was
set to NA (0) = 100, and the mutation probability
Pmut = 0.1.

Figure 4: Tracing Target (Balls of red, blue and green)commands, respectively. (6) Return to (1).
In steps (2) and (4), the commands are determinedby the errors and the gains GI and GM . Here we treat
the gains GI and GM as proportionality constants, although the gains have to be determined, considering
the weight and the inertia moment of MieC and thecharacteristics of the motors, etc. The execution cycle (1)-(3) and the execution cycle (4)-(5) depend on
the execution speeds of image-processing and motorcontrol, respectively. These execution cycles are considered to determine the gains GI ,GM . Here, theseparameters GI ,GM are optimized applying sLs-GA.
3.1.3

3.2

Experiments and Discussion

In this experiment, all 100 agents act for 10 seconds.
Up to 1,000 seconds, X is Red. After 1,000 seconds,
X is Blue.
We gained the following results. The history of
the population ratio of SC ∈ {R, G, B} is shown in
Fig. 6. The histories of the population ratios of GI ∈{0.0, 0.2, 0.4, · · · , 1.8} and GM ∈ {0.0, 0.6, 1.2, · · · , 5.4}
are shown in Fig. 7, respectively. The horizontal axes
express the time [in seconds]. The vertical axes express
the ratios of genes, respectively.

Agent Action

One agent controls MieC during 0.1 second in the order of the list. We give each agent three genes SC ,GI and GM . SC takes one of 3 values {R, G, or B}.
GI takes one of 10 quantized values {0.0, 0.2, 0.4, · · ·,1.8}. GM takes one of 10 quantized values {0.0, 0.6,

©ISAROB 2006

(1)

552

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006environment.

[population ratio]

1

0.8
0.6
0.4

0

1000

1800

[second]Figure 6: The history of the population ratio of SC[population ratio][population ratio]

0.8

0.8

1

0.4

0.4

This research was supported in part by the
Telecommunications Advancement Organization ofJapan.
1.8

0.6

1.0

References

0.4

0.2
0

Acknowledgements

1

0.6

0.2

0

1000

1800

[second](A) Ratio of G I

0

0

1000

[1] K. Nakayama, H. Matsui, K. Shimohara, O. Katai,“Proposal of Serially and Dynamically Separating
Genetic Algorithm and Its Application to Optimization of Robot Control Systems,” Proceedingsof the Tenth International Symposium on Artificial
Life and Robotics, pp. GS12-2(CD-ROM), 2005.

1800[second](B) Ratio of G M

Figure 7: The history of the population ratio of GI
and GM[2] K. Nakayama, H. Matsui, K. Shimohara, “Emergence System Design by Serially and Dynamically
Separating Genetic Algorithm (In Japanese),”IPSJ Symposium Series, Vol. 2005, No. 11, pp. 7982, 2005.

In Fig. 6, up to 200 seconds, SC converged on the
optimal value R. After 1,000 seconds, X is Blue. Then,
most SC are changed to B around 1,200 seconds.
In Fig. 7(B), up to 600 seconds, GM converged on
1.8. These genes are unaffected by the difference of the
tracing targets. Then the learned value of GM that isfor red ball tracing is used for blue ball tracing.
In Fig. 7(A), up to 200 seconds, GI converged on
0.4. At around 900 seconds, most GI are changed to
1.0. That result shows that the control system of MieC
adapts to the lowering of the battery voltage. Whenthe battery voltage of MieC lowers, motor gain GM
should be a bigger value. However, in this experiment,
GM is already maximum. For this reason, GI waschanged.
From the above discussion, it is believed that sLsGA can optimize not only the control parameters but
also the structures of the control system. The sLs-GA
is efficient for the optimization of a control system for
MieC as an actual intelligent robot under an unknown©ISAROB 2006

Conclusion

In this paper, we verified the effectiveness of theadaptive design of a control system by a physical experiment. The experimental result showed that the
proposed sLs-GA designed not only the optimal control parameters but also the optimal structures of acontrol system for a tracing problem of a physicalrobot.

Red

0.2
0

4

Blue

553

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Design of Mass Estimated Adaptive Friction Compensator for Position
Control of Linear Motor System
Jin Woo Lee
Dept. of Electrical Engineering
Dong-A University

Jin Ho Suh
Dept. of Electrical Engineering
Dong-A University

Kwon Soon Lee
Dept. of Electrical Engineering
Dong-A University

840, Hadan-dong, Saha-gu, Busan,
Korea

840, Hadan-dong, Saha-gu, Busan,
Korea

840, Hadan-dong, Saha-gu, Busan, Korea

jinwoo@donga.ac.krsuhgang@donga.ac.kr

kslee@dau.ac.kr

Keywords: Linear motor servo system, Friction, Adaptive control, Mass estimatoraverage deflection behavior of the elastic bristles. It can be
described by Eq. (2).

Abstract
In this paper, we propose an adaptive control algorithm toimprove the position accuracy and reduce the nonlinear friction
effects for linear motion servo system. Especially, theconsidered system included not only the variation of the massof mover but also the friction change by the normal force. To
adapt these problems, we designed the controller with the massestimator and the compensator by observing the variation of
normal force.

F = σ 0 z + σ1

dz
+ σ 2vdt(2)

where, z is average deflection of elastic bristles, v iscoefficientofmover’s velocity. σ 0 , σ 1 , and σ 2 are
stiffness, damping coefficient, and coefficient of viscous,
respectively.
The equation of bristle deflection z and the positive definite
function that relies on factors such as surface material
characteristics can be described by Eqs. (3) and (4)respectively.

1. Introduction
In the general mechanical servo system, the friction
deteriorates the performance of the controller by its nonlinear
characteristics. Especially, the friction phenomenon causes
steady-state tracking errors, limit cycles in the position andvelocity control system, even though gains of the controller are
tuned well in the linear system model. Even if the sensor isused higher accuracy level, it is difficult to improve thetracking performance of the position to the same level with ageneral control method such as PID type. Therefore, many
friction models were proposed and compensation methods havebeen researched actively [1].
In this paper, we consider that the variation of mover’s mass
is various by loading and unloading. The normal force variation
occurs by it and other parameters. Then, we performed
simulation to evaluate its adaptation and performance of
position accuracy for control systems for other control systems.

σ0 v
dz
=v−
z
dt
g (v )
g (v) = FC + ( FS − FC )e −( v / v s )

(3)
2

(4)
where, FC is Coulomb friction level, FS is stiction force
level, and vs is Stribeck velocity. The steady state relation isz& = 0 , the steady state friction

FSS can be rewritten as Eq. (5).

Fig. 2 shows the surface of steady state friction model for the
mass, velocity, and friction force.
F ≈ FSS = g (v) sgn(v) + σ 2v
= {FC + ( FS − FC )e − (v / vs ) }sgn(v) + σ 2v
2(5)

2. Mathematical modelingFig. 2 Steady state friction surfacel for scale factor θ

Fig. 1 Configuration of the considered system and eachcomponent

3. Adaptive Controller Design with Mass
Estimator

The linear motion servo system with nonlinear friction andvariation of mover’s mass can be shown as Fig. 1. Fig. 1 shows
mover’s mass M1 is changed to M1 + M 2 . The system
dynamic equation can be described by Eq. (1).
M

d 2x
=u−F
dt 2

Let assume the parameters of dynamic friction and the initial
mover’s mass are known. But, these are change during the timeof control because of loading and unloading. Therefore, we
designed the mass estimator that can scale the position
controller H(s) and constrict the estimates of the normal force
factor more rapidly.
Fig. 3 shows the proposed control system and the systemequation with control is described as follows:(1)

where, x is mover position, M is mass, u and F arecontrol input(thrust force) and friction force, respectively.
LuGre dynamic friction model F is considered the model ofbristles reaction between two surfaces. And it based on the©ISAROB 2006

M554

d 2 xdd 2x= u − F = − Mˆ H ( s )e + Mˆ+ Fˆ − F
2
dt 2
dt

(3)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006= −ζ T Qζ − θ1

σ 0 v ~2g (v )z − 2ε 2 ≤ −ζ T Qζ < 0(10)

4. Simulation Results
In this paper, we performed the numerical simulations toverify the proposed control system about the position tracking
control using Matlab/Simulink module. These results included
the asymptotic tracking performances for the distance errors,
the friction estimates, and the mass and normal force estimate,etc. Fig. 5 and Fig. 6 show the results of the displacement andits error, respectively.

Fig. 3 Control system structure with the mass estimatedadaptive controller and compensator
To design the mass estimator, it needs to assume there are not
any more the friction and other external forces in the system by
the ideal compensator. Then, the relation can be obtained asfollows:
u=M

d 2x
a
= Ma =
θ2dt(4)

where, θ 2 is the estimates of mass, and we can know
θ 2 = 1 / M = a (t ) / u (t ) . Then, its estimate can be written as Eq. (5)and shown by Fig. 4.
1aˆ (t )
(5)
θˆ =
=
2

Mˆ (t )

Fig. 5. Distance variations(solid line) for the reference
trajectory (dotted line)u (t )

Fig. 6. Variations of the position error

Fig. 4. Block diagram of the mass estimator for the plant
without actual friction and compensator

5. Conclusions
In this paper, we consider that the variation of mover’s mass
is various by loading and unloading. The normal force variation
occurs by it and other parameters. Therefore, the proposed
control system was composed of the main position controller
and the friction compensator part. And a mass estimator was
designed by the adaptive control law.

To analysis the convergence of the mass estimator, let define
the estimate of system output is ε = a − a) , and the error of
estimate is θ~2 = θˆ2 − θ 2 . Then, ε can be rewritten as follows:~

ε = a − aˆ = θ 2u − θˆ2u = −θ 2u(6)

In order to minimize ε , the cost function is defined by
ε 2 ( a − θˆ2u ) 2(7)J (θˆ2 ) =
=
2
2
)
)
When ∇J (θ 2 ) is (a − θ 2u )u = 0 , J (θˆ2 ) has a minimum.
Therefore, the equation of θ~2 can be rewritten as Eq. (8) by

Acknowledgement
This
work
was
supported
by
grant
No.
R01-2005-000-10418-0 from Korea Science and Engineering
Foundation in Ministry of Science & Technology.

using the gradient method.

References

dθˆ2
(8)
= −γ 2∇ = γ 2 (a − θˆ2u )u = γ 2ε udt)
where, θ 2 (0) = 1 / M (0) and γ 2 > 0 . To analysis the stability of[1]

B. Armstrong-Helouvry, et. al., “A Survey of Models, Analysis
Tools and Compensation Methods for the Control of Machineswith Friction,” Automatica, Vol. 30, No. 7, pp. 1083-1138, 1994.
[2] C. Canudas de Wit, et. al., “A New Model for Control of Systems
with Friction,” IEEE Trans. on Automatic Control, Vol. 40 , No.
3, pp. 419 - 425, 1995.
[3] C. Vivas, et. al., “Gain-scheduling Control of Systems with
Dynamic Friction,” in Proceedings of the 41st IEEE Conferenceon Decision and Control, pp. 89-94, 2002.
[4] K. K. Tan, et. al., “Robust Adaptive Numerical Compensation forFriction and Force Ripple in Permanent-Magnet Linear Motors,”
IEEE Trans. on Magnetics, Vol. 38, No. 1, pp. 221- 228, 2002.

the whole system, it can be chosen Lyapunov function
candidate V as follows:V = ζ T Pζ +

1 ~

~
z2

1 ~

θ2+
+ θ22
γ1 1
k
γ2

(9)

Then, dV / dt can be obtained as Eq. (10) under the conditions
that are θ~&2 = θ&ˆ2 − θ& = γ 2εu − θ&2 = −γ 2u 2θ~2 and θ&2 = 0 .
~~~σ v 2 ~ ~ σ 0 v ) 1 dθ1 2θ 2θ&2dV= −ζ T Qζ − θ1 0 ~z + θ1 (− zz+)+dtg (v )g (v )
γ 1 dtγ2= −ζ T Qζ − θ1

©ISAROB 2006

σ 0 v ~2g (v )~z − 2u 2θ 2 2555

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Implicit Principle Discovery in Tourism Geography Information
Based on Data Mining
Du Junping
School of Computer Science
Beijing Technology and Business
University, Beijing, 100037

Guo Wensheng
School of Information Engineering
University of Science andTechnology, Beijing, 100083

Abstract
their background information, however is has tobe made explicit for the computer. Based on thedata mining theory, we tried to discover the
implicit principles in the geography information
with the consideration of the features of the
tourism industry.

We made study in geography data sets using
data mining technology and established asemantic network with the use of implicit
information in the data. We got implicit
principles in geography information withsupervised classification technology andunsupervised classification technology. Somevaluable conclusions are presented.

1

2 Patterns In the Geography Information
In GIS, there is some knowledge about the
distribution and location of the geometricalelements, their connections to each other, theiraccumulation in special places and so on. Thosecharacteristics make the information of a data set
complete and allow humans to interpret data. In
order to make this knowledge to be interpretableby computer, we need a translation in two
respects: first a language translation, but
moreover a semantic translation. Those
catalogues, which describe the meaning of aword and determine its sense depending on thecontext, are called ontologies. To enrich the
ontology, our ambition is focused on teaching
the computer to learn spatial concepts and tocombine knowledge with higher concepts
automatically. Some are hidden in the spatial
data, some exist in pure geometry and most ofthem are exist in the combination and interactionof the spatial elements. Spatial data mining is theapproach to extract the implicit information.

Introduction

China has large amount of tour resources andtourism grows up rapidly. In order to provide
tour destination information to the tourists,
including tour resources, facilities, tour lines,
enterprises and products, each tour destination
has set up GIS. But GIS provide only the searchservice of geography features to the users, and it
can not meet the complicated semantic searchdemand.
Semantic search in the geography information
requires the implicit principles in theinformation. This implicit information does notexist in the level of pure geographical features,but in the level of the relationships among thefeatures, their extent, density, frequency,
neighborhood, uniqueness and more. Thisknowledge often is well known by humans with

Using typical queries using search engine anduser scenarios with spatial background, there is a
lot of useful information stored in thegeographical data sets. If a user wants to search
for a hotel in the centre of the city, at least theSupported by Beijing Natural Science Foundation ofChina (4042012), Key Project of Beijing Educational
Committee（KZ200510011009） and

Project of

National Science Foundation of China (60442003)©ISAROB 2006

556

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

search engine has to know, where the city center
is. This knowledge can be discovered in vector
data, but it is usually not explicitly stored in the
database.
Indeed we recognize streets and houses and
we are able to get further facts. Humans canlocate the theater or museum by the special
shape of this building. The interaction of thestreets and houses and their concentrationinduces at least the information, that it is a town.
We also can identify larger buildings in theupper part and distinguish them from smaller
ones in the south. A computer can calculate thesefacts too. The big challenge is the following
reasoning process. Humans interpret the larger
buildings as the inner part of the town, because
they know about old farmyards and the typicalformation of a town. The smaller buildings
represent a courtyard of one family house. We
are able to locate the main street leading throughthe town as well, because of the structure of thesettlement. Therefore humans can detect the city
centre approximately without difficulty.
Some characteristics of the elements can be
determined with simple GIS functionality, suchas to calculate an area/size or to count theexistence of special objects. The evaluation ofother properties, such as density, distribution orneighborhood is more complicated. The analysis
of distances is an essential part to get knowledgeof these aspects. However, the handling ofthreshold values or absolute numbers is lesshelpful, because it depends on the context, if an
attribute or a characteristic is really specific andoutstanding. Most of the time those values are ofinterest and shed light on something. Clustering
algorithms can be used to identify groups ofelements respectively to their neighborhood.
Among clustering algorithms, those are
preferable that do not need threshold values.
Moreover the combination of properties andtheir calculated values raise a problem. Logic
operations have to be extended by weighting andquantifiers, which depend on the importance,©ISAROB 2006

relevance, quality of the attribute values andsignificance of elements.

3 Data Mining and Principles Discovering
As mentioned above there are rules implicit inspatial data, however there are two different
ways of approaching the goal of extraction
implicit knowledge. These two kinds ofextraction models are on the one hand, to define
the rules a priori (association rules) and to apply
them to the data, on the other hand, to let thecomputer find the rules by itself by exploring thedata. Both ways can get more knowledge. The
second case brings up unknown knowledge or
inherent information, which may be useful tolearn more about the data set, but can not be
useful as well. Both methods are usually known
as data mining and will be described andexamined. They are discerned into supervisedand unsupervised classification.
3.1 Supervised ClassificationThe
supervisedclassification
impliesknowledge discovery on the basis ofpredetermined models respectively spatial
association rules. Supervised classification starts
from a set of classified examples for a concept tobe learnt. From this set classification schemes
for the concepts are derived, e.g. using machinelearning approaches, or also Maximum
Likelihood classification. In principle every kindof knowledge representation can be used to forma classification scheme, especially rule-based
systems or semantic networks. We will depict
the process by the help of decision trees. Everybranch represents the existence of a distinctiveclassification feature. Depending on the result of
the inquiry, the adequate branch will be followed
further. In the end, the model leads to aclassification into different categories of one
issue. However the scheme includes someessential problems. The sequence of thevalidation of a distinctive classification feature
is one determining factor. The need of high557

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

quantitative and qualitative data is necessary to
be able to calibrate the model. The concept of“city center” can be implemented by suchmethod. For example, if a point satisfies suchcondition, we can define it as the center of thecity.

the outgoing lines can be important. Different
types of nodes can be distinguished andclassified.
While investigating the concept of the city
centre with supervised models, we introduced
the criteria of crossroads in the centre. A
crossroad is a node with at least four outgoinglines, which were expected primarily in the city
centre, as there many roads come together. The
tests turned out in an unexpected result of thisinvestigation. The condition to find crossroads inthe city centre depends on the size of the town.
There seems to be a rule regarding the relation
between the structure of the centre, the spatial
arrangement of streets and the size of the city.
Typical structures in the city centre are shown,
depending on the dimension of the town. In
small towns, often a big street leads through andmainly TEE-junctions can be found, whereas inmedium size cities the expected structure
dominate, meaning that three or more majorroads will meet in the city centre. In large cities
the opposite trend can be observed: major streets
will run around the city but in the centre itself
only minor streets or even pedestrian areas will
be located.
The intention was to look into detail, if there
are reasoning mechanisms to cut settlement
areas into partitions, especially if the lines willmeet approximately orthogonal. Among otherthings it came up, that highways will be
represented by separate clusters with solely oneedge (ELLjunctions), with the exception of theaccess roads. Naturally there are only a few
intersections with highways, the parts between
are direct polylines without branches.
It could be one of a criteria to determine
highways respectively to distinguish their accessroads from their carriage-ways in data sets. It
can be very helpful to validate further structures
like the neighborhood of settlement areas in thevicinity of a highway access.

On the contrary, we could have included
distinctive features, which do not correspond to
the reality, and have only been valid for a small
test data set. This implies, that the setup of suchmodels has to be done very carefully, possiblyusing large test data sets in order to gain theinformation from and to perform tests forverification of the derived rules. Furthermore, aspecific inference scheme has to be designed toapply the rules to the data, which takes theprobability or the importance of a condition to arule into account.
3.2 Unsupervised Classification
The method aims at leaving the process ofknowledge discovery to the computer itself. Thatmeans the computer has to discover rules,
separations into categories, similarities in data
sets without any predefined restrictions. Sincesuch rules are induced from a finite set ofexamples, they cannot be verified, but only
falsified. Thus, there has to be a validation of theutility of the detected information. It may
happen, that rules will be found, which are
obvious and do not give further knowledge. It isanother process of learning to distinguish usefuland non-useful rules.
A way to analyze geometric objects is todetermine their characteristics and to try to findregularities among them. Such regularities then,in turn, can be considered as representatives fora certain class of objects or a class of objects in acertain context or environment. For linear
objects or even networks of linear objects, thenodes are such a characteristic, including thenode degree, i.e. the number of outgoing lines
from the node. Furthermore, also the angles of©ISAROB 2006

On the basis of described processes, we areable to examine the data sets and the above

558

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

mentioned results in more detail, whereby
supervised and unsupervised models can hardly
be kept apart at this stage. The following factors
could be decisive for further (supervised andunsupervised) interpretations: size of a single
mesh, length of segments between nodes,
frequency of occurrence of high-orderrespectively significant nodes, in depth study ofthe type/shape of the nodes. To get on without
predefinition of thresholds or the preliminary
fixing of minimal and maximal values, it is ouraim to continue the search for broader
regularities, for example in the combination
between above mentioned criteria.We willconcentrate on both ways, supervised andunsupervised methods. Both can support
knowledge
discovery
andduringtheimplementation of algorithms, both data mining
models will influence each other.

4 Conclusion
This paper explains the data mining in thespatial database of the GIS using two classified
methods of the data mining. Some valuable
implicit principles are also presented. These
principles contribute to the establishment of thegeography information search engine based on
the semantic net, meeting the requirements ofthe semantic search demanded by users. In
practice, the conclusions from the data mining
are a powerful tool by which the tourists canknow about the destination and the prospect of
this application will be very wide.

References[1] David Hand, Heikki Mannila, Padhraic
Smyth. Principles of Data Mining，2003[2] Wu Mingrui, A neural network algorithm
used in the pattern rogognition. Software
Journal，2001[3] Olivia Parr Rud. Data Mining Cookbook，
Modeling Data for Marketing, Risk, andCustomer Relationship Management，2003©ISAROB 2006

559

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

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©ISAROB 2006

563

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Remarks on tracking method of neural network weight change foradaptive type neural network direct controller
Takayuki Yamada
Department of Computer and Information Sciences
Faculty of Engineering
Ibaraki University
4-12-1 Nakanarusawa, Hitachi, Ibaraki, 316-8511, Japan

Abstract
A neural network usually learns so as to minimize ascalar value such as a cost function. This scalar value is
useful for a confirmation of neural network learning
performance. However, this confirmation may not becorrect for neural network controllers because a plant
dynamics affects the cost function. My previous paper
proposed a new tracking method of the neural network
weight change as an examination of the neural network
controller learning performance and it was applied to alearning type neural network direct controller. This paper
shows the simulation results when the tracking method isapplied to an adaptive type neural network direct controller.
1. Introduction
Many studies have been undertaken in order to apply
both the flexibility and the learning capability of neuralnetworks to control systems.[1]-[3] Learning rules ofneural network weights are usually designed so as tominimize the error between a plant output (or neuralnetwork output) and a desired output (teaching signal). The
essence of neural network learning is nothing but the changeof the neural network weights. However, in order toexamine the performance of the neural network learning,most researchers use a cost function (squared error between
the desired output and the neural network output (or theplant output)). This is because it is not practical toexamine the huge number of the neural network weights andthe cost function is a scalar value which is easily dealt with.
However, the neural network weight change is not always
reflected in the cost function. This problem is especiallyserious in neural network controller applications. This isbecause the performance of the cost function is affected bydynamics of the plant. My previous paper[1] proposed a new
tracking method of the neural network weight change as an
examination of the neural network controller learning©ISAROB 2006

564

performance. This tracking method can realize to observe
the neural network weight change directly. It was also
applied to a learning type neural network direct controller
and its usefulness was confirmed. The learning type of theneural network controller is useful to confirm thecharacteristics of the proposed tracking method. This isbecause it is simplest and the neural network weightchanges slowly in comparison with an adaptive typecontroller[2]. On the other hand, the adaptive type neuralnetwork controller requires less convergence time. This isbecause the neural network weights of the adaptive typecontroller are changed at every sampling time and we canrealize an on-line neural network controller when this typecontroller can be applied to practical systems.
T h i s paper applies the tracking method of theneural network weight change to the adaptive type neuralnetwork direct controller for a second order discrete time
SISO (single input and single output) plant.
The
simulation results confirm the usefulness of the tracking
method for the adaptive type neural network direct controller.
I also obtain interesting phenomena which are not observedon the simulation for the learning type neural network directcontroller.
2. Tracking method of neural network
weight changeThis section explains the tracking method of theneural network weight change briefly. This tracking
method is applied to the adaptive type neural network directcontroller for SISO plant. For this selection, an output
layer of the neural network has one neuron, the weights
between the output layer and a hidden layer can be expressed
as a vector ω and the weights between the hidden layer andan input layer can be expressed as a matrix W. To
simplify, the neuron number of the input layer is equal tothat of the hidden layer. That is, the weight matrix W is

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the square matrix. The tracking method uses the following
steps.

network weight change to the adaptive type neural network
direct controller. The simulated plant is follows:(Tracking method of neural network weight change)(1) We can derive one weight vector Γ from the neuralnetwork weight vector ω and weight matrix W as follows:

Y(k) = - a1 Y(k-1) - a2 Y(k-2)+U(k-1) +bU(k-2) -a3 Y(k-3)+ Cnon Y2 (k-1)ΓT = [ω 1ω n W11

W1n W21

W2n

Wn1

Wnn ]

(1)

where n is the neuron number both the input layer and thehidden layer.
(2)We must define a standard vector Γ 0. Any vector, which
has same order as that of the weight vector Γ, can be selected
as this standard vector, for example, the weight vectors
derived from the initial neural network weights, the final
neural network weights and so on.
(3) We can calculate an inner product of the weight vector Γand a standard vector Γ0 because these vectors have sameorder. We can also calculate an angle between the weightvector Γ and the standard vector Γ 0 as follows:
X = Γ cos θ , Y = Γ sin θθ = cos-1 (<Γ0 ⋅ Γ >
Γ0 ⋅ Γ

For this simulated plant, the neuron number n inboth the input and hidden layers is 4. The neural network
input vector I is defined as the following equation.
IT(k) = [Yd (k+1) Y(k) Y(k-1) U(k-1)]

z -1
U
Plant

We select the following sigmoid function f(x) as the input
output relation of the hidden layer.

(7)

Where Xg is the parameter which defines the sigmoid
function shape. The plant input U(k) equals the neuralnetwork output composed as follows:(8)

The block diagram of the adaptive type neural network directcontroller is shown in Fig.1. The learning rule of thisneural network controller is shown in the following
equations.

Y
Wij(k+1) = Wij(k-1)+ηε(k)ω i(k-1)Ij(k-1)
n×f,{∑ Wij(k-1)Ij(k-1)}

-

j=1

(9)

+

n

ω i(k+1) = ω i(k-1)+ηε(k)f{∑ Wij(k-1)Ij(k-1)}(10)
Where η is the parameter to determine the neural network
convergence speed. We select the weight vector derived
from the initial neural network weights as the standard
vector Γ0 of the equations (2) and (3)j=1

Fig.1 Block diagram of neural network direct controller
for second order discrete time plant.
3. Simulation
This paper applies the tracking method of the neural©ISAROB 2006

(6)

U(k) = ω T(k)f{W(k)I(k)}(4) We can draw a new weight performance on the 2D planeby the use of X and Y in equations (2) and (3).

z

(5)

(3)

and the vector Γ, and Γ is the norm of the vector Γ.

Ydε(k)=Yd(k)-Y(k)X {1-exp(-4x/Xg )}f(x)= g2{1+exp(-4x/Xg )})NeuralNetwork

Where Y(k) is the plant output, U(k) is the plant input, k is
the sampling number, a1, a 2 & b are the plant parameters, a 3
is the parasite term and Cnon is the nonlinear term. For thissimulation, a1=-1.3, a2=0.3, b=0.7, a 3=-0.03 and Cnon=0.2
are selected. The rectangular wave is also selected as thedesired value Yd. The output error ε is defined as follows:(2)

Where <Γ0 ⋅ Γ > is the inner product between the vector Γ0

z -1

(4)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0.01

0.8

Plant output

Plant output
Desired value

Y

0.005

0.4

0

0

500
Sampling number

0
2.255

1000

2.2555
X

2.256

Fig.5 Expansion of track around initial stage of learning.

Fig.2 Plant output (k=1~1000).

0.061

0.8

Y

Plant output

Plant output
Desired value
0.06050.4

0.06
2.2658

0
4000

4500
Sampling number

5000

Fig.3 Plant output (k=4000~5000).

0.08

Y

0.04

0
2.252

2.256

2.26
X

2.264

2.268

Fig.4 Track of neural network weight change.

©ISAROB 2006

566

2.2659X

2.266

Fig.6 Expansion of track around final stage of learning.
There is less meaning in the cost function for theadaptive type neural network controller. This is because
the neural network weights are changed at every samplingtime. We usually use the plant output as an examination of
the neural network performance. Figure 2 shows anexample of the plant output (k=1~1000). The solid line
and the broken line show the plant output and the desired
value respectively. As shown here, the plant output
converges with the desired value. Figure 3 shows the plant
output (k=4000~5000) of this example. As shown here, asmall error remains, but the plant output converges with thedesired value and whole control system is stable. As
shown in these figures, the neural network learning seems
to be well performed. Figure 4 shows the track of theneural network weight change. It is drawn by the use of
our tracking method explained in the section 2. In thisfigure, I draw a circle at the point (X,Y) of eqs.(2)&(3).
Since we select the vector derived from the initial neuralnetwork weights as the standard vector Γ0, the neural

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

IEEE Transaction on Neural Networks, Vol.1, No.1, pp.427, 1990

0.06

Y

network learning begins on the X axis. As shown in thisfigure, the track of the neural network weight change
separates into two trajectories as learning progresses. The
difference between two trajectories becomes to be large andthey do not converge in this example. Figure 5 shows the
expansion of the track around the initial neural network
learning stage. The circle is drawn and the solid line shows
the sampling time sequence. As shown here, the track of
the neural network weights change separates after several
sampling time. Figure 6 shows the expansion of the righttrajectory of the track around the final neural network
learning stage. As shown here, the neural network weightchange is not finished although the plant output converges
with the desired value.
Figure 7 shows an anotherexample of the track of neural network weight change (a3=0,
C non=0). Figures 8 and 9 show the expansions of Fig.7.
As shown in these figures, the track of neural network
weight change is separated into two trajectories. Abovephenomena are not observed in the application to thelearning type neural network direct controller.

0.03

0
2.37

2.4
X

2.43

Fig.7 Track of neural network weight change.

0.02

Acknowledgment
T h e author wishes to express his thanks to
Mr.Yasuhiro Uematsu, graduated student, Ibaraki University,
for his programming and simulation.
References[1] Takayuki Yamada, “Remarks on tracking method ofneural network weight change for learning type neuralnetwork direct controller”, Proceedings of AROB 10th ‘05(The Tenth International Symposium on Artificial Life andRobotics 2005), pp.624-627(2005)[2]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.784791(1991)(in Japanese)[3]K.S.Narendra and K.Parthictsarathy, “Identification andControl of Dynamics System Using Neural Networks”,©ISAROB 2006

567

0.01

0
2.409

2.412

2.415
X

2.418

2.421

Fig.8 Expansion of track around initial stage of learning.

0.0519Y

T h i s paper applied the tracking method of theneural network weight change to the adaptive type neuralnetwork direct controller. The simulation results confirm
the usefulness of the tracking method for the adaptive typeneural network direct controller. I also obtain interesting
phenomena which are not observed on the simulation for thelearning type neural network direct controller. The track of
the neural network weight change are separated into two
trajectories.

Y

4. Conclusion

0.05185

0.05182.37182.3719X

2.372

Fig.9 Expansion of track around final stage of learning.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Situation Recognition Mechanism
Based on the Fuzzy ART for a Communication Robot
K. Iwatsubo*

Y. Haga**

Y. Hoshi**Musashi Institute of Technology
1-28-1 Tamazutsumi, Setagaya-ku
Tokyo, 158-8557, Japan**Bandai Co.,Ltd.
1-4-8 Komagata, Taito-ku
Tokyo, 111-8081, Japan

Abstractof the sensors for the achievement of the high recognizable accuracy explode the perceptual space of a robot.
When the perceptual space is exploded, we face on thediﬃculty that deﬁnes each situation. Therefore it isdesirable that a robot categorizes the perceptual spaceautonomously. In this paper, we propose the methodof categorizing the perceptual space using Fuzzy ART
algorithm. The Fuzzy ART has a feature which is creating new neurons. The proposed system is expected
to improve the recognizable accuracy of a robot by utilizing this feature.
We demonstrate the usefulness of the proposed
method by applying the function of the human bodydetection to a communication robot. The human bodydetection system is accomplished by the integration of
the Fuzzy ART and BP (Back Propagation). The heat
detection sensor called ‘pyroelectric sensor’ is installedin the robot, and the human body detection is achievedby measuring body temperature. After the learning, arobot can distinguish a human body and an obstacle
using the integration of the Fuzzy ART and BP.

In this paper, we propose the new method of categorizing the perceptual space using the Fuzzy ART(Adaptive Resonance Theory) algorithm. The Fuzzy
ART, which is a kind of the neural network, is anarchitecture to classify the vector. To categorize theperceptual space is to classify the information of environment acquired by a robot. When a robot properly
categorizes the information of environment, a robot
can recognize present situation around a robot itself.
We also demonstrate the usefulness of the proposed
method by applying the function of the human bodydetection to a communication robot.
Keywords:
Back Propagation, Communication
Robot, Fuzzy ART Neural Network, Human Body Detection, Pyroelectric Sensor, Situation Recognition

1

Introduction

The technology of robots has made rapid progress. Recently developed robots are able to do more complex
actions compared with traditional one. However, considering many applications of robots in the future, thedevelopment of a well behavior based robot is desirable. To achieve autonomous behavior of a robot, arobot should recognize a present situation around arobot itself using sensors installed in a robot. Thena robot selects a suitable behavior for the recognizedsituation, and executes the appropriate action. Up
to now, the ﬁeld of the artiﬁcial intelligence has beenstudied from the viewpoint of acquiring an appropriate action for the situation. However, the deﬁnitionof the situation needed a human’s design in order toacquire actions of a robot in past studies.
The number of sensors installed in the robot andthe resolution quantization determine the volume ofperceptual space for a robot. Increasing the numberof sensors and improving the resolution quantization©ISAROB 2006

B.T. Nohara*2
2.1

The Fuzzy ART Neural Network
OutlineThe ART network, which is a kind of the neural network, was originally proposed by Carpenter and Grossberg[1]. The ART network uses binary inputs. For
analog inputs the Fuzzy ART was designed[2][3]. In
this study, we use the Fuzzy ART since input is analog
value and the perceptual space is easily constructed by
the Fuzzy ART. Fig.1 shows the structure of the Fuzzy
ART used in this study. The Fuzzy ART consists ofthe comparison layer, named F1-layer, and the recognition layer, named F2-layer.
An input vector to the F1-layer ﬁts a neuron in theF2-layer. When no neuron is ﬁtted, a new neuron is

568

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

as follows:
Weight vector
Recognition䇭Layer

F2
1

2

Wi = (W1,i , W2,i , L , WN ,i )

䊶䊶䊶

i

䊶䊶䊶

Ai∗ =

M

1

2

3

䊶䊶䊶

old
= β(X ∧ Wiold
Winew∗∗ ) + (1 − β)Wi∗ ,(3)

where β (0 < β < 1) is the learning speed ratio.
If Ai∗ < ρ, which means that the input vector does
not ﬁt the best match neuron, then the next best
match neuron is searched again. The search for thebest match neuron is repeated until Ai∗ ≥ ρ is satisﬁed as follows:

Vigilance parameter

N(2)

Then the ﬁtness is compared with the value of vigilance parameter ρ. If Ai∗ ≥ ρ, then the input vector
ﬁts the best match neuron, and the weight vector of
the best match neuron is updated as follows:

Wi*

F1

|X ∧ Wi∗ |N
.
|X|N

ρ

Comparison Layer
Input vector

X = ( X 1 , X 2 ,L, X N )Fig. 1: Structure of the Fuzzy ART• to select the second largest Ti (Eq.(1)) (*)• to calculate Ai∗ (Eq.(2)) corresponding to Ti

created in the F2-layer. The signiﬁcant feature of the
Fuzzy ART is to create new neurons. Neurons in theF2-layer have the weight vector as a template of memory pattern. The ﬁtness is calculated by comparing
with the input vector and the weight vector in the F2layer. The vigilance parameter ρ (0 < ρ < 1) is a
threshold value which decides whether a new neuron
should be created.

2.2• if Ai∗ ≥ ρ, thenold= β(X ∧ Wiold
Winew∗∗ ) + (1 − β)Wi∗ (Eq.(3))• if Ai∗ < ρ, then go to (*)
If there is no neuron that should be ﬁtted in the F2layer, a new neuron is created in the F2-layer. The
weight vector of a new neuron is set to the input vector
as follows:

Operation

WM+1 = (X1 , X2 , · · · , XN ).

An input vector X(0 < Xi < 1) to the F1-layer isrepresented as a N-dimensional vector, where N is thenumber of perceptual input of a robot. After the input vector X is put to the F1-layer, the weight vector
Wi of the i−th neuron in the F2-layer is comparedwith the input vector X. The weight vector Wi has
the same dimension of the input vector. The valueof selection intensity Ti , which is the level of excitation of the i-th neuron in the F2-layer, is calculated asfollows:
Ti =|X ∧ Wi |N,
α + |Wi |N

The Fuzzy ART network is constructed by repeating
the renewal of the weight vector Wi and the creationof a new neuron in the F2-layer.

2.3

Perceptual space represented by the
Fuzzy ART

We consider the perceptual space of a robot. The perceptual space represented by the Fuzzy ART is accomplished using the complement coding. The extended modiﬁed from the original input vector
input vector X
X is written using the complement coding as follows:(1) = (X1 , · · · , XN , 1 − X1 , · · · , 1 − XN ).
X



X

where α(0 < α < 1) is the choice parameter, andN|Wi |N and X ∧ Wi are deﬁned by |Wi |N = i=1 |Wi |and X∧Wi = [min(X1 , W1,i ), · · · , min(XN , WN,i )], respectively.
The neuron with the largest value of selection intensity is selected as the best match neuron i∗ , andthe ﬁtness Ai∗ of the best match neuron is calculated©ISAROB 2006

(4)

(5)

 is a 2N-dimensional vector. Let the exNote that X
 i corresponding to X
 be detended weight vector W
noted by i = (W1,i , · · · , WN,i , W 1,i , · · · , W N,i ).
W

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(6)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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1.0

study, the detectable distance is 1.0[m], and the horizontal, detectable range is -30[deg]∼+30[deg].

1.0
p2,1 ' (1 − W ′1,1 ,1 − W ′2,1 )

p2,1 (1 − W 1,1 ,1 − W 2,1 )

3.2

p1,1 (W1,1 , W2,1 )W1,1 or 1-W1,1

0

Fig.3 shows our communication robot ‘BN-16’ used inthis study. This robot has two driving wheels installed
in both sides and a free wheel in the rear side. The pyroelectric sensor and the infrared sensor are installedin three directions (the front, the front-left, and thefront-right ) of the robot as shown in Fig.4. In theinfrared sensor used in this study, the detectable distance is 1.3[m], and the horizontal, detectable range is-15[deg]∼+15[deg]. The function of human body detection is accomplished by using pyroelectric sensors
and infrared sensors.

p1,1 ' (W1′,1 , W2′,1 )1.00

W '1,1 or 1-W '1,11.0

ˆ = (W , W , W 1,1 , W 2,1 ),
W
1
1,1
2 ,1
p1,1 = (W1,1 , W2,1 ),ˆ ′ = (W ′ , W ′ , W ′1,1 , W ′ 2,1 ),
W
1
1,1
2 ,1

p2,1 = (1 − W 1,1 ,1 − W 2,1 ).

p2,1 ' = (1 − W ′1,1 ,1 − W ′2,1 ),p1,1 ' = (W1′,1 , W2′,1 ),where W1,1 ≥ W1′,1 , W2,1 ≥ W2′,1 ,W 1,1 ≥ W ′1,1 , W 2,1 ≥ W ′2,1.

Fig. 2: Perceptual space in the Fuzzy ART

Here, we deﬁne the following two points p1,i and p2,i in
the N-dimentional space using the complement coding:p1,i=(W1,i , · · · , WN,i ),

p2,i

=

(1 − W 1,i , · · · , 1 − W N,i ).

3.3

3.1

(7)

Experiment
The human body detection and a pyroelectric sensor

We demonstrate the usefulness of the proposed
method by applying the function of the human bodydetection to a communication robot. The function ofhuman body detection is necessary for a communication robot since a robot works required tasks ordered
by a robot’s user. Many industrial applications perform the function of human body detection using animage sensor so far. However the system using an image sensor is too expensive to apply to the consumer
market of communication robots. Therefore we utilizea ‘pyroelectric sensor’ instead of an image sensor as
the detection of human bodies.
The pyroelectric sensor detects the change of temperature caused by infrared rays radiated from humanor animals. In the pyroelectric sensor used in this©ISAROB 2006

Total system

The proposed method stated in Section 2 is applied
to the perceptual space of BN-16. The human bodydetection system is accomplished by the integrationof the Fuzzy ART and Neural Network that uses BP(Back Propagation). In the human body detection
system, the perceptual space of BN-16 is categorizedusing the Fuzzy ART, and each categorized space isrecognized using BP. Fig.5 shows the outline of thesystem of human body detection by the integrationof the Fuzzy ART and Neural Network. Total system
consists of the F1-layer, the F2-layer, and the F3-layer.
The network between the F1-layer and the F2-layer
is constructed by the Fuzzy ART, and the network
between the F2-layer and the F3-layer is assembled by
Neural Network that uses BP. The pyroelectric sensor
detects the diﬀerence of the temperature, so we let the

The perceptual space categorized by the Fuzzy ART
becomes the linear space spanned maximally by thetwo points p1,i and p2,i . Fig.2 shows an example ofthe two-dimensional perceptual space. We update theextended weight vector based on Eq.(3). So, the normof the weight vector decreases and the created perceptual space expands gradually.

3

Communication robot

pyroelectric and infrared sensors

0.7[m]
driving wheel
infrared sensor

0.3[m]

pyroelectric sensor

Fig. 3: Communication Fig. 4: Robot’s sensors:robot: BN-16
Pyroelectric sensor andInfrared sensor

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Fuzzy ART
F1

Ps(t − 2)Ps(t − 1)

F2

1

1

1.0

Is (t )Is (t )

2

2

F3
Human body1

Ps(t )

0

Obstacle
3

3

2

4

丵丵丵

Is (t )

1.0

Neural Network3

Non-object

Ps(t )

1.0

0

Ps(t )

1.0

Fig. 6: Perceptual space in Fig. 7: Perceptual space
the early learning stage after the learningM

can autonomously categorize the perceptual space using the proposed method. We applied the proposedsystem to the robot with the function of tracking a human body to demonstrate the usefulness. The communication robot can track a human body while avoiding
obstacles in the experiments. The robot can distinguish between a human body and an object.

X = ( Ps (t − 2), Ps (t − 1), Ps (t ), Is(t ))Fig. 5: The system of human body detection

input vector be
X(t) = (Ps(t − 2), Ps(t − 1), Ps(t), Is(t)),(8)

where Ps(t), Ps(t − i) are the value of the pyroelectric
sensor at time t and the value of i-sampling earlier
time, respectively and Is(t) is the value of the infrared
sensor at time t.
The output of this system is as follows: whether ahuman body is detected or whether an obstacle is detected or whether nothing is detected. In the learning
process, to obtain a desirable output, the neuron of
the F2-layer is created as occasion demands and theweight vector between the F2-layer and the F3-layer
is updated using the teacher data.
The total system works as follows: the vector X
is inputted to the F1-layer. Then the ﬁtness Ai in
the F2-layer is calculated. The output value of theF3-layer is obtained as the product-sum of the ﬁtness
and the weight vector between the F2-layer and theF3-layer. The largest value in the output of the F3layer is the recognition result of the total system. If
the output of the F3-layer is not a correct result, theweight vector between the F2-layer and the F3-layer
is updated to output a correct recognition result.

4

3.4[2] G.A. Carpenter, S. Grossberg, and D.B. Rosen“Fuzzy ART: Fast Stable Learning of Analog Patterns by an Adaptive Resonance System,” Neural
Networks,Vol. 4, pp. 759-771, 1991.

In this paper, we propose the new method of categorizing the perceptual space using the Fuzzy ART
algorithm. We demonstrate the usefulness of the proposed method by applying the function of the humanbody detection to a communication robot. The proposed method is eﬀective as the method of categorizing the perceptual space. We have such an advantageof the proposed method that the perceptual space ofa mobile robot is categorized in real time. The proposed method is expected to be applied to the situation recognition mechanism of the controlled object
such as mechanical pets and the industrial robots, etc.

References[1] G.A. Carpenter, S. Grossberg “The ART of Adaptive Pattern Recognition,” IEEE Computer,Vol.
21, No.3, 1988.

Results in the experiments

After the learning process, the recognizable accuracy
reaches about 90[% ], and the robot can distinguish between a human body, an object, and non-object withhigh rate. Fig.6 and Fig.7 shows the perceptual spaceconstructed by Ps(t) and Is(t), in the early learning
stage, and after the learning, respectively. The robot©ISAROB 2006

Concluding remarks[3] Russell Beale, and Tom Jackson Neural Computing: An Introduction, KAIBUNDO, 1993.

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Multi-module neural network system with concept formation and primitive consciousness
Yasuo Kinouchi*, Katsumasa Masuda*, and Shoji Inabayashi *** Tokyo University of Information Sciences, 1200-2 Yatoh-cho Wakaba-ku Chiba-shi 265-8501 Japan,
kinouchi@rsch.tuis.ac.jp** Pacific Technos Corp., 8-4-19 Tajima Sakura-ku Saitama-shi 338-0837 Japan,
ina@pacific-systems.co.jp
Since these functions are difficult to execute on
current neural networks, such as the widely used
back-propagation type, we paid attention to thenecessity of links between nodes and devised a simple
neural network. In this new neural network, the weight
between nodes is changed by a similar way to Hebb's
rule. After learning, when a link between nodes isnecessary, the value of its weight is given as 1. When alink is unnecessary, it is given as 0. When the necessity
is uncertain, it is given as #. Under the assumption that
input signals of the modules are expressed by 1 or 0,
depending on the presence of micro features, the groupof weight values expresses concepts or images. By
outputting the weight value, concepts and images canbe recollected as sets of micro features. Combining
concept formation and recollection functions of theneural network allowed us to create an associative
memory system that can be minutely controlled.
The above neural networks were used to design a
module with concept formation and association, a
module for episodic memory, and a module for
integration. The module for integration selects efficientand useful concepts for the system by using a
two-cycle voting method that we devised. The concepts
selected by this module are returned to the module
which recognized the concept, and displayed as a dim
imagery on a working memory by using therecollection function. The content in the working
memory is again input to the module for concept
formation together with the input from the external
world. This means that the system considers the images
on working memory to be a kind of reality similar to
the images from the surrounding environment.
Repeating this process, the system can adapt to theenvironment autonomously.

Abstract
To develop an information processing system thatworks in a way similar to primitive “thinking,” wedesigned an adaptive system with concept formationand primitive consciousness in a working memory.
This system is mainly composed of concept-formation
modules, episodic memory, and integration modules. A
number of micro features are input into the concept
formation modules, and converted into a small numberof signals representing concepts. These conversions
corresponding to recognition are formed slowly
through learning in the entire system, and details of theconversion function are memorized on weights as
average occurrence ratio of micro features. The
episodic memory module quickly memorizes two or
more signals corresponding to concepts that occur atthe same time as one scene. The integration module
selects efficient and useful concepts for the entire
system by means of a two-cycle voting method.
Selected concepts are sent back to the concept
formation modules. The module that originally output
the returned concept reversely converts them into akind of micro feature signal representing an imagery of
the concept; that is, this reverse conversion
corresponds to recollection, and displays the imagery
in the working memory. These virtual and dim
imageries reproduced in a working memory and themechanism of reproducing the imageries are principal
parts of our primitive consciousness.
1. Introduction
We have designed a multi-module neural network
system that adapts to the environment using concept
formation and primitive consciousness with a working
memory. A lot of discussions about consciousness havebeen done so far. However, understanding
consciousness still faces many problems. Haikonen has
already paid attention to the importance of inner speech,
and has developed an implementation method based ona neural network [1], but this method is not necessarily
clear because of the structure of a main association unit
is not concrete. Though Taylor[2] and Koch[3]explained consciousness in the brain after a detailed
research of brain functions, both explanations are notenough to clarify a structure based on neural networks.
Although a system with primitive consciousness must
be capable of concept formation, recollection ofconcepts as an inverse operation of recognition, andassociation between concepts that can be controlled indetail, they did not consider these functions.

©ISAROB 2006

2. Consciousness, concepts, and images
We assumed that our consciousness is classified
into two displays of inner spaces according to oursenses in daily life.
(1) Real world and real-time spaces: This space
displays the external environment and the state insideof our body. We feel this display as a reality throughsenses such as sights and sounds. Each sense isindependent, and each space is not integrated across
two or more senses. The system itself cannot change
the displayed content. The content of these spaces does
not influence an action decision of the system directly.
(2) Thinking space: In the thinking space, all senses,
such as sight and sound, are integrated, and images and

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concepts considered the most important by the system
are displayed on each occasion. When we recall a past
scene or imagine a future forecast, images are
displayed. The content displayed here is informationthat has spread not in the real-time world but in thewide space-and-time world. Moreover, the system itself
can change the content of the space. The display shows
information integrated at the highest level in a system.
The content influences an action decision of the system
directly. The thinking space is a kind of “virtual space”
where our thinking is displayed.
We think that an important part of consciousness
belongs to the thinking space, which is therefore
mainly discussed in this paper. However, to treat thisthinking space independently of the usual word
“thinking”, it is called “virtual space” in this paper.

features are input into these sub-modules, and each
sub-module has two layer as shown Figure 2.

We consider primitive consciousness to be a kindof virtual space with the following functions.
a. Useful information for the system is selected andgenerated by the integration of several senses, pastexperiences, and forecasts about the future.
b. The selected and generated information aredisplayed in virtual space as a dim imagery.
c. The displayed content influences the action of thesystem and the content of virtual space.
d. The system associates various concepts and images
from the content of virtual space.
e. The system intentionally simulates and forecasts
future events by operating the images in virtual space.
f. The system displays the system itself in virtual
space as an image.

The nodes on the first layer are connected with all thenodes on the second layer. There are weights between
the two layers. The input, a cluster of micro features, attime t from the preprocessing module to the first layer
is given by X(t) = (x1,x2,...,xi,...,xm). Here, xi has threestates: “1”, “0”, and “#”. When the i-th feature exists inimage X, xi is given as 1. When the i-th feature isabsent, xi is given as 0. The # mark shows that image X
is not influenced by the existence of the i-th feature. In
the basic processing mode, the first layer outputs R(t) =(r1,r2,...,ri,...,rm), which is R(t)= X(t), to the second layer.
The weights of the links between the first layer and thesecond
layer
are
given
by
WCj(t)=C
C
C
C
C(w 1j,w 2j,...,w ij,...,w mj). Here, w ij (0≦wCij≦1) is
the weight of the link that connects node i on the firstlayer and node j on the second layer. The total input of
node j on the second layer is calculated from
ECj = Σ (ri * f(wCij) ) /m
(i=1,m),(1)
where f(wCij) is a step function that converts analog
variable wCij to discrete variables “1”, “0”, and “#”according to the following equations.
f(wCij) = 0(0≦wCij≦d),Cf(w ij) = #(d<wCij≦1-d),Cf(w ij) = 1(1-d<wCij≦1)
The operation “*” means comparing two inputs,
as shown in Table 1.

In regards to the above functions, images andconcepts become important basic units. Images usually
range widely from an extremely ambiguous image to aclear image. An image that is clear and general is calleda concept in this paper. That is, the concept is treated asa subset of the image. Moreover, the function forgenerating and controlling images and concepts is
indispensable in order to achieve the function of
consciousness. In this study, we used Kinouchi andInabayashi’s methods [4][5] for generating concepts
from images and associating between images andconcepts in a simple layered neural network.
3. Configuration and functions of system
As shown Figure 1, this system is mainlycomposed of three kinds of modules: concept
formation, log(episodic memory), and integration.
Parallel stimulations are input to the system as a timeseries. The inputs from the environment are converted
into signals of micro features by sensors andpreprocessing.

Input A
Input B
Output

3.1 Concept formation moduleThis module is composed of a visual sub-module,
an auditory sub-module, and a body sub-module. Micro©ISAROB 2006

573

Table 1.
0 0 0 1 1 1 # ##0 1 # 0 1 # 0 1 #1 0 d# 0 1 d# d# d# d#(0≦d#≦1)

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Only one node j with the highest value ECj
becomes excited and emits signal 1 to othermodules. And it varies weights wCij according to
the next equation, in which constant kC is alearning rate coefficient with a small value.
wCij(t+1)= wCij(t) + kC (x(t) -wCij(t))(2)
As this process is repeated, wCij slowly
approaches the average value of occurrence rateof xi =1, under the condition that node j is
excited. When f(wCij) of node j equals 1, microfeature xi belongs to those images or concepts
represented by node j. When f(wCij) of node jequals 0, xi does not belong to those images orconcepts. And when f(wCij) of node j equal #, xi
does not affect the images or concepts. As awhole, images and concepts are formed andmemorized slowly in concept formation modules.
After the concepts and images are formed, they
are used for recognition and association
between them.

corresponding to concepts recognized in aconcept formation module to which nodes
belong but also supported from other modules
according to coincidence of nodes excitation.

In the concept formation module, selected
nodes act in the reverse direction, and output
values of f(wCij) are put into working memory.
Images or concepts represented as the weightsf(wCij) are therefore displayed on the working
memory, so we can feel imageries in a virtual
space or thinking space. Using the two-cycle
action, even when many conditions exist at thesame time, a useful node for the system can beappropriately
selected.
Moreover,signalpropagation in the reverse direction is a kind of
positive or negative feedback, so this process
sustains “attention” at the micro feature level.

3.2 Integration module
The main function of the integration
module is selecting efficient and useful images
and concepts for the entire system from outputsof the concept formation modules and theepisodic memory module. This function isformed in a two-step process on the structure
shown in Figure 3, in which node i and node j
are mutually connected and have links in theboth directions. The link from node i to node j
has a weight wIij (0≦wIij≦1).
First step: Efficient images and concepts are
selected according to the weights determined in
the first step. This step is comprised of
two-cycle actions.

Second step: The results of selection signals
in the first step are sent to the motor module,
and action of system is determined andexecuted in the environment. Only when yi(t),output of node i, equals 1, wIij is correctedbased on the following equation corresponding
to the yj(t), output of node j. Constant kC is alearning rate coefficient with a small value.
wIij(t+1) = wIij(t) + kI (yj(t) - wIij(t))
On repeating these operations, wIij approaches
conditional probability p(yj=1|yi=1).

First Cycle: Each node in the concept
formation modules is excited according to theECj , and outputs of the modules are input to theintegration module, and propagate to othernodes. Then gain of nodes j, EIj, are calculated
by each output of node i, yi(t), according to next
equation.
EIj = Σ( yi(t)・wIij(t)) / n (i=1,n).
(3)I
E j shows the degree how the excitation of node j
expected according to past selection results.

3.3 Log (episodic memory) module
The log module memorizes a group ofconcepts that the integration module selectedeach time in order of occurrence. The group ofconcepts selected by the integration module
corresponds to one scene of our consciousness,and the log module functions like an episodic
memory. Figure 4 shows the configuration of thelog module consisting of a two layer neuralnetwork with feedback. Concept-formation
modules memorize the value of the average

Second Cycle: Excitation of each node j isdetermined according to the ECj calculated in
the concept formation module and EIj calculated
in first cycle. Only several nodes exciting with alarge value of g(EIj) + ECj are selected in the
integration module based on modified “winnertakes all” rule, where g(EIj) = 2EIj –1. Using
function g, EIj
has both excitation andsuppressing effect. Selected nodes are not only

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through the working memory and concept-formation
module. When information in the working memory ismore important than that of the stimulation from theenvironment, the information relating to the working
memory is recalled to the working memory again. The
circulating operation, that is, the concept formation
module, integration module, and working memory
execution, is a kind of association operation. In
addition, if the log module joins this circulation, it
becomes association based on episodic memory.
The concept selected by the integration module isoutput to the motor module and influences the action
decision directly. Though the decided action acts on theenvironment through an effector (a kind of muscle), thesensor in the effector perceives movement, and sends
the sensed signal to the body sub-module at the sametime. This signal influences the integration module andworking memory. This means that the system canchange the content of the working memory byintentional action. These actions correspond to humanintentional thinking or simulating the future.
Micro features of e.g. an apple are bundled andrepresented by a node in a second layer in the visual
sub-module. When the node is stimulated, an image ofan apple is recollected and we feel an apple in theworking memory. Similarly, micro senses in our bodybundled and represented by a node in a second layer inthe body sub-module. When the node in the bodysub-module is stimulated, an image of something is
recollected. We expect that something felt in theworking memory is a kind of sense of self.

input by using a small learning coefficient.
However, the log module memorizes amomentary value of the input in every case byusing a large learning coefficient. Moreover, thelog module has a recollection function as well asa memorizing function. The log module recalls asimilar scene to the scene memorized in thepast, the scenes that are associated with thefirst recalled scene are recollected one after
another, and they are output to the integration
module.

4. Total system operation
4.1 Three-stage growth of the system
The functions of the entire system “grow up”
by three-stage learning as explained below.
First stage: This stage corresponds to that ofa baby in the human analogy. Only concept
formation modules work at this time. Images
and concepts are formed in learning without ateacher. The system at this time does not havedecision ability, and episodic memory does not
function.

5. Conclusion
We have designed an adaptive system withconcept formation and primitive consciousness that canbe executed as thinking. This system uses a simple
linkage-oriented neural network. We are now
developing a simulation program of the entire systemon a computer to clarify its operational characteristics.

Second stage: This stage corresponds to an
infant. The integration module forms links
based on the degree of simultaneous occurrenceof images and concepts that the concept
formation modules formed before. The concept
formation modules start learning with a teacher.
At this time, the output signal of anotherconcept-formation module is used as a teacher
signal. The system at this time has only low
level decision ability, and the episodic memory
does not function sufficiently.

Acknowledgments
We thank Mr. Ryohei Kobayashi for his usefulsuggestion.
References[1] Haikonen P (2003), The cognitive approach to conscious
machines. Imprint Academic[2] Taylor JG et al. (2000), The frontal lobes and executivefunction, IJCNN2000[3] Koch C (2004), The quest for consciousness. Roberts &Company Publishers[4] Kinouchi Y, Inabayashi S, and Sasho T et al. (2001), A
structure of an associative memory system with concept
formation based on mutual feature reactions. IJCNN'01[5] Kinouchi Y, Inabayashi S, and Sasho T et al. (2000), An
adaptive associative memory system based on autonomous
reaction between image memories. Artificial Life andRobotics, Vol.4, pp.53-56, Springer

Third stage: This stage corresponds to a child.
High level decision becomes possible by using thelinks in the integration module with the images andconcepts in the concept-formation modules, which
actively learn with the teacher signal. The episodic
memory functions sufficiently.
4.2 Conscious operation in the system
The functions of consciousness are completed in
the third stage. Important information selected by the
integration module influences states of the system©ISAROB 2006

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A Bayesian approach to blind source separation with variable number of sources
Jun-ichiro Hirayama, Shin-ichi Maeda and Shin Ishii
Graduate School of Information Science
Nara Institute of Science and Technology
8916-5, Takayama, Ikoma, Nara, 630-0192, Japan
E-mail: {junich-h, ichi, ishii}@is.naist.jp
Abstractand hence the number of presented sources changes
with time. This may degenerate the performance ofsource separation without considering it.
In this article, we propose a novel BSS/ICA
method, called Switching ICA, to address the BSS
with unknown and variable number of sources in noisy
situations. We incorporate a dynamical model of nonstationary sources that may abruptly appear or disappear through time into the generative model of noisy
ICA. The generative model then becomes a kind of
hidden Markov model (HMM) in which the observation process is described as the data generation processof noisy ICA. We employ the variational Bayes (VB)
method to derive a deterministic and computationallyeffective approximation of the Bayesian inference forthis model.

Most of the existing independent component analysis (ICA) algorithms assume that the number oftrue sources is constant for all samples. In practicalsituations, however, signals often have difficult nonstationarity such that each source signal abruptly appears or disappears and hence the number of presented
sources changes with time. In this article, we propose
a novel ICA method, called Switching ICA, to address
the blind source separation with unknown and variable number of sources in noisy situations. We incorporate a dynamical model of non-stationary sourcesthat may abruptly appear or disappear through time
into the generative model of noisy ICA, and the variational Bayes (VB) method is employed to realize an effective Bayesian inference for this model. In a simulation experiment using an artificial dataset, our method
correctly recovers the original source signals and theirvarying number, showing a superior performance compared to the existing ICA algorithms in the presenceof noise.

2
2.1

1

introduction

Generative modelLet xj,t (j = 1; : : : ; d) denote observations fromd channels at time step t, which are linear mixturesof unknown and mutually independent source signals
from n channels, si,t (i = 1; : : : ; n), plus Gaussian
noise. The probabilistic generative model of an observation vector xt = (x1,t ; x2,t ; : : : ; xd,t )0 is then given
byBlind source separation (BSS) is the problem of recovering unknown source signals when only their mixtures are observed, without knowing the actual mixing
process. Independent component analysis (ICA) [8] iscurrently the most promising approach to BSS, which
recovers original source signals by exploiting the statistical independence among them, which can be assumed in many situations.
Most of the existing BSS/ICA algorithms assume
that the number of true sources is known and constantfor all samples. In practical situations, however, thenumber of sources is rarely known a priori, and realworld signals often have difficult non-stationarity suchthat each source signal abruptly appears or disappears©ISAROB 2006

Switching ICA

xt = Ast + ν t ;(1)

where st = (s1,t ; s2,t ; : : : ; sn,t )0 is the source vector,
d × n matrix A is the mixing matrix, and ν t is thenoise vector which is assumed to be distributed as an
isotropic Gaussian with a mean zero and a variance¿ −1 .

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2.2

Source model

2.4

Each source signal si,t is modeled as

Variational Bayesian inferencep(yi,t ) = ‰i (yi,t );(3a)p(si,t | yi,t ; zi,t = 1) = N(si,t | 0; φi (yi,t ));(3b)

A Bayesian inference for the model described aboveis performed by the variational Bayes (VB) method [5].
Let ξ = {st ; y t ; z t |t = 1; : : : ; T } denote the set oflatent variables,= {A; ¿ } the set of observation
model parameters, and ω = {πi ; r1i ; r0i | i = 1; : : : ; n}the set of dynamics model parameters. The VB
method solves a functional optimization problem toobtain the probability distribution q(ξ; ; ω) that wellapproximates the true posterior p(ξ; ; ω|X), whereX = (x1 ; x2 ; : : : ; xT ). We introduce the factorization
assumption: q(ξ; ; ω) ≈ q(ξ)q( ; ω) to allow the solution to have closed forms with respect to q(ξ) andq( ; ω). For the parameters , we assume a matrix
normal-Gamma conjugate prior:¡¢p0 ( ) = Nd×n A|M 0 ; ¿ −1 I d ;G−1
Ga(¿ |κ0 ;λ0 ); (6)0where yi,t ∈ {0; 1} is a latent variable to indicate
each of the two Gaussian components, and ‰i (yi,t )and φi (yi,t ) respectively represent the mixing rates
and component variances depending on yi,t . On theother hand, the source si,t takes consistently zero when
zi,t = 0, as represented bywhere Nd×n (· | ·; ·; ·) denotes a matrix normal distribution 1 , and Ga(· | ·; ·) is a Gamma distribution. Forω, we also use a conjugate prior which is given by a
product of Beta distributions.
According to the VB method, the approximate posterior for st , conditional on y t and z t , is derived assi,t = zi,t ui,t ;(2)where zi,t is an indicator variable which takes zero (inactive) or one (active), called switching variable, andui,t is the original signal of source i. To represent
the original signal, ui,t , we use a mixture of Gaussian(MoG) as in existing studies [11, 4]. For simplicity, we
use a scale mixture [3] of two Gaussian distributions torepresent super-Gaussian signals with its parameters
being fixed and not adjusted by learning. Then theconditional model of si,t given zi,t = 1 isp(si,t | zi,t = 0) = –(si,t );(4)q(st | y t ; z t ) = R

where –(·) denotes the Dirac’s Delta function.

2.30µt (y t ; z t ) = V̂ t (y t ; z t ) h¿ Ai xt ;We assume a Markov process on the switchingvariables. Following strictly the assumption of mutual independence among sources, we assume zi,t fori = 1; 2; : : : ; n are independent of each other. The
initial and transition probabilities of
Qnthe switchingvariables are then
given
by
p(z
)
=
1
i=1 p(zi,1 ) andQnp(z t | z t−1 ) = i=1 p(zi,t | zi,t−1 ), respectively, where
we assume(7d)where ˆ (xt ; st ) ≡ exp (hlog p(xt | st ; A; ¿ )iA,τ ).
V t (y t ; z t ) is the prior covariance matrix for st , which
is a diagonal matrix whose i-th diagonal element iszi,t φi,t (yi,t ). Note that both the prior and posterior variances for a source i degenerate to zero if
zi,t = 0. The expectations h¿ Ai and h¿ A0 Ai are given
by Eq. (10) below. Let l(xt ; y t ; z t ) denote the denominator in Eq. (7a). The marginal likelihood of z t isgiven asXe(xt ; z t ) =l (xt ; y t ; z t ) p(y t );
(8)

1−z

p(zi,1 ) = πi i,1 (1 − πi ) i,1(5a)
izi,t−1h1−zzp(zi,t | zi,t−1 ) = (1 − r0i ) i,t r0i i,th
i1−zi,t−1z1−z
× r1ii,t (1 − r1i ) i,t
(5b)

yt

1 A matrix normal distribution is defined as

Here, πi is the probability for initial presence of thei-th source, and r0i , r1i ∈ [0; 1] are probabilities thatthe i-th source switches from active to inactive andfrom inactive to active, respectively. The parameters, π = (π1 ; π2 ; : : : ; πn )0 , r 1 = (r11 ; r12 ; : : : ; r1n )0
and r 0 = (r01 ; r02 ; : : : ; r0n )0 , are unknown and estimated.

©ISAROB 2006

(7a)

= Nn (st | µt (y t ; z t ); V̂ t (y t ; z t )); (7b)
¡›
fi
¢−1
; (7c)
V̂ t (y t ; z t ) = ¿ A0 A + V t (y t ; z t )−1

Markov switchingzˆ (xt ; st ) p(st | y t ; z t )dst ˆ (xt ; st ) p(st | y t ; z t )

dn

d

n

Nd×n (A | M, V , K) = (2π)− 2 |K|− 2 |V |− 2
ű
ţ
ď
1 č
× exp ¡ tr (A ¡ M)0 V −1 (A ¡ M) K−1 ,2
where A ∈ <d×n , M ∈ <d×n , K ∈ <n×n , and V ∈ <d×d . M
denotes the mean of A; K and V are two covariance matrices
of A [10].

577The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

which is the summation over 2n possible values of then-dimensional binary vector y t . Using the marginal
likelihood, the approximate joint posterior of z t for
all t is?
?
zi,t
= 1 and the black color zi,t
= 0. The number ofactive source signals varied with time: two, one andthree for t ∈ [1; 100], t ∈ [101; 200] and t ∈ [201; 300],
respectively. We set the inverse variance of Gaussian
noise at ¿ ? = 6 (SNR ≈ 16:81). The prior hyperparameters were set at: M 0 = 0, G0 = 0:01I n ,κ0 = λ0 = 1×10−3 , u· = w· = 0:5, and the process wasiterated 500 times to converge. The hyperparametersin the trial distributions were determined randomly,
except that the mean parameter of the mixing matrix
M was initialized by the one estimated by an existing
ICA algorithm, in order to avoid poor local maxima.

q(z 1 ; z 2 ; : : : ; z T )=TY1e(x1 ; z 1 )ˆπ (z 1 )e(xt ; z t )ˆr (z t ; z t−1 ); (9)Cξt=2where ˆπ (z 1 ) ≡ exp(hlog p(z 1 | π)iπ ) andˆr (z t ; z t−1 ) ≡ exp(hlog p(z t | z t−1 )ir0 ,r1 ). Cξ isthe normalization term. Because Eq. (9) is a simple
HMM whose transition and observation processes are
given by ˆr (z t ; z t−1 ) and e(xt ; z t ), respectively, themarginal distributions, q(z 1 ); : : : ; q(z T ), are exactly
calculated by the standard Forward-Backward algorithm [12]. The pairwise posteriors q(z t ; z t−1 ) can also
be exactly calculated for updating the approximate
posterior for πi ; r1i ; r0i which are given as Beta distributions. The detail of Bayesian learning of HMM canbe found in [9]. Taking expectations with respect tothe posteriors, the following statistics are calculated:
Rss = hSS 0 i + G0 , Rxs = XhSi0 + M 0 G0 , Rxx =0
XX 0 + M 0 G0 M 00 and Rx|s = Rxx − Rxs R−1
ss Rxs ,where S = (s1 ; s2 ; : : : ; sT ). Then the approximate
posterior for A and ¿ is given asq( ) = Nd×n (A | M̂ ; ¿ −1 I d ; Ĝ

−1

True
8
6
4
2
0
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-4
-6
-8
8
6
4
2
0
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-4
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8
6
4
2
0
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-4
-6
-8

0

0

0

50

50

50

100

100

100

150

150

150

time

SwICA

200

200

200

250

250

250

300

300

300

8
6
4
2
0
-2
-4
-6
-8
8
6
4
2
0
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-4
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6
4
2
0
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-8

0

50

100

150

200

250

300

0

50

100

150

200

250

300

0

50

100

150

200

250

300

time

Figure 1: Examples of true and estimated source signals and the switching variables)Ga(¿ | κ̂; λ̂); (10)whereM̂ = Rxs R−1
ss ;κ̂ = κ0 +3Ĝ = Rss ;d(n + T );2

The right panel of Fig. 1 shows a typical example of recovered source signals and the switching variables. The bar graph denotes the maximum a posteriori (MAP) estimate of the switching variables, where
the black color and white colors represent zero andone, respectively. We can see that our Switching ICA(SwICA) correctly recovered the three source signals
and the changes in their number by identifying theactive/inactive terms for each source.
We then compared our SwICA with three ICA
methods. One is JADE [6], a well-known ICA algorithm which tries to minimize cross-cumulant, andthe second is an ICA using natural gradient with anonholonomic constraint [1] (referred to as NG-N).
It has been reported that NG-N correctly performed
BSS in low-noise situations even when some source
signals abruptly disappeared. The other one is a restricted version of our Switching ICA such that themodel no longer assumes dynamics on the switchingvariables (referred to as NoDyna). In NoDyna, theswitching variables, zi,t for t = 1; 2; : : : ; T , were considered as independent Bernoulli samples as p(zi,t =1) = 1 − p(zi,t = 0) = rzi , instead of obeying the

(11a)

⁄
1 £
λ̂ = λ0 + tr Rx|s : (11b)2

Simulations

We examined the basic performance of Switching
ICA with a dataset synthesized by the mixing process in Eq. (1) from artificially-generated source signals. The number of mixtures and that of all potential
source signals were set as d = n = 3, and the timeseries length was T = 300. The three source signals
were generated from the scale-MoG in Eq. (3) with
φ0 = 0:5, φ1 = 2:5 and ‰ = 0:25 (which were assumed
to be known) under a specific switching assumption
with an unknown mixing matrix. The switching values and source signals used to synthesize the observations are shown in the left panel of Fig. 1. The bar
graph at the top of each panel shows the value of the?
true switching variables, zi,t
for t = 1; 2; : : : ; T (? denotes the true value), where the white color represents©ISAROB 2006

578

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Markov process in Eq. (5); the n parameters, rzi for
i = 1; 2; : : : ; n, were simply estimated using a conjugate prior given as the Beta distribution, in a similar way to learning the dynamics parameters in theMarkov process. The NoDyna algorithm used thesame setting as that of SwICA described above.
Each of the four algorithms was conducted for 50
runs, by applying different Gaussian noises and using different initial hyperparameters for SwICA and
NoDyna. To evaluate the estimation performance, weused the following two performance measures: average
Source-to-Distortion Ratio (aSDR) and Amari’s Performance Index (PI) [2], which measure the estimation
performance of source signals and mixing matrix, respectively. The former was a slight modification of
the Source-to-Distortion Ratio proposed in [7], whichbecomes high when the absolute correlation between
true and estimated sources for every i is high, while
it becomes low when the absolute correlation is low.
The latter evaluates the reproducibility of the recovered mixing matrix up to scale and permutation ambiguities, which is zero when the mixing matrix wasappropriately recovered.
SNR=16.81

stationary situations where the number of presented
sources dynamically changes over time. We are currently working on applying our method to more difficult, i.e., realistic, situations.
A major deficit of the Switching ICA is its highcomputational cost. Actually the cost grows in an
exponential order of the number of potential sources,n; then, it is intractable when n is large. This difficulty
may be addressed by assuming further factorizationon the trial distributions, as utilized in [4], or by some
Monte Carlo techniques.

References[1] S. Amari, T. P. Chen, and A. Cichocki. Nonholonomic
orthogonal learning algorithms for blind source separation. Neural Computation, 12(6):1463–1484, 2000.
[2] S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind source separation. In Advancesin Neural Information Processing Systems, volume 8,
pages 753–763, Cambridge MA, 1996. MIT press.
[3] D. F. Andrews and C. L. Mallows. Scale mixtures ofnormal distributions. J. Royal Stat. Soc. B, 36:99–
102, 1974.

SNR=16.81[4] H. Attias. Independent factor analysis. Neural Computation, 11(4):803–851, 1999.

1.2
1

20

[5] H. Attias. Inferring parameters and structure of latentvariable models by variational Bayes. In Proc. 15th
Conf. on Uncertainty in AI, pages 21–30, 1999.

0.8
PI

aSDR (dB)

21

19
18

0.6
0.4

17

0.2

JADE NG−N NoDyna SwICA[6] J. F. Cardoso and A. Souloumiac. Blind beamforming
for non-Gaussian signals. IEEE Proc. -F, 140(6):362–
370, 1993.

JADE NG−N NoDyna SwICA

Figure 2: Comparison of the four algorithms’ performances for 50 runs.

[7] R. Gribonval, L. Benaroya, E. Vincent, and
C. Févotte. Proposals for performance measurement in source separation. In Proc. 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), number 763–
768, 2003.

In Fig. 2, the left and right panels respectively show
the box-plots of aSDR and PI for the four algorithms.
We can see that the aSDR of NG-N is apparently
higher than that of JADE and the PI of NG-N is lower
than that of JADE. This result confirmed the effectiveness of NG-N when the number of sources changes
with time in comparison to the standard ICA method.
It can be seen that, however, SwICA exhibits the best
performance among the four algorithms, by further
improving both aSDR and PI. Especially, the improvement in PI is not shown by NoDyna, which implies theimportance of assuming Markov dynamics on switching variables.

4[8] A. Hyvarinen, J. Karhunen, and E. Oja. Independent
Component Analysis. Wiley, 2001.
[9] D. J. C. MacKay. Ensemble learning for hidden
Markov models. Technical report, Cavendish Laboratory, University of Cambridge, 1997.
[10] T. Minka. Bayesian linear regression. Technical report, MIT, 2000.
[11] E. Moulines, J.-F. Cardoso, and E. Gassiat. Maximum
likelihood for blind separation and deconvolution of
noisy signals using mixture models. In Proc. ICASSP,
volume 5, pages 3617–3620, 1997.

Conclusion[12] L. R. Rabiner. A tutorial on hidden Markov models
and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989.

In our experiments, the Switching ICA showed ahigh performance of source separation in highly non-

©ISAROB 2006

579

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Controller Design for a Motion Generator Based on Dual Linear Motors
Dong Hoon Lee
Sang Yeol Seo, Woo Seok Choi

Se-Han Lee, Han Sung Kim

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

Abstract

manufacturing gets costly. But since the motion
generator developed in this research only deals with arm
movement, it makes the use of electrical motor possible,
which brings system composing and controlling simple.
And the similar concept of active impedance used inthis research has been widely studied in robot
engineering. In robotics, when a manipulator is incontact with an object, the relation between the force
exerted on the object by the manipulator and the motion
at the contact point is modeled with impedance such as
inertia, damping and stiffness and then the appropriate
position/force control is carried out.[1]The concept of active impedance in this research isbasically the same with the impedance in robotics, but
has many differences in application purpose and method.

In this research a 2-dimensional arm motion generator
composed of two linear motors was developed. The
inertia, damping and/or stiffness characteristics of the
motion generator can be changed on the real-time basis
by properly regulating the force generated by the linear
motors. That is, active impedance is implemented
without actual change in the physical structure of the
motion generator. A motor force control is carried out by
regulating the input currents supplied to the linear
motors. In the control system the time delay due to thesampling period has an adverse effect on the stability ofthe system. Furthermore, disturbances caused bycharacteristics of the motion generator exist.
To cope with these difficulties, 2-DOF controller
combined with the LQ-servo and H-infinity controller
was used. This motion generator can be used to measurekinesthetic sense associated with the human arm andthus leads to developing the products for which thekinesthetic sense is taken into account.

1. Introduction
As lifestyles modernize, products with not only
function in its mind but products with kinesthetic sensealong with its function draw attention to customers. Thatis, people consider the sensibility of a product as well as
its performance. In recent years, there are lots of efforts
and considerations to apply this even from the designingand early stages of development.
The object of this research is to develop an arm
motion generator necessary for quantification andimplementation of kinesthetic sense, which mainly
relates to arm movement. In most mechanical systems,
motion has close relations with mechanical impedance,
which consists of inertia, stiffness, and damping. The
arm motion generator developed in this research mustmeasure the kinesthetic sense difference experienced bya person by alternating the impedance or must let theperson experience the desired 'sense'. But varying theimpedance physically is very tedious, costly, and time
demanding. The arm motion generator controls the force
outputs of the linear motors as to have the same effect as
to physically changing the impedance.
These kinds of motion generators are used in various
flight and vehicle simulators and vigorous research ofthis continues today. However, these motion generators
mainly use pneumatic systems to generate an entire body
movement, and thus systems get complicated and©ISAROB 2006

580

2. Implementation of Active Impedance
A motion generator capable of implementing two
dimensional plane motion is made by combining two
linear motors perpendicularly as shown in Fig. 1. As an
operator moves the lever with his hand to generate somemovement pattern, displacement, velocity, andacceleration involved in this pattern are detected. Basedon this information, motor driving current is regulated toget desired active impedance that is active inertia, active
damping, and/or active stiffness. By implementing thiskind of active impedance, by varying inertia, stiffness,
and/or damping, one can measure the kinesthetic sensemost suitable for the operator.
This chapter will explain about implementation ofactive impedance using the motion generator mentioned
above.
∆cy

∆ky
∆my

m y,cy,k yym x ,cx ,kx

∆mx

∆cx

x

∆kx

Fig.1. Configuration of the motion generator and conceptof active impedanceThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(1)

the operator must experience acceleration of a =F /(m+∆m) (< ao) for the same input of F. If appropriate
current is supplied to the motor to generate force of F1 =∆ m.a to the opposite direction of the force given by theoperator, the net force input on the moving part becomesF − F1 = ( m + ∆ m) a − ∆ m ⋅ a = m ⋅ a(2)and so the acceleration felt by the operator is not ao but a,which is the acceleration earned from force F applied on
active inertia M, even when the operator puts force F on
mass m. In this way, active inertia larger than actual
inertia can be implemented.
While, given torque constant (force constant in case of
linear motor) of the motor KT and current supply i, theforce generated by the linear motor Fm becomes
Fm = K T ⋅ i . And supplying the current of
im = − ∆ m ⋅ a / K T(3)to the motor could generate the effect of active inertia M.
Here, the letter m at the current stands for the current
relating to mass(active inertia). Also, the negative signmeans that the force generated by the motor should be in
the opposite direction to the force applied by the operator.
A plus sign(+) should be used as to get active inertia lessthan the actual inertia, because the force from the motor
should be in the same direction with the force applied by
the operator.
Active damping C and active stiffness K can bepresented in the same manner,

2.2 Complex Active Impedance Implementation
Inertia, damping, and stiffness do not exist in simple
forms but rather in complex structures in actual
mechanical systems. That is why implementing complex
active impedance is required to let us reach closer toactual systems. When an operator applies force to thearm motion generator (to the lever connected to thelinear motor), the motion generator also exerts the same

©ISAROB 2006581

= M ⋅a + C ⋅v + K ⋅ x(4)When considering a reaction of an instant, thecomplex form of the inertia, damping, and stiffness
would not be known. But by considering that thereaction due to inertia be proportional to acceleration,
damping to velocity, and stiffness to position, one canmeasure the form of time-varying reaction, which leads
to classification of the each component of the total
reaction.
1

0

-1

.1

0 .0

Velocity (m/s)

M = m + ∆m

Ftotal = Finertia + Fdamping + Fstiffness2
Acceleration (m/s )Impedance in an electrical system is a measure ofcurrent response in a circuit to certain AC voltage.
Electrical impedance consists of inductance, capacitance,
and/or resistance. Impedance in a mechanical system isdefined in similar concept as a measure of motion
response to force applied to the system, and consists ofinertia, damping, and stiffness. Active impedance,
mentioned in this research, means getting the same effectof physically changing the impedance of the moving partof the linear motor with only generating proper force
from the motor.
Let us first consider active inertia. When an operator
applies force F to the lever of the linear motor in which
moving part has mass of m and no energy is supplied,
acceleration of ao = F / m is experienced. Assuming that
we add active inertia of ∆m to mass m of actual movingpart,amount of force to the opposite direction, as noted in the
Newton’s law of action-reaction. This force is called
reaction force. Thus, when the operator generates motion
by exerting force on the lever connected to the motor, theoperator will experience reaction to inertia, damping,and stiffness all in complex. Expressing this in an
equation gives,

Displacement (m)2.1 Active Impedance

-.2

.1
0 .0
-.1

0 .0

.2

.4

.6
.8
T im e (se c )

1 .0

1 .2

Fig. 2 Displacement, velocity and acceleration due to thearbitrary motion generated by the operator
Consider the case where an operator intentionallycreates vibration by holding the lever of the motion
generator as shown in Fig. 2. The velocity andacceleration in the figure can be obtained by using thebasics of the measurement of the position of the linear
motor moving part using the encoder. First, when thevalues of active impedance M, C, K which are to be
implemented are set, additional impedance ∆ m, ∆ c, ∆ kcan be calculated by using Eqn. (4). By multiplying
these values of additional mass, damping, and stiffnessby the acceleration, velocity and displacement obtained
above and then adding this value would get the force
which the linear motor should generate. At this time, thetotal reaction felt by the operator is expressed in Eqn (5),but for this to happen, the motor has to generate thefollowing force.
Fmotor = ∆ m ⋅ a + ∆ c ⋅ v + ∆ k ⋅ x(5)

To generate this force, the current that must be supplied
to the linear motor isi total = im + ic + ik =− (∆m ⋅ a + ∆ c ⋅ v + ∆ k ⋅ x)
(6)
KT

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3. Motion Generator System
3.1 Motion Generator System Structure
Fig. 3. shows an arm motion generator composed of
the two linear motors, drive circuit to run linear motors,
TMS320 DSP board used as controller, and parallel port
connected PC used to collect and analyze experimental
data. A voltage driven BLDC (Brushless DC) drive,
exclusively for motors, is used to drive the linear motor.
The main controller TMS320 DSP board can be
operated at 40MHz speed and it carries out algorithms
necessary to control the motor. An A/D converter
measures analog current supplied to the motor anddigitizes it, then sends it to the DSP, while the D/Aconverter transforms the output digital signal from thecontroller to analog voltage to send it to the motor drive.

DSP

D/A

Controlsignal

A/D

Mem

PWM
driver

LQ-servoum+Motor #2

Motiongenerator

Internal Plantu

Actual Plant

The force for the active impedance is proportional tothe current supplied to the motor. But since most motor
drivers are usually voltage-driven, the current is notdirectly controlled but it is controlled indirectly bycontrolling the voltage. That is, to supply desired current,
the voltage of the linear motor must be controlled by thecontroller. The relation between the allowed input
voltage u, and the current flowing through the motor i iscommonly known as
di(7)u − KE v = L + Ri
dt
where L and R denote the inductance and resistance ofthe linear motor winding, respectively, and KE the backemf constant. Since the motion is produced by
combination of force the operator applies and force themotor generates, the displacement, velocity andacceleration of the motion generator depends mostly
upon the force arbitrarily applied by the operator.
Therefore, the term KE v on the left-hand side of Eqn. (7)varies arbitrarily, and this is treated as a disturbance inthe control system. In this case the plant can be
represented by
1(8)
G p ( s) =Ls + R
Since the current output for motor drive is measured
by the A/D converter and fed back to the control system,
the time delay of at least the sampling period T due to
A/D conversion and computation time occurs. As for the
linear motor, the electric time constant τe is about

582

+
d
+

+
+

3.2 Motion Generator Control Systemym+ H Controller∞∆mFig. 3. Schematic of experimental setup(9)

1 − 0.72615 z −1

roots locus corresponding to Eqn. (9) can say that Kp
gain is to be limited less than 16 according to gain
limitation characteristic depending on sampling period in
discrete system,
To cope with such a problem, 2-DOF control system
including internal plant and H-infinity controller, whichis to regard modeling error, shown in Fig. 4. wasemployed in this research. Time delay mentioned before
will be absorbed in modeling error.

+

Motor #1

©ISAROB 2006

0.10784 z −1

G p ( z) =r

SensorsignalMonitoring &data analysis

1,250µs, while the sampling period of the controller is Ts= 400µs. It is generally preferable that the sampling
period be less than 1/10 of the plant time constant.
It is interesting to investigate the effect of this timedelay scale on the control system. First take z-transform
Eqn. (9) then ,Nominal Plant+ yFig. 4. Block diagram of 2-DOF current controller
including internal plant and H-infinity
controller.

3.3 H-infinity Control Formulation
Time delay was considered multiplicative modelingerror ∆m in Fig. 4. before. The multiplicative modelingerror is generally accepted as a problem related to sensor
noise system.[2]A proper weight functions WS(s), WT(s) may be used
to mixed sensitivity H-infinity control problem asfollowing.
WS ( s) S ( s )WT ( s )T ( s ) ∞

<γ

(10)

where S(s), T(s) is sensitivity and co-sensitivity function.

γ is positive real number.

Generalized plant corresponding for Eqn. (10) and its
block diagram are as following At γ ≈ 0.14, obtained
controller K(s) satisfying Eqn. (10) and block diagram in
Fig. 4. [3]3.4 LQ-Servo Formulation
LQ-servo is used to control internal plant. Though
LQ-servo problem is a kind of trial and error, LQ-servo
has merit to obtain control gain considering control input
constraint.[4] In this research, control input weightmatrix R was chosen not to over maximum motor drive
output.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Current(A)The experimental condition is as following, when
active stiffness, 500 N/m, active damping, 100 Ns/m,and active inertial, 5kg, are applied respectively, an
operator activates the motion generator with arbitraryreciprocal motion.
In the plots, Fig. 5., Fig. 6., Fig. 7., dotted lines
labeled ref. are current corresponding to displacement,
velocity, and acceleration respectively caused by theoperator and solid lines are current controlled by thecontroller proposed in this research. Though aconventional PI controller in itself is good controller,
gain adjust constraint due to discretization exists.
Feed forward controller, having LQ-servo form, has
no feed back loop and H-infinity controller tocompensate the difference between internal plant andnominal plant has properties to guarantee robust stability
against time delay characteristic, which was obstacle
factor to give high gain in PI controller. By the reason
mentioned before, no stability problem may occur andsufficient performance may be forecasted. The
experimental results shown in Fig. 5., Fig. 6., Fig. 7. canback up the performance of controller proposed in thisresearch.
1.5 (a)1.0(b)1.5 (a)(b)ref.
M with PI

00.5
Time(sec)

1.0

ref.

1.0
0.5

0.0
-0.5
-1.0
-1.5
0

M with0.5

H ∞ control

1.0

1.5

Fig. 7. Following response of Current corresponding toactive inertia using 2-DOF controller including
H-infinity controller (a) and (conventional PI
controller (b), respectively.

ref.
K with PI

0.5
0.0

Acknowledgements-0.5This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

ref.
K with H ∞ control

-1.0
-1.5
0

stiffness of the motion generator. Numerous kinesthetic
senses relating to arm movement can be measured due tothe ability to change the impedance, such as inertia,
damping, and stiffness, on real-time basis.
In this research, a 2-DOF controller was employed tocope with some inherent problems with current controlof the linear motor. The control system shows reasonable
performance on both disturbance rejection and tracking.
This type of research can be related to virtual reality
engineering and give a user the chance to experiencevarious kinds of virtual kinesthetic senses, and it is also
expected to be applied to mechanical structures thatgenerates various forms of forces.

Current(A)4. Performance of the Motion Generator

0.2 0.4 0.6 0.8 1.0
0.2 0.4 0.6 0.8Time(sec)

References
Fig. 5. Following response of Current corresponding toactive stiffness using 2-DOF controller including
H-infinity controller (a) and conventional PI
controller (b), respectively.
1.5

(a)

ref.
C with PI(b)

Current(A)

1.0
0.5
0.0
-0.5
-1.0
-1.50

ref.
C with H ∞control
0.5
0.5 1.0 1.5
Time(sec)

1.0

1.5 2.0

Fig. 6. Following response of Current corresponding toactive damping using 2-DOF controller including
H-infinity controller (a) and conventional PI
controller (b), respectively.

5. Conclusions
In this research, an arm motion generator capable oftwo-dimensional movement is made by perpendicularly
connecting two linear motors. By controlling the force
generated by these motors on the motion generator, theactive impedance is implemented, which has the same
effect as physically changing the inertia, damping, and©ISAROB 2006

583

[1] M. W. Spong, M. Vidyasagar, Robot Dynamics andControl, Wiley & Sons, pp.252-254, 1989.
[2] S. Bahram, and M. Hassel, CONTROL SYSTEMDESIGN USING MATLAB, Prentice-Hall, New
Jersey, pp.404, 1993.
[3] Z. H. Kemin, J.C. Doyle and K. Glover, ROBUSTand OPTIMAL CONTROL, Prentice-Hall, New Jersey,
pp.459-pp.462, 1996.
[4] D. Ken, S. Thompson and B. Barraclough, The Art of
CONTROL
ENGINEERING,
Addison-Wesley,
England , pp.603 – 604, 1997.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Stiffness Analysis of a Limited-DOF Parallel Manipulator including theCompliance of Both Joints and Links
Han Sung Kim*, Chang-Rok Shin, Joon-Wu Kim, and Se-Han Lee
School of Mechanical and Automation Engineering
Kyungnam University
449 Wolyoung Dong, Masan, Kyungnam, 631-701, Korea (hkim@kyungnam.ac.kr)
To overcome the above shortcomings, parallel manipulators with fewer than six degrees of freedom have been
investigated.[3~12] Limited-DOF parallel manipulators havethe advantages of relatively simple forward-kinematics,
larger workspace, lower inertia, and requiring less parts over
6-DOF counterparts. A limited-DOF parallel manipulator
costs less than a 6-DOF counterpart and, hence, it may be
more economic to employ such manipulators forapplications for which 6-DOF is not necessary. Among
limited-DOF parallel manipulators, 3-DOF translational
parallel manipulators[6~9] and 3-DOF rotational parallel
manipulators[10,11] have been focused.
In the beginning design stage of manipulators requiringhigh speed, accuracy, and stiffness, the stiffness analysis
may be very essential and the most important step. For 6DOF parallel manipulators, various methods of the stiffness
analysis have been published.[12~14] However, the stiffness
analysis for limited-DOF parallel manipulators has been alittle investigated. Among the research results, Zhang and
Gosselin[15~18] analyzed limited-DOF parallel manipulatorswith passive constraining limbs. However, the method ofusing virtual joints is not systematic. It is well known that
the Jacobian and stiffness matrices of a 6-DOF parallel
manipulator are 6×6 matrices. However, it is not clear as towhat is the best way to express the Jacobian and stiffness
matrices of a limited-DOF parallel manipulator. For theJacobian matrix, Joshi and Tsai[19] presented that a 6×6
Jacobian matrix including actuations and constraints should
be used in order to analyze all the singularities and toprevent an erroneous design.[20,21] Since the stiffness matrix
is basically based on the Jacobian matrix, the stiffness
matrix should contain the information on the stiffness due toactuations and constraints and should be a 6×6 matrix evenfor a limited-DOF parallel manipulator.
In this work, the reciprocal screws of actuations andconstraints are determined by using the theory of reciprocal
screws. The reciprocal screws are the reaction forces due toactuations and constraints, which eventually result in theinfinitesimal deflections of joints and links in a serial chain.
Using the 6×6 Jacobian matrix, the stiffness matrix becomes
the sum of the stiffness matrices of actuations andconstraints. The joint stiffness values can be precisely
determined by modeling the compliance of joints and links
in each serial chain. The stiffness values of a joint can be

Abstract
This paper presents a stiffness analysis method for alimited-DOF parallel manipulator, which takes into accountof elastic deformations of joints and links. A limited-DOF
parallel manipulator is defined as a spatial parallel
manipulator which has less than six degrees of freedom.
Differently from the case of a 6-DOF parallel manipulator,
the serial chains in a limited-DOF parallel manipulator are
subject to constraint forces as well as actuation forces. The
reaction forces due to actuations and constraints in eachlimb can be determined by making use of the theory ofreciprocal screws. It is shown that the stiffness model of an
F-DOF parallel manipulator consists of F springs related to
the reciprocal screws of actuations and 6-F springs related to
the reciprocal screws of constraints, which connect themoving platform to the fixed base in parallel. The 6×6
stiffness matrix is derived, which is the sum of the stiffness
matrices of actuations and constraints. The six spring
constants can be precisely determined by modeling thecompliance of joints and links in a serial chain as follows;
the link can be considered as an Euler beam and the stiffness
matrix of rotational or prismatic joint can be modeled as a
6×6 diagonal matrix, where one diagonal element about therotation axis or along the sliding direction is zero. By
summing the elastic deformations in joints and links, thecompliance matrix of a serial chain is obtained. Finally,
applying the reciprocal screws to the compliance matrix of aserial chain, the compliance values of springs can be
determined.

1

Introduction

A Parallel manipulator typically consists of a moving
platform that is connected to a fixed base by several serial
chains, called limbs. In general, parallel manipulators havesome advantages of high speed, payload, accuracy andstiffness over serial manipulators. Among various parallel
manipulators, the 6-DOF Stewart-Gough platform has
attracted the most interests of researchers and industries,
since all the linear actuators are under only
tensional/compressive forces. [1,2] However, the manipulator
has some disadvantages such as complex forward
kinematics, small workspace, and requiring many parts.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

precisely determined by using the analytic or experimental
data of bearings, and the stiffness matrix for revolute andprismatic joints becomes a 6×6 diagonal matrix, where one
diagonal element about the rotation axis or along the sliding
direction is zero. The links in a serial chain are modeled as
Euler-Bernoulli beams. Summing the elastic deformations in
all the parts, the total compliance matrix of a serial chain
can be obtained. Since the total compliance matrix is
generally singular, it is impossible to get the inverse matrix.
Instead, multiplying the total compliance matrix by thereciprocal screws yields finite deflections about or along thereciprocal screws. From the finite deflections, thecompliance values of springs can be obtained.

p2

leg 1(C1 )Moving platformpa

Stiffness Analysis

In this work, a limited-DOF spatial parallel manipulator
with F-DOF ( 3 ≤ F < 6 ) is considered. As shown in Fig. 1,
it is assumed that the moving platform is constrained by mnumber of serial-kinematic chains and each limb may haveat most one actuator. The limbs having an actuator areindexed first such as i = 1,2, K , F , and the other limbs withno actuator are indexed as i = F + 1, F + 2,K, m . Let thedegree-of-freedom associated with all the joints of the ith
limb be defined as the connectivity, Ci , of that limb.[22,23]
Each limb constrains the moving platform by 6 − Ci numberof constraints, and the total number of all the independent
constraints should be six.
The overall Jacobian matrix for a limited-DOF parallel
manipulator can be expressed by[19]J = [J a

J c ]∈ R 6×6 ,

[

1

F

]

J c = sˆ c ,1,1 L sˆ c , 6 −C ,1 L sˆ c ,1, m
i

i

]

p

p

leg m(C m )leg F (C F ) leg F + 1(C F + 1 )K = J a [ka ] J aT + J c [kc ]J cT

(5)

(6)

where [ k a ] and [ k c ] are the diagonal matrices of which the
diagonal elements are the spring constants related toactuations and constraints, respectively.
When pure force is exerted on a limb with somecompliance, it generates infinitesimal translational motion
along the line of the force. On the other hand, when purecouple is applied to a limb, it generates infinitesimalrotational motion about the axis of the couple. Hence, theinfinitesimal translational and rotational motions can bemodeled as the deflection of a linear spring placed along theline of the force, and the deflection of a rotational springwith the axis of the couple, respectively. The stiffness of alimited-DOF parallel manipulator can be modeled that themoving platform is supported by six independent springs,
since there exist six reciprocal screws in a general case.
Among the springs, F springs are related to the actuations
and 6-F springs are related to the constraints.

(3)

constraints that are reciprocal to all the joint screws, and ŝ a,i
are the reciprocal screws of actuations that are reciprocal to
all the joint screws except the active joint screw, Ŝ a ,i . It isinoted that the overall Jacobian matrix of a limited-DOF
parallel manipulator consists of the actuation and constraintparts and the rank should be six.
It is well known that the stiffness mapping between
applied wrench and infinitesimal twist at the end-effector isexpressed in terms of the stiffness matrix, K©ISAROB 2006

p

Using Eqs. (2) and (3), the stiffness matrix can be expressedin terms of the Jacobian matrices of actuation andconstraints by

In the Jacobian matrix, ŝc ,k ,i are the reciprocal screws ofwˆ = K δDˆ ,a

K = J [k ]J T

(2)

L sˆ c , 6 −C , m ∈ R 6×( 6 − F ) .

pand the stiffness matrix is given by

where the Jacobian matrix of actuations and the Jacobianmatrix of constraints are given by[19][pFig. 1 Structure of an F-DOF parallel manipulator.
(Note: a: active joint, p: passive joint)(1)

J a = sˆa ,1 / sˆaT,1 Sˆ a ,1 L sˆa , F / sˆaT, F Sˆ a , F ∈ R 6×F and,p3Compliance Modeling of Serial Chains

In this section, we present a method to determine thespring constants related to actuations and constraints in a
serial chain. In deriving the compliance model of a serial
chain, the flexibility in joints and links is considered. First,
without loss of generality, only revolute and prismatic joints
are considered, since a spherical joint is equivalent to three

(4)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

⎡ L3
⎢ 3EI
x
⎢
⎢
⎢ 0
⎢
⎢ 0
⎢
CL = ⎢⎢ 0⎢⎢ L2⎢⎢ 2 EI x⎢⎢ 0⎣

intersecting non-coplanar revolute joints, a universal joint isequivalent to two intersecting revolute joints, a cylindricaljoint is equivalent to the sum of a revolute joint and aprismatic joint along the revolute joint axis, and so on. The
compliance matrix of a 1-DOF joint is given by⎡cl ,r⎢0⎢⎢0
CJ = ⎢
⎢0
⎢0
⎢
⎣⎢ 0

0

0

0

0

cl ,r
0

0
cl ,a

0
0

0
0

0

0

cθ ,r

0

0
0

0
0

0
0

cθ ,r
0

0⎤
0 ⎥⎥
0⎥
⎥
0⎥
0⎥
⎥
cθ ,a ⎦⎥

(7)

L3
L2
L
Fy −
M x , δz =
Fz ,
3EI y
2 EI xAEθx = −
θy =

0

L
AE

0

0

0

L
EI x

0

0

0

0

L
EI y

0

0

0

0

0

0
L2
−
2 EI y

⎡ PR
T =⎢i
⎣03×3

P

pˆ i Pi R ⎤
⎥
P
R ⎦
i

(12)

where Pi R denotes the rotation matrix from the ith frame tothe moving frame and P p̂i denotes the skew-symmetricmatrix representing the vector from the origin of the moving
frame to that of the ith local frame expressed in the moving
frame. The total deflection of a serial chain can becalculated by the sum of all the deflection in joints and links
given by(8)nδDˆ = ∑ ( PiT iCi PiT T ) wˆ .

(13)

i =1

Therefore, the compliance of a serial chain expressed in themoving frame can be written byinertias about the x, y, and z axes, respectively. I p is thepolar moment of inertia. Then, the compliance matrix of alink is given by

xi′+1 θ xr ( yi )a ( zi )zi′+1θzLink ixir ( xi )zi
yiFig. 2 Compliance model of 1-DOF joints.

©ISAROB 2006(11)

where the transformation matrix of screws from the ith
frame to the moving frame is given by

where A and L are the area and length of a link, E is themodulus of the longitudinal elasticity, G is the modulus of
the transverse elasticity. I x , I y and I z are the moment ofa ( zi )(10)

δDˆ i = PiT iδDˆ i and wˆ i = PiT −T i wˆ i

LL
L2
M y , θz =Mz
Fx +2 EI x
EI y
GI pr ( xi , y i )(9)where iCi denotes the compliance matrix of the ith part
expressed in the ith local frame and i ŵi denotes the wrench
applied on the ith part expressed in the ith local frame.
Expressing the infinitesimal twist and wrench with respect
to the moving frame (P) gives

P
i

L
L2
Mx ,
Fy +
EI x
2 EI y−δDˆ i = i Ci i wˆ i

Second, the links in a serial chain are considered as
Euler-Bernoulli beams, implying that the shear effect andinertia of rotation of the beam section are ignored, and beam
sections stay plane and perpendicular to the neutral axis.
Forces and moments elastically deform the tip of each link.
The elastic deformations at the tip of a link are written as
follows:

δy =L2
2 EI x

L3
3EI y

⎤
0 ⎥
⎥
⎥
0 ⎥
⎥
0 ⎥.
⎥
⎥
0 ⎥
⎥
⎥
0 ⎥
⎥
L ⎥
GI p ⎥⎦i

cl ,a = ∞ , for a prismatic joint.

L3
L2
Fx +
My
3EI x
2 EI yL2
2 EI y0

The infinitesimal twist at the ith joint or the ith link
expressed in the ith local frame can be expressed bywhere the axis of a joint is aligned with the z-axis of a local
frame, and the radial direction of a joint is placed on the xy
plane of a local frame as shown in Fig. 2. The subscripts, land θ denote the linear and rotational stiffnesses, and thefollowing subscripts, a and r denote the axial and radial
directions. It is noted that cθ ,a = ∞ , for a revolute joint, and

δx =

0

0

L

θ y y′

i +1

δx
δy

xi +1

δz
yi +1

Fig. 3 Compliance model of a link.

586

zi +1

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[9]

Kim, H. S., and Tsai, L. W., 2002, “Evaluation of aCartesian Parallel Manipulator,” 8th International
Symposium on Advances in Robot Kinematics, 24~28
June, Caldes de Malavella, Spain, pp. 21~28.
[10] Gosselin, C. M., and Angeles, J., 1989, “The Optimum
Kinematic Design of a Spherical Three-Degree-ofFreedom Parallel Manipulator,” ASME Journal of
Mechanisms, Transmissions, and Automation in
Design, Vol. 111, No. 2, pp. 202~207.
[11] Fang, Y. F., and Tsai, L. W., 2004, “Structure Synthesisof a Class of 3-DOF Rotational Parallel
Manipulators,” IEEE Trans. on Robotics Automation,
Vol. 20, No. 1, pp. 117~121.
[12] Gosselin, C., 1990, “Stiffness Mapping of Parallel
Manipulators,” IEEE Transaction on Robotics andAutomations, Vol. 6, pp. 377~382.
[13] Griffis, M., and Duffy, J., 1993, “Global Stiffness
Modeling of a Class of Simple Compliant Couplings,”
Mechanism and Machine Theory, Vol. 28, No. 2, pp.
207~224.
[14] Tahmasebi, F. and Tsai, L. W., 1995, “On the Stiffnessof a Novel Six-Degrees-of-Freedom Parallel
Manipulator,” Journal of Robotic Systems, Vol. 12, No.
12, pp. 845~856.
[15] Zhang, D., and Gosselin, C. M., 2001, “Kinetostatic
Modeling of N-DOF Parallel Mechanisms with a
Passive Constraining Leg and Prismatic Actuators,”
ASME Journal of Mechanical Design, Vol. 123, pp.
375~381.
[16] Zhang, D., and Gosselin, C. M., 2002, “Kinetostatic
Modeling of Parallel Mechanisms with a Passive
Constraining Leg and Revolute Actuators,”
Mechanism and Machine Theory, Vol. 37, pp. 599~617.
[17] Zhang, D., and Gosselin, C. M., 2002, “Kinetostatic
Analysis and Design Optimization of the Tricept
Machine Tool Family,” ASME Journal of
Manufacturing Science and Engineering, Vol. 124, pp.
725~733.
[18] Zhang, D., and Gosselin, C. M., 2002, “Parallel
Kinematic Machine Design with Kinetostatic Model,”Robotica, Vol. 20, pp. 429~438[19] Joshi, S. A., and Tsai, L. W., 2002, “Jacobian Analysis
of Limited-DOF Parallel Manipulators,” Journal of
Mechanical Design, Vol. 124, pp. 254~258.
[20] Zlatanove, D., Bonev, I., and Gosselin, C., 2002,“Constraint Singularities as C-Space Singularities,”
Proceedings of the 8th Advances in Robot Kinematics,
edited by J. Lenarcic and F. Thomas, Kluwer
Academic Publishers, pp. 183~192.
[21] Han, C., Kim, J., Kim J., and Park, F. C., 2002,“Kinematic Sensitivity Analysis of the 3-UPU Parallel
Mechanism,” Mechanism and Machine Theory, Vol.
37, pp. 787~798.
[22] Tsai, L. W., 1999, Robot Analysis: The Mechanics ofSerial and Parallel Manipulators, John Wiley & Sons,
New York, NY.
[23] Tsai, L. W., 2000, Mechanism Design: Enumeration ofKinematic Structures According to Function, CRC
Press, pp. 221~247.

nC = ∑ ( PiT iCi PiT T ) .
i =1

(14)

The joint compliance values related to the reciprocal screwsof actuations and constraints are calculated by
cl = δx ⋅ f and cθ = δθ ⋅ m(15)

where f = 1 and m = 1 .

4

Conclusions

In this paper, the stiffness analysis for a limited-DOF
parallel manipulator using the theory of reciprocal screws
and considering the elastic deformation in joints and links
by the reciprocal screws is presented. For an F-DOF parallel
manipulator, it is derived that there exist F number ofreciprocal screws related to actuations and 6-F number ofreciprocal screws related to constraints. The stiffness matrix
for a limited-DOF parallel manipulator becomes the sum of
the stiffness matrices of actuations and constraints. The
practical values of spring constants are obtained bymodeling the compliances of joints and links in each serial
chain. In the further works, this analytic method will be usedin the design optimization of a parallel-kinematic machine
tool and the effectiveness of the suggested stiffness analysis
method will be verified through experiments.
REFERENCES

[1]
[2]
[3]
[4]
[5]

[6]

[7]

[8]

Stewart, D., 1965, “A Platform with Six Degrees ofFreedom,” Proc. Institute of Mechanical Engr.,
London, England, Vol. 180, pp. 371~386.
Gough V. E., and Whitehall S. G.., 1962, “Universal
Tire Test Machine,” Proceedings 9th Int. TechnicalCongress F.I.S.I.T.A., Vol. 117, pp.117~135.
Clavel, R. 1988, “Delta, a Fast Robot with Parallel
Geometry,” 18th International Symposium on
Industrial Robots, Sydney, Australia, pp. 91-100.
Pierrot, F., Reynaud, C., and Fournier, A., 1990,“DELTA: A Simple and Efficient Parallel Robot,”
Robotica, Vol. 8, pp. 105~109.
Siciliano, B., 1999, “The Tricept Robot: Inverse
Kinematics, Manipulability Analysis and Closed-Loop
Direct Kinematics Algorithm,” Robotica, Vol. 17, pp.
437~445.
Tsai, L. W. and Joshi, S., 2000, “Kinematics andOptimization of a Spatial 3-UPU Parallel
Manipulator,” ASME Journal of Mechanical Design,Vol. 122, No. 4, pp. 439~446.
Wenger, P., and Chablat, D., 2000, “Kinematic
Analysis of a New Parallel Machine Tool: The
Orthoglide,” Advances in Robot Kinematics, Edited by
J. Lenarcic and M. L. Stanisic, Kluwer Academic
Publishers, London, pp. 305~314.
Kim, H. S., and Tsai, L. W., 2003, “Design
Optimization of a Cartesian Parallel Manipulator,”
Journal of Mechanical Design, Vol. 125, No. 1, pp.
43~51.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Robust Adaptive Control of Robot Manipulator
Based on DSPs
Sung Hyun Han, Uhn Zoo Na, Woo Song Lee, Haeng Bong Shin, and Yoon Kyu Kim
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

severely limited in precise tracking of fast trajectories andsustaining desirable dynamic performance for variations of
payload and parameter uncertainties (R. Ortega et al., 1989; P.
Tomei, 1991). In many servo control applications the linear
control scheme proves unsatisfactory, therefore, a need for
nonlinear techniques is increasing.
Digital signal processors (DSP's) are special purpose
microprocessors that are particularly suitable for intensive
numerical computations involving sums and products ofvariables. Digital versions of most advanced control algorithms
can be defined as sums and products of measured variables,
thus can naturally be implemented by DSP's. DSPs allow
straightforward implementation of advanced control algorithms
that result in improved system control. Single and/or multiple
axis control systems can be controlled by a single DSP.
Adaptive and optimal multivariable control methods can track
system parameter variations. Dual control, learning, neuralnetworks, genetic algorithms and Fuzzy Logic control
methodologies are all among the digital controllers
implementable by a DSP (N. Sadegh et al., 1990; Z. Ma et al.,1995). In addition, DSP's are as fast in computation as most 32bit microprocessors and yet at a fraction of their prices. Thesefeatures make them a viable computational tool for digital
implementation of advanced controllers. High performance
DSPs with increased levels of integration for functional
modules have become the dominant solution for digital control
systems. Today's DSPs with performance levels ranging from 5
to 5400 MIPS are on the market with price tags as low as $3 (P.
Bhatti et al., 1997; T. H. Akkermans et al., 2001). In order todevelop a digital servo controller one must carefully consider
the effect of the sample-and-hold operation, the sampling
frequency, the computational delay, and that of the quantization
error on the stability of a closed-loop system (S. A. Bortoff,1994). Moreover, one must also consider the effect of disturbanceson the transient variation of the tracking error as well as its steadystate value (F. Mehdian et al., 1995; S. H. Han et al., 1996).
This paper describes a new approach to the design ofadaptive control system and real-time implementation using
digital signal processors for robotic manipulators to achieve theimprovement of speedness, repeating precision, and tracking
performance at the joint and cartesian space. This paper isorganized as follows : In Section Ⅱ, the dynamic model of therobotic manipulator is derived. Section Ⅲ derives adaptive
control laws based on the model reference adaptive control
theory using the improved Lyapunov second method. Section
Ⅳ presents simulation and experimental results obtained for adual-arm robot. Finally, Section Ⅴ discusses the findings anddraws some conclusions.

ABSTRACTWe describe a new approach to the design and real-time
implementation of an adaptive controller for robotic
manipulator based on digital signal processors in this paper. The
Texas Instruments DSPs(TMS320C80) chips are used in
implementing real-time adaptive control algorithms to provide
enhanced motion control performance for dual-arm robotic
manipulators. In the proposed scheme, adaptation laws arederived from model reference adaptive control principle basedon the improved direct Lyapunov method. The proposed
adaptive controller consists of an adaptive feed-forward andfeedback controller and time-varying auxiliary controller
elements. The proposed control scheme is simple in structure,
fast in computation, and suitable for real-time control.
Moreover, this scheme does not require any accurate dynamic
modeling, nor values of manipulator parameters and payload.
Performance of the proposed adaptive controller is illustrated
by simulation and experimental results for robot manipulator
consisting of dual arm with four degrees of freedom at the joint
space and cartesian space.
Keywords : Adaptive Control, DSP(TMS320C80), Dual-Arm
Robot, Real Time Control, Real-Time Implementation
I. INTRODUCTION
At recent there are mach advanced techniques that aresuitable for servo control of a large class of nonlinear systems
including robotic manipulators (P.C.V. Parks, 1966; Y.K.Choi
et al., 1986; Y.M.Yoshhiko, 1995). Since the pioneering workof Dubowsky and DesForges (1979), the interest in adaptive
control of robot manipulators has been growing steadily (T. C.
Hasi, 1986; D. Koditschck, 1983; A. Koivo et al., 1983; S.
Nicosia et al., 1984). This growth is largely due to the fact that
adaptive control theory is particularly well-suited to robotic
manipulators whose dynamic model is highly complex and may
contain unknown parameters. However, implementation ofthese algorithms generally involves intensive numericalcomputations (J. J. Craig, 1988; H. Berghuis et al., 1993).
Current industrial approaches to the design of robot arm
control systems treat each joint of the robot arm as a simple
servomechanism. This approach models the time varying
dynamics of a manipulator inadequately because it neglects the
motion and configuration of the whole arm mechanism. Thechanges in the parameters of the controlled system aresignificant enough to render conventional feedback control
strategies ineffective. This basic control system enables amanipulator to perform simple positioning tasks such as in the
pick-and-place operation. However, joint controllers are©ISAROB 2006

588

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

q& r

+

E&

PV
PB

q&& r

PI ( t )
PA

q&

+
+

ADAPTATIONSCHEMEROBOTIC

Fig. 1. Adaptive control scheme of Robotic Manipulator with eight joint MANIPULATOR

q

PC

qr

E
+

PP

and (4) as

II. MODELING

J (q )T T (t ) + D (q ) q&& + N (q, q& ) + G (q ) = τ (t )The dynamic model of a manipulator-plus-payload is derived
and the tracking control problem is stated in this section.
Let us consider a nonredundant joint robotic manipulator inwhich the n×1 generalized joint torque vector τ(t) is related
to the n×1 generalized joint coordinate vector q(t) by thefollowing nonlinear dynamic equation of motion

D ( q) q&& + N ( q, q& ) + G (q ) = τ (t )(5)Substituting equations (2) and (3) into equation (5) yields∆ M p J ( q ) T [ J ( q ) q&& + J& ( q , q& ) q& + g ]+ D ( q ) q&& + N ( q , q& ) + G ( q )(1)on the manipulator dynamics. This equation can be written as[ D (q) + ∆M p J (q)T J (q) ] q&&+[ N (q, q&)

vector, and G (q) is the n×1 gravitational loading vector.
Equation (1) describes the manipulator dynamics without any
payload. Now, let the n×1 vector X represent the end-effector
position and orientation coordinates in a fixed task-related
cartesian frame of reference. The cartesian position, velocity,
and acceleration vectors of the end-effector are related to thejoint variables by+ ∆M p J (q)T J& (q, q&) q& ] +[ G(q) + ∆M p J (q)T g ] = τ (t )Tis symmetric and positive-definite. Equation (7) constitutes anonlinear mathematical model of the manipulator-plus-payload
dynamics.

III. ADAPTIVE CONTROL SCHEME(2)

The manipulator control problem is to develop a control
scheme which ensures that the joint angle vector q(t) tracks any
desired reference trajectory qr (t ) , where qr (t ) is an n×1

where Φ(q) is the n×1 vector representing the forward
kinematics and J(q) = [∂Φ(q)/∂q] is the n×n Jacobian matrixof the manipulator.
Let us now consider payload in the manipulator dynamics.
Suppose that the manipulator end-effector is firmly grasping a
payload represented by the point mass ∆M p . For the payload

vector of arbitrary time functions. It is reasonable to assume
that these functions are twice differentiable, that is, desired
angular velocity q& r (t ) and angular acceleration q&&r (t ) exist andare directly available without requiring further differentiation of
qr (t ) . It is desirable for the manipulator control system toto move with acceleration X&& (t ) in the gravity field, the end-

achieve trajectory tracking irrespective of payload mass ∆M p .

effector must apply the n×1 force vector T (t ) given by

The controllers designed by the classical linear control scheme
are effective in fine motion control of the manipulator in theneighborhood of a nominal operating point Po . During the(3)

where g is the n×1 gravitational acceleration vector.
The end-effector requires the additional joint torqueτ f (t ) = J (q) T T (t )gross motion of the manipulator, operating point Po andconsequently the linearized model parameters vary substantiallywith time. Thus it is essential to adapt the gains of thefeedforward, feedback, and PI controllers to varying operating
points and payloads so as to ensure stability and trajectory
tracking by the total control laws. The required adaptation laws
are developed in this section. Fig. 1 represents the block diagramof adaptive control scheme for robotic manipulator.

(4)

where superscript T denotes transposition. Hence, the total
joint torque vector can be obtained by combining equations (1)©ISAROB 2006

(7)

where the modified inertia matrix [ D ( q ) + ∆M p J ( q ) J ( q ) ]X (t ) = Φ ( q )T (t ) = ∆M p [ X&& (t ) + g ](6)Equation (6) shows explicity the effect of payload mass ∆M p

where D(q ) is the n×n symmetric positive-definite inertia
matrix, N (q, q& ) is the n×1 coriolis and centrifugal torque

X (t ) = J (q ) q& (t )X (t ) = J& ( q, q& ) q& (t ) + J ( q) q&& (t )= τ (t )589

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

DSPs for motion control of a dual-arm robot. The adaptive
scheme developed in this paper will be applied to the control ofa dual-arm robot with eighth axes. Fig.2 represents link
coordinates of the dual-arm robot.

Nonlinear dynamic equation (7) can be written asτ (t ) = D* (∆M p , q, q& ) q&&(t ) + N * (∆M p , q, q& ) q& (t )+ G * (∆M p , q, q& ) q(t )(8)where D * , N * , and G * are n×n matrices whose elements arehighly nonlinear functions of ∆M p , q , and q& .
In order to cope with changes in operating point, the controller
gains are varied with the change of external working condition.
This yields the adaptive control law

τ (t ) = [ PA ( t ) q&&r (t ) + PB ( t ) q& r (t ) + PC ( t ) qr (t ) ]+ [ PV (t ) E& (t ) + PP (t ) E (t ) + PI (t ) ]where PA (t ) , PB (t ) , PC ( t )(9)are feedforward time-varying

adaptive gains, and PP (t ) and PV (t ) are the feedback adaptive
gains, and PI (t ) is a time-varying control signal corresponding
to the nominal operating point term, generated by a feedback
controller driven by position tracking error E(t) defined as
q r (t ) − q (t ) .
The gains of adaptive control low in equation (9) are defined
as follows:

PA (t ) = a1[ p a1 E + pa 2 E& ][q&&r ]T+ a 2 ∫0 [ pa1 E + p a 2 E& ][q&&r ]T dt + pa (0)t

PB (t ) = b1[ pb1 E + pb 2 E& ][q& r ]Tt+ b2 ∫0 [ pb1 E + pb 2 E& ][q& r ]T dt + pb (0)PC (t ) = c1[ pc1 E + pc 2 E& ][q r ]T
+ c2 ∫0 [ pc1 E + pc 2 E& ][qr ]T dt + pc (0)t

PI (t ) = λ2 [ pi 2 E ] + λ1 ∫0 [ pi1 E ]T dt + pi (0)t

PP (t ) = p1[ p p1 E + p p 2 E& ][ E ]T
+ p 2 ∫0 [ p p1 E + p p 2 E& ][ E ]T dt + p p (0)t

PV (t ) = v1 [ pv1 E + pv 2 E& ] [ E& ]T+ v ∫t [ p E + p E& ][ E& ]T dt + p (0)
2 0

v1

v2

Fig.2. Link coordinates of dual-arm robot

Table Ⅰ Link parameters of robot.
Length ofInertia ofGear ratio ofMass of link(kg)link(kg)link(kg)link
m1 15.0067 I10.35
I10.1538
r1
1/100

(10)

(11)

(12)

m2

8.994

I2

0.3

I2

0.0674

r2

1/80

m3

3.0

I3

0.175

I3

0.045

r3

1/200

m4

1.0

I4

0.007

I4

0.0016

r4

1/75

m5

15.067

I5

0.35

I5

0.1538

r5

1/100

m6

8.994

I6

0.3

I6

0.0674

r6

1/80

m7

3.0

I7

0.175

I7

0.045

r7

1/200

m8

1.0

I8

0.007

I8

0.0016

r8

1/75

(13)

Table Ⅱ Motor parameters of robot
Rotor inertia(kg·m2)

(14)

Jm1

(15)

Jm2

v

Jm3

where [ p p1 , pv1 , pc1 , pb1 , p a1 ] and [ p p 2 , pv 2 , pc 2 , pb 2 , pa 2 ] areJm4

positive and zero/positive scalar adaptation gains, which arechosen by the designer to reflect the relative significance of
position and velocity errors E and E& .

Jm5
Jm6

IV. SIMULATION AND EXPERIMENT
Jm7

A. Simulation
Jm8

This section represents the simulation results of the positionand velocity control of a four-link robotic manipulator by the
proposed adaptive control algorithm, as shown in Fig.2, anddiscusses the advantages of using joint controller based-on©ISAROB 2006

5.0031×10-5
1.3734×
10-5
0.8829×
10-50.2256×
10-5
5.0031×10-5
1.3734×
10-5
0.8829×
10-50.2256×
10-5

B. Experiment

590

Torque constant(K m/a)
Ka1
Ka2
Ka3
Ka4
Ka5
Ka6
Ka7
Ka8

21.4839×1
0-2
20.0124×1
0-2
20.0124×1
0-2
17.6580×1
0-2
21.4839×1
0-2
20.0124×1
0-2
20.0124×1
0-2
17.6580×1
0-2

Back emf constant(V s/rad)
Kb1
Kb2
Kb3
Kb4
Kb5
Kb6
Kb7
Kb8

214.8592×
10-3
200.5352×
10-3
200.5352×
10-3
176.6620×10-3
214.8592×
10-3
200.5352×
10-3
200.5352×
10-3
176.6620×
10-3

Amaturewindingresistance(ohms)
Ra1

1.5

Ra2

4.2

Ra3

9

Ra4

20

Ra5

1.5

Ra6

4.2

Ra7

9

Ra8

20

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Memory Map
I/O Interface

HOST COMPUTER(Pentium PC)

Global Memory(32Kbyte)

I/O Map
I/O Interface

MAIN BOARDDATA BUS. ADRESS BUS...

Fig. 3. Experimental set-up
The performance test of the proposed adaptive controller has
been performed for the dual-arm robot at the joint space andcartesian space. At the cartesian space, it has been tested for the
peg-in-hole tasks, repeating precision tasks, and trajectory
tracking for B-shaped reference trajector. At the joint space, it
has been tested for the trajectory tracking of angular positionand velocity for a dual-arm robot made in Samsung Electronics
Company in Korea. Fig. 3 represents the experimental set-up
equipment. To implement the proposed adaptive controller, weused our own developed TMS320C80 assembler software. Also,
the TMS320C80 emulator has been used in experimental set-up.
At each joint of a dual-arm robot, a harmonic drive (with gear
reduction ratio of 100 : 1 for joint 1 and 80 : 1 for joint 2) has
been used to transfer power from the motor, which has aresolver attached to its shaft for sensing angular velocity with aresolution of 8096 (pulses/rev). Fig. 4 represents the schematic
diagram of control system of dual-arm robot. And Fig. 5
represents the block diagram of the interface between the PC,
DSP, and dual-arm robot.
The performance test in the joint space is performed toevaluate the position and velocity control performance of thefour joints under the condition of payload variation, inertia
parameter uncertainty, and change of reference trajectory.

AXIS 1JOINT CONTROLLER(TMS320C80 DSP)
axis 1,2

AXIS 2

AXIS 3JOINT CONTROLLER(TMS320C80 DSP)
axis 3.4

AXIS 4

AXIS 5JOINT CONTROLLER(TMS320C80 DSP)
axis 5,6

AXIS 6

AXIS 7JOINT CONTROLLER(TMS320C80 DSP)
axis 7,8

AXIS 8ServoInterfaceCurcuitServo Driver(FARA SCD)
ROBOT
(Dual-Arm with 8 Axis)

Fig. 5. The schematic diagram control system of dual-arm robot.
Fig.6 represents the B-shaped reference trajectory in thecartesian space. Fig. 7 shows the experimental results of theposition and velocity control at the first joint with payload 4 kg
and the change of reference trajectory. Fig. 8 shows theexperimental results for the position and velocity control at thesecond joint with 4 kg payload. Fig.'s 9 and 10 show theexperimental results for the position and velocity control of the
PID controller with 4 kg payload. As can be seen from theseresults, the DSP-based adaptive controller shows extremely
good control performance with some external disturbances. It is
illustrated that this control scheme shows better control
performance than the exiting PID controller, due to small
tracking error and fast adaptation for disturbance.

Teaching
pendant

R3

①

Y

R4

R2(271,210mm)

R5
0

R6

X

R7

☺
R1

R9

R8

R10
R11
R12
R13
R14
R15

start

Fig. 4. The block diagram of the interface between the PC. DSP,
and dual-arm robot.

©ISAROB 2006

R0(271,210mm)

R16
R17(237,213mm)

Fig. 6. The B shaped reference trajectory in the cartesian space.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig. 7. (a)-(d) Experimental results for the position and velocity
tracking of adaptive controller at the first joint with 4kg payload.
Fig. 10. (a)-(d) Experimental results of PID controller for the
position and velocity tracking at the second joint with 4kg
payload.
Ⅴ. DISCUSSION AND CONCLUSIONS

A new adaptive digital control scheme is described in thispaper using DSP(TMS320C80) for robotic manipulators. The
adaptation laws are derived from the direct adaptive techniqueusing the improved Lyapunov second method. The simulationand experimental results show that the proposed DSP-adaptive
controller is robust to the payload variation, inertia parameter
uncertainty, and change of reference trajectory. This adaptive
controller has been found to be suitable to the real-time controlof robot system. A novel feature of the proposed scheme is theutilization of an adaptive feedforward controller, an adaptive
feedback controller, and a PI type time-varying control signal to
the nominal operating point which result in improved tracking
performance. Another attractive feature of this control scheme
is that, to generate the control action, it neither requires acomplex mathematical model of the manipulator dynamics nor
any knowledge of the manipulator parameters and payload. The
control scheme uses only the information contained in theactual and reference trajectories which are directly available.
Futhermore, the adaptation laws generate the controller gains
by means of simple arithmetic operations. Hence, thecalculation control action is extremely simple and fast. Thesefeatures are suitable for implementation of on-line real-time
control for robotic manipulators with a high sampling rate,particularly when all physical parameters of the manipulator
cannot be measured accurately and the mass of the payload canvary substantially. The proposed DSP-based adaptive
controllers have several advantages over the analog control andthe micro-computer based control. This allows instructions anddata to be simultaneously fetched for processing. Moreover,most of the DSP instructions, including multiplications, areperformed in one instruction cycle. The DSP tremendously
increase speed of the controller and reduce computational delay,
which allows for faster sampling operation. It is illustrated that
DSPs can be used for the implementation of complex digital
control algorithms, such as our adaptive control for robot systems.

Fig. 8. (a)-(d) Experimental results for the position and velocity
tracking of adaptive controller at the second joint with 4kg
payload.

Fig. 9. (a)-(d) Experimental results of PID controller for the
position and velocity tracking at the first joint with 4kg payload.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

T. H. Akkermans and S. G. Stan, 2001, “Digital servo IC
for optical disc drives”, Contr. Eng. Pract., vol. 9, no. 11, pp.
1245-1253.
P. Bhatti and B. Hannaford, 1997, “Single-chip velocity
measurement system for incremental optical encoders”, IEEE
Trans. Contr. Syst. Techn., vol. 5, no. 6, pp. 654-661.

REFERENCES

P.C.V. Parks, July 1966, "Lyapunov Redesign of Model
Reference Adaptive Control System," IEEE Trans. Auto. Contr.,
Vol. AC-11, No. 3, pp. 362-267.
Y.K. Choi, M.J. Chang, and Z. Bien, 1986, "An Adaptive
Control Scheme for Robot Manipulators." IEEE Trans. Auto.
Contr., Vol. 44, No. 4, pp. 1185-1191.
Y.M. Yoshhiko, June 1995, "Model Reference Adaptive
Control for Nonlinear System with unknown Degrees, "In theproceeding of American Control Conference, pp.2505-2514,
Seatle.
S. Dubowsky, and D.T. DesForges, 1979, "The Applicationof Model Reference Adaptation Control to Robot
Manipulators," ASME J. Dyn. Syst., Meas., Contr., Vol. 101,
pp. 193-200.
T.C. Hasi, 1986, "Adaptive Control Scheme for Robot
Manipulators-A Review." In Proceeding of the 1987 IEEE
Conference on Robotics and Automation, San Fransisco, CA.
D. Koditschek, 1983, "Quadratic Lyapunov Functions for
Mecanical Systems," Technical Report No. 8703, Yale
University, New Haven, CT.
A. Koivo and T. H. Guo, 1983, "Adaptive Linear
Controller for Robot Manipulators." IEEE Transactions andAutomatic Control, Vol. AC-28, pp. 162-171.
S. Nicosia and P. Tomee, 1984, "Model Reference Adaptive
Control Algorithm for Imdustrial Robots," Automatica, Vol. 20,
No. 5, pp. 635-644.
J. J. Craig, 1988, "Adaptive Control of Meduanical
Manipulator," Addison-wesley.
H. Berghuis, R.Orbega, and H.Nijmeijer, 1993, "A Robust
Adaptive Robot controller," IEEE Trans., Robotics andAutomation, Vol. 9, No. 6, pp. 825-830.
R. Ortega and M.W. Spong, 1989, "Adaptive Motion
Control of Rigid Robots: A Tutorial," Automatica, Vol. 25, pp.
877-888.
P. Tomei, Aug. 1991, "Adaptive PD Controller for Robot
Manipulators," IEEE Trans. Robotics and Automation, Vol.7,
No.4.
N. Sadegh and R. Horowitz, Aug. 1990, "An Exponentially
Stable Adaptive Control Law for Robot Manipulators," IEEE
Trans. Robotics and Automation, Vol. 9, No. 4.
Z. Ma, J. shen, A. Hug, and K. Nakayama, October 1995,"Automatic optimum Order Assignment in Adaptive Filters,"
international conference on signal Processing Applications &Technology, Boston pp. 629-633.
T. A. Lasky and T. C. Hsia, July 1994, "Application of a
Digital Signal Processor in Compliant Control of an Industrial
Manipulator," Proceedings of American Control Conference.
K. Michael and P. Issa, June 1995, "Digital Signal
Processor : A Control Element," In Proceedings of American
Control Conference, Seatle, pp. 470-474.
S. A. Bortoff, Feb. 1994, "Advanced Nonlinear Robotic
Control Using Digital Signal Processing," IEEE Trans. Indust.
Elect., Vol. 41, No. 1.
F. Mehdian and M. Wirth, June 1995, "Adaptive Control of
Robotic Manipulators Using DSPs," In Proceedings ofAmerican Control Conference, Seatle, pp.480-481.
S. H. Han, J. Lee, D. Ahn, M. Lee, and K. Son, July 1996,"Implementation of Robust Adaptive Controller of Robotic
Manipulator using DSPs, "In Proceedings of EleventhInternational Conference on System engineering, Lasvegas, pp.
668-673.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Study on Image Based Visual Feedback Controlof Robot System
Sung Hyun Han and Uhn Zoo Na
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

Hong Rae Kim,Yoon Kyu Kim, and Chang Rok Shin
Dept. of Mechanical Eng.,Graduate School, Kyungnam Univ., Masan, 631-701, Korea.
Tel: +82-55-249-2590 Fax: +82-55-249-26

Abstract
This paper presents how it is effective to use many features for improving the speed and the accuracy of the visual servo
systems. Some rank conditions which relate the image Jacobian and the control performance are derived. It is also proventhat the accuracy is improved by increasing the number of features. Effectiveness of the redundant features is evaluated by
the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world
coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm Robot
manipulator made in Samsung Electronic Co. Ltd..
Key Words : Visual Feedback Control, Robot System, Image Based, Visual Tracking, Real Time Control.

based on limited hardware. Accordingly, visual servoing
scheme which utilizes many features effectively is required.
Furthermore exploiting the information carefully from thefeatures will give robust and accurate control performance[2],
Sanderson et al. proposed a feature-based approach anddefined the Jacobian of ideal inverse interpretation which
was considered as the infinitesimal change of the relative
position and orientation between the camera and the object
in the environment.
Newman et al. proposed an adaptive control law basedon a single input single output model and a feature selection
criterion were proposed [1], [2]. The criterion addressed the
choice of which feature should be used to control eachactuator, where the number of selected features is equal to
the number of the actuator. Feddema et al. [3], also studied
the selection method of the features to make the Jacobian
good condition. Real time experiment of gasket tracking
showed that the proper selection of features is necessary tominimize the effect of image noise.
Papanikolopoulos et al. [2], [3] experimentally
examined many control algorithms including ProportionalPlus- Integral, pole assignment and linear quadratic gaussian.
Some adaptive control schemes were also examined in [8].
These approaches do not consider to use the redundantfeatures which are defined as the features whose number ismore than the degrees of freedom of the robot manipulator.
Chaumette el al. [2] and Espiau el al. [3] derived theinteraction matrix, and introduced the concept of task
function. Chaumette [2] extended the task function approachto the complex features. Jang and Bien [10] mathematicallydefined the “feature”, and derived the feature Jacobian

1. Introduction
Recently, robots can perform assembly and material
handling jobs with speed and precision yet, compared tohuman workers robots, are hampered by their lack of sensory
perception. To address this deficiency considerable research
into force, tactile and visual perception has been conducted
over the past two decades.
Visual servoing is the fusion of result from many
elemental areas including high-speed image processing,kinematics, dynamics, control theory, and real-time
computing. It has much in common with research into active
vision and structure from motion, but is quite different fromthe often described use of vision in hierarchical task-levelrobot control systems. Many of the control and vision
problems are similar to those encountered by active visionresearchers who are building robotic heads. However thetask in visual servoing is to control a robot to cope with its
environment using vision as opposed to just observing theenvironment.
Most visual servoing problems can be considered as
nonlinear control problems with the gray level of each two
dimensional pixel array being an observation. The difficultyof the problem is the size and the nonlinearity. The size ofthe observation is larger than ten thousand and they havenonlinear interaction with each other. A few researches basedon the stochastic models of the two-dimension observation
are found, but most visual servoing schemes uses thefeatures of the image as the observation. To manipulate
objects with complex shapes, it is important to deal withcomplex features such as spheres and cylinders. However,
the time extracting complex features will become too long

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006[

matrix. The authors [2] derived the image Jacobian, and used
its generalized inverse and PD control to generate the hand
trajectory. These schemes are based on the generalized
inverse of the Jacobian. Redundant features can be used.
However, the parameters to improve the control performanceare very limited and the controllability of the redundantfeatures are not discussed.
The authors proposed a linearized dynamic model of thevisual servo system and linear quadratic control scheme for
redundant features [1], [3]. The controllability problem wasdiscussed but the performance improvement by utilizing theredundant features was not presented.
This paper presents how the control performance of thefeature-based visual servoing system is improved byutilizing redundant features. Effectiveness of the redundantfeatures is evaluated by the smallest singular value of theimage Jacobian which is closely related to the accuracy inthe world coordinate system. Usefulness of the redundantfeatures is verified by the real time control experiments. To
illustrate the accuracy of the redundant visual servo system,
real time experiments on the Dual-Arm robot with eight
joints are carried out. Translation and rotation step responsewith three, four and five features are examined in thisexperiment.

by ξ ≅ ξ1 Lξ n

] . Then the system model for n feature

T T

Tpoints is defined by the map ψ : R → R
angle θ to the feature vector ξ as follows:m

operation region Μ θ ⊂ R m . We restrict the robot motion in
the operation region. Thus the robot Jacobian J robot isinvertible in the working area. It is useful to introduce thefeature manifold Μ , which is defined by

Μ = {ξ ∈ R 2 n : ξ = ψ (θ ),θ ∈ Μ θ }T

Camera Model

Kinematic Model

Robot
Kinematics Camera
PositionObjectPosition
Featurein Camera Projection
ObjectCoordinate
Image
System+Object Position

Fig. 1. System Modeling••ξ = Jθ
where the 2n × m matrix J is defined by⎡ J im( 1 ) ⎤⎢⎥J ≅ ⎢ M ⎥ c J ARM⎢ J im( n ) ⎥⎣⎦f being the focal
The matrix
(1)

⎡ f
⎢−
z
(i )
J im ≅ ⎢ i⎢⎢ 0⎣

Suppose that there are n feature points, namely

p i = [X i Yi Z i ]T (i = 1,...., n) , on an object and thecorresponding positions in the image plane areξ i = [xi y i ]T (i = 1,...., n) . Assume that the shape and the size

J

(i )
im

0
−

f
zi

(4)

(5)

is given by [14], [12]

xi
Zi
yi
Zi

xi yi
f
2
yi + f 2
fx2 + f 2
− i
f
xi yi
f

⎤
yi ⎥
⎥ (6)
⎥
− xi ⎥⎦cand called the image Jacobian [12]. J ARM is the robot
Jacobian expressed in the camera coordinate system. Sinceof the object are known and constant (i.e., the object is arigid body). Then ξ i for i = 1,...., n become functions of thejoint angle θ . Let us define a 2n dimensional feature vector©ISAROB 2006

(3)

The features on the feature manifold is called theadmissible features. If the features are admissible, then the
robot Jacobian is invertible by definition. In equation (3), θ
represents joint angle.
Differentiation of the system model yields

system [XYZ ]T and in the image plane [xy ]T .

Y ] ( f / z)(2)where m is the number of the joints of the robot.
Since the task must be carried out in the nonsingularregion of the robot, the nonsingular region is called the

The object image moves with the joint angle to theobject image, which is composed of the kinematic model andthe camera model as shown in Fig.1. Suppose that a camera
is mounted on the robot hand and the object does not move.
The kinematic model is a map from the joint angle to a
position of the camera. Since the camera is on the robot
hand, the camera position is uniquely defined by the joint
angle θ based on the kinematic structure of the robot. The
camera model is a map from the position of the camera to thecamera to the image of the object. The object image is
generated by the perspective of the relative position between
the camera and the object. The perspective projection is a
map between two different representations of the position of
the object, i.e., the representations in the camera coordinate[x y ]T = [Xfrom the joint

ψ (θ ) ≅ ξ

2. System Modeling and Formullation

The perspective projection withlength of the lens is given by

2nthe vector J ARM θ& ∈ R is the linear and angularvelocities of the camera expressed in the camera coordinate
c

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system,

J

(i )

⎡− 1
⎢ 0
⎢
⎢ p
⎢
1 ⎢ q
N =
r ⎢ 0
⎢
⎢ 0
⎢ 0
⎢
⎢⎣ 0

becomes the infinitesimal change of theimposition of the camera. Moreover,J (i) ≅ J im( i ) c J ARM

becomes the infinitesimal change of the features according to
the infinitesimal change of the joint angles.
The degenerated features are the features for which theextended image Jacobian is not full rank. The degeneratefeatures should be avoided because the inverse map (the map
from ξ to θ ) becomes singular. Thus, when the number ofjoints is m ,rank J (θ ) = m ∀θ ∈ Μ θ
(7)

0
− 1
0

0
0
1

0
r
0

− r
0
0

0
p
q

0
0
1

0
0
0

0
0
0

0
0

− p
− q

0
r

− r
0

Then we obtain

⎤
⎥
⎥
⎥
⎥
r ⎥
− r⎥
⎥
0 ⎥
0 ⎥
⎥
0 ⎥⎦
0
0
0

(10)

J = MN c J ARM , where M =[M M M M ] . It is straightforward to see thatT1

is required for all admissible features. To satisfy thiscondition n ≥ m / 2 is an obvious necessary condition, but it
is not sufficient for some cases.
For example, consider a general six degree of freedom(m = 6) . In this case, n ≥ 3 is necessary. If n = 3 ,rank J 〈6 the camera lies on the cylinder(Fig. 2) which
includes the three points and the axis of which isperpendicular to the plane containing these points. For any
attitude of the camera, J is singular. Thus n = 3 is not
sufficient and n ≥ 4 is desirable. For the case of n = 4 , wehave the following theorem.

T
2

T
3

T T
4

1

x1

y1

1

x2

y2

1

x3

y3

det M = 1
1x2x3y2 • 1y3
1x3x4

y3 • 1
y4
1

x4
x1

y4 •
y 1 (11)

1

x4

y4

1
1

x1
x2

y1
y2

Thus M is invertible because any three feature points
are not collinear. On the other hand, if p 2 + q 2 ≠ 0 , the firstsix rows of N is linearly independent. If p = q = 0 , the first
four and the last two rows are linearly independent. Thus
rank N = 6 . finally, since all features are admissible,c

J ARM is invertible. Therefore, the extended image Jacobian
J is full rank.

FeaturePoints

3. Visual Feedback System

For evaluating the performance of the feature-based
visual feedback system, it is useful to discuss the ratio of thejoint angle error to the feature vector. The following theorem
shows that increasing the number of the feature point is aneffective way to improve the performance.
Let the joint error be ∆θ = θ − θ d and the feature error

Fig. 2. Singular Cylinder
Theorem 1: Suppose that there are four points on a plane
and the corresponding feature vector is admissible. Then theextended image Jacobian is full rank if any three featurepoints out of them are not collinear in the image plane.
Proof : Let the plane on which the four points exist be
Z = pX + qY + r . Then Z i satisfies Z i = pX i + qYi + r for
i = 1, L ,4 . Substituting (1) into this yields
f − pxi − qyi
f
(8)
=
Zi
r
And substituting this into (6) yields

J im( i ) = M i N

be ∆ξ = ξ − ξ d . Define the worst joint/feature error ratio

ER , called sensitivity, as follows:∆θ1=
ER = supβ min ( J )||∆ξ ≠ 0 ∆ξ

where β min ( J ) is the minimum singular value of J . Then(9)

the sensitivity ER decreases strictly by increasing thenumber of non-degenerated features on the object.
Let J n be the image Jacobian for n feature points andJ n +1 be the image Jacobian obtained by adding an extra
feature point to the already existing feature points.
Then we havewhere M i and N are defined by⎡f
Mi = ⎢
⎢⎣ 0

0

xi

yi

0

0

x i2 / f

f

0

0

xi

yi

xi yi / fxi yi / f ⎤ ,⎥y i2 / f ⎥⎦⎡ J ⎤J n +1 = ⎢ ( nn+1) ⎥⎣J⎦©ISAROB 2006

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where J ( n +1) is the 2 × m image Jacobian corresponding to
the newly added feature point. It is straightforward to see thatβ min ( J n ) ≤ β min ( J n +1 )(14)

the 3D work space will be improved by using 5 features. We
carried out many step tests to this observation.
The first experiment is a step motion in vertical axis.
The object is moved upward for 120mm (i.e., in ω Z
direction). The camera is controlled to keep the features at
the initial positions. Thus the initial values and the reference
values are the same. The object motion is considered as a
disturbance for the plots of the features in the image plane.
On the other hand, the object motion becomes the stepchange of the reference position for the plots of the camera
motion in the world coordinate system. Since Dual-Arm
robot has only 6 degrees of freedom, the orientation of theobject changed slightly. Thus, the reference orientation is[2.8, 0, -1.8] degrees expressed in the Euler angles, say
p ,η , φ .

The equal sign holds only if each row of J ( n +1) islinearly dependent to J n , i.e., only if J n +1 is not full rank.
Since we assumed that the features are not degenerated, theequal sing should be dropped. Thus adding extra featurepoints strictly increases the minimum singular value.
This theorem says that we can reduce the joint angle
error by increasing the number of feature points.
Linearizing the model (2) with the feature vector being
the state vector yields an uncontrollable model because ξ
can not move arbitrarily in R .2 n [13]. A simple way to avoid
this problem is to map ξ ∈ Μ onto the tangent space of Μ
by using the following transformation.
z = J dT (ξ − ξ d )(15)
where J d = J (θ d ) is the image Jacobian at the desired
point[16]. Note that z and θ are one-to-one in theneighborhood of θ d . The dynamics of the feature error on
the tangent space of the manifold Μ is given byheight :40 mm
8 mm

8 mm

120mm

60 mm
120mm

120mm

Fig. 3. Configuration of Feature Points

z& = J dT J (θ )θ&(16)&
Thus, for a simple continuous time control law θ = − Kzwith a positive definite constant matrix K yields an
asymptotic stability if J dT J (θ ) is positive definite. It isshown that this condition is satisfied fairly large region about
θ d [13].

4. Experiments and Discussion
As shown in Fig.3, the objects are white boards withthree, four and five black marks. Three points are arranged tomake a regular triangle with edge length 120mm. Four points
are on corners of a square with edge length 120mm. All
marks are on a plane except the one of five points at thecenter of the square, which has height 60mm. Dual-Arm
robot holds the objects and a camera(Fig.4). The world
coordinate system ω X − ω Y − ω Z is at the base of theDual-Arm robot. A nominal camera position is almost in
front of the plane on which the marks exist. To avoid thesingular cylinder(Fig.2) the optical axis and the normal axisof the object plane are not aligned. The distance is about
1000mm. The features are the x and y coordinates of thecenter of the image of each mark. Computing their minimum
singular values at the reference position gives

β min = ( J 3 ) = 0 .35 ,β min = ( J 4 ) = 0 .65 ,β min = ( J 5 ) = 3 .60

Fig. 4. Experimental Setup
Table 1. Specification of Dual Arm Robot(17)

Thus accuracy of the position control of the camera in©ISAROB 2006

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Content

Unit

Spec.

Remark

1st Arm
2nd Arm
Workspace
Z AxisR AxisMaximum Reach
PayloadMax. Resultant Vel.
Plane
PositionZ
Axis
Repeatability
M Axis
WeightCoincident Control
Axis No.

Deg
Deg
Mm
Deg
Mm
Kg
m/sec
Mm
Mm
Deg
Kg

180
450
150
±180
(350+260)
2.5
5.4
0.05
0.02
0.05
200
8Axis
(4+4)

High-speed
1,2 Axis
1,2 Axis

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig.4. Response in Image Plane for 3 Points.

5. Conclusions
In this paper, it has been presented how the control
performance of the feature-based visual servo system isimproved by utilizing redundant features. Effectiveness of
the redundant features is evaluated by the smallest singularvalue of the image Jacobian which is closely related to theaccuracy in the world coordinate system. It shows that theaccuracy of the camera position control in the world
coordinate system was increased by utilizing redundantfeatures. Real time experiments on dual-arm-robot werecarried out to evaluate the improvement of the accuracy andspeed by utilizing the redundant features. The results verifies
that the minimum singular value of the extended image
Jacobian plays an important role for performance
improvement of the feature-based visual servoing.

References[1] K. Hashimoto et al.,1991, “Image-based dynamic visual
servo for a hand-eye manipulator,”in MTNS-91, Kobe,
Japan, pp. 609~614.
[2] Sung Hyun Han, Man Hyung Lee, Hideki Hashimoto,2000, “Image-Based Servoing Control of a SCARA
Robot” KSME International Journal, Vol. 14, No. 7, pp.
782~788.
[3] P. K. Allen, A. Timccnko, B. Yoshimi, and P. Michelman,
1993, "Automated tracking and grasping of a moving
object with a robotic hand-eye system," IEEE Trans.
Robotics and Automation, vol. 9, no. 2, pp. 152~162.

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Lateral-shearing Interferometer for optical testingof DVD pick-up lenses
Byoung Chang Kim*, Seo Heon Kim, Yong Kwan Kwon and Won Il. Kim
School of Mechanical and Automation Engineering
Kyungnam University
449 Wolyoung Dong, Masan, Kyungnam, 631-701, Korea(bckim@kyungnam.ac.kr)
thorough analysis of alignment errors in this particular
double-pass interferometry reveals that 10 out of the entire
18 degrees-of-freedom of the three components have[3]
significant effects on measurement results. For thatreason, the testing method turns out to be a timeconsuming, laborious task even to the skillful operator andhardly ever applicable to the total quality inspection of all
the produced lenses.

Abstract
We present a lateral-shearing interferometer speciallydevised for production-line inspection of aspheric lenses.
The interferometer constitutes four right-angle prisms,
whose relative sliding motions provide the lateral shear andphase shifts necessary for measuring the beam wavefront
collimated by the lens under test. The prisms are held
together face to face using index-matching oil, so
environmental disturbances from external vibration andatmospheric turbulence can be minimized. Further, a special
phase-measuring algorithm of least-squares is adopted tocompensate for the phase shift error caused by the thickness
variation in the index-matching oil holding the prisms.
Experimental results prove that the interferometer provides ameasuring repeatability in the order of one hundredth of thelight wavelength.

In recent years various types of lateral-shearing
interferometer have been investigated with particular[4]
intention of testing of aspheric lenses and surfaces. They
all allow single-pass wavefront measurement without thereflecting mirror needed in the Fizeau interferometry. Thisadvantage reduces necessary alignment work significantly
and subsequent total measuring time. In this paper we
presents a special lateral-shearing interferometer, which
has been devised for production-line inspection of asphericobjective lenses. This interferometer has a high immunity
to external vibration and atmospheric disturbance, since
the whole interferometer is composed of four pieces ofsquare prisms held together face to face using indexmatching oil. The relative sliding displacements of theprisms can readily generate both the lateral shear andphase shifts necessary to determine the wavefront to bemeasured. As a result, the lateral-shearing interferometer
provides a great flexibility in handling various types andsizes of aspheric lenses. In addition, the latest computervision technology can be well incorporated so thatnecessary testing operation can be implemented
automatically in unmanned manner.

1. Introduction
Optical pickup objectives used in data storage devices suchas compact discs (CD) or digital versatile discs (DVD) aremade in aspheric shape and fabricated in large volumes
using the injection molding process. In this massproduction, one critical problem is the optical testing ofproduced lenses especially for production-line quality
assurance. The optical testing involves measuring theconverging or collimated wavefront from the objectives toreveal any surface defects and aberrations. In fact, a wide
variety of interferometric techniques are available for the[1]
purpose. Among them, the Fizeau interferometry is mostpopularly used in industry, being well incorporated with[2]
latest computer-vision technology. As the objective has
been designed to focus the incoming parallel beam at a
certain depth location underneath a protective disc layer, ablank disc plate with controlled thickness is included in thetesting. Without the disc plate, an excessive amount of
spherical aberration would dominate in the generated
wavefront, leaving out other types of aberrations such as
astigmatism and coma rarely observable. Consequently,
three optical components of the objective itself, the discplate, and the reflecting mirror should be precisely aligned
along the optical axis to obtain accurate testing results. A©ISAROB 2006

2. Interferometer design
The lateral-shearing interferometer configured in thisinvestigation is shown in Figure 1. A coherent HeNe laser
is used as light source. The laser beam is focused using a
microscope objective and then collimated by the lens being
tested. A disc plate is placed before the lens for the same
reason as in the Fizeau interferometer of Figure 1 to avoid
excessive spherical aberration. The collimated beam would
be a plane wave if the lens were perfect being free fromaberrations; otherwise it would have a distorted wavefront.

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3. Phase measurement

MO:Microscope objective
D :Disk
LT :Lens under test

Figure 2 shows the wavefronts of the two beams involvedin the lateral-shearing interferometry. Let the wavefront of
one beam be represented by z=w(x,y). Then the otherwavefront can be expressed as w(x-s,y)+d, where s is theamount of lateral-shear in the x-direction and d is the path
difference between the beams along the optical axis. Whenthe two wavefronts are combined, the intensity ofinterferometric fringes is obtained as

DP:Dove prism
P1,P2,P3,P4:Right-angle prisms

Phase shift

IL:Imaging lensCCD:CCD camera

LateralPZT
MOshearing-

DPD

I(x,y) = D(x,y) + V(x,y) cos[k(Δwx(x,y) - d)]

LT

(1)

IL

where Δwx(x,y) = w(x,y) - w(x-s,y); k is the wavenumber,
k=2π/λ; and D(x,y) and V(x,y) are the intensity mean andmodulation amplitudes, respectively. The phase termΔwx(x,y), defined as the wavefront difference, has to be
determined from the measured intensity of interferometricfringes. In doing that, there are additional unknowns ofD(x,y) and V(x,y) as in Eq.(1), so Δwx(x,y) can not simply
be extracted from single intensity measurement. Therefore,
multiple intensity measurements are necessary to be madewith different phase shifts. Let the path differences bevaried with phase shifts such as d=do, d1, ..., dm. Then
Eq.(1) is extended as

CCD

Figure 1. Optical configuration of the lateral-shearing
interferometer under investigation
The lateral-shearing interferometer measures the wavefront
using four pieces of square prisms. The prisms are attachedface to face using index-matching oil to allow their relative
sliding motions necessary for lateral shear and phase-shift.
For convenience, the prisms are referred to as P1, P2, P3,
and P4, respectivel. The prism P3 is fixed stationary atcenter in the assembly, and its inclined surface divides theincoming beam 50:50, half the beam to reflect and theother half to transmit. The separated beams are thenreflected back by P1 and P4 respectively, and combined
together with a predetermined amount of lateral shear to
produce interferometric fringes.

Iij = Di + Vi cos(φi -φj)where φI = kΔwx(x,y) and δj = kdj . For convenience of
expression, the subscript i implies the location of (x,y) andj indicates the j-th phase shift.

The amount of lateral shear is adjusted by sliding P4 in thez-direction, being twice the displacement of P4. The
direction of lateral shear in the xy-plane is also variable byrotating the dove prism placed between the test lens andthe interferometer; the collimated beam swivels twice theangular rotation of the dove prism. Another important
aspect of the interferometer design is that phase shiftingnecessary for effective fringe analysis is simply introducedby pushing P2 along the inclined surface of P3. P1 isconstrained to move only in the z-direction. The movementof P2 in the x- and z-directions causes no lateral shear in
the x-direction, but only changes the optical path in the zdirection to induce phase shift. A micrometer is used to set
the position of P4 with a predetermined amount of lateralshear. And a piezoelectric ceramic actuator is used to pushP2 in steps during measurement to generate a series ofphase shifts. A ball joint is inserted between the actuatorand P2, so that P2 can slide along the inclined surface of
P3 in the x- and z-directions while the actuator elongates
only in the x-direction. The motion of P1 is kinematicallyguided by a set of preloaded springs to move only in the zdirection. The refractive index of the index-matching oil isselected to be the same as that of the prisms.

©ISAROB 2006

(2)

Figure 2. Two wavefronts involved in the lateral-shearing
interferometry
In determining φi from Eq.(2), a suitable phase-measuring
algorithm is required. Much work has in fact been done on
establishing effective phase-measuring algorithms, and as

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[5]computed Δwx(x,y). One drawback of the lateral-shearing
interferometry is that w(x,y) can not be determined simply
by unwrapping Δwx(x,y). The reason is that Δwx(x,y)
represents the phase differences between w(x,y) and ashifted part of itself, i.e., Δwx(x,y) = w(x,y) - w(x-s,y), notbeing the phase deviations from a reference as in the
Fizeau interferometry. Therefore, a special phasereconstruction algorithm is needed to estimate w(x,y) fromΔwx(x,y). As results of recent rigorous work on thesubject, several estimation algorithms are available andthey may be categorized as either modal or zonal phasereconstruction techniques. [7] In the former, w(x,y) is
described as a sum of modal functions, such as the Zernike
polynomials. Then the coefficients of the modal functions
are determined by approximating the measured Δwx(x,y)
in terms of the sheared forms of the model functions
considering the amount of lateral shear. On the other hand,in zonal phase-reconstruction technique, w(x,y) is
computed point by point on a grid of measured phasedifference points by the application of a least-squares fit to
the measured Δwx(x,y). The two phase-reconstruction
techniques are capable of minimizing the propagation of
measurement errors of Δwx(x,y) in determining w(x,y).
However, they require a considerable amount ofcomputation especially for large size matrix inversion orrecursive multi-dimensional steepest search. In addition,
due to computational truncation errors, they tend to fail togive out precise solutions when w(x,y) is a nearly plane
wave with small aberrations. The DVD objectives
generally have minute aberrations whose total root-meansquare values range within a few hundredth wavelength, so
the problem of computational truncation errors becomessignificant. [3]results many novel algorithms are available. Most
existing algorithms have their own advantages and are
distinguished from each other by the required number andspacing of phase shifts. However, they all assume thatphase shifts be exactly induced as intended. If there are
errors in actual phase shifts due to miscalibration or
external disturbance, no accurate measurement isguaranteed. In our lateral-shearing interferometer, one
problem is that the amounts of phase shifts are not easy to
be precisely controlled. The reason is that the indexmatching oil is squeezed in and out when the prisms are
moved for phase shifting. Consequently the oil thickness
does not remain constant, causing a significant level ofinaccuracy in phase shifting. This problem can beovercome by adopting an adaptive algorithm in that actual
true values of phase shifts are identified directly frominterferograms. Here we adopt a least-squares algorithm inwhich a residual error function related to phase shift errors
is to be minimized to determine true solutions of phase[6]
shifts.
The first step of the least squares phase-measuring
algorithm is to define the differential intensity such as
Jij = Iij - Ii0 = Ci(cosδj - 1) + Sisinδj(3)where Ci and Si are obtained from Eq.(2) such as
Ci=Vicosφiand Si=Visinφi In fact, the differential
intensity Jij represents the variation of intensity induced by
the phase shift δj, assuming δ0 = 0. Then, besides Ci andSi, δj is also treated as unknowns and determined so as tominimize the residual function of

In this investigation, a simple phase-reconstruction
algorithm with less computation is employed which is well
suited for fast inspection of DVD objectives. When theamount of lateral shear s is taken small, the tangential slopeof the wavefront may be well approximated such as

E = [Jij - Jij*]2 = [Ci(cosδj - 1) + Sisinδj - Jij*2 (4)where Jij* denotes the measured value of Jij. The
necessary conditions for the minimization of the function E
are given by the equations ofE/ ∂ Ci = ∂ E/ ∂ Si = ∂ E/ ∂ j = 0.

∂w(x , y) 1
≈ ∆w x (x , y) .
∂x
s

(5)

This approximation becomes more valid if the measuredΔwx(x,y) bears no drastic changes, which is generally thecase of DVD objectives. Recovering the whole wavefrontw(x,y) can then be done using two wavefront slopes along
two orthogonal directions such as

Adopting the special numerical method that has beenthoroughly described by the authors in the reference, theabove equations can readily be solved. When δj, Ci, andSi are obtained, the wavefront difference Δwx(x,y) is
finally determined asw(x,y) = ∫ [Δwx(x,y) =φi/k = (1/k) tan-1(Si /Ci ). (6)4.

∂w(x , y)∂w(x , y)
dx +
dy]
∂x
∂y

(8)

in which Δwy(x,y) represents the tangential slope ofwavefront along the y-direction. As previously explainedin Figure 1, in our interferometer the direction of lateralshear is simply switched to the y-direction by swiveling the

Wavefront reconstruction

Now the wavefront field w(x,y) is to be obtained from the©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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dove prism by 45 degrees. Then Δwy(x,y) can bemeasured following exactly the same procedure forΔwx(x,y).
Reconstruction of the whole field of wavefront isperformed by integrating the measured values of Δwx(x,y)
and Δwy(x,y) as in Eq.(8). In doing that, many integration
paths may be taken to reach a certain point (x,y). In nature,w(x,y) is supposed to be a point function, thus its
computed result should be identical regardless of
integration paths. In practice, the errors in Δwx(x,y) andΔwy(x,y) tend to propagate as the integration proceeds. If
this is the case, the reconstructed w(x,y) varies withintegration paths and becomes inaccurate. The concept isthat every point value of w(x,y) is determined by making
the most of two orthogonal paths; one incremented fromthe x-neighbor point and another from the y-neighbor point.
This is logically expressed asw(x , y) =1dydx
∆w x (x − dx , y)} + {w(x , y − dy) +∆wy(x , y − dy)}][{w(x − dx , y) +
2
s
s

(9)
This averaging is performed for every point in recursive
manner. As results, errors residing in Δwx(x,y) andΔwy(x,y) are averaged out as the integration progresses.
Once the whole field of w(x,y) is obtained, the aberrationsof the test lens is identified by fitting the Zernike circularpolynomials.[1] For that, it is necessary to fit the computed
data points of w(x,y) in unit radius circle by rescaling their
the x- and y-coordinates. The amount of shear is taken
usually as 5 % the pupil diameter of the lens being
measured. Thus interference occurs only in an ellipsoidal
area contracted in the sheared direction, so different scale
factors are needed for rearranging the x- and y-coordinates.
Further, the Gram-Schmidt orthogonalization is needed toconvert the Zernike polynomials to be orthogonal over the[1]
rescaled discrete data points.

6. Conclusions
A lateral-shearing interferometer has specially beendevised and tested for the optical testing of aspheric lenses.
The interferometer is configured with four square prisms
whose relative sliding motions readily provide lateralshearing and phase-shifting necessary for evaluating thebeam wavefront collimated by the lens under test. The
prisms are attached face to face using index-matching oil,so undesirable disturbances from external mechanicalvibration and atmospheric turbulence can be minimized.
Further, a special phase-measuring algorithm of leastsquares has been adopted to compensate for the phaseshifting errors caused by the thickness variation in theindex-matching oil holding the prisms. Measurements
results proved that the interferometer has a repeatability ofless than λ/100.

Acknowledgements
This work is supported by the New University for Regional
Innovation (NURI) program from the Minister of Educationand Human Resources Development, Korea.

References[1] D. Malacara(1992), Optical Shop Testing, John Wiley& Sons, Inc., 2nd edition.
[2] P.F. Forman(1979), Interferometric examination oflenses, mirrors and optical systems, Proc. SPIE, 163,
pp.103.
[3] B.C. Kim(1998), Performance test of aspheric lenses
using optical interference, Korea Advanced Institute of
Science and Technology, MSC thesis.
[4] M. Murty(1970), A compact lateral shearing
interferometer based on the Michelson interferometer,,Appl. Opt. 9(5), pp.1146.
[5] K. Creath(1988), Phase-measurement interferometry
techniques, Progress in Optics, 26, pp.349.
[6] G. Han and S. Kim(1994)., Numerical correction ofreference phases in phase-shifting interferometry by
iterative least-squares fitting, Appl. Opt. 33(31),pp.7321.
[7] G. Harbers, P. Kunst, and G. Leibbrandt(1996), Analysisof lateral shearing interferograms by use of Zernike
polynomials, Appl. Opt. 35(31), pp.6162.

5. Experimental results
0.30µ m0.00µ m− 0.30 µ m− 0.60 µ m(a) A representative shearing interferogram (b) A reconstructed wavefront

Figure 3. Experimental results obtained from a DVD objective

An exemplary shearing interferogram obtained from DVD©ISAROB 2006

objectives is shown in Figure 3, together with its
corresponding wavefront finally reconstructed. The level of
total wavefront distortion of the objectives is usually in therange of a few hundredth wavelength in root-mean-squarevalues. For phase-shifting fringe analysis, 5 interferograms
are sequentially measured with different amounts of phaseshift. Then the phase-measuring algorithm of least squares
determines the wavefront difference

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Permanent Magnet Biased Magnetic Bearings and Robotic Applications
Jin Seok Won

Tae Hyun Son

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

Uhn Joo Na

Sung Hyun Han

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
performance limits of the permanent magnet biased
magnetic bearings. Fan et al (1997) presented systematic
design procedures for permanent magnet biased
magnetic bearings. Fukada and Yutani (1998) studied thefrequency response of permanent magnet biased
magnetic bearings.

Abstract
The theory for a novel fault-tolerant 4-active-pole
homopolar magnetic bearing is developed. If any one
coil of the four coils in the bearing actuator fail, theremaining three coil currents change via an optimal
distribution matrix such that the same opposing pole, Ccore type, control fluxes as those of the un-failed bearing
are produced. The hompolar magnetic bearing thus
provides unaltered magnetic forces without any loss ofthe bearing load capacity even if any one coil suddenly
fails. Numerical examples are provided to illustrate thenovel fault-tolerant, 4-active pole homopolar magnetic
bearings.

Fault-tolerance of the magnetic bearing system is ofgreat concern for highly critical applications of turbomachinery since a failure of any one control components
may lead to the complete system failure. Maslen andMeeker (1995) introduced a fault tolerance of an 8-poleheteropolar
magnetic
bearing
actuatorwithindependently controlled currents and experimentally
verified it (Maslen et. al., 1999). They utilized flux
coupling in a heteropolar magnetic bearing that allows
the remaining coils to produce force resultants identicalto the unfailed bearing. Na and Palazzolo (2001)
provided the optimized realization of fault-tolerantmagnetic bearing actuators so that fault-tolerant control
can be realized for an 8-pole bearing for up to 5 coils
failed, and experimentally verified it (Na et. al, 2002).

1 Introduction
A magnetic bearing system is an active control device
that suspends the spinning rotor magnetically without
physical contact as well as suppresses vibrations.
Magnetic bearings find greater use in high speed, highperformance applications such as flywheel energy
storage systems since they have many advantages overrolling element bearings (Bitterly, 1997). Unlike
electromagnetic heteropolar bearings shown in theprevious works (2003, Na, 2005), permanent magnet
biased homopolar magnetic bearings have a uniquebiasing scheme that directs the bias flux flow into theactive pole plane where it energizes the working air gaps,
and then returns through the dead pole plane and theshaft sleeve. Use of rare earth permanent magnets suchas Samarium-Cobalt and Neodymium-Iron-Boron yields
a very high efficiency when the permanent magnets areused as the source of bias flux to energize the air gaps
and electromagnets are used to supply control fluxes in
the active plane. Sortore et al (1990) and Allaire et al(1992) presented design methods and experimental
verifications for permanent magnet biased magnetic
bearings. Similarly, Maslen et al (1996) also showedboth analytical and experimental results on the designand construction of permanent magnet biased magnetic
bearings. Lee et al (1994) provides design, testing and©ISAROB 2006

Fault tolerant scheme applied to heteropolar
magnetic bearings was modified and extended topermanent magnet biased homopolar magnetic bearings
in Na’s work (Na, 2004). He presented the optimized
realization of fault tolerant 8-pole homopolar magnetic
bearings, so that the same magnetic forces can berealized up to any combination of 5 coils failed out of 8
coils. However, the overall load capacity of the bearing
is reduced as coils fail. The same magnetic forces arethen preserved up to the load capacity of the failed
bearing.

2 Bearing Model
A 4-active pole, permanent magnet biased
homopolar magnetic bearing is analyzed. Assuming that
eddy current effects and material path reluctances are
neglected, Maxwell’s equations are reduced to theequivalent magnetic circuit for the homopolar magnetic
bearing. The reluctance in air gap j of the active poleplane is;603

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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Rj =

gj

µ 0 a0g j = g 0 − x cos θ j − y sin θ jlaw and an adaptive current distribution mechanism. A
simple PD feedback control law is used to stabilize thesystem. While the feedback control law remains
unaltered during the failure the appropriate current
distribution matrix T can be continuously updated
according to the failure status of coils (Na et. al., 2002).
The fault-tolerant control system is simulated on ahorizontal rigid rotor supported on the 4-active polehomopolar bearings. The symmetric rotor used in thisanalysis has mass of 12 kg, the polar moment of inertia
of 0.05 kgm2, and the transverse moment of inertia of
0.36 kgm2 about the mass center. Two magnetic bearings
are located at 0.22 m either side of the mass center. An

(1)
where
(2)

Applying flux conservation law to the magnetic circuit
results in one more equation. Flux to current relations on
the magnetic bearings is described as,(3)RΦ = H + NI
The 4-active pole homopolar magnetic bearing utilizes
4 independent coils each driven by its power amplifiers
so as to obtain the flexibility of a coil failure.
(4)
B = ςA−1R −1 ( H + NI )The magnetic forces developed in the active pole plane
are described as;

f x = BT

∂D
B
∂x

(5)

f y = BT

∂D
B
∂y

(6)

−5

unbalance eccentricity of 1.0 × 10 m is applied at bothbearing locations with relative phase angle 90°. The
power amplifiers are simply modeled with the DC gainof 1 Ampere/Volt. The sensors are also simply modeledwith a DC sensor gain of 7874 volt/m. The closed loop
bearng stiffness and damping can be adjusted by tuning
the PD control gains (Keith et. al., 1990). The designed
PD controller has the proportional gain of 60 and thederivative gain of 0.05. The proportional control gain of
K p is tuned to produce the positive closed loop bearing

3 Fault Tolerant Control

stiffness. The derivative control gain of K d is tuned to

The 3-D magnetic field model is constructed for thedesigned homopolar magnetic bearing. A commercial
magnetic field software (OPERA3D) is used for the 3-D
field calculation. Two cases of currents, which are the~normal current I1 = T vc and the 4th coil failed current

add enough closed loop bearing damping.
The transient response from normal operation withno failure to fault-tolerant control with the 4th coil failed
for both bearings was simulated for nonlinear force
modeled bearings at 10,000 RPM. The nonlinear
magnetic bearings are used for a more stringent test.
Figure 2 shows transient current inputs from the normal
unfailed operation through failure of the 4th coil of the
bearing A failed at 0.05 sec. The transient response of
the displacements at the bearing A is shown in Fig. 3.
Displacements and flux density variation at Bearing A
remain unchanged even after the 4th coil fails, while thethree remaining currents are redistributed via T4 after the
4th coil fails at 0.05 seconds. This means that any one
coil out of four coils is free to fail while bearing
properties such as the load capacity and stiffness remain~
invariant, if T is replaced by T1 , T2 , T3 , and T4 shortly

I 2 = T4vc , are applied on the 3-D magnetic bearing
model at the centered position to calculate magnetic
fluxes and magnetic forces. The feedback voltages are
simply assumed to be vcx = 3.0 cos Ωt , vcy = 3.0 sin Ωt ,t ∈ [ 0, 2π / Ω ] . 12 current sets of I1 and I 2 att = [π /(6Ω), 2π /(6Ω),. . . .,12π /(6Ω)] are applied on
the model such that the corresponding magnetic fluxes aswell as magnetic forces should be calculated at eachcurrent sets. Flux distribution in the active pole plane
driven with the current set is shown in Fig. 1.

after failure.

Fig. 2 Current Plot for Normal Operation to the 4th
Coil Failed Operation

Fig. 1 Flux Distribution Calculated with 3-D Finite
Element Model by Current Set
The fault tolerant control system consists of two
independent parts, which are a feedback voltage control©ISAROB 2006

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Allaire, P.E., Mikula, A., Banerjee, B.B., Lewis, D.W.,
and Imlach, J., 1989, “Design and Test of a Magnetic
Thrust Bearing,” Journal of the Franklin Institute, Vol.
326, pp. 831-847.
Bitterly, J.G, and Bitterly, S.E., 1997, “Flywheel
Based Energy Storage System,” U.S. Patent No.
5,614,777.
Fan, Y., Lee, A., and Hsiao, F., 1997, “Design of a
Permanent/Electromagnetic
Magnetic
BearingControlled Rotor System,” Journal of the Franklin
Institute, Vol. 334B, pp. 337-356.
Fukata, S., and Yutani, K., 1998, “Characteristics ofElectromagnetic Systems of Magnetic Bearings Biased
with Permanent Magnets,” Proceedings of the Sixth
International Symposium on Magnetic Bearings, pp.
234-243.
Keith, F. J., Williams, R. D., and Allaire, P. E., 1990,“Digital Control of Magnetic Bearings Supporting a
STLE
Tribology
Multimass
Flexible
Rotor,”
Transactions , Vol. 33, pp. 307-314.

Fig. 3 Displacement Plot for Normal Operation to
the 4 Coil Failed Operationth

4 Conclusion
A fault-tolerant control scheme is developed for thepermanent magnet biased, 4-active-pole homopolar
magnetic bearings. The present work extends Na’s
previous result (Na, 2004) such that unique fault tolerant
schemes are realized for 4 active pole homopolar
magnetic bearings. If any one coil out of four coils
suddenly fails, the remaining three coil currents can beredistributed in a way such that the same C-core type,control fluxes as those of an un-failed bearing arepreserved in the active-pole plane.
The bias voltage linearization can be realized for thefailed bearing in a manner such that the magnetic forces
are decoupled by using a modified current distribution
matrix after a coil failure. The Lagrange Multiplier
optimization with equality constraints is utilized to
calculate the optimal distribution matrices for the failed
bearing.
Three dimensional finite element magnetic bearing
model is also constructed. The magnetic forces
calculated with the 3-D finite element model are wellmatched with those calculated with the 1-D circuit model.
The fault-tolerant control system is simulated on ahorizontal rigid rotor supported on the 4-active polehomopolar bearings. Dynamic simulation shows thatvery much the same vibrations as well as the same fluxes
are maintained throughout the failure event.
.

Lee, A.C., Hsiao, F.Z., and Ko, D., 1994, “Analysis
and Testing of a Magnetic Bearing with Permanent
Magnets for Bias,” JSME International Journal, Series
C , Vol. 37, pp. 774-782.
Lee, A.C., Hsiao, F.Z., and Ko, D., 1994,“Performance Limits of Permanent-Magnet-Biased
Magnetic Bearings,” JSME International Journal, Series
C , Vol. 37, pp. 783-794.
Maslen, E.H., Allaire, P.E., Noh, M.D., and Sortore,
C.K., 1996, “Magnetic Bearing Design for Reduced
Power Consumption,” ASME Journal of Tribology , Vol.
118, pp. 839-846.
Maslen, E.H., Meeker, D.C., 1995, “Fault Toleranceof Magnetic Bearings by Generalized Bias Current
Linearization,” IEEE Transactions on Magnetics, Vol.31,
pp. 2304 – 2314.
Maslen, E. H. , Sortore, C. K., Gillies, G. T., Williams,
R. D., Fedigan, S. J., and Aimone, R. J., 1999, “A Fault
Tolerant Magnetic Bearings,” ASME Journal ofEngineering for Gas Turbines and Power, Vol. 121, pp.
504-508.
Na, U.J., 2004, “Fault Tolerance of Homopolar
Magnetic Bearings,” Journal of Sound and Vibration,
Vol. 272, pp. 495-511.
Na, U.J., 2005, “Fault Tolerant Control of Magnetic
Bearings with Force Invariance,” Journal of Mechanical
Science and Technology, Vol. 19. pp. 731-742.

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

Na, U.J., Palazzolo, A.B., and Provenza, A., 2002,“Test and Theory Correlation Study for a Flexible Rotor
on Fault-Tolerant Magnetic Bearings,” ASME Journal of
Vibration and Acoustics, Vol. 124, pp. 359-366.
Sortore, C.K., Allaire, P.E., Maslen, E.H., Humphris,
R.R., Studer, P.A., 1990, “Permanent Magnet Biased
Magnetic Bearings – Design, Construction and Testing,”
Proceedings of the Second International Symposium on
Magnetic Bearings, pp. 175-182.

References
Allaire, P.E., Maslen, E.H., Humphris, R.R., Sortore,
C.K., Studer, P.A., 1992, “Low Power Magnetic Bearing
Design for High Speed Rotating Machinery,”
Proceedings of the NASA International Symposium on
Magnetic Suspension Technology, pp. 317-329.

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Fundamental Study of Dielectric Elastomer as Artificial Muscle
Junnici Hayashida (Matsue National College of Technology)
Fengzi Dai (Matsue National College of Technology)
Yutaka Fujihara (Matsue National College of Technology)

expansion and contraction of the artificial muscle.
In the experiment, the dielectric elastomer film isimpressed about 4kV. The large electric field is
occurred between the electrodes. And bothelectrodes attract each other. In our experiments,
the three kinds of film of silicon rubber, acrylic
form elastomer and urethane rubber are used.
These films occur elastic deformation, and expandin the horizontal direction.

Abstract
In this paper, we focus attention on the soft
actuator that generally called artificial muscle.
The artificial muscle can move linearly and issmall and lightweight. Also, a basic experiment
was done for the aim of the application of thedielectric elastomer EAP (Electro Active
Polymer) as the artificial muscle.
Key word: EAP, Artificial Muscle

1.

Introduction [1]Presently, the needs of small size and weightsaving for actuator are increased further by thedevelopment of the technology. For example,
the miniature camera mounted in the cellular
phone needs a small actuator. At the same time,
the robot that mimics human begins to arrive onmarket. However, the movement of that robot is alittle awkward motion. It is believed that therotation movement by the servo motor causes the
awkward motion of robot. Contrarily, the humanmuscle moves linearly in contrast to the robot.
From this example, we see that the actuator whichmake a linearly movement is necessary.

2.

Fig.1

3.

Basic experiment [2] [3] [4]The stress that occurs between the electrodes canbe expressed by the equation (1).

Dielectric elastomer EAP artificial
muscle [2] [3]P = ε 0ε r E 2

The dielectric elastomer EAP artificial muscle
is structured to hold a soft electrode between two
sheets of dielectric elastomer as shown in Fig.1.
The thickness of the film is 100μm. A soft
electrode is used to avoid disturbing the©ISAROB 2006

Structural drawing

where,
P:stress, ε r :relative permittivity, ε 0 :permittivity in vacuum, and E :electric fieldstrength.

611

(1)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

By the experiment, we confirm that thedisplacement of the film and the relationshipbetween the electric field strength and the stressare correspond to the equation(1). And also we
examined that which film among the threematerials is suitable for artificial muscle.

By another experiment, an influence to thedisplacement can be examined when conductive
grease is used for electrode and when carbon
powder is used for electrode. But this experimentis not done because material was not enough as
for the urethane rubber.

4.

Experimental result andconsideration

Fig.4 indicates the relationship between thedisplacement of film and the electric field strengthfrom the result of the experiment. This figure
shows that the displacement of film increases
according to the curve of parabola. Therefore, theresult of the relationship between thedisplacement and the electric field strength iscoincided with the equation (1).
Fig.2

Photograph of experimental sample

2500

Displacement[μm]

2000

1500

1000

500

Fig.3

Structural drawing of experimental sample

00

The three kinds of material, silicon rubber,acrylic form elastomer and urethane rubber wereused as the experimental sample. Each sample
was fixed on the acrylic frame shown in Fig.2.
Fig.3 is the structural drawing of Fig.2. The
reason of the fixation to the flame is to preventthe film from the slackening and to guard against
the failure of the electric discharge that happens
easily in the experiment using the high voltage.
Conductive grease was used for the electrode.
And the diameter of the electrode is 15mm. The
voltage of 0V∼4kV was applied to the space ofthe electrode. The displacement of the film wasmeasured by image processing from the computer.

©ISAROB 2006

20
40
60
80
Electric field strength [MV/ｍ]

100

Urethane rubber（conductive grease）
Silicon rubber（conductive grease）
Silicon rubber（carbon powder）
Acrylic form elastomer(conductive grease）
Acrylic form elastomer（carbon powder）

Fig.4 Graph of electric field strength displacement
And it is clarified that acrylic form elastomer
has the biggest displacement among these threematerials the second is the silicon rubber, andurethane rubber became the smallest. In other612

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006preliminary experiment, we have got the resultthat the relative permittivity of silicon rubber is
3.18 and the relative permittivity of acrylic form
elastomer is 3.98. Supposing the relative
permittivity of the material had a much effect on
the displacement, the acrylic form elastomer andthe silicon rubber would be similar of
displacement.
But, the result clearly shows that the size of thedisplacement of silicon rubber and the acrylic
form elastomer is different. Therefore, it can be
estimated that not only the electric character but
also the mechanical character such Young’s
module greatly influence the displacement.
Dose the electrode also has any effect on thedisplacement. In Fig.4, the displacement using
powder electrode was bigger than that using
conductive grease. When the powder electrode isused, the contact point of the conductive particle
is conducting mode. But, when conductive grease
was used, the grease exists between the
conductive particles. Therefore, it can be assumed
that the displacement of conductive grease issmaller than that of the conductive powder.
From these results, acrylic form elastomer issuitable as the material for artificial muscle.
Because the displacements of other materials are
small, application is limited. However, theconductive grease is used for the electrodebecause the conductive grease is easy to be usedin the experiment.
Furthermore, by the visual observation, it isconfirmed that the response of acrylic form
elastomer is slower than silicon. However, theresponse time of acrylic form elastomer is about
in one second, acrylic form elastomer had no
problem to use as the actuator that is linearlymotion type.

5.

saving and the small sizing can be done. The
structural and the cross section drawings areshown respectively in Fig.5 and Fig.6.

Fig.5

Fig.6

Cross section

The application example as
actuator
The parts that fix the lens are wrapped with theelastomar film. And the parts are pushed up by the
spring. Therefore, the structure is very simple.
And, there is an advantage that the number ofparts used decreases. However, because the highvoltage power supply is used, it is necessary to
pay attention to need the electrical discharge.
The movement for focus adjustment responds
at once when the voltage was applied. And themechanical noise and electromagnetic waves canbe prevented.

In this paper, the actuator is used for the focal
control of the lens as application example. And,the trial production of application was made.
In the past, the focal control of lens
used the electromagnetic motor or the ultrasonicmotor that consist of many machine parts. These
motors can accurately decide the position for
focal adjustment. While on the other hand, thesesystems need high reliability,
complexmechanisms and precise parts to move accurately.
When the dielectric elastomer EAP artificial
muscle was used, both complex mechanism andprecise parts are not needed. Because the artificial
muscle can operates linearly, therefore the weight©ISAROB 2006

The structural drawing

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6.

Conclusions

In this paper, the possibility of the soft actuatorby the dielectric elastomar EAP artificial muscle
was researched. The basic experiment of artificial
muscle was done. And the possibility of theapplication of artificial muscle was confirmed.
The results obtained by the experiment wereshown as follows.
1)

The displacement of acrylic form elastomer
occurred the biggest of experiment sample.

2)

The acrylic form elastomer is suitable as thematerial of the application.

3)

The actuator for the focal control of the lens
was produced, and the operation was
confirmed.

Reference[1] Haruo Sugi, The muscle is mysterious(Japanese), p.p16-64, Koudansya, (2003)[2] Yoshihito Osada, The front of soft actuator
development (Japanese), NTS, (2004))[3] Scott Stanford, Neville Bonwit, Ron Pelrine,
Roy Kornbluh, Oibing Pey, Seiki Chiba,“Electroactive Polymer Artificial Muscle(EPAM) for Biomimetic Robotics”, Second
conference on artificial muscle, National
Institute of Advanced Industrial Science andTechnology, (2004)[4] Yoseph Bar-Cohen, Electroactive Polymer[EAP] Actuators as Artificial muscles,
p.p529-581, SPIE, (2004)©ISAROB 2006

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618

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Prediction of the Aperiodic Time Series of a Visual Target by Humans
Manabu Shikauchi, Shin Ishii and Tomohiro Shibata
Graduate School of Information Science,
Nara Institute of Science and Technology,
8916-5 Takayama, Ikoma, Nara 630-0192, JAPAN
Abstract

uously moving target.

It is advantageous for animals and humans to predictchanges in the environment. To uncover mechanisms forsuch a prediction is one of the most important topics inbrain science.
In our study, aperiodic time series generated by an
AR process were sequentially presented by means of asmall target at a regular time interval, and subjects wereasked to predict the next target position. The result showsthat the effects of learning were not clearly observed,
while it was observed that the subjects tried to predict
the next target position based on a low-order AR model,
and some performance-improvement was also observed
through long-term trials.

2

Methods

2.1

Visual stimulus

We used the AR process to produce the time series presented to the subjects (the left panel of Figure 1), because
the AR process is aperiodic, and then the possibility thatthe subjects predict based only on memorized patterns canbe excluded. In addition, a quantitative analysis becomes
possible by using the AR process; if the optimal prediction
defined later is achieved by subjects, the distribution of theprediction error would be close to the noise distribution that
the AR process employed. If subjects have no knowledge
about the AR model and its parameters, then the subjects
have to learn the model based on the presented time series
in order to achieve the optimal performance.
In this study, we adopted a first-order AR model as thesimplest case:

Keywords
Aperiodic time-series, auto-regressive process, online
learning, prediction.

1 Introduction

S(t) = 0.5S(t − 1) + ξ(t),where S(t) and ξ(t) denote the target position and the system noise following the normal distribution N (µs , σs ), attime t. In this study, the mean µs and the standard deviation(SD) σs were set to -0.4 [mm] and 18.8 [mm], respectively.
As contrasted to the task using the AR model (AR task),
we prepared a task in which the time series produced by thecorresponding AR process were shuffled (Random task), as
shown in Figure 1. The linear prediction theory tells us thatthe following prediction is optimal in the AR task:

It is advantageous for animals and humans to predictchanges in the environment. To uncover mechanisms forsuch a prediction is one of the most important topics inbrain science. We are concerned that humans learn the underlying dynamics of objects and that humans can do themost suitable prediction when the time series from the objects are aperiodic but have hidden linear dynamics.
Shibata et al.[1] investigated the ability of prediction inhumans utilizing smooth pursuit eye movements which are
specific to primates. They conducted a task in which eachsubject was asked to pursue aperiodic target motions generated by an Auto-Regressive (AR) process. Their results
suggested that subjects were capable of (1) online learning of a hidden linear dynamics in the AR process (the AR
model), and (2) performing predictive tracking similar tothe optimal filtering to some extent.
This study aims at investigating whether such predictive
ability of humans as reported by Shibata et al. works forvisual perception as well. In our study, discrete time series
was used in contrast to Shibata et al’s work using a contin-©ISAROB 2006(1)

P (t) = 0.5P (t − 1),(2)

where P (t) is the predicted position at time t. The prediction error distribution in this case follows the system noise
distribution N (µs , σs ).

2.2

Subjects

Two healthy male subjects (MO and TU), whose eyesights were normal (the subject TU had corrected eyesight),
were participated in the experiment.

619

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

AR process

Random time series

65
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[Trial]25

Figure 1: Target time-series of the AR task (left) and therandom task (right).

Figure 2: One trial consisting of a fixation period of1500 [ms] and a prediction period of 1500 [ms]. See thetext for more detail.

2.3 Procedure
the target disappeared 1500 [ms] later with a notification
sound. Then, a prediction period of 1500 [ms] was started
and finished with another sound. In the prediction period,
each subject was required to predict the next target position
and keep gazing at the position (prediction gaze) until thesubject heard a sound. After a 1000 [ms] rest, the next trial
was started.
One block consisted of 25 trials, and two block types
were designed. One was the AR block where the timeseries were generated by an AR process, and the other was
the random block where the time series were an ordershuffled variation of the ones produced by the AR process.
Each subject took part in the experiment of 48 blocks (1200
trials) where 24 random blocks and 24 AR blocks wererandomly aligned.
Note that all time series were different in each blockwith the others, due to the probabilistic nature of the ARprocess, although they were generated by the same AR
model with the same noise function ξ(t).

Each subject was required to predict the target positionat the next discrete time step, but not informed about theway to generate the target time-series. Subjects presented
their prediction by their gaze at a point on a monitor. Although this study does not focus on the eye movement, wechose an eye movement as a device for pointing out theprediction rather than, for example, a hand movement, because one-dimensional eye movement was considered tohave higher measurement accuracy with less physical efforts.
A horizontal graduated ruler was presented on the monitor because subjects participated in a pre-experiment reported the difficultly to gaze at a certain point without theruler. The ruler was presented just beneath the horizontal
location where the target was presented. The scale resolution of the ruler was 5 [mm] and the range was ±65 [mm](view of ±4.6 [deg]).
We also needed to correct the time series generated by
the continuous-valued AR process to fit the discrete-valued
scales. In this study, we took a very simple correction
method that each original position was shifted to the nearest scale. Then, every position in the time series took oneof 27 quantized values.
A target was presented by a standard PC (DELL, Pentium -746 [Hz]) on a TFT color LCD monitor of 15.4
[inch]. Subjects sat down, and fixed their head on the position of 80 [cm] from the monitor. To fix the subject head,
we used a bite bar and a chin rest. Eyelink (SR Researchcompany) was used for measurement of eye movements,
and a sampling frequency was 250 [Hz].
Figure 2 shows one trial consisting of a pair of a fixation period and a prediction period. In a fixation period in
the t-th trial, a fixation target S(t) appeared on the monitor, and the subject gazed at the target (target gaze) until©ISAROB 2006

2.4

Analysis

We conducted two types of analyses. One was to examine whether humans were able to perform good prediction
based on some learning related to the hidden linear dynamics of the AR model. The other was to investigate howmuch complicated model humans actually utilized.
For the former analysis, the SD of the prediction error
was used as an indicator to evaluate human’s prediction andlearning. The SDs of prediction error over trials and blocks
were defined respectively as√∑Nblock(En (t) − µ)2
SDtrial (t) = 1/(Nblock − 1) n=1
(3)

620

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006√
∑Ntrial1/(Ntrial − 1) t=1(En (t) − ν)2 ,(4)
where En (t) denotes the prediction error in the t-th trialof the n-th block, Ntrial the number of trials in eachblock, Nblock theof blocks in each task, and∑Nnumberblockµ = (1/Nblock ) n=1
En (t). Ntrials and Nblock were
set to 25 and 24, respectively, throughout the experiment.
Note that En (t) = Sn (t + 1) − Pn (t + 1), because subjects
predicted the next position Sn (t + 1) in the t-th trial.
Analysis of variance (ANOVA) was used for statisticalanalysis. We detected significant difference with the condition of p-value < 0.05.
As for the second analysis to investigate how much complicated model humans actually utilized, AR models werefitted to the subject’s behavioral data, and the selection of
the model’s order was performed according to the Akaike’s
information criterion [2]: AIC(q) = Ntrial ·logσs2 +2(q +1), where q is the order of the estimated model. Note thatthe choice of the 0-th order model means the preference ofa model with no-dynamics but drove only by a noise.
Matlab (Mathworks) and its statistics toolbox were used
for all statistical analyses.

SD of Prediction Error [mm]SDblock (n) =ARRandom

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SD of Prediction Error [mm][Trial]3 Results

subject : TU
AR
Random

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[Trial]Figures 3 and 4 show the results of SDtrial (t) andSDblock (n), respectively, in the AR task and the randomtask done by the two subjects.
The results show that the difference between the ARtask and the random task was not statistically significant
in most trials and blocks for both the subjects.
Because, as Figure 3 indicates, a monotonic decreaseof SDtrial (t) was not observed, SDtrial (t) was divided
into 5 sections of every 5 trial, and multiple comparison(Scheffe’s) was used in each trial section for a statisticalanalysis. For subject MO, there was no significant difference in SDtrial (t) section. For subject TU, SDtrial (t) of
solely the 4th section decreased temporarily. In short, there
was no learning effect observed between trials.
Because, as Figure 4 indicates, a monotonic decreaseof SDblock (t) was not observed, SDblock (t) was divided
into 4 sections of every 6 block, and multiple comparison(Scheffe’s) was used in each block section for a statisticalanalysis. For subject TU, a tendency to decrease was observed. In short, there was a learning effect between blocks
for this subject.
The block count was collected about the AR order ofprediction time series, which was identified based on AIC(Table 1). The block count was 0 about the identified orders
being 7th- 24th. The number of blocks with the 0-th order
was the largest for both the subjects and each task. The
rates of blocks with the 0-th, the 1st, and more than the 2nd©ISAROB 2006

subject : MO

Figure 3: SDtrial (t) of MO and TU. The solid line shows
the SDtrial (t) of the AR task, while the broken line
SDtrial (t) of the random task. Error-bars show 95% confidence intervals.

in the AR task were each 63%, 17% and 21% for subject
MO, and were each 50%, 29% and 21% for subject TU.

4Discussions

The results have shown that the effects from learning were not clearly observed, but some performanceimprovement was observed through long-term trials. Figure 3 exhibits wide error-bars corresponding to 95% confidence intervals. These wide error-bars might be a reason
why we were not able to see the different in performance
between the AR task and the random task as well as thedecrease in errors over trials, part of which could be due tothe small number of samples. We also found that there wasno large difference between the system noise input to the
AR task and the random task, which might prevent us fromfinding the difference in the subject’s behaviors between
these tasks.
Another analysis has suggested that the subjects tried to621

SD of Prediction Error [mm]

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

subject : MO

Table 1: The number of blocks with 0-th-6th.
the AR task
0th 1st 2nd 3rd 4th 5th 6th
MO
15
4
3
2
0
0
0
12
7
4
0
0
0
1
TU

AR
Random

60
50
40
30
20
10

MO
TU

0
1

5

10

15

20

24

0th
20
18

1st
2
4

the random task
2nd 3rd 4th
1
1
0
0
2
0

5th
0
0

6th
0
0

SD of Prediction Error [mm][Block]subject : TU

tion explicitly driving motion perception pathways in thebrain [3, 4], while, in our study, the time series were carefully designed not to be perceived as apparent motion [5].
Therefore, our study would suggest that perception of underlying continuous motions is necessary for accurate prediction and learning of discrete time series. To make sure
this conjecture will be an interesting future work.

AR
Random

60
50
40
30
20
10

Acknowledgement
A part of this study has been supported by the Inamori
research grant.

0
1

5

10

15

20

24

[Block]References

Figure 4: SDblock (t) of prediction error with each block.
there are the result of MO, TU from the top. The AR(solidline) is of the AR task and the random (broken line) is of
the random task. Error-bar is 95% confidence intervals.

[1] T. Shibata, D. Kawawaki, Y. Fujiwara, K. Doya and
M. Kawato: “Prediction and learning of aperiodic visual target motion in human smooth-pursuit eye movements”, Technical report of IEICE, 103, 602, pp. 13–
18 (2004).

predict the next target position by using some dynamics,
because the 0-th order was much identified in the randomtask compared to in the AR task, and in contrast, the firstor later order was much identified in the AR task comparedto the random task. The retrospective reports of the subjects revealed that they did not imagine an underlying AR
model nor the fact that two types of the task were mixed.
The identified order higher than the first order in the caseof the AR task may suggest that they tried to use a complicated rule and/or switched their strategies, which utilized
the memory of past time-series.
Our current study is based on the Shibata et al.’s study
which examined smooth pursuit eye movements of humans. They showed that humans were able to predict thetarget motion generated by a third-order AR process withthe help of online learning of the AR model. Comparing
this study to the Shibata et al.’s study, the most salient difference seems to lie on the perception of the target motion, i.e., their presented stimuli were perceived as a mo-©ISAROB 2006

[2] H. Akaike: “On the use of a linear model for the identification of feedback systems”, Ann Inst Statist Math,
20, pp. 425–439 (1968).
[3] H. Komatsu and R. Wurtz: “Relation of cortical areas
MT and MST to pursuit eye movements. I. Localization and visual properties of neurons”, J Neurophysiol,
60, 2, pp. 580–603 (1988).
[4] 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 (in press).
[5] J. v. d. Steen, E. Tamminga and H. Collewijn: “A comparison of oculomotor pursuit of a target in circularreal, beta,, or sigma motion”, Vis Res, 23, 12, pp.
1655–61 (1983).

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Robotic Emotion Generation using Dynamics-based
Information Processing
Minoru HASHIMOTO

Yuki KATOH

Department of KANSEI Engineering
Shinshu University

3-15-1 Tokida, Ueda, Nagano 386-8567, Japan

Mechatronics Division
Harmonic Drive Systems Inc.
1856－1 Maki, Hotaka, Azumino, Ngano 399-8305, Japanhashi@shinshu-u.ac.jp

yuki.kato@hds.co.jp

Abstract

Freeman [4] finds that there is chaotic dynamics in biologicalpattern recognition of the olfactory system.
Okada et al. [5]-[7] proposed a polynomial design of thenonlinear dynamics for brain-like information processing ofwhole body motion of robots. This technique allows us toperform dynamic information processing using the nonlinear
dynamics network with the polynomial configuration.
Therefore, this method is useful, not only for motion
generation of humanoids, but also for information processingof the robotic emotional generation.
In this paper we propose a framework to generate
robotic emotions based on the dynamics-based information
processing with the polynomial design during human-robot
communication. We assume face to face communication as ahuman-robot interaction. The human facial expression is used
as an external stimulus for the robot. The robot reacts
continuously to the human facial expression.
This paper will address the following issues: In section
2 the emotion system that we utilized is explained. In section
3 the computation method of the vector field is describedbased on the polynomial approximation. In section 4 thedesign of dynamics for discrimination is proposed. In section
5 we describe conditioning of the emotion part. In section 6
the method of the robotic facial expression based on theemotional state is addressed. Then we report on theexperimental results in section 7. Lastly, we make theconclusion in section 8.

This paper provides a framework to generate robotic
emotions during human-robot communication. The proposed
method utilizes dynamic systems to make continuous information
processing for emotion generation. The synchronization andentrainment of the dynamic system are used for information
processing.
The emotion generation system consists of
discrimination, emotion and expression parts.
This paper
describes the design of the vector filed for the continuous
discrimination of stimuli and emotion generation. We realize
dynamic discrimination simply by using the proposed method.
By introducing a conditioning the emotional response develops
over time. The proposed method is examined by the simulations
and experiments. The results show the effectiveness of thecontinuous discrimination, the emotion generation and theexpression.
Index Terms - Dynamics, Facial recognition, Communication
robot, Robotic emotion, Human-robot interaction.

1 Introduction
Emotion plays a very important role in our daily
communication. Although there are many communication
robots, which have emotion-like responses [1], in most cases
these responses do not react to the external stimuli that are not
pre-programmed. In order to make human-like emotion it isimportant that the robot develops the intrinsic emotional states
based on the communication with human, and changes the
response according to the intrinsic emotional state.
On the other hand neural scientists investigate theinformation processing of emotions in the brain. The
emotional states we subjectively experience are the end resultof information processing that occurs unconsciously as thebrain decodes the significance of stimuli in order to shape
appropriate behaviour. LeDoux [2] finds that the amygdala
structure within the brain is important in responding toemotions, particularly fear. The amygdala mediates fear andother responses and actually processes information more
quickly than other parts of the brain, allowing for a rapid
responses that can by life saving before other parts of thebrain have had a chance to react. The roles of amygdala
structure are discriminations of stimuli, combinations between
stimuli and responses, and generations of commands forresponses [3]. The relationships between stimuli andresponses are installed inherently in the human body. The
relationship develops by conditioning from experiences.
Information processing is performed by dynamic processes.

©ISAROB 2006

Fig.1 Structure of the emotional system.

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2 Emotional Generation System

where,

a101 L a1nn ⎞⎛a⎟⎟
A = ⎜⎜ 100⎝ a200 a201 L a2 nn ⎠V = (Vs1 Vs 2 Vs 3 L VsL )Figure 1 shows a structure of an emotional system which
consists of a discrimination part, an emotion part andexpression part. The discrimination part identifies the kind of
stimuli such as facial expressions in the environment. In theemotion part the discriminated stimulus is examined theinternal state, and the combinations between stimuli are
generated by classical conditioning. Lastly, in the expression
part the expression command trajectories are generated basedon the emotional state that is output from the emotion part.
.

Θ = (θ( X s1 ) θ( X s 2 ) θ( X s 3 ) L θ( X sL ) )
1
⎞
⎛
⎟
⎜
⎜ xsk 2 ⎟θ( X sk ) = ⎜ xsk1 x 2 sk 2 ⎟
⎟
⎜
M
⎟
⎜
⎜ xn xn ⎟⎝ sk1 sk 2 ⎠Θ # means a pseudo inverse matrix of Θ .

VshFig.2 Schematic drawing of thevector field.

4 Design of Vector Field for Discrimination
4.1 Discrimination using vector fields
The discrimination part distinguishes between stimuli
by using vector fields. The time series data of a stimulus isobtained through the sensors such as a camera. The robot has
to identify a kind of stimuli from the time series data. Then avector field of dynamics is used. The vector field consists of
input dimensions and output dimensions. Figure 4 shows anexample of facial expression discriminations in which there
are two input dimensions and one output dimension. The
axes of the input dimensions, X1 and X2, show the data ofmouth width and eye height, respectively. We make a
trajectory of the attractor based on the time series data of the
facial expression as shown in Fig.5 (a). The output dimension
indicates the symbol of the facial expression. The output
dimension is constructed by time series data which increaselinearly as time (Fig.5 (b)). Combining the input and output
dimensions a discrimination vector field such as in Fig.6 isobtained. The vector field is written by equation (4). The
vector field with the region of the entrainment is generated bya polynomial design method described in the former session.

X sh
Fig.3 Making the vector field.

3 Making Vector Field
An example of a vector field in the two dimensional
space is shown in Fig.2. The vector filed is formed aroundarbitrary curve C. The curve C is assumed as an attractor, andregion D is a basin of entrainment around the attractor. The
curve C is a function of discrete time t and consists of，where
X = {X(1), L , X(t ), L , X( m )}, ( X ∈ R N )N expresses the dimension of the state space. We make avector Vsh which directs from arbitrary point X sh in thebasin D to the curve C as shown in Fig.3. The vector isdefined by the following equation.

Vs h = γ ( X (k ) − X sh ) + ( X (k + 1) − X (k ))(1)where, γ is a constant, 0 ≤ γ ≤ 1 . We make many vectors

x&1 = f1 ( x1 , x 2 , y )x& 2 = f 2 ( x1 , x 2 , y )Vsh (h = 1,2L L) in the basin D in this way andapproximate them by a polynomial expression. When X ∈ R ,the polynomial is as follows,y& = f 3 ( x1 , x 2 , y )

2

n

n

v sh1 = x& sh1 = ∑∑ a1ij x shi 1 x shj 2
i =0 j =0
n

(2)

n

v sh 2 = x& sh 2 = ∑∑ a 2ij x x
i =0 j = 0

where, Vsh = (vsh1 , vsh 2 )

T

and

i
j
sh1 sh 2

X sh = ( xsh1 , xsh 2 )T . The

values of the parameters a1ij , a 2 ij can be obtained by theleast-squares method.

A = VΘ #©ISAROB 2006

Fig.4 Discrimination using dynamics.

(3)

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Fig. 5 Attracter of input and output spaces. (a)Input space. (b)Output space.

(a)(b)
Fig. 9 Examples of the stabilized discrimination space.

5 Emotion Part

The emotion part recognizes the meaning of the stimulus
and evaluates the value based on the viewpoint of selfpreservation. The meaning recognition gives a relationshipthat the input stimulus is connected to the output response. We
design the basic relationships between stimuli and responses
according to the biological inherent responses. Relationships
are developed by classical conditioning.
The information
processing of the conditioning is performed in the emotion
part using the symbols of the stimuli which are obtained fromthe discrimination part (Fig.10).

Fig.6 Vector field of discrimination space.

4.2 Stabilization
When the input data is located in the entrainment
region, the discrimination vector field outputs a correct
symbol of the output dimension. However, if the input data is
located outside of the entrainment region of the attractor in the
input dimension, the output signal diverges, since the vector
field is not defined outside of the entrainment region. In orderto stabilize the output signal we introduced another vector
field outside of the initial entrainment region. The new vector
field has an attractor located on the origin of the space asshown in Fig.7. We call this space a stabilized vector field.
An example of the stabilized vector field is shown in Fig.8.
The initial attractor increases in a monotone in the input spaceas an example. The stabilized vector field whose attractor is
located on the origin is constructed on the outside of the initialentrainment region. The behaviour of the discrimination spaceis examined with the sample input data. The sample input
data x1 (t ), x2 (t ) is given, and the output data y (t ) is
computed. At first, the input signal behaves on the outside ofthe entrainment region, and then the input signal follows the
trajectory of the attractor as shown in Fig.9(a). The black spot
is starting point of the state. There is no divergence on the
output signal, and the output signal increases after the input
signal goes into the entrainment region. The other example isshown in Fig.9(b). The input signal follows the trajectory of
the attractor at the beginning, and then the signal changes the
direction of motion to the reverse direction.
The output
signal increases as the input signal increases at first, and thenthe output signal decreases after the direction changes.

Fig. 10 Schematic drawing of the emotion part.

Figure 11 shows an example of the relationships ofstimuli and responses. We obtain symbols of stimuli from thediscrimination part such as y d (1) , y d ( 2 ) and y d ( 3) . The emotion
part outputs the symbols of responses, z1 and z 2 , related tothe stimulus symbols. In the figure the solid lines show theinherent relation that we design in advance. The weight
coefficients for the stimuli y d (1) and y d ( 2 ) are written by thefollowing equation.

w1 = (1 0) T , w 2 = ( 0 1)T(5)

The stimulus y d ( 3) does not have any relationship to the
response at first. Now we give meaning to the stimulus y d ( 3)through conditioning. The stimulus y d ( 3) is input first, andthen the stimulus y d (1) is input.
response
yd

( 3)

→ yd

In this case an inherent

occurs. If the input-output response(3)→ z1 is repeated, then the new relation y d → z1

z1
(1)

is generated. Namely, the stimulus y d ( 3) is given meaning.
Fig. 7 Stabilized vector field.

©ISAROB 2006

w 3 = ( 1 0 )T

Fig.8 An example of stabilized vector field.

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6. Expression Part
yd

(1)

yd

( 2)

w12 w21yd( 3)

The expression part generates the actual data to control
the robotic facial expression. Input signal of the expression
part is the output signal from the emotion part. By using theemotional state we compute the output data in the expression
part, and we combine the data of each emotion to make theactual trajectories of the robot expression. Since the output
signal from emotion part is continuous, we obtain thecontinuous trajectory of the robot expression by mapping theemotional state. The function is written by the following
equations.

w22 w31

w11

w32
z2

z1

Fig.11 Classical conditioning of stimuli

We need a learning algorism for the conditioning. The
information processing of the conditioning is performed by
the output-dependent learning based on Hebb learning [8].

o ( p ) = ∑ si gi( p)( zi )(11)i

H ij (t + 1) = ayd( j)

U i (t ) = ∑ Wij (t ) yd

+ bH ij (t )

(7)

( j)

(8)

gi

where, ojzi (t ) = f e (U i (t ))(9)∆Wij (t ) = c(1 − Wij (t ))( H ij (t ) − H 2 ) zi (t )

(10)

( p)

2

( zi ) = ai , p + bi , p zi + ci , p zi + d i , p zi

( p)

3

(12)

is the p th characteristic quantity for the facial( p)( zi ) is a mapping function from the i themotional state to the p th characteristic quantity. si is a
expression.

where, yd ( j ) is a stimulus input signal to the emotion part,gicoefficient for normalization. The value of zi changes 0 to 1.

H ij is a variable determined by equation (6), U i is the

By using the equations (11) and (12), we computed an
example of a facial expression. We use five characteristic
quantities for the facial expression as shown in Fig.13. Figure
14 shows the trajectories of the characteristic quantities andthe facial expression when z1 = 0 → 0.4 , andinternal state, zi is an output of the emotion part, ∆Wij is achange rate of the weight coefficient Wij , a, b and c areconstants.
We simulate an example of the conditioning by using thelearning algorism described above. The input-output response
(1)
( 2)
(1)
and yd ( 2 ) are they d → y d → zi is repeated, where y dz2 = 0 → 1.0 , linearly.

conditioned and unconditioned stimuli to the response zi ,respectively.

The

value

of

the

constantsarea = 0.03, b = 0.99, c = 0.05 and H 2 = 0.1 . The simulated

result is shown in Fig.12. The variable H i1 changes after theconditioned stimulus is input.
The weight coefficient
wi1 increases over time, and then the response zi occurs by
not only yd ( 2) but also yd (1) .

Fig.12 Simulated result of conditioning. (a)Conditioned stimulus andthe variable, (b) Unconditioned stimulus, (c) Response, (d) Weight
coefficient between the conditioned stimulus and the response.

©ISAROB 2006

Fig.13 Characteristic quantities for the facial expression.

(a)(b)
Fig.14 An example of the facial expression. (a) Trajectories. (b) Facial expression.

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continually. The face image change in time series is shown in
Fig.17. In addition, the output signals from each identification
device are shown in Fig.18.
The output signal of each identification device changes
from 0 to 1, when the memorized change of facial image
occurs. On the other hand, the output signal keeps around 0,
when the facial image is not a memorized one. We can findthat expressions A and D are distinguished precisely, but theidentification precision between expressions B and C is not as
good. As we can find in Fig.16, the trajectories of attractorsof expressions B and C are located in closed area. We believethat this is the reason why the separation between B and C is
not precise. The problem may be solved by adding anotherdimension in the input space, so that the separation of the
trajectories can be exactly determined. In addition, if we candetect the characteristic quantities precisely, then theseparation between B and C improves. In our experiment weused a simple extraction method for the characteristic
quantities.

7 Experiment

Figure 15 shows the experimental setup for theemotional generation system. We use two PC's to construct a
discrimination part, an emotion part and an expression part.
The discrimination is performed by PC1. The image signal
from a CCD camera is connected to an image processing
board (IP 5005 made by Hitachi) in PC1, and thecharacteristic quantities are obtained. PC2 computes theemotion and expression parts and shows the robotic facial
expression on the display.

7.3 Conditioning and robotic facial expression
We conducted experiments of the robotic emotional
generation to evaluate the proposed information processing.
We determined the initial relationships between the external
stimuli and the robotic facial expressions as shown in Fig.19.
The relationships are changed by conditioning. Figure 20
shows the results of the conditioning between the stimuli (2)and (4). The stimuli y d ( 2 ) and y d ( 4 ) are conditioned by it

Fig.15 Experimental setup.

7.1 Design of the vector fields for facial expression
We use four characteristic quantities for facial
discrimination, i.e., height of eye, distance between eye andeyebrow, width of mouth, height of mouth. We measurechanges in the characteristic quantities from a neutral
expression to another expression using the former extraction
method, and then we obtain time series data of thecharacteristic quantities. The time series data become thesample data of the attractors of the dynamics. We measured it
five times for the same expression. We compute the average ofthe data. The average data is approximated with fifth orderpolynomial to get a smooth curve. We use one dimensional
data as the output signal. The output signal increases from 0 to
1 in a monotone as shown in Fig.5. We make an attractor infive dimensional vector filed. The space consists of four
dimensional data of characteristic quantities and one
dimensional data of the output signal. We designed theentrainment region of the attractor so that the actual
measurement data of characteristic quantities are included in
the region.

being repeated in turn. Figure 21 shows the expressions in thesegments TA and TC . In the case of segment TA , theexpression is determined by the initial relationships. On theother hand, in segment TC the expression is changed from theinitial relationships due to conditioning.

Fig.16 Attractors of facial expressions

7.2 Discrimination of facial expression
We conduct identification experiments of facial
expressions in real time with an animated image of a humanface. We used four kinds of expression change from Neutral
to four characteristic expressions of A,B,C,D as shown in
Fig.16. We designed four vector fields using the trajectories
beforehand and considered it identification device forexpressions. We input the characteristic quantities provided
from face image processing into each identification device©ISAROB 2006

Fig.17 Sequence of facial expressions in discrimination experiment

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8 Conclusion

We propose a framework to generate robotic emotion
for human-robot communication based on the dynamics-based
information processing. The proposed method utilizes
dynamic systems to make continuous information processing
for emotional generation. The synchronization andentrainment of the dynamic system are used for information
processing. The vector fields are utilized to make information
processing of time series data continuously. The emotional
generation system consists of discrimination, emotion andexpression parts. We realized dynamic discrimination simply
by using the proposed method. By introducing a conditioning
the emotional response developed at time. We showed that the
new computational framework was effective for face to facecommunications based on an experimental work.

Fig.18 Experimental results of facial discrimination

Acknowledgement
We are grateful to Assoc. Prof. Michael Honywood of
Shinshu University who provided critical assistance on ourmanuscript. This research was supported by The Mitsubishi
Foundation.
Reference
Fig.19 Initial relationships between the stimuli and the expressions.
[1] For example: Masahiro Fujita, “AIBO: Toward the Era of Digital
Creatures,” The International Journal of Robotics Research, Vol.20,
No.10, pp.781-794, 2001.
[2] Joseph Ledoux, The Emotional Brain: The Mysterious Underpinnings ofEmotional Life, Touchstone Books, 1998.
[3] Gen Matsumoto and Taketoshi Ono, Brain Science of Emotion andConsciousness -what is human beings-, Baifukan Pub., 2002. (inJapanese)[4] W.J.Freeman, “Simulation of Chaotic EEG Pattern with a Dynamic Modelof the Olfactory System,” Biol.Cybern.56, pp.139-150,1987.
[5] Masafumi Okada, Koji Tatani and Yoshihiko Nakamura, “Polynomial
Design of the Nonlinear Dynamics for the Brain-Like Information
Processing of whole Body Motion,” Proceedings of the 2002 IEEE
International Conference on Robotics and Automation, Washington, DC,
May, pp.1410-1415, 2002.
[6] Masafumi Okada, Daisuke Nakamura and Yoshihiko Nakamura,“Hierarchical Design of dynamics Based Information Processing System
for Humanoid Motion Generation,” Proceedings of the 2nd International
Symposium on Adaptive Motion of Animals and Machines, Kyoto, March,
SaP-III-1, 2003.
[7] Masafumi Okada and Yoshihiko Nakamura, “Design of the Continuous
Symbol Space for the Intelligent Robots using the Dynamics-based
Information Processing,” Proceedings of the 2002 IEEE International
Conference on Robotics and Automation, New Orleans, LA, April,
pp.3201-3206, 2004.
[8] Yukifumi Shigematsu, Hiroshi Okamoto,Kazuhisa Ichikawa,Gen
Matsumoto: Temporal Event Association and Output-Dependent
Learning. A Proposed Scheme of Neural Molecular Connections, Journalof Advanced Computational Imtelligence,Vol.3, No.4, 1999.

Fig.20 Experimental results of conditioning.

(a)(b)
Fig.21 Experimental results of expression. (a) Expression duringTA .

(b)Expression during TC .

©ISAROB 2006

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Data Manipulation of DNA Relational Database
Yutaka Kita∗ ,∗Satoshi Kashiwamura∗ , Atsushi Kameda∗∗ ,and Azuma Ohuchi∗,∗∗Masahito Yamamoto∗,∗∗ ,Graduate School of Information Science and Technology, Hokkaido University
Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan{ki-yu, kashiwa, masahito, ohuchi}@complex.eng.hokudai.ac.jp∗∗

CREST, Japan Science and Technology Agency (JST)
Honmachi 4-1-8, Kawaguchi 332-0012, Japan
kameda@complex.eng.hokudai.ac.jp

Abstract

molecules.
In studies of DNA relational database, Arita et
al. showed the feasibility of relational algebra (RA)through in vitro experiments [3], and Katsányi showed
the feasibility of relational algebra with theoreticalmodels that consist of basic biomolecular reactions [4].
However, in their data models, one DNA molecule
is represented as the tuple of relational databases.
Therefore, such operations as the extraction of only
information of attributes will become troublesome.
In this paper, we construct a DNA relational
database with a simple data model in which one DNA
molecule stores one data. Therefore, our data model
simply extracts only information of attributes, mucheasier than conventional models. We showed some experimental results that are considered important partsand discussed possibility of practical application of the
proposed model as well as the limit of it.

DNA memory is a recording medium that utilizes
the advantages of DNA molecules. However, only
a few article have studied the possibility of DNA
databases. In this paper, we suggest a DNA relational
database with a simple data model. We show a mathematical model and chemical experimental results thatconsist of data manipulation and relational algebra.
key words: DNA database, simple data model, relational algebra

1

Introduction

Recently, many papers have dealt with DNA memory, which is a recording medium that utilizes the stability and minute characteristics of DNA molecules.
Kashiwamura et al. described the use of nested PCR
to do hierarchical memory operations to constructlarge DNA memory [1]. However, most DNA memory is memory as storage, and so there are insuﬃcient
points to manage the data as a database.
Storing data in DNA molecules represent data asarrangement of base sequences. It is easy to deal withgenome DNA in a DNA database because they canbe directly manipulated by chemical reactions. In addition, storing and processing individual genetic information in electronic data has many problems from theviewpoint of the protection of individual information.
Reif et al. invented a method that makes a databaseof DNA molecules without changing them into digital media and retrieves the data from DNA molecules[2]. Even in a large DNA database, the processing
time is fairly constant because chemical reactions are
fairly constant due to massive parallel reaction of DNA©ISAROB 2006

2
2.1

Model
datamodel

A relation is denoted by R, and a test tube thatconsists of DNA molecules representing R is denotedby U . Given two variables i = 1, 2, ..., n and j =1, 2, ..., m,
R(A1 , A2 , ..., An ) =

(1)

(1)

{(v1 , v2 , ..., vn(1) ),
(2)

(2)

(m)

, v2

(v1 , v2 , ..., vn(2) ), ...,
(v1

(m)

, ..., vn(m) )},(j)

where Ai is an attribute value, and vi is a elementof the relation. A tuple of attributes is denoted by

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(j)

(j)

(j)

tj = (v1 , v2 , ..., vn ). Given tj , we denote IDj that
stores the value of j. All data are represented bysingle-stranded DNA (ssDNA) with the attribute andthe tuple information shown in Fig. 1.

3

RA Operations

In this section, we describe packaging methods for
ﬁve RA operations (Union, Diﬀerence, Projection, Selection, and Cartesian product) in test tubes with basic operations. The other operations can be expressedby utilizing these ones.

3.1

Union

Take the union of sets with two union compatible
relations Rx and Ry .

Figure 1: Representation of data with ssDNA

Rz := Rx ∪ Ry do :2.2

Uz := M erge(Ux , Uy)

Basic Operations

This program can be executed by mixing two tubes
that represent Rx and Ry .

We deﬁne the basic experimental operations fordatabase processing and use a mathematical model
based on Reif’s RDNA model [5], which has slightly
diﬀerent notation for operations than the models ofReif’s and Katsányi’s.

3.2

Take the diﬀerence of sets with two union compatible relations Rx and Ry .

• M erge(Ux , Uy ): Mix the DNA molecules in thetest tubes Ux and Uy .
• Amplif y(U, F W, RE): Execute ampliﬁcation reactions for DNA molecules in test tube U withsequence sets of forward primers FW and reverse
primers RE.
• Append(U, S, E)): Append corresponding sequence set S to DNA molecules in test tube U
and contain any subsequence E at the 3’ end.

Rz := Rx − Ry do :S1 := Amplif y(Uy , ø, RE)
where RE = {IDj |j = (1, 2, ..., m),
50 end biotinylated}
S2 := Separate− (S1 )Uz := Get(Ux , −S2 )where, IDj represents comprementary strands of IDj .
Amplif y(Uy , ø, RE) operation is an extension reactions. Moreover, we utilize aﬃnity separation to execute Separation− (S1 ) and Get(Ux , −S2 ) operations.

For the double-stranded DNA (dsDNA) in test tube
U and one of the ssDNA’s end is biotinylated, operations are following:• Separate+ (U ): Extract ssDNA whose ends are
not biotinylated.
• Separate− (U ): Extract ssDNA whose ends are
biotinylated.

3.3

Projection

In relation R, an arbitrary subset of {A1 , A2 , ...An }is denoted by X. Create a new relation to abstract
attributes, that are assigned by subset X, from relationR.

For the DNA molecules in test tube U , operations
are following:

R0 := Πx R do :S1 := Amplif y(U, X, RE)
where X = {Ai |i = {10 , 20 , ..., k 0 } ⊆ {1, 2, ..., n}},• Get(U, +S): Extract ssDNA that contains any
subsequence S.
• Get(U, −S): Extract ssDNA that does not contain any subsequence S.

RE = {IDj |j = (1, 2, ..., m), 50 end biotinylated}
U 0 := Separate+ (S1 )Using the basic operations, we can describe data manipulations (Insert, Delete, Update, and Select). Here,
Select means referring to one data. For selecting tuples, we describe in RA operation.

©ISAROB 2006

Diﬀerence

Amplif y(U, X, RE) operation is a simple polymerase
chain reaction (PCR), and Separation+ (S1 ) operation
is executed by aﬃnity separation.

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3.4

Selection

sequences may form secondary structure in which
DNA molecule anneals with itself and may preventthe intended experimental reactions. To solve thisproblem, we predict free energy (∆G) and secondary
structure of combined sequences of GC-Templates by
Mfold [8] and generated data strands and transition
molecules. To examine the success of the experiments,
we used PolyAcrylamide Gel Electrophoresis (PAGE).
For two relations (R1 and R10 ), we performed
Cartesian product operations shown in Fig. 2. First,
we mixed the corresponding transition molecules witheach test tube that represent R1 and R10 and performed ST-PCR to execute Append(U, S, E). The appropriately reacted data strands were 64-mer, which
we checked by measuring their length with gel electrophoresis.

We denote selection condition as F. Abstract tuples
that satisfy condition F from relation R.
R0 := σF R do :S1 := Amplif y(U, F W, F )where F W = {Ai |i = (1, 2, ..., n)},F = {IDj |PF (tj ), 50 end biotinylated}
U 0 := Separate+ (S1 )PF (tj ) is predicate that become true when tuple tj
satisﬁes condition F. This Selection program can be
executed in similar chemical operations of the Projection program.

3.5

Cartesian ProductThe
multiplicationoftworelationRx (A1 , A2 , ..., An ) and Ry (A10 , A20 , ..., An0 ). In otherwords, create Rx × Ry (A1 , A2 , ..., An , A10 , A20 , ..., An0 )Rz := Rx × Ry do :S1 := Append(Ux , S, Ex )S2 := Append(Uy , S, Ey )where Ex = {IDj |j = (1, 2, ..., m)},Ey = {IDj 0 |j 0 = (10 , 20 , ..., m0 )},S = {IDf (j,j 0 ) |tj ∈ Rx , t0j 0 ∈ Ry }Figure 2: Cartesian product between R1 and R10 andexample of ST-PCR. State molecules corresponding todata represent strands and transition molecules to strands
that consist of IDj and IDf (j,j 0 ) . After ST-PCR, only
state molecules are extended because 3’ end of transition
molecules are aminated.

,Uz := M erge(S1 , S2 )here, we deﬁne f (j, j 0 ) that returns speciﬁed value that
depends on j and j 0 that distinguish tuple (tj , t0j 0 ) that
represents the concatenation of tuples of tj and t0j 0 . We
utilize State Transition PCR (ST-PCR) [6] to execute
Append(U, S, E) operation. By ST-PCR, we prevent
DNA molecules from forming complete dsDNA.

4

To complete Cartesian product operations, we
mixed post-ST-PCR samples and described the relation of after Cartesian product between R1 and R10 as
R2 . However, this experiment was insuﬃcient to verify
the operation. Therefore, we executed additional relational Selection operations that extracted the elementsof tuple (t1 , t010 ) from relation R2 . We perfromed PCR
with forward primer mix solution (A1 , A2 , A3 , A10 ,and A20 ) and reverse preimer IDf (1,10 ) . Target bandsof data strands of relation R2 (64-mer) were ampliﬁed,
as shown in Fig. 3.
However, we could not verify whether only data
strands of tuple (t1 , t010 ) were ampliﬁed from this experiment. Thierefore, we executed a second PCR for
post-Selection solution. However, since R2 consistedof 30 data strands, it’s diﬃcult to perform and show
PCR with all primer sets. Therefore, we selected ﬁveprimer sets (p(1, f (3, 20 )), p(2, f (1, 10 )), p(3, f (3, 10 )),p(4, f (1, 10 )), and p(5, f (2, 10 ))). Assuming that Selection operation was succeeded, ampliﬁcation reaction

Experiments

We set two relations R1 (3 × 3) and R10 (2 × 2) toverify whether we can execute RA operations in vitro.
Here, we showed experiments of Cartesian product.
We also describe ssDNA that contains Ai and IDj as
data(i, j), a set of primers that correspond Ai and IDj
as p(i, j), and transition molecule for Ri as tRi (j, j 0 ).
In this paper, we consider 16-mer ssDNA as 1 unit
and assign these units to attribute Ai , tuple informa(j)tion IDj and data element vi . We employed a sequence set of GC-Templates in Template Method [7].
However, since sequences of GC-Templates aren’t designed to consider a thermodynamic viewpoint, some

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a mathematical model of data manipulation and RA
operations. A simple data model contributes to simplicity of data manipulation experiments compared toexisting models. We conﬁrmed successful data reference and RA operations in vitro and showed results ofCartesian product operation. In experiments of Cartesian product, we demonstrated the robustness of ourmodel in terms of continuous PCR reactions. This result will be a stepping stone to continuous action of
DNA relational database.
However, to realize a DNA relational database,
many problems need to be solved. To realize referenceof database, we must check robustness for continuous
aﬃnity separation and verify feasibility that we canobtain tuple information using data element. In addition, the size of our database is still too small to use
bioinformatics. We must cooperate with large DNA
memory on this point. As the future work, we willsolve these problems and construct DNA relational
database system.

Figure 3: Results of Selection operation extracting ele-ments of tuple (t1 , t010 ) from relation R2 . Bands show data
strands of R2 (64-mer) were ampliﬁed. However, ampliﬁed
data strands were not ensured as elements of tuple (t1 , t010 ).

is observed in case of using p(∗, f (1, 10 )) and not otherprimer sets. The results of the second PCR shown in
Fig. 4. The target bands of data strands (64-mer) appeared in samples of PCR with p(∗, f (1, 10 )), while few
data strands were ampliﬁed with other primer sets.
These experiment results show that Cartesian product and Selection operations could be executed. We
considered Projection operation feasible operation because the operation of Selection and Projection operations use similar chemical experiments except theprimer sets. Moreover, we showed the feasibility of the
RA Join operation because Selection after Cartesian
product corresponded to Join.

References[1] S. Kashiwamura, M. Yamamoto, A. Kameda, T. Shiba,
and A. Ohuchi, “Hierarchical DNA memory based onnested PCR”, 8th International Workshop on DNAbased Computers (DNA8), in Lecture Notes In Computer Science, pp. 112-123, 2003.
[2] J. H. Reif, T. H. LaBean, M. Pirrug V. S. Rana, B.
Guo, C. Kingsford, and G. S. Wickham, “Experimantal
Construction of Large Scale DNA Databases with Associative Search Capability”, 7th International Meetingon DNA-Based Computers (DNA7), in Lecture Notes
in Computer Science, pp. 231-247, 2002.
[3] M. Arita, M. Hagiya, and A. Suyama, “Joining andRotating Data with Molecules”, Proc. of IEEE 4thInternational Conference on Evolutional Computaion(ICEC’97), pp. 243-248, in IEEE press, 1997.
[4] I. Katsányi, “On implementating Relational Databaseon DNA Strands”, Acta Cybernetica, vol. 16(2), pp.
259-270, 2003.
[5] J. H. Reif, “Parallel biomolecular computation:Models
and simulations”, Algorithmica, vol. 25, no. 2-3,
pp.142-175, 1995.

Figure 4: Result of the second PCR for relation R2 anda series of experiments corresponding to RA Join operation. We used ﬁve primer set (two target and three nontarget) described in the relation table. After the ﬁrst PCR,
most data strands in the test tube were element of tuple (t1 , t010 ), data strands ampliﬁed only with p(∗, f (1, 10 )).
Moreover, proportion of data strands remained fairly constant through consecutive operations.

5[6] K. Hasimoto, A. Kameda, M. Yamamoto, and A.
Ohuchi, “State Transition Model Based on DNA Polymerization”, Proc. of the International Technical Conference on Circuit/Systems, Computers and Communications (ITC-CSCC’03), pp. 1889-1892, 2003.
[7] M. Arita and S. Kobayashi, “DNA Sequence Design
Using Templates”, New Generation Computing, vol.
20, pp. 263-277, 2002.

Discussion & Conclusion[8] M. Zuker, “Mfold web server for nucleic acid folding
and hybridization prediction”, Nucleic Acids Research,
vol. 31, pp. 3406-3415, 2003.

We proposed a DNA relational database model inwhich one DNA molecule stored one data and showed©ISAROB 2006

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Stability evaluation method of DNA tile structure
Naoki Iimura1 ,Masahito Yamamoto1,2 ,2Atsushi Kameda ,Azuma Ohuchi1,21

Fumiaki Tanaka1

Graduate School of Engineering, Hokkaido University
North 13, West 8, Kita-ku, Sapporo 060-8628, Japan
http://ses3.complex.eng.hokudai.ac.jp
Tel:+81-11-716-2111(ext.6498),Fax:+81-11-706-7834

2

CREST, Japan Science and Technology Cooperation (JST)
Honmachi 4-1-8, Kawaguchi 332-0012, Japan

E-Mail : {i˜mu, masahito, ,fumi95,kameda,ohuchi}@complex.eng.hokudai.ac.jp

Abstract

Binary counter and silver nanowire with DNA
nanostructure have been researched [3]. Nanodevices
and smaller circuits for application are expected.

DNA tiles formed from self-assembly by a hybridization property of DNA molecules have attracted
attention as a new calculation technique and newnanostructures. When nanostructures are composedof DNA tiles, base sequences that correctly and stably
hybridize are required. We have to design base sequences (the row of the base) that become such stabletiles. In conventional research, however, sequence design that considers the structure stability of tiles can
not be carried out. The purpose of this paper is to propose an evaluation method for the structure stabilityof DNA tiles.

Figure 1: DNA Nano-structure made by DNA tiles
Sequences tightly bonding in tile conformation are
indispensable and must be designed to constitute DNA
tiles. Conventional research SEQUIN considers only
complementary sequences in designing sequences [4].
However, it is difficult to see that the tile is well generated because tile structure may not be stable thermodynamically.
In DNA computing, many base sequence design
techniques for constituting a desired structure havebeen studied. An evaluation of a sequence by thetemperature at which a double strand DNA molecularcomes off to two single strand DNA moleculars (Melting temperature : Tm) or free energy (∆G) calculatedby the adjoining base is emphasized [5]. Despite that
free energy can be used as a standard of the stability ofhybridization, it has never been used for the sequence
design of DNA tiles by the above evaluation methods.
We propose an evaluation method for the structurestability of DNA tiles, in which a thermodynamicallystabilized tile structure can be designed by hybridization prediction based on free energy. This method

key words - DNA tile, DNA nano-structure,
DNA computing, sequence design, free energy

1

Introduction

DNA is a double helix consisting of two single strand DNA molecules held together by specificbonded base pairs: guanine (G) and cytosine (C)sticks, and adenine (A) and thymine (T) sticks. DNA
forms one spiral double strand DNA molecule fromtwo single strand DNA molecules when this property
acts in parallel. This complementary chemical bond iscalled “hybridization.”
Winfree et al. generated DNA tile conformation bymaking two or more DNA molecules meet[1][2]. Eachtile has an adhesive end called “sticky end” that associates according to complementarity. Branched DNA
tiles with sticky ends constitute nanostructure. (Figure1)©ISAROB 2006

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is effective, especially, when more complicated nanostructures are required and two or more kinds of tiles
are required.

2

necessary hybridization between molecules fortile construction reacts surely, and so tile canform efficiently.
(2) The sum of ∆G when two single strand DNA
molecules mis − hybridize for tile comformation

Proposal method

Sequences that hybridize undesirably may become competition that obstructs necessary hybridization. Therefore, since hybridization undesirably must be unstable, ∆G has to be high.

We use free energy (∆G) based on the Nearest
Neighbor method [7] [8] for evaluation. Free energychanges with base sequences because it depends on secondary structure and hybridization strength between
molecules. ∆G becomes lower as the structure (hybridization) is stabilized. Furthermore, ∆G generally
becomes lower as the number of bonded base pairs increases in single strand DNA molecule and between
two single strand DNA molecules.
∆G when a single strand DNA has secondary structure is lower than when it does’t have secondary structure. Moreover, if the number of base pairs is thesame, ∆G becomes lower as the number of G andC base pairs increases because hybridization strength
when G and C hybridize is stronger than when A andT hybridize.
We evaluate stability based on ∆G when single
strand DNA molecules have secondary structure, whendesired hybridizations react, and when undesired hybridizations react.

2.1(3) The sum of the absolute value of ∆G when two
single strand DNA molecules hybridize desirably
Necessary hybridization must react more accurately and be tighter. ∆G has to be low. Because
this item is the absolute value of ∆G, the ∆G
becomes lower as the tile is more stable.
We need to find the sequence that increases thevalue of the above items heuristically or the design
sequence by optimization.

3

Validity of evaluation items

In this section, we show that a tile designed by the
proposed evaluation items is more stable than a conventional tile. We compared tiles generated randomly
with conventional tiles by our evaluation items.
We used 4×4 DNA tiles as a subject of verification(Figure 2). This 4×4 DNA tile, which consists of one
long sequence CORE, four short SHELLs and ARMs
(Figure 3), needs the most sequences and is the mostcomplicated of any DNA tiles. In addition, this tile
creates a silver nanowire and arranges protein regularly [3].

Points of evaluation

To produce a stable tile hybridized desirably andthat doesn’t hybridize undesirably, ∆G hybridized desirably is reduced, and ∆G hybridized undesirably isheightened.
The primary consideration in designing stabler tiles
are the following two points.
1. The bond of the desirable complementary sequence is tight.
2. No sequence easily has secondary structure.

2.2

Evaluation items

Taking the above points into consideration, we propose evaluation items. The tile structure becomes
more stable as the value of each item becomes larger.
(1) The sum of ∆G When each single strand DNA
molecule has the stablest secondary structure

Figure 2: 4×4DNA tile Figure 3: The kinds of sequene in 4×4 DNA tile

Hybridization between single strand DNA
molecules may be obstructed by secondary
structure. Because a sequence that has large∆G cannot easily have secondary structure,

We generated 10,000 sets of tiles randomly and thenevaluated them by the above evaluation items. At
this time, complementary bases were arranged on a

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position that should be arranged as complementarybases for tile formation.
The length of the sequence was the same as the conventional tile (Yan et al [6]) and the GC% (the contents of G and C) was set to 55-65% (in conventional
work, 59%) to compare with tiles made by conventional work SEQUIN.

3.1

ondary structure. We discovered sequences that makethe value larger than the conventional sequence.
The graphs in Figures 7 and 8 show the distribution of I2 and I3 . There are also more stable tiles thanthe conventional tile in each graph. In I2 , Yan’s tile isin the top 2%, so we revealed that conventional work
can design a sequence that doesn’t hybridize undesirably. In I3 we revealed that sequence design based on
only complementary sequence improved hybridization
strength.

Application to 4×4 DNA tiles

In 4×4 DNA tiles, the longest sequence CORE easily tends to have secondary structure. We examined a
distribution of CORE’s ∆G beforehand, when CORE
had secondary structure. Then, we adopted CORE,
which is in the lowest 10% of distribution when sequences are generated randomly.
Item 1: (I1 ) is the sum of ∆G when nine single
strand DNA molecules have the stablest secondary
structure.
Item 2: (I2 ) is the sum of ∆G of single strand DNA
molecules that mis-hybridize with each other undesirably. We don’t want to allow Core and Core (and Arm
and Arm ,and Shell and Shell) to hybridize with eachother. If this ∆G is low, undesirable hybridization
reacts.
Item :3 (I3 ) is the sum of ∆G of single strand DNA
molecules that desirably hybridize with each other. In
this case, sequences that hybridize with SHELL (a) are
three sequences: (b), (c), and (d) (Figure 4). However,these three sequences have to hybridize to sequences
except SHELL (a), that is , a duplication occurs. We
divided SHELL (a) into four partitions and used thesubsequences to avoid such duplication (Figure 5).

Figure 6: Distribution of I1

Figure 7: Distribution of I2

Figure 4: Before partition Figure 5: After partition

4

Results
Figure 8: Distribution of I3

The graph in Figure 6 illustrates the distributionof I1 and the evaluation values of the conventional
tile (Yan et al [6]). As the value of the graph becomes large, sequence doesn’t easily have the sec-

©ISAROB 2006

The value of each item was uneven. Figure 9 shows
an average of each evaluation value. The effect of I2

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inevitably, became stronger, in particular because calculation time to calculate the value of I2 increased.
Based on Figure 9, weight was adjusted as the absolute value of each item’s average became equal. Figure
10 shows the sum of each item weighted by the above
weight.
The results of our experiment show that we can design more stable tiles based on our three items. However, we can do so only if I2 is taken into consideration.
From this point we might proceed to examine the influence of each item with chemical experiments.

well by chemical experiments and visualized by atomic
force microscopy (AFM).

References[1] Erik Winfree, Furong Liu, Lisa A.Wenzler andNadrian C.Seeman, “Design and self-assembly of
two-dimensional DNA crystals”, Nature, Vol.394,
pp.539-544, 1998.
[2] Erik Winfree, “DNA Computing by Selfassembly”, in NAE’s The Bridge33(4):pp.31-38,
2003.
[3] Hao Yan, Sung Ha Park, Gleb Finkelstein, John
H.Reif, Thomas H.LaBean, “DNA-Templated SelfAssembly of Protein Arrays and Highly Conductive Nanowires”, Science, val.301, pp.1882-1884,
2003.
[4] Nadrian C.Seeman, “DeNovo Design of Sequence
for Nucleic Acid Structural Engineering”, Jornal of
Biomolecular Strucure & Dynamics, ISSNO. 7391102, Vol.8, 1990.

Figure 9: Average of evaluation items[5] Fumiaki Tanaka, Atsushi Kameda, Masahito Yamamoto, Azuma Ohuchi, “Design of nucleic acidsequence for DNA computing based on a thermodynamic approach”, Nucleic Acids Research,
val.33, pp.903-911, 2005.
[6] http://www.sciencemag.org/cgi/data/301/5641/
1882/DC1/1[7] A.M.Zuker, B.D.H.Mathews, C.D.H.Turner, “ALGORITHMS AND THERMODYNAMICS FORRNA SECONDARY STRUCTURE PREDICTION: A PRACTICAL GUIDE”, In RNA Biochemistry and Biotechnology, J. Barciszewski &B.F.C. Clark, eds., NATO ASI Series, Kluwer Academic Publishers, 1999

Figure 10: Distribution of the sum of evaluation items

5[8] Mirela Andronescu, Rosalia Aguirre-Hernandez,
Anne Condon, Holger H.Hoos, “RNA soft:a suiteof RNA secondary structure prediction and design
software tools”, Nucleic Acids Research, val.31,
No.13, pp.3416-3422, 2003.

Summary

We proposed a stability evaluation method for DNA
tiles. Our method designed stabler sequences thermodynamically. We also investigated that a sequence designed by our method is expected to be more stable
than the conventional tile.
We will design a tile sequence based on our method
and actually validate that tiles will be constituted©ISAROB 2006

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Evolutionary emergence of the 16S /18S-ribosomal RNA
from a poly-tRNA structure containinga “(5') tRNAGly-spacer-tRNACys-spacer-tRNALeu (3')” region.
Koji Ohnishi 1, 2,3 Shouken Hokari 2
ohnishi@sc.niigata-u.ac.jp
1

hokari_s@mail.goo.ne.jp

Department of Biology, Faculty of Science, 2Graduate School of Natural Sciences,and 3Center for Interdisciplinary Research,
Niigata University,

Ikarashi-2, Niigata, 950-2181, Japan

ABSTRACT: Small subunit (SSU- or 16S/18S-)

the 16 tRNAs in the trnD tRNA gene cluster

rRNAs were found to have a base-sequence( Poly-tRNA theory, See [1] ~[4] for details.).

region homologous to the "(5') tRNAGly-spacer-

In this paper, evolutionary origin of SSU-rRNA

tRNACys-spacer-tRNALeu (3')"

from poly-tRNA structure was analyzed based onregionoftheBacillus subtilis trnD tRNA cluster and its homo-sequence similarity analyses.

logue tRNA-clusters in other bascteria (such as
the E. coli trnGCL tRNA cluster). Furthermore,

2.

Homology between 16S rRNAs and (5')

the codon-interacting helix 28 region of thetRNAGly-spacer-tRNACys-spacer-tRNALeu(3')

ribosomal A site in both E. coli and Saccharo-regionmyces cervisiae SSU-16S-/18S-) rRNAs wereThe(5')tRNAGly-spacer-tRNACys-spacer-tRNALeu

found to be homologous to tRNA (especially,(3') region of the trnD tRNA cluster in the trrnDtRNAGly) and proto-tRNAGly. These results strong-

operon was aligned against the trnGCL tRNAly suggest that earliest peptide-making apparatus

cluster in E. coli,, which evidently shows strong

would have been a poly-tRNA ribozymic bio-

homology with each other (Figure 1). Since themachine.

trnGCL cluster is widely distributed in eubacteria,
this tri-tRNA region was shared by the latest

1. Introduction

primitive organism commonly ancestral to all of

The trnD-tRNA gene cluster of the Bacillus

the living biotic phyla.

On the other hand, thesubtilis trrnD operon has been proposed to be a

SSU- and LSU-rRNAs were analyzed by Harr-relic of an earliest peptide-synthesizing RNA bio-

Plot similarity search, resulting in a finding that amachine for synthesizing a “trnD-peptide” (=most similar region between SSU/LSU rRNAs“NSEVM DFTYWHQBCLL”) whose amino acidand the B. subtilis trnD-tRNA cluster (containing(aa) sequence is in the order of aa specificities of

16 tRNAs and spacers) was discovered to reflect©ISAROB 2006

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Figure 1.

Alignment of tRNA

Gly

-tRNA

Cys

Leu

-tRN

regions in B. subtilis (BS) trnD tRNA gene cluster(in trrnD operon from GenBank locus BACTGTRND) and the E. coli (EC) trnGCL tRNA gene cluster.
Asterisks indicate base-matches.

Figure 2. Alignment of tRNAGly-tRNACys-tRNLeu regions against eubacterial 16S
rRNAs. Base-match levels are evaluated by Pnuc(m,n) values (ref.[2]-[4])shown in the box, meaning the probability that m or more base-matches occurs
by chance in randomly selected n-base-long alignments. Data from BenBank.

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significant homology between 16S-(SSU-)rRNA

rRNA helix 28 region and the reconstructed

and the GCL tri-tRNA cluster region of the trnD

proto-tRNAGly.(reconstruction in [4]). The corres-tRNA-cluster. As shown in Figure 2 and Figure 3,

ponding helix 28-containing region of the S.

the B. subtils

tri-tRNA region shows a 53.8%

cervisiae 18S rRNA is also aligned in Figure 4,(134/249) base-match and P nuc(134,249) = 0.28 E-

together with the trnGCL homologue region of

21 (See refs. [2]-[4] for the Pnuc value.). Thus the

eubacterial 16S rRNAs found in Figure 2.

16S-(and SSU-)rRNA region aligned against theresults strongly tell us a genuine homology of thetri-tRNA (Fig.2) is now concluded to have been

helix 28 with tRNA from which the A site would

derived from trnGCL tRNA cluster.

have been derived.

3. Origin of the codon-recognizing A-site

4. References:

helix 28 of the SSU-rRNA from tRNA

[1] Ohnishi K.(1993), Evolution from semi-tRNA

Helix 28 of the ribosomal A site is known toThese

to early protein-synthesizing RNA molecule.

In:

have critical role in recognizing codons in mRNA,

Endocytobiology V (Sato S. et al., eds), Tubingen

and is a portion of the 3’ minor domain of SSU-

Univ.Press. Tubingen, pp.407-414.

rRNA (ref. (5)). In the E. coli 16S rRNA, the[2] Ohnishi K. et al.(2002a), Viva Origino 30: 63-

helix 28 is constructed by base-pairing a sequence78.(http://www.origin-life.gr.jp/3002/3002063/

region, 921 /UGACGGGGGCCCCB/ 934 (over-

3002063.html)

lined in Figure 4), with another sequence region,[3] Ohnishi K. et al. (2002b), Origin of most1384 /CGGGCCUUGUACA/ 1396.

primitive

The helix 28 and its vicinities in SSU-rRNAs

interactions between primitive tRNA ribozymes.

were analyzed for finding sequence region(s) of

Genome Informatics 13: 71-81 (http://www.jsbi.org/

rRNAs possibly homologous to some tRNA(s) or

journal/GIW02/GIW02F008.html)

tRNA-clusters.

Finally, the former region (E.

[4] Ohnishi,K., Ohshima,M., Furuichi,N.(2005),

coli 921-934) and its downstream region was

Evolution from possible primitive tRNA-viroids

found to show close sequence similarity to tRNA,to early poly-tRNA-derived mRNAs. A new

as shown in Figure 4.

approach from the poly-tRNA theory, Genome

An alignment of the bases

mRNAs

and

genetic

codes

via

915-1001 of the E. coli 16S rRNA with theInformatics 16(2): 94-103.

tRNAGly region of the E. coli trnGCL tRNA[5] Bulygin, K. et al. (2005) The first position of acluster elucidated a 50.0 % base-match (= 41/82),

codon placed in the A site of the human 80S

and a base-matching probability by chance,

ribosome contacts nucleotide C1696 of the 18S

Pnuc(41,82) = 0.97 E-6.

rRNA as well as positions S2, S3, S3a, S30, andA similar level of base-identity was also found between the E. coli 16S©ISAROB 2006

S15. Biochemistry 44: 2153-2162.

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Figure 3. Homologue of the tRNAGly-tRNACys-tRNLeu region of the B. subtilis trnD-tRNA cluster in thesecondary structure of the E. coli 16S rRNA. Based on the alignment in Figure 2.

Figure 4.Alignment of the A site helices 28 of the E. coli 16S- and the Saccharimyces cervisiae 18SrRNAs against the tRNAGly-tRNACys- regions from eubacterial tRNA

Gly

-tRNA

Cys

Leu

-tRN

regions. Base-matche levels are given in the Figure. See text for the overlined regions (helix 28) of SSU-rRNAs,©ISAROB 2006

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Stochastic Analysis of Schema Distribution in Multiplicative Landscape
Hiroshi Furutani, Susmu Katayama
Takao Ito
Makoto Sakamoto
Ube National College of Technology
University of Miyazaki
Ube City, 755-8555
Miyazaki City, 899-2192

Abstract

In population genetics, researchers also encountered
this type of di culties in treating the evolution of anite population by Markov chain model 2]. One of
the approaches to avoid this problem is to restrict the
search space within the rst order schemata 3]. We
apply this approach to the present problem, and consider the evolution of rst order schemata in the GA
on the multiplicative landscape with the nite population size.

In this study, we investigated the inuence of genetic drift on the performance of a GA on the multiplicative landscape. We performed the theoretical investigation of frequencies of the rst order schemata,and calculated their changes in time by using the
Wright-Fisher model. We showed that this mathematical theory reasonably predicts various quantities
including the ultimate distribution of the rst orderschemata.

2 Mathematical Model

1 introduction

We consider the processes of selection, mutationand crossover, and investigate the inuence of the population size N . We use the tness proportionate selection and uniform crossover. The population evolves indiscrete and non-overlapping generations.
Individuals are represented by binary strings of thexed length `. The number of genotypes is n = 2` ,and the ith genotype is identied with the integer i.
We use the representation i =< i(`)  i(1) >.
population size N is xed, and N =PnThe;1 N (t). Here N (t) is the frequency of the ithi
i=0 i
genotype at generation t. The relative frequency xi (t)is xi (t) = Ni (t)=N .
The average tness of the population f(t) is

In this paper, we study the inuence of nite population size on the performance of genetic algorithms(GAs). We focus on the eect of genetic drift by the
random sampling in the selection process. When weapply a GA to a given problem, we choose the population size N intuitively. We do not have any applicable
theory to guide the choice of N . If we choose a small
N to reduce the cost of calculations, there appears the
problem of genetic drift. The main part of the eectof genetic drift may disappear by averaging repeatedtrials. However there are several cases in which its effect remains nite even after averaging. An example
of such cases is a GA on the multiplicative landscape.
If one uses a small N , the risk of poor performance becomes high by the undesirable eect of genetic drift.
The theoretical analysis of GAs with nite N is
far more complicated than the deterministic approachassuming innitely many N . The most representative
approach is Markov chain analysis of Nix and Vose 1].
The Nix and Vose Markov model includes selection,
mutation and crossover and can calculate the exacttransition matrix. However, it is in general di cult toobtain an analytical expression of Markov process inclosed form. Furthermore the dimension of the transition matrix increases exponentially with string length` and population size N . This makes numerical simulations impossible for realistic values of ` and N .

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f(t)



nX
;1
j =0

fj xj (t)(1)where fi is a tness of the ith genotype. We use thetness function of multiplicative form

fi =

Ỳ

k=1

( 1 + i(k) s ) (s 0):(2)Here the parameter s represents selection strength.

2.1 Deterministic Equations
The deterministic equations for selection and mutation are described here. For proportionate selection,

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3.1 Multiplicative Landscape

the relative frequency at the next time step xi (t + 1)
is given byxi (t + 1) = ff(it) xi (t) (i = 0 : : :  n 1)
;It is natural to assume that the population is inlinkage equilibrium at t = 0, and the deterministic
theory predicts that the population is in linkage equilibrium at all generations under the action of selection
and crossover. Using equation (5), we have the averagetness in the product form

(3)

To show equation (3) is under the action of selection,
we use the notation

Sbxi (t) = ff(it) xi (t)f(t) =

where Sbxi means the frequency xi after selection.

Ỳ
k=1

f

h0k ]+(1+s)h1k ] =A schema is the set of all strings with certaindening values at xed positions. It is represented bythree types symbols, 0,1 and 4]. The bits 0 and 1
are dening bits, and is a wild card. The order ofschema ( ) is the number of dening bits.
We also use the notation showing explicitly theorder of schema, the positions of dening bits, andtheir binary values, = (k) i(b1 ) : : :  i(bk )]. Here,k = ( ), and b1 < : : : < bk are positions of dening
bits. In the similar manner, we use the notation for
the relative frequency h( ),h( ) = h(k) i(b1 ) : : :  i(bk )]:We will use a shorthand notationh(1) i(k) = 1] h1k ] h(1) i(k) = 0] h0k ]:In this analysis, the notion of linkage is very important, and the second order linkage disequilibrium
coe cient D is dened as 5]Dk m] = h(2) i(k) = 1 i(m) = 1] h1k ]h1m ]: (4)When each gene evolves independently, a population
is in linkage equilibrium, while if there are any correlations among genes at dierent loci, it is in linkage disequilibrium. When the population is in linkage
equilibrium, all D coe cients are zero, Dk m] = 0.
In this state, the frequency of genotypes i is given interms of the rst order schema frequencies
O H

k=1

f~i =

H

H

1 + ( 1)i(k) (1 + s)
;

g

(8)

;

h1k ](t + 1) = (1 )h1k ](t) + h0k ](t)
with mutation rate , and h0k ] = 1 h1k ].
;;4 Stochastic Models
We review here the Wright-Fisher model for theevolution of a haploid population whose size remains
constant at N . The number of the rst genotype N0
takes the values of 0 1 : : : N , and that of the second genotype is given by N1 = N N0 .
We consider the selection process of random sampling. Let us assume there are N1 (t) = i copies of therst genotype at the current generation. If we randomly choose the ospring from the population, the(5)f

3 Deterministic Modelg;

We consider the evolution of the GA on the multiplicative landscape in the deterministic model6].

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k=1

f

;

;

hi(k)]:

Ỳ

Under the assumption of linkage equilibrium, we canobtain the schema equation of the rst order schemata
for selection. The evolution equation of h1k ] isSb h1k ](t) = h0 ]((1t) +s(1) h1sk)](ht1) ](t) : (9)kk
If mutation is included in the GA, the evolution
equation is

!

k=1

(7)

The Walsh transform of the tness function is obtained as

H

O H

xi =

(1+s h1k ]): (6)To show the assumption of linkage equilibrium explicitly, we giveỲf(eq) =
gk :





Ỳ

k=1

gk = 1 + s h1k ]:

H

!

Ỳ

Then we may dene the tness function of the rst
order schemata

2.2 Schema Theorem

H

g

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probability P (j i) of N1 (t + 1) taking the value of jfrom the possible values of 0 : : :  N is given by thebinomial distribution,
j

f

g


 i ji N ;j :1(10)
P (j i) = Nj
NN
The probability P (j i) species the process of random

0.5
N=50

;

j

FIXATION
0.4

jqj (t + 1) =

N
X
i=0

P (j i) qi (t):j

PROBABILITY

sampling, and the future behavior of the process only
depends on its current frequencies. Thus this process
is a Markov chain. The states i = 0 and i = N are
absorbing states, and other states are transient states.
If the process enters either of the absorbing states,
it will stay there forever. The state i = N means
the allele 1 is xed while the allele 0 is lost in the
population.
Let qi (t) be the probability that
PNthe population isin N1 = i at t, with the condition i=0 qi (t) = 1. The
process is described by

0.3

0.2
EXTINCTION

0.1

0

0

50

100

150

200

250

GENERATION(11)

Figure 1: Fixation and extinction Probabilities of thefavorable schema. N = 50.

The generalization of the Wright-Fisher model tothe alleles with dierent tness values is straightforward. We dene the tness values as0)fi = 11 + s ((ii == 1).
We assume s 0, and i = 1 is the favorable allele.
The transition probability is given by


0.5



N bj (1 b)N ;j (12)jb = (1 +(1s)i++s)Ni i :It is to be noted that both N1 = 0 and N1 = N are
P (j i) =
j

N=200

;

0.4

PROBABILITY;

also absorbing states, and there is a nite risk of theloss of favorable allele 1.
If mutation is included, we modify this equation by
the analogyN
P (j i) = j cj (1 c)N ;j 
c = (1 )b + (1 b)s)ib = (1 +(1s)+i + N i:
j

;

;

(13)

FIXATION

0.2

0.1
EXTINCTION

;

0

0

100

200

300

400

500

GENERATION;

5 Numerical Results

Figure 2: Fixation and extinction Probabilities of thefavorable schema.N = 200.

We carried out GA calculations on the multiplicative landscape with the tness proportionate selection.

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0.3

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The crossover was included by uniform crossover withthe crossover rates  = 1, and mutation rate was = 0:001. The string length was ` = 8. The calculations were performed repeatedly, and results wereaveraged over 1000 runs.
Figures 1 and 2 also show the xation and extinction probabilities of the rst order schema h1k ] with
N = 50 and N = 200, respectively. In the result of
N = 50, the gure tells that about 20% of the favorable schema is lost On the other hand, with the larger
population size of N = 200, the extinction probability
is very small, meaning the eect of genetic drift is very
weak.

0.2
N=200

PROBABILITY

0.15

0.1

0.05

0

0

0.2

0.4

0.6

0.8

1

FREQUENCY

0.6
N=20

Figure 4: Ultimate distribution of the favorable
schema. N = 200.

0.5

PROBABILITY

0.4

In the GA calculation with crossover, the evolutionof the rst order schema h1k ] is well reproduced by thestochastic model. The genetic drift gives undesirableeect on it when N is small. The analysis of the rst
order schema shows that this problem is caused by theextinction of the favorable schema. .

0.3

0.2

0.1

0

0

0.2

0.4

0.6

0.8

1

References

FREQUENCY

1] Nix, A. E., Vose, M. D.: Modelling Genetic Algorithm with Markov Chains. Annals of Mathematical and Articial Intelligence. 5 (1992) 79{882] Ewens, J.W. J.: Mathematical Population Genetics. I. Theoretical Introduction, Second Edition.
Springer-Verlag, New York (2004)
3] Asoh, H., Muhlenbein, H.: On the Mean Convergence Time of Evolutionary Algorithms without
Selection and Mutation. Parallel Problem Solvingfrom Nature, Lecture Notes in Computer Science,
866, Springer-Verlag, New York, (1994) 88-97
4] C. R. Stephens and H. Waelbroeck, Evolutionary
Computation, 7, 109 (1999).
5] H. Furutani, in Foundations of Genetic Algorithms 7, (Morgan Kaufmann, San Francisco,2003), p.9.
6] H. Furutani, Proceedings of the Simulated Evolution and Learning Conference, SEAL'02, 230(2002).

Figure 3: Ultimate distribution of the favorable
schema. N = 20. Abscissa is the relative frequency ofthe rst order schema h1k ].
Figures 3 and 4 show the ultimate distributions ofthe rst order schema h1k ] at t = 500 for N = 20
and 200. These gures suggest that the genetic driftcauses a force to push members of the population toundesirable direction. We also note that the predictions of Wright-Fisher model reproduce the numericalresults very well.

6 Summary
We studied the evolution of the GA on the multiplicative landscape by investigating the inuence of genetic drift. Within the framework of the innite population model, the assumption of linkage equilibrium
holds at all generations if the initial state is at linkage equilibrium. Therefore, the system is completely
determined by the rst order schema frequencies h1k ].

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Integrated multimedia understanding based on
Mental Image Directed Semantic Theory
Masao Yokota
Fukuoka Institute of Technology
yokota@fit.ac.jp

Genci Capi
Fukuoka Institute of Technology
capi@fit.ac.jp
In this paper we focus on the semantic processing of
sensory and motory data represented in the formal
language Lmd, simulating the interactions between robots
and their environments including humans.

Abstract

The authors have proposed the Mental Image Directed
Semantic Theory (MIDST) and have been developing the
integrated multimedia understanding system IMAGES-M
as robotic intelligence. This paper focuses on the semantic
processing of sensory and motory data in IMAGES-M,
simulating the interactions between robots and theirenvironments including humans.

2. Brief sketch of Lmd

The MIDST has modeled mental images as “Loci inAttribute spaces” [1], [2]. An attribute space correspondswith a certain measuring instrument just like a barometer,a map measurer or so and the loci represent the
movements of its indicator.
A general locus is to be articulated by “Atomic locus”
formalized as the expression (1) in first-order logic, where“L” is a predicate constant.
L(x,y,p,q,a,g,k)(1)
The expression (1) is called “Atomic locus formula”
whose arguments are referred to as ‘Event Causer’,‘Attribute Carrier’, ‘Initial Attribute Value’, ‘Final
Attribute Value’, ‘Attribute Kind’, ‘Event Kind’ and‘Standard Attribute Value’, respectively.
The interpretation of (1) is as follows, where “matter”
means “object ” or “event”.
“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 monotonouschange or constancy of the attribute in time domain and inspace domain, respectively. The former is called‘temporal event’ and the latter, ‘spatial event’.
For example, the motion of the ‘bus’ represented by S1
is a temporal event and the ranging or extension of the‘road’ by S2 is a spatial event whose meanings or concepts
are formalized as expressions (2) and (3), respectively,
where the attribute is “physical location” denoted as A12.
We think that the verb ‘run’ used in S2 must reflect the
motion of the observer’s attention [2].
(S1) The bus runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gt,k)∧bus(y)(2)(S2) The road runs from Tokyo to Osaka.
(3)(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gs,k)∧road(y)1. Introduction

Recently, there have been developed various kinds ofrobots intended for practical use. We assume that robots
suitable for collaboration with humans should be equippedwith good capabilities for(C1) understanding of real environments, and(C2) communication with ordinary people.
More concretely, these robots can recognize well theirenvironments by their miscellaneous sensory organs to act
appropriately by their miscellaneous actuators, and avail
themselves of miscellaneous kinds of communication
media such as natural language, body language, map, etc.
For such a purpose above, we think it is most essential
to develop a formal language for representing andcomputing semantic contents conveyed by various
information media such as text, picture, sensory andmotory data, etc. This kind of formal language shouldhave at least a descriptive power for spatio-temporal
events that people or robots perceive in the real world.
Yokota, M. ([1], [2]) has proposed a semantic theory
for natural languages so called ‘Mental Image Directed
Semantic Theory (MIDST)’. In the MIDST, word
concepts are associated with omnisensual mental imagesof the external or physical world and are formalized in anintermediate language Lmd, based on first-order predicate
logic while the other knowledge description schema suchas [3], [4] are too linguistic (or English-like) to formalize
omnisensual mental images.
The Lmd is employed for many-sorted predicate logic
and has been implemented on several types ofcomputerized intelligent systems [1], [5]. There is afeedback loop between them for their mutual refinement
unlike other similar theories [6], [7].

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For an extreme example, consider such a report fromcertain sensors as (6) represented in Lmd, where ‘A29’ isthe attribute ‘taste’. This locus formula can be translated
into the English sentence S3 by TPU, but it is semanticallyanomalous because a ‘desk’ has ordinarily no taste.
(∃x)L(_,x,Sweet,Sweet,A29,Gt,_)∧desk(x)(6)(S3) The desk is sweet.
These kinds of semantic anomalies can be detected in
the following processes.
Firstly, assume the postulate (7) as the commonsenseor default knowledge of “desk”, stored in KB, where‘A39’ refers to the attribute ‘vitality’, and the special
symbols ‘*’ and ‘/’ are defined as (8) and (9) representing‘always’ and ‘no value’, respectively.
(λx) desk(x) ⇔ (λx) (…L*(_,x,/,/,A29,Gt,_)∧… ∧ L*(_,x,/,/,A39,Gt,_ ) ∧ …)
(7)
(8)
X* ⇔ (∀p,q)X Π ε(p,q)(9)
L(…,/,…) ⇔ ~(∃p) L(…,p,…)Secondly, the postulates expressed by (10) and (11) in
KB are utilized. The formula (10) means that if one of twoloci exists every time interval, then they can coexist. The
formula (11) states that a matter has never different
values of an attribute at a time.
(10)
X ∧ Y* ⊃. X Π Y(11)
L(x,y,p,q,a,g,k) Π L(z,y,r,s,a,g,k) ⊃. p=r ∧ q=s
Lastly, IE detects the semantic anomaly of “sweet desk”
by using (7)-(11). That is, the formula (12) below is finallydeduced from (7)-(11), which violates the postulate (16),that is, “ Sweet ≠ / ”.
L(_,x,Sweet,Sweet,A29,Gt,_) Π L(z,x,/,/,A29,Gt,_)(12)
These processes above are also employed for dissolving
syntactic ambiguities in people’s utterances such as S4. IE
rejects ‘sweet desk’ and eventually adopts ‘sweet coffee’
as a plausible interpretation.
(S4) Bring me the coffee on the desk,
which is very sweet.
If multiple plausible interpretations of a text or anothertype of information are represented in different locus
formulas, it is semantically ambiguous. In such a case,
IMAGES-M will ask for further information in order for
disambiguation.
Furthermore, if two different representations areinterpreted into the same locus formula, they areparaphrases of each other. The detection of paraphrase
relations is very useful for deleting redundant information,
for cross-media translation, etc. [8].

The expression (4) is the conceptual description of the
English word “fetch”, implying such a temporal event that‘x1’ goes for ‘x2’ and then comes back with it, where ‘Π’and ‘•’ are instances of the tempo-logical connectives,‘SAND’ and ‘CAND’, standing for “Simultaneous AND”
and “Consecutive AND”, respectively.
In general, a series of atomic locus formulas with suchconnectives is called simply ‘Locus formula’.
(∃x1,x2,p1,p2,k) L(x1,x1,p1,p2,A12,Gt,k)• (L(x1,x1,p2,p1,A12,Gt,k)ΠL(x1,x2,p2,p1,A12,Gt,k))∧x1≠x2 ∧p1≠p2(4)
In order for complete representation of temporal
relations, we have introduced a concept called ‘Empty
Event (EE)’ and symbolized as ‘ε’ which stands
exclusively for time elapsing. For example, (5) represents‘X1 during X2 ’.
(5)(ε1•X1•ε2) Π X2
The image model presented here is also valid forformalizing word concepts (i.e. coding) of actions because
any action must be measured with sensors for its
formalization. That is, grounding words on actions isequivalent to grounding words on sensations of actions
Sensors and actuators are assumed to collaborate very
closely in feedback or feed-forward ways in cybernetics
and there is a hypothesis that some kinds of sensations (orperceptions) and actions are encoded in the same way inorganisms [12]. If not, real-time coordination of multiple
sensors and actuators would be impossible. ‘Mimicking’may be a good support for this hypothesis.

3. Systematic computation of Lmd

The integrated multimedia understanding system
IMAGES-M works as the main intelligence of a robot,
employing locus formulas as intermediate conceptual
representations through which it can integrally understandand generate sensory data, speech, visual image, text, andmotory data.
IMAGES-M is one kind of expert system equipped withfive kinds of user interfaces for multimedia
communication, that is, Sensor Data Processing Unit
(SDPU), Speech Processing Unit (SPU), Image
Processing Unit (IPU), Text Processing Unit (TPU), andAction Data Processing Unit (ADPU) besides Inference
Engine (IE) and Knowledge Base (KB). Each processing
unit in collaboration with IE performs mutual conversion
between each type of information medium and locus
formulas.
The fundamental computations on Lmd by IMAGES-M
are to detect semantic anomalies, ambiguities andparaphrase relations. These are performed as inferential
operations on locus formulas at IE.
Detection of semantic anomalies is very important toavoid succession of meaningless computations or actions.

©ISAROB 2006

4. Interaction between a robot and its world

A robot is to solve some kinds of problems in its world.
Such problems can be classified roughly into two
categories as follows.
(CP) Creation Problem:e.g.) house building, food cooking, etc.

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the transit event XT is the reversal of such X-T that has
been already detected as ‘abnormal’ by the robot.
For example, if XG is given by (17) in advance, XT isalso represented as the underlined part of (19) while X-T as(21). Therefore the job here is quite the same that was
described in the previous section.
XG •X-T• XC•XT• XG(20)
L(z1,C9,20,p,A28,Gt,k) ∧ room(C9) ∧ actor(z1) (21)and(MP) Maintenance Problem:e.g.) fire extinguishing, room cleaning, etc.
In general, an MP is relatively simple one that the robot
can find and solve autonomously while a CP is relatively
difficult one that is given to the robot, possibly, by humans
and to be solved in cooperation with them.

4.1 Definition of a problem and a job for a robot

A robot must determine its job to solve a problem in theworld. In general, the robot needs to interpolate sometransit event XT between the two events, namely, ‘Current
Event (XC)’ and ‘Goal Event (XG)’ as shown by (13).
(13)
XC•XT•XG
According to this formalization, a problem XP is definedas XT•XG and a job for the robot is defined as its
realization.
The events in the world are described as loci in certain
attribute spaces and a problem is to be detected by the unitof atomic locus by the inference employing such a
postulate as (14) implying ‘Continuity in attribute values’.
Therefore, the problem XP in (15) is to be inferred as (16).
(14)
L(x,y,p1,p2,a,g,k)•L(z,y,p3,p4,a,g,k) .⊃. p3=p2(15)
L(x,y,p1,p2,a,g,k)•XP• L(z,y,p3,p4,a,g,k)(16)
L(z’,y,p2,p3,a,g,k) • L(z,y,p3,p4,a,g,k)

5. Application to robot manipulation

The intelligent system IMAGES-M, still underdevelopment, is intended to facilitate integrated
multimedia information understanding, including crossmedia operations. At present, IMAGES-M, installed on a
personal computer, can deploy SONY AIBOs, dog-shaped
robots, as actors and gather information about the physicalworld through their microphones, cameras and tactile
sensors. Communications between IMAGES-M andhumans are performed though the keyboard, mouse,microphone and multicolor TV monitor of the personal
computer.
Consider such a verbal command as S6 uttered to therobot, SONY AIBO, named ‘John’.
(S6) John, walk forward and wave your left hand.
Firstly, late in the process of cross-media translationfrom text to AIBO’s action, this command is to beinterpreted into (22) with the attribute ‘shape (A11)’ andthe values ‘Walkf-1’ and so on at the standard of ‘AIBO’,
reading that John makes himself walk forward and wave
his left hand. Each action in AIBOs is defined as anordered set of shapes (i.e., time-sequenced snapshots of
the action) corresponding uniquely with the positions of
their actuators determined by the rotations of the joints.
For example, the actions ‘walking forward (Walkf)’ and‘waving left hand (Wavelh)’ are defined as (23) and (24),respectively.
L(John,John,Walkf-1,Walkf-m,A11,Gt,AIBO) ∧L(John,John,Wavelh-1,Wavelh-n,A11,Gt,AIBO) (22)Walkf={Walkf-1, Walkf-2,…,Walkf-m}(23)
Wavelh={Wavelh-1, Wavelh-2,…, Wavelh-n}(24)Secondly, an AIBO cannot perform the two events (i.e.,
actions) simultaneously and therefore the transit event
between them is to be inferred as the underlined part of(25) which is the goal event here.
L(John,John,Walkf-1,Walkf-m,A11,Gt,AIBO)• L(John,John,Walkf-m,Wavelh-1,A11,Gt,AIBO)•
L(John,John,Wavelh-1,Wavelh-n,A11,Gt,AIBO) (25)Thirdly, (26) is to be inferred, where the transit event,underlined, is interpolated between the current event andthe goal event XG (=(25)).
L(John,John,p1,p2,A11,Gt,AIBO)• L(John,John,p2,Walkf-1,A11,Gt,AIBO)• XG(26)

4.2 CP finding and solving

Consider a verbal command such as S5 uttered by ahuman. Its interpretation is given by (17) as the goal event
XG. If the current event XC is given by (18), then (19) withthe transit event XT underlined can be inferred as the
problem corresponding to S5.
(S5) Keep the temperature of ‘room C9’ at 20.
L(z,C9,20,20,A28,Gt,k)∧ room(C9) ∧ actor(z)
(17)
(18)
L(x,C9,p,p,A28,Gt,k) ∧ room(C9)L(z1,C9,p,20,A28,Gt,k)• L(z,C9,20,20,A28,Gt,k)∧ room(C9) ∧ actor(z1)(19)
For this problem, the robot is to execute a job deploying
a certain thermometer and actors ‘z1’ and ‘z’. The
selection of the actor ‘z1’ is performed as follows:
If 20-p <0 then z1 is a cooler, otherwise
if 20-p >0 then z1 is a heater, otherwise
20-p =0 and no actor is deployed as z1.
The selection of ‘z’ is a job in case of MP described in thenext section.

4.3 MP finding and solving

In general, the goal event XG for an MP is that foranother CP such as S5 given possibly by humans andsolved by the robot in advance. That is, the job in this caseis to autonomously restore the goal event XG created inadvance to the current event XC as shown in (20), where©ISAROB 2006

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Science and Technology, Japanese
numbered 14580436 and 17500132.

Referencesp[1] M.Yokota: “An approach to natural language
understanding based on a mental image model,” Proc.
of the 2nd International Workshop on Natural Language
Understanding and Cognitive Science, pp.22-31, 2005.
[2] M. Yokota, et al: “Mental-image directed semantic
theory and its application to natural language
understanding systems”, Proc. of NLPRS’91, pp.280287, 1991.
[3] J.F. Sowa: Knowledge Representation: Logical,
Philosophical, and Computational Foundations, Brooks
Cole Publishing Co., Pacific Grove, CA, 2000.
[4] G.P. Zarri: “NKRL, a Knowledge Representation Tool
for Encoding the ‘Meaning’ of Complex Narrative
Texts”, Natural Language Engineering - Special Issue
on Knowledge Representation for Natural Language
Processing in Implemented Systems, 3,pp.231-253,
1997.
[5] S.Oda, M.Oda & M.Yokota : “Conceptual Analysis
Description of Words for Color and Lightness for
Grounding them on Sensory Data”, Trans.of JSAI,16-5E,pp.436-444, 2001.
[6] R.W. Langacker : Concept, Image and Symbol,
Mouton de Gruyter, Berlin/New York, 1991.
[7] G.A. Miller & P.N.: Johnson-Laird : Language andPerception, Harvard University Press, 1976.
[8] M.Yokota & G.Capi: “Cross-media Operations
between Text and Picture Based on Mental Image
Directed Semantic Theory,” WSEAS Transactions on
Information Science and Applications, 10-2, pp.15411550, 2005.
[9] M.Amano, et al : “Linguistic Interpretation of Human
Motion Based on Mental Image Directed Semantic
Theory,” Proc. of IEEE AINA2005, pp.139-144,
Taipei , 2005.
[10] M.Amano, et al : “Cross-media Translation between
Human Motions and Texts Based on Mental Image
Directed Semantic Theory,”
Proc. of IEEEICDCSworkshop2005, pp.707-713, Ohio, 2005[11] M.Yokota & G.Capi: “Integrated Multimedia
Understanding for Ubiquitous Intelligence Based on
Mental Image Directed Semantic Theory,” IFIP
EUC’05 UISW2005, Nagasaki, 2005 (to be presented).
[12] W. Prinz : “A common coding approach toperception and action”, In Relationships between
perception and action (Neumann, O. and Prinz, W.
eds.), pp.167-201, Springer-Verlag, 1990.

q

Fig.1. Actions as temporal changes in angularities of thejoints in an AIBO (Sony)

Fig.2. AIBO behaving in accordance to the command‘Walk forward and wave your left hand’Finally, (26) is interpreted into a series of the joint
angles in the AIBO as indicated by the values ‘p’ and ‘q’of ‘Angularity (A45)’ in Fig.1 and it acts as shown in
Fig.2.

6. Discussions and conclusions

The authors formalized the performances of a robot in
Lmd and applied it to robot manipulation by text as asimulation of robot-world interaction. This is one kind ofcross-media operation via locus formulas as alreadyreported [8], [11]. At our best knowledge, there is no othersystem that can perform cross-media operations in such aseamless way as ours, which leads to the conclusion that
employment of atomic locus formulas has made the logicalexpressions of event concepts remarkably computable andhas proved to be very adequate to systematize cross-media
operations. This is due to their medium-freeness and goodcorrespondence with the performances of miscellaneous
devices, which in turn implies that locus formula
representation may make it easier for the devices to sharea task than macro-command representation.
From the simulation results, we conclude that Lmd canbe a universal language for robots. Our future work will
include establishment of learning facilities for automatic
acquisition of word concepts from sensory data andhuman-robot communication by natural language underreal environments.

Acknowledgements

This work was partially funded by the Grants from
Computer Science Laboratory, Fukuoka Institute ofTechnology and Ministry of Education, Culture, Sports,©ISAROB 2006

Government,

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Associative Learning Method in Hyper-Column Model
Atsushi Shimada
Department of Intelligent Systems
Kyushu University
6-1 Kasuga-koen Kasuga Fukuoka 816-8580

Naoyuki Tsuruta
Department of Electronics Engineeringand Computer Science
Fukuoka University
8-19-1, Nanakuma, Johnan, Fukuoka 814-0180

Rin-ichiro Taniguchi
Department of Intelligent Systems
Kyushu University
6-1 Kasuga-koen Kasuga Fukuoka 816-8580
8-19-1, Nanakuma, Johnan, Fukuoka 814-0180
Abstract

object locale, illuminant, and so on. Therefore,techniques to reduce the system size to a realizable scale are necessary.

In this paper, we propose an associative learning
method in Hyper-Column Model (HCM). HCM is amodel to recognize images, and consists of Hierarchical Self-Organizing Maps (HSOM) and Neocognitron(NC). HCM complements disadvantages of HSOM andNC, and inherits advantages from them. There is aproblem, however, that HCM does not suit general image recognition in HCM since its learning method is
an unsupervised one with competitive learning which
is used by Self-Organizing Map (SOM). Therefore, we
extended HCM to a supervised learnable model withan associative learning of SOM. We have found that
an ability of HCM with the associative learning is superior to the one with unsupervised learning.

1

3. In such cases, ANNs need a very large numberof neurons. Therefore, learning methods which
depend on the initial states of the connection
weights cannot perform well, even if a networkmodel such as the multilayered perceptron is theoretically very powerful. Thus, learning methods
whose performance does not depend on the initialstates of connection weights are needed.
Tsuruta proposed Hyper-Column Model (HCM)[1]
which is a new image recognition model combining Hierarchical Self-Organizing Map (HSOM)[2] andNeocognitron (NC)[3]. The learning method is quite
simple with unsupervised learning, but it is powerfulenough since it does not depend on the initial states of
the connections. In addition, HCM can reduce the dimensionality of general images and can perform better
than HSOM and NC, since HCM overcomes the disadvantages of HSOM and NC and inherits their advantages directly. HCM cannot, however, show highability when the boundary between categories is very
complicated, such a case that objects of diﬀerent categories have similar features. Therefore, a new learning algorithm for the HCM is proposed. The proposed algorithm is associative learning method, which
is very powerful to separate the similar input data
which should belong to diﬀerent categories on the feature map.

Introduction

Recently, applications of Artiﬁcial Neural Networks(ANNs) have expanded into general image recognition
such as face recognition and visual surveillance. In
these applications, there are three problems.
1. In spite of the high dimension of the input images,
the regions of object parts occupy small areas of
the whole space. Therefore, it is necessary to reduce the dimension to eliminate the redundancy
and to allow the network to learn according to theregion actually expanding the object.
2. The absolute dimensionality of the image region
is very large even if the redundancy is eliminated,since images have large variations as a function of©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

HSOM and NC

2.1

Hierarchical(HSOM)

Self-Organizing

2. Maps are organized according to the continuityof data distribution in the space deﬁned by the
distance between images using template matching. Therefore, it is hard to organize maps incases where the distance between images does not
smoothly vary with locale and scale of target object. In such cases, a heuristic, such as image
blurry, must be adapted or the number of training
samples must be increased so that the variationsof distance are smooth enough.

Map

The HSOM is a two layers of SOM network connected as any feedforward neural network: every unit
in the sending layer is connected to every unit in thereceiving layer. The basic SOM deﬁnes a mappingfrom the input data space onto a regular array of neurons. All neurons (1 · · · u · · · N ) are fed the same input
data I. Each neuron has a weight vector W u . Whenthe neurons are fed I, they are activated according to
the similarity between I and W u . In practical applications the Euclidian distance is usually used as thesimilarity measure, only neuron c can be activated,
where||I − W c || = min(||I − W u ||)(1)

3. The recognition method is also based on the distance of images and, therefore, is regarded as oneof nearest neighbor method. Therefore, a highaccurate segmentation of target region and a normalization of locale and size should be needed.

2.2u

In the training phase, each time a training data item
is input, te winner is selected according to Eq. (1) andis trained according to the following equations:

Neocognitron (NC) is proposed by Fukushima[3] asa hierarchical network consisting of several layers ofneuron-like cells. The lowest stage of the network is
the input layer. Each succeeding stage has a layer consisting of cells called S-cells followed by another layerof cells called C-cells. S-cells are the feature-extracting
cells. The C-cells are inserted on the network to allow
for positional errors in the features.
The “structural” advantages of NC are summarized
in the following two points, which can alleviate thedisadvantages of HSOM.

W u (t + 1) = W u (t) + hcu (I(t) − W u (t)) (2)µ¶||rc − ru ||2
hcu = α(t) · exp −(3)
2σ 2 (t)hcu is a neighborhood kernel function. With increasing ||rc − ru || and t, hcu converges to zero. α(t) is amonotonically decreasing function of t (0 < α(t) < 1),and σ 2 (t) deﬁnes the width of the kernel.
In the case of HSOM, the neurons (1 · · · v · · · M ) in
the second layer are fed the index of the winner neuron
in the ﬁrst layer as input data. The training algorithm
in the second layer is same as that in the ﬁrst layer
but the number of neurons M is smaller than N . This
small number of neurons help the HSOM to integrate
the features extracted in the ﬁrst layer.
Characteristics of the HSOM are summarized in thefollowing points.

1. Every feature map is rather small since NC has ahierarchical structure of divide-and-conquer type.
This characteristic resolves the disadvantage 1 of
HSOM.
2. Toward the disadvantage 2 and 3 of HSOM, NC
does not need any preprocesses of blurring nor
normalization due to its shift invariant recognition mechanism. NC, also, needs small number oftraining sample data because of its learning mechanism.

1. HSOM solves the complex region problem, when asuﬃciently dense data set is given for continuous
variation of the input data.
2. HSOM cab be a good preprocess to resolve thedimensional reduction problem.

The original NC has the following two disadvantages when it is directly applied to general image
recognition. The advantages of HSOM match withthose disadvantages.

In case where the HSOM is used directly for general image recognition, the following three problems,
however, arise.

1. The learning process of NC is strongly depends
on the initial state of weight vectors, and the feature extraction does not often perform well due
to its simple competitive learning without neighborhood learning.

1. Image recognition methods based on HSOM are
regarded as a memory-based method. Therefore,
in case where the dimension and the size of data
distribution are large, the network size increases.

©ISAROB 2006

Neocognitron (NC)650

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 1: Structure of HCM
input part I and an associative part T . As a result, thenetworks can be considered as a supervised learning
machines.
µ
¶ µ
¶
I
0
X=a
+
(4)
0
T

2. The shift integration layer integrates only shifted
patterns, and does not requantize feature spaces.
Therefore, its dimension reduction is not enoughfor variation of target shape, which requires large
number of network neurons.

3

where a > 1 in order that the input part can aﬀect
the information of the map more than the output partdoes. The weight vector W u is represented as follows,

Hyper-Column Model (HCM)µHyper-Column Model (HCM) is proposed for visual
recognition of objects with variations in its position,size, and orientation[1]. HCM is a kind of competitive
neural networks with unsupervised learning. The network is composed of hierarchical layers derived from
NC by replacing the unit cell plains (each C-cells andthe lower directly connecting S-cells) in NC with twolayers HSOM. These HSOM cell plains allow features
extraction through the ﬁrst SOM layer followed by features integration in the second SOM layer, as shownin Fig. 1. This feature integration process allows thecell plains to integrate more features in lower numberof neurons. NC does not perform such feature integration process.

3.1

Wu =

4
4.1

¶
(5)

Experimental Results
Condition

The input data were images of human hands which
consist of 10 categories shown in Fig. 2. Each category has 10 images for training data, and 500 images
for test data. The size of images was 100 × 100, andeach pixel had an 8-bit gray value. The neurons ineach feature extraction layer were arranged in a ring
shape. In experiment, training data were learned by
the traditional learning (HCM) and by the associative
learning (AHCM).

Associative Learning in HCM

The structure of HCM is similar to the one of NC,
but HCM uses unsupervised learning algorithm that isused in SOM. In general image recognition problems,
it is known that the recognition ability with supervised learning method is superior to the one with unsupervised learning method. Therefore, introducing a
supervised learning method into HCM is needed.
We introduce a method of supervised learning in
SOM proposed by Ichiki[4] into HCM. In this learning
method, we can regard the input vector as the one
which is composed of diﬀerent two components; an©ISAROB 2006

W Iu
W Tu

4.2

Learning result

The maps generated by each HCM are shown in
Fig. 3. The horizontal axis shows the sample number.
From number 0, each 10 samples belong to the same
category. The vertical axis shows the neuron number.
HCM cannot generate one neuron cluster for the wholeof one category 1, 2, 6, 7 and 8. On the other hand,
AHCM can do it in most cases.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(a) category 1(b) category 2(c) category 3(d) category 4(e) category 5(f) category 6(g) category 7(h) category 8(i) category 9(j) category 10

Figure 2: Training images

0GWTQPPWODGT

0GWTQPPWODGT


















































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5CORNGPWODGT

5CORNGPWODGT(b) Associative learning(a) Traditional learning

Figure 3: Correspondence between maps of HCM and sample data in experiment

4.3

Recognition accuracy

ditional learning. However, HCM has a problem that
it runs out of neuron when the number of category increases. We are now researching for reconstruction of
the network and incremental learning.

The average rate of correct recognition of HCM was
72.5%. The images of category 7 tended to be misunderstood as category 2. There are some overlaps
between the neurons which belong to category 2 andcategory 7 as can be expected from Fig. 3(a). The
images which belong to category 9, also, tended to be
misunderstood as category 6. In most images whichwere not recognized correctly, the position of thumb
was nearby the palm. We think that HCM with unsupervised learning could not create an appropriate
boundary between category 6 and category 9 since theimages of category 9 are similar to the ones of category
6.
On the other hand, the average rate of correct
recognition of AHCM was 93.3%. The associative
learning method gave good results without decreasing
the recognition rates.

5

References[1] Naoyuki Tsuruta, Rin-ichiro Taniguchi, and
Makoto Amamiya. “Hypercolumn Model: A Combination Model of Hierarchical Self-Organizing
Maps and Neocognitron for Image Recognition”.
Systems and Computers in Japan, 31(2):49–61,
2000.
[2] J. Lampinen and E. Oja. “Clustering propertiesof hierarchical self-organizing maps”. Journal of
Mathematical Imaging and Vision, 2(3):261–272,
1992.
[3] K. Fukushima. “Neocognitron: A self-organizing
neural network model for a mechanism of patternrecognition unaﬀected by shift in position”. Biological Cybernetics, 36(4):193–202, 1980.

Conclusions

An associative learning method in Hyper-Column
Model (HCM) was proposed. In the experiments described in this paper, the recognition ability of HCM
with the associative learning was superior to the tra-©ISAROB 2006

[4] Ichiki H., Hagiwara M, and Nakagawa M. “Kohonen feature maps as a supervised learning machine”. Proc. of International Conference on Neural Networks, pages 1944–1948, 1993.

652

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Incremental acquisition of behavioral concepts
through social interactions with a caregiver
T. Taniguchi
Dept. of Mechanical Eng. and Sci.
Kyoto University
Kyoto, Yoshida-honmachi 606-8501

T. Sawaragi
Dept. of Mechanical Eng. and Sci.
Kyoto University
Kyoto, Yoshida-honmachi 606-8501

Abstract
We describe a novel modular reinforcement learning architecture, the reinforcement learning schemata
model (RLSM), based on the schema model proposed by Piaget. In our previous work, we describedthe dual-schemata model, which enables autonomous
robots to obtain concepts representing their environment and/or surrounding objects incrementally, basedon sensory-motor interaction with their environment.
By applying this basic idea to reinforcement learning, we have developed an incremental reinforcement
learning architecture. The results shows that by using RLSM, autonomous robots can obtain several behaviors incrementally through interactions with theircaregiver, who designs various reward functions.
Keywords: Reinforcement learning, schema model,social interaction.

1

Figure 1: Abstract diagram of social interaction
autonomous robot to obtain behavioral concepts incrementally by diﬀerentiating its behavioral concept
when it encounters an environment that has not been
experienced before. In the next section, we discuss social interaction in the context of reinforcement learning. In the third section, we describe our RLSM. Finally, we describe the results of testing RLSM under
the condition that an autonomous robot’s caregiver
sometimes switches reward functions.

Introduction

How can we enable autonomous robots to acquire
behaviors incrementally through natural interactions
with their environment? To give a reasonable solution to this problem in the context of reinforcement
learning is the main purpose of this paper. Here,”environment” includes not only the physical environment but also the human caregiver interacting with therobot. Human infants acquire a number of behaviors
through interactions with their surroundings, particularly their mothers, who interact kindly with themvery frequently. However, an autonomous robot cannot obtain behavioral concepts incrementally throughsuch interactions because reinforcement learning usually requires that a reward function, which represents
a behavior that should be acquired by the robot, beﬁxed during the robot’s learning phase. To overcome
this problem, we propose a novel incremental modular reinforcement learning method, the reinforcement
learning schemata model (RLSM), which enables an©ISAROB 2006

2

Social interaction based on reinforcement learning

Social interaction has many features on which focus can be placed. In the human cognitive development process, the ﬁrst social interactions emerge between an infant and his or her mother. The infant
learns several behaviors, which are considered somewhat meaningful and diﬀerent by his or her mother,through interaction. However, an infant cannot recognize explicit diﬀerences between various behaviors.
It may be very diﬃcult to notice the overt diﬀerences
because they do not exist in the physical phenomena,
only in the mother’s covert intentions. Therefore, it isimportant for the infant to recognize the diﬀerences ofthe mother’s intentions from the series of interactions

653

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

with his or her mother, or other caregivers.
From the viewpoint of reinforcement learning, thistime series can be represented by a series of two vectors
and a scalar: sensor vector st , motor vector at , and reward signal rt . We assume that the diﬀerences in the
caregiver’s intentions are encoded in this time series
data (Fig. 1). Caregivers usually control reward functions to communicate their intentions to an infant. A
cognitive development autonomous robot should thus
be able to learn several behavioral concepts by receiving corresponding rewards.
Figure 2: Reinforcement learning schemata model

3

RLSM:
reinforcement
schemata model

learning

inner prediction function. The experiences accommodate its inner functions.This cyclic process is calledan equilibration process. However, if every schema refuses to assimilate a novel experience, the RLSM creates a new schema for the situation which produces
that experience. This process is called diﬀerentiation.
These equilibration and diﬀerentiation processes are
the basic concepts of our schemata model.

A modular learning architecture manages several
learning modules to learn dynamics and/or multiple
objective behaviors. The ”mixture-of-experts” modular learning architecture proposed by Jacobs et al.[5] iswidely used for pattern matching problems and stateprediction problems. In the context of reinforcement
learning, Singh proposed the compositional Q-learning
architecture which extends mixture-of-experts to reinforcement learning [3]. In this architecture, a compositional task is decomposed into several elemental tasks,and each elemental task is learned by an expert. The
achitecture has several Q-tables, and a Q-table is considered as an expert. However, the number of experts
is ﬁxed. Since a cognitive development autonomous
robot has to learn new behaviors incrementally, thenumber of experts should be ﬂexible. Takahashi et al.
proposed a modular reinforcement architecture [2], inwhich a new Q-table is created for each interaction
context. However, the criteria are based on the environmental dynamics, which are not related to rt , but
to st and at . Therefore, the tables do not reﬂect theintentions of the caregiver who designed the reward
functions.

3.1

3.2

Algorithm

RLSM is formulated based on Q-learning. The
λ-th schema has three functions: state-action-valuefunction Qλ , standard deviation estimator function
σ λ , and Qλ ’s second order statistics function Q(2)λ .
Q(2)λ is a supplementary function used to calculate
σ λ . In temporal diﬀerence (TD) learning including
Q-learning, errors in the value function cannot been
observed directly, so the TD errors must be considered. Therefore, Q(2)λ is considered for the purposeof estimating Qλ ’s standard deviation. TD-error δt(2)
and secondary TD-err δt for each λ-th schema arecalculated using
δtλ
(2)λ
δt

= rt + γV λ (st+1 ) − Qλ (st , at ) and=

Basic concepts(1)

rt2 + 2γrt V λ (st+1 ) + γ 2 Qλ(2) (st+1 , a∗t+1 )(2)−Qλ(2) (st , at ),where
In the context of a developmental learning, we previously proposed the dual-schemata model which enables an autonomous robot to obtain an environmental model incrementally [1]. By ”schema” we meana term in Piaget’s developmental psychology [6]. By
extending this concept to reinforcement learning, wedeveloped our reinforcement learning schemata model(Fig. 2), in the sameway that Singh extended mixtureof-experts to reinforcement learning. A schema assimilates experiences that are predicted correctly by its©ISAROB 2006

V λ (st ) =a∗t=Q(st , a∗t ),(3)
λargmaxa Q (st , a),(4)and γ is a discount parameter. Each function is updated using these errors:
Qλ (st , at ) ← Q(st , at ) + αδt
Q

654

(2)λ

(2)
λ(st , at ) + αδt(st , at ) ← Qσ̂ λ =
Q(2)λ − (Qλ )2 .
(2)

(5)
(6)
(7)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

By dividing δt by estimated standard deviation σ̂t , weobtain a dimensionless number, Rtλ , as the subjective
error:Rλ (t)λR̆ (t + 1)≡=∼

Vλ (t)=

np=|δtλ /σ̂tλ |

(8)
λ

λ

(1 − p)R (t) + pR̆ (t) ∞s1
exp(− )Rλ (t − s)ds
τ
τ
0

(9)

χc (np R̆λ (t), np )
1+p
,
1−p

(10)

Figure 3: Left: simulation space; right: Khepera’s
sensory-motor system(11)

where χc (x, n) is a chi-squared one-sided cumulative
function, P (X < x), R̆λ is a temporal weighted average of the subjective errors， and Vλ is the λ-thschema activity. ”Schema activity” means how near
the robot’s facing environment and reward function
are to the λ-th reinforcement learning schema. The
p is a parameter representing how long a schema retains a previous recognition. In continuous time, time
constant τ = t/(1 − p) corresponds to p; t is thecontinuous time for one step in discrete time.
A reinforcement learning schema decides whether
to assimilate an experience or reject it by referring
to schema activity Vλ . If all the schemata reject anincoming experience, diﬀerentiation is inisiated and a
new schema is created. This algorithm enables an autonomous robot to notice qualitative changes in a timeseries of st , at and rt and to obtain novel behavioral
concepts incrementally. Signiﬁcance parameter α isset, and the probability, P (λ), with which λ-th schemais selected is deﬁned asµ(H λ ) =P (λ) =

sgn(Vλ (t) − α)µ(H λ )λ−1
(1 − µ(H k )),4.1

The simulation space and a Khepera’s sensorymotor system are illustrated in Fig. 3. We used Webots, produced by cyberbotics, to simulate Khepera’s
dynamics. The square simulation space was enclosed
by walls 2 m long and 10 cm high. A light source was
located at a height of 10 cm at the center of the space.
Khepera has two wheels as a motor system, and theirrotational velocities can be set independently at eachtime step. Khepera’s sensory system comprises an infrared sensor, a light sensor, and a GPS. Q-learning
usually requires a discrete state space. Therefore, we
divided Khepera’s x,y coordinates and its angle of direction, obtained from the GPS, into six parts, anddeﬁned 216(= 6 × 6 × 6) states．The action space wasalso made discrete by deﬁning ﬁve representative motor outputs: i.e. forward, back, right, left, and stop.
Forward and back move the robot about 30 cm perstep，and right and left rotate it about 60◦ per step.
The values of the infrared sensor (ds) and the lightsensor (ls) were limited to between 0 and 1. They
were used only to calculate the rewards.
We prepared three reward functions:

(12)
(13)

k=0

where


sgn(x) =10

if
x > 0,
otherwise

(14)

=

ds

(15)

r
r3

=
=

1.5 ∗ ls
1.8 ∗ vf orward ,

(16)
(17)

where 0 ≤ vf orward ≤ 1 is the value given when
Khepera advances. These reward functions mean thatthe caregiver wants the robot to face a wall, to receive more light, and to go forward, respectively. The
meta parameters for reinforcement learning were set
to α = 0.2 and γ = 0.8. The meta parameters for theschemata model were set to p = 0.999 and α = 0.0001.
We investigated whether RLSM enabled Khepera
to obtain several behavioral concepts, i.e. reinforcement learning schemata, and recall them while it wasinteracting with the environment, in which the reward

Evaluation

We evaluated RLSM by using 2D simulation for the
Khepera mobile robot [7].

©ISAROB 2006

r1
2

and µ(H λ ) is the truth value of hypothesis H λ , whichmeans that the robot’s facing environment corresponds to the λ-th schema. H 0 is a dummy hypothesis
deﬁned to simplify the equation (µ(H 0 ) = 0).

4

Conditions

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5

Summary

We have described a novel reinforcement architecture, RLSM, for a dynamic environment in which
a caregiver designs several reward functions to train
an autonomous robot. Using RLSM enables a robot
to obtain several learning modules, called reinforcement learning schemata, without any explicit indication except for sensor vectors, motor vectors, andrewards, which are usually given in reinforcement
learning. This kind of constructive learning process
will enable development of cognitive development autonomous robots.

Figure 4: Top: schema diﬀerentiation process andtransition of schema activities; bottom: selected reinforcement learning schema

Acknowledgements
This work was supported in part by the Center of
Excellence for Research and Education on Complex
Functional Mechanical Systems. (The 21st Century
COE program of the Ministry of Education, Culture,
Sports, Science and Technology, Japan).

References
Figure 5: time course of averaged rewards after Khepera experienced three reward functions[1] T. Taniguchi, T. Sawaragi (2004), “Design and
Performance of Symbols Self-Organized within an
Autonomous Agent Interacting with Varied Environments ”, IEEE International Workshop on ROMAN, in CD-ROM

functions were switched in turn. Each trial consistedof 200 steps．Khepera’s action at each step was determined by Boltzmann selection. In each trial, theinverse temperature was set to β = 0 during theﬁrst
100 steps, to β = 1 during the next 50 steps, and to
β = 3 (almost greedy) during the ﬁnal 50 steps. The
reward functions were set to r1 for (0 < trial ≤ 750),
r2 for (750 < trial ≤ 1500), and r3 for (1500 < trial ≤2250). Subsequently, each reward function was usedfor 250 trials in turn.

4.2[2] Y. Takahashi et al.(2004), “Modular Learning System and Scheduling for Behavior Acquisition in
Multi-Agent Environment.” RoboCup 2004 Symposium papers and team description papers, in
CD-ROM[3] S. P. Singh (1992),“The Eﬃcient Learning of Multiple Task Sequences”, Neural Information Processing System 4, pp. 251–258[4] R.S. Sutton, A.G. Barto (1998),“Reinforcement
Learning: An Introduction”, MIT Press

ResultAs shown in Fig. 4, each schema activity, Vλ , transited successfully, and the schema, initially only one,diﬀerentiated into three, and three behavioral concepts
corresponding to the three reward functions were organized. Each schema was selected as shown in Fig.
4, and the averaged rewards transited remaining at
a high level as compared with simple Q-learning as
shown in Fig. 5. This shows that RLSM is better
than simple Q-learning in a dynamically changing environment.

©ISAROB 2006

[5] R.A. Jacobs et al.(1991),“Adaptive Mixtures of Local Experts”, Neural Computation, vol. 3, no. 1,
pp. 79–87[6] J.H. Flavell (1963), “The Developmental Psychology of Jean Piaget”, Van Nostrand Reinhold[7] Webots. http://www.cyberbotics.com. Commercial Mobile Robot Simulation Software.

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A Step towards Artificial ‘Kansei’
Kaoru Sugita Genci Capi Masao Yokota
Fukuoka Institute of Technology{sugita, capi, yokota}@fit.ac.jp
Abstract(2) Knowledge Processing Agent (Kn) evaluates
mental images received from the other agentsbased on its memory (e.g. knowledge), producing
other mental images such as “It is false that theearth is flat.”(3) Emotion Processing Agent (Em) evaluates mental
images received from the other agents based on its
memory (e.g. instincts), producing other mental
images such as “I like the food.”(4) Response Processing Agent (Re) converts mental
images (i.e. encoded physical actions such as “I’llwalk slowly.”) received from the other agents into
real physical actions against W.
A performance P for a stimulus X with a result Y ateach agent can be formalized as a function by theexpression (1).
Y=P(X),(1)where
P: a combination of atomic performances describedlater,
X: a spatio-temporal distribution of stimuli from W to
St or a mental image for another agent, andY : a series of signals to drive an actuator for Re or amental image for another agent.

Various types of robots developed in recent years canplay their roles according to programmed actions tostimuli. However, they have not yet come to understandsuch a mental function of their human partners as is called
Kansei. In this paper, Kansei for a robot, so called,'Artificial Kansei' is proposed as one of the functions of amulti-agent mind model in the view of artificial or robotic
individuality.

1. Introduction
In recent years, there have been developed various
types of robots in Japan. However, they are to play theirroles according to programmed actions to stimuli andhave not yet come to understand such a mental function of
their human partners as is called Kansei. The authors haveproposed a human mind model of human mind consistingof Stimulus, Knowledge, Emotion and Response
Processing Agents (see Fig.1) and simulated human-robot
communication based on it [1]. Besides this, we have tried
to describe the meanings of Kansei expressions such as‘heart-calming’, ‘fantastic’, ‘soft’, ‘thick’, ‘grotesque’, etc
related to visual images of crafts and to retrieve them bythese expressions as queries [4, 5, 6]. In this paper, we
describe 'Artificial Kansei (AK)', namely, Kansei for arobot as tight collaboration of Knowledge and Emotion
Processing Agents of our mind model, and verbalizationof Kansei information so called ‘Kansei expression’ by
Response Processing Agent in the view of artificial or
robotic individuality.

St

Re

Em

W
St: Stimulus Processing Agent.
Kn: Knowledge Processing Agent.
Em: Emotion Processing Agent.
Re: Response Processing Agent.
W: World surrounding human mind,
including his/her body.

2. Multi-agent mind model
Figure1 shows the multi-agent mind model proposedby the authors [1]. This is a functional model of humancentral nervous system consisting of the brain and thespine. The basic performances of its agents are as follows.
(1) Stimulus Processing Agent (St) receives stimuli
from the world (W) and encodes them into mental
images (i.e. encoded sensations) such as “I sensed
something oily.” (if verbalized in English.)©ISAROB 2006

Kn

Fig.1. Multi-agent model of human mind.
A performance P is assumed as a function formed
either consciously or unconsciously. In a conscious case, a
set of atomic performances are to be chosen and combined

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according to X by a meta-function, so called,‘Performance Selector (PS)’ assumed as ‘Conscience’.
On the contrary, in an unconscious case, such a
performance as associated most strongly with X is to be
applied automatically [8](∃x1,x2,p1,p2,k) L(x1,x1,p1,p2,A12,Gt,k)• (L(x1,x1,p2,p1,A12,Gt,k) ΠL(x1,x2,p2,p1,A12,Gt,k))∧x1≠x2 ∧p1≠p2(5)
In order for complete representation of temporal
relations, we have introduced a concept called ‘Empty
Event (EE)’ and symbolized as ‘ε’ which stands
exclusively for time elapsing. For example, (6) represents‘X1 during X2 ’.
(ε1•X1•ε2) Π X2
(6)
The image model presented here is also valid forformalizing word concepts (i.e. coding) of actions because
any action must be measured with sensors for its
formalization. That is, grounding words on actions isequivalent to grounding words on sensations of actions.
Sensors and actuators are assumed to collaborate very
closely in feedback or feed-forward ways in cybernetics
and there is a hypothesis that some kinds of sensations (orperceptions) and actions are encoded in the same way inorganisms [7].

3. Mental image description
The Mental Image Directed Semantic Theory (MIDST)
has modeled mental images as “Loci in Attribute spaces”[3], [7]. An attribute space corresponds with a certain
measuring instrument just like a thermometer, map
measurer or so and the loci represent the movements of its
indicator. The performance of ‘Attribute space’ is themodel of ‘Atomic performance’ introduced in Section 2.
A general locus is to be articulated by “Atomic locus”
formalized as the expression (2) in first-order logic, where“L” is a predicate constant.
L(x,y,p,q,a,g,k)(2)
The expression (2) is called “Atomic locus formula”
whose arguments are referred to as ‘Event Causer’,‘Attribute Carrier’, ‘Initial Attribute Value’, ‘Final
Attribute Value’, ‘Attribute Kind’, ‘Event Kind’ and‘Standard Attribute Value’, respectively.
The interpretation of (2) is as follows, where“matter” refers to “object” or “event”.
“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 and inspace domain, respectively. The former is called‘temporal event’ and the latter, ‘spatial event’.
For example, the motion of the ‘bus’ represented by S1
is a temporal event and the ranging or extension of the‘road’ by S2 is a spatial event whose meanings orconcepts are formalized as expressions (3) and (4),respectively, where the attribute is “physical location”
denoted as A12. We think that the verb ‘run’ used in S2
must reflect the motion of the observer’s attention [4].
(S1) The bus runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gt,k)∧bus(y)(3)(S2) The road runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gs,k)∧road(y)(4)

4. Artificial Kansei
It is well known that emotion in a human can beaffected by his/her world, namely, W in Fig.1. For
example, a person’s evaluation of live image of an object(i.e. image output from St) expressed by such words as‘favorite’, ‘beautiful’, ‘tasty’, etc can vary depending on
his/her emotional bias such as ‘hungry’, ‘depressed’, etc.
Kansei is one of mental functions with emotion
involved but has a more complicated phase than pureemotion originated from instincts or imprinting. For
example, sweet jam may be nice on toast but not on pizza
for certain people knowledgeable about these foods. For
another example, people can be affected on theirevaluation of an art by its creator’s name, for example,‘Picasso’. These are good examples of Kansei processing
as emotional performance affected by knowledge inhumans.
Therefore, Kansei can be defined as human emotion
toward an object affected by its information for him/her,so called, ‘concept’, including his/her intellectual pursuits,
traditions, cultures, etc concerning it. In this sense, Kansei
is assumed to be reasonable among the people sharing
such concepts unlike pure emotion. These hypothetic
considerations are formalized as (7) and (8).

The expression (5) is the conceptual description of the
English word “fetch”, implying such a temporal event that‘x1’ goes for ‘x2’ and then comes back with it, where ‘Π’and ‘•’ are instances of the tempo-logical connectives,‘SAND’ and ‘CAND’, standing for “Simultaneous AND”
and “Consecutive AND”, respectively.
In general, a series of atomic locus formulas with suchconnectives is called simply ‘Locus formula’.

©ISAROB 2006

IP(x)= PE(S(x))IK(x) =PE(S(x)∧O(x)) = PE(S’(x))where
PE(X): Performance of Em for mental image ‘X’,
IP(x): Mental image as pure emotion for object ‘x’,
IK(x): Mental image as Kansei for object ‘x’,
S(x): Live image of object ‘x’ from St,

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O(x): Concept of object ‘x’ from Kn,
S’(x): Unified image of live image and concept.

pragmatically. Here, semantic understanding means
connecting symbols to conceptual images of objects andpragmatic understanding means connecting symbols toreal objects by unifying conceptual images withperceptual images as shown in Fig.4.

Figure 3 shows an example of Kansei processing in our
mind model, where perceived, induced and inspired
images correspond to S(x), S’(x) and IK(x), respectively,
while Fig.2 is for pure emotion with IP(x) as the inspired
image.
These two inspired images are to be verbalized in Re
as ‘Fragrant!’ and ‘Appetizing!’, labeled in Fig.2,
respectively. The essential difference between them isassumed to reside in whether or not they are affected by
O(x), namely, the concept of ‘chocolate cream bread’,
inferred by Kn from the shape and the smell. Whereas,
pure emotion for an object can be a special case of Kansei
processing without knowing or recognizing what it is.

Fig.4. Semantic and pragmatic understanding
However, humans and robots can be equipped withsensors, actuators and brains of different performances
and their vocabularies may well be grounded on quite
different sensations, physical actions or mental actions.
And in turn such a situation may bring inevitably different
kinds of semantics to them, so called, “Natural Semantics(NS)” for humans and “Artificial Semantics (AS)” forrobots.
For example, consider such a scenario as follows.
…A human ‘Kate’ and a humanoid robot ‘Robbie’
encounter at the terrace in front of the room where a
Christmas party is going on merrymaking. Kate says
“Robbie, please fetch me some delicious food from thenoisy room.” Robbie replies “OK, Kate.”….

Fig.2. Example of pure emotion

For a happy end of this dialog, Robbie must have agood knowledge of Kate’s NS for Kansei and translate it
into its AS appropriately enough to find out the real
objects referred to by her words. In this case, Robbie needat least to interpret Kate’s statement as the expression (10)reading “If Robbie fetches Kate some food delicious forher from the room noisy for her (E1), then consecutively
it makes Kate happier (E2),” or as its logical equivalent,the expression (11) reading “It is not the case that Robbie
fetches Kate the food … and consecutively it does notmake Kate happier.” Both the expressions are adopted in
MIDST as the canonical conceptual structures of animperative sentence.
E1 →c E2(10)~(E1•~E2)
(11)
where
E1 ⇔ (∃x1,x2,k1,k2,k3,k4) (L(R,R,K,x2,A12,Gt,k1)•(L(R,R,x2,K,A12,Gt,k1)ΠL(R,x1,x2,K,A12,Gt,k1)))
Π(L(K,x1,Delicious,Delicious,B08,Gt,k2)
Π L(K,x2,Noisy,Noisy,B08,Gt,k4)∧ food(x1) ∧ room(x2)Fig.3. Example of Kansei processing
In the MIDST, the concept of an object ‘x’ is given
as an integrated omnisensory mental image of its
properties and its relations with other objects involved.
For example, the concept of ‘chocolate cream bread’ canbe given by (9), reading that x is bread, sweet due tochocolate cream, fragrant of itself, etc, where A29 andA30 refer to ‘Taste’ and ‘Odour’, respectively.
(λx)chocolate_cream_bread(x) ⇔(λx∃y,k1,k2)L(y,x,Sweet,Sweet,A29,Gt,k1) ΠL(x,x,Fragrant,Fragrant,A30,Gt,k2) ∧bread(x) ∧chocolate_cream(y) ∧ …
(9)

5. Human-robot communication
For comprehensible communication with humans,robots must understand natural language semantically and©ISAROB 2006

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E2 ⇔ (∃e1,e2,k7) L(E1,K,e1,e2,B04,Gt,k7) ∧ e2>e1.

References

The special symbols and their meanings in theexpressions above are:‘X→c Y’ =‘If X then consecutively Y’, R=‘Robbie’,
K=‘Kate’, A31=‘Sound’, A32=‘Color’, B08=‘Kansei’ and
B04= ‘Happiness (=degree of happiness)’.
As easily imagined, these values of the attribute Kansei(B08) greatly depend on their standards (i.e. k2 and k4)
that are most closely related to ‘Individual’ or ‘Purposive’
standard shown in Table 1 (see APPENDIX).
By the way, Robbie’s task is only to make E1 cometrue where each atomic locus formula is associated withhis actuators/sensors. Of course, Robbie believes that hewill become happier to help Kate, given by expression(12) where ‘B03’ is ‘trueness (=degree of truth)’and‘KB‘ is a certain standard of ‘believability’. That is
emotionally to say, Robbie likes Kate. Therefore, thisexample is also very significant for intentional sensing
and action of a robot driven by logical description of its
belief.
(∃p)L(R,E,p,p,B03,Gt,KB) ∧ p>KB
∧ E = E1 →c E2[1] Yokota,M., Shiraishi,M. and Capi,G.: “Human-robot
communication through a mind model based on the
Mental Image Directed Semantic Theory,” Proc. of
the 10th International Symposium on Artificial Lifeand Robotics (AROB ’05), pp.695-698, 2005.
[2] Minsky, M: The society of mind, Simon and Schuster,
New York, 1986.
[3] Yokota, M. & Capi,G.: “Cross-media Operations
between Text and Picture Based on Mental Image
Directed Semantic theory,” WSEAS Trans. on
INFORMATION SCIENCE and APPLICATIONS,
Issue 10, 2, pp.1541-1550, Oct. 2005.
[4] M.Fukuda, K.Sugita and Y.Shibata, “Perceptional
Retrieving Method for Distributed Design Image
Database System,” IPSJ Journal, Vol.39, No.2,
pp.158-169, 1998.
[5] K.Sugita, A.Miyakawa and Y.Shibata, “Relation
Between Kansei Words and the Room Space inDigital Traditional Japanese Crafting System,” TheInternational Conference on Advanced Information
Networking and Applications (AINA), 159-162, 2003.
[6] K.Sugita, T.Ishida, A.Miyakawa, Y.Shibata, “Kansei
Retrieval Method using the Quantitative Feature of
Traditional Japanese Crafting Object,” Proc. of The
6th International Workshop on Multimedia Network
Systems and Applications (MNSA'2004), 112-117,
2004.
[7] Prinz, W.: “A common coding approach to perception
and action”, In Relationships between perception andaction (Neumann, O. and Prinz, W. eds.), pp.167-201,
Springer-Verlag, 1990.
[8] Brooks, R. A.: “A robust layered control system for amobile robot”, IEEE Journal of Robotics andAutomation, RA-2, pp.14-23, 1986.

(12)

6. Discussion and conclusion
Our mind model is much simpler than Minsky’s [2] but
the locus formula representation can work for representing
and computing mental phenomena fairly well [1, 3]. For
realizing a plausible Kansei, it is most essential to find out
functional features of Em and to deduce from them suchlaws that rule PE. The most important problems to besolved are how to realize the attribute space of Kansei andhow to build its corresponding atomic performance. In
order to solve these problems, we will consider theapplication of soft computing theories such as neuralnetwork, genetic algorithm, fuzzy logic, etc. in the future.

APPENDIX
Table 1 Examples of standards.
Categories of standards
Rigid Standard
Species Standard
Proportional Standard
Individual Standard
Purposive Standard
Declarative Standard©ISAROB 2006

Remarks
Objective standards such as denoted by measuring units (meter, gram, etc.).
The attribute value ordinary for a species. A short train is ordinarily longer than a long pencil.
‘Oblong’ means that the width is greater than the height at a physical object.
Delicious food for one person can be too poor for another.
One room comfortable enough for a person’s sleeping may be uncomfortable for his jogging.
The origin of an order such as ‘next’ must be declared explicitly just as ‘next to him’.

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A Spoken Language Interface to a Mobile Robot ∗Johan Bos
Dipartimento di Informaticà
Universita di Roma “La Sapienza”
via Salaria 113 00196 Roma, Italy

Tetsushi Oka
Dept. of Information and Systems Engineering
Fukuoka Institute of Technology
3-30-1 Wajiro-higashi, Fukuoka 811-0295 Japan
2

Abstract

2.1
We describe a spoken dialogue interface to a mobile robot, which a human can direct to speciﬁc locations, ask for information about its status, and supply
information about its environment. The robot uses
an internal map for navigation, and communicates its
current orientation and accessible locations to the dialogue system. In this paper, we focus on linguistic andinferential aspects of the human-robot communication
process.

1

Speech Recognition

The robot that we developed is able to communicate
with humans in spoken language, rather than typed.
In our system, we use Nuance’s speaker-independent
speech recognition system (www.nuance.com), which allows language models to be speciﬁed in the GSL(Grammar Speciﬁcation Language), a form of context
free grammars. Rather than coding the grammarbased language model in GSL directly, we use ageneric domain-independent, but linguistically motivated grammar as a starting point. The grammar inquestion is a uniﬁcation grammar for English, whichgets translated into GSL, using the UNIANCE compiler [1]. We did not adopt the standard slot-ﬁllingparadigm for semantic interpretation, but used UNIANCE’s feature to employ a sophisticated compositional semantics involving λ terms which are passed as
the value of a single slot for the recognised sentence.
As a result the output of the speech recognition component is a genuine semantic representation, and nofurther parsing is required before handing it over tothe dialogue manager.

Introduction

The use of spoken language is arguably the mostnatural way of establishing a communication channel
between a human and a robot. A successful implementation of a talking robot requires a good understanding of many diﬀerent aspects of conversation, ranging
from acoustic signals, the syntactic structure of thelanguage, the meaning associated it, and the underlying goals derived from it. This paper presents anapproach to human-robot dialogue understanding. In
the framework that we introduce, the meaning of utterances of the dialogue participants and other information of the situation are represented as logical formsof compositional semantics, and logical inferences are
drawn to manage the direction of the dialogue fromthe robot’s point of view: when to perform which actions, how to answer a question, and whether to show
agreement or disagreement towards the user’s contribution.

2.2

Semantic Interpretation

Discourse Representation Theory (DRT) [5] is used
for meaning representation in the system. It is a formal theory of discourse interpretation, covering a wide
variety of natural language phenomena including referring anaphoric and deictic expressions, quantiﬁed expressions, plural noun phrases, and a wide spectrumof presuppositional expressions [3].
We use Discourse Representation Structures(DRSs) to represent the meaning of the dialogue between user and system. There are computational implementations that provide means to extend existing
linguistic grammars with DRS-construction tools that
we use to design the semantic interpretation component. There is a standard translation from DRSs to∗ This work was conducted at ICCS, School of Informatics,
University of Edinburgh.

©ISAROB 2006

Natural Language Understanding

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2.3

formulas of ﬁrst-order logic that behaves linear on thesize of the input, which gives us the means to implement logical inference.
DRT was initially designed to deal with texts, so
we use an extension of standard DRT that enables us
to cope with the semantics of imperatives and interrogatives. We use a modiﬁed DRS-language covering
these extensions:

Inference

The DRS-language used for representing the semantic content is useful for dealing with various linguistic
phenomena, but there are no eﬃcient inference engines
available that work directly on DRSs. Since there are acouple of eﬃcient theorem provers and model builders
for ﬁrst-order logic available, we translate the DRSs
to ﬁrst-order formulas for inference tasks.
The core of the translation to ﬁrst-order covering
our extended DRS-language is based on the “standard translation” from DRT to ﬁrst-order logic [5].
Basically, it introduces existential quantiﬁers for discourse referents, unless discourse referents are declaredin the universe of the antecedent DRSs of an implication, in which case they will undergo universal quantiﬁcation. Sets of DRS-conditions are translated into
a conjunction of ﬁrst-order formulas. To deal with themodal operators in our extended DRS-language, we
use the ideas of the relational translation for modal
logic to ﬁrst-order formulas [6]. This means that allbasic DRS-conditions of arity n are translated intoﬁrst-order relations of arity n+1, where the additional
argument plays the role of a possible world (a state).
DRS-action-terms are translated into three-place relations where the ﬁrst argument denotes the current
state, the second argument describes the actions, andthe third argument denotes the resulting state. The
full translation is given in [7]. We illustrate the translation with an example.

Syntax of DRSs
1. If {x1 , . . . , xn } is a set of discourse referents, and{γ1 , . . . , γm } is a set of DRS-conditions, thenthe ordered pair h{x1 , . . . , xn }, {γ1 , . . . , γm }i is a
DRS;
2. If B1 and B2 are DRSs, then so is (B1 ;B2 ). (mergeof DRSs)
In DRS conditions, DRS-action-terms are used
to deal with imperatives in dialogue, following [4].
We distinguish between atomic and composite DRSaction-terms:
Syntax of DRS-action-terms:
1. If B is a DRS, then δB is a DRS-action-term;
2. If A1 and A2 are DRS-action-terms, then so are(A1 ;A2 ) (sequence) and (A1 |A2 ) (free choice).
Syntax of DRS-conditions:
1. If R is a relation symbol for an n-place predicate and x1 . . . xn are discourse referents thenR(x1 ,. . . ,xn ) is a DRS-condition;
2. If x1 and x2 are discourse referents, then x1 = x2
is a DRS-condition;sax

3. If B is a DRS, then ¬B, 2B, 3B, ?B are DRSconditions;
4. If B1 and B2 are DRSs, then B1 ∨ B2 , B1 ⇒ B2 ,B1 ?B2 are DRS-conditions;system(s)kitchen(x)5. If x is a discourse referent and B a DRS, then x:B
is a DRS-condition;et! δ

6. If A is a DRS-action-term, and B a DRS, then[A]B and hAiB are DRS-conditions;
7. If A is a DRS-action-term then !A is a DRScondition.

light(y)in(y,x)⇒switch(e,s,t)
t:
on(y)

Figure 1: Example DRS paraphrasing the utterance“Switch every light in the kitchen on”.

Clauses 1 and 2 deals with basic DRS-conditions as
in standard DRT [5]. Clause 3 introduces negation,
the modal operators, and yes-no questions. Clause
4 covers disjunction, conditionals, and wh-questions.
Hybrid DRS-conditions, formed by combining a discourse referent and a DRS, are introduced in clause
5. Clause 6 describes necessary and possible eﬀects ofactions, and clause 7 introduces DRS-conditions that
describe actions that are commanded.

©ISAROB 2006yConsider the DRS for Switch every light in the
kitchen on! in Figure 1. This example will get thefollowing ﬁrst-order translation:∃w∃s∃a∃x (possible world(w) ∧ system(w,s)∧ kitchen(w,x) ∧ ∃v∃a (action(w,a,v) ∧ ∀y662

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(light(a,y)∧
in(a,y,x)(switch(w,e,s,t) ∧ on(t,y)))))→∃e∃t
1. Construct a (ﬁnite) set of DRSs B for a new utterance with respect to the previous DRS Bold ;We make use of two kinds of inference engines:
theorem provers, and model builders. A theorem
prover, when given a ﬁrst-order formula, tries to decide whether it is true in all possible models. A modelbuilder, on the other hand, tries to construct a possible model for the input formula. Figure 2 describes
this idea in the form of an algorithm. Step 1 illustrates
the fact that due to potential ambiguities appearing in
the utterances conveyed by the user, semantic interpretation normally result in a set of DRSs. In Step2, we make interpretation of the dialogue sensitive tocontext (see Section 2.4). We start with the empty
set of interpretations (Step 3) and attempt to ﬁnd
consistent interpretations for each of the DRSs (Step4). The theorem prover and model builder work in
a complementary way. As soon as a proof is foundthe model builder does not need to further attempt toﬁnd a model because it will never succeed in doing so.
However, if a model is found, the theorem prover canstop its attempt to ﬁnd a proof, because it will never
be able to do so. Finally, the system is instructed on
how to react on the set of interpretations found (Step
5 and 6).
As the algorithm implies, the models generated by
the model builder play a crucial role in interpretation. Figure 3 shows the model generated for the DRS
of Figure 1. There are two interesting things aboutmodels. First, the ﬂat representations of these models (they contain no recursion, in contrast to DRSs)
ensure that they are extremely easy to process— allquantiﬁcation and boolean structures are explicit inmodels. Second, the models produced by the modelbuilder are minimal models, and contain no irrelevant
information.
We use the model to deduce what actions need to be
performed by the robot, or to answer questions posedby the user. For instance, in the model in Figure 3 adesired action for the robot is to supply power to thelights d6 and d7.
The algorithm itself is implemented on top of theinformation-state approach to dialogue modelling [8],in which an agent’s information state is updated on
the basis of observed dialogue moves, leading to theselection of a new dialogue move to be performed by
the agent. This is realised by a set of update rules,
linking preconditions to eﬀects. The dialogue manager
repeatedly computes the eﬀects of those update rules
whose preconditions are satisﬁed by the current information state. Preconditions are expressed in terms ofcurrent values in the information state, while the ef-©ISAROB 2006

2. Construct a DRS C representing situational
knowledge;
3. Initialise the set of interpretations I to ∅;4. For each Bi ∈ B:(a) Translate the compound DRS (Bi ; C) to theﬁrst-order representation φi ;(b) Compute appropriate background knowledge
stated as the ﬁrst-order theory θi for φi ;(c) Attempt to build a model for (θi ∧ φi ) by
simultaneously performing:
i. Give (θi ∧ φi ) to a model builder, possibly resulting in a model Mi ; (for consistent interpretations).
ii. Give ¬(θi ∧φi ) to a theorem prover, possible resulting in a proof (for inconsistentinterpretations);(d) If a proof is found cancel 4(c)i. If a model isfound add hBi , Mi i to I and cancel 4(c)ii;
5. If I = ∅ perform a misunderstanding act and quit.
Else perform an understanding act and select a
preferred interpretation hBp , Mp i from I;
6. Use the information in Mp to decide whether toperform any actions. Replace Bold by Bp .

Figure 2: Update algorithm for inference-based dialogue management

D={d1,d2,d3,d4,d5,d6,d7,d8}
F(possible_world)={d1,d2,d3}
F(system)={(d1,d4),(d2,d4),(d3,d4)}F(kitchen)={(d1,d5),(d2,d5),(d3,d5)}F(action)={(d1,d2,d3)}F(light)={(d1,d6),(d2,d6),(d3,d6),(d1,d7),(d2,d7),(d3,d7)}
F(in)={(d1,d6,d5),(d2,d6,d5),(d3,d6,d5),(d1,d7,d5),(d2,d7,d5),(d3,d7,d5)}
F(poweron={(d2,d6),(d2,d7)}F(off)={(d1,d6),(d1,d7)}
F(on)={(d3,d6),(d3,d7)}

Figure 3: Example model

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This allows us to combine the situational knowledge
with the information from the dialogue, enabling tointerpret actions directly in the current situation.

fects will change these values. The 26 update rules inour current system deal with establishing contact withthe user, initiating clariﬁcation dialogues (when therecognition conﬁdence score is below a certain threshold), answering questions, acknowledging requests andconﬁrming or denying statements.

2.4

Background Knowledge

The interpretation algorithm in Figure 2 integrates
background knowledge in two ways: in Step 2 situational knowledge is combined with the DRS; andin Step 4b other background knowledge is computed.
These other sources of background knowledge can bedivided in ontological knowledge, world knowledge andknowledge linking language to robot primitives.
Ontological knowledge comprises the various relationships between concepts that appear in the application domain. The two main relations that are
expressed are subsumption and disjointness. For instance, we need to express that in all possible worlds
a kitchen is a region, and that in all possible worlds a
kitchen is not a corridor. Both can easily be coded inﬁrst-order logic:

Mobile Robot Control

3.1

Control Architecture

The control system is a looping algorithm reading
sensory input and writing motor output at regular intervals. The sensory input comprises the readings ofsonars, infrared sensors, bumpers and odometry. The
sonars and infrared sensors are used for detecting occupied space and obstacles, and the bumpers notify
if the robot hit an obstacle or wall. The odometrycomponent measures the position and orientation of
the robot. The motor output comprises translationaland rotational velocity of the robot, and pan-tilt-zoom
information for the camera unit.
We use units called “behaviours” to implement
robot control. A behaviour has its own internal memory and uses the sensory input and motor output of
the control system. While executing, it computes the
motor output based on the sensory input and internal memory and updates the memory at regular intervals. It terminates when certain conditions are fulﬁlled. For example, a behaviour to move forward repeatedly sends velocity commands in a forward direction and terminates when an obstacle is detected or
the robot has reached a speciﬁed location.
Behaviours are described as object-oriented classes(Figure 4 ). An instance of a behaviour class can becreated using diﬀerent parameters such as speed, direction, duration, or goal.
The robot needs to be able to deal with multiple
goals. For instance, the robot may receive a new verbal command from the user when it is already undertaking an action. In that case, the robot must suspend
the current behaviour and create and execute another.
In our control system, we use a stack of behaviour instances to pursue multiple goals given by the user. The
control system creates instances of behaviour classes,
pushes them into the stack and deletes them when
they terminate.
The behaviour that is executed is the one on the
top of the stack. Any new command suspends the current executing behaviour and starts a new behaviour.
When the new behaviour terminates, it is popped of
the stack and the previous one resumes execution. The
use of a stack simpliﬁes the way the dialogue systemsends commands to the control system. It doesn’t need∀w∀x(kitchen(w,x)→region(w,x))∀w∀x(kitchen(w,x)→ ¬corridor(w,x))World knowledge subsumes all generalisations relevant to the application domain. The so-called ‘frame
axioms’ belong to this class, and rules expressing physical laws. The frame axioms state properties of objects
when certain actions are performed. For instance, if arobot moves from one region to another, its position
will be diﬀerent, but the positions of all other objects
normally remain unaltered. Again, this can be relatively easily coded in ﬁrst-order logic.
Knowledge linking natural language to robot primitives can be seen as a set of meaning postulates, rulesthat map non-logical symbols expressing the meaningof actions to symbols understood by the robot. Put
diﬀerently, these rules establish the interface between
the instruction language (i.e., English) and the hardware of the robot. An example of such a rule is “toswitch on a device” and its translation into a DRS(Figure 1), which is mapped to the action poweron, as
can be seen in the model generated for it (Figure 3).
Situational knowledge, ﬁnally, comprises the information of a speciﬁc situation in time. In the application domain of mobile robots, this typically consists of
the regions and objects that are accessible to the robot,
and the current positions of movable objects (including
the robot itself). We do not code this knowledge directly into ﬁrst-order knowledge, but rather use DRSs.

©ISAROB 2006

3

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4

Look
Turn

NavigateToCell

Navigate

NavigateToRegion

3

0

7

1

6
8 9 12Behaviour
ListenNavigateHome

0

1

3

6

8

4

7

9

10

Explore101211
FollowWall

11

Figure 4: Part of behaviour class hierarchy
Figure 5: Representation of the environment
send commands to explicitly suspend, resume, terminate or remove behaviours to achieve multiple goals.
There are robot primitives creating simple behaviours such as go(Distance, Speed), turn(Angle,
Speed) and look(Pan,Tilt), as well as commands introducing complex behaviours like follow wall(Distance)and move to region(N). In addition, sequences of actions such as seq(zoom(100), look(0, 20), turn(90, 20))can be commanded, in which case three behaviour instances are created and pushed into the stack.

3.2

Table 1: Semantic labels
RegionDescription0

the oﬃce

1

the hallway

3, 4, 8-12

the corridor

6

the rest room

Semantic labelxλp.(;p(x))
office(x)xλp.(;p(x))
hallway(x)xλp.
;p(x))
corridor(x)xλp.(;p(x))
rest room(x)Internal Map and Situational Knowledge

4
An internal map of the environment is used to plana path to the target location and navigate the robot
there following the path and avoiding obstacles. There
are three layers in the map. The geometrical layer
uses an occupancy grid to represent occupied and freespace in the environment. The topological layer is automatically constructed from the occupancy grid by
subdividing the free space into distinct topological regions corresponding to rooms or parts of the corridor(Figure 5). This is possible by creating a Generalised
Voronoi Diagram [9, 10, 11].
The numbers in the geometrical map shown in Figure 5 are identiﬁers of topological regions which canbe seen as nodes of an undirected graph. A further
layer of representation connects the map of the navigation system with a vocabulary of semantic symbols
used by the dialogue system. This is done by associating semantic descriptions to regions (Table 1). These
descriptions can be arbitrarily complex. For instance,
the DRS λp.(h[x, y], [oﬃce(x ), of (x , y), tim(y)]i; p(x ))could be used to denote Tim’s oﬃce.

©ISAROB 2006

Implementation and Evaluation

We implemented a talking mobile robot system,
Godot, integrating a dialogue interface and a control
system on an RWI Magellan Pro mobile robot platform with an on-board PC and a laptop computer on
the top (Figure 6).
Godot is a complete end-to-end system for humanrobot communication, where users can start a new dialogue simply by addressing the robot in spoken language, with the robot responding in real time. Although we have not yet reached the stage of carrying out formal usability studies, we have tested
Godot when visitors to our department unfamiliar
with Godot have controlled its movements and its
camera with success. Another evaluation took placeover two days in the Scottish museum, where museum visitors could address the Godot in a simulatedhouse. This was a diﬀerent environment from Godot’s
usual one (the basement of our department) but our
modular approach allowed us to quickly adapt it—
in fact we only needed to change the internal map.
In these interactions with new users we collected the665

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Acknowledgements

spoken data and used that to improve the system,by extending the vocabulary and grammar constructions. We have published videos of Godot in action at
http://www.ltg.ed.ac.uk/godot/.

This work was supported by IBL for mobile robots(EPSRC GR/M90160) and the EU Project Magicster(IST 1999-29078). We thank Nuance for permission
to use their software and tools.

References[1] Johan Bos, “Compilation of Uniﬁcation Grammars
with Compositional Semantics to Speech Recognition Packages,” COLING 2002. Proceedings of the
19th International Conference on Computational
Linguistics, 106–112, 2002. 1982.
[2] Hans Kamp, “A Theory of Truth and Semantic
Representation,” Formal Methods in the Study ofLanguage, 277–322, 1982.

Figure 6: Godot, the mobile robot

5[3] A. Rob Van der Sandt, “Presupposition Projection
as Anaphora Resolution,” Journal of Semantics,
vol.9 333-377, 1992.

Conclusions[4] Alex Lascarides, “Imperatives in Dialogue,” Proceedings of the 5th International Workshop on Formal Semantics and Pragmatics of Dialogue (BIDIALOG), 1–16, 2001.

We have developed an eﬀective interface between
natural language semantics and the robot control
layer, thus enabling users to refer to locations in a natural way, rather than resorting to expressions like go to
grid cell 45-66 or you are in region 12. The framework
is practically domain-independent: a change of environment would only require a change of the internal
map and possibly a new lexicon.
Our robot employs an inference-based approach todialogue understanding, with the aim to ﬁnd a consistent semantic representation capturing the meaningof the dialogue. If a consistent interpretation cannot
be found, we have got a signal that something is goingwrong in communication. Such a situation might arise
from disagreement or misunderstanding between dialogue participants. Inference also contributes to ambiguity resolution (for instance the resolution of pronouns and other anaphoric expressions) and helps ﬁnding preferred interpretations. Moreover, with the helpof model building, inference can be used for performing actions and answering questions, too. The resulting dialogue system is formulated on an abstract level,
and is easily portable to robots in new domains or with
diﬀerent applications.
Future work will address the interpretation of vague
expressions (the end of the corridor ), metonymic expressions (go to the door, where an artifact is interpreted as a location), together with commands which
require Godot to reason and talk about its current activities (continue going to the kitchen).

©ISAROB 2006

[5] H. Kamp and U. Reyle, From Discourse to
Logic; An Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and DRT,
Kluwer, 1993.
[6] Robert C Moore, “Reasoning about Knowledge
and Action,” SRI International Technical Report,
1980.
[7] Johan Bos, ”Implementing the Binding and Accommodation Theory for Anaphora Resolution
and Presupposition Projection,” Computational
Linguistics, vol.29, 2, 179–210, 2003.
[8] David Traum et al, “A model of dialogue moves
and information state revision,” Trindi Technical
Report D2.1, 1999.
[9] Jean Claude Latombe, “Robot Motion Planning,”
Kluwer Academic Publishers, 1991.
[10] Sebastian Thrun, “Learning Maps for Indoor Mobile Robots,” Artiﬁcial Intelligence, vol.99(1), 21–
71, 1998.
[11] Christian Theobalt, ”Navigation on a Mobile
Robot,” Master’s Thesis, University of Edinburgh,
2000.

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Performance Evaluation of Evolutionary Multiobjective Optimization
Genci Capi1, Koco Bode2 and Masao Yokota1
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
Recently multiobjective evolutionary algorithm(EA) ([4], [5]) has been proven to give good results byfinding better tradeoffs between different objectivefunctions. However, a crucial issue is to determine the
EA parameters like the population size, number ofinteractions, crossover and mutation probability, in orderto improve the performance..

Abstract
In this paper, we focus on the performance ofevolutionary multiobjective optimization algorithm.
Especially, how the population size and number ofinteractions affect the quality and distribution of optimal
Pareto solution. The performance is tested in humanoid
robot gait generation task. The humanoid robot gait isgenerated based on two conflicting objectives: minimum
energy and minimum torque change. The preliminary
results show that population size and number ofinteractions greatly effect the performance ofmultiobjective evolutionary algorithm.

In order to see the effect of these parameters on theperformance of multiobjective EA, we considered a thehumanoid robot gait generation based on two conflicting
objective functions: minimum consumed energy (MCE)and minimum torque change (MTC) ([6]). In ourprevious works, we considered the humanoid robot gait
generation during walking and going up-stairs ([7]) and areal time gait generation ([8]), where each criteria wassatisfied separately. The results showed that MCE gaithas different characteristics compared to MTC. MCE gait
was similar with that of human. Another advantage of
MCE criteria is also related to the operation time when abattery actuates the motors. On the other hand, due tosmooth change of torque and consequently of linkaccelerations, the disturbance of robot stability was smallin MTC humanoid robot gait. Based on theseobservations, it would be effective to generate ahumanoid robot gait utilizing the advantages of each
optimization criteria. Our method produces a set of nondominated Pareto front solutions from which we canselect the preferable humanoid robot gait.

KEY WORDS
Actor-critic Reinforcement Learning, Genetic Algorithm,
metaparameters.

1. Introduction
In most of real world problems several objectives, often
conflicting ones, must be satisfied simultaneously inorder to obtain an optimal solution. Evolutionary
algorithm is one of the most prominent class ofmetaheuristics for tracking multiobjective optimization
problems.
In order to solve multiobjective problems different
approaches have been proposed. Syswerda and Palmucci([1]) proposed a combination of all objectives into asingle one using different weighting coefficients for eachone of them. Charnes and Cooper ([2]) and Ijiri ([3])
developed a method where the targets or goals areincorporated into the problem as additional constraints.
However, both these methods require accurate
information on the objectives, to determine theappropriate weights.

©ISAROB 2006

Some preliminary results presented in this paper
show that performance of multiobjective EA is greatly
affected by the parameters such as population size andtermination criteria. Especially, the number ofinteractions influences the quality of the solution and the
distribution of nondominated solutions in the obtained
Pareto optimal set.

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2. Multiobjective Evolutionary Algorithm

3. Humanoid Robot Gait Generation

In multiobjective optimization problems there are many
(possibly conflicting) objectives to be optimized,
simultaneously. Therefore, there is no longer a single
optimal solution but rather a whole set of possible
solutions of equivalent quality. In contrast to fullyordered scalar search spaces, multidimensional search
spaces are only partially ordered, i.e. two different
solutions are related to each other in two possible ways:
either one dominates the other or none of them isdominated. Consider without loss of generality thefollowing multiobjective maximization problem with mdecision variables x (parameters) and n objectives:

During motion, the arms of the humanoid robot will be
fixed on the chest. Therefore, it can be considered as afive-link biped robot in the saggital plane, as shown in
Fig. 1. The motion of the biped robot is considered to becomposed from a single support phase and an
instantaneous double support phase. The friction force
between the robot’s feet and the ground is considered to
be great enough to prevent sliding. In addition, theposture at the beginning and at the end of the step isconsidered the same.

y = f ( x ) = ( f 1( x 1 ,...... x m ),...,f n( x 1 ,...... x m ))The humanoid robot gait synthesis problem during
walking, consists on finding the joint angle trajectories,
to connect the first and last posture of the biped robot for
which the CE or TC is minimal. For the MCE costfunction, it can be assumed that the energy to control the
position of the robot is proportional to the integration of
the square of the torque with respect to time, because thejoint torque is proportional with current. Therefore,
minimizing the joint torque can solve the MCE problem.
The cost function J, which is a quantity proportional tothe energy required for the motion, is defined as follows:(1)

where x = ( x1 ,......x m ) ∈ X , y = ( y1 ,...... y n ) ∈ Y andwhere x is called decision (parameter) vector, X
parameter space, y objective vector and Y objective space.
A decision vector a∈X is said to dominate a decision
vector b∈X (also written as a ≻b) if and only if:∀i ∈ {1,...., n} : f i (a ) ≥ f i (b) ∧∃j ∈ {1,...., n} : f j (a) > f j (b)

(2)

En =

The decision vector a is called Pareto-optimal if andonly if a is nondominated regarding the whole parameter
space X. Pareto-optimal parameter vectors cannot be
improved in any objective without causing degradation inat least one of the other objectives. They represent in thatsense globally optimal solutions. Note that a Paretooptimal set does not necessarily contain all Pareto
optimal solutions in X. The set of objective vectors
corresponding to a set of Pareto-optimal parameter
vectors is called "Pareto-optimal front".

tf

0

0

∫

∫

(3)

where: tf is the step time, τ is the torque vector, ∆τ jumpand ∆t are the addition torque applied to the body link tocause the ZMP to jump in the ne supporting foot and its
duration time, and C is the constraint function, given as
follows:m3

θ3

In extending the ideas of single objective EAs tomultiobjective cases, two major problems must be
addressed: 1. How to accomplish fitness assignment andselection in order to guide the search towards the Paretooptimal set. 2. How to maintain a diverse population inorder to prevent premature convergence and achieve awell distributed, wide spread trade-off front. Note thatthe objective function itself no longer qualifies as fitness
function since it is a vector valued and fitness has to be ascalar value. Different approaches to relate the fitness
function to the objective function can be classified withregard to the first issue. The second problem is usually
solved by introducing elitism and intermediate
recombination. Elitism is a way to ensure that good
individuals do not get lost (by mutation or set reduction),
simply by storing them away in an external set, which
only participates in selection. Intermediate recombination,on the other hand, averages the parameter vectors of twoparents in order to generate one offspring.

©ISAROB 2006tf1
( τ T τdt + ∆τ 2jump ∆t + Cdt ) ,
2

z1
O1
z θ
i
O

m4
θ4

+

x1
m2
θ2

Searching
Area P(xp,yp)

x
m5
θ5

m1
θ1

P(xp,yp)
ZMPf

R

ZMPjump

RN

step length
Ff

Fig.1 Five link biped robot in the saggital plan.

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the foot. The foot length is 0.18m. A DC servomotor
actuates each joint. The control platform is based on
Common Object Request Broker Architecture (CORBA),
which allows an easy updating and addition of newmodules.
Due to difficulties of binary representation when
dealing with continuous search space with large
dimension, real coded GA [8] is used in this study. The
decision variables are represented by real numbers within
their lower and upper limits. We employed a standard
crossover operator and the non-uniform mutation. In all
optimization runs, crossover and mutation probabilities
were chosen as 0.9 and 0.3, respectively. On all
optimization runs, the population size was selected as 50
individuals and the optimization terminated after 100
generations. The maximum size of the Pareto-optimal set
was chosen as 50 solutions.
In the following, we present the results for the step
length 0.42m and step time 1.2s. Fig. 3 shows the Paretooptimal trade-off front after 100 generations. We canobserve the existence of a clear tradeoff between the two
objectives. In addition, the obtained reference solution
set has a good distribution (similar to uniform
distribution). One of the interesting features of theresulting Pareto front is the almost exponential relation
between the MCE and MTC cost functions. Results in
Box 1 and Box 5 are at the extreme ends of the Pareto
front. Box1 represents Pareto solutions with high valueof MTC function, but low energy consumption. Based on
the Pareto-optimal solutions, we can choose whether togo for minimal CE (Box 1 in Fig. 3) at the expense of aless smoothness in the torque or choose someintermediate result. If we are interested for a low
consumed energy humanoid robot gait, without
neglecting the smoothness in the torque change, theresults shown in Boxes 2, 3 are the most important. The
results in Box 2, show that by a small increase in theenergy consumption (2.2%), we can decrease the MTC
fitness function by around 12.1%. Also, the energy canbe reduced by 14.5% for a small increase in the MTC
cost function, as shown by Box 4 solution.

Fig.2. “Bonten-Maru” humanoid robot.

 0 - if the constraints are satisfied,C=c i - if the constraints are not satisfied,c denotes the penalty function vector. The individuals
that not satisfy the constraints have a low probability tosurvive in the next generation. We consider the following
constraints during the humanoid robot gait generation:•••

The walking to be stable or the ZMP to be within thesole length.
The distance between the hip and ankle joint of theswing leg must not be longer then the length of theextended leg.
The swing foot must not touch the groundprematurely.

The MTC model is based on smoothness at the torquelevel. Uno et al., 1989 argue that human arm motion is
generated based on MTC cost function. The cost is theintegrated squared torque change summed over the joints
and the movement. In the MTC, the objective function to
be minimized is expressed as follows:

2300
2200

tf

T

2

2100

tf

1  dτ   dτ  ∆τ 
Jtorquechange = (    dt +   + Cdt). (4)2  dt   dt  ∆t 
0

∫

MTC

∫

0

2

2000

3

1900

4. Results

4

1800
1700
220

In the simulations, we use the parameters of the “BontenMaru” humanoid robot (Fig. 2). The “Bonten-Maru” is
1.2m high, each leg has 6 degrees of freedom and iscomposed by three segments: upper leg, lower leg and©ISAROB 2006

1

240

260

280

5
300

320

340

MCE
Fig. 3. Pareto front after 100 generations.

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In the following we changed the number ofinteractions to 40 while the population size is considered
50, the same with previous simulation. The optimal
Pareto solution is shown in Fig. 5. This figure shows that
nondominated solutions in the obtained Pareto optimal
set are not well distributed. The fitness values of bothobjective functions are nearly the same with the results
shown in Fig. 3. The performance of EA with 100
interactions is slightly better for MTC cost function.

Joint torque [Nm]

τ1
τ2

20

τ3
τ4
τ5

0
-20

0

0.5

Time [s]1

1.2

5. Conclusion(a) Box 1 results (MCE).

In this paper we gave some preliminary results on how
parameters such as number of interactions, population
size influence the performance of multiobjective EA. We
checked the performance on a humanoid robot gait
generation task based on two conflicting objectivefunctions. The results show that the proposed approach isefficient for solving multiobjective optimization in thatmultiple Pareto-optimal solutions can be found in one
simulation run. In addition, number of interactions
greatly affects the distribution of nondominated solutionsin the obtained Pareto-optimal. In addition, the results
show that the optimal gait reduces the energy
consumption and increases the stability during the robot
motion.

τ1

Joint torque [Nm]

τ2

20

τ3
τ4
τ5

0

-20

0

0.5

1

1.2

Time [s](b) Box 3 results.

τ1

Joint torque [Nm]

τ220

References:τ3[1] Syswerda, G., and Palmucci, J., 1991, The application of

τ4
τ5

genetic algorithms to resource scheduling, in: Proc. of the
Fourth Int. Conference on Genetic Algorithms, R. K. Belew
and L. B. Brooker, ed., Morgan Koufmann, California, pp.
502-508.
[2] Charnes, A., and Cooper, W. W., 1961, Management
models and industrial application of linear programming,Vol. 1, John Wiley, New York.

0

-20

0

0.5

1

1.2

Time [s](c) Box 5 results (MTC).

[3] Ijiri, Y., 1965, “Management goals and accounting
for control”, North-Holland, Amsterdam.
[4] Coello, C. A. C., 1999, “A cornprehensive survey ofFig. 4. Torque vector and optimal gait of different
optimal solutions selected from Pareto-front.

evolutionary based multiobjective optimization techniques,”
Knowledge and Information Systems, 1(3):269-308.
[5] Herrera, F., Lozano, M., and J. L. Verdegay, 1998,
Tackling real-coded genetic algorithms: operators and tools
for behavioral analysis, Artificial Intelligence Review, 12(4),265-319.
[6] Uno, Y., Kawato, M., and Suzuki, R., 1989, Formulationand control of optimal trajectory in human multijoint arm
movement, Biol. Cybernet. 61:89-101.
[7] Capi, G., Nasu, Y., Barolli, L., and Mitobe, K., Takeda, K.,
2001, Application of genetic algorithms for biped robot gait
synthesis optimization during walking and going up-stairs",
Advanced Robotics Journal, 15(6):675-695.
[8]Capi, G., Nasu, Y., Barolli, L., and Mitobe, K., 2003, "Realtime gait generation for autonomous humanoid robots: acase study for walking", Robotics and Autonomous Systems,
42(2):107-116.

Iteration 40; Individs in Pareto set = 3
2200
2150

Fitness 2

MTC

2100
2050
2000
1950
1900
1850
220

230

240
250
Fintess 1

260

270

MCE

Fig. 5. Performance of EA with 40 interactions.

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Mobile Robot Navigation by Visual Perception with Vision Decision Agent in an
Unstructured Environment
Chandima 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

visibility graphs, Voronoi diagram, cell decomposition
method, potential ﬁeld method, etc. Some of thosemethods do not give the minimum path to the goal
and when there are many sharp turning points in thepath, it makes navigation complicated. One of theadvantages in our navigation system is that we canreduce the number of sharp turning points and guarantee the smooth navigation along the path.
Our navigation system is capable of identifying the
obstacle locations, shapes of the obstacles and sizes of
obstacles. It can readily make the shortest path upongiving the goal. Introducing circular object modelingand tangential path planning methods, we can add
additional advantages to our navigation system, compared to the previous work on vision-based navigation
systems [2][3][4]. This leads to the processing time tobe small and to reduce the complexity of the system. A
vision decision agent is introduced into the navigation
system so that it can learn the dynamic behavior of
the environment. Simplicity, reliability, accuracy andquickness of our navigation system result in a successful result in a dynamic environment.

We present a vision-based robot navigation system
in an unstructured environment. This system operates under two stages: namely online path planningupon receiving the current image and robot navigation related to the current planned path. We make asimulator that can simulate the minimum path upongiving the starting and the goal point. The robot using
this system can acquire the coordinates of the planned
path and navigate along the path smoothly. The intent of this project is to create a system that allows
robot to navigate in a dynamic environment in a waythat facilitates path planning and goal oriented tasks
with the minimum hardware requirements.

1

Introduction

Digital image processing [1] has been used in theﬁeld of robotics to ﬁnd out many solutions for the robot navigation problems. This is due to the fact that
digital image processing provides a rich set of featuresof the environment at anytime which will direct a robot to the goal controlling the navigation behavior of
the robot in real time. Many robot navigation systems
also focus on producing a detailed metric map of theworld using expensive hardware, such as a laser scanner. The system we introduce here facilitates pathplanning and does not require expensive range sensors.
Some vision-based systems [2][3] require expensive andcomplicated hardware. We have to reduce the amountof hardware so that it will not aﬀect performances of
the navigation system since our priority goes to system performances. The present navigation system wehave designed relies on the image data received fromthe camera as the sole input to navigate the robot.
This is the highlighting point in our system.
In path planning problems, conventional vision systems have used diﬀerent planning methods such as©ISAROB 2006

2

The Present System Overview

Our experimental setup includes a USB camera, atest bed and a computer. Khepera miniature robot isused in this experiment. The plan view of the robot
environment is captured by the camera and is read by
the computer where the robot is connected througha serial line to process the captured image. The image processing part of the system processes the image
for detailed information of the robot environment. In
identifying the edges of objects, Canny edge detection method is performed. To ensure faster navigation and to ease path planning, each detected obstacle
is modeled by a circle in the path planning part ofthe system so that each circle just ﬁts each obstacle.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Sensor(USB camera)

Image
processing

Knowledge from
Previous image

Image comparison
moduleObstacles

Filled gaps in the edges

Knowledge fromcurrent image

KheperaRobot
Environment

Planneragent

Actuator

Decision
agent

(a)

ActionFigure 2: (a): Robot environment and (b): Edge detected, dilated image

Figure 1: System architecture

the structuring element used to probe the input image. The pixels in the structuring element containing
1’s deﬁne the neighborhood of the structuring element.
Due to some light-colored, small clusters of pixels in
the image can end up with breaking edges. Therefore the edge detected image is passed through theprocess of morphological dilation to obtain the continuous edges for all the objects in the image (seeFig. 2(b)). The size and the shape of the structuring element (see Fig. 3(a)) are selected to be “3” and“disk” respectively.

Modeled obstacle can now be considered as circularobstacles. To make sure safe navigation of the robot,
modeled obstacles are further expanded to keep an adequate clearance between the obstacle and the robot.
The navigation system reads the coordinates of theplanned path and converts the coordinates to real axis
coordinates. The robot is then fed with the real axis
coordinates and smooth navigation could be obtained
along the planned path. The robot navigation is executed in parallel with decisions made by the vision
decision agent implemented to observe the changes ofthe environment.

3

3.1.3

The present vision-based navigation system can be
split into three main parts, namely
1. Image processing,
2. Path planning, and3. Robot navigation.
The architecture of our navigation system can be illustrated as shown in Fig. 1.

Image Processing

3.1.1

Image acquisition and conversion

3.1.4

Morphological dilation

Dilation adds pixels to the boundaries of objects in animage. An essential part of the dilation operation is©ISAROB 2006

Labeling the objects

Objects in the binary image are labeled so that thenavigation system can easily obtain the necessary information of the relevant objects when needed to proceed. Labeling the objects helps the system to distinguish the objects, their locations, boundary coordinates and area, etc. With the fact that the pixels ofa particular object (1’s) are connected together, thesystem can group the connected pixels and label connected component accordingly. Each set of connected
pixel components represents the objects in the image.
Fig. 3(b) shows the method of labeling the connected

Plan image of the robot environment is acquired
through a USB camera. Software program is set tocapture the image continuously with a given time interval. The captured image is RGB image as shown in
Fig. 2(a). The RGB image is then converted to a graylevel image.
3.1.2

Morphological filling

The navigation system needs the understanding of exact number of objects in the image to proceed fromthe edge detection and the dilation. Morphologicaldilated edge detected image shows the clear and continuous boundaries of objects. Due to the irregularshape of the body of a particular object, it tends tolocate the edges inside the main boundary of an object. This may mislead the navigation system that
there may be many objects than the actual numberof objects. To overcome this problem, morphologicalﬁlling is carried out in the entire dilated edge detected
image so that this ﬁlling technique will ﬁll out all thearea inside the main boundary of each object. Morphologically ﬁlled image; binary image looks similar tothe ﬁgure in Fig. 4(a).

Architecture of Vision-Based Navigation System

3.1

(b)

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0
0
0
1
0
0
0

0
1
1
1
1
1
0

0
1
1
1
1
1
0

1
1
1
1
1
1
1

0
1
1
1
1
1
0

0
1
1
1
1
1
0

Object no. 3

Object no. 1

Radius of the structuring element(3)
Origin
0
0
0
1
0
0
0

Disk shaped structuring element

(a)

0
0
0
0
0
0
0
0

0
1
1
1
0
0
0
0

0
1
1
1
0
0
0
0

0
0
0
0
1
1
1
0

0
0
0
0
1
1
1
0

0
1
0
0
0
0
0
0

0
1
1
0
0
0
0
0

Goal point

Bounding box

0
1
1
0
0
0
0
0

1

Robot path3

Object no. 2

4

(a)

(b)

tangential points, robot will follow the circle. And thisresultant path is taken as the ﬁrst segment of the ﬁnal
path. The tangential point, related to the goal point,of this segment of the path is made as the starting
point for the next stage of path planning. This process
is repeated until the path planning part of the navigation system ﬁnds the minimum path up to the goal
point. A planned path upon giving the goal point isshown in Fig. 4(b).

Object modeling

Path planning process may be complicated if the
shapes of objects are irregular. It is desirable to makethe irregular shaped objects in the image have regularshaped objects. Modeling of objects helps to ease thepath planning and reduce the complexity in calculation. Modeling objects as circular objects is found to
be one of the simplest methods in object modeling.
It is needed to ﬁnd out the center of each object inmodeling the objects into circular objects. The centerof objects is taken as the center of the circle which isused to model the objects. Since the objects are in
irregular shapes, exact center of an object can not befound but the near center of an object can be foundby securing the near symmetry of the object. For thispurpose, the system ﬁnds rectangular bounding boxes
which can just ﬁt the objects and the center of eachbounding box is taken as the near center of each object. And this near center of an object is treated as thecenter of modeling circle (see Fig. 4(a)). The navigation system is fed with these modeled circular objects
for the next stage of path planning process. Object
modeling and approximation are shown in Fig. 4(b).

3.3Robot Navigation

We have ﬁrst performed our experiment in the static environment. Coordinates of the planned path arein the pixel coordinates pertaining to the image axes.
And these pixel coordinates have to be converted to
the real coordinate system of the test bed to get theexact location of the path in the test bed. To makethe robot navigate through the planned path, the navigation system obtains the key turning points in theﬁnal path and calculates the initial position of the robot, the initial direction of the robot, angles of thelines joining two adjacent turning points of the ﬁnal
path to the horizontal axis and the distances between
adjacent turning points. All measurements are madewith reference to the real test bed axis. The robot understands the distance to be traveled and the angle tobe turned in terms of the time. The robot navigation
takes place along the path shown in Fig. 4(b). Although our navigation system mainly works on image
data retrieved from the captured image without any
expensive hardware, we have shown the accuracy andsimplicity of our system.

Path Planning

The present navigation system uses the tangential
method to make the path planning. The path planningpart of our navigation system ﬁrst identiﬁes the closest obstacle to the starting point which lies on the line
joining starting point and the goal point. It ﬁnds thetwo optimum tangential points of the interested circle;
one is related to the starting point and the other is related to the goal point which give the minimum path
between the starting and goal points when avoiding
the ﬁrst obstacle. At this stage, it records a part ofthis path from the starting point up to the tangential
point which is related to the goal point. Between these©ISAROB 2006

Starting point
Inner circle---Modeled circular object
Outer circle---Expanded edge of the circular object

Figure 4: (a): Labeling the objects in the image and(b): Object modeling and path planningcomponents in the binary matrix of a particular image. Labeling the objects in the image is shown in
Fig. 4(a).

3.2

6

2

(b)

Figure 3: (a): Structuring element and (b): Labelingthe connected components in a binary image

3.1.5

5

4

Dynamic Navigation

We have designed our navigation system to takechanges in the environment into account. A vision decision agent is introduced to facilitate the robot navigation under dynamic conditions. The vision decisionagent decides the suitable action that the robot should

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Goal point

Current image
Image processing
Current image data
Immediate previous image data

Change of theenvironment
is identified
here

Comparison of current image
and the previous image

Change in image data

No

Continue
navigation

Starting point

Yes

Safety region is
not blocked but
alternative
path is preferredReplanning

Safetyregion of theplanned pathis blocked

Obstacle crosses
the safety region
and moves awayfrom the robot

Change is not
significant
enough to affectthe planned path

Replanning

Waiting

Continue navigation

Path before the environment changes
Path after the environment changes

Figure 6: Dynamic path planning
while robot navigating in its environment. For example, the system can detect when the objects movein the environment and the existing objects leave theenvironment and the new objects enter into the environment. The proposed navigation system was able
to navigate the robot under the dynamic conditions.
The highlighting points of this work were that our system mainly worked on the vision data and we haveused the minimum number of hardware requirements
thus allowing the system to be simple but to performed well without any drawbacks. We have shown
the faster path planning method and the safer navigation method by incorporating circular object modeling and tangential path planning method. This has
reduced the calculation time to a considerable extent.
Thus, we have presented a vision-based navigation system with a reduced complexity and faster response.

Vision date base is updated

Figure 5: Flow chart of vision decision agent
undergo. The vision decision agent constitutes of image comparison network which uses the priori knowledge and the current knowledge of the environment tostudy for possible changes in the environment while
updating the knowledge base of the environment eachtime when it receives a new image. The architectureof the vision decision agent is shown in Fig. 5. Basedon the outcome of the image comparison module, thevision decision agent decides the three actions thatshould be executed by the navigation system. Threedecisions are:• Continue navigation: Robot navigates continuously unless the safe region of the planned pathis blocked or the navigation system did not ﬁnd
any signiﬁcant changes in the environment.

References• Waiting: The robot waits if an obstacle crosses
the safe region of the planned path and moves
away from the robot.

[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, New York, NY: Addison-Wesley,
1992.

• Replanning: The navigation system plans analternative path from its current position if thesafety region of the planned path is blocked by
obstacles or the environment has changed signiﬁcantly so that the system can ﬁnd an alternative
minimum path.

[2] I. Ohya, A. Kosaka and A. Kak,“Vision-basednavigation by a mobile robot with obstacle avoidance using single-camera vision and ultrasonicsensing,” IEEE Trans. on Robotics and Automation, vol. 14, no. 6, pp. 969–978, December 1998.
[3] J. Gaspar, N. Winters and J. S. Victor,“Visionbased navigation and environmental representations with an omnidirectional camera,” IEEE
Trans. on Robotics and Automation, vol. 16, no.
6, pp. 890–898, December 2000.

Navigation along the newly planned path when theobject no. 1 left the environment is shown in Fig. 6.
The navigation system executes the decision given by
the vision decision agent and guides the robot accordingly until it reaches the goal point.

5[4] J. Weng and S. Chen,“Visual learning with navigation as an example,” IEEE Intelligent Systems,
vol. 15, no. 5, pp. 63–71, Sep-Oct. 2000.

Conclusion

The present vision-based navigation system was capable of identifying the changes in the environment©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Guiding Mobile Robots in Ubiquitous Environment Using Binocular Vision System
and Intelligent Decision Making
Janaka Chaminda Balasuriya,
Keigo WatanabeandKiyotaka Izumi
Department of Advanced Systems Control Engineering
Graduate School of Science and Engineering
Saga University, 1-Honjomachi, Saga 840-8502, Japan
jcbala@lycos.com, {watanabe, izumi}@me.saga-u.ac.jp
Abstract

MasterThis paper describes an intelligent ubiquitous environment that is comprised of binocular vision system and adaptive fuzzy neuro based decision-making
system for guiding a mobile robot. The integrated
binocular vision system is mounted in the environment (rather than on one single robot) so as to get
information about the whole area. This information
after analyzing and processing is distributed among
the robot units for navigation purposes. Potential ﬁeld
path planning techniques including mapping and navigation, allow safe navigation through the dynamic environment when carrying out dynamic target tracking
forming a robust and cohesive intelligent ubiquitous
system.

1

Obstacle(Static)
Obstacle(Dynamic)

Obstacle(Static)
Goal(Human)Figure 1: Target tracking in dynamic environment,
where master, central intelligent system consists ofbinocular vision system and fuzzy logic decision making.

Introduction

responses are a must. These need much more advanced
features such as fast response sensors, high speed processing equipment, etc. On the contrary, if the sensors have a slower response time, the estimation is nolonger suﬃcient for a real-time target tracking [2].
Hence many real-time tracking systems use intelligent prediction strategies based on fuzzy logic decision
making to predict the potential object position at thenext time period. Information gathered in such a way
is used to speed up the detection procedure and tocontrol motion parameters of a tracker device.

Target tracking and path planning are used inmany ﬁelds such as robotics, intelligent systems, autonomous vehicles, guidance systems, etc. There aremany approaches in the areas of research to fulﬁll the
dream of a fully functional automated moving system
in a real-time environment. This simplest form of a
tracking and guidance system can be considered as
guiding a robot to a stationary position in a static
environment. But, there will be many critical issues
to consider when the target is moving and the environment is dynamic. According to Luo et al. [1], in
addition to the sensory detection module in a general
position based tracking system, there are many limitations to address, such as estimation/prediction of theobject position from noisy sensory measurements, motion control of the tracker to track the moving object,
etc.
When the target is a dynamic object with a complex
nature of movement or target tracking is performed ina dynamic environment, accurate detection and fast

©ISAROB 2006

Slave
(Robot)Obstacle(Static)

2

Target System

Navigate a slave (e.g. robot) to meet a goal (e.g.
human) in motion while avoiding static and dynamic
objects in the surrounding environment according tocommands issued by a master (intelligent system consisting of ﬁxed binocular vision system and fuzzy logic

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decision-making).
The environment is continuously monitored by thebinocular vision system to identify static and dynamic
objects. Here, objects like desks, chairs, standstill
humans, robots, etc. are identiﬁed as static objects,
whereas moving humans, robots, vehicles, animal, etc.
are identiﬁed as dynamic objects. Next a goal (e.g.
a human, vehicle, etc.) and a slave (e.g. a ubiquitous robot, a blind person, autonomous vehicle, etc.)
are identiﬁed within this environment. Once a goal
and a slave are identiﬁed, all the other objects (staticor dynamic) in the environment will be considered as
obstacles. Continuously analyzing the changes in theenvironment, master guides the slave until the slave
reaches the goal by fuzzy logic decision-making (seeFigure 1).

3

T=0
Trackerr

Shortest distance (d)O
Master

VHg

TargetT=t

X

Figure 2: Position and velocity analysis.
Similarly velocity of each dynamic object is obtained, concerning one object at a time, one after eachother. This process continues in a loop updating thedecision-making systems. Since there can be many objects in the concerned area in many sizes and shapes,
considerable amount of time can be expected to be
wasted for one round of analysis. In order to minimize
such time, simpliﬁcation process is applied initially as
described below.

5

Experimental Setup

Experimental setup to check the objective functionality of the software modules is shown in Figures 3 (a) and 3 (b), where Figure 3 (a) shows thereal setup while Figure 3 (b) shows more generalized/simpliﬁed version of the same. Figure 3 (a) illustrates a “target” (movable toy), “tracker” (Khepera,
programmable mobile robot for target tracking) and
one obstacle (as the ﬁrst stage). The simpliﬁcation
process includes the same objects (as in Figure 3 (a))
covered with speciﬁc colour top. This conversion isused to decrease the excessive burden on calculations
involved, so that it will permit the initial identiﬁcation of “target” (in blue colour), “tracker” (in green)and “obstacle” (in red) and later obtain the locationsof them. In this way, it will be possible to search forcolour matching/searching, without using complex object matching algorithms.
Some results obtained from the stereo visionsystem, while trying to catch the motion of the target
are shown in Figure 4. Here the target is tracked andits outer regions are marked with white colour andPosition and Velocity Analysis

Position and velocity of the target, tracker and obstacles (static and dynamic) are obtained in the manner as illustrated in Figure 2. (Only target and tracker
are shown). Master is positioned at one end of theconcerned area, looking at the entire place. Give thecoordinates of the tracker R with reference to its position, which can simply be written as equation (1)omitting z components considering the ground plane:©ISAROB 2006

βθIntelligent SpaceR = (x, y) = (r cos θ, r sin θ)

VRg

VRh

In an era of “intelligent objects”, it is also possible to think about “intelligent space”, as proposed
by Hashimoto et al. [3]. Intelligent space is an environmental system, which is able to support humanin informative and physical ways. Most of intelligent
systems interact with humans in a passive space, but
in intelligent space, an environment containing humans and artiﬁcial systems is capable of self-suﬃcient.
Artiﬁcial systems like sensors, cameras, etc. become
agents of intelligent space and simultaneously humans
and artiﬁcial systems like robots become “clients” ofit. Consider a situation where a robot is lacking insensors to navigate around an intelligent space. At
such instance it can get the necessary guidance fromthe intelligent space very easily. The ultimate goal ofintelligent space is to accomplish an environment thatcomprehends intentions of humans and assist them [4].

4Place of meeting

Y

(1)

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Toy

Obstacle

T = t1

T = t2

T = t3

T = t4

Movable Robot(a) Real situation

Target (Blue)

Obstacle (Red)

Figure 4: Tracking target.

where VHg is velocity of the target (human) with reference to the ground, VRh is velocity of the tracking
robot with relative to the human and VRg is velocityof the tracking robot with reference to the ground.
In order to make VRh positive, i.e. the robot beable to meet the human (in case of following the human in straight behind), magnitude of VRg should behigher than magnitude of VHg . Also, magnitude of
VRh will decide the time taken for the meeting of thetwo parties.

Tracker (Green)(b) Modiﬁed for simpliﬁcation

Figure 3: Experimental setup to check the functionality of the software modules. Real objects are coveredin special colours to make the identiﬁcation and tracking processes more easier.

7

In an environment with continuously changing surroundings, it is necessary to make the necessary corrections from time to time. Once obtained the initial
calculations of the relative velocity (speed, direction
and hence by having a rough idea of time taken for
the whole tracking job), it is necessary to apply it to
the tracking device. Next it is necessary to adjust it astime proceeds or environment changes. This may benecessary to analyze target details as well as obstaclesin the vicinity as described as follows.
Figure 5 illustrates path planning with respect tothe obstacle position and motion. In the event of ﬁnding the most suitable path in a dynamic environment,
it is necessary to consider all possible paths at a speciﬁc time. Consider the tracker position at T = 0 withthe target position at T = t (next position predictionof the target). For quick target tracking, next positionof the target is considered with the current position of

all the other surrounding area is indicated by black
colour. (Grid lines are added later for clariﬁcation).

6

Fuzzy and Relative Velocity

In the case of dynamic motion analysis, it is necessary to apply relative motion mechanisms. Once therobot starts to move, its speed and direction should
be adjusted appropriately according to relative motion. Although the main objective is to track the target (i.e. to go and meet the moving human), necessary
adjustments should be performed considering the relative motion as given by
VRg = VRh + VHg©ISAROB 2006

Environment with Obstacles

(2)

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References

Shortest Distance at T = 0
Direction of
Obstacle Motion
Directionof Target
MotionPath 4
T=t

Path 2Path 3
T=0

T=0[1] R. C. Luo, T. M. Chen, and K. L. Su, “Target tracking using hierarchical grey-fuzzy motion
decision-making method,” IEEE Transactions on
Systems, man, and Cybernetics - Part A: Systems
and Humans, vol. 31, no. 3, May 2001, pp. 179–
186.

Obstacle

T=t

Path 1[2] R. C. Luo and T. M. Chen, “Target tracking bygrey prediction theory and look-ahead fuzzy logic
control,” in Proc. IEEE Int. Conf. Robotics andAutomation, vol. 2, Detroit, MI, USA, May 10–
15, 1999, pp. 1176–1181.

TargetTracker

Figure 5: Path selection.

[3] H. Hashimoto, P. T. Szemes, and K. Morioka,“Intelligent space — Robots and spaces —,” inProc. International Symposium on Electronics for
Future Generations, Tokyo, March 2004, pp. 383–
388.

the tracker. With reference to the obstacle position at
T = 0, path 1 or 2 seems to be acceptable. At T = t,obstacle moves a little bit and path 2 will no longer be
acceptable as safe and it should be shifted as path 4
to avoid any possibility of collision. At the same time,
path 1 can be re-adjusted to path 3 making it little
shorter than path 1 to reach the target.

8[4] H. Hashimoto, J. H. Lee, and K. Morioka, “Human robot interaction via intelligent space,” inProc. International Conference on Control, Automation and Systems (ICCAS2002), Jeonbuk,
Korea, October 2002, pp. 512–517.
[5] D. J. Coomb, “Real time gaze holding in binocular robot vision,” Technical Report, University ofRochester, Dept. of Computer Science, 1992.

Summary[6] J. Chung and H. S. Yang, “Fast and eﬀective multiple moving targets tracking method for mobile
robots,” in Proc. IEEE Int. Conf. Robotics andAutomation, vol. 3, Nagoya, Japan, May, 1995,
pp. 2645–2650.

Attempt of implementing an intelligent decisionmaking system has been presented for guiding an autonomous mobile robot in ubiquitous environment using binocular vision system. Although much work has
to be performed in the future in this regard, much beneﬁt as well as certain parts that need improvements
had been identiﬁed as follows. Time consumed forthe studious calculations and processing of the vast
amount of software programs is a major problem in
the real time system. It has created a delay in taking
the correct decision at the speciﬁc instance of time. In
order to overcome such drawbacks, minimizing the total number of software programs or making them mucheasier for processing is currently under consideration.
Application of a sense of “experience” to the central
system (i.e. to keep certain instruction for a speciﬁc
instance such as how the system responded for a particular situation in the past so as to apply them again
if a similar situation occurs in the future. So in thatcase, it is not necessary to re-calculate once again minimizing the processing time to a great extent) is also
being analyzed.

©ISAROB 2006

[7] Z. Lin, V. Zeman, and R. V. Patel, “On-line robot
trajectory planning for catching a moving object,” in Proc. IEEE Int. Conf. Robotics and Automation, vol. 3, Scottsdale, AZ, 1989, pp. 1726–
1732.
[8] B. R. Chang and S. F. Tsai, “An intelligent prediction method for short-term time series forecast on engineering education,” in Proc. International Conference on Engineering Education,
Manchester, U.K. August 2002.
[9] J. Spletzer and C. J. Taylor, “Sensor planning andcontrol in a dynamic environment,” in Proc. ofIEEE International Conference on Robotics andAutomation (ICRA ’02), vol. 1, May 2002, pp.
676–681.

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Kansei and Human Experience Analysis for Mobile Robot Navigation in
Ubiquitous Environment
Janaka Chaminda Balasuriya,* 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, {watanabe, izumi}@me.saga-u.ac.jp**ashu@u-aizu.ac.jp
Abstractcated task comprising of many analysis, calculations
and procedures.
As Simmons et al. [1] describe, recent research inmobile robot navigation has utilized autonomous mobile robots in service ﬁelds. To operate them in an environment with people, it requires more than just navigation. The robots should recognize and act according
to human social behavior to share the resources without conﬂict [2]. Sometimes, even when humans interact with each other, it leads to resource conﬂict. At
such times humans use social rules to maintain order[3] [4].

Researchers are trying to construct an intelligent
system with the help of the advance in technology. There were many successful milestones that had
passed achieving ‘humanoid robots’, that are little
closer to humans but not at whole. Although, the expectation level of the intelligence of these new systems
is still unknown, it can be fairly assumed that reachinga goal at least up to an intelligence level of a humanbeing, may be a big success. In order to be more close
to the human, it is necessary to analyze the behavioral
patterns of humans in order to apply those for theother artiﬁcial intelligent machines. Especially, decision making of humans should be thoroughly analyzed.
We humans take a decision not just thinking about thecurrent situation but considering many previous similar instances, simply known as ‘experience’. Will there
be any improvement to intelligent systems, if they also
have this ‘experience’ ? This research project discusses
the possibility of applying ‘experience’ to path planning for mobile robots in ubiquitous environments.

1

2

In experience analysis, the reasoner remembers previous situations similar to the current one and uses
them to help to solve the new problem. Rememberedcases are used to suggest a means of solving the newproblem, suggest a means of adapting a solution that
does not ﬁt, warn of possible failures, and interpreta situation. Reasoning based on previous experienceis another word for using old solutions to solve newproblems and demands. Using old cases it is possible
to explain new situations, argue new solutions, reasoning from precedents to interpret a new situation,
create an equitable solution to a newer problem, etc.
[5].
While analyzing the way that how people solve
problems, it can be seen that reasoning based on experience is in continuous use. For obvious cases, the way
attorneys, doctors, and mediators work can be seen.
It can also be seen using extensively in day today liv-

Introduction

Being a human, have you ever noticed or did you
ever consider how easy for us to walk along a crowded
pathway? Crossing at the correct place avoiding incoming traﬃc, selecting the speed, as we desired or
according to the environment are not complicated task
for any normal human. It is just another matter or day
today life styles and we do not even bother to think
about it. But how about making a robot to do thesame thing? Needless to say, it will be very compli-©ISAROB 2006

Experience Analysis

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ing by general public such as meal planning, shopping,traveling, etc. Although it is imprudent to think thatold solution will be valid for the new case each time,
it can still be validated for the new case since the oldcase facilitates to generate a plausible answer easily.
This reduces considerable time of processing the wholesituation once again.

Although the actual size or distances to the sides ofthis personal space vary drastically with culture, work
performed, environmental conditions, etc., a robot
working with humans should have an understandingof it in order to prevent unnecessary repercussions.
Human

Front Side

3

30 cm

Social Rules

As Nakauchi and Simmons [2] explained, there aremany aspects to the social rules and behaviors in human society. Among them, standing in line is one
such example for the most highly socialized and crucial skills required for autonomous robots that operate
in an environment with humans. For example, at various supermarkets, grocery stores, post oﬃce, banks,etc., humans stand in line and wait their turn. If aperson does not understand or obey the social rules,
he will not be able to get those services. This is true
for an autonomous robot. If the robot does not ‘understand’ the social behaviors of humans and if it treats
the humans in front of it as obstacles, then it will never
be able to achieve its goals. Hence automated robots
should be designed to ‘understand the social behaviors
of humans and to work in accordance to them.

4

80 cm

Personal Space

Generalized
Circular ShapeFigure 1: Personal human space.

4.1

Size and distance

Personal space can be generalized to an average sizeof 80 cm in front, 20 cm behind, and 30 cm in sides fora human as shown in Figure 1. Calculating the above,
Nakauchi and Simmons [2] had assumed that the personal space of the person in front is identical to thepersonal space of the subject (who is going closer).
But having diﬀerent size subjects (such as diﬀerent
size robots), this should be varied and adjusted appropriately. Consider a human, generalized to a circularshape of 45 cm of diameter. Then these distances canbe considered as a percentage of 177.8% in front, 44.4%
behind and 66.7% in sides as the following propotion
yields:(1)(a dist/gcd) × 100%

Personal Space

Personal space can be considered as a person’s own
territory/area which is very close to his physical body.
The person feels uncomfortable if other people moved
into this area and always tries to keep it restricted toothers in the vicinity. This ‘personal space or ‘humanterritory has been studied in the ﬁeld of cognitive
science as mentioned by Malmberg [4], Sack [3] andmany others. The shape of this space can be modeled
as an oval, which has wider space towards the directionof one’s own eyes as shown in Figure 1. A typicalexample for the eﬀect of personal space can be seen in
many day today activities of humans such as people
forming and moving in lines. When people form a line,
in general, they keep a certain distance away from eachother people. They also usually stand so that they face
towards the person in front. These phenomena can be
described based on the concept of personal space. The
idea is that the person who is standing in line keeps
enough distance to the person in front so that person
in front does not feel uncomfortable. On the other
hand, he stands close enough to the person in front toavoid other people cutting in the line [2].

©ISAROB 2006

20 cm

where a dist is average distance and gcd is generalized
circular diameter. Hence for any sized subject (robot),
appropriate personal space can be obtained by using
the above propotion.

4.2

Safe and personal distance

It is not necessary to consider this personal space
while in motion because it involves many calculations
and precautional stages. Rather, while in motion, safe
distance (sd) from the nearest object is analyzed. Consider a situation as given in Figure 2. When robot
R requires travel to the human A, within a path

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where there is an obstacle and other humans. At time
T = 0, obtaining the positions of target (human A)
and Tracker (Robot R), tentative safest and shortest
path is obtained (for this, previous experiences if any
can be used for fast accessing and reducing unnecessary calculations to speed up the process). Then considering the generalized circular diameter (gcd) of the
tracker (robot R) at that instance, already obtained
path is readjusted keeping the safe distance. The gcd
is not a constant due to the fact that many posturesthat the robot can have such as stretching arms, carrying some objects, etc. but as a general rule, once
calculated, the gcd can be considered as a constantthroughout an operation. This safe distance has been
considered as half of the generalized circular diameter(i.e. if gcd = d, then sd = d/2). Once this is done,
the whole path will be considered as a strip of width
gcd+2sd or simply 2gcd in order for the robot to travel
along that path.

Blue / Green

Happy

sd

5

Robot RT = 0 2gcd

Robot R
T=t

Human
A

Human
B

Figure 2: Safe and personal path planing.

©ISAROB 2006

Mouth
Indication
Eye Colour

Emotional States

Humans are full of emotions and they show them inmany direct or indirect ways even without consciousness. Many such feelings are expressed in human faceand recognized by fellow companions without muchdiﬃculty. Understanding of these emotional states
is very useful in interaction and communication withhumans. While in conversation, idea is expressed invoice means by humans (whether the speaker reallymeans it or not) but the true intentions are shown byother means of the body such as special gestures ofhands, change in face, pupil dilation of eyes, directionof face/eyes, etc. Sometimes these gestures are usedby experts to ﬁnd out the true nature of individuals.
Although specialized knowledge is required to do suchthings, just by having basic instinct, ordinary people
do the interactions without much eﬀort or rather unintentionally. Consider few cases as follows:
If a person is frowning, others try to get away andnobody dares to move closer (understanding that, that
person is in anger and unable to have any deals withhim). Sometimes, the same reaction may be returnedby the viewer. In another situation, ‘smile’ indicatesthe gesture of goodwill (can be misused as well). In an
event of meeting, indicate friendliness, etc., smiling isused always. There is no doubt reaching a person witha smile. It is always better to have a smile in returnin order to indicate the same good feeling. Althoughsuch a behaviour is not unusual to a human being,
how about to a robot (when interacting with humans)?
Will not it be much easier and more comfortable for
us to have a robot companion that shows its feelings
as well? Even without speaking a single word, a single
gesture of the robot may represents thousand words
that we humans understand.

pd

Obstacle

Feelings

Figure 3: Two human feelings in ‘Feelings’ fuzzymodel.

Now consider an instance that robot R meets a human B while traveling along the path. At such times
it is unnecessary to change ‘safe distance’ to ‘personal
space’ as this human is just another obstacle along
the path and that the robot has no intentions of interacting with him. Further having a distance of sd
is enough to move along the way. (If the robot has
to consider personal space every time it meets a human, it will cause many problems due to certain space
restrictions of a particular area, unnecessary calculations involved, etc.) Finally when the robot reaches its
target human A at T = t, it needs to consider personal
space so that interaction is going to take place. Then,
robot motion is decided so as to arrange the spacearound it, according to the personal space criteria.

gcd

Unhappy

Distance
Distance
ChangeYellow / Red

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5.1

Indication of feelings

In a human face, a large number of muscles support
to show many feelings. Although it will not be possible to have a similar face mechanism for a robot as
an initial stage, an indication of eye colour (shades ofdiﬀerent colours) and the shape of the mouth as shownin the Figure 3 can be used to indicate basic feelingsof a robot.
Consider a situation where a robot is moving along
a pathway and a human is coming closer to it (orrather blocking the pathway). Having a sense of ‘personal space’ (or ‘feeling’ danger to own motion or to
the self), robot can indicate its unhappy or unstablestate by indicating the colour ‘yellow’ in his eyes. If
the person continues his motion and the level of uncertainty of the robots gets increased, then the eye colour
can be changed towards ‘orange’ (‘red’ can be used toindicate extreme unhappiness).
Once the ‘unhappy’ situation is subsidized, the eyecolour can be returned back to its normal. Similarly,
happy or good feeling can be indicated by green followed by blue. In this way, robot is capable of showing
its inner ‘feelings in certain extinct to the surrounding
human beings.

5.2

Eye
Colour
= 0.376

2
3
4
5
6
7
0

1

0

1
0

1

0

1

Figure 4: Rule viewer for ‘Feelings’ fuzzy system.
Similar to the human nature, experience is gathered in many acquaintances with similar situations andthis requires many involvements with time. Although
there may be speciﬁc instances slightly diﬀerent to theprevious, having an intelligent decision-making can besupposed to assist such times. These are currently
under consideration.

Fuzzy and feelings

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] R. Sack, Human Territory, Cambridge University
Press, UK, 1986.

Summary[4] M. Malmberg, Human Territoriality: Survey of
behavioural territories in man with preliminary
analysis and discussion of meaning, Mouton Publishers, 1980.

In this research project, giving a robot a sense of
feelings (that we humans understand) according tocertain experiences has been analyzed. The robot gets
this feeling as an experience while it moves with othersubjects including humans. Although it seems to beeasy to reach a human (for us), it is not so for an artifact such as a mobile robot. There are many otherfactors that should be considered in addition to thesafe distance. They include reaching speed, angle ofmeeting (such as face to face, side to side, from behind), etc. and these are also should be analyzed fora better system.

©ISAROB 2006

Mouth
Indication= 0.424

1

The complete system will be comprised of several
fuzzy models for initial path planning, obstacle detection, path adjustments, etc. One such module used to
anticipate the feelings due to personal space is shownin Figure 3. The input membership functions for distance and rate of change of distance with output functions for mouth indication and eye colour are shownin Figure 4, where rule viewer for some input values
for the above system is given.

6

Distance
Change= 0.693

Distance= 0.392[5] J. L. Kolodner, “Improving human decision making through case-based decision aiding,” AAAI,AI Magazine, Summer 1991, pp. 52–68.
[6] T. Gilovich, “Seeing the past in the present: Theeﬀects of associations to familiar events on judgments and decisions,” Journal of Personality andSocial Psychology, vol. 40, no. 5, 1981, pp. 797–
808.

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A Natural Language based Human Friendly Network Robotic System
Chandimal Jayawardena
Keigo Watanabe
Kiyotaka Izumi
Department of Advanced Systems Control Engineering
Saga University
Saga 840-8502, Japan
Abstract

systems. However, there, the authors’ concentration wason object recognition aspects.
Online robots are controlled through a web browser.
They are different from conventional teleoperation in several aspects. In addition to the well known problems related to time delay, supervisory control, and stability, online robots need to be designed to be operated by nonspecialists around the clock via human-friendly interfaces.
Therefore, new strategies are needed to coordinate simultaneous users handling large variations in demand and timedelay.
In most existing network or online robotic systems, input to the robot is given by filling forms or selecting commands. In some recent research, integrating object recognition capabilities with online robots has been addressed[5].

Network robotic systems, specially which are operated
over the Internet, are meant to be used by non-experts.
Therefore, user-friendly interfaces would provide many
advantages. In developing user friendly interfaces, natural language communication is mandatory. This paper
presents a network robotic system in which a user cancommand a robot manipulator to identify and pick objects
placed on a table. Some toys are used as objects. User
may use commands such as “pick the elephant.” The objects having complicated shapes are identified independentof their location and the orientation. To do this, two dimensional object recognition techniques are extended to three
dimensional object recognition utilizing a standard feedforward neural network.

1 Introduction

2 System Overview

Network robotic systems are very important in the sensethat they combine robots, which are fast and precise, withremote control capabilities. Specially, with the advent of
the Internet, a new class of robots has emerged with newadvantages and features. During the last one and half
decades, there have been a number of Internet robotic systems. Using Internet as the medium of communication for
network robotic systems has become increasingly popular
due to advantages such as, low cost, easy accessibility, easy
maintenance, possibility of allowing public participation,etc.
Since Internet based tele-robots are meant to be usedby non-experts and the expected user base is very highand diverse, the human-robot interaction should be as natural and user-friendly as possible. In most existing Internet based robotic systems [1][2][3][4],user interaction withrobot is via filling forms, selecting commands, clicking
with mouse, etc. In contrast, a user-friendly framework fortele-operation using natural language commands would be
more appropriate.
Sanz et al. [5] proposed for the first time, the importanceof using natural words to refer to objects in tele-operation©ISAROB 2006

The network robotic system presented in this paper allows the manipulation of objects of different shapes locatedon a table using a sub-set of the natural language. The objects used have complicated three dimensional shapes.
User can see the table and may ask the robot to pick
any one of the toys. For example, user may say “pick theelephant.” This kind of user interaction is important in
network or online robotic systems because it enhances theuser-friendliness. However, efficient object recognition is
one of the challenges. In [5], an object recognition systems
for online robots which was based on two dimensional information of an image was presented. The objective of theapproach presented in this paper is to integrate three dimensional object identification to the online robotic system.
Considering the restrictions imposed on online robotic systems which operates over the Internet, this approach needs
to be simple and efficient.
The modules of the system are shown in the Fig. 1. They
are, image acquisition, object feature extraction, neural network module, knowledge base, lexical symbol identification, command execution and communication module. The

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

“Pick the elephant”

Image
Acquisition

Object Feature
Extraction

Neural Network
ModuleObservation

Verbal Command
Knowledge Base

Command
Execution

Lexical Symbol
Identification

TCP/IP
Network
Semantic Symbols

Communication
ModuleStatus Updates

Robot Manipulator

Figure 1: System overview.

3.3

functions of these modules are discussed in the next sections.

The challenge in object recognition is to identify object with complicated three dimensional shapes placed on
the table. However, considering the constraints imposedon online network robotic systems, the approach needs tobe simple and efficient. Since the object can be placed inarbitrary orientations and in arbitrary places, the object descriptors used should be invariant to rotation and translation.
Hu descriptors are well-known for two dimensional
shape recognition. They have the particularity of being
invariant to scale, translation and rotation [6]. In our approach, we have tried to approximate 3D object identification with 2D information obtained from orthogonal camera
images. To achieve this a neural network technique wasused.

3 Implementation
3.1

Experimental Setup

The system shown in the Fig. 1 has a client-server architecture. The server side consists of a PA-10 industrial manipulator and the controller, object table, three USB cameras, and a PC running WindowsXP. The three cameras
were placed over, in front of, and in a side of the object
table.
The client side consists of a PC running WindowsXP
and a microphone. Voice recognition was performed using
IBM ViaVoice SDK.
Both the server and the client were connected to an Ethernet network and the communication was performed using
TCP/IP sockets.

3.2

3.4

Learning

To train the neural network, first a set of training data
was collected. Each object was placed in different orientations and images were taken from all three cameras simultaneously. Some of those images are shown in Fig. 2.
Then, those images were binarized and the first and second
Hu descriptors of the shapes were found as follows.
For a two dimensional function f (x, y), the moment oforder (p + q) is defined as:
 +∞  +∞xp y q f (x, y)dxdy
(1)
mpq =

Image Acquisition

For the image acquisition DirectX technology was used.
Camera images taken from all three cameras are further
processed to extract object features. Low resolution versions of images are sent to the communication module andthey are sent to the user in order to provide 3 dimensional
details of the workspace.

©ISAROB 2006

Object Recognition−∞

for p, q = 0, 1, 2, ...

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Table 1: Object representaion.
Object Descriptor
Up
H1
H2
H1
H2
Elephant-1 0.017479 0.033349 0.015421 0.029573
Elephant-2 0.018220 0.033008 0.014670 0.029938
...
...
...
...
...
Pig-1
0.016445 0.029444 0.018702 0.031850
Pig-2
0.014417 0.031383 0.015995 0.037169
...
...
...
...
...
Car-1
0.015429 0.028767 0.016730 0.031066
Car-2
0.017421 0.043342 0.016040 0.035854
H1: Fist Hu descriptor, H2: Second Hu descriptor.
Front

If f (x, y) is piecewise continuous and has nonzero values only in a finite part of the xy-plane, moments of allorders exist, and the moment sequence (m pq ) is uniquelydetermined by f (x, y). Conversely, m pq uniquely determines f (x, y).
The central moments are defined as:
 +∞  +∞µpq =(x − x)p (y − y)q f (x, y)dxdy (2)−∞the output layer is equal to the number of object classes.
In this case, it is three to classify the three object classes“elephant,” “pig,” and “car.”

3.5

−∞


x

(x − x)p (y − y)q f (x, y)(3)yThe normalized central moments are defined as:
ηpq =

µpq
µγ00

(4)

where γ = p+q
2 + 1.
From the normalized moments of order up to three, it ispossible to derive seven invariant moments or Hu descriptors. In this work, the first and second Hu descriptor, φ 1
and φ2 were used such as
φ1 = η20 + η02(5)2
φ1 = (η20 + η02 )2 + 4η11

(6)

4 Summary

Vectors of all six descriptors were used as the training
vectors of the neural network. For each object, sixteen
samples were collected. Table 1 shows some such examples of objects.
The neural network used was a standard feed forward
back-propagation neural network. Number of neurons in©ISAROB 2006

Results

After the network was trained, it was tested with sometest data. Test images were obtained by placing all objects on the table in arbitrary orientations and positions. As
explained above, three images were taken from the threecameras and the Hu descriptors were calculated as before.
Some sample test images are shown in the Fig. 3.
In addition to the Hu descriptors, from the top camera
view, center of area of each object is calculated and eachobject is marked with an object number. However, without
identifieng which object is the “elephant,” a user commandsuch as “pick the elephant” cannot be executed. For that,
Hu descriptor vector for each object is fed to the previously
trained neural network and the objects are identified. Sometest data are shown in the Table 2.
The trained neural network was tested with twenty test
images and it was found that these two object can be correctly identified with 100% accuracy. Once a user issue acommand such as, “pick the elephant,” the correct object
can be identified and picked independent of the object location and the orientation.

where x = m10 /m00 and y = m01 /m00 .
If f (x, y) is a digital image, the Eq. (2) becomes:
µpq =

Side
H1
H2
0.022898 0.039438
0.023288 0.043842...
...
0.018614 0.031869
0.016219 0.029566...
...
0.050733 0.072174
0.029486 0.046491

In this paper, possibility of introducing three dimensional object recognition capabilities to network robotic
systems was discussed. Although it is only a portion of thetotal network robotic system, it is vital in providing userfriendly natural language controlled interfaces.

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Table 2: Test Data.
ObjectNo.

Object Center
x
y
Front
(pixels) (pixels)
H1
H2
1
169
99
0.0172438 0.030375
2
196
134
0.0176682 0.034507
3226
620.0179100 0.032484
H1: Fist Hu descriptor, H2: Second Hu descriptor.

Object Descriptor
Up
H1
H20.0199281 0.041321
0.0275677 0.080579
0.0180720 0.032840(a) Front viewSide
H1
0.0182495
0.0259410
0.0175880(a) Up viewH2
0.033830
0.080527
0.033244

ObjectIdent.
elephantcarpig(a) Side view(a) Front camera view

Figure 3: Sample test images — elephant, pig and car.

References[1] K. Goldberg, M. Mascha, S. Gentner, N. Rothenberg,
C. Sutter, and J. Wiegley, “Desktop teleoperation via
the world wide web,” in Proc. IEEE Int. Conf. Robotics
Automation, 1995, pp. 654–659.

(b) Up camera view

[2] K. Goldberg, J. Santarromana, G. Bekey, S. Gentner,
R. Morris, J. Wiegley, and E. Berger, “The telegarden,”in Proc. Assoc. Computing Machinery Special Interest Group Computer Graphics Interactive Techniques,
1995, pp. 135–1140.

(b) Side camera view

[3] P. Saucy and F. Mondana, “KhepOnTheWeb: Open access to a mobile robot on the internet,” IEEE Robot.
Autom. Mag., vol. 7, no. 1, pp. 41–47, Mar. 2000.

Figure 2: Sample training images — elephant.

[4] D. Schulz, W. Burgard, D. Fox, S. Thrun, and A. B.
Cremers, “Web interface for mobile robots in public
places,” IEEE Robot. Autom. Mag., vol. 7, no. 1, pp.
48–56, Mar. 2000.

In this approach, main objective was to develop a system which was able identify objects with complex shapes
irrespective of the object location and orientation; but at
the same time, the simplicity and the efficiency of the approach was considered. Therefore, a well known technique
for two dimensional shape recognition was extended two
identify three dimensional object by utilizing a standard
feed-forward back-propagation neural network.

[5] P. J. Sanz, R. Marı́n, and J. S. Sánchez, “Including efficient object recognition capabilities in online robots:
From a statistical to a neural-network classifier,” IEEE
Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, vol. 35, no. 1, pp. 87—96,
2005.
[6] M. K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. of Info. Theory, vol. 8, pp. 179–
187, 1962.

It was found that once trained, the neural network could
correctly classify the objects. However, in this experiment
only three objects were used.

©ISAROB 2006

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Some Properties of Coupled Van del Pol Oscillators with
Inhibitory or Excitory Connections
Kiyotaka Izumi, Akihito Tajima, Guang Lei Liu, and Keigo Watanabe
Dept. of Advanced Systems Control Engineering,
Saga University
1-Honjomachi, Saga, 840-8502, Japan
Abstractparameters need to be designed carefully, when this oscillator is applied to a real system.
We examine the inﬂuence that the design parameter ina single Van del Pol oscillator gives to the oscillation generated in this paper. Furthermore, we discuss the inﬂuenceof connection weights for output oscillations, in which two
Van del Pol oscillators are coupled with such connection
weights. The eﬀectiveness of the systematic design of the
Van del Pol oscillator is examined by some numerical simulations, though we need to examine about the stability
condition of Lyapnov to discuss the stability of oscillations
rigorously.

Fundamental properties of a central pattern generator(CPG) using Van del Pol oscillators are examined to generate some locomotion patterns for a semi-looper-like worm
robot, where such a robot adopts a locomotion of green
caterpillar as a basic locomotion pattern, but sometimes it
can realize a locomotion of looper. Assume that the robot
is composed of simple three serial links, as a fundamental
research. Several simulations are conducted for examining how to design the CPG by using inhibitory or excitory
connections to obtain a stable rhythmic response, and forsearching the design parameters to produce valid amountof amplitude and frequency.

2
1 Introduction

When some Van del Pol oscillators are coupled respectively, the dynamics of ith oscillator is given by)(2ẋi − c2i xai + di ,(1)ẍi = ai b2i − xai

Robots which move any narrow passage such as a tube
is considered in our study. Those robots are being materialized by various mechanisms and the research is advanced
toward commercialization. The present research handles asemi-looper type robot that can be composed of a serial link
structure that is the most simple structure. The semi-looper
type robot can realize both of the motion patterns for thelooper and the green caterpillar. We examine a method forrealizing a motion pattern generation based on a CPG for
the semi-looper type robot.
There is a Van del Pol oscillator as one of the realization
methods of an oscillator[1]-[4]. It is very diﬃcult to deduce design parameters for an analysis target, because the
Van del Pol oscillator is expressed with a nonlinear equation of motion. Most of conventional research analyze theoscillator by numerical analysis, and decide the ﬁnal design parameters by trial and error when the object to be
applied is ﬁxed. An example of its application is the gait
acquisition of leg type robots.
Also, it is known that the output of the Van del Pol oscillator is sensitive to a design parameter. The response islargely changed to a slight change of parameters. The output is sometimes in the inclination diverged. Therefore, the©ISAROB 2006

Van del Pol Oscillator

where xai denotes the connection variable and λi j denotes
the connection weight which are related by
xai =

n
∑

λi j x j ,

(2)

j=1

where n is the number of oscillators. Coeﬃcients ai , bi , ci
and di in equation (1) have to be designed in an appropriatemanner. In particular, bi is called the amplitude parameter,
ci is called the frequency parameter and di is called oﬀset
parameter; those characterizes the shape of an output oscillation.
The analysis of Van del Pol oscillator is diﬃcult, because its dynamics include the nonlinear term which is the2and ẋi . Most conventional researches discuss
product of xai
the results of numerical analysis. There are no results on
the systematic design of any oscillators; parameters of an
oscillator are designed through trial and error.
Here, a single oscillator is analyzed by a numerical approach to design parameters. The amplitude parameter b

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Table 1: Conditions for the analysis of amplitude parameter
Parameters
Valuesa7d20b2
1 ∼ 93c2
21 ∼ 1600
Sampling interval 12.5 [ms]Table 2: Conditions for the analysis of frequency parameter
Parameters
Valuesa7d20b2
5 ∼ 80c2
6.25 ∼ 6400
Sampling interval
12.5 [ms]

100
2

50

2

Frequency parameter c

2

Amplitude parameter b

6000

c =21
2
c =34
2
c =40
2
c =1002c =4002
c =1600

4000
2

b =5
2
b =10
2
b =20
2
b =40
2
b =80

2000

0

0
0

10
Amplitude A

20

0

5
10
Frequency f [Hz]Figure 1: Property of amplitude parameter b

Figure 2: Property of frequency parameter cis analyzed by the results of some simulations. Simulation
conditions are tabulated in Table 1. The corresponding results are shown in Fig. 1. It is found from the results thatthe amplitude of an output depends on the amplitude parameter b2 and is independent of the frequency parameterc2 . The solid line in Fig. 1 is the ﬁtted curve approximatedwith a quadratic polynomial as shown by

ﬁxed, then the output frequency becomes high, setting theamplitude parameter in a small value. Note however that,when the amplitude parameter is set in a small value, theoutput amplitude becomes small.
Considering with the application for a real system, we
may not generate desired amplitude and oﬀset by setting
any parameters. In this situation, the desired amplitude andoﬀset can be realized by using an output equationb2=

2.4400445 × 10−1 A2 + 6.8580147 × 10−2 A−7.1860883 × 10−2(3)

yi = ki xi + oi

The parameter a can alter the output amplitude, but it ismore sensitive to its variation than the parameter b2 . In
our check, the region of a for generating a periodic signal
is about from 7 to 55. If the parameter a is set to a large
value, then the output of the oscillator diverges. It is foundfrom this discussion that we have to adjust the parameterb2 to alter the output amplitude, ﬁxing the parameter a in asmall value.
Next, the frequency parameter c is analyzed by using
the results of some simulations. Simulation conditions arelisted in Table 2. The corresponding simulation results areshown in Fig. 2. When the amplitude parameter b2 is ﬁxed,
the output frequency becomes high depending on the frequency parameter. Moreover, if the frequency parameter is©ISAROB 2006

(4)

where ki denotes an amplitude coeﬃcient and oi denotes anoﬀset.
From discussions aforementioned, the design approachof a single Van del Pol oscillator is summarized as follows:.
1. Set parameters a and d. The parameter a is set to 7
and the parameter d is set to 20 in all simulations of
this paper.
2. Design the amplitude parameter b2 using equation (3).
Note that the output amplitude becomes large with increasing the amplitude parameter b2 , though the designed b2 has to be readjusted.

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Table 3: Designed parameters of two VDP oscillators
VDP i aib2i
c2i
diVDP 1 7 24.7 334.2 20
VDP 2 7 6.08 46.2 20

Output of VDP osillator xi

10Output of VDP oscillator xi

x1

x1

10

x2

x2

0

–10
18

0

20
Time t [s]22

Figure 4: Outputs of VDP oscillator with λ12 = λ21 = 0.1–1020Time t [s]

22

x1

10
Output of VDP oscillator xi

18

Figure 3: Output of VDP oscillator with λ12 = λ21 = 0

3. Design the frequency parameter c2 according to thediscussions of Fig. 2.
4. Adjust the output amplitude and oﬀset using equation(4), if necessary.

0

–10
18

3 Connection Weights of Coupled Oscillator

20
Time t [s]22

Figure 5: Outputs of VDP oscillator with λ12 = 0.1 andλ21 = −0.1

The 1st oscillator (VDP1) is designed by the proposed
design approach, whose amplitude is 10 and frequency is
1 [Hz]. The 2nd oscillator (VDP2) is designed, in which
the amplitude is 5 and the frequency is 0.5 [Hz], using thesame manner. Designed parameters are listed in Table 3.
The VDP1 response is represented by x1 , and the VDP2 response is represented by x2 as shown in Fig. 3. According
to the notations of the equation (1), Fig. 3 can be obtainedby using

The result of the ﬁrst case is shown in Fig. 4. Each oscillator has the same frequency as about 0.75 [Hz] which isaverage value of each desired frequency. The phase diﬀerence between the VDP1 and VDP2 becomes constant. The
amplitude of the VDP2 becomes smaller than the desired
value.
Figure 5 is the result of the second case. The frequencyof the VDP1 is identical with the VDP2, as in the ﬁrst case.
The frequency is about 1.3 [Hz] which is the reciprocal
value of the averaged frequency for both oscillators. The
phase lag in the VDP2 relative to the VDP1 is 1/4 period. The trend on the output amplitude is similar to theﬁrst case.
The frequency of the third case is the same as that of the

λ11 = 1, λ12 = 0, λ21 = 0, λ22 = 1
in equation (2). The VDP1 and the VDP2 can generateeach desired oscillation as found from Fig. 3. The eﬀect ofconnection weight is analyzed by simulation results in fourcases such that λ11 and λ22 are set to 1 in all cases; the ﬁrst
case is λ12 = λ21 = 0.1; the second case is λ12 = 0.1 andλ21 = −0.1; the third case is λ12 = −0.1 and λ21 = 0.1; andthe fourth case is λ12 = λ21 = −0.1.

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x2

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x1

x2

0

–10

x2

0

–10
18

20
Time t [s]2218

Figure 6: Outputs of VDP oscillator with λ12 = −0.1 andλ21 = 0.1

20Time t [s]22

Figure 7: Outputs of VDP oscillator with λ12 = λ21 = −0.1

4second case, as shown in Fig. 6. However, the phase lead in
the VDP2 relative to the VDP1 becomes 1/4 period. Other
characteristics of oscillations are substantially the same as
those of other cases.
Finally, for the fourth case, we have similar discussions
about the amplitude and the frequency to the ﬁrst case. The
only diﬀerence from the ﬁrst case is that the present phaseshift is 1/2 period as shown in Fig. 7.
From discussions mentioned above, the following
points are summarized about the sign combination of connection weights in two Van del Pol oscillators:

Conclusions

In this paper, we have studied on the design approach toa single Van del Pol oscillator and on the numerical analysis of coupled two Van del Pol oscillators with connection weights. The systematic design approach was demonstrated in a single oscillator in order to generate the desired oscillation. The relationship between the connection weights and the output responses was clariﬁed throughsome simulations. Results obtained here gave some important points to design the Van del Pol oscillator.
In future works, the present results will have to be discussed with other conditions and three or more coupled oscillators.

• About the frequency: Each oscillator has the same
frequency. When the sign of λ12 and λ21 is identical,
the frequency becomes the averaged value between
the VDP1 and the VDP2. If these signs are diﬀerent then the frequency becomes the reciprocal valueof the average between the VDP1 and the VDP2.

References[1] J. S. Bay and H. Hemami, “Modeling of a Neural Pattern Generator with Coupled Nonlinear Oscillators, ” IEEE
Trans. on Biomedical Engineering, Vol. 34, No. 4, pp. 297–
306, 1987.

• About the phase shift: The phase shift is 0, when
the sign of both connection weights is plus. When λ12
is plus and λ21 is minus, the phase lag becomes 1/4
period. If λ12 is minus and λ21 is plus, then the phaselead becomes 1/4 period.

[2] J. J. Collins and S. A. Richmond, “Hard-Wired Central Pattern Generators for Quadrupedal Locomotion,” Biological
Cybernetics, Vol. 71, pp. 375–385, 1994.
[3] T. Zielińska, “Coupled Oscillators Utilised as Gait Rhythm
Generators of a Two-Legged Walking machine,” Biological
Cybernetics, Vol. 74, pp. 263–273, 1996.

• About amplitude: The amplitude of the oscillator,
designed with smaller amplitude than other one, becomes a little bit small in the actual response.

[4] L. Liu, A. B. Wright, and G. T. Anderson, “TrajectoryPlanning and Control for a Human-Like Robot Leg with
Coupled Neural-Oscillators,” Proc. of The 7th Mechatronics
Forum International Conference and Mechatronics Educational Workshop, Paper ID M2000-72, 2000.

As very important results, it is suggested that the phaseshift can be designed by the sign combination of connection weights.

©ISAROB 2006x1

10
Output of VDP oscillator xi

Output of VDP oscillator xi

10

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Optimal Design Method for Region Setting in Fuzzy Energy Switching
Control of Underactuated Manipulators
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

Table 1: Deﬁnition of input torque, generalized coordinates and physical parameters

Underactuated manipulators have some passive or
free joints in general, where the number of inputs is lessthan the degrees of freedom. These systems have complex structural properties, and they have to control
a lot of generalized coordinates by few inputs. However, reducing the number of actuators brings some advantages such as lightweighting, compactiﬁcation andcost reduction. We here propose a switching control
method based on fuzzy energy regions. We also discuss
the application of a logic-based approach to underactuated manipulators with drift terms as considered inthis research. Then we compare a switching control
method based on a logic method with that based on
the present fuzzy energy regions. The eﬀectiveness ofthe proposed method is demonstrated with some simulations.

1

Symbols
τ1
θ1 , θ2
m1 , m2
l1 , l 2
lg1 , lg2
I1 , I2µ1 , µ2

design parameters related to energy regions and gains
of some partial stabilizing controllers based on thecomputed torque method[4]. It is interesting to discuss
about the energy region related parameters. In particuler, we are here concerned with a logic-based switching method that has been proposed by Hespanha etal[5]. Note however that this method was applied tononholonomic systems without drift terms in the original paper.
In this paper, we thus discuss the application of thelogic-based switching method to underactuated manipulators with drift terms. Here, we compare it witha switching control method based on fuzzy energy region. We show the eﬀectiveness of design parameters
through some simulations.

Introduction

In general, manipulators used for industry and academic laboratory have actuators to drive each joint.
Then, these actuators are appropriately controlled
to achieve the desired motion. On the other hand,underactuated manipulators handled by our research
have some passive or free joints without actuators andbrakes. These passive or free joints cannot generate
dynamic torque at all. Due to the existence of such
underactuated joints, the desired reference cannot bedesigned arbitrary, so that we cannot use the same
control approach as used for the manipulators withfully actuated joints. To control underactuated manipulators a lot of researches have been studied.[1, 2, 3].
The present authors have also already proposed aswitching control, in which some partly stable controllers were designed by computed torque method andswitching low was obtained as the index of controller
directly by fuzzy reasoning. In our method, it needs to©ISAROB 2006

Physical meaning
Input torque [Nm]Link angles [rad]Mass of link [kg]Link length [m]Distance between joint
and center of gravity [m]Moment of inertia [kgm2 ]Viscous coeﬃcient [Ns/m2 ]2Underactuated Manipulator

Figure 1 shows two-link underactuated manipulator where the ﬁrst joint is active and the second joint
consists of a passive joint. In Table 1, τ1 denotes theapplying torque of 1st joint, θ1 , θ2 denote the angle ofeach link, and the physical parameters are shown in

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3

Energy Regions

3.1

Deﬁnition of energy

Energy is deﬁned by using general coordinates. Desired joint angle of each link is θdi , and the error of
joint angle is denoted by4

ei = θdi − θi .

(2)

Then, the energy of each link is deﬁned by
4

Ei = e2i + ė2i .

(3)Figure 1: Two link underactuated manipulator

4

Switching Control

4.1

Logic based switching

Boundary curves πi , (i = 1, 2) are deﬁned by()
πi (E1 ) = Ai 1 − e−E1 /T

where Ai is the amplitude of πi and T is the convergence ratio. It is assumed that Ei ’s converge monotonically when the partly stable controllers Ci are applied
for a system. The logic based switching function is deﬁned by{j
if E1 ∈ Rjˆ(5)I=
max{i(= 1, 2) : E1 ∈ Ri } if E1 6∈ Rj

Figure 2: Region approximations for a logical switching

Then, the ideal energy response becomes as shown in
Figure 2.

it. The dynamical model of the underactuated manipulator is given as follows:
M (θ) θ̈ + h(θ, θ̇) = τ

4.2[

M11 (θ) M12 (θ)
M12 (θ) M22 (θ)]

M11 (θ) =2
2+ I2 )+ m2 l12 + I1 ) + (m2 lg2(m1 lg2+2m2 l1 lg2 cos θ2

M12 (θ) =2+ I2 ) + m2 l1 lg2 cos θ2(m2 lg2

M22 (θ) =2+ I2
m2 lg2h(θ, θ̇) = [h1 (θ, θ̇) h2 (θ, θ̇)]T
h1 (θ, θ̇) = −(m2 l1 lg2 )(2θ̇1 θ̇2 + θ̇22 ) sin θ2 + µ1 θ̇1h2 (θ, θ̇) =©ISAROB 2006

Fuzzy energy region based switching

We design controller 1 (C1 ) and controller 2 (C2 )for a two link manipulator. They are used as partial
stabilizing controllers to stabilize each link. We candeﬁne energy region by each controller.
If an exponential function is used, design parametersof boundary curve to divide energy region are denotedby the amplitude and the time constant. Control responses of manipulators depend on these parameters.
Such parameters need to be set in ideal way. But
it is diﬃcult to set at once. Therefore, the boundary curve is denoted by fuzzy expression in this research. If a boundary curve has fuzziness denoted by
the present fuzzy reasoning, then there appears an advantage of present method in setting the design parameters roughly. We ﬁrst consider a straight line approximation shown in Figure 2. After such an approximation, fuzzy sets for E2 can be deﬁned for E1 ≤ E1a

(1)

where
M (θ) =

(4)

m2 l1 lg2 θ̇12 sin θ2 + µ2 θ̇2692

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Figure 5: Switching control system
Figure 3: Membership functions for E1 ≤ E1avalue of each gain is ﬁxed, region set parameters arechanged. Actually, each gain parameter is set to
Kp1 =25.0, Kv1 =10.0, Kp2 =25.0, Kv2 =10.0. Parameters of fuzzy energy region (method A) are set to
E1a =4.0, E2a =1.0, E2b =3.5, and parameters of logic
based (method B) are set to T =2.5, A1 =1.0, A2 =3.5.
The initial state vector is set tox(0)To compare the logic based switching with the fuzzyenergy region based switching, we evaluate the total
error energy of each link. The evaluation function wasapplied for last 15 seconds when the control converged
to zero roughly. Results using the fuzzy energy based
switching are shown in Figure 6 and Figure 7, andthe results of logic based switching are shown in Figure 8 and Figure 9. Then, the evaluation function inmethod A was 6.819, whereas the evaluation function
in method B was 2.525. Thus, both methods converged with a satisﬁed condition. Note however thatthe convergence result of method B is a little better
than that of method A.

Figure 4: Membership functions for E1 > E1aand E1 > E1a cases as shown in Figure 3 and Figure 4,
where E1a , E2a and E2b 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:

If E2 = S
If E2 = M
If E2 = M
If E2 = B

Then s1 = 1
and It−1 = 1
and It−1 = 2
Then s4 = 2

Then s2 = 1
Then s3 = 2

6Here, 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.

5Conclusions

In this paper, a control method of underactuatedmanipulator using partly stable controllers has been
designed by computed torque method. For switching mechanism, we compared the fuzzy energy region
based switching with the logic based switching. Fuzzyenergy region based switching was better in the number of switching times than logic based one. Regarding the joint angle response, the fuzzy energy region
based switching was better than the logic based one.
However for the evaluation of convergence by using a
cost function, the logic based switching was a slightly
better than the fuzzy energy region based one.

Simulations

For partial stabilizing controller, set the ith link
proportional gain Kpi , derivative gain Kvi , and region parameters E1a , E2a , E2b . At this stage, the©ISAROB 2006T= [θ T (0) θ̇ (0)]T= [ 0 π/4 0 0 ]T

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Figure 6: Energy with the fuzzy energy based switching

Figure 8: Energy with the logic based switching

Figure 9: Link angles with the logic based switching
Figure 7: Link angles with the fuzzy energy based
switching[4] 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.

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.

[5] J. P. Hespanha and A. S. Morse, “Stabilization ofnonholonomic integrators via logic-based switching,” Automatica, vol. 35, pp. 385–393, 1999.

[2] H. Arai, K. Tanie, and N. Shiroma, “Nonholonomic control of a three-DOF planar underactuated manipulator,” IEEE Trans. on Robotics andAutomation, vol. 14, no. 5, pp. 681–695, 1998.
[3] I. Fantoni, R. Lozano, and M. W. Spong, “Energy based control of the pendubot,” IEEE Trans.
on Automatic Control, vol. 45, no. 4, pp. 725–729,
2000.

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Transport Devices : Devices which transport theparts from one work cell to another work cell of therobot ( for e.g., conveyors and carousels ).

ElbConveyors are the linear type of part feeders andcalled as linear transport devices. Their function is topresent the parts or objects to the robot for
manipulation. They are used to transport the parts
linearly from robot to robot or from one robotic
work cell to another robotic work cell. They are used
to transport parts / objects for manipulation either
from left to right or from right to left and are driven
by electric motors at either ends. It can be used fora multi robot work cell.

VE

Roll

Roll
Tool

z

0

x

Robot 10

Rotating
centralpedastal

Tool

Elb

Part

Rotary turn tablezθx

CarouselFig. 3 A carousel00Robot 2

Part Holding Devices / Fixed Tools : Devices which
are used to hold a sub part in proper position andorientation ( clamps , jigs , vices ).

Example : automated assembly lines. The parts areapproached from the above with r3 = – i3 i.e.,
approach vector opposite to z0 of base. A four axis
robot is sufficient instead of a Five axis robot. The
1st and 2 nd axis are used to position the tool over thebelt. The 3 rd axis places the tool at proper elevation
above the object. The 4th axis orients the tool in the
direction of the object. Exchange of parts ( swapping) between two robots is not possible. It is possible if
the conveyor belt is driven in reverse direction. Any
number of robots can be accommodated on eithersides of the conveyor belts [2].

Part Storage Devices : Devices which are used tostore the manufactured parts ( bins / containers ).

2. Design of mechanical fixture & robot
INRI is a cylindrical coordinate robotic unit
having its workspace as a solid cylinder and has two
positional axes, viz., the base and the verticalprismatic motion (termed as the major axes) and one
orientation axes, viz., the roll (termed as minor axes)
which allows the wrist to be oriented in the
direction of the object. The robot designed iscapable of sensing and picking of objects andplacing them in its work volume in a particular
position and orientation with a high degree ofprecision and accuracy.
A part feeding mechanism for the robotic
system, viz., a conveyor belt on which the parts arearriving one after the other is to be designed andfabricated along with the robotic system. Thoughthought of this work as a technology demonstration
project, the INRI can be put to many uses such as in
assembly operations or in the educational field. Thereis also plenty of scope for future enhancements suchas remote controllability of the system, internet based
controllability and the object sensing / obstacle
avoiding techniques such as using the computer vision
for mechanical systems.

Fig. 2 A conveyor belt mechanism
Carousels are rotary transport devices ( rotary part
feeders ). They transfers parts back and forth between
two or more than two robots. The parts areapproached from above. SCARA’ s are sufficient. A
carousel consists of a rotating table with a centralpedestal. Robots are mounted in a circular fashionaround the rotary turn table. Swapping of parts
between robots is possible, since the table can berotated or turned in the reverse direction. They areused to accommodate only a few robots since thediameter of rotary turn table places a restriction onnumber of robots. If the number of robots has to be
increased , then the diameter of the turn table has to
be increased . Then, eccentricity, i.e., balancing of thetable comes into picture. Carousel is shown in Fig. 2
below.

©ISAROB 2006

VE

The entire mechanical assembly of the robotic
system is being divided into three parts, viz., Baseassembly, Armassembly,End-effector (Gripper)
assembly, Conveyor
belt
assembly. The
entiremechanical assembly of the designed and fabricatedrobotic system is shown in Fig. A conveyor
mechanism is a one DOF system which is moving ina linear fashion along a straight line and is driven
by a conveyor DC servomotor with a 90° powertransmission and which has the same specifications
as that of the vertical extension / roll motor.
A conveyor mechanism is a one DOF system
which is moving in a linear fashion along a straightline and is driven by a conveyor DC servomotorwith a 90° power transmission and which has the696The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

same specifications as that of the vertical extension /roll motor. It consists of a belt which is moving onrollers (in order to prevent the sag) over which theobjects are coming one after the other and is asshown in Fig. 5.1. The length of the conveyor belt
being ( 45 × 5 ) cms. The speed of movement of theconveyor belt mechanism being approx. 12 cms / min.
The electronic unit serves as an driver interface
between the mechanical hardware and the computing
system [3]. The electronic circuitries designed andfabricated by us consists of the following sub-parts,
viz.,

3. Software design application
An efficient VB GUI was developed to do the PickaNd Place (PNP) application by using the robot [5]. The
GUI developed consists of various menu boxes,
instructions such as File menu, motor selection list box,
motor selected text box, direction option buttons, start
button, Stop button, reset button, conveyor on / off, autoteach facility, The run screen, single stepping feature, run
through, code box, clear, step, run and stop (Fig. 5).
START APPLICATION

1. Driver interfacing unit - is used to drive theactuators (motors) and consists of the drive power
amplifiers with its peripheral chips such as the
decoders and the buffers . The driver unit consistsof two L293D IC’s , each driver IC being used todrive two motors . The conveyor belt is driven by
a FET IRF z 44 N. The designed and fabricateddriver unit acts as the interface between thecomputer system and the mechanical unit (robot)
and was tested on a general purpose PCB .

DISPLAY THE GUI

SELECT THE MOTOR

SELECT THE MODE

2. Controller unit - which consists of a IC circuitry(such as decoders 74138, buffers 74LS245) to giveproper control signals to the robotic system forsatisfactory operation. The data to the control unit
comes from computer through the parallel portinterface .

IS MODE=CW

Perform CCW motion on
START untill s\w limit hasreached or operation is stopped
by STOP
Perform CW motion on
START untill s\w limit hasreached or operation is stopped
by STOP

3. Sensing unit - to sense the objects ( using limit
switches , etc.,. ) and to avoid the collisions of therobot with the obstacles , to find whether the object
has been gripped properly or not. When both thelimit switches (which are connected in series) getsclosed after grasping the object, a signal will besent to the parallel port through the status pin
which makes the gripper motor cut off from the
supply preventing further motion of the gripper anda message will be displayed on the GUI indicatingthat the object has been gripped.

Do you want to EXIT
application

EXIT Application

Fig. 4 Flow chart module of software to do PNP job

4. Power supply unit - to supply power to thevarious electronic circuitries. The power to thedriver cards and the driver IC’s is obtained fromthe standard laboratory made regulated power
supply . A 7805 regulator IC is used to provide 5
volts to the various IC chips .
The robotic system developed and controlled by
the computer employes a GUI developed in Visual
Basic 6.0. VB is selected as it is a simple, yet
powerful, windows platform based programming
language, which provides us with a complete set oftools to simplify complex rapid application
development [4].

Fig. 5. GUI to do PNP operation©ISAROB 2006

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5. APPLICATION OF INRI : PNP OPERATION

Fig. 6 Robot picking up the object form the conveyor
belt, transporting the object to the destination(container) & placing the object in the container

6. Conclusions
A novel design and implementation of a work space
fixture for a 2 DOF cylindrical robot was done
successfully and the work space fixture, i.e., the 1 DOF
conveyor belt mechanism was tested for various types ofoperations.

References[1] Robert Schilling, Fundamentals of robotics, PHI.
[2] Hodges Bernard, Industrial Robotics, Jaico Pub.
[3] T.C.Manjunath (2005), Fast Track to Robotics, 2ndedition, Nandu Publication, Mumbai, India.Fu,
Gonzales, Lee, Robotics-Control Sensing & Vision,
Mcgraw Hill.
[4] T.C.Manjunath (2005), Fundamentals of Robotics,
3rd edition, Nandu Publication, Mumbai, India.
[5] Groover et.al., Industrial Robotics, MGH.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Remarks on texture synthesis and texture segmentation with the aidof multi-level logistic model of Markov Random Field
Tomasz Kubik††

Masanori Sugisaka∗

Institute of Computers, Control and Robotics, WrocÃlaw University of Technology,
Janiszewskiego 11/17, WrocÃlaw 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: texture synthesis, texture segmentation, Markov Random Field

Abstract

to the pixel s). The neighborhood system for S is:

Markov Random Field (MRF) method is used for
texture modeling. This method makes use of parameterized random field models. The article concerns
MRF method from two points of view: texture synthesis and texture segmentation. Texture synthesis,
in general, relies on a joint distribution sampling while
the model of a random field is fully known. The common algorithms employed here are: Metropolis sampler and Gibbs sampler. Texture segmentation can bedone in two ways. Assuming, that unknown parameters of known distribution can be estimated and classified within regions of interest, the classification results
for all regions in the image analyzed provide the clue
for the final segmentation. The other proposition assumes, that there are several reference random fields
models which are fully known. The model that fits
the best to the region of interest defines the results ofclassification. Thus the segmentation comes from theclassification performed for the whole image.

1

σ = {σs | ∀s ∈ S}

where σs stands for the set of neighbors of s. The
neighborhood systems of 1st, 2d, 3rd, . . ., kth ordercan be defined as in the Figure 1a). The pair (S, σ)constitutes a graph, in which elements S are nodes,
and arcs are defined by the neighborhood system σ.
A clique c for (S, σ) is a subset of S. The clique cancontain one pixel, c = {s}, or a pair of neighboring
pixels, c = {s, s0 }, or a triple of neighboring pixels, c ={s, s0 , s00 }, etc. The cliques containing single pixels,
pairs, and triplets are denoted, respectively, as:
C1 = {s | s ∈ S}
C2 = {{s, s0 } | s0 ∈ σs , s ∈ S}
C3 = {{s, s0 , s000 } | s, s0 , s00 ∈ S all are neighbors}(3)
TheS collectionofcliquesisdenotedasC=S
C1 C2 C3 · · · . The different types of cliques corresponding to the neighborhood system of the 2nd order,
are shown in the Figure 1b).

Introduction

1.1

The method of MRF is based on the random fieldmodel where the form of its joint and parameterized
distribution is known. This section provides some basic definitions used in the model definition (plese referto [1] for details).
Let field of view S be a regular, 2 dimensional, anddiscrete lattice:
S = {(i, j) : 1 ≤ i ≤ N1 , 1 ≤ j ≤ N2 }Random field model

Let X = {X1 , . . . , Xm } (m = N1 ·N2 ) be the familyof probabilistic variables defined on the set S, and leteach variable Xs can take a value xs ∈ L. Family X
is called a random field. The probability that randomvariable Xs takes the value xs is denoted as P (Xs =xs ), what can be written as P (xs ). The joint probability is denoted as P (X1 = x1 , . . . , Xm = xm ), what canbe shortened to P (X = x), where x = {x1 , . . . , xm }and X = {X1 , . . . , Xm }, or written simply as P (X).
Thus any image can be regarded as a realization ofa random field where pixel colors follows the random
field joint probability distribution.

(1)

Let pallet L be a finite set of n colors: L = {1, . . . , n}.
The function x : S → L defines an image. The value of
this function for s ∈ S is denoted as xs (color assigned©ISAROB 2006

(2)

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a)

b)
C1

5

4

3

4

5

4

2

1

2

4

3

1

1

3

4

2

1

2

4

5

4

3

4

5

To highlight that this potential depends on parameters, it can be rewritten as V2 (xs , xs0 , β̄), where β̄ =(β1 , . . . , β4 ). Finally, considering only cliques includedin C2 , the conditional probability P (xs | xS−{s} ) is
given by:

C2

b1

b2

b3

C3

b4

C4

P

e− s0 V2 (xs ,xs0 )PP (xs | xS−{s} ) = P− s0 V2 (x0s ,xs0 )
x0s e

(9)

P
where s0 denotes the sum over all neighbors of s, forwhich (s, s0 ) is a clique in C2 . Using double indexing
this probability can be rewritten as follows:

Figure 1: a) Neighborhood systems of nth order (fields
marked with 1 denotes the neighboring system of the
1st order, fields marked with 1 and 2 - neighboring
system of the 2nd order; 1,2 i 3 - of the 3rd order,etc.) b) Cliques for the neighborhood system of the
2nd order with parameters βk , k = 1, 2, 3, 4, related to
the four different clique types.

P (xi,j | xS−{(i,j)} ) = P

e−L(xi,j )x0i,j e−L(x0i,j )

(10)

where

1.2

MRF modelL(ai,j )= V2 (ai,j , ai−1,j ) + V2 (ai,j , ai+1,j )+V2 (ai,j , ai,j−1 ) + V2 (ai,j , ai,j+1 )+V2 (ai,j , ai−1,j−1 ) + V2 (ai,j , ai+1,j+1 )+V2 (ai,j , ai+1,j−1 ) + V2 (ai,j , ai−1,j+1 )(11)Thus, if all pixels in a clique have the same color, L =2β1 + 2β2 + 2β3 + 2β4 . If all colors are different, thenL = −2β1 − 2β2 − 2β3 − 2β4

The joint probability distribution design is a basic
task in the MRF based texture modelling. X is called
MRF on S with respect to σ iff the following conditions
holds, [?]:
P (x) > 0, ∀x ∈ X(positivity)

P (xs |xS−{s} ) = P (xs | xσs ) (Markovianity)

(4)
(5)

where S − {s} denotes all pixels in S except s, xS−{s}denotes colors of all pixels in S except s, and xσs ={xs0 | s0 ∈ σs } denotes the colors of neighbors of s.
The conditional probability P (xs | xS−{s} ) is defined as:
P (xs | xS−{s} ) = Pe−Px0s ∈L ec∈A Vc (xs )−P

0c∈A Vc (xs )2

If a model of a random field is fully known, texture
synthesis can be realized as the joint distribution sampling. The common algorithms used for such sampling
are: Metropolis sampler [2, 3], and the Gibbs sampler[4]. Both algorithms produce an image with a texture in an iterative way, starting from an image withpixel colors generated from a palette L with uniform
random distribution function. The iterations continue
arbitrary number of times.
In the Metropolis sampler at each iteration step a
copy of a current image is generated. In this copy acolor for a chosen pixel is generated randomly from Lwith uniform random distribution function. Then the
copy becomes a new current image, if the probability
obtained from a model is higher for it then for thecurrent image.
The scheme of a Gibbs sampler is following. For arandomly chosen pixel at each iteration step all possible colors are tested. The color which agree the mostwith a model of a random field (under condition, thatcolors of the pixels in the neighborhood of the pixel ofinterest are are known) is accepted as a new color.

(6)

where x0s takes all possible values of colors which canbe assigned to the pixel s, and A is a set of cliques
including pixel s. Vc (·) represents the potential of aclique c. The potentials of cliques containing morethen one pixel, in a multi-level logistic model (MMLmodel), are defined as follows:
if all pixels in the clique c

ζc
have
the same color,(7)
Vc (x) =−ζc otherwise.
where ζc is a potential of a clique c. Let potentialof a clique containing a pair of pixels (a cliques inC2 ) for the neighborhood system of the 2nd order,
where parameters βk , k = 1, 2, 3, 4 are assigned as in
Figure 1b), is defined as follows:½
if xs = xs0−βk0
V2 (xs , xs ) =(8)βkotherwise©ISAROB 2006

Texture syntesis

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3

Texture segmentationof the moving window used was 12×12, 15×15, 20×20.

Texture segmentation can be done in two ways: 1)by the estimation of unknown parameters of the known
random field model, followed by their classification (estimation method), 2) by the choice of such random
field model from a set of models, which fits the best
to the samples observed (best fit method).
In both solution an image being segmented isscanned with a moving window. The size and the position of this window defines a region of interest containing the texture. The texture is recognized by theparameters classification (estimation method) or by
the best candidate selection (best fit method). The
position of the center of the moving window defines
the pixel in the original image, which should labelledaccording to the texture recognized. The outcome ofthis procedure is a segmented image.
The estimation method requires some computation
to be performed. Such computation can be regarded as
MRF parameters recovering on the base of an observed
texture, where this texture is MRF realization. Classification, that follows parameters estimation, takes vector of parameters as a feature vector. The result of theclassification is a label corresponding to the texture.
The best fit method (by means of the highest likelihood function value) requires that all MRF models of
textures are known. If they are known, the result of
the method are labels, which correspond to the best
fitting texture models found. The texture models canbe supplied by an expert user (by specifying parameters) or build on the base od samples. The set ofsamples for all distinguishable textures can be createdby cutting homogeneous regions from a test image.
Then, the parameters of the models can be recoveredin the same way as in the estimation method. Afteraveraging they can be used for segmentation in thebest fit method.

4

5

Texture synthesis and texture segmentation by theuse of the MLL model of the MRF and the segmentation method presented is easy to implement for thecase of artificial textures. It has been observed, thatthe best fit method of texture segmentation is much
faster then the estimation method. The only step thatcost much of the computation time is the initial set of
texture model parameters approximation (if they are
not known from the begining). Nevertheless, the results of two segmentation methods are comparable.
Working with a moving window gives bad segmentation result on the borders of two textures. Thus akind of a method that gives better accuracy in suchcase should be applied.
The use of the method proposed for natural textures is not so promising. The reason is that natural textures have models that are unknown, and must
be discovered first. This brings much more difficultyin the method application. It was tested (result notshown) that the MLL model used does not fit to thenatural textures. For natural texture modelling pleaserefer to [6].

References[1] S. Z. Li: Markov Random Field Modeling in Computer Vision Spinger-Verlag, London, UK, (1995).
[2] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller: Equations ofstate calculations by fast computational machine.
Journal of Chemical Physics, 21:1087–1091, (1953).
[3] J. M. Hammersley and D. C. Handscomb: Monte
Carlo Methods Wiley, New York, (1964).

Experiments[4] S. Geman and D. Geman: Stochastic relaxation,
Gibbs distribution and the Bayesian restoration ofimages. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 6(6):721–741, (1984).

In the experiments conducted MLL model of random field was employed (see equation (10)). The
model was described by four parameters: β1 , β2 , β3 ,β4 . Texture synthesis method was based on the Gibbs
sampler. Parameters estimation procedure used in
the article was an implementation of kind of gradientsearch algorithm, [5]. Classification of the parameters
estimated was done with the aid of artificial neural network based classifier. The classifier was trained withdata provided from the reference textures models.
The results of segmentation with respect to the segmentation method are shown in the Figure 3. The size

©ISAROB 2006

Conclusions[5] Y. Yu and Q. Cheng: MRF parameter estimation
by an accelerated method. Pattern Recognition Letters, 24:1251–1259, (2003).
[6] R. D. Paget: Nonparametric Markov Random
Field Models for Natural Texture Images. Master’s thesis, The University of Queensland, St Lucia,
Queensland 4072, Australia, (1999).

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a)

b)

a)
b)
c)
d)
e)
f)

c)

d)

original parameters
β1
β2
β3
β4
1.0 1.0 -0.5 -0.5
1.0 -0.8 0.5 -0.5
0.3 0.3
0.3
0.3
0.5 1.0 -0.5 0.7
1.0 1.0 -1.0 1.0
2.0 2.0 -1.0 -1.0

β11.087171.044650.320296
0.74194
1.53794
2.4197

a)
b)
c)
d)
e)
f)

e)

f)

estimated parameters
β2
β31.09241-0.565433-0.899957 0.558655
0.314226
0.288998
1.30031-0.843298
1.48114-1.6005
2.45401-1.24345

β4-0.566961-0.54535
0.281211
0.704515
1.53289-1.30294

Figure 2: Synthesized artificial textures along with the parameters used for their synthesis (original parameters),
and parameters that were estimated for them (inside a moving window of the size 20×20). The images are enlarged
for better visualization.
segmented images
best fit, N = 15best fit, N = 12

estimation, N = 15

estimation, N = 20

test image

Figure 3: Results of texture segmentation with respect to the segmentation method, where N comes from the sizeof the moving window used which was N × N .

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Tension-based Movement Corrections of the Artificial Agent
Matosiuk B., Grzyb B.
Faculty of Mathematics, Physics and Computer Science
Maria Curie-Sklodowska University
pl. Marii Curie-Skodowskiej 1, 20-031 Lublin, Poland
bartosz@matosiuk.net, beata@gabri.pl

Abstract

A tension accumulator produces a spiketrain whose
frequency is proportional to the current tension level.
The tension dischargers cause such motions of therobot that it can get sensory patterns causing possibly
rapid discharge of a tension accumulated at a given accumulator. The corner-quitting module shown in Fig.
1 is a simple example of cooperation between a tension
accumulator and a tension discharger.

This paper presents results of an experimentwith a simulated mobile robot equipped with a
psychodynamic-like controller called PreBrain-M wehave built. The theory of machine psychodynamics,
inspired by the works by Sigmund Freud, deals withpleasure-seeking robots, where the pleasure is a variable that substantially increases when a certain tension rapidly discharges. The PreBrain-M is a modular
system that contains two types of devices: tension accumulators and tension dischargers. The communication between the devices is based on time-series called
spiketrains. The most essential part of our PreBrainM is the circuit responsible for smart chasing of objects. Owing to this circuit the robot moves toward
anticipated location of the pursued object. The PreBrain’s performance was compared with the performance of a controller which always moves in the direction it currently sees the object of chasing.

1

Figure 1: The corner-quitting module gets activatedwhen simultaneous signals come from both left andright IR sensor, which means that the robot stuck inthe corner. In such a case a related tension is being
accumulated. Then the module’s tension discharger
sends appropriate signals to the drivers of the robot’s
motors.

Introduction

The theory of machine psychodynamics, inspiredby the works by Sigmund Freud, is being developed at
the Advanced Telecommunications Research Institute(ATR), Kyoto, Japan in the framework of the Artificial Brain Project [1]. In his work Beyond the Pleasure Principle Freud proposes that the course taken
by mental events is invariably set in motion by an
unpleasurable tension, and that it takes a direction
such that its final outcome coincides with a loweringof that tension [2, p.3]. Hence, machine psychodynamics deals with pleasure-seeking robots, called psychodynamic robots, where the pleasure is a variable that
substantially increases when a certain tension rapidly
discharges [3]. PreBrains is the name of a family ofcontrollers for psychodynamic robots.
Psychodynamic architecture employs two types ofdevices: tension accumulators and tension dischargers.

©ISAROB 2006

Although such approach is a great simplification of
the processes taking place in a human brain it is possible that during growth of the controller’s complexity
some extraordinary features would emerge. An example that confirms such statement is an experiment
performed by the ATR Artificial Brain Group members that showed how easy the complex phenomenonof the robot’s hesitation could occur when an agent
were controlled by a PreBrain in which contradictorytensions fought for dominance [4]. The PreBrain-M
we present contains modules for corner quitting andsmart chasing of objects of interest..

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

PreBrain-Building Platform

PPNNs are graphs composed with axons that represent delays and nodes that process received spiketrain. Although it was proved that a PPNN could
be built for every Boolean function [5], this paradigm
does not provide a natural method to synthesize more
complex arithmetical operations on spiketrains. Hencewhen it is necessary (i.e. when a design requires morethan the BrainCAD library devices) the programmed
C++ modules are being used. The designer’s task is
to define a module’s output upon values read fromthe input. Created functions are still operating on the
spiketrains sent to each destination module during every clock.

PreBrain controllers are being created using theBrainCAD - a CAD-style toolkit for rapid prototyping of distributed data-processing systems. It offers alibrary of ready-made tension accumulators and dischargers, as well as a convenient GUI for designingconnections among arbitrary number of tension accumulators and dischargers. Under the current versionof the BrainCAD, the designs can be run on multiple
stations in real-time regime in synchronous mode. The
toolkit also containins several useful, build-in modules,
a number of generic mathematical functions and a simulator of a mobile robot (for a visual observation of the
behavior of the agent controlled by the PreBrain).
The tension is a function of the sum of received excitations, discharging signals, and spontaneous leakage.
In the PreBrain-M it is defined the same way as in[6], i.e. by a set of values representing the accumulation/discharge time. During every clock, which isa PreBrain’s measure of time, each tension accumulator produces a spiketrain whose frequency changes
according to the formula:


Ai,t
Di,t
u
+
+
S
TA,i,tTD,i,tΘi,t+1 = G Θi,t + if

An intrinsic element of the PreBrain platform isa simulated agent representing a Breitenberg-style
robot. In its basic version the robot equipped witha set of IR sensors that enables it to receive a signals
from the environment and two motors - each equippedwith a FCV (frequency-to-voltage) module. Since PreBrain controller can only excite the motors with unary
spike trains, each FCV has a positive and negative
input and produces a positive or negative voltage depending on the difference between the frequencies provided to its inputs. Hence, the speed and direction ofrotation of each of the robot’s wheels depends on the
distribution of the frequencies of the four spiketrains
sent to FCVs.

t - time (in clocks),
Θt - tension volume,u - parameter determining the direction of uncontrolled change of tension (1 - increase, -1 - decrease),Ai,t = (Ai,t,0 + ... + Ai,t,7 ) - i-th tension accumulation
signal,Di,t = (Di,t,0 + ... + Di,t,7 ) - i-th tension discharge
signal,Si - time of uncontrolled charge/discharge,
TA,i,t - accumulation time (from 0 to 1) assumingthat Ai,t contains only 1s,
TD,i,t - discharge time (from 1 to 0) assuming that
Di,t contains only 1s,
f - frequency of clocking (clocks per second).

3

The Motion-Correction Module (MCM) has been
written in C++ and compiled as a BrainCAD-library
module. Attached to the PreBrain together witha handful of dedicated tension-accumulation devices,
MCM enables the robot to estimate the future positionof the object that is being chased [Fig. 2].
MCM uses the V-Green module (from the BrainCAD library), which simulates a camera-based tracking of a movement of a small green object. The output
data is an angle-range within which the robot sees theobject. The camera’s visual field is divided into fivesectors. The appearance of the object in a given sector results in an increase of a related tension (T1, T2,
T3, T4, or T5). This event implies the accumulationof the tension Tlr or Trr (depending on the side of thevisual field where the object was detected) or the discharge of all tensions (when the object was detectedin the central sector). Figure 3. shows a scheme of the
PreBrain-M and explains the role of MCM.

Function G(x) will return 1 x≥1x x ∈ (0, 1)
G(x) =0 x≤0In PreBrain the spiketrains are used to exchange
information within the PreBrain elements. It is necessary to employ such data carrier since the tension dischargers could be implemented in two different ways; a
C++ client or a Pulsed Para-Neural Network module.

©ISAROB 2006

Motion-Corrector Module

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Figure 3: A scheme of PreBrain-M. The controlled agent’s visual field is divided into 5 sectors related to five basic
tension accumulators (T1, T2,..., T5). When the object of interest is seen in a particular sector, a related tension
gets increased, which causes an increase of one of three auxiliary tensions - Tlr for the left side of the vision field,
Trr for the right side, and Treset for the central sector. An increase of Treset causes decrease of all the othertensions, which results in a repeatitive work of the system. The frequency resulting from the tension accumulated
in Tlr or Trr is appropriately weakened to avoid harmful oscillations and the obtained sub-frequencies are added toand subtracted from the positive and negative input, respectively, for both motors.
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References[1] Buller A. (2004) From q-cell to artificial brain, Artificial Life and Robotics, 8 (1), pp. 89-94.
[2] Freud S. (1920/1990) Beyond the Pleasure Principle, W.W.Norton & Co, pp. 3-4.
[3] Buller A., Hemmi H., Joahimczak M., Liu J.,
Shimohara K., Stefanski A (2004) ATR Artificial Brain Project: Key Assumptions and Current
State Proceedings, Ninth International Symposium
on Artificial Life and Robotics (AROB 9th ’04),
January 28-30, 2004, Beppu, Japan, pp:166-169.

Figure 2: Motion-Correction Module’s goal is to increase the effectiveness of chasing the object of interest through enabling the robot to move towards not theactual position of the object but toward an estimatedposition.

4[4] Liu J. & Buller A. (2004) Tensions and Conflicts: Toward a Pleasure-Seeking Artifact, The 5th
IFAC Symposium on Intelligent Autonomous Vehicles (IAV’2004), July 5-7, Lisbon, Portugal.
[5] Eeckhaut H. & Van Campemhout J. (2003) Handcrafting Pulsed Neural Networks for the CAMBrain Machine, The Eight International Symposium on Artificial Life and Robotics (AROB 8th’03), January 24-26, Beppu, Japan, 494-498.

Experiment

The goal of our experiment was to find out whether
the efficiency of the PreBrain could be substantially
increased by a support of the tension-driven MCM(Motion-Corrector Module).

[6] Li J., Buller.A, Liu J (2004) Robot’s behavior
driven by internal tensions regulated by pulsed
para-neural networks, Tenth International Symposium on Artificial Life and Robotics (AROB 10th’05) February 4-6, 2005 at Beppu, Japan.

We used the controller described in [6], which isequipped with the circuit allowing robot to follow agreen ball by always heading straight toward it. We
also used the feature build-in the simulated robot thatrecords the distance between the robot and the ball inevery clock during the 30 minutes pursue. For the second run the same initial conditions were assured but
this time MCM was attached to the PreBrain. The log
files analysis revealed that the tensions based motioncorrector module increased the frequency of the robots
rendezvous with the ball almost twice. The research is
being continued. The next generation of the PreBrains
should be able to learn the described behaviors. The
learning should be psychodynamic, i.e. reinforced bypleasure signals.

Acknowledgements

This research was supported by Gdansk
Artificial Brain Research Initiative (GABRI;
http://www.gabri.pl).
The authors would also
like to express their gratitude to Dr Andrzej Buller
for his great help in editing this paper.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Development of a Mobile Robot for Video and Thermal Monitoring of Railway
TunnelsN. M. Bykova

S. V. Yeliseev

A. A. Loukianov

A. P. Khomenko

bykova@iriit.irk.ru

eliseev s@rkt.iriit.irk.ru

aloukian@iriit.irk.ru

khomenko@rkt.iriit.irk.ru

Research and Development Department,
Irkutsk State University of Railway Transport,
664074, Chernyshevsky st. 15, Irkutsk, Russia.
Abstract

to the system (ultrasonic, radio-locating, etc.) andextend a set of data parameters to be monitored.
The Severo-Muisk railway tunnel is one of the mostcomplex railway tunnels in the world. Its peculiarity
is not only its large length (15,3km), the most laying
depth (1km) but as well extremely adverse geological, hydrological, seismic, and mining conditions of
the region where it is situated; moreover, there wereconsiderable problems and delays in the process of
its building. Dependable and safe exploitation of the
Severo-Muisk tunnel (SMT) largely depends on theeﬀectiveness of the measures on monitoring and assessing of its technical state [1]. One of the important
methods of monitoring of the state of the tunnel is
a visual examination, i.e. examination of state, defects and damages of its internal vault (destructions,corrosion, cracks, etc.) and rail way. Valuable information of the current state of the tunnel may be
got applying the thermal imaging monitoring in infrared frequency range, it allows to reveal humidiﬁcation and cracks with subterranean water leaking.
Also there is a problem that is extremely importantfor safe exploitation of the tunnel and it is monitoringof the drainage system state. The drainage system isdesigned to oﬀtake water that is one of the main factors of destruction. Drainage system monitoring aswell as revealing of its defects (leakings, wet spots)
can be carried out by both visual and thermal imaging examinations (monitoring of thermal springs). A
serious danger is interstices in the space behind thelining of the tunnel, they can be revealed by radiolocation approach.

The paper presents a project for development a
railway mobile robot for inspection and monitoring ofrailway tunnels. The mobile robot has to be equippedwith a number of sensors and instruments and a computer system to process and store collected sensory
information. The basic set of sensors includes video
cameras, infrared cameras, railway track inspection
sensors, positioning sensors. Additional sensors andinstruments can be installed. The paper discusses
the structural and hardware layout of the mobile
robot, the architecture of robot control system andsensory information processing and storage system.
The mobile robot functions, operation modes andcontrol principles are considered. The paper focuseson advantages of automatic inspection and monitoring compared to usual visual checkups performed bya human operator. A special emphasis is placed on
the problem of reliable mobile robot positioning inside the tunnel using visual information.
Keywords: mobile robot, image processing, monitoring, defect detection

1

Introduction

A joint use of video and thermo cameras when examining constructions and equipment allows not only
to control them in optical and infrared wave bands
but promotes automation of data processing of thermal imaging monitoring. This is particularly actualfor constant temperature images of lengthy constructions: tunnels, bridges, reservoirs, tubings, etc.
The present article considers the development anduse of automated monitoring and diagnostic system,
based on mobile rail robot, for automated technicalstate monitoring of railway tunnels, in particular the
Severo-Muisk railway tunnel. The considered system
is designed to collect and process large amount of sensory and measurement data when carrying out video
and thermal-imaging inspections in the tunnel. It is
possible to add additional sensors and instruments©ISAROB 2006

2

Mobile robot for monitoring

The suggested mobile robot (Fig. 1) represents
a moving (on the rail way) autonomous platform(truck) (1) equipped with a power unit (2) to set thetruck in motion and provide the installed equipmentwith energy. The equipment of the mobile robot includes: lighting equipment (3), video cameras (4),thermal imaging cameras (5), additional monitoring

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Figure 1: The scheme of proposed mobile robot
subsystems (6); a complex of subsystems: a) a computer vision subsystem, b) a measuring data logging
subsystem and c) the platform movement control subsystem (7); subsystems and transducers of the platform positioning (8).
The obtained data are bound by location with theuse of an odometer with periodical adjustment to external labels. The logged information is recorded inexternal media or processed in real time.

estimate of robot location can be obtained withthe help of platform wheel encoder sensors. The
accurate positioning is accomplished with thehelp of external labels on the tunnel surface or
railway crossties. We are developing electronic
counter device in order to use crossties for positioning purposes.
• Database for storing numerical results of image
processing, thermal images, monitoring protocols, and other data. The database is to store
the processing results with coordinate binding
to a deﬁnite section of the SMT.
• Digital archive of tunnel surface images.
• The platform movement control subsystems.
• Software for automated control of diagnostic mobile robot, system setup and diagnostics.
• Software for visualization, processing and analysis of collected sensory data, automatic report
generation.

3

Visual detection of defects of the tunnel lining. Detecting of defects in the images obtained by
the video subsystem is carried out by the computer
vision subsystem with the help of image processing
algorithms (pattern recognition). The image processing algorithms may be directed to detecting of typical
defects of the tunnel lining which can be detected visually. When a defect is detected the computer visionsubsystem saves its image in the archive and makesa corresponding record in the current inspection protocol.
Temporal visual monitoring of the tunnel
lining. In order to perform this task, primarily
the system saves the initial state of the lining in the
database in the form of coordinate-bound archive images and their processing results. These saved initial
data then are used as reference data to compare withthe data of the following video inspections. If there

Figure 2: Architecture of the system for sensory information processing
The automated monitoring and diagnostic system
has in its structure the following components (Fig. 2):• Video subsystems consisting of the set of video
cameras.
• Thermal imaging subsystem consisting of the setof thermal imagers.
• Computer vision subsystem processing the images, coming from the video cameras, in realtime.
• Subsystems for comparison and matching ofvideo and thermal images.
• The platform positioning subsystems allowing tobind the obtained information to the coordinatesof a monitored section of the SMT. Approximate©ISAROB 2006

Functions of mobile robot

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are any distinctions between the current and reference data the system makes a record in the protocolof video inspection (about the disparities found), also
an image is saved in the archive for visual comparison.
Temporal thermal monitoring of the tunnel
surface. The obtained thermal images are analyzed
and compared to the reference data in the database.
At the same time the thermal images are matched
to video images of the same scene. The temperature
anomalies are revealed by searching irregular temperature patterns in thermal images or by comparing
averaged measurements to the reference data or datafrom previous inspections. Discovered temperature
anomalies are saved in the database and logged in
the inspection protocol.
Collection of statistics. The repeating inspections will help to build a rich quantitative and qualitative statistics on tunnel lining conditions and theirchanges over tunnel life. The quantitative statistics
will be collected for numerical analysis results. The
qualitative statistics will be collected in form of digital images archive. The statistics will help to understand and predict the dynamics of the developmentof tunnel conditions changes and defects.
Monitoring of railway tracks. If proper instruments and sensors are installed on a mobile robot it
can be used to monitor the condition of railway tracks
inside the tunnel.
The functions of the considered system may be
expanded by installing additional sensors and instruments, namely: (a) laser rangeﬁnders for monitoringof the tunnel lining geometry; (b) radio-locating devices in order to detect water- or air-ﬁlled openings(interstices) in the space behind the tunnel lining.
The components and software of the monitoringand diagnostic mobile robot may become reference
prototypes for building the automated mobile robots
to monitor the state of other railway objects.

4– Multilevel monitoring of the SMT state.
At the ﬁrst level the monitoring of a limited set ofparameters is carried out by an accelerated technique,the sections for a complete monitoring are selected
automatically or semiautomatically according to theresults of this monitoring.
– Sampling monitoring of a number of the
SMT sections. The parameters and sections forsampling are determined by the monitoring program
or in a random way.
The proposed mobile robot and its information
processing system oﬀer the following advantages overmanual inspection procedures:• Increasing of productivity and improving thequality of monitoring procedures;• Automating monitoring procedures;• Recording of the initial conditions in the tunnel
and their temporal changes to give exhaustive
evaluation of the tunnel state at any moment;• Automatic collection of digital images archive forany section of the tunnel in order to help visualassessment of the lining state for any section of
the tunnel at any time.
• Building up of the statistics and dynamics of theparameter changes of the tunnel state during its
exploitation;• Collected statistics can be used to analyze, assess and predict tunnel conditions in order toimprove safety and perform damage and preventive repairs.
• Collected statistics can be used in future research and design works to build long railway
tunnels.

5

Robust and accurate positioning of the mobile
robot is one of the principal tasks of its control system. If this problem is not solved, the monitoringand diagnostic data collected by the robot cannot bebound to a concrete section of the tunnel. Positioning system based on wheel encoders and electronic
counter for crossties can fail or receive incorrect initial data. In order to increase robustness and fault
tolerance it is necessary to develop a robust method
for robot positioning that can recover the correct position autonomously.
This task may be solved with the help of computer
vision data. The robot’s position in the tunnel is deﬁned by distance x between the robot and one of thetunnel portals. In order to ﬁnd x it is suggested [2]to use one-dimensional particular case of probabilistic

Operation principles and the eﬃciency of the mobile robot

The suggested automated monitoring and diagnostic system will operate in one of the following modes:– Complete automated monitoring of the
SMT state along its whole length. The characteristic feature of this mode is the large amountof collected and processed data as well as the timeneeded for this operation since the inspection is carried out at slow speed.
– Complete automated monitoring of the
SMT carried out for specific sections of thetunnel. Transition of the mobile robot into a speciﬁed section is carried out automatically at full speed.

©ISAROB 2006

Robust and accurate mobile robot
positioning using computer vision
data

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6

approach of localization:x = H(P (x)),(1)

The subject of our paper is the development of
automated monitoring and diagnostic system on thebasis of a mobile robot moving on a railway tracks
and equipped with a set of sensors and instruments.
This mobile robot system is intended to monitor thestate of the Severo-Muisk railway tunnel. The structural layout, control principles and operating modes
are considered here. We suggest the scheme of a control system and the system architecture for sensor
information processing of the robot. A special attention is given to the system of the robot positioning in
the tunnel. We have succeeded in showing that theprobabilistic approach of localization with the use of
the computer vision data can be applied for solving
the problem of position estimation of the robot in thetunnel (in case of an error or failure).

where P (x), x ∈ X – probability density function; H(·) – statistical estimator function (herearg max P (x) can be used). Function P (x) is estimated as a conditional probability density function
P (x|a1 , . . . , aN , L1 , . . . , LN ) that depends on the
robot movements ai and computer vision data Li ,obtained at speciﬁc location in the tunnel. The computer vision data can be composed from the image
parameters and primitives extracted by image processing and pattern recognition algorithms [3].
After the platform makes a movement ak the probability density function P (x) is updated by the following recursive formula
P (xk |xk−1 ) P (x)k−1 dxk−1 , (2)P (x)k =X
where P (xk |xk−1 ) – conditional probability density
function for movement ak that changed the positionof the platform from the coordinate xk−1 to coordinate xk . This conditional PDF is calculated on basisof an error model for robot positioning system andsensors.
When the computer vision data Lk is received thefunction P (x) is changed by the following recursive
update formula
P (x)k = µ P (Lk |x) P (x)k−1 ,References[1] S.V. Eliseev et al, “Inspection Results and Expertise of Technical Condition of Severomuisk Railway Tonnel,” Technical report, Irkutsk State University of Railway Transport, Irkutsk, 2003.
[2] A.A. Loukianov, M. Sugisaka, “A Selective Vision and Landmark based Approach to Improve
the Eﬃciency of Position Probability Grid Localization,” Trans. of IEEJ Journal (C: Electronics, Information and Systems), Vol. 123, No. 4,
pp. 804-809, 2003.

(3)

where P (Lk |x) is the conditional probability density for obtaining vision data Lk at position x, µ =−1
P (x)k– normalizing multiplier. ConditionalX
PDF P (Lk |x) is derived from the results of the previous inspections, which are stored in the system
database. Expressions (2) and (3) imply that the
movements and observations correspond to Markov
assumptions, which are easily satisﬁed under conditions of a single-track tunnel.
The considered above method allows to solve a
problem of the global localization of the mobile robot
in the tunnel. It also allows to recover correct mobile
robot position after sensor system failure or incorrect
initial position data. But the accuracy of this method
is not so high.
For accurate positioning of the robot (position error within 4 to 7 cm) it is suggested by [4] to use
local positioning technique. This technique is basedon the visual servo control [5, 6]. Mismatching between coordinates of visual landmarks on the initial(made from the target robot position) and current
video images is used as an error signal for the servo
controller. In order to compare and match the visuallandmarks the probabilistic approach, based on [7],was proposed in [4].

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Conclusions[3] K.Rohr, “Landmark-Based Image Analysis: Using Geometric and Intensity Models,” Computational Imaging and Vision Series, Dordrecht:
Kluwer Academic Publishers, Vol. 21, 2001.
[4] A.A. Loukianov, “A Probabilistic Relaxation
Method for Visual Landmarks Matching in Accurate Positioning of Mobile Robots,” Int. Journalof Computer and Systems Science, No. 1, 2006.
[5] G.D. Hager, S. Hutchinson, P. Corke, “A Tutorial on Visual Servo Control,” IEEE Trans. on
Robotics and Automation, Vol. 12, No. 5, 1996.
[6] J. Chen, W. E. Dixon, D. M. Dawson,“Homography-based visual servo tracking control
of a wheeled mobile robot,” In Proc. Of IEEE
Conf. on Intelligent Robots and Systems, Vol. 2,
2003.
[7] W. J. Christmas, J. Kittler, M. Petrou, “Structural matching in computer vision using probabilistic relaxation,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, 1996.

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servos are used to impart motion to the robot. A computer isused for the visual display of the user program.
AC synchronous motors capable of generating a torqueof 10 kg-cm at 60 rpm are used to drive the wheels.
Specification of AC motor used for mobile base, arm base,arm shoulder are 230 V AC, 60 rpm, torque 7 kg-cm,
weight 1.5 kg, and the specifications of the elbow motor,
roll and gripper motor are DC Motor, 4 kg-cm torque, 0.5A
current rating, bi-directional, 60 r.p.m. The mobile AC
motors are mounted on the lower surface of the hylam sheetwith the help of brackets made from aluminum in order toreduce the weight [2].
The two wheels of diameter 12 cms, plastic made witha rubber tyre over it is coupled to the main driving motors
using a chain of gearing systems and supported between
two ball bearings. The motors are coupled to the wheels
using aluminum shafts via the spur gears of 12 and 48
teeth’s. Since the hylam base has to carry the entire weightof the robot, it was necessary for us to increase thetorque rating at the wheel shafts. This was achieved byusing spur hard nylon gears of 1:4 ratios. The smaller gear
attached to motor has 12 teeth’s and the bigger gear
attached to the wheel has 48 teeth’s. The two driving
wheels of 10 inches are placed along the diameter ofthe circular hylam sheet below it . The rubber tyre
helps the robot better gripping of the surface.

Fig. 2 : A pictorial view of the designed & fabricatedmobile OCTAGON
Shoulder assembly : The shaft is fitted with a pair ofparallel aluminum plates that move along with the shaft.
The motion is actuated by a very compact mechanism ofworm and worm wheel. The double helical worm is fitted
on the motor shaft, while the worm wheel is fitted on theshaft, which provides elbow rotation. The speed ratio of thisarrangement is 1:15, that is, the worm wheel makes one
complete revolution when the worm makes 15 revolutions.
The motor is fixed on the circular aluminum plate on thebase assembly. To shoulder axis, 1st link of length 250mm
is attached. Limitation of the shoulder motion is < 90°.

Two castor wheels are used as guiders, one at the front
and the other at the back. A 1:4 spur gear arrangement isused for reducing the speed to 15 rpm and increases thetorque by 40 kg-cm at the wheels. The arm mounted on thetrolley has a 4 DOF and consists of arm base; arm shoulder,arm elbow and the arm roll motion with the grip motion.
Worm and worm wheel arrangement is used for theshoulder and elbow joints because even when the arm is in
the intermediate position, it won’t fall down and thus acts alocking mechanism. The fabricated length of the upper arm
and the forearm is 24 cms and 20 cms.
Base assembly of the 3 axis robot arm is slightly off
center of the octagonal acrylic platform. It consists of
two small circular plates one above the other (bottom plate
being fixed and the upper one rotating w.r.t. thebottom) with a thrust bearing in between them toprovide a smooth base rotation of 300° either in theclockwise or in the anticlockwise direction. A big AC
synchronous motor is attached at the bottom of the acrylic
platform. A worm and a worm wheel assembly of 1:30 ratio
is keyed to the motor shaft in the horizontal plane. A central
rotating shaft is attached to the big rotating worm wheel.
This shaft is extended from the bottom of the platform to
the top circular plate and is welded at the center. Limitswitches may be used at the extreme ends of the double
plates. The entire arm (with the shoulder assembly, elbow
assembly and the gripper assembly) is mounted on theupper rotating plate [3].

©ISAROB 2006

Elbow joint motion : The motion is actuated by a very
compact mechanism of worm and worm wheel. The double
helical worm is fitted on the motor shaft, while the worm
wheel is fitted on the shaft, which provides elbow rotation.
The speed ratio of this arrangement is 1:30, that is, theworm wheel makes one complete revolution when theworm makes 30 revolutions. The motor is fixed on thelower plate of the inner arm. The position of the motor is so
adjusted that it does not obstruct the rotation of the outerarm. The plates of both the arms are coupled to the shaft
with the outer arm plates inside the inner arm plates. The
shaft moves in a pair of Brass bushes in the inner arm
plates. Contact between the two plates is avoided by a pairof thrust bearings between them. The upper plate of theouter arm is pinned to the shaft motor. Actuating the motion
is a AC motor; small in size with considerable torque andspeed. The motor is light in weight (200 gm) and easy tocontrol, thus making it an ideal choice for actuation.
Limitation of the elbow motion is 120°.
Gripper : The robot being a PNP-type has a gripper as theend-effector (Fig. 3). The gripper is of parallel jaw type,which works on the principle of left-hand / right-handscrew. These 2 jaws run on the LH / RH screw. The LH /RH screw is made by tapping a brass rod with LH die fromone end and RH die from other end. This ensures that thegripper jaws move in the opposite direction, that is, the jaws

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to the PC through the parallel port using the RS232C. The
program sends the byte combination to energize the desired
motors, which reaches the driver circuit through the 3-meterdata cable to the input of the driver circuit. The i/p section
has also a T T L short circuit prevention logic. This circuit
is employed to ensure that the motor driver IC does notreceive a logic 1 signal on both the CW / CCW inputs
which may cause the motors to get stalled. The L293D has
2 direction inputs for each motor, which should be activatedexclusively and not simultaneously. The TTL short circuit
prevention logic makes sure that if both signals (CW /CCW) for a motor are high simultaneously then input to thedriver on both the direction lines will be Logic 0. The ExOR gate output will be logic 1 only if one of the inputs is
Logic 1. Output is then logically ANDed with the 2 inputs
to yield the final outputs for CW (clockwise) and CCW.
The input connector is used to connect any input signal to
be fed back to the PC from the driver circuit. It sends themto the PC via the unused lines of the parallel port. We use 3
lines from the status port of the parallel port connector as
feedback signals to indicate Base Reset position, Elbow
Reset position and Gripper status (limit switch) [5].

move either towards each other to grip an object or awayfrom each other to release it. The LH / RH screw is coupled
to a small DC tape motor shaft ( 12V, 1A) via a bevel gear
arrangement in the ratio 1:1 as shown in the Image. One
limit switches are placed at the inner ends of thefinger. This limit switch is connected to the controllercard and is used to prevent the further closing of thefingers after the object has been gripper properly.
Another limit switch is attached to the extreme end of
the maximum opening of the gripper which is also
connected to the controller card. The maximum openingof the gripper is 75 mm [4].

Fig. 3. Designed gripper and motor mounting

3. Electronics Design

To all motors

The mechanical set up forms the skeleton of the robot
and what adds intelligence to it is the electronics and thesoftware module. The block diagram of the designed
robotic control system is shown in Fig. 4. The electronic
design is divided into various sections and finally, theintegration is done. Electronic system consists of various
cards like the power supply card, controller card, and drivercard, interfacing card, sensing and the feedback cards. The
software module is designed for maximum robot control &working efficiency in a user-friendly java environment. It isso designed that the user can have complete control overeach movable part of the robot. Also the user can easily
maneuver the robot & make it traverse a path towards theobject to be picked. He / she can then manipulate thedifferent limbs of the robotic arm so that the gripper comescloser to the object & eventually picks it up. Throughout
this process the software interface guides the user throughthe usage of various parts of the robot & provides him withresponses from the robot. The robot is interfaced to thecomputer using the parallel port. If there are obstacles in
the path of motion of the robot, then the software takes careof this by initiating an alternative path around the obstacles
making sure that the robot reaches the desired destination.
The design of the software is kept modular and structured
properly. The robot can be controlled in 18 modes as given
in Table 1. The various electronic circuitries designed are

Limit
Switches

Motor
DriverCard

Interfacing
Circuitry

UltrasonicSensors

Controller
Card

RemoteControl
With
Interface

ADC Cards

Keyboard
Potentiometers

Power
SupplyFig. 4 : Designed and fabricated interfacing card
Output section : consists of the dual motor driver IC
L293D, a quad push-pull driver capable of delivering
output currents of up to 1A or 600mA per channel
respectively. Each channel is controlled by a TTLcompatible logic input and each pair of drivers (a fullbridge) is equipped with an inhibit input which turns off all
four transistors. A separate supply input is provided for the
logic so that it may be run off a lower voltage to reduce
dissipation. Additionally, the L293D includes the output
clamping diodes within the IC for complete interfacing withinductive loads. Both devices are available in 16-pin
Batwing DIP packages. They are also available in PowerS0IC and Hermetic DIL packages. The enable lines for the
ICs are kept at logic 1 to enable all the ICs. The outputs of
the ICs are given to diode bridges, which are used tointerface to inductive loads [6]. The motors are connectedacross the bridges. Electronic views are shown in Fig. 5.

Motor driver circuit : is the interface medium between the
PC and the Robot as shown in Fig. 4. It is designed using
IC’s L293D and consists of an :Input section : in the motor driver circuit which consists ofa 25 pin D male connector which connects the driver circuit©ISAROB 2006

Display
Card

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Power supply circuit : is designed to give a 12V output at
5A and 5V at 1A. A 5V / 12V supply is chosen because thecumulative current drawn when all the motors are energizedis around 3A. Thus as a safety measure, a 5A supply is
selected. The power supply consists of the step downtransformer, the filter circuit, the bridge rectifier and theregulator circuit. A fuse of 5 A is used to protect the circuitfrom an overload of current drawn from the mains. The
power supply unit consists of two circuits namely, a 5V /1A supply employing a 78L05 IC regulator and a 12V / 5A
supply employing a 338K IC regulator [7].

Fig. 6 : Java based graphical user interface formaneuvering the robot

5. Conclusions
A mobile octagon with a 4 axes robot arm wasmodeled, simulated, designed, fabricated indigenously withthe local available components and successfully
implemented and is in operation. The mechanical assembly
was divided into 3 units, viz., base, arm and the endeffector assembly. The electronic section was done in 3
stages, viz., power supply, controller and the driver unit.
The robot was controlled using a GUI developed in JAVAin various modes. A brief kinematic analysis of the robot
was also carried out. A number of pick and place operations
were successfully performed by the developed robot byusing teaching mode, manual mode and programming
modes.

Fig. 5 Various views of the robot / electronics
MobileArm

Forward

Arm base clockwise

Backward

Arm base anticlockwise

Forward Left

Arm shoulder up

Forward Right

Arm shoulder down

Backward Left

Arm elbow up

Backward Right

Arm elbow down

Spin Clockwise

Arm roll clockwise

Spin Anti-clockwise

Arm roll counter-clockwise

References[1]. Robert, J. S., “Fundamentals of Robotics : Analysis
and Control”, PHI, New Delhi.
[2]. Klafter, Thomas and Negin, “Robotic Engineering”
PHI, New Delhi.
[3]. Fu , Gonzalez and Lee, “Robotics : Control , Sensing ,Vision and Intelligence”, McGraw Hill.
[4]. Ranky, P. G., C. Y. Ho, “Robot Modeling, Control &Applications”, IFS Publishers, Springer, UK.
[5]. T.C.Manjunath (2005), “Fundamentals of Robotics”,
Nandu Publishers, 3rd Revised Edition, Mumbai.
[6]. T.C.Manjunath (2005), “Fast Track To Robotics”,
Nandu Publishers, 2nd Edition, Mumbai.
[7]. Ranky, P. G., C. Y. Ho, “Robot Modeling, Control &Applications”, IFS Publishers, Springer, UK.
[8]. Groover , Weiss , Nagel and Odrey, “Industrial
Robotics”, McGraw Hill.

Arm gripper open
Arm gripper close
Table 1 : Table to show 18 motions of OCTAGON

4. 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. 6. The softwaremodule application facilitates the user interaction with
OCTAGON and has many in built features [8].

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Designing the robotic arm using stepper motors.
This phase includes the interfacing of a pair of
stepper motors with the computer to allow for a two-axis
motion of an artificial arm so as to perform tasks, which
are out of the reach of the individual.

• The workspace geometry of the arm. (i.e., theworkspace allowed to the arm due to the close
proximity with the human operator.
• The load bearing capacity of the actuator.
• The environment in which the arm is subject tooperate.
• The budget of the individual.

Creating a Front-End software interface or GUI.
The third and final phase of the project includes thecreation and implementation of a user-friendly, pointingdevice compatible (mouse operable), GUI - Graphical
User Interface. By far the most challenging part of thiswhole undertaking has been to get the devices to work
upon interfacing with the computer. The Visual C codesused for development and the preliminary failures during
the course of this project were a challenge in itself [2].

It would be fair to say that depending upon thecustom requirements of each individual, the robotic arm
may be modified to suit the needs of the person. The
arm needs to be made sturdy so as to be able to handle
the rigorous everyday chores put to it by the user. The
actuators need to have a long life and need to be able tohandle worst-case scenario handling conditions.
Therefore, the materials used to construct the arm need
to be able to handle large loads without shearing.
However, for building our prototype, we have made useof a lightweight, sturdy material that is equally effective.
First, corresponding to the design, a 1:1 template istraced onto the material and the material is cut at thesetraced edges. The cutout parts are then attached together
in their specific sequence so as to create the individual
sections of the arm. Starting from the base each section
is attached to its corresponding sections using therequired joint structure. The completed frame of therobot is now ready to be fitted with the actuators.
The actuators are attached to the arm in such a waythat they do not hinder the motion of the arm itself. For
this purpose, generally they are placed away from thework envelope of the arm itself. Moreover, the actuatorsneed to be powerful enough to be able to drive the arm
without causing any backlash or slip. This needs to betaken care of especially because we do not want the arm
to slip from its position while lifting a heavy load. The
actuators we have used are of the digital type (Stepper
Motors X2). Thus, a suitable drive mechanism needs tobe implemented to run the arm itself [2].

The Front-End Software has been designed using
the Microsoft Visual Studio©. Using Visual C++, which
is one of the most powerful developer tools available inthe market today, we were able to create a good-looking
Graphical User Interface (GUI) to allow a user tointeract with both : The Robotic Arm & The Relay
Controller simultaneously. This was a challenge in itself,
as initially we were finding new ways to be able tomultiplex the computer signals so as to operate both ofthe circuits at once. The front-end software program
executable called “ROBO.EXE” is used to run theinterfaced devices. A detailed view followed by a brief
description of each function of the interface follows suit.

4. Mechanical design of the robot arm
As mentioned earlier, the robotic arm is made out of
a compressed fibrous plastic material and is popularly
known as foam-board. The foam-board used for ourdesigning purpose was 3 mm thick. The whole processof creating the robotic arm involves the following steps :First, based on the requirements of the process thatthe arm is going to be used for, the arm is designed on
paper and also using some CAD (Computer Assisted
Design) software. The arm needs to be simulated, itstrajectories need to be calculated, its work-envelope
geometry needs to be thought out before actually startingto build the arm. Why this needs to be stressed isbecause, we need to design the arm keeping in mind theneeds of a disabled individual. What he / she can andcannot do. For the purpose of simplicity however, wehave kept the design of the robotic arm as minimal andsimple as possible. The following points need to be kept
in mind while designing the robotic arm:

The robotic arm is made out of a compressed
fibrous plastic material and is popularly known as foamboard. The foam-board used for our designing purpose
was 3mm thick. An important feature of the arm thatmight not appear at first sight is the mutual coupling of
two joints; namely the shoulder and the wrist. The
shoulder and wrist joints are mutually coupled by aparallel bar arrangement so as to provide compliant
motion to the gripper when it is lifting or placing levelsensitive objects like a glass of water or the like.
Thus, this eliminates the need to have sophisticatedtrajectory calculation for the wrist and also the need toplace a heavy motor at that point. The material used forconstructing the robotic arm needed to be light anddurable. The most suitable material we could find forthis purpose was a fibrous plastic created by compacting
a resin called “foam-board”. This foam board can beeasily cut and pasted like ordinary cardboard but has thestrength of a hard plastic. Therefore it can withstand a lotof shear-force as compared to cardboard.

• The specific disability / disabilities of the individual.
(The arm may be customized according to thedisability of the individual.)• The requirements and purposes the arm must be able
to fulfill. (i.e., the maximum and minimum reach of
the arm.)• The tasks that need to be performed by the arm. (suchas lifting an object, or stirring a spoon, etc.)©ISAROB 2006

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It is clear from the schematic shown in the GUI thatthe robotic arm has three degrees of freedom from just
two axes. The two axes being the rotary axis passing
through the base’ revolute joint and that passing throughthe shoulder’s revolute joint. Both the axes are rotationaland hence this robotic arm can also be described as an
RR-Robotic arm. The material used for constructing therobotic arm needed to be light and durable. The mostsuitable material we could find for this purpose was afibrous plastic created by compacting a resin called“foam-board”. This foam board can be easily cut andpasted like ordinary cardboard but has the strength of a
hard plastic. Therefore it can withstand a lot of shearforce as compared to cardboard [3].
An important feature of the arm that might notappear at first sight is the mutual coupling of two joints;
namely the shoulder and the wrist. The shoulder andwrist joints are mutually coupled by a parallel bar
arrangement so as to provide compliant motion to thegripper when it is lifting or placing level-sensitive
objects like a glass of water or the like. Thus thiseliminates the need to have a sophisticated trajectory
calculation for the wrist and also the need to place aheavy motor at that point. The arm is controlled with thehelp of a pair of stepper motors, one each for the baseand the shoulder. The base motor has its axis vertical andis attached directly into the base, while the shoulder
motor has its axis horizontal and is attached just below
the shoulder joint.
VDD

The performance of the controller depends on theproper functioning of the power supply unit. The powersupply not only converts A.C into D.C, but also provides
output voltage of 5V, 1A DC to run the motors. Notethat the relays operate directly on the 12V supply. The
essential components of the power supply aretransformer, rectifier, filters and regulators (Fig. 3).

6. Interfacing unit
The robot is interfaced to the computer’s parallel
port using a interfacing device. The original IBM-PC’sParallel Printer Port had a total of 12 digital outputs and5 digital inputs accessed via 3 consecutive 8-bit ports inthe processor's I/O space. The ports have been colorcoded for ease of reference. The Blue pins refer to the
Output ports, while the Red pins refer to the input ports.
The Green pins denote ground.
• 8 output pins accessed via the DATA Port .
• 5 input pins (one inverted) accessed via the
STATUS Port .
• 4 output pins (three inverted) accessed via the
CONTROL Port .
• The remaining 8 pins are grounded.

VCC

IN

GND

U3
1

5. Design of the power supply

OUT

3

D3
2

D3
J1
2
1

+

+
C1

C1

C2

C2

Fig. 4. Parallel port
D3

D3

7. Relay controller / stepper controller

VDD

J1

2
1

J1

2
1

VDD

J1

S1
S2
S3
S4

TO MOTOR
1

2
1

TO MOTOR
2

J1

The control circuit used for controlling the roboticarm is one of the most intriguing and complex parts ofthis entire undertaking. The task facing the designer wasto create a circuit, which would perform a two-fold task
for serving the purpose. Firstly, it needed to isolate thelow-power output-port signals of the LPT1 port.
Secondly, it needed to be able to drive the motor after
this isolation, which would definitely require a complex
switching mechanism. A schematic of the control circuit
is shown in Fig. 5. Here too, the signals arriving fromthe port are first isolated to disallow any stray voltages
or currents from passing back to the sensitive computer
port [3].

2
1

J1

2
1

J1

2
1

S5
S6

S7
S8

Fig. 3. Power supply circuitry
It was possible to use ordinary DC motors for thispurpose with a suitable reduction ratio. But, that would
greatly diminish the precision of the arm. Moreover, toavoid subjecting the arm to sudden strains or evenlocking due to limited motion of the joints, it was
decided to use stepper motors instead. However, we domake use of a DC motor for the gripper, which is being
controlled by a relay.

©ISAROB 2006

These isolated signals are then given a driving
capability using a buffer. Subsequently, the bufferedsignals move on to the GATE terminal of an N-channel
MOSFET. On receiving the requisite level of GATE
voltage, the NMOS switches ON thus allowing the drive
to that particular winding of the stepper motor.
However, since the signal arriving from the port is in the717

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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version of the circuit performed outstandingly due to its
much more simpler and rugged design.
Another modification was that of the software used
for the switching purpose. The native C-language
initially used, was scrapped. Instead, modules of thesoftware were created in Visual C++, which were thenlinked in a compiler and run in compatibility mode withthe operating system. Each module contained reference
to various classes used mainly for designing the frontend of the interface. The schematic below shows thecircuit used for relay switching:form of a square pulse, so too at the output of the NMOS
we would observe a square pulse but of a higher driving
capability. Now, these square pulses arrive in succession
to the coils of the stepper motor, thus rotating the motor
depending upon the sequence of their excitation.
The Relay Controller is intended for use mainly inswitching applications. Initially, there were plans tomake use of a series of 8 relays dedicated for switching
ON / OFF the various devices, but since the robotic arm
was making use of the same parallel port LPT1, asolution had to be found to multiplex the relay control
signals as well as the robotic arm control signals. This
was achieved by cutting down the required number ofrelays to 4. Moreover, the initial circuit proposed wasthat of a logical device controller, which made use of anarray of combinational logic devices to switch the relays,
as well as to sense the present condition of the relay(whether the device is actually ON or OFF). This idea
was abandoned after the failure of the design during
testing phase [4].

Fig. 4 Relay controller / switching circuit
The switching circuit is basically used for switching
ON / OFF those devices that are connected to the supply
through the relay. When the relay switches ON, thedevice will obtain the required drive and will thus switch
ON. The relays used for this project have a rating of 12V/ 200 Ω. A transistor BC548 controls the relays, which
perform the switching depending upon the BASE drive
they are given. The resistors are used for biasing thetransistor and also limiting the current to the LED, whichdisplays the status of the relay. A flywheel diode is used
to dissipate the residual EMF present in the relay coils.
A supply of 12V DC is given to the P-terminal of
the LED, while the loose ends of the biasing resistors areconnected to ground. Now, when an ON signal arrives
from the port pin to the BASE of the transistor, the NPNtransistor tends to switch ON allowing the supply current
to flow through it. This supply current in-turn energizes
the plunger-coil of the relay. When the plunger-coil isenergized, the electro-magnetic coil forces the plunger toswitch from its N/C terminal to its N/O terminal. This inturn allows the supply to be given to the device, which isconnected between the terminals of the relay. The device
thus switches ON [4].

8. Conclusions
The design and implementation of a automatedblind navigator for the physically handicapped persons
using mobile robots is presented in this paper.
REFERENCES[1] Robert Schilling, Fundamentals of robotics, PHI.
[2] Hodges Bernard, Industrial Robotics, Jaico Pub.
[3] T.C.Manjunath (2005), Fundamentals of Robotics,
3rd edition, Nandu Publication, Mumbai, India.
[4] Fu, Gonzales, Lee, Robotics-Control Sensing &Vision, Mcgraw Hill.

Fig. 5 Stepper motor controller circuit diagram
However, a new and improved version of the circuit
was developed, to ensure fail-safe operation even in
extreme conditions. Where the previous circuit failed tooperate due to its complex logic involved, the new©ISAROB 2006

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chassis) and soldered to the ground plate. The ballbearing we used first was replaced by a cloth hanger.
This also led to better steering of the micromouse
because of the reduced weight in the front .

3. CONSTRUCTION IDEAS
Two complete different ways of designing the
micromouse are possible. One is the conventional
analogue method with comparators and analogue
control of the motors. The second, the digital way caneither be realized by using digital logic devices or byusing a microprocessor. The opportunity of using a
microprocessor is the easy and convenient adjustmentof the software for different tasks and to increase theoverall speed without any change of hardware or thetrial and error method with a lot of variable resistors.
Therefore, we used a microcontroller, which isvery fast in operation and because of its reduced
instruction set easy to handle. The hardware
architecture allows execution of one instruction in only
one processor cycle, because the data and the address
bus are completely separated. Also, input and output of
the microcontroller are very easy to use, because they
can be directly accessed by the program. The motor
control will be realized with two half bridges, whichcontain each 2 logic level MOSFET’s one for runningand one for braking. These transistors can be directly
driven by CMOS-Logic devises and are capable ofhigh voltages and high current.

4. Stepper Motor
A stepper, or stepping motor converts electronic
pulses into proportionate mechanical movement. Eachrevolution of the stepper motor’s shaft is made up ofa series of discrete individual steps. A step is definedas the angular rotation produced by the output shaft
each time the motor receives a step pulse. Thesetypes of motors are very popular in digital control
circuits, such as robotics, because they are ideallysuited for receiving digital pulses for step control.
Each step causes the shaft to rotate a certain numberof degrees.
A step angle represents the rotation of the output
shaft caused by each step, measured in degrees. Fig. 3
below illustrates a simple application for a stepper
motor used. Each time the controller receives an input
signal, the motor is driven a certain incremental
distance. Like in addition to the drive mechanism in a
printer, stepper motors are also popular in machine
tools, process control systems, tape and disk drive
systems, and programmable controllers. The mostpopular types of stepper motors are permanent magnet(PM) and variable reluctance (VR).

Driving backwards was thought not to be necessaryduring the design process. For wall detecting, pulsed
infrared sensors are used. The pulsing of the sensors
increases their working distance enormously andmakes variable resistors unnecessary. With thesesensors, it is also possible to read out wheel
reflectors. We tried to built up the micromouse ascompact and light as possible for having sufficientspace for turning and running the micromouse
between the walls of the maze. Reduction of weight
results in a better mobility.

Fig. 3 Stepping motor (CW / CCW) used in our work
Fig. 3 above shows a permanent magnet stepper
motor with four stator windings. By pulsing the stator
coils in a desired sequence, it is possible to control
the speed and direction of the motor. The figure also
shows the timing diagram for the pulses required torotate the PM stepper motor. This sequence of
positive and negative pulses causes the motor shaft torotate counterclockwise in 90° steps. The waveformsof Fig. 3 above illustrate shows how the pulses can beoverlapped and the motor made to rotate
counterclockwise at 45° intervals.
The direction of rotation is determined byapplying the pulses to either the clockwise or counterclockwise drive circuits. Rotor displacement can bevery accurately repeated with each succeeding pulse.
Stepping motors are generally operated without
feedback, which simplifies the control circuit
considerably. One of the most common stepper motor
drive circuits is the unipolar drive, shown in Fig. 3.
This circuit uses bifilar windings and four Darlington
transistors to control the direction of rotation and the
stepping rate of the motor.

Fig. 2 : Mouse layout - Top view
Mechanical Construction
From the Fig. 2, it can be seen that the
gearboxes are mounted as close as possible to reduce
the size of the chassis. Everything on the chassis was
mounted in a way that we could get as close as
possible to the maze ground to get the centre ofgravity down. For minimum weight the sensor arms
are build out of epoxy (the same material as the©ISAROB 2006

5. BLOCK DIAGRAM
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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
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This module uses the technique of On-Off keyed
modulation. Local Oscillator is based on PLL. The module
is high performance, simple to use and miniaturized.

• As shown in the block diagram port 0 is connected to IR
sensor module through the Schmitt trigger 74HCT14
which is used for the TTL compatibility.
• The Port 1 is also connected to the IR sensor module
through the Schmitt trigger.
• Port 2 is connected to the RF communication module• Port 3 is connected to the Stepper Motor throughULN2803 which is used as a current boosters

SCHMITT TRIGGER :It is used to provide TTL compatibility it eliminates
noise. It provides 6 inverting buffers with Schmitt trigger
action. They are capable of transforming slowly changing
input signal into sharply defined distortion free output
signals.
ULN2803 :It is a current amplifier. The required current rating
for stepper motor is 0.7 ampere per phase. One ULN cangive current rating of 0.22 to 0.28 amperes. Thus to get therequired rating of 0.7 amp we are using three ULN which
are mounted one over the other. ULN2803 is a 18 pin IC
which provides current stability and is easily available.
SENSORSThe Fig. 6 shows the circuit diagram of one of thesensors. The IR-Diode is pulsed by the MOSFET (small
duration pulses are given). When the IR-Transistor detects
reflected light (which means a wall), the input of thecomparator LM339 (very high current stability & acting as
a comparator ) is pulled to ground and at the output of thecomparator occurs a high impulse. The 100nF coupling
capacitor blocks DC and low frequent signals. Therefore,
ambient light cannot interfere. We are using common
anode sensors. These are infrared sensors. The number of
the IR sensor is TSOP series IR DIODES. Voltage ratingof IR sensor is 3.3 volt. We are using these sensors
because it has affordable cost with good sensitivity.
Actually, we need accuracy and not larger range i.e. thesensor should not sense anything beyond 2.5 cm range.

Fig. 4 Block diagram of the system
Components List :89C51RD2 - (2 in number)
ULN2803 - (6 in number)
Schmitt Trigger - 74HCT14 - (4 in number)
IR Sensors Module - (24 in number)
Transmitter Module - (24 in number)Resistances - 1K & 5K
Stepper Motor (MACT) - (2 in number)
9 Voltage battery
Microcontroller (89C51RD2)
For controlling the micromice, we are using
microcontroller, i.e. 89C51RD2 one for each micromice.
89C51RD2 is a high performance CMOS flash version of
the 80C51 CMOS single chip 8-bit microcontroller. It
contains a 64k byte flash memory block for code and fordata. The 64kb flash memory can be program either inparallel mode or serial mode with the ISP capability orwith the software. The programming voltage is internallygenerated from the standard Vcc pin.

Arrangement of sensors:
12 sensors are used in one micromice .two are at thefront side, 5 for left stepper motor and 5 for right stepper
motor. Out of this 5 sensors the central one is used for theright or left turn. The other two sensors besides the center
one are used to control the straight. The extreme two
sensors normally not used and are there for corner point
detection.

Here, we are using all the 4 ports of the controller.
• For programming we assert the pin no 29 low.
• External crystal is connected to the pins 18 and 19.
• Pins 10 and 11 are used to program the controller
serially into the flash RAM which is 64K bytes.
• To reset the system we give high voltage to the pin 9.

Communication Module :The communication between the two mouse isachieved through a Crystal Tuned PLL Based ASK
Module.
+ 6V Battery

Block Diagram :3 x 4.7k

1k

4.7k

Controller
input

FET OP02

100 nf

26

GND Battery

Fig. 6. Schematics of one Sensor-stage

6. The Software Design

Fig. 5 Overall block diagram of the system

The software for the vehicle must be able todrive the mouse straight ahead and to turn right

Crystal Tuned PLL Based ASK Module:©ISAROB 2006

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around the corner. For driving straight ahead thesoftware has to control the motors in an suitable way.
Therefore a PWM (Pulse Width Modulation) with aresolution of 16 bits for each wheel was developed.
The program has to check the sensors in order tocontrol the vehicle by detecting the walls. During theinitialization the I/P’s and O/P’s are set. Also, all theneeded variables are presetted or cleared and the ISR(Interrupt Service Routine) for PWM is started. Thenthe program waits for the START button (Input RA2of the controller to be pressed. After that the
microcontroller drives the vehicle along the wall, until
no sensor has wall detection. Then, the TURN
subroutine is started and the wallcounter decreased byone. The mouse stops and if not all walls are done,
the mouse starts with the next straight driving
process.

used by us for the downloading of the program that isstored in the computer in to the micro controller throughthe bus called RS232. The screen shot of the software is asshown in Fig. 1.
7. WORKING OF THE SYSTEM
Micromouse One :The first micromouse is designed to since themagnetic ink path and propagate along the same. The
micromouse is composed of three sensors, viz. central,right, and left. As long as the central sensor isassorted the micromouse will propagate straight line
path. If any one of the right or left sensors getsasserted the respective rotational motion will be
initiated at the directive stepper motor so that themouse will take turn. As soon as the left / rightsensors gets disserted the mouse will continue thestraight line path. The action and motion of the firstmouse is transmitted using transmitting mechanism.

The ISR is called with every timer overflow. At
first a helping counter is increased. The ISR checks
now, if the desired PWM value is higher or less thanthe helping counter. If the desired PWM value isless, the appropriate PWM output is set to low andotherwise to high level. With every call of the ISR
the timer has to be presetted again. The above
diagram shows in an elegant way, how the PWM
actually works. The bits Q1 .. .. Q4 represent thecorresponding bits of the PWM helping counter. The
ISR compares them with the desired PWM andswitches the outputs high or low, depending on the
result of the comparison.

Micromouse Two :Micromouse two receives the signals forwarded
by micromouse one. Micromouse two detects theshorts path towards micromouse one by reading theposition of micromouse one and traverse it.
Micromouse two is well equipped with the wall
sensors with the help of wall sensors directing path
to it self.

8. Conclusions
Overall, it was felt that the project was successful
in all areas; mechanics, electronics and software. All the
primary aims were achieved and in addition the majorityof the secondary aims were achieved. The project tackled
new areas of design that required innovative ideas.. Itsconstruction has proved the design concepts used and withsome minor adjustments and improvements, these could
be taken forward to produce an even better test bed. A
possible extension to this project has been proposed andconcerns the processing. An application was consideredusing the designed micromouse.

Driving along the wall :Depending on the sensors, the mouse has either
to drive straight on or to steer left or right. The
control algorithm is a modified bang-bang mechanism.
There are 5 different stages :Stage 1: Only mid sensor has contact - drive ahead.
Stage 2: Mid and right sensor has contact - drive
slightly right.
Stage 3: Only right sensor has contact - drive rightStage 4: Mid and left sensor has contact - drive
slightly left.
Stage 5: Only left sensor has contact - drive left.

APPLICATION :The concept of our project was implemented for the
purpose of identifying vehicles over a decided territory.
This particular project developed as per the requirement
put forward by the MUMBAI traffic police department
hence the application has to be enhanced to the realistic
implementation. As far as the implementation is concernedan ordinance has to be established where in all vehicles
should be equipped through the identity transmitting
module. The reception can be established through cells
defined and established by private and government cellularcommunication provider. Since it is in the public interest
the police department is into process to sanction an
ordinance and by 2010 the ordinance will be implemented
in full proof manner.

When none of the sensors have contact, a gap
counter is decreased and when this counter is equal
to zero, the mouse starts the stop and then theturning process. The turning process is a very simple
subroutine, which brakes the right wheel and turns
with the left one around, until the first (the right)
wall-sensor has contact. Due to the force of inertia
and the wheel-slip the vehicle turns a bit further andthe as next started straight driving routine adjusts the
car again straight towards the wall. The Maze Logic is
resolved using the matrix positional coordinates.
Two types of software’s design
KEIL - has simulator which contain functionally richlibrary and good support and compatibility with 89C51.
CX51 provides the flexibility of programming in C and thecode efficiency and speed of assembly language. It is
ground up implementation dedicated to generating
extremely fast and compact code for the 8051
microprocessor. FLASH- FLASH is the software that is©ISAROB 2006

References[1]. Ayala, K.J. : The 8051 Microcontroller :Architecture, Programming and Applications, 2nd
Edition. Minneapolis: West Publishing Company,
1997.Calcutta.
[2]. Hughes, F.W. Op Amp Handbook. 1st Edition.
Englewood Cliffs: Prentice-Hall Inc., 1981.
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Design Of A Intelligent Sensing Interface For
A Flexible Manufacturing System
T.C. Manjunath
Research Scholar, IEEE / SPIE / IOP / ISSS Student member,
101 B, Interdisciplinary Programme in Systems and Control Engineering, ACRE Building,Indian Institute of Technology Bombay, Powai, Mumbai-400076, Maharashtra, India.
Email : tcmanju@sc.iitb.ac.in ; Phone : +91 22 25780263 ; FAX : +91 22 25720057
The paper is organized as follows. Section 2
gives a brief introduction to the input section of thesensors. Section 3 discusses about the infra-red
detection circuitry. In section 4 , the sensor block –diagram interface to the robotic system and to thecomputer is presented. Working of the comparator
circuit and the limit switches is discussed in section
5. The obstacle avoidance algorithm is presented in
section 6. Ultrasonic sensing system design is given
in section 7.
The paper concludes with theconclusions in section 8 followed by the references.

Abstract
This paper deals with the design, fabrication andimplementation of a sensor interface for a Flexible
Manufacturing System (FMS) which is capable ofsensing the dead ends of the rail track on which themobile robot system is moving and does somemanipulation task. A provision for sensing the limits of
the joints, the hard home position of the flexible
manufacturing system is designed and developed as aproject work. The developed sensor system consists oflimit switch sensing circuit, a reset detection circuitry forthe mobile flexible manufacturing system, a infra-red
detection circuitry and a ultrasonic obstacle avoidance
circuitry, thus making the system a closed loop control
system.

2. Input section
The input card is used to give inputs to computerfrom robot. The inputs to computer are actually the statusof various limit switches, IR detectors circuit to takeappropriate action to stop further movement of motor andrather stop at that particular position. The input cardcomposes of various IC’s like LM324, 74LS76, 74LS245
and transistor, resistor and LED [2]. Transistors andLED’s are used to indicate weather desired position isreached or not. In our circuit, we have used the limit
switches for

Keywords : Flexible manufacturing system, Limitswitches, Computer, Infra-red detection circuitry.

1. Introduction
Sensors are feedback devices which are used tosense the maximum limits of various actions of the robots
like the gripper close / open, sense the dead ends of thejoints of a robot, thus making it very intelligent. They areused to sense the changes in the environment (detect the
obstacles) and get adaptable to the environment(overcome the obstacle and proceed to the destination).
There are various types of sensors such as limit switches,
IR detectors, proximity switches, cameras, tachometers,
thermocouples, thermistors, potentiometers, range
sensors, ultrasonic sensors, inductive sensors, lightsensors, hall effect sensors, gyroscopes, laser sensors,
light sensors, opto-couplers, encoders, incremental
encoders, positional encoders, shaft encoders, synchros,etc., [1].
Sensors make the robotic system a close loop
feedback control system. Here, in our work, we havedesigned a powerful sensing system for a mobile robotic
system, which is moving on a railway track. In thiswork, a full-fledged autonomous flexible manufacturingsystem (FMS) is designed, fabricated and implemented
using good sensing techniques with an artificial
intelligence provider algorithm. Various type of sensing
circuitries is used to make the system closed loop andprovide intelligence to the automated system, INARMS
which is shown in Fig. 1.

©ISAROB 2006

Fig. 1 The designed and assembled flexible
manufacturing system with the sensing interface.
GRIPPER :-Two limit switches are connected in parallelfor detecting closing action and one for detection the723

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Because the same change occurs to both elementseven if the infrared quantity of the background in theplace to detect with the sensor changes, little change ofthe output occurs even if it occurs. When the person or
the animal crosses the sensor, the quantity of the infrared
rays which enter both elements becomes not equal andthe change of the voltage appears in the output. IR
circuit used is shown in Fig. 2.

extreme limit. In the home position, the gripper remain
in fully opened position so that limit switches used for
detecting extreme and goes to NO position.
ROLL ACTION :- Two limit switches are used for
detecting the home position of roll motor .In the home ofroll motor and 90° rotation ( clockwise ) of roll motor,
the home roll limit switch get to it’s NO position .
PRISMATIC ACTION :- Two limit switch are installedat extreme end of vertical extension assembly to detect
two end limits. In home position of robo the VE goes tofully upward position, so that bottom limit switch of VE
goes to it’s NO position.
In this way, two limit switches are used forcontrolling individual action of gripper, roll andprismatic motion.

3. Infra-red detection circuit
The IR detector circuit is used to detect maximum &minimum limit of the base assembly. IR circuit is also
used for detecting forward & reverse stop point. We areusing pair of IR detector’s to detect base minimum limit(0 degree) & maximum limit (180 degree ).

Fig. 2. Designed and fabricated IR circuitry

A pair of IR detector’s to detect forward & reverse
stop point are used. Hence in all there are 10 inputs but
since we are using parallel port in SPP mode number of
inputs to computer get reduced to 5. So we multiplexed
all the inputs such that two states of each action are given
to two inputs of J-K flip flop, & taking single ‘Q’ output[3].

4. Block-diagram for the sensor interface
To all motors

Limit
Switches

As the infrared sensor device, PZT (Lead[Pb:Plumbum] Zirconate Titanate) is used. This material
has the nature that the electric charge in the surface isdivided into the positive electric charge and the negativeelectric charge in the usual condition (Spontaneouspolarization). The distribution of the electric charge isdisordered when the infrared rays lash this material andthe voltage occurs. The infrared sensor outputs thechange of this voltage.

Motor
DriverCard

Interfacing
Circuitry
UltrasonicSensors

Controller
Card

RemoteControl
With
Interface

ADC Cards
Keyboard

Potentiometers

The infrared sensor has the kinds such as the single
type, the dual type, the quad type. The dual type is often
used to detect the move of the person or the animal. The
two identical shape elements are used for the dual-type
sensor. And, it is put for the pole of the element tobecome opposite.

PowerSupplyFig. 3. Designed and fabricated sensing interfacing card

5. Working of comparator circuit for
the limit switches

When the change of the infrared quantity occurs,
being simultaneous with the element which was put inthis way, because the occurring voltage is opposite, it
denies each other and the voltage doesn't appear in the
output. The output voltage changes only when there is adifference in the quantity of the infrared rays which enter
both elements.

©ISAROB 2006

DisplayCard

The input to computer requires proper logic states,that is 0 or 1. Hence, to achieve this, we have usedcomparator circuit for each limit switches [4]. The
comparator action is achieved by connecting resistive
bridge at input of comparator ( LM 324 ) & placing thelimit switches in bridge arm connected to inverting input
of comparator as shown in Fig. 4. When the switch is
closed then voltage at inverting input is high then non-724

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inverting input. Hence output is logic 0. But when thelimit switch get into it’s NO position, then voltage at
non-inverting is high & output goes to logic 1 , indicating
extreme limit reach.
Consider limit switch state for prismatic motion.
When the robot is in home position then lower prismatic
limit switch will be in NO position & upper limit switch
will be in NC position. Hence output of lower
comparator will be logic 1 & that of the uppercomparator will be logic 0.This two inputs are given to k
& j inputs of J-K flip flops respectively. This causes
output Q to be logic 0 indicating home position [5].

On the other hand when VE comes to it’s extreme
lower position then state of comparators will be exactly
reverse of previous case. Hence, output of flip flop goesto logic 1, indicating / detecting extreme lower end. This
Q output is used to signal computer to stop rack motor
via IC 74245 buffer. In this way all the output ofindividual comparator are to given to J-K flip flops &output Q’s of all J-K are given to computer indicating totake stop action’s.

6. Obstacle Sensing Avoidance Algorithm
Here, the operations are actually carried out or
taught to the robot instead of just assuming theparameters. Here in lies the advantage of using
remote control over the keyboard to program therobot, however typing the program through the
keyboard based on assumed parameters is fastermethod of programming. If there are obstacles in thepath of motion of the robot, then the software takes careof this by initiating an alternative path around the
obstacles making sure that the robot reaches the desired
destination. However, if there are too many obstacles
then the robot may not reach the desired position
according to the program [6].

VCC

8

3.3K

4

RA

U5
VCC RESET

7

6

THOLD

RB5
1K

3CLK

OUT
CVOLT

U1

2
4
CLK 1
16
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R5
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Fig. 5 Limit switch sensing circuitry©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Fig. 9 Ultrasonic transmitter & receiver pair

7. Ultrasonic Sensors / Limit Switches / Pots
Ultrasonic sensors [7] are mounted at four points on
the links to prevent the collision of the robot with the
obstacles. These sensors give a signal to the computer
through the parallel port and the computer in turnprocesses this signal and in turn avoids the collision byturning the arm in another direction. Limit switches areused at the inner surfaces of the grippers to sense whether
the gripper has held / picked up the object or not.
Potmeters are mounted at the joints to accurately measure
the movement.

Fig. 7 Reset detection sensing unit PCB’s
If you drive an automobile, you know the practicalapplication of the Pauli exclusion principle: Two objects
cannot occupy the same space at the same time. What’strue for automobile is even true for robots. An
autonomous robot has to keep itself from colliding withobstacles. Obstacles might take the form of a wall or
post, or they may be mobile like a dog, a person, or
another robot.

8. Conclusions

Since robot can’t know the position of moving object
in advanced, it must have some way of detecting
obstacles in real time. Luckily, detecting obstacles
doesn’t require anything as sophisticated as machine
vision. A much simpler system will suffice. Some robots
use SONAR (sometimes called SODAR when used in air
instead of water) or RADAR. An simple system is to use
ultrasonics.

A sensor interface was designed and installed in theflexible manufacturing system which is used to provide
closed loop feedback action of the system, thus avoiding
collision of the system with the obstacles. The sensing
inputs are taken to the computer, processed and necessary
action is taken, thus providing artificial intelligence to the
FMS.

References[1] Robert Schilling, Fundamentals of robotics, Prenticehall.

[2] Hodges Bernard, Industrial Robotics, Jaico
publication.

[3] Klafter, Cheimieleswski, Negin, Robotics
Engineering Approach , Prentice hall.

[4] Fu, Gonzales, Lee, Robotics-Control Sensing &Vision, McGraw Hill.

[5] T.C.Manjunath (2005), Fundamentals of Robotics,

3rd edition, Nandu Publication, Mumbai, India.
[6] T.C.Manjunath (2005) , Fast Track to Robotics, 2ndedition, Nandu Publication, Mumbai, India.
[7] National Semiconductor , Data sheets for linear IC’s.

Fig. 8 Limit switches sensing unit PCB’s©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Application of Uncertain Variables to Knowledge-based Decision Making in Complex
Systems with Uncertain and Random Parameters1
Z.Bubnicki
Institute of Information Science and Engineering, Wroclaw University of Technology
Wyb. Wyspianskiego 27, 50-370 Wroclaw, POLAND
e-mail: zdzislaw.bubnicki@pwr.wroc.pl

Abstract

2 The general formulation of the decision
problem using uncertain and randomvariables

The paper is concerned with the application of
uncertain variables to decision making based on an
uncertain knowledge. The system under consideration
contains two kinds of parameters: uncertain parameterscharacterized by an expert in the form of certainty
distributions and random parameters described byprobability distributions. The general formulation of
the decision problem using uncertain and randomvariables is described. The decision making for a
hybrid system with the cascade structure is presented.
The possibilities of the application to allocation
problem in a complex of parallel operations are
discussed. A simple example illustrates the approachpresented.

In the definition of the uncertain variable x we
consider two soft properties (i.e. such properties ϕ ( x )that for the fixed x the logic value v [ϕ ( x )] ∈ [0,1] ):~ x ” which means “ x is approximately equal to“x =x ” or “ x is the approximate value of x ,” and~ D ” which means “ x approximately belongs to“x∈x
the set D x ” or “the approximate value of x belongs
to D x ”. The uncertain variable x is defined by a setof values X (real number vector space), the function~ x ) (i.e. the certainty index that x =~ x,h( x ) = v ( x =given by an expert) and the following definitions forD x , D1 , D2 ⊆ X :~v( x ∈ D x ) = max h( x ) ,Keywords: uncertain systems, uncertain variables,
knowledge-based systems, decision making systems

x∈D x

~
~D ),
v( x ∉ D x ) = 1 − v( x ∈x

1 Introduction~D ∨x∈~ D ) = max{v( x ∈~ D ), v( x ∈~ D )},v( x ∈
1
2
1
2

The uncertain variables have been introduced anddeveloped as a tool for analysis and decision making ina class of uncertain systems described by traditional
mathematical models or by relational knowledge
representations
withunknownparameterscharacterized by an expert [1–5]. In the paper [6] anapproach for the system with uncertain and randomparameters has been presented. The purpose of thispaper is to extend this approach and to show how theuncertain variables may be applied to a class ofcomplex systems consisting of two subsystems withdifferent descriptions of the uncertainty. Section 3 isdevoted to a hybrid system with a cascade structure
containing random and uncertain parts. Section 4
presents some remarks concerning the complex ofparallel operations. We shall start with the shortpresentation of the decision problem based on theuncertain variables. Details of the uncertain variables
theory and its applications may be found in theauthor’s books cited above.

~D ∧x∈~ D ) = min{v ( x ∈~ D ), v ( x ∈~ D )}.
v( x ∈
1
2
1
2
The function h ( x ) is called a certainty distribution. In
the case of C-uncertain variable x , the certainty index
that x approximately belongs to D x is defined asfollows:~ D ) = 1 [v ( x ∈~ D ) +1− v (x ∈~ D )] (1)vc ( x ∈xxx2
where D x = X − D x . The application of C-uncertain
variable means better using of the expert’s knowledge,
but may be more complicated.
Let us consider a static plant with the input vectoru ∈ U and the output vector y ∈ Y , described by arelation R (u, y; x ) ⊂ U × Y where the vector of
unknown parameters x ∈ X is assumed to be a valueof an uncertain variable described by the certainty
distribution h ( x ) given by an expert. If the relation R
is not a function then the value u determines a set of1This work was supported by the Polish State Committee for Scientific Research under Grant No. 3T11A 031 28(2005-2007)©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

~ with the probability density
random variable w
f ( w)

possible outputs

D y (u; x ) = { y ∈ Y : (u, y ) ∈ R(u, y; x )} .

(2)

and x is a value of an uncertain variable x with thecertainty distribution h( x) . Let us apply an approachtaking into account the additional requirement for the
first element: z ∈ D z were D z is given by a user. The
probability that the requirement is satisfied

For the requirement y ∈ D y ⊂ Y given by a user, we
can formulate the following decision problem: For the
given R (u, y; x ) , h (x ) and D y one should find thedecision u* maximizing the certainty index that the setof possible outputs approximately belongs to D y (i.e.

∆~ ∈ D (u )] =P( z ∈ D z ) = P( wp(u1 )
w 1

where Dw (u1 ) = {w ∈ W : D z (u1 ; w) ⊆ D z } . Then twocases of the decision problem may be formulated:belongs to D y for an approximate value of x ). Then~u* = arg max v [ D y (u; x ) ⊆ D y ] = arg max max h( x)
u∈U

(5)

1. To determine u2* using according to version I or II,u∈U x∈Dx (u)(3)

with u1 = u1* where u1* is the value of u1 maximizing(5).

Let us now consider the system described by arelation R (u, y ; x, w) where x ∈ X and w ∈W are thevectors of uncertain and random parameters,~ are independent uncertain
respectively. If xi and wi
and random variables for different i, respectively then

2. To determine (u1* , u2* ) according to version I or II,

where D x (u ) = {x ∈ X : D y (u; x ) ⊆ D y } .

subject to the constraint p(u1 ) ≥ p where p is a given
value.
Example 1. Consider very simple example with

u1 , u2 , z , y , x, w ∈ R + (one-dimensional, non-negative
variables) and the relations in the form of inequalities:f ( w) = ∏ f i ( wi ) .

h( x ) = min hi ( xi ) ,ii

wu1 ≤ z ≤ 2wu1 ,For the system as a whole one may apply the following
general approach described for one element in [4, 7]:
1. To determine D y (u ; x, w) andxu2 + z ≤ y ≤ 2 xu2 + z(6)The elements 1, 2 may be for example production
units, u1,2 may denote amounts of raw materials andaccording to (2) and (3), with R(u, y ; x, w) in place ofz, y – amounts of products where z is used as anadditional raw material for the second element. The
inequalities (6) are reduced toxu 2 + wu1

R (u , y ; x ) .

≤ y ≤ 2( xu 2 + wu1 ) . Let D y = [ y1 , y 2 ]. Then∆v [ D y ( u ; x , w) ⊆ D y ] =v ( u, w)(4)v (u1 , u2 , w) =2. To find u* maximizing~
E[v (u, w)] = ∫ v (u, w) f ( w)dw

maxx∈D x ( u , w )h( x )(7)

where the set D x (u, w) = D x (u1 , u2 , w) is determinedby the inequalityW(version I), or to find u* as the expected value~ )] whereE[u( wu( w) is obtained by maximization of(4) (version II). In the next section a particular form of
the structure and a special approach to our problem
based on a decomposition will be presented.

( y1 − wu1 )u2−1 ≤ x ≤ 0.5( y 2 − 2 wu1 )u2−1 .

(8)Assume that x has a triangular certainty distribution
presented in Fig. 2. Let us apply C-uncertain variables.
h ( x)

3 Hybrid system with cascade structure1

Let us consider the system with the cascade
structure (Fig. 1) in which the elements 1 and 2 aredescribed by the relations R1 (u1 , z ; w)
and

z

1

y

1
2

2

1

x

Fig. 2. Example of certainty distributionu1

u2

Then, using (1), (7) and (8), under the assumptiony 2 ≥ 2 y1 , we can obtain the following function

Fig. 1. Cascade structurev c (u1 , u2 , w) :R2 (u2 , z, y ; x ) , respectively, where w is a value of a

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the greatest possible value u1* = (0.5z 2 − z1 )( pwm ) −1

v c (u1 , u 2 , w)
−1
0.5( y 2 −2 wu1)u 2 for u2 ≥ y1 + 0.5 y 2 − 2 wu1
=  1− ( y1 − wu1 )u 2−1 for y1− wu1 ≤ u 2 ≤ y1+ 0.5 y 2 −2wu10
for u2 ≤ y1 − wu1 .

and by using (9) with u1 = u1* we have~ )u * = y + 0.5 y − (0.5z − z ) p −1.
u 2* = y1 + 0.5 y 2 − 2 E( w
1
1
2
2
1

It may be shown that u2 (u1 , w) maximizing vc for the

4 Task allocation problem

fixed value u1 is as follows:u2 (u1 , w) = y1 + 0.5 y 2 − 2wu1

(9)The similar approach may be applied to an
allocation problem in a complex of operations. To
explain To explain the idea for the hybrid complex
with uncertain and random parameters, let us start withthe uncertain complex ([4]) containing k parallel
operations described by the inequalities Ti ≤ x i u iand vc is then the following

vc (u1 , w) = ( y 2 − 2 wu1 )( 2 y1 + y 2 − 4 wu1 ) −1 . (10)
It is easy to show that for y 2 ≥ y1 , (10) is anincreasing function of u1 . Assume that w ≤ wm , i.e.

f ( w) = 0

forw ≥ wm .

Using

the

(i ∈1, k ) where u i is the size of a task assigned to theassumption

i-th operation, Ti is the execution time, xi > 0 is−1y1 − wu1 ≥ 0 (see (8)) we obtain u1* = y1wm
, andputtingu1*

assumed to be a value of an uncertain variable xi
described by a certainty distribution hi ( xi ) given byu2* ( w) = y1 + 0.5 y 2~ )] = y + 0.5 yu * = E[u * ( winto (9) yields−1− 2 wy1 wm. Finally,
2
2
1
2
~
−1
− 2 E( w) y w . Let us take into account the additional

an expert and x1 ,..., x k are independent variables. The
complex may be considered as a decision plant
described in Sec. 2 where y is the execution time of

1 m

requirement for the first element: z ∈ D z = [ z1 , z 2 ] ,the whole complex T = max{T1 , ..., Tk } , x = ( x1 , ...,

z1,2 ≥ 0 , z2 ≥ 2z1 . In this case D w (u1 ) is determinedu = (u1 , ..., u k ) ∈ U . The set U ⊂ R k isdetermined by the constraints: u i ≥ 0 for each i andu1 + ... + u k = U where U is the total size of the task
to be distributed among the operations (Fig. 3). The
initial requirement given by a user is T ≤ α for the
given α.
xk ) ,

by the inequality z1u1−1 ≤ w ≤ z 2 (2u1 ) −1 . For
example, assume the rectangular probability density:−1f ( w ) = wm
for 0 ≤ w ≤ wm and f ( w) = 0 otherwise.
Then, according to (5)p(u1 )−1−1
u1 ≥ 0.5z 2 wm
(0.5z 2 − z1 )( wm u1 ) for

−1
−1
−1
for z1 wm ≤ u1 ≤ 0.5z 2 wm
=  1 − z1 ( wm u1 )
−1

0
for
u1 ≤ z1 wm.
(11)
Now we can consider two approaches:
1. We determineU−1
u1* = arg max p(u1 ) = 0.5z 2 wm(9) with u1 = u1* we have~ ) z w −1 = y + 0.5 y − 0.5z
u2* = y1 + 0.5 y 2 − E( w
2 m
1
2
2
which may be used when z2 ≤ 2 y1 + y2 . For example,
by

.
.
uk .

.
.
.

y =Τ

using

According to the general formulation of thedecision problem presented in [4], the allocation
problem may be formulated as an optimization
problem consisting in finding the optimal allocation

for z1 = 1 , z 2 = 3 , y1 = 2 , y 2 = 5 , wm = 0.75 we
1
obtain u1* = 2 , u2* = 3 and p(u1* ) = 1 − 2 z1 z 2−1 =3which means that the requirement z ∈ [ z1 , z 2 ] is toostrong. For z1 = 0.5 we obtainu * that maximizes the certainty index of the soft
property: “the set of possible values T approximately
belongs to [0, α ] ” (i.e. belongs to [0, α ] for an
approximate value of x ).
Now let us consider the complex consisting of two
subcomplexes: the uncertain subcomplex described by2p(u1* ) = . Hence, u1*

3
is the decision for which the requirement concerning
the first element is satisfied with the greatest
2
probability equal to .
3
2. For the requirement p(u1 ) ≥ p we obtain from (11)©ISAROB 2006

Plant

Fig. 3. Complex of parallel operations as a decision
plant

u1

and

Allocationu1
u2

Ti ≤ xi u i for i ∈ 1, k 1 and the random subcomplex
described

byTi ≤ wi u ifori ∈ k1 + 1, k1 + k 2(k1 + k 2 = k ) where wi ≥ 0 are values of random~ described by the probability density
variables w
i729

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

f i ( wi ) ,∆( wk1 +1 ,..., wk1 +k2 ) = w.

The

the first subcomplex u i ( wi ) should be determinedallocation

with Di (u i ) = [0, α u i ] andproblem corresponding to version II in Sec. 2 is then~ )] where
as follows: To find the decisions u * = E[u ( w
i

i

U−

u i (w) are the decisions maximizing the certainty
index that the requirement T ≤ α is approximately
satisfied for the fixed w, E denotes the expected value.
Let us introduce the notation
 x uwi u i

5 Conclusions
It has been shown how to apply the uncertain
variables as a tool for decision making in a complex
system consisting of two parts: the part with uncertain
parameters characterized by an expert and the part withrandom parameters described by probability
distributions. The approach presented in the paper has
been applied to the control of a production processdescribed by uncertain relational knowledge
representation and to a load distribution in a
multiprocessor system. The approach may be extended
for a complex computer system with distributed
knowledge [8].

~v i (u i ) = v [τ i ( u i ) ≤ α ] . In the second subcomplex τ i isa deterministic variable and vi (u i ) = 1 for u i ≤ α wi−1 ,vi (u i ) = 0 otherwise. For the triangular certainty
distribution it is easy to show [4] that the solution of
our distribution problem is as follows:
1. If

α

i = k1 +1i∑ w <U(12)then in the second subcomplex u i ( wi ) = α wi−1 and in
the first subcomplex u i ( wi ) should be determined in
the same way as for the uncertain complex describedabove withU−

k1 + k2∑α

References[1] Bubnicki Z (2001), Uncertain variables and theirapplications for a class of uncertain systems.
International Journal of Systems Science 5:651659[2] Bubnicki Z (2001), Uncertain variables and theirapplication to decision making. IEEE Trans. on
SMC, Part A: Systems and Humans 6:587-596[3] Bubnicki Z (2002), Uncertain logics, variables andsystems. Springer-Verlag, Berlin, London, N.
York[4] Bubnicki Z (2004), Analysis and decision making
in uncertain systems. Springer-Verlag, Berlin,
London, N. York
[5] Bubnicki Z (2005), Modern control theory.
Springer-Verlag, Berlin, London, N. York
[6] Bubnicki Z (2005), Learning algorithms anduncertain variables in knowledge-based patternrecognition. Proceedings of the 10th International
Symposium on Artificial Life and Robotics, Oita,
Japan, Feb 4-5, 2005, pp. 236-239[7] Bubnicki Z (2003), Application of uncertain
variables in a class of control systems withuncertain and random parameters. Proceedings ofthe European Control Conference, Cambridge,
UK, 2003.
[8] Bubnicki Z (2002), Application of uncertain
variables to decision making in a class of
distributed computer systems. Proceedings of 17th
IFIP World Computer Congress, Montreal,
Canada, Aug 25-30, 2002, Kluwer Academic
Publishers, Norwell, MA, Vol. “Intelligent
Information Processing”, pp. 261-264(13)

i = k1 +1 wi

instead of U.
2. If (12) is not satisfied then in the first subcomplex
u i = 0 and in the second subcomplex u i may be any
number satisfying the condition u i ≤ α wi−1 andu k1 +1 + u k1 + 2 + ... + u k1 + k2 = U .
Then the requirement T ≤ α is exactly satisfied(v(u ) = 1) for the given values wi . As a particularsolution one may accept u i = α cwi−1 wherek1 + k 2

c =U( ∑

αi =k +1 wi) −1 .

1

In such a way in the cases 1 and 2 we obtain thesolution u i (w) for the fixed w. Finally, one should~ )] .
determine u * = E[u ( wiiThe similar approach may be applied for a resource
distribution problem, i.e. when the operations aredescribed by Ti ≤ xi u i−1 and Ti ≤ wi u i−1 where u i isthe amount of the resource assigned to the i-th
operations. If
k1 + k2w∑ αi < U(14)

i = k1 +1

then in the second subcomplex u i ( wi ) = wi α −1 and in©ISAROB 2006(15)

instead of U. If (14) is not satisfied then v(u ) = 0 for
any distribution u.

In the first subcomplex τ i is a value of τ i = xi u i andk1 + k 2w∑ αi

i = k1 +1

fori ∈1, k1
for i ∈ k1 + 1, k1 + k 2 .

τ i (u i ) =  i i

k1 + k2

730

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Firing Cell: An Artificial Neuron with a Simulationof Long-Term-Potentiation-Related Memory
Jacek Bialowas

Beata Grzyb

Pawel Poszumski

Dept. of Anatomy and Neurobiology
Medical University of Gdansk
Debinki 1, 80-211 Gdansk, Poland

Faculty of Mathematics, Physics
and Computer Science
Maria Curie-Sklodowska University
Pl. Marii Curie-Sk odowskiej 120-031 Lublin, Poland
E-mail: beata@gabri.pl

Institute of Oceanology
Polish Academy of Science
Powstancow Warszawy 55
81-712 Sopot, Poland

Abstract: We propose a computational model of neuron,called firing cell (FC), properties of which cover suchphenomena as attenuation of receptors for external
stimuli, delay and decay of postsynaptic potentials,
modification of internal weights due to propagation of
postsynaptic potentials through the dendrite, modificationof properties of the analog memory for each input due toa pattern of short-time synaptic potentiation or long-time
synaptic potentiation (LTP), output-spike generation
when the sum of all inputs exceeds a threshold, andrefraction. The cell may take one of the three forms:
excitatory, inhibitory, and receptory. The computer
simulations showed that, depending on the phase of input
signals, the artificial neuron’s output frequency may
demonstrate various chaotic behaviors.

3. If (after an arrival of an action potential at aparticular input) the accumulated local potential forpostsynaptic region of each input synapse increasebeyond the value of the local threshold (about-70mV), the NMDA receptor causes an opening of
the previously blocked channel for an influx ofcalcium ions [4][5][6], and thus the phenomenon ofLong–Term Potentiation (LTP),

4. On the most excitatory neural inputs the long-term
memories can be activated, which results in an
increase of LTP through an activation of NMDA
receptors,

5. The amplitude of the single excitatory postsynaptic
potential (EPSP) is not an absolute constant; indeed,
severalvalueshas
beenproposedbyneurophysiologists (see Andersen [3]),1. Introduction

6.

The paper presents a new approach to building
computational models of selected brain circuits, aimed todevelop a tool supporting an interpretation of recordingsof electric potentials in living neural tissue. Suchcomputational models must reflect the most essential
properties of biological neurons and their assemblies, but,in order to not to increase the necessary computational
power beyond necessity, not too much more.
We propose that, as for a single neuron, at least thefollowing neurophysiological facts should be covered inan model of the discussed kind:1.

Some properties of the real neuron such as transiently
after hyperpolarisation in refractory time and presynaptic
regulatory mechanisms as reuptake of released
neurotransmitters we did not take into consideration.
As for currently available simulators of life-likeneural circuits, they allow to simulate the circuits built of
thousands of cells, but neglect some important propertiesof single cell (see Ambros & Ingerson [8]; Izhikevich [9]or tend to go deeply into detailed biophysics andbiochemistry of a single neuron; so, in case of simulationof large neural networks, the complexity of the neuron’smathematical description requires hardly available
computational power e.g. Hines [10]; Sikora [11]; Traubet al. [12]).
It seems to be commonly believed in the research thata useful neural model must be of “channel-type” i.e. it
must reflect opening of ionic channels at the cell
membrane of dendrite as a reaction to action potential
arrival at the synapse and opening ionic channels at theinitial segment of axon for generation and propagation ofaction potentials (see Bower & Beman [13]). We proposethat in neural simulations the computationally-expensive

Each input signal causes some changes of thepostsynaptic potential at the area of the input point;
there are typical patterns of the increment and decayof the excitatory (EPSP) and inhibitory (IPSP)
potential [1][2],2. There is a set of commonly accepted neurophysiological data: time courses, amplitudes ofvarious postsynaptic potentials, action-potential
amplitude, and the thresholds for neural activation
causing action potentials and removing the block of
NMDA canals [1][3],©ISAROB 2006

Internal weights change due to propagation of
postsynaptic potentials through the dendrite [7].

731The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

model of biophysical phenomena can be replaced with amodel based on a set of three shift register and that no
essential output properties are lost because of such
replacement.
The neuron properties we consider are frequency
coding, a memory of a single input value, and aprocessing algorithm that facilitates a non-linear potential
summation. Despite such a simplification, the proposedmodel demonstrates such phenomena as attenuation ofreceptors for external stimuli , delay and decay ofpostsynaptic potentials, modification of internal weights
due to propagation of postsynaptic potentials through thedendrite, modification of properties of the analog
memory for each input due to a pattern of LTP, outputspike generation when the sum of all inputs exceeds athreshold, and a refraction.

simulations we enhanced this value for EPSP by two. The
object can then adjust all values proportionally to a given
number of inputs.

2. Firing Cell (FC)The model of neuron we propose is called FC (firing cell)and occurs in three versions: excitatory, inhibitory andreceptory (in this paper we discuss only the first one). FC
consists of a dendrite, body and axon (Fig. 1). The
dendrite is a string of compartments. Each compartment
contains an input synapse. Each synapse is being checked
and after a detection of an action potential the values of
the table of a certain shift register are being changed
according to the typical time course of EPSP or IPSP.
Each of the registers is a string of data-boxes. The firstdata-box of one of the registers is reserved for the actual
value of activation of the related compartment. Every 0,5msec of the simulated time the actual values of all
registers are checked and their weighted sums arecompared with related thresholds. Generally, there is apositive correlation between the weight and a related
synapse’s proximity to the cell body.
The output takes at a given clock the value equal to 1,
if and only if the accumulated postsynaptic potential at
the level of the neuron’s body got at least as high as thethreshold that equals -50mV. The axon provides theoutput signal to the compartments of the dendrites of thedestination FCs. The switching signal is being backpropagated to all dendrite compartments. The period
between two state updates, i.e., between a given clock
and the previous clock, is an equivalent of 0.5 msec of
the simulated time. When an action-potential isgenerated, the registers are reset to the value of resting
potential (using the switching signal) for and then theactivity of all compartments is inhibited for 1.5 msecperiod of refraction.The values of the resting potential
and threshold as well as other initial parameters can be
input independently for each neuron before eachsimulation session.
The minimal value of the single postsynaptic potential
is calculated as dividing the difference between thethreshold and the Kalium Equilibrium Potential -90mVby the number of inputs. For practical reasons, in first©ISAROB 2006

Fig. 1. Firing Cell (FC) in the configuration usedin the experiments described in this paper.
We can consider the mentioned shift registers above
as a very short-term local memory unit that remembers
related events for up to 15 msec. The biological nervous
system uses them to avoid the troublesome necessity ofstrict synchronization that takes pace in conventional
computers. Yet we should also have a memory related toeach input. The memory must work according to LTP
rule. In each state-updating cycle the program calculates(based on actual values in the registers and appropriate
weights) an accumulated local potential for postsynaptic
region of each input synapse. If, after an action potential
arrival at a particular input, the postsynaptic potential
increase beyond the value of the local threshold (ca.
-70mV), the second register (which simulates the rise of
calcium ion content inside the cell) change and shift thecontents of their data-boxes. Thus, the function operatingon the registers substitute a time-consuming solving ofdifferential equation related to the strength of synaptic
potentiation versus calcium ion charge. The enhanced
strength remains for a time calculated as power functionof the charge.
If after some time an additional charge appears, theperiod of the calculated synaptic potentiation
substantially increases. In the same pattern we can easily
include for each input the third register, which cansimulate the slower influence of neuromodulatory
substances on theirs receptors; however, in our
preliminary investigations we did not use them.

3. Experiment
We used the simulated FC as a model of a pyramidal
hippocampal cell under conditions such as in the classical
LTP experiment by Bliss and Lomo [14]. We configured
the FC in such a way that it had 13 excitatory inputs and

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

3 inhibitory inputs. Two cases were tested. In the firstcase the assumed EPSP amplitude was 5mV (Fig. 2), in
the second it was 7 mV.

processing-related mechanisms. In both tested cases thefrequency of action-potential generation and the values of
LTP increased after the training and in the case of EPSP
amplitude 7mV the frequency were substantially higher
both before and after the training.
For demonstration of regular, periodic or chaotic
behavior of a spiking neuron we used the Poincare returnmaps. Our experiments showed differences between theneuron’s behavior before, during, and after the training.
(Fig. 3) We also noticed that change of initial values
implicate change of FC's behavior. For detailed
description we need further statistical calculations withnumerous simulations at various initial values.

1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
-50mV
-80mV

a
1.00
1.00
1.00
1.00
1.00
1.81
2.03
2.07
2.11
1.90
1.88
1.88
1.88
-50mV
-80mV

b
Fig 2. Behavior of an untrained FC (a) and trained FC(b) for EPSP 5mV. The peaks located on the input lines
show history of action potential arrivals, while thevertical peaks above the threshold (-50mV) show action
potential generation by FC. The zig-zag-shaped line
between the threshold and resting potential (-80mV) is
the plot of accumulated postsynaptic potential. The
training of 400 msec at 100Hz was with action potentialthth
spike trains on the 7th, 8 and 9 input (b-thick lines).
The frequency of action potentials and the state of thelong term memory associated with the related input (realnumbers on the left) increase after training (b). An
animation of 2 sec of the firing cell’s work is available at
the web page of GABRI (http://www.gabri.pl)

In the first case each FC’s synapse substituted ca.
1000 biological synapses, whereas in the second caseeach FC’s synapse substituted ca. 1400 biologicalsynapses. In both cases three of the excitatory inputs
were subject to a stimulation that lasted for 400msec withspiketrains of the frequency 100Hz.
The results of the experiment confirmed thebiological plausibility of FC as for information-©ISAROB 2006

Fig 3. The Poincare return maps from total simulations
time 2,5 sec. For EPSP 5mV (a) and 7mV (b). I(n) interval between two subsequent spikes. Note thepassages between various musters of spike trains.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[6] Bliss T. V. P. &. Collingridge G. L. (1993)

4. Concluding Remarks

Asynaptic model of memory: long-term potentiationin the hippocampus, Nature 361, pp. 31-39.

The way of information coding in the brain is still amystery. Some neuroscientists suggest that encoding of
information using firing rates is a very popular coding
scheme used by cerebral cortex [15]. In the FC model thisidea is followed thorough elaborating mainly themechanisms that facilitate a frequency-based coding of
information. To the best of our knowledge, no similarsolutions have been published to date.
The realistic modeling requires a lot of computational
power. Note that Amaral & Ishizuka [16] calculated
12,000 synapses on a single rat’s hippocampal neuron.
The idea of representing near 1000 biologicalsynapses by each of the FC’s synapse is justified by thefact that during experiments in vivo a single electrode fortechnical reasons excites simultaneously multiple fibers.
Hence, in information-processing oriented simulation
there is no reason to calculate separately the states of
thousands of synapses.
The set of functions employed in the FC model
should be a subject to further simplification toward afuture implementation in hardware, for which analog
memories and field transistors (as processing devices) arebeing considered. The silicon neuron by Mahovald andDouglas [17] seems to well solve the problem of thegeneration of action potentials. A satisfactory model must
properly react frequency and phase of action-potential
spike-trains. Some reported solutions e.g. Elias &Northmore [18] seem to be a good step in this direction.

[7] Bekkers J. M. & Stevens Ch. F. (1990) Two
different ways evolution makes neurons larger, In:
J. Storm-Mathisen, J. Zimmer, & O. P. Ottersen(Eds.) Progress in Brain Research 83, AmsterdamNew York-Oxford: Elsevier, pp. 37-45.

[8] Ambros-Ingerson J., Granger R., & Lynch G.
(1990) Simulation of paleocortex performs
hierarchical clustering, Science 247, pp. 1344-1348.

[9] Ishikevich E. M. (2003) Simple model of spiking
neurons, IEEE Transactions on Neural Networks,
14, pp. 1569-1572.

[10] Hines M. (1989) A program for simulation of nerve
eqations with branching geometries, International
Journal of Biomedical Comput. 24, pp. 55-68.

[11] Sikora M. A., Gottesman J., Miller R. F. (2005) “Acomputational model of the ribbon synapse”,
Journal of Neuroscience Methods 145, pp. 47-61.

[12] Traub R. D., Contreras D., Cunningham M. O., etal. (2005) “Single-Column Thalamocortical
Network Model Exhibiting Gamma Oscillations,
Sleep Spindles, and Epileptogenic Bursts”, Journalof Neurophysiology 93, pp. 2194-2232.

[13] Bower J.M. & Beeman D. (2003) The Book of
GENSIS: Exploring Realistic Neural Models with
the GEneral NEural SImulation System,
http://www.genesis-sim.org/
GENESIS/bog/bog.html

Acknowledgment: This research was supported by
Gdansk Artificial Brain Research Initiative (GABRI).
http://www.gabri.pl[14] Bliss T. V. P., & Lomo T. J. (1973) Long lasting
potentiation of synaptic transmission in the dentate
area of the anaestaetized rabbit following
stimulation of the perforant path, J. Physiol.
(London) 232, pp. 331-356.

References[1] Atwood H. L. &. MacKay W. A. (1989) Essentials
of Neurophysiology, Toronto: B.C. Decker Inc.

[2] Schmidt R. F. (1976) in R. F. Schmidt (Ed.)[15] Rolls E. T. & Treves A. (1998) Neural Networks

Fundamentals of Neurophysiology, New York,
Heidelberg, Berlin: Springer-Verlag, pp. 64-92.

and Brain Function, Oxford: Oxford University
Press.

[3] Andersen P. (1990) Synaptic integration in[16] Amaral D. G. & Ishizuka N. (1990) Neurons,

hippocampal CA1 pyramids, In: J. Storm-Mathisen,
J. Zimmer, & O. P. Ottersen (Eds.) Progress in
Brain Research 83, Amsterdam-New York-Oxford:
Elsevier, pp. 215-222.

numbers and the hippocampal network, In: J.
Storm-Mathisen, J. Zimmer & O. P. Ottersen (Eds.)
Progress in Brain Research 83, Amsterdam–New
York–Oxford: Elsevier, pp. 1-12.

[4] Muller D.,

Joly M. & Lynch G. (1988)
Contributions of quisqualate and NMDA receptors
to the induction and expression of LTP, Science
242, pp. 1694-1697.

[17] Mahovald M. & Douglas R. (1991) A siliconneuron, Nature 354, pp. 515-518.
[18] Elias J. G. & Northmore D. P. M. (1998) Building
Silicon Nervous Systems with Dendritic Tree
Neuromorphs, In: W. Maass & Ch. M. Bishop(Eds.), Pulsed Neural Networks, Cambridge, Mass.:
A Bradford Books/The MIT Press, pp. 135-156.

[5] Grant S. G. N., O'Dell T. J., Karl K. A., et al. (1992)
Impaired long-term potentiation, spatial learning,and hippocampal development in fyn mutant mice,
Science 258, pp. 1903-1910.

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Land-cover/land-use mapping based on color feature extraction∗
Tomasz Kubik†
Adam Iwaniak‡

Witold Paluszyński†PrzemysÃlaw Tymków‡†

Institute of Computers, Control and Robotics, WrocÃlaw University of Technology,
Janiszewskiego 11/17, WrocÃlaw 50-372, Poland
E-mail: {Tomasz.Kubik|Witold.Paluszynski}@pwr.wroc.pl‡
Department of Geodesy and Photogrammetry, Agricultural University of WrocÃlaw
Norwida 25/27, WrocÃlaw 50-372, Poland
E-mail: iwaniak@ar.wroc.pl, tymkow@kgf.ar.wroc.pl
Keywords: image segmentation, chromacity moments

Abstracton remotely sensed data. This subject has particular interest to government agencies responsible for
economic or environmental policy at regional and up
to global scales. The solutions, including per-parcel
or per-field classification with further processing by
the use of statistical classification filters, have been
already published, [2, 3, 4].
The article presents a method of automatic mapping of land-cover/land-use that can be used withaerial photographs or satellite images as sources of remotely sensed data. The process of mapping is implemented as color feature based image segmentation.
The use of colors or multi-spectral information inimage segmentation is usually done by setting ranges
for them, and classifying individual pixels according
to the pixel range membership. Better results canbe achieved when the local statistics of analyzed image are taken under consideration. Such statistics isthe base of methods using texture analysis, where thespace relationship between pixels is concerned, [5]. In
the solution proposed in this article the local statistics
of colors, which does not take space dependency into
account, are used instead. This statistic is provided
in the form of chromacity moments, [1], which serveas features for classification ending up with image segmentation.
Originally, the segmentation based on chromacity
moments was invented for the images taken as photographs in the RGB format. But there is no reasons
against its use for the satellite image segmentation.
It is enough to substitute RGB components with thevalues of different spectrum channels.

The article presents a method of automatic mapping of land-cover/land-use that can be used withaerial photographs or satellite images as sources of remotely sensed data. The method is based on image
segmentation technique in which local color features
are employed.
The color feature extraction is based on the conceptof chromacities, as defined within the CIE XYZ color
space, and chromacity moments, [1]. In this solution
chromacities are characterized by two attributes: thechromacity diagram, and the chromacity distribution
histogram. The nature of both attributes is capturedby the set of moments forming the feature vector. This
vector reflects color statistics of the region of interest.
The size of the region of interest is defined by thesize of the custom window, which is moved across theimage in order to calculate feature vectors at each window’s position. The feature vectors obtained are used
for classification. Classification of the feature vectors
provides labels for the pixels defined by the window
centers. The outcome is a segmented image.
The important issues in such segmentation are features and classifier choices. The article addresses thisproblem, showing the way of feature weighting andnormalizing, and presents the classifier, which can be
successfully applied in this case. The illustration of thesegmentation proposed are results of the experiments
conducted with aerial and satellite images.

1

Introduction

Current concerns about land-cover/land-use
changes have lead to an increased demand for methods and techniques of automatic area estimates based2∗ This research was supported in part by the Committee for
Scientific Research through grant 5T12E02924.

Let I(i, j) for an image I denote the color of its
pixel at position i, j, where 0 ≤ i ≤ Lx − 1, and 0 ≤©ISAROB 2006

735

Methods

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Scaled and discrete chromacity distribution and its
trace can now be expressed as follows:j ≤ Ly − 1, and Lx , Ly correspond to the image width
and the image height (in pixels).

2.1

Ds (xs , ys ) = card{(i, j)|I(i, j) → (xs , ys )}RGB to CIE XYZ conversion

There are some slightly different formulas for RGB
to CIE XYZ color space conversion. In the article it isassumed that this conversion is given by the equation:

 


X
0.607 0.174 0.200R Y  =  0.299 0.587 0.114   G (1)
Z
0.000 0.066 1.111
B

2.2


 1, ∃(i, j) : I(i, j) → (xs , ys )Ts (xs , ys ) =

(8)

2.4

Chromacity moments

For the given scaled and discrete distribution with
its trace, the chromacity moments can be calculated
as follows:
XX
k,l
MD
xks ysl Ts (xs , ys )(9)(xs , ys ) =The chromacities, x, y, z, apart from their physicalinterpretation, can be calculated as follows:Z
YX, y= , z=
L
L
L

Xs ,Ys 0, ¬∃(i, j) : I(i, j) → (xs , ys )Xs ,Ys

Chromacity

x=

(7)

Xs ,Ysxs(2)
MTk,l (xs , ys ) =ysXX

xks ysl Ds (xs , ys )(10)

where L = X + Y + Z. It should be pointed out,that z value in the equation above is redundant. It
can be calculated from x and y according to the rule:z = 1 − x − y. Moreover, the value of each of the three
chromacities falls into the range [0, 1], (x, y, z ∈ [0, 1]).

where MD is used to denote the chromacity distribution moments, and MT is used to denote the momentsof the trace of MD .

2.3

2.5

xs

Chromacity distribution and chromacity trace(3)

where K is the number of pixels with chromacities
equal to x and y. This can also be written as:
D(x, y) = card{(i, j) | I(i, j) → (x, y)}Trace of the distribution, T (x, y), is given by:
if exist a pixel with1,chromacities (x, y),T (x, y) =0, otherwise.

(4)

(5)

or:
½
T (x, y) =1,0,∃(i, j) : I(i, j) → (x, y)¬∃(i, j) : I(i, j) → (x, y)(6)

So T (x, y) is in fact the projection of D(x, y) onto theplane x, y.
The distribution D, and the trace of distribution T ,need to be scaled and discretized in order to calculate
the chromacity moments. Let Xs , Ys ∈ ℵ denote thescaling factors. Let xs = Int(Xs x), ys = Int(Ys y)
represent scaled values, where xs , ys ∈ ℵ, and Int is afunction returning the integer part of its argument.

©ISAROB 2006

Classifiers

The chromacity moments are the features thatmight be classified. The simple k-nearest neighbors
method would be a good candidate for the classifier
choice, but there is a problem with a distance measure. The problem is caused by the fact, that the values of the chromacity moments depend on the orderof moments used. The chromacity moments of higher
orders have values much higher comparing to the chromacity moments of lower orders. Therefore the classifiers utilizing a simple distance measure in the feature
space are not appropriate. To use the Euclidian norm
the chromacity moments have to be normalized, or
weighted with appropriate weights.
The next candidate for the classifier choice could be
a neural network based classifier (with a simple threelayer, feed-forward neural network architecture). This
classifier works well, but the problem of normalizationof the chromacity moments remains.
Both classifiers mentioned in this subsection wereused in the experiments described hereafter precededby a kind of feature normalization.

Chromacity distribution, D(x, y), is given by:
D(x, y) = K,ys3

Experiments

The experiments were conducted as follows. At firsta set of small reference images was prepared by an

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4

expert. These images were extracted from the original images representing areas with different land covertypes. The extraction was done in such way, that eachreference image contained only one kind of a separable land cover type (i.e. it was a representative of one
class only). The reference images were processed thenin order to create the training data. The processing
was done by: moving a square window of a custom size
across the image; and calculating chromacity moments
at each window position.

Calculation of chromacity moments makes no sense
for the square windows smaller then 10 by 10. But thebigger this size is, the more extensive and expensive
the computation becomes. Moreover, the values of thechromacity moments are getting higher and can resultin overflow errors. Thus some kind of normalization isessential.
The normalization can be done through the divisionof each moment by the moment of 0 order. This is a0,0
good solution for the MD moments (MDis always
equal to Lx × Ly , where Lx and Ly define the sizeof the moving window, and therefore can be omitted
during classification). But it does not hold for TD case0,0(TD
can vary and therefore might be crucial for theclassification). Another way of normalization might be
the division of each moment by the value Xsk ·Ysl . Thissolution was successfully applied in the experiments
performed.

The training data created contained a set of pairs:
a vector consisting of the chromacity moments calculated (input) and a vector representing the land covermembership for each distinct class (output). The sizesof input and output vectors depend on the number ofclasses considered and the order of the chromacity moments used. Thanks to the knowledge of an expert,
each output vector had 1 at the position corresponding to the class it should represent, and 0 elsewhere.
The training data, prepared separately for satellite images and aerial photographs, were used to build two
different classifiers. They were used later in the image
segmentation.

References[1] G. Paschos: Fast color texture recognition using
chromaticity moments. Pattern Recognition Letters,
21:837–841, (2000).

The segmentation of aerial photographs and satellite images was done similarly as the training data creation. There was a moving window (of the same size
as in the training phase) and the calculation of thechromacity moments. But this time the chromacity
moments were further classified by the proper classifier built. This classification provided labels for the
pixels defined by the current window position.

[2] S. Berberoglu, C. Lloyd, P. Atkinson, and P. Curran: The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Computers and Geosciences, 26(4):385–396, (2000).

The size of the input vector used throughout theexperiment was 18 = 2 ∗ 32 (for k + l ≤ 3 there is 32
possible MD moments, and the same number of MT
moments). The size of the output vector corresponded
to the number of selected classes. The scaling factors
Xs and Ys were set to the value of 100. The size ofthe moving window was 20 × 20 pixels.

[3] X.-H. Liu, A. Skidmore, and H. Van Oosten: Integration of classification methods for improvementof land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 56(4):257–268,(2002).
On the[4] Y. Venkatesh and S. Kumar Raja:classification of multispectral satellite images using the multilayer perceptron. Pattern Recognition,
36(9):2161–2175, (2003).

The satellite images used in the experiments were
provided in the form of 4 spectra channels with 256
value levels, representing the same region covered by
the different land cover types. Three of the channels
were used as pseudo RGB components. The set of reference images used to build the classifier, the original
image, and the segmentation results are shown in the
Figure 2. The resolution of these images was 4 m.

[5] M. Tuceryan and K. Anil: The handbook of Pattern recognition and computer vision, chapter 2.1
Texture Analysis, pp. 207–248 World Scientific Publishing Co., 2nd edition, (1998).

The aerial photographs used in the experiments
were provided in RGB format originally. The set ofreference images used to build the classifier, the original image and the segmentation results are shown in
the Figure 1. The resolution of these images was 0.5 m.

©ISAROB 2006

Conclusions

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

grass

forest1

forest2

original image

field

segmented image

Figure 1: Reference images (top) and the test image with segmentation results (bottom) used in the experimentwith aerial photographs

housesgardens

fieldsfactory

original image

river

forest1

forest2

segmented image

Figure 2: Reference images (top) and the test image with segmentation results (bottom) used in the experimentwith satellite images©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Functional Motif Scanning Algorithm for Invertebrate EST Analysis
Makoto Ohkubo and Futoshi Aranishi
Department of Biological and Environmental Sciences
Miyazaki University
Miyazaki 889-2192, Japan

ABSTRACTEST sequence

Expressed Sequence Tag (EST) analysis is one of
the functional genomics techniques and a mining methodof physiological mechanisms by comprehensive analysisof the expressed genes. This technique is based on thesystematical homology search of gene information on theinternational DNA databases. However, very little gene
information of invertebrates has been accumulated on thepresent databases, and the molecular structures of theirgenes quite differ from those of vertebrate genes. A new
algorithm for invertebrate EST analysis, termed fmEST
algorithm, consisting of the systematical homology
search and functional motif scanning has been developed.
In this research, a total of 200 unidentified invertebrate
ESTs including 100 arthropod ESTs and 100 mollusk
ESTs were analyzed for the purpose to evaluate validityof the functional motif scanning for invertebrate EST
analysis. A total of 18 and 21 ESTs were determined tocontain the functional motif sequences for arthropod andmollusk, respectively. Their nucleotide lengths were
distributed from 388 to 954 in arthropod and from 222 to742 in mollusk. These ESTs were obviously identified as
any functional genes, while they did not show significant
homology to the gene information on the international
DNA database by BLAST homology search. Results
suggest that the functional motif scanning is a valuableapproach enabling to break new grounds in invertebrate
EST analysis.

Homology search

Clustering analysis

Low homology

High homology

Unidentified EST

Identified EST

Protein motif scanfmEST
Clustering analysis

Arrangement test
Identify

Figure 1. Scheme of fmEST algorithm. Gray boxes
indicate the traditional procedure of EST analysis.

1 Introduction

An EST dataset for analyzing the expressed genes
usually contains from 500 to 2000 EST clones. Theselarge numbers of the gene information accumulated on
the DNA databases are well applicable to broad scientificfields such as medical, pharmacology, microbiology, andphylogeny [2-5]. To date, functional genomic researches
about invertebrate taxa have been increasingly required
from physiological, ecological, and evolutional interesting [6-11]. The gene information on microorganisms andvertebrates, however, occupy the majority of the present
databases [12]. In addition, the molecular structures ofvertebrate genes quite differ from those of invertebrate
genes. We have developed a new analytical algorithm forinvertebrate EST analysis, termed as fmEST algorithm
involving the traditional systematical homology search
and a new “functional motif” scanning [12].

Expressed Sequence Tag (EST) analysis is one of
the functional genomics techniques and a mining methodof physiological mechanisms by comprehensive analysisof the expressed genes. EST is the nucleotide sequence
information of cDNA, a complementary single strand of
DNA to mRNA, which is expressed in the sample tissue,
cell and specimen.
The molecular biologists are used to identify EST
clones as the functional gene homologues by comparing
their nucleotide sequences with the gene information
recorded in the international DNA databases. Based onthese findings, the molecular biologists can survey thephysiological mechanisms occurred in different levels ofanimal, tissue, organ, or cell [1].

©ISAROB 2006

Arrangement test

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sequence for the annotation of the contig containing theseed sequence.
For example, an unidentified EST clone (GenBank
accession CI999386) was obtained from EST analysis of
kuruma prawn Marsupenaeus japonicus. Although it did
not show a significant match to the carboxypeptidase
gene of sea hare Aplysia californica (GenBank accession
U37755) by BLAST homology search (Score = 46.1,
Expect value = 0.079), it was determined to contain thefunctional motifs of zinc carboxypeptidase (InterProScan
accession IPR000834) by functional motif search. Twolocal short parts of the deduced amino acid sequences,
PEFKYVANMHGNEV and LPSMNPDGWQ, which are
the zinc-binding sites involved in the catalysis of carboxypeptidases, were well conserved in the prawn EST andsea hare enzyme gene by CLUSTALW analysis (Figure2). These results enabled us to annotate the unidentified
prawn EST as a gene encoding zinc carboxypeptidase.

The functional motifs are functionally importantsecondary structures of proteins, such as receptor binding
domains, immunological epitopes, and catalytic sites of
enzymes. They are encoded by short nucleotide sequence(generally less than 20 nucleotides) and highly conserved
during evolution. The functional motifs are a significant
clue to annotate large EST dataset [13].
In this research, we analyzed the functional motif
sequences from a total of 200 unidentified invertebrate
ESTs including 100 arthropod ESTs and 100 mollusk
ESTs, all of which showed no significant similarity to the
known functional genes by the homology search, for the
purpose of evaluating validity of the functional motif
scanning for invertebrate EST analysis.

2 fmEST algorithm
The traditional systematic approach to EST analysis
consists of BLAST homology search and subsequent
CLUSTALW [14] clustering analysis of EST dataset to
the gene information on the DNA databases. Several
studies on invertebrate EST analysis have been reported
that these traditional techniques enabled us to identify alimited part of EST dataset [6-11], because of less gene
information of invertebrates than vertebrates on the DNA
databases and divergent molecular structures between
invertebrate and vertebrates genes. BLAST homology
search therefore allows us to obtain a little information of
the functional gene as a candidate for a certain EST clone
having a short homologous nucleotide sequence.
The fmEST algorithm consists of two analysis steps,
traditional systematical approach and functional motif
scanning (Figure 1). Following BLAST homology search,
the nucleotide sequence of the unidentified EST clones
was deduced to amino acid sequence and then applied to
the functional motif search programs; InterProScan (http://www.ebi.ac.uk/InterProScan/), PROSITE (http://www.
expasy.org/prosite/), and Pfam (http://www.sanger.ac.uk/
Software/Pfam/). Subsequently, its nucleotide sequence
was applied to CLUSTALW clustering analysis as a seed

3 EST dataset and analysis
A total of 200 unidentified invertebrate EST clones
containing 100 arthropod ESTs (50 crustacean and 50
insect ESTs) and 100 mollusk ESTs (50 bivalve and 50
gastropod ESTs) were obtained from the GenBank under
the accession numbers from CI999365 to CI999414 inkuruma prawn M. japonicus, DT777319 to DT777368 inflour beetle Tribolium castaneum, DR409845 to DR
409894 in akoya pearl oyster Pinctada fucata, and DT
725365 to DT725414 in snail Biomphalaria glabrata,
respectively (Table 1).
Functional motif scanning was interactively carriedout using InterProScan functional motif search programon the web (http://www.ebi.ac.uk/InterProScan/), which
is operated by the European Bioinformatics Institute. The
option of “translation table” was set to “Standard Code”
for all ESTs. The option of the “minimum open reading
frame size” was set to 20, 50, 100, and 150 for individual
ESTs. The other parameters are set to default values.

Accession No.
U37755
CI999386------MDTSKMLS--QCVLVFAAAVAIVSYQSIEASQDTEENGSKKTESTIFFHHTYEEMCVHSSADTMKYCWGHVGVLLLLLVASVCVQAASVAKPGTSGNGTPSKSEFVFKHHNNEE** ***:: ..::: *. .*. **: .... :* **. **U37755
CI999386

MVSLMYEVNKACPEVTRIYNLSEPSVEKRNLTVLEITENPGVHVPGKPEFKYVANMHGNELEQVLRETAEKCKDVTRLYALSEPSVRNVPLWVIEFSDNPGQHDLLEPEFKYVANMHGNE: .:: *. : * :***:* ******.: * *:*:::*** *:*************

U37755
CI999386

VVGKEMVLYFLVALCEEYKRGDKLANFIVSQTRVHVLPSMNPDGWQKAYKELQEKGEAGWVLGRELLLALAHYLCQGWKDGDEEIKKLIKSTRIHLLPSMNPDGWQLATDTGGKDYLR-*:*:*::* : **: :* **: : ::..**:*:********** * . :.

Figure 2. Arrangement of deduced amino acid sequences of unidentified EST and sea hare carboxypeptidase gene.

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Table 1. Summary of EST datasets for functional motif search
PhylumClass

SpeciesNo. of clones

Av. nt length

Accession No.

Arthropoda

Crustacea
InsectaMarsupenaeus japonicus
Tribolium castaneum

50
50

480
890

CI999365 - CI999414
DT777319 - DT777368

Mollusca

Bivalvia
Gastropoda

Pinctada fucata
Biomphalaria glabrata

50
50

350
518

DR409845 - DR409894
DT725365 - DT725414

4 Results and discussion
6

The lengths of the nucleotide sequences of ESTs
obtained from various animals are generally distributed
from 100 to 1000. Those of 200 EST dataset were
distributed from 152 to 1025 in arthropod and from 147
to 743 in mollusk. The average nucleotide sequencelength of 50 ESTs of flour beetle was estimated to be 890,
which was higher than other species analyzed in thisresearch (Table 1). Insect taxa are the most understoodinvertebrate class in recent molecular biology [15], andtheir EST clones with long nucleotide sequences could
be easily prepared rather than other invertebrate taxa. In
contrast, the average nucleotide sequence length of 50
ESTs of akoya pearl oyster was lower than the others. It
is probably due to much less information on molecularbiological techniques available for bivalves.
The lengths of the nucleotide sequences of 18 and
21 ESTs containing the functional motif were distributed
from 388 to 954 in arthropod and 222 to 742 in mollusk,
respectively (Figure 3). This result indicates that thefunctional motif sequences are difficult to be found in
short ESTs less than approximately 200 nucleotides inlength. Several studies on invertebrate EST analysis havereported that EST datasets generally contain from 500 to2000 ESTs, and approximately 20 to 30 % of these ESTs
can be identified as any functional genes [6-11]. In thisresearch, a total of 39 ESTs from 200 arthropod andmollusk ESTs were determined to contain the functional
motif by functional motifs scanning (Figure 4). Thesefindings suggest that at least additional 100 ESTs might
be identifiable as any functional genes by the fmEST
algorithm in conventional invertebrate EST analysis.

Number of fmEST

Mollusk

4
3
2
1
01101- 201- 301- 401- 501- 701- 801- 901- 1001100 200 300 400 500 600 800 900 1000 1100

Nucleotide length (bp)

Figure 3. Distribution of nucleotide length of arthropod
and mollusk ESTs containing functional motif.

Arthropod
InsectMollusk
Bivalve

Crustacean

fmEST
Gastropod

fmEST

5 Conclusions
This research demonstrated that the functional motif
search allowed us breaking new grounds in invertebrate
EST analysis. However, the BLAST homology search
program has no ability to refer to functional motifs,
whereas it automatically deduces nucleotide sequences toamino acid sequences. In addition, the conventional
automated EST annotation programs are not incorporated
with the functional motif scanning program [16]. The
molecular biologists thus require heavy labor for analysisof the functional motif sequences of large numbers of
ESTs. A novel form of automated EST annotation system©ISAROB 2006

Arthropod

5

Figure 4. Proportion of ESTs containing functional
motif in invertebrate EST datasets analyzed.

incorporated with fmEST algorithm is proposed toinspire molecular biology for a variety of invertebratetaxa, which are involved in human familiar sciences suchas agriculture, fisheries, and ecology.

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006[11] Ljunggren, E.L., Nilsson, D., and Mattsson, J.G.
2003. Expressed sequence tag analysis of Sarcoptes
scabiei. Parasitology, 127 139-145.
[12] Ohkubo, M., and Aranishi, F. 2004. What does
invertebrate EST analysis acquire from bioinformatics? "Simulated Evolution and Learning (Kim,
J.-H., ed.)" Genome Informatics pp. 1-5, KAIST,
Daejeon, Korea.
[13] Whisstock, J.C., and Lesk, A.M. 2003. Predictionof protein function from protein sequence andstructure. Q. Rev. Biophys., 36 307-340.
[14] Thompson, J.D., Higgins, D.G., and Gibson, T,J.
1994. CLUSTAL W: improving the sensitivity of
progressive multiple sequence alignment throughsequence weighting, position-specific gap penalties
and weight matrix choice. Nucleic Acids Res., 224673-4680.
[15] Boutros, M., and Perrimon, N. 2000. Drosophila
genome takes flight. Nat. Cell Biol., 2 E53-E54.
[16] Hotz-Wagenblatt, A., Hankeln, T., Ernst, P.,
Glatting, K.H., Schmidt, E.R., and Suhai, S. 2003.
EST Annotator: A tool for high throughput EST
annotation. Nucleic Acids Res., 31 3716-3719.

Acknowledgment
We thank Professor Ikuo Yoshihara, Department ofComputer Science and Systems, Miyazaki University, for
his helpful suggestions to the preparation of this article.

References[1] Asamizu, E., and Nakamura, Y. 2004. System forexpressed sequence tags (EST) analysis. Tanpakushitsu Kakusan Koso, 49 1847-1852.
[2] Angus, A.C., Ong, S.T., and Chew, F.T. 2004.
Sequence tag catalogs of dust mite-expressed
genomes: utility in allergen and acarologic studies.
Am. J. Pharmacogenomics, 4 357-369.
[3] Samuels, A.K., Weisrock, D.W., Smith, J.J.,
France, K.J., Walker, J.A., Putta, S., and Voss, S.R.
2005. Transcriptional and phylogenetic analysis offive complete ambystomatid salamander mitochondrial genomes. Gene, 349 43-53.
[4] Evans, D.L., Kaur, H., Leary, J. 3rd., Praveen, K.,
and Jaso-Friedmann, L. 2005. Molecular characterrization of a novel pattern recognition proteinfrom nonspecific cytotoxic cells. Dev. Comp.
Immunol., 29 1049-1064.
[5] Faria-Campos, A.C., Cerqueira, G.C., Anacleto, C.,
de Carvalho, C.M., and Ortega, J.M. 2003. Mining
microorganism EST databases in the quest for new
proteins. Genet. Mol. Res., 31 169-177.
[6] Gross, P.S., Bartlett, T.C., Browdy, C.L., Chapman,
R.W., and Warr, G.W. 2001. Immune gene discovery by expressed sequence tag analysis ofhemocytes and hepatopancreas in the PacificWhite Shrimp, Litopenaeus vannamei, and theAtlantic White Shrimp, L. setiferus. Dev. Comp.
Immun., 25 565-577.
[7] Lehnert, S.A., Wilson, K.J., Byrne, K., and Moore,
S.S. 1999. Tissue-specific expressed sequence tags
from the black tiger shrimp Penaeus monodon.
Mar. Biotechnol., 1 465-476.
[8] Supungul, P., Klinbunga, S., Pichyangkura, R.,
Hirono, I., Aoki, T., and Tassanakajon, A. 2004.
Antimicrobial peptides discovered in the black
tiger shrimp Penaeus monodon using the EST
approach. Dis. Aquat. Organ., 61 123-135.
[9] Jenny, M.J., Ringwood, A.H., Lacy, E.R., Lewitus,
A.J., Kempton, J.W., Gross, P.S., Warr, G.W., andChapman, R.W. 2002. Potential indicators of stressresponse identified by expressed sequence tag
analysis of hemocytes and embryos from theAmerican oyster, Crassostrea virginica. Mar.
Biotechnol., 4 81-93.
[10] Lee, Y.H., Huang, G.M., Cameron, R.A., Graham,G., Davidson, E.H., Hood, L., and Britten, R.J.
1999. EST analysis of gene expression in earlycleavage-stage sea urchin embryos. Development,
126 3857-3867.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Molecular Clocks in Aquatic Invertebrates
Futoshi Aranishi and Takane Okimoto
Department of Biological and Environmental Sciences
Miyazaki University
Miyazaki 889-2192, Japan

Genes to Clocks

Mitochondrial Clock is Precise or Imprecise?

The discovery of the molecular clock – a relatively
constant rate of molecular evolution – provided an insight into the mechanisms of molecular evolution, andcreated one of the most useful tools in biology [1-3]. The
unexpected constancy of rate was explained by assumingthat most changes to genes are effectively neutral — thatis, driven by drift not selection [4-7].
The molecular clock is a 'sloppy' clock. The neutral
theory predicts several sources of variation in rate ofmolecular evolution that is influenced by mutation rate,
drift pattern, and population size. Although the molecular
clock allows time estimates of events in evolutionary
history, which provides a method for testing a wide rangeof biological hypotheses from the origins of the animal
kingdom, 1) stochastic fluctuations in substitution rate ofgenes over time in lineages make date estimates imprecise, and 2) variation in rate between lineages can cause
substantial bias in date estimates. Attempts to use themolecular clock to date evolutionary divergences mustaccount for these sources of imprecision and bias, andvariation in rate must be expressed in confidence intervals around date estimates [8].
The molecular clock hypothesis is the basis of themodern molecular phylogenetic approach for dating andresolving evolutionary history. Such approach requires
the comparison of orthologous genes. Orthologous andparalogous genes usually possess different evolutionary
fates. In general, orthologs keep the same functions in
species, but, particularly over a long time span, paralogs
diverge functionally and may become pseudogenes or get
lost [9].
The mitochondrial genome certainly represents amajor model system in studies on evolutionary genomics
in metazoan, because of its peculiar features, such as
exclusive presence of orthologous genes, uniparental inheritance, lack of recombination, small size, and constantgene content. In 800 million years (My) of evolution, thegene content of metazoan mitochondrial genomes hasremained practically frozen, but an array of evolutionary
processes has taken place; variations in the genetic codeand evolution of codon usage, arising of compositional
asymmetry between the two strands, rearrangements ofgene order, changes in base composition, and lineagespecific nucleotide substitution rates and evolutionary
patterns [10].

The mitochondrial cytochrome c oxidase I subunit(COI) gene is suggested to have evolved neutrally, whichmeans nucleotide alterations of this gene have occurredconstantly. The COI therefore has been frequently usedin resolving phylogenetic relationships and evolutionary
history of various metazoan taxa. We first attempted toestimate its evolutionary constant for nucleotide alterations based on the genetic distances in the Kimura’s twoparameter model [11].
Five anthropoid organisms belonging to the family
Hominidae (phylum Vertebrata, class Mammalia, order
Primates) were used, because they possessed relatively
short evolutionary history, and numerous fossil records
revealed their evolutionary nodes. These facts indicate
that, if an appropriate orthologous gene is employed, themolecular clock calculated from the genetic distancesamong these organisms allows precise time estimates of
evolutionary events, which well correspond to other nonmolecular information including the fossil records.
The complete nucleotide sequences of the COI gene
were obtained from the GenBank under the accession
numbers of J01415 for human Homo sapiens, NC001646
for orangutan Pongo pygmaeus, NC001645 for gorilla
Gorilla gorilla), NC001644 for bonobo Pan paniscus,and NC001643 for chimpanzee Pan troglodytes. Multiple sequence alignments were performed using the program CLUSTAL W ver. 1.7 [12], and genetic distanceswere calculated from multiple sequence substitutions by
the two-parameter model [11]. Distance phylogram wasconstructed by the unweighted pair group method witharithmetic mean using the program MEGA ver. 2.1 [13].
Non-molecular information indicated that the node
between orangutan and other four species was distributed
around 8 My ago. In addition, the node between humanand Pan taxa, chimpanzee and bonobo, was expected to
be 4 to 6 My ago. Comparison of these findings with the
distance phylogram of the COI gene, 0.01 distance (D) in
the two-parameter model resulted in corresponding to1.08 My (Figure 1). The D values from the nodes between two Pan species, human and the genus Pan, andGorilla and the genera Pan and Homo to their present
status were calculated to be 0.020 D (2.156 My), 0.046 D(4.998 My), and 0.055 D (5.929 My), respectively. All ofthese time estimates of evolutionary events correspond tonon-molecular information, indicating that the molecular
clock of the COI gene based on the genetic distances inthe two-parameter model could be a precise evolutionary
clock with the 0.01 D at 1.08 My.

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Figure 1. Distance phylogram of nucleotide sequences
encoding the mitochondrial COI gene from 5 Hominidae
species. Numbers of nucleotide alteration indicated by
the scale are the Kimura’s two-parameter distances.

the sea. They thrive in water that is rich in phytoplankton.
One of the most commercially important oysters worldwide is Crassostrea gigas, named as Japanese or Pacificoyster in English, and kaki or magaki in Japanese.
The classification of oysters is based on the formand structure of larval shell, mode of reproduction, life
history, adult shell morphology, and foot shape, but theirtaxonomic positions have never been clearly established[15]. There is a hypothesis available for the evolution
and classification of oysters; all species of the genus
Crassostrea originated from a common fossil ancestor C.
gryphoides and have remained unchanged for about 10
My [16].
On the basis of this hypothesis, the taxonomic positions of species of the genus Crassostrea could be established, if an approximate molecular clock enabled us toestimate times and orders of their classification events inevolutionary history. In the following part, we attempted
to illustrate the phylogenetic relationships of species of
the genus Crassostrea together with those of the genus
Ostrea by means of the data set of nucleotide sequences
encoding the mitochondrial COI gene (Table 1).

Evolution of Oysters

Mitochondrial Clock in Oysters

Oysters are widely distributed throughout tropicaland subtropical areas (Figure 2). They are benthic marineorganisms inhabiting near-shore, shallow water, bay, andestuary locations [14]. The "true oysters" are more than
100 members of the family Ostreidae, and this includes
edible oysters, which are mostly classified in the genera
Ostrea or Crassostrea (Table 1).
Species belonging to the genus Ostrea generally
live continually immersed in sea water and brood theirfertilized eggs for various proportions of the period fromfertilization to hatching. They can thrive in water with a
not too thick concentration of phytoplankton. Otherwise,
species belonging to the genus Crassostrea generally livein the intertidal zone and broadcast sperm and eggs into

For a total of 13 Ostreidae species including 9 and 4
ones of the genera Crassostrea and Ostrea, respectively,
nucleotide sequences encoding the mitochondrial COI
gene were recorded in the GenBank (Table 1). As thesesequences varied in the position and length, they werearranged to be 579 bp for the complete correspondences,and then subjected to multiple sequence alignments forcalculation of the genetic distances in the two-parametermodel as above. In order to date evolutionary events ofoysters using the molecular clock of the COI gene alongwith their non-molecular information, it should be considered whether the fossil species of C. gryphoides was
the common ancestor of either the genus Crassostrea or
the family Ostreidae.

Chimpanzee
BonoboHuman
GorillaOrangutan
0.08 0.06 0.04 0.02 0.00

Table 1. List of mitochondrial COI nucleotide sequences of oysters analyzed in this study
Phylum

Class

Order

FamilyGenus

Species

Mollusca

Bivalvia

Ostreoida

Ostreidae

Mollusca

Bivalvia

Ostreoida

Ostreidae

Crassostrea gigas
nipponaariakensis
sikamea
hongkongensis
belcheriiredeleiangulata
virginica
Ostreaequestris
chilensis
aupouria
angasi* GenBank accession number.
** Grobal distribution on Figure 1.

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Common name

GenBank* Distribution**

Japanese oyster
Iwagaki oyster
Suminoe oyster
Kumamoto oyster
Hongkong oyster
Lugubrious oyster
Philippine oyster
Portuguese oyster
Eastern oyster
Crested oyster
Chilean oyster

AF177226
AF300616
AY632559
AY632568
AY632557
AY038077
AY038078
AF152567
AY905542
AY376609
AF112289
AY376630
AF540598

Australian oyster

1
1
2
2
3
4
5
6
7
8
9
10
11

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

6

4

1

5

7

2

8

3

11

10

9

Figure 2. Global distribution of Crassostrea and Ostrea oysters.
1 – C. gigas, C. nippona; 2 – C. ariakensis, C. sikamea; 3 – C. hongkongensis; 4 – C. belcheri; 5 – C. iredelei;
6 – C. angulata; 7 – C. virginica; 8 – O. equestris; 9 – O. chilensis; 10 – O. aupouria; 11 – O. angasi

C. gigas

C. angulata
C. sikamea
C. ariakensis
C. hongkongensis
C. nippona
C. iredalei
C. belcheri
C. virginica
O. equestrisO. aupouria
O. chilensis
O. angasi
0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

Figure 3. Distance phylogram of nucleotide sequences
encoding the mitochondrial COI gene from 13 Ostreidae
species listed in Table 1. Numbers of nucleotide alteration indicated by the scale are the Kimura’s two-parameter
distances.

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745

The distance phylogram of the COI gene indicatedthat the D values of the nodes between the genera Crassostrea and Ostrea, and between C. virginica and other
Crassostrea taxa to their present status were 0.142 D and
0.127 D, respectively (Figure 3). When the fossil C.
gryphoides could be located at the respective nodes, 0.01
D in the two-parameter model resulted in corresponding
to 0.71 My from 0.142 D and 0.79 My from 0.127 D.
Both of these values were apparently lower than the 0.01
D at 1.08 My in case of the family Hominidae.
It is noteworthy that the distribution areas of two
species, C. virginica and C.angulata are isolated far fromother Crassostrea species, which are concentrated at theEast Asia (Figure 2). The taxonomic status of C. gigas
and C. angulata has been often questioned since no morphological or genetic differences had ever been observed
between the two Crassostrea species. In addition, therecent molecular-based research using the COI and 16S
ribosomal RNA (16S rRNA) genes as genetic makers
concluded that C. angulata was certainly identical with C.
gigas collected from Taiwan [17]. These facts indicatethat only C. virginica is geographically (Figure 2) andgenetically (Figure 3) isolated from other Crassostrea
taxa.
We hypothesized that the fossil C. gryphoides may
not be an ancestor of C. virginica but a common ancestorof other Crassostrea taxa. Based on this hypothesis, if
the fossil species could be located on the node between C.
virginica and others, its D value was determined to be
0.915 D. In addition, 0.01 D in the two-parameter model
resulted in corresponding to 1.09 My. Interestingly, thisvalue is well coincident with the 0.01 D at 1.08 My incase of the family Hominidae.
The mitochondrial COI gene is suggested to have
evolved neutrally, particularly in vertebrate taxa, but it is
not verified in invertebrate taxa because of much smaller
numbers of its molecular phylogenetic and evolutionary
studies on invertebrate taxa than vertebrate taxa. In thisThe Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[7] M. Kimura, and T. Ohta (1971) Protein polymerphism as a phase of molecular evolution. Nature
229:467–469.
[8] F. J. Ayala (1999) Molecular clock mirages.
BioEssays 21:71–75.
[9] C. Saccone, C. Caggese, A. M. D’Erchia, C.
Lanave, M. Oliva, and G. Pesole (2003) Molecular
clock and gene function. J. Mol. Evol. 57:277–
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[10] C. Saccone, C. Gissi, A. Reyes, A. Larizza, E.
Sbisa, and G. Pesole (2002) Mitochondrial DNA inmetazoa: degree of freedom in a frozen event.
Gene 286:3–12.
[11] M. Kimura (1980) A simple method for estimating
evolutionary rates of base substitutions throughcomparative studies of nucleotide sequences. J.
Mol. Evol. 16:111–120.
[12] J. D. Thompson, D. G. Higgins, and T. J. Gibson(1994) CLUSTAL W: improving the sensitivity of
progressive multiple sequence alignment throughsequence weighting, position-specific gap penaltyes and weight matrix choice. Nucleic Acids Res.
22:4673–4680.
[13] S. Kumar, K. Tamura, B. I. Jakobsen, and M. Nei(2001) MEGA2: Molecular Evolutionary Genetics
Analysis software. Bioinformatics 17:1244–1245.
[14] D. Hedgecock (1995) The cupped oyster and thePacific oyster. “Conservation of Fish and Shellfish
Resources: Managing Diversity (J. Thorpe, G. Gall,
J. Lannan, and C. Nash, eds.)”. Academic Press,
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[15] B. -H. Park, M. -S. Park, B. -Y. Kim, S. -B. Hur,and S. -J. Kim (1989) “Culture of the Pacific
Oyster (Crassostrea gigas) in the Republic ofKorea”. FAO, Rome.
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article, we introduced our preliminary study supportingthat the COI gene could have altered at the constant ratefrom invertebrate though vertebrate taxa, if C. virginica
and other Crassostrea taxa had evolved from different
origins.
The complete mitochondrial nucleotide sequenceswere determined for only two Crassostrea species in thefamily Ostreidae. Those of C. gigas (NC001276) and C.
virginica (NC007175) are, however, entirely divergent inlength of nucleotide sequence, number and arrangementof genes, and structure of the control region. This fact islikely to support our assumption of different evolutionary
histories between C. virginica and other Crassostrea taxa
inhabiting in the East Asia.

Perspectives in Oyster Molecular Clock
Various deviations, which are caused by rate heterogenety between lineages from molecular clocks, havebeen observed for most genetic markers [18]. However,recent approach to molecular clock calculations provides
the potential to obtain robust temporal reconstructions byassuming any number of local molecular clocks within a
global phylogeny [19].
In addition to the COI gene, a data set of nucleotide
sequences encoding the mitochondrial 16S rRNA is alsoavailable in the GenBank for 9 Crassostrea and 4 Ostrea
species, the same taxa as we here analyzed using the COI
gene. Our next investigation is to reconstruct molecular
clock calculations based on two local clocks of the COI
and 16S rRNA genes that are suggested to have evolved
divergently.

References[1] E. Zuckerkandl, and L. Pauling (1962) Moleculardisease, evolution, and genic heterogeneity. “Horizons in Biochemistry (M. Kasha, and B. Pullman,eds.)”. Academic Press, New York, pp. 97–166.
[2] E. Zuckerkandl, and L. Pauling (1965) Evolutionary divergence and convergence in proteins.
“Evolving Genes and Proteins (V. Bryson, and H.
J. Vogel, eds.)”. Academic Press, New York, pp.
97–166.
[3] E. Margoliash (1963) Primary structure and evolution of cytochrome c. Proc. Natl. Acad. Sci. USA.
50:672–679.
[4] M. Kimura (1968) Evolutionary rate at the molecular level. Nature 217:624–626.
[5] M. Kimura (1969) The rate of molecular evolution
considered from the standpoint of population
genetics. Proc. Natl. Acad. Sci. USA. 63:1181–
1188.
[6] M. Kimura (1983) “The Neutral Theory of Molecular Evolution”. Cambridge University Press,
Cambridge.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Self-Organizing Map of Species using Ribosomal Protein DNA Sequences
Yusuke HIGASHI
Graduate.School of Engineering,
Univ. of Miyazaki,
1-1, Gakuen-Kibanadai-Nishi,
Miyazaki,889-2192,Japan

Maki YOSHIHAMA
Frontier Science Research Center,
Univ. of Miyazaki,
5200, Kiyotake-Kihara,
Miyazaki,889-1692,Japan

Ikuo YOSHIHARA
Faculty of Engineering,
Univ. of Miyazaki,
1-1, Gakuen-Kibanadai-Nishi,
Miyazaki,889-2192,Japan

Kunihito YAMAMORI
Faculty of Engineering,
Univ. of Miyazaki,
1-1, Gakuen-Kibanadai-Nishi,
Miyazaki,889-2192,Japan

Naoya KENMOCHI
Frontier Science Research Center,
Univ. of Miyazaki,
5200, Kiyotake-Kihara,
Miyazaki,889-1692,Japan
Abstract

lution.
We try to clarify intron evolution by comparing
across species. Grantham clariﬁed the feature of codon
frequency in species gene by multiple classiﬁcation
analysis[1]. And, Abe classiﬁed species based on SOM
using codon frequency, and demonstrate eﬀectivenessfor genome analysis[2][3][4]. Those researches focusedon transcriptional region (exon and intron) , not applied to intron.
The ribosomal protein gene involves useful debris
for clarifying intron evolution[5]. For example, all eukaryotic have it and the coding region (exon) is saved
across species, etc. We analyze the ribosomal protein
gene for clarifying intron evolution.
We clarify whether unique information of species is
included in intron for to make clear intron evolution.
First, we make map using base sequence of transcriptional region, and conﬁrm that species are classiﬁed.
Next, we apply to base sequence of intron region.

Eukaryotic gene has exon and intron. Exon is translated into protein, but intron is not. The intron has
many unknown things one of which is so-called intron
evolution or biological evolutional process. Comparing across species is a good way to make clear intron
evolution. The ribosomal protein gene involved debris
useful for clarifying intron evolution. We try to clarify
intron evolution by analyzing base sequence of ribosomal protein gene using classiﬁcation of species by
self-organizing map (SOM). The input data is codon
frequency that is appearance of frequency of three continuous bases. We prepare two kinds of data; one involves both intron and exon and the other does only
intron. We experimented for to obtain maps of classiﬁed species.

1

Introduction

Eukaryotic gene has exon and intron. Exon is translated into protein, but intron does not directly contribute to protein synthesis. The intron has many unknown things one of which is so-called intron evolution
or biological evolutional process. Comparing acrossspecies is an eﬀective method to clarifying intron evo-

©ISAROB 2006

2

Self-Organizing Map

SOM is a kind of neural networks, and it is nonlinear projection form high-dimensional data to twodimensional map[6][7]. It can see similarity of high-747

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0dimensional vector in two-dimensional map, and highsimilarity of vectors is shown cluster in map. SOM isused by engineering, economic science and linguistics,
etc that is ﬁeld of analyzing large and complex data.

2.1

J-1

0
Competitive layer

Algorithm
I-1

SOM has input layer and competitive layer, andthose units are connected completely as shown in
Figure1. The input vector of n piece is assumed
(n)
(n)
(n)
to be x(n) (= x1 , x2 , ..., xk ), each nodes of competitive layer are shown by i(= 0, 1, .., I − 1) andj(= 0, 1, ..., J − 1).
The weight vector of eachnode of competitive layer is assumed to be wij (=wij1 , wij2 , ..., wijk ). Figure2 shows the learning process. In step1, the weight vectors on competitive layer
are initialized randomly. In step2, the input vector
is presented. In step3, the node on competitive layer
with weight vector that is minimum Euclidian distancewith input vector is searched. This node is called winner node. The Euclidian distance is deﬁned as follows.
X (n)(xk − wk )2(1)
E=

Weight : wij

(n)

x1

(old)

= wij

(old)

+ α(t)(x(n) − wij)(3)β(t) = max{0, βinit − t}

(4)

Step1

Presenting input vector
Step2

Searching winner nodeUpdating weight vector
Step3

No
End conditionYes

αinit and βinit are each initial value.

Classiﬁcation Experiments

3.1

Ribosomal Protein Gene

End

Step4

Figure 2: The learning process• The coding region (exon) is saved across species.

This study does classiﬁcation species experiments.
The used data is the ribosomal protein gene, because this gene has four eﬀective features for intron
analysis[5].

• Number of intron in 1-kilo base is more than othergenes.
In this paper, the input data to SOM is codon frequency that appearance of frequency of three continuous bases. We calculate codon frequency form base se-• All eukaryotic species has it.
• Number of intron region is more than other genes.

©ISAROB 2006

xk

Initialization(2)α(t) = max{0.01, αinit (1 − t/T )}

(n)

(n)

x5

Start

t(= 0, 1, ..., T ) represent iteration, α(t)(0 < α(t) < 1)
represent learning rate, and β(t) represent area of the
update. α(t) and β(t) is deﬁned as follow.

3

(n)

x4

Figure 1: The structuer of SOM

In step4, the weight vector include in area of centeron winner node are update conform presented input
vector. The weight vector is updated as follow.
(new)

x3

Input layer

k

wij

(n)

(n)

x2

748

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

DNA
1000base

Species
Arabidopsis thaliana
Caenorhabditis elegans
Dictyostelium discoideum
Drosophila melanogaster
Homo sapiens
Plasmodium falciparum
Schizosaccharomyces pombe
Saccharomyces cerevisiae
Total

1000base 1000base 1000base 1000base

a c g t g c t g a c t c t c t g c t c a c t c t ……t……t a gExon
IntronFigure 3: The codon frequency of transcriptional region

ID
A
B
C
D
E
F
G
H

Number of pattern
Method 1
Method 2
273
119
66
21
61
22
130
58
356
306
73
24
80
10
102
36
1141
596

Table 1: The dataset information

DNA

1000base

1000base

Parameter
Map size
αinit
βinit
Iteration

1000base

Method 1
34 × 340.5
17100 epochs

Method 2
25 × 250.5
14100 epochsa c g t g c t g a c t c t c t g c t c a c t c t ……t……t a gTable 2: The parameters of SOM

Exon
IntronFigure 4: The codon frequency of intron region

3.3
quence of the ribosomal protein gene for experiments.
Codon frequency of each species is made tow datasets
by each methods.

Figure5 and Figure6 shows experimental results ofeach dataset. Figure5 is a map obtained in the dataset
made from transcriptional region (method 1), and Figure6 is a map obtained in the dataset made from intron
region (method 2). The displayed character on each
map corresponds to species shown in Table1. The ’-’expresses not plotting at all, and the ’*’ expresses plotting more than two species. Those results mean thatspecies classiﬁes it by seeing Figure5 and Figure6.

Method 1: Codon frequency of transcriptional region
Base sequence of transcriptional region is cut out
every 1-kilo base as shown in Figure3. In addition,codon frequency is calculated form 1-kilo base.
Method 2: Codon frequency of intron region
The intron regions are picked up from the transcriptional region and they join, and base sequence of joined intron region is cut out every 1kilo base as shown in Figure4. In addition, codon
frequency is calculated from 1-kilo base.

4Here, the codon frequency is calculated by shifting every one base.

3.2

Discussion

Figure5 is making map using transcriptional region,and this map is classiﬁes species. Therefore, transcriptional region include unique information of species.
Next, Figure6 is experimental result of mapping using intron region. This map is classiﬁes species justlike analysis of transcriptional region.

Experimental Conditions

The information of those dataset is as shown in Table1. The ID in Table1 is used to identify species on
the map.
The parameters of SOM to each dataset are asshown in Table2.

©ISAROB 2006

Experimental Results

In spite of what exon region translated protein, intron region do not directly contribute to protein synthesis. However, as the result, intron region haveunique information of species.

749

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 20065

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Figure 5: The result of method1

Figure 6: The result of method2

Conclusions

ing Hidden genome signatures, Genome Research,
13:693-702, 2003

We tried to clarify intron evolution by analyzing
sequence of the ribosomal protein gene using classiﬁcation of species by SOM. The input data is codon frequency. Two datasets are prepared codon frequency
of base sequence of transcriptional region (exon andintron) and base sequence of intron region. We obtained maps of classiﬁed species; one involves both
intron and exon and the other does only intron. Experiment shows that base sequence of intron region has
unique information of species.
The future works are to develop method of discovering for information of intron region based on obtained
map, and to compare obtained map with evolutionary
tree, etc.

[3] T. Abe, S. Kanaya, M. Kinouchi, Y. Kudo, H.
Mori, H. Matsuda, C.D. Carpio, T. Ikemura, andT. Ikemura, Gene classiﬁcation method based onbatch-learning SOM, Genome Informatics, 10:314315, 1999
[4] S. Kanaya, M. Kinouchi, T. Abe, Y. Kudo, Y. Yamada, T. Nishi, H. Mori, and T. Ikemura, Analysis
of codon usage diversity of bacterial genes with aself-organizing map: characterization of horizontally transferred genes with emphasis on E. coil
157 genome, Gene, 276:89-99, 2001

Acknowledgements[5] A. Nakao, M. Yoshihama, and N. Kenmochi, RPG:
Ribosomal Protein Gene database, Nucleic Acids
Res, 32.D168-D170, 2004

This research is partly supported by MEXT grant(c) 17500146 in 2005

[6] T. Kohonen, Self-organizing maps, Springer-verlag
Tokyo, ISBN4-431-70700-x, 1996 (In Japanese)

References[7] H. Tokutaka, S. Kishida, K. Fujimura, Applicationof self-organizing maps, Kaibundo Syuppan. Co,
ISBN4-303-73230-3, 1999 (In Japanese)[1] R. Grantham, C. Gautier, M. Gouy, R. Mercier,A. Pave, Codon catalog usage and the genome hypothesis, Nucleic Acids Res, 8:r49-462[2] T. Abe, S. Kanaya, M. Kinouchi, Y. Ichiba, T.
Kozuki, and T. Ikemura, Informatics for unveil-©ISAROB 2006

750

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Non-transcriptional Region Analysis of Dictyostelium Discoideum using 1/f Noise
Masakazu SATO
Graduate School of Engineering,
University of Miyazaki
1-1,Gakuen-Kibanadai-Nishi,
Miyazaki, 889-2192,Japan
Kunihito YAMAMORI
Faculty of Engineering,
University of Miyazaki
1-1, Gakuen-Kibanadai-Nishi,
Miyazaki, 889-2192, Japan

Ikuo YOSHIHARA
Faculty of Engineering,
University of Miyazaki
1-1,Gakuen-Kibanadai-Nishi,
Miyazaki, 889-2192, Japan

Moritoshi YASUNAGA
Graduate School of Systems and Information,
Engineering, University of Tsukuba
1-1-1,Tennoudai,Tsukuba,
Ibaraki, 305-8573,Japan

Abstractand Miyagi [1][2][3] showed that several fractal groupcorrelations exist in a partial sequence and total sequence of bacteriopahge φ-X174. The lengths of partial sequences are 64, 128, 256, and 512 etc., because
they employed FFT with radix two. To avoid limitation of 2n , we developed a FFT program with mixed
radix (2, 3, 5) that enables to analysis various lengthsof DNA base sequences.
As a ﬁrst step of research on non-coding region,
we analyze non-transcriptional region which is also
non-coding. The process of synthesizing protein bytranscription of a part of DNA is called gene expression (Figure 1). In non-transcriptional region,
information to control the gene expression is included. We analyze non-transcriptional region of
Dictyostelium Discoideum using exponent α of 1/f α .
Dictyostelium Discoideum is known as an important model organism [6][7], because it has the simple
growth cycle.

The eukaryotic genome has regions that do not contribute explicitly to synthesize protein. Those noncoding regions occupy most part of DNA base sequences of eukaryotic genome. To ﬁnd embedded information in non-coding region is important to clarify
evolution of species.
As a ﬁrst step of research on non-coding region,
we analyze non-transcriptional region which is also
non-coding. We analyze DNA base sequences ofDictyostelium Discoideum with 1/f noise in chaostheory. Dictyostelium Discoideum is so-called modelorganism that has been investigated worldwide.

1

Introduction

The eukaryotic genome consists of two kinds of regions. One is coding region that operates to synthesize
protein. The other is non-coding region that does notoperate to synthesize protein. Most of DNA base sequences of eukaryotic genome are non-coding region.
For instance, 95% or more of human genome is noncoding. To ﬁnd embedded information in non-coding
region is one of the most important tasks for clarifying
evolution of species. We develop a method to ﬁnd thisinformation using 1/f noise in chaos theory.
Li and Kaneko [4] , and Voss [5] applied the 1/fnoise method in DNA base sequences. They suggest
the power spectrum of DNA base sequences shows
power-law scaling similar to the 1/f α noise at low frequencies. The exponent α of 1/f α shows the gradient of the regression line of power spectrum. Takushi©ISAROB 2006

Figure 1: The Process of synthesizing Protein

751

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

2

Dictyostelium Discoideum

Step1 Numeric conversion of DNA base sequences
Step2 Calculation of power spectrum

Dictyostelium Discoideum is a kind of primitive
eukaryote. Life cycle of Dictyostelium Discoideum
consists of four growth stages based on timing of gene
expression (Figure2).

Step3 Linear regression at low frequency by leastsquare method (Figure3)
Step4 Calculation of gradient (−α) of regression line

(1) Vegetative stage that lives independently withamoeboid and proliferates. (V stage)-1.5

power spectrum
regression line

-2(2) Aggregating stage that assembles in the center of
the aggregate and forms pseudoplasmodium. (Astage)-2.5log(power spectrum)-3(3) Slug stage that begins to move pseudoplasmodium. (S stage)

-3.5
-4
-4.5
-5
-5.5
-6

(4) Culminating stage that forms a fruiting bodyfrom pseudoplasmodium. (C stage)

-6.5
-7

And amoeba appears by germinating of spore of fruiting body.

0

0.5

1

1.5
2
log(frequency)

2.5

3

3.5

Figure 3: Power Spectrum and Regression Line

4.1

Numeric Conversion of DNA Base Sequences

DNA base sequences consist of four kinds of bases;
A (Adenine), C (Cytosine), G (Guanine), and T(Thymine). It is necessary to convert each bases into
the numerical value before Fourier transform. Eachbases are converted into complex number, because thedistance between four bases can arrange as evenly as
possible (Figure4).
Figure 2: Life Cycle of Dictyostelium Discoideum

3• A· · ·(1 + r1 ) + (1 + r2 )i• G· · ·(−1 + r3 ) + (1 + r4 )i• C· · ·(1 + r5 ) + (−1 + r6 )i• T· · ·(−1 + r7 ) + (−1 + r8 )i

1/f Noisern ∈< −0.1, 0.1 >When gradient of graph obtained by the spectral
analysis is -1, it is called the 1/f noise. The 1/f noise
is observed in natural phenomena such as ﬂame of candle, the breeze, and the classical music etc. It is said
that the 1/f noise is related to a comfort sensation.
As for 1/f α noise, if the gradient increases, an irregular transition decreases. On the other hand, if thegradient decreases, an irregular transition increases.

4

Calculation of α of 1/f α

The exponent α of 1/f α is calculated by the following procedure.

©ISAROB 2006

Figure 4: Numeric Conversion of DNA Base Sequences

752

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4.2

Fourier Transform and Power Spectrum

The DFT (Discrete Fourier Transform) of discrete
data fn (n = 0, 1, · · · , N − 1) calculate by the following
formula.
Fk =

N
−1
X

fn e

−i2πknN(k = 0, 1, · · · , N − 1)

Figure 6: Movement of Position of a Partial Sequence
average of α of partial sequence in transcriptional region. The α of non-transcriptional region is larger
than α of transcriptional region. These results meanthat DNA base sequences of non-transcriptional region is more regular than that of the transcriptional
region. Average of α converge from L ≈ 500 in nontranscriptional region.
Figure 9 shows the transition of average of α ineach growth stage (V stage, A stage, C stage, S stage)at L = 48. In V stage and C stage, the average ofα increases in front of TSS. This increase might be
an inﬂuence of the change in frequency of appearanceof each bases in front of TSS as shown in Figure 10,
Figure 11 . However, such an increase is not clearlyobserved in each result before the average is calculated.

(1)

n=0

fn :DNA base sequences expressed by numerical value
Next, the power spectrum is calculated by the following formula.
Sf =| Fk |2(2)
FFT with mixed radix is employed to analyze various
lengths of a partial sequence.

5

Experimental Data and Method

We used DNA base sequence of Dictyostelium
Discoideum. These data were created on experiments
in Univ. of Tsukuba. Each data consists of nontranscriptional region (2000 bases) and transcriptional
region (100 bases)(Figure 5). 2000 position is the transcriptional start sites (TSS).

1.1

A-stage
S-stageC-stageV-stage

1

Average of a0.9

• V stage · · ·141 data
• A stage · · · 92 data
• C stage · · · 58 data
• S stage · · · 63 data

0.8

0.8
0.75
0.7

0.7

0.65
0.6

0.6

0.55
0.5

0.5

0.45
0.4

0.4

0

100

200

300

400

20

500

30
600

40
700

50

60

70

800

80
900

90

100

1000

1100

L

Figure 7: Avarage of α in Non-transcribed Region

Figure 5: DATA of Dictyostelium Discoideum

0.8

A-stage
S-stageC-stageV-stage

0.75

The length (L) of a partial sequence is decided , andthe position of a partial sequence is moved one by one(Figure 6). The α of 1/f α of each a partial sequenceis calculated. In the case DNA base sequence is M
bases, a partial sequence is from m − L + 1 to m.
(m = L, L + 1, · · · , M )0.7

Average of a

0.65

0.6

0.55

0.5

0.45

6

0.4

Result and Discussion

30

40

50

60
L

70

80

90

100

Figure 8: Average of α in Transcribed Region

Figure 7 shows the average of α of partial sequence
in non-transcriptional region, and Figure 8 shows the©ISAROB 2006

20

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0.7

Vstage-48(+0.0)
Sstage-48(+0.3)
Cstage-48(+0.6)
Astage-48(+0.9)

1.8

Cstage-A
Cstage-G
Cstage-C
Cstage-T

0.6

1.6
0.5
Frequency of appearance

Average of α

1.4

1.2

1

0.3

0.2

0.8

0.1

0.6

0.4

0.4

0

500

1000
1500
Starting position of partial sequence

0

2000

Figure 9: Transition of Average of α in Each GrowthStage (L=48)
0.7

500

1000
Position

1500

2000

Figure 11: Frequency of Appearance of Each Bases in
C Stage

Vstage-A
Vstage-G
Vstage-C
Vstage-T

0.60This research is partly supported by MEXT grant(C) 17500146 in 2005

Frequency of appearance

0.5

0.4

References

0.3[1] E. Takushi and H. Miyagi, ”Fractal Packing of the DNA Sequence of Bacteriophage φX174,” Bull.Fac.Sci.,Univ.Ryukyus ,No. 70, pp.4346,2000.(in Japanese)

0.2

0.1

0

0

500

1000
Position

1500

2000

Figure 10: Frequency of Appearance of Each Bases in
V Stage[2] E. Takushi and H. Miyagi, ”Fractal Packingof the DNA Sequence of Bacteriophage φ-X174(II),” Bull.Fac.Sci.,Univ.Ryukyus ,No. 71, pp.2123, 2001.

7[3] E. Takushi and H. Miyagi, ”Fractal Packingof the DNA Sequence of Bacteriophage φ-X174(III),” Bull.Fac.Sci.,Univ.Ryukyus ,No. 72, pp.4347,2001.

Conclusion

We analyzed non-transcriptional region of DNA
base sequence of Dictyostelium Discoideum using exponent α of 1/f α . The analyses showed that DNA sequences of non-transcriptional region are more regularthan that of the transcriptional region. In addition,
experiments showed that the average of α increases infront of TSS in V stage and C stage. This paper mentions non-transcriptional region, but analysis of intron
will be mentioned at the conference.
Future work is to apply our method to comparing
DNA base sequences across species.

8[4] W. Li and K. Kaneko, ”DNA correlations,” Nature,Vol.360, pp.635-636,1992.
[5] R. F. Voss, ”Evolution of Long-Range Fractal Correlations and 1/f Noise in DNA Base Sequences,”
phys.Rev.Lett., , Vol.68 , pp.3805-3808, 1992.
[6] I. Yoshihara and T. Yamaguchi and K. Yamamori
and M. Yasunaga, ”Feature Ectraction of Cellular Slime Molds using 1/f ﬂuctuation,” Memoirsof the Fac. of Engineering, Miyazaki Univ., Vol.32,
pp.277-282, 2003.(in Japanese)

Acknowledgements[7] T. Onitani and I. Yoshihara and K. Yamamori
and M. Yasunaga, ”Extraction of Feature Patterns Embedded in Non-Transcribed Region of
Dictyostelium Discoideum,” SEAL04, SWP-8, No.
123, 2004.

We are most grateful to Prof. Hideko URUSHIHARA at University of Tsukuba and also Prof. Naoya
KENMOCHI and Dr. Maki YOSHIHAMA for providing us the information on DNA sequences.

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A Bio-inspired Tracking-camera System
Kazuya Hayashi*, Noriyuki Aibe*, Yoshiki Yamaguchi*, Yorihisa Yamamoto†,
Moritoshi Yasunaga*, and Ikuo Yoshihara‡*Systems & Information Engineering, University of Tsukuba,
1-1-1 Ten-ou-dai Tsukuba Ibaraki, 305-8573, Japanhayashi@islab.cs.tsukuba.ac.jp†Yamamoto System Design Inc.,
3481 Oozone Tsukuba Ibaraki, 300-3253, Japan‡Faculty of Engineering, University of Miyazaki,
1-1 Gakuen Kibanadai Nishi Miyazaki Miyazaki, 889-2192, Japan

Abstract
We propose a bio-inspired reconfigurable trackingcamera system using FPGAs. In the system, a wide-lenscamera captures an entire image while a zoom-lens camera
tracks a target in the image magnifying it. In the current
system, probabilistic neural network (PNN) and mosaicprocessing are used for the image recognition and preprocessing, respectively. Thanks to the FPGA-based design,
not only PNN and the mosaic-processing but other recognition and pre-processing algorithms can be implemented
onto the system also to adapt the various images and targets.

bio-inspired approach.
Thanks to the reconfigurability using FPGAs, not only
the PNN and the mosaic-processing, but other algorithms
can be implemented also in the system. Furthermore, in thesystem, parameter-tuning and servomotor-modeling, which
are usually carried out offline using computer simulations,can be done online using FPGAs, i.e., different parametersand models can be implemented onto the FPGAs in the system and tested automatically and quickly, so that high-speed
system-setting can be achieved.

2. Bio-inspired tracking-camera system

In order to prevent increasing number of crimes andaccidents, novel approaches are required in the videosurveillance systems to achieve much higher precision in
the image recognition than the conventional ones. Some
new approaches, for example, using a CMOS cameraarray[1] or multiple cameras[2] have been proposed already.
In the above background, we propose an image recognition and tracking camera system that can give us highrecognition-rate based on high resolution images of the targets.
The system has two features: The system is reconfigurable using FPGAs with which it can adapt to different algorithms for image recognition and its preprocessing. And
the system is designed based on the eye-system in which
we watch a target carefully while we see an entire image
roughly. In order to mimic the eye-system, two cameras areused in our system: One wide-lens camera captures an entire image, while the other zoom-lens camera tracks a target
with a servomotor magnifying it. Moreover, we use probabilistic neural network (PNN)[3] that is one of promisingneural networks as a recognition algorithm in the recognition part. The system is thus designed totally based on the

Figure 1 shows the system configuration. The system
is mainly composed of three blocks: Camera-block, Videoimage Recognition Block, and Servomotor Control Block.
The camera-block consists of the above-mentioned two
cameras(see Fig.2). The zoom-lens camera is controlledwith two servomotors (MS1 and MS2) that move the camera perpendicularly and horizontally. An image from thewide-lens camera is input to the PNN processor[4][5] andthe position of the target in x- and y-axis is detected in it.
The position is then feed back to the position controller inthe camera-block to train the zoom-camera on the target,
and the zoom-camera outputs the magnified high-resolution
image of the target. The magnified high-resolution image
is sent to the PNN processor again and its information orproperty is retrieved in it.
The system can be applied, for example, to a facerecognition in the video-surveillance as follows. Faces innatural scenes are detected with the wide-lens camera andthe PNN processor, and their positions are detected first.
The zoom-camera is then trained on the faces and outputs
the magnified face-images that are candidates. Those candidates are sent to the PNN and are finally recognized in the
PNN compared with the face-images that are registered inadvance. In the next section, we describe the PNN and the
motor controller in details.

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1. Introduction

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Wide Camera

Video Image
Recognition Block
ObjectDetector
PNN ProcessorxyZoom Camera
MS1*

Position Controller

MS2*

Servomotor
Control Block

ObjectRecognizer
PNN Processor

Camera Block*MS1/2 : Servomotor with Potentiometer

Figure 1. Outline of the BI-Camera System

Figure 2. Photograph of the Camera Block

Figure 3. Photograph of Video Image Recognition and Servomotor Control Blocks(unknown pattern) and Sij is the jth pattern in the category
Ci . The superposition of the kernel functions that is calculated in the summation layer constructs the estimator of theprobabilistic density function p (X|Ci ) of the category Ci ,that is,Np¢
1 X ¡K X − Sij ,(1)
p (X|Ci ) =N
where Np is the number ofp j=0
sample patterns in each category. The neuron in the decision layer selects the highest
p (X|Ci ) and its category Ci is output as the answer (i.e.,
predicted category) to the input pattern (unknown pattern)
X.
The kernel function is the kernel in the PNN as it isnamed, and we adopted the uniform function
¯
¯
½
¡
¢
1 : ¯xk − (sij )k ¯ ≤ σ2i(2)
K X − Sj =0 : otherwise(for all k, k = 1, 2, ..., n)

3. Video Image Recognition Block
Video Image Recognition Block mainly executes two
processes. One process is to detect the position of thetarget based on the input image from the wide-lens camera. And the other process is to recognize the target basedon the high resolution images from the zoom-lens camera.
Figs. 3 and 4 show a photograph of the developed FPGA
board and a detailed system configuration, respectively.
As mentioned above, PNN is currently used for positiondetection and recognition of the target. We have proposed
a PNN processor[4] and its reconfigurable architecture using FPGAs[5] already. PNN is one of the statistical pattern recognition techniques, and is now applied to many realworld applications. PNN consists of feed-forward three layers, that is, a kernel layer, a summation layer, and a decision
layer. Each neuron¡(node) in¢ the kernel layer calculates thekernel function K X − Sij , where X is the input pattern

because it is suitable for the digital hardware implementation. The smoothing parameter σ determines the width of
the kernel function. Determining σ, which is just the learning in PNN, is much easier than other learning algorithms
because it is the only one parameter in the network.
Fig. 5 shows a circuit block diagram of¡ the¢ PNN processor. An adder and a subtracter are for sij 1 + σ/2 and¡ i¢sj 1 − σ/2 in Eq. (2), respectively and they compose¯¡ ¢ ¯¯¯
the kernel function. The condition ¯xk − sij k ¯ ≤ σ/2
in Eq. (2) is examined with two comparators sequentiallycomponent k. The counter increases by one if¯in every¡ ¢ ¯¯¯¯xk − sij k ¯ ≤ σ/2 is satisfied for all components, while
the calculation for Eq. (2) is canceled at the RS-FF once it is
not satisfied. Finally, the number of kernels that include theinput pattern, which is equal to Eq. (1), is set to the counter.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Camera Block
BURR-BROWN ST Microelectronics
MC33079
OPA541MAXIM
MAX536TECHNO SHOWA
TSD-25SSG
TILT
MOTOR

Zoom
Camera

POWER
AMP

PRE
AMP

POWER
AMP

PRE
AMP

DAC

PAN
MOTOR
TSUKASAELECTRIC
TG-85B-BG-60-KA

Potentiometer

To HostComputer
24

FPGA Board (240x180mm)

MIDORI SOKKI
CPP-35

Video Image Recognition Block
Xilinx XCV1000E-6HQ240C

NTSC
Decoder

Pre-Processor

88

Xilinx XCV300E-6PQ240

Sample Pattern
Memoryi

PNN Processors

Sj

Servomotor
Control Block

8x8

17

TV Monitor
TV Monitor

X

Unknown Pattern 8(3 types mosaics)

8

NTSC
Encoder24

Wide
CameraCommon Data Bus

Potentiometer

Xilinx XCV300E-6PQ240ABCLCD Controller

ANALOG DEVICES
AD7858LCD Character
Display

Servomotor Controller
PS/2 Keyboard
Controller

ADC
2

4
5

PS/2 Keyboard

Figure 4. Configuration of the BI-Camera System

4 Servomotor control block
The servomotor control block controls the servomotorsof the zoom-lens camera to track the target according to thedetected position. The FPGAs in the block are used not only
to control the servomotors but also to identify the servomotor feedback loop. In the case of system identification, asignal oscillator for a servomotor input and a data recorderfor the servomotor output are implemented in the FPGA because of estimating parameters for the servomotor control
for the high-precision tracking. The measured data are sent
to a PC (host computer) and parameters are estimated. In
this estimation, we used a model as shown in Fig.6.
From Fig.6, input-output relation is expressed as follows:
1
β
(3)
· V
θ=
s+α s
T
Where α is KTRIKE and β is K
RI . Using some mesured
data to Eq.(3), we can determine optimum parameter of servomotor. After determining parameter of servomotor, reconfiguring FPGAs for control of servomotor, we can control servomotor using the same FPGAs. Using the same
FPGAs, the system do not need external oscillator and etal., we can realize miniaturization of whole system scale.
Gain and phase properties, namely Bode plot, are shownas Fig.7 and Fig.8 respectively. As frequency turning up,
the gain of servomotor as shown Fig.7 come down, and the
response of servomotor as shown Fig.8 gradually delay. To©ISAROB 2006

research motor specification precisely such that, we could
realize high accuracy performance for zoom-lens camera to
track.

5 Conclusions
We proposed a bio-inspired reconfigurable trackingcamera system that is equipped with a wide-lens and azoom-lens camera. The system is designed based on theeye and the brain system in the organism. We discussed theimage recognition process, in which the probabilistic neural network (PNN) is used, and the motor control process
in the system in detail. Thanks to the FPGA-based design,different algorithms for the recognition and other processes
can be implemented in the system. And the motor identification process is also carried out using the FPGA without
any simulations that are indispensable in the conventional
motor control system. The system is now being developedand some fundamental evaluation results will be reported at
the conference.

Acknowledgment
This research is supported in part by the 2005 Cooperationof Innovative Technology and Advanced Research in Evolutional
Area and the 2005 grant of Japan Society for the Promotion Science(No.16360197)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 5. Configuration of one PNN Processor.

Figure 6. Block Diagram of a Servomotor with
Descrete-Time Feedback

References[1] Ryutaro Oi, Marcus Magnor, and Kiyoharu Aizawa, “A SolidState, Simultaneous Wide Angle-Detailed View Surveillance
Camera“, VMV2003, Munich, Germany, November 19-21,
2003.

Figure 7. Magnitude (Bode Plot)[2] Sohaib Khan, Omar Javed, Zeeshan Rasheed, and Mubarak
Shah, “Human Tracking in Multiple Cameras“, The EighthIEEE International Conference on Computer Vision, Vancouver, Canada, July 9-12, 2001.
[3] D.F.Specht, “Probabilistic Neural Networks and the Polynomial Adeline as Complementary Techniques for Classification”, IEEE Trans. Neural Networks,Vol.1, No.1, pp.111-110
1990.
[4] Noriyuki Aibe, Ryousuke Mizuno, Masanori Nakamura,
Moritoshi Yasunaga, and Ikuo Yoshihara, “Performance Evaluation System for Probabilistic Neural Network Hardware”,
The journal of Artificial Life and Robotics (Springer-Verlag),Vol.8, No.2, pp.208–213, 2004.
[5] Ryosuke Mizuno, Noriyuki Aibe, Moritoshi Yasunaga, andIkuo Yoshihara, “Reconfigurable Architecture for Probabilistic Neural Network System”, IEEE International Conference
on Field-Programmable Technology (ICFPT) 2003, University of Tokyo, Tokyo, Japan, December 15-17 , pp.367-370,
2003.
[6] XILINX Inc. (2002, July). Virtex–E 1.8V Field Programmable
Gate Arrays Data Sheet (DS022-1 v2.3). [Online]. Available:
http://www.xilinx.co.jp/bvdocs/publications/ds022.pdf©ISAROB 2006

Figure 8. Phase (Bode Plot)

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

A robot semantic recognition using support vector machine
Hung-Chuan, Lai, Chia-Te Chou, Shi-Jinn Horng

Gwo-Cheng Chao

Dept. of Computer Sci.&Info.Eng., National
Twaiwan University of Science and Technology;
Taiwan Information security Center (TWISC)
D9115002@mail.ntust.edu.tw#43,Sec.4,Keelung Rd.,Taipei,106,Taiwan,R.O.C

Graduate Institute of Networking and Multimedia
National Taiwan Univ., Taipei, Taiwan

the commander and understanding the command. By matching
the sentence from the speaker and the action, the robot will
follow the legal speaker’s command.
We use the most popular learning machine—Support Vector
Machine (SVM) to train a model. Due to the characteristics of
SVM that it’s easy to train and it can identify the speakers
accurately and fast, we can take the speech features of a small
group of people as the sample to process the training, and avoid
the robots following other’s commands. Furthermore, the robots
will be able to understand legal commands and to work properly
and efficiently.

Abstract
Due to the development of the speech recognition whichleads to various biometric applications, this field of research
attracts a lot of attentions. In this paper, we use the
Mel-Frequency Cepstrum Coefficients as the speaker’s feature
vectors and support vector machine as the training model, thenthe recognized speech is used to control the behavior of the robot.
The robot will be activated by the specified action which
trained by speaker’s speech, in other words, we need to know thecontent of the speech in advance, for example stand up, sit down,up, down, turn left, turn right and so on. In the experiment weshould transform our speech from analog signal to digital data,
we use Mel-Frequency Cepstrum Coefficients as our feature
vectors, and use support vector machine as our training model.
The speech is trained by a computer and then stored in the ROM
of DM642 first, During testing, the test speech will go throughthe microphone of DM642 and A/D converter, and then thefeatures vectors of it is extracted. Then we compare it to the
speaker’s model stored in the ROM, if it is successful, we will
send the signal of the result to the control center of the robot by
RS232 and the robot will perform the action. The better equal
error rate of the voice, the less wrong action of the robot. In our
experiment, the equal error rate is 0.5%, it is very effective.

2. Support Vector Machine
2.1 Optimal Separating Hyperplane
SVM is a method of machine learning, and is used inpattern classification and regression extensively. Because SVM
can decrease training error (or called empirical risk) and testingerror (or called risk) at the same time, SVM becomes the mostpopular algorithm in machine learning. SVM was built by Vapnik[6][7]. The simple binary classification problem is showed asfollows: Given training set S={(xi,yi)}, samples in the set arexi ∈ IRd, which belong to one of the two classes labeled asyi ∈ {-1,+1}. The goal is to establish the equation of a hyperplanethat divides S completely (no misclassification), this means that
we can find some hyperplane so that all positive examples are on
one side of the hyperplane, while all negative examples lie on theother side. This idea is illustrated at Fig.1. If we can obtain largest
margin in separating two classes (see Fig. 1), then we candecrease the test errors (risks). Due to the reason there might be
not only one hyperplane which can be used to separate twoclasses, with the idea of margin above, Vapnik and Chervonenkis
describe an Optimal Hyperplane (OH), which means the optimal
hyperplane can separate two classes and have the largest margin.

1. Introduction

With the progress of the times, we have different robots help
us to do many things in our daily life. Some robots help us with
our housework, some robots are used to help the military, andsome robots are used in the medicine [4]. They can not only save
our energy, but to prevent people from some injury. In this thesis,
we use speech technique to analyze speech data, so that the
robots will be able to extract from a certain speaker’s speech.
Moreover, the robots will be capable of identifying the identity of©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

This is the SVM.
To summarize up, the SVM first non-linearly transforms
from low dimension space to a high dimension space by innerprobuction, then finds the OH in this space.
Different kernel functions form different SVM algorithm.
Here are three kind of kernel function that are often used:• Polynomial: K(xi, xj) = (γxiT xj + r)d, γ> 0.
• Radial basis function (RBF): K(xi, xj) = exp(−γ||xi − xj||2), γ> 0.
• Sigmoid: K(xi, xj) = tanh(γxiT xj + r) where γ, r, and d are
kernel parameters.
Here, we use the RBF kernel function as our method.

Fig. 1 Optimal hyperplane and margin

3. System Design

It can be probed that in this case the linear classifier that solve theproblem is:lf(x) = sgn{

Σ α y x x +b}
i i

T

i

(1)

Two important components contribute to this system. The
first components is the speech based verification system and thesecond component send the signal to control the robot’s behavior.
These two components are described below.

(2)

3.1 Speech Utterance Verification System

i =1where is the solve of next equation
Minimize

W(a) = -aTl+

Subject toaTy = 0

1 T
a Qa
2

The system includes three important stages: preprocess,feature extraction and pattern comparison. The preprocess
stage aims to remove silent parts from the raw audio signal,as this part does not convey speaker dependent information.
Then in order to deal with the channel distortion or
convolution noise that is introduced by a microphone, thezero’th order cepstral coefficients are discarded and theremaining coefficients are appended with delta feature
coefficients [5]. The cleaned audio signal is converted to12th order Mel-frequency cepstrum coefficients (MFCC) [3],complemented by their first and second derivatives, for a
total of 36coeffiecients.

0≦a≦ClWhere (a) i = αi, (l) i =l , (Q) ij = y i y j x i T x j. It can be proved that
there are only a few are not equal to 0, and the corresponding
samples are support vectors. So the sum in Eq. (1) operates only
on support vectors. B is the threshold value of classification, andcan be calculated by and one of the support vectors.

2.2 Kernel Function

In real occasions, linear classifiers are not suitable, because
these problems may have inherently nonlinear structures. An
appealing property of SVMs is that they can be easilytransformed into nonlinear learners by the kernel technique. This
technique maps input data into a higher-dimensional space andconstructs a separating hyperplane there. The higher dimensional
space is called the feature space, as opposed to the input space towhich input data belongs. In the dual problem, calculation ofeither the optimal function Eq. (2) or the classification function
Eq. (1) involves only the inner product (x‧i yi) of training samples.
So only the inner production is needed for transformation to thehigh dimension space, which is implemented by the functions in
the low dimension space, without knowing the form oftransformation. In order not to directly calculate the inner product(xi‧yi), we use the following connotative kernel function k(xi, yi)=(xi‧yi) instead. By using kernel function to calculate data’s innerproduct value in feature space, we do not have to project data intofeature space. In this case, the corresponding classification
function changes toFig. 2 the flow of the speaker identificationlf(x) = sgn{

Σ α y K(x‧x )+b}i i

©ISAROB 2006
i =1

i

(3)

760

Mel-frequency cepstrum coefficients are defined according
to the susceptibility of people’s ears. Psychophysical studies
have shown that human perception of the frequency contents
of sounds for speech signals does not follow a linear scale.
The Logarithmic waveform appears in order to catch theimportant features in voice syllables when the voice is inhigh frequency. If the voice expresses with Mel-frequency
scale, it is a linear frequency space under 1000Hz, above
1000Hz it is a logarithmic space. Its analysis procedure isshown in Fig. 3, the operations of all parts are listed asfollows.

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

SVM hardware architecture is shown in Fig. 4. The speechmodels are trained first and then stored in the ROM. After
converting the testing speech through A/D converter, FFT,
triangle filter, and DCT, testing features would be generated
and be stored in RAM. Next, SVM hardware substitute thetesting features and the speech models which has beenltrained into f(x) = sgn{

Fig. 3 MFCC feature parameters extraction procedurecolumns in Arbitration Register. One is Mn(boolean) which
represents if the testing speech and the original nth sentenceMel(f)=2595*log10(1+f/700)(4)
The last step is to transform Log Mel Spectrum into thetime domain, which means we can receive mel-frequency
cepstrum coefficients. After transforming the speech signal
spectrum into cepstrum, it can describe the Local Spectral
Properties of the frame completely.
Because of mel-frequency coefficients being a real number,so we use the Discrete Cosine Transform (DCT) to transform
it into time domain. The formula is as follow:

are the same. Another is the correct number standing for a
threshold value. We call it is a successful matching if Mn is
not less than the number. If there’s only one true value in Mn,
the testing speech will be presumed as the original sentences.
If there are more than one true value, the control unit will
check the correct numbers and take the testing speech withthe largest as the original sentences.

1 π
C(k ) = Σ log{|Y (m) |}cos{k (m − ) }, k=1,2,…12 (5)
2 M
m=1
M

xi

We take 12 orders Mel-Frequency Cepstrum Coefficients asfeature coefficients. Coefficient of the Zeroth Order is theaverage logarithm energy of frames. It carries less speech
information which can be omitted [2].

-xj

exp & γ
Table

X

ABS

K (xi, xj)

Fig.5 K(xi, xj) Hardware Architecture

3.2 System Hardware Architecture

Fig.5 is the hardware architecture of K(xi, xj) = exp(−γ||xi −
xj||2),because of γis a constant, we use the method of Table
to design the hardware of exp and γ.

The speech identification algorithm is preceded by
TI-DM642 emulation board in our system, and we havealready established various kinds of actions withcorresponding sentence within the robot. After identifying
the sentences, we will send the signal of the result to thecontrol center of the robot by RS232 and the robot will
perform the action. In this paper, we use this idea of
hardware and software co-design to design this system, using
DSP to execute FFT, triangle filter, and DCT program, anddesign SVM part into hardware.

RAM
Test
Featurexj
K(xi, xj)

ROM
Model
Feature

+

+

Sgn
b

(α i * yi , b , xi )lFig. 6 f(x) = sgn{f(x ) Hardware ArchitectureΣ α y K(x‧x )+b} Hardware Architecture
i i

i

i =1

Instruction
ROM

4. ResultAarbitration Register
M1 Correct #RAM
Test Feature

M2 Correct #

f(x)
(3)

DSP
A/D
ROM
Model
FeatureM3 Correct #ActionCommandMl Correct #SVM Hardware Architecture

Fig.4 System Hardware Architecture‘iwill be outputted into Arbitration Register. There are twocompute the mels for a given frequency f in Hz:©ISAROB 2006

i i

i =1

Therefore we can use the following approximate formula to

FFT
Tri.
DCT

Σ α y K(x‧x )+b} . Then, the result

761

In our system identification test, the correction rate of SVM
is 99.67%. In speaker verification test, the performance wasmeasured in terms of the Detection Error Tradeoff (DET) curve[1]. Fig.7 shows the results of speaker verification system basedon SVM. The DET curve shows that the Equal Error Rate (EER)The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

for SVM is 0.5%

Fig. 7 DET curve of speaker verification

5. Conclusion

The advantage of our system is that our/the robot only
follows the instructions from the correct user. Anyone who tries
to command the robot should register first or the robot will reject
his request. The disadvantage is that we need to connect a
microphone to the robot. In the future, we can combine the image
processing technology and it will help us do housework, like pickthe newspapers up, sweep the floor and so on.

Reference[1] Alvin M., George D., Terri K. etal (1997). The DET curve inassessment of detection task performance. Proceedings ofEurospeech, 1997pp. 1895-1898[2] Douglas AR (1995), “Robust Text-Independent Speaker
Identification Using Gaussian Mixture Speaker Models,” IEEE
Trans. on Speech and Audio Processing, Vol. 3, No. 1, Jan. 1995,
pp.72-83.
[3] Molla KI, Hirose, K (2004), “On the effectiveness of MFCCs
and their statistical distribution properties in speaker
identification,” Virtual Environments, Human-Computer
Interfaces and Measurement Systems, 2004. (VECIMS). 2004
IEEE Symposium on 12-14 July 2004 pp.136-141[4] Murphy RR, Riddle D, Rasmussen E (2004), “Robot andHuman Interactive Communication,” 2004. ROMAN 2004. 13th
IEEE International Workshop on 20-22 Sept. 2004 pps.301-306[5] Rabiner L, juang BH (1993), Fundamentals of speech
recognition. United State: Prentice-Hall International, Inc[6] Thomas E, Samuel K, Hong-Goo K etal (2005), “Aninformation-theoretic perspective on feature selection in speakerrecognition,” Signal Processing Letters, IEEE Vol.12, Issue
7, July 2005 pp.500-503[7] Vapnik, VN (1999), “An overview of statistical learning©ISAROB
2006Networks,” IEEE Transactions on Vol. 10, Issue 762
theory.
Neural5, Sept. 1999 pp.988-999

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Flowering analysis based on image segmentation
Hiroko Kamikokuryo Masahiro Yokomichi
University of Miyazaki
1-1, GakuenKibanadaiNishi, Miyazaki, Japan
yokomich@cs.miyazaki-u.ac.jp
Abstract
The analysis of flowering situation by means of human being is very ambiguous because of individual variations. This paper
proposes an approach for expressing the flowering situation numerically by means of image segmentation. It has been
pointed out by several researchers that the segmentation of the region of petals from natural image is difficult. In this paper,
we consider the case where the flower has tubular flowers. In such case, the tubular flowers must be surrounded by petals, so
this rule can be used as a domain-specific knowledge. In this paper, it is shown that the task of extraction of the petals
becomes easily by natural image experiments.

1 Introduction
At the present time, the flowering situation is analyzed byhuman being, which is not accurate because of individual
variations. For example, in Japan, the situation that “cherry
blossom are in full bloom” is defined as “80 percents of all
flowers are in bloom (not necessary in full bloom).[1]”However, in practice, it is not easy for human beings tocheck this condition exactly. Therefore, certain computer
vision-based approach is needed.
Recently, many authors have studied the image processing
for flower images. In order to analyze the floweringsituations from image, it is needed to extract the region ofeach flower correctly. In the case where natural images areused, the flowers are seen with various sizes and shapes.
Furthermore, the background colors are not uniform. Thus,
simple template matching cannot be applied. For thisproblem, several researchers have proposed the use of thedomain-knowledge about the colors of each flower [2], [3]and [4]. Using the domain-knowledge, the regions offlowers can be extracted properly. However, each flower
cannot be distinguished if several flowers are overlapping.
In this case, the outline of the flower that hides another one
has weak edge. Thus several flowers form single region sothat we cannot estimate the position and the shape of eachflower properly. We should note that, this case is alsodifficult one even if we use the pattern matching-based
region extraction methods.
In this paper we restrict the species of the flowers to those©ISAROB 2006

having central tubular flower (CTF, for short). For suchspecies, CTF’s can be seen separately even if the petals areoverlapping. Therefore, by extracting CTF at first, we canextract the region of each flower at least partially.
We describe the background removal, flower region
segmentation, and an estimation of a position of an individual
flower in Chapter 2, describe an individual flower and the total
analysis method of a tree in Chapter 3, and describe an
experiment method in Chapter 4.

2 Preprocessing
In this section, the elimination of the background, theextraction of the region of flowers, and the estimation of theposition of each flower is explained.

2.1 Elimination of the backgroundIn the natural images, the colors of flowers, leafs, andbackgrounds are not unique. So the extraction of the domainof the flowers by certain threshold value is very difficult.
However, it is known that the colors of flowers are limited
to some colors. This can be used as the domain-specificknowledge [2] for the extraction of the domain of flowers.
The colors of the flowers are limited to red, blue, yellow,
white, orange, pink, and purple. On the other hand, there arefew flowers whose colors are black, gray, brown, and green.
Actually, on this earth, ratio of a color of a flower of the natural
world, 33% are white, 28% are yellow, 20% are red, and purple

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plus blue is 17%. These other colors are 2%. On the flower
image, black and gray pixel are region of almost shadow, and abrown pixel is region of such as a shadow, a branch, and soil.
Therefore, without the loss of gene racy, we can consider thesecolors by an expert skill method as background colors.
According to this definition, we remove a background by color
segmentation with threshold value (See Fig. 1).

2.3.1 Clustering in the color space
In order to eliminate the non-flower region, we use k-meansclustering method on color space. In the following, we
assume that the range of the rate of the radius of the CTF
and that of the flower is already known, that is,
Tmax := max{R / r}
Tmin := min{R / r}

are known, where R denotes the radius of the flower and rdenotes that of CTF. We note that this is not so unrealistic
assumption if the spacies is already known. In this case, foreach CTF, the region of its petal must be inside of the circle

Fig. 1 Example of background elimination (left: original
image, right: processed image)of radius rTmax whose center is that of the CTF. For all

2.2 Searches for CTF
Even if we can eliminate the background regions by themethod described above, we cannot distinguish each flower.
This is because there may be some flowers that are hided byother ones. In such case, the contours of such flowers that are
overlapped have weak edges, so the edge detection-based
contour extraction methods do not work well.
In this paper, we restrict the class of the flowers such ascosmos and sunflower having CTF. For such flowers, even if thepetals are overlapped, CTF’s are not. Therefore, if we can extract
CTF’s from each flower region, we can estimate the position ofeach flower. Conversely, we can expect that any non-backgroundregions that contain no CTF’s are not the regions of flowers suchas noise or other objects. The result of the searching CTF for theimage shown in the previous section is depicted in Fig. 2.

pixels in the circle, clustering is yielded in color space.
If we use the k-means clustering, the choice of the number ofclusters is very important. The number of the clusters should
be determined according toz The number of the colors the flower has;z How much does the color of flowers change when theflower is shadowed.
After clustering, pixels whose colors are far from the center of
each cluster can be considered as the part of other objects.

Fig.3 Example of extracted flower region
Fig. 3 shows the result of the clustering of the colors in the
non-background region for Fig. 2. We can see that the
non-flower regions are removed properly.
2.3.2 Extraction of the contour

Fig. 2 Example of searched CTF

2.3 Flower region extraction
Because we were able to grasp an individual flower position,
we extract an individual flower region. And we extract ancontour with an contour extraction filter. But we cannot extract
an contour of a flower piled up only by this processing, because
petal is few changes of the density. Therefore we demand an
approximate petal region and pull a boundary line.

©ISAROB 2006

If each flower is seen separately, the region of it is aconnected region with similar color which contains single
corresponding central tubular flower. Thus it can beextracted by smoothing and edge detection. On the other
hand, if overlap occurs, multiple CTF’s are included insingle region. Furthermore, the outline in the overlapping
region has weak edge, thus extracting it is very difficult (SeeFig. 4).
. In this paper, the main objective is to analyze the flowering

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situation rather than the extracting the shape of each flower
completely. Thus It is sufficient if we can estimate R / r foreach flower. Before explaining the estimation scheme, we
assume that there are no central tubular flowers that arecompletely surrounded by other flowers. If this assumption
is satisfied, for each central tubular flower, at least a part ofthe outlines is seen as edge whose distance to the central
tubular flower ranges in [ rTmin : rTmax ] .

Fig. 4 Overlapping flowers (left: original image, right:
extracted outlines)

3. Flowering analysis
One simple approach to analyze the flowering situation fromimage is to analyze the area of the region of background (denoted
as Sbg ) and that of the flowers (denoted as S fl ). If the flowering
progresses, the ratio S fl / Sbg also increases. However, thecorrelation of these two values depends on each image, and when
the flowering is near the full bloom, this ratio may saturates. Thus
it may be difficult to analyze the flowering situation by thismethod.
On the other hand, if the flowering progresses, the value
R / r also increases. Thus, we can analyze the flowering situation
by analyzing the distribution of R / r for all flowers in the input
image. The relationship between this ratio and the floweringsituation does not depend on each image, thus we expect that we
can estimate the flowering situation by this ratio precisely.

Suppose that a CTF is located at ( x0 , y0 ) and its maximum
radius is r > 0 . For each angle 0 ≤ θ < 2π , search the

4. Experimental results0boundary of the corresponding region of petal on the line
segmentx = x0 + t cos θ
y = y0 + t sin θ , r0Tmin ≤ t ≤ r0Tmax .

If there is a boundary pixel, then it is the boundary of thepetal. On the other hand, the flower and some other flower
overlap in this direction. About all directions which has

Fig. 5 Searching the outline of each flower in theoverlapping case.
boundary, the mean value of R is computed and it is theestimate (See Fig. 5).

©ISAROB 2006

We prepared four natural images of cosmos (See Fig. 6).
In Fig. 6a, single flower is seen and it is seen clearly. In Fig.
6b, single flower is seen too, but, not clearly. In Fig. 6c, we
can see three flowers are seen and they overlap. In Fig. 6d,many flowers are seen and some of them are overlapping.
In applying the k-means clustering method described in thesection 2.3.1, the number of the clusters is set to 3. This isbecause we think that single flower is considered to be seen
with three colors that are its original color, shadow and thecolor of the other flowers petal.
We show a result of search for CTF’s and corresponding
flower regions for Fig. 7. The percentage in the index ofeach image means how much flowers are detected correctly.
Only circular template matching is incompetent as search for
CTF’s. In fact, ambiguous regions with orange color in Fig.
6b are detected. However, we were able to judge that they
are not flower region because they have no CTF’s.
When a flower crowds like Fig. 6d, extraction of CTF’s isstill difficult. In addition, it is inconvenient for a searchwhen a stem and a leaf interrupted the flower. We observedthat some flowers are detected smaller. This affects theprecision of the extraction of flower region.

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And, it is thought that analysis precision of flowering
improves by using an animation.

Referencesab[1] “Japan Meteorological Agency” http://www.jma.go.jp/
jma/index.html[2] Hong.A, Chen.G, “A flower image retrieval method
based on ROI feature”, J Zhejiang Univ SCI 2004 5(7):764-772cdFig. 6 Input images[3] H.Takeichi, Y.Takeuchi, “Flower Information Retriev
al Using Picture”, IEICE Vol.102 No.707 PRMU2002-2
42, (2003-3, In Japanese)[4] T.Saitoh, T.Kaneko, “Automatic Recognition of Wil
d Flowers”, IEICE D-II Vol.J84 No.7 P1419-1429,(2001,
In Japanese)a (100%)b (100%)c (100%)d (83%)

Fig. 7 Experiment result

5. Conclusion
In this paper, we showed a preprocessing method forflowering analysis when we use a natural flower image forflowers that have CTF. We were able to extract a flower
region from an input image by an experiment. In addition,
we were able to grasp a position of an individual flower.
However, extraction of a bud is necessary if we consider it
to be "flowering = a bud opens" as described in Chapter 1.
We thought about only a species of flowers that have CTF.
It is difficult to grasp a position of an non-outstanding
tubular flower. However, we may do grasp of a position if
we detect a bud from observation by an animation image.

©ISAROB 2006

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Recognition of Word Expressed by Sign Language Using Thermal Image Processing
Y.Yamagichi1, Y.Yoshitomi2 and H.Fushimi3

1:KKC Information System Corp.,369 Minamitsuji-cho Kamigyo-ku, Kyoto, 602-8466, JAPAN
2:Dept. of Environmental Information, Fac. of Human Environment, Kyoto Prefectural University,
1-5 Nakaragi-cho Shimogamo Sakyo-ku Kyoto 606-8522 JAPAN, E-mail:yoshitomi@kpu.ac.jp
3:NTT DATA SANYO System Corp,2-5-5 Keihanhondouri Moriguchi, Osaka, 570-8686, JAPAN

Abstract

In our researches, hands have been detected as parts
having human temperature. In this paper, our approach to on-line sign-language recognition with thermal image processing is open and then we discuss theperformance of our method.

A basic method for recognizing sign language has
been developed with thermal image processing. In
this method, the hand-traces of word expressed by
Japanese sign language are inputted into computer as
sequential coordinates with thermal image processing,then the word is recognized through nding out themost similar hand-traces in the database to that fromthe word. The 47 Japanese words expressed by sign
language were recognized with 71% accuracy.
Keywords: Japanese sign language recognition,
thermal image, image processing, hand tracing

2 Hand tracing method
An example of input image is shown in Figure 1.
The owchart for tracing hand in performing sign language is shown in Figure 2. In this section, the detailprocedure is explained.

1 Introduction
There have already been several researches on signlanguage recognition. However, in these researches,some special apparatus such as data-glove has beenused for inputting signal of sign language into a computer. For preparing system of sign-language recognition, hand tracing has been tackled with skin color
detection on visible-ray image[1].
We have started a new approach for aiming signlanguage recognition with skin region detection on
infrared-ray image [2, 3, 4]. The principle of thermal image generation with infrared-ray comes fromwell-known law by Stefan and Boltzmann, which isexpressed as W =   T 4 , where W is radiant emittance (W/cm2),  is emissivity,  is Stefan-Boltzmann
constant, T is Temperature (K). For human skin,  isestimated at 0.98 to 0.99 [5]. In this study, however,
the approximate value of 1 is used as  for humanskin. The values of  for almost all substances except
human skin are lower than that for human skin[5]. Accordingly, human skin is easily extracted in the scene
having its circumstances whose temperature are lower
than that of human skin, when the range of skin temperature is selected for producing the thermal image,using the value of 1 for [6].
1©ISAROB 2006

Figure 1: Input image.

2.1 Thermal image generation

1

Under the condition that emissivity is set as 1,
thermal images extracting regions of human skinare produced by a thermal video system (Nikon
LARD-3ASH) with infrared rays and these images
are recorded on 8-mm-videotape. The detected temperature range is 302.3 to 306.7 K . Then, they are
inputted into computer with an 8-mm-video camera(SONY Handycam CCD-TRV86 NTSC) and avideo-signal input-board (SYBERTEK CT-3000A).
The input-images in a computer have a spatial resolution of 320 240 pixels and a gray level of 256.
2

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2.2 Judgment of beginning and nal
framesStart

For nding out easily the beginning of sign language
on a video lm, a subject wearing a jacket with long
sleeves sits on the seat and puts its left and right hands
on its corresponding knees before and after performing sign language. After segmentation of input image,the area having the value of 1 on the speci c region on
subject's knee-parts is measured. Then, at the frame
where the area rstly becomes less than the value ofthreshold, which has been experimentally decided beforehand, the forthcoming processing for tracing hands
is started(Figure 3). After processing several frames,at the frame where the area on the speci c region on
subject's knee-parts becomes larger than the value ofthreshold, the processing for tracing hands is terminated.

Image input

Real-time processing

Bmp file saving

while hands aren’t on knees

Image input

Are hands on knees ?

No

YesPreprocessing

File processing
up to final file

Prediction of hand movement
Reading a file
Are hands overlap ?
Yes

Preprocessing

Hands
separartionNo

Are hand and headoverlap ?
No

Prediction of hand movement

Yes
Are hands overlap ?

Figure 3: Recognition of start of sign language by measuring number of pixel. Left: more than threshold,
Right: less than threshold (judged as start).

Yes

Judgement of hand region

Hands
separartion

No

Hand center tracking stepwise

2.3 Preprocessing

Are hand and headoverlap ?
Yes
No

Real-time processing

After the judgment that the movement of hand fora sign language has started, the gray-level of meaningless parts that are characters and relatively hot regions
on the subject's knees on which the hands stayed fora while is set as 0. Then, the averaging of gray levelon local area is performed for erasing noise and thesegmentation with a constant threshold is performed,
followed by easements of small regions.

Templatematching
Judgement of hand region

Hand center tracking stepwise

2.4 Prediction of hand movement

File processing

The coordinate of center of gravity for each handat t + 1t is predicted with Taylor series up to second
order, where the rst order di erential coecient of
coordinate and the second one are approximately obtained in the usual way with the coordinates at t 21t,
t 1t and t. However, this prediction cannot be usedat the rst and second frames from the beginning ofsign language. Therefore, at these two frames, the region whose x coordinate of center of gravity is smaller

End

Figure 2: Flowchart for tracing hands in performing
sign language.

©ISAROB 2006

0

0

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between the regions having the second or third largest
area on the segmented image at the frame is judged
as a right hand-region. On the other hand, at these
two frames, another region having the second or thirdlargest area on the segmented image at t +1t is judged
as a left hand-region.

2.5 Judgment of hand region

Figure 4: Region for template matching.

The rectangular partial-image circumscribing theleft hand region at t is moved so that the coordinateof center of gravity for left hand region corresponds tothe predicted one at t +1t, and the moved rectangularpartial-image is compared with the image at t + 1t .
For the right hand region, the same procedure is performed. Then, the situation of hand region at t +1t is
categorized into the following three cases; (a) the left
and/or right hand-region overlaps face region, (b) theleft and right hand-regions overlap each other, (c) the
two hand-regions are isolated.
When the predicted left and/or right hand-region
at t +1t overlaps face-region recognized as the largest
region at t + 1t, the situation is judged as (a). If not(a), the next process is as follows. When the region
whose coordinate of center of gravity is the nearest
from that of the predicted left hand-region at t + 1t
is the same as that of the nearest from the predicted
right hand-region, the situation is judged as (b). If
neither (a) nor (b), the situation is judged as (c).
In the case of (a), the template matching mentioned
later is performed. In the case of (b), the shrink ofregion mentioned later is performed.
In the case of (c), the region whose coordinate ofcenter of gravity is the nearest from that of predictedleft hand-region among some regions on the segmented
image at t + 1t is judged as the left hand-region at
t + 1t. The right hand region at t + 1t is also foundout in the same manner as that for left hand-region.
In the case of (a) where the left and/or right handregion overlaps face region, the hand region at t + 1t
is found out with the usual template matching. At
rst, the rectangular partial-image circumscribing left
hand-region at t is set as the template. Then, therectangle region circumscribing the left or right handregion at t and the face region at t+1t is used as searcharea for template matching(Figure 4). In Figure 4, thesmallest rectangle denotes a hand-region at t, while thesecond largest one denotes a face-region at t + 1t.
It takes much more time for tracing hands througha sign language with template matching, comparedwith that without template matching. Therefore, inthe case of (a), it is dicult to perform an on-line processing to trace hands through a sign language. Ac-©ISAROB 2006

cordingly, the frames after starting template matching
are stored as BMP les, and the processing for tracing
hands until the sign language is nished is performed
just after storing the nal frame of sign language.
In the case of (b) where the left and right handregions overlap each other, the shrink on the segmented image is preformed until the region is divided
into two regions (Figure 5). After dividing the region,
the left and right hand-regions are judged in the samemanner as case (c).

Figure 5: Separation of connected hand-regions bycontraction.

3 Sign language word recognition
The training data that consists of successful hand
tracing for each sign language are used for recognizing the testing sign language. The time for sign language used as training data is transformed to equal itto that of a testing sign language in calculating thesum of each di erence between a coordinate of testing sign language and that of training data. In thisprocess, the coordinate of training data at the transformed time is calculated with inside linear interpolation using two coordinates at the nearest and second
nearest transformed time to the time of the testing
sign language. The testing sign language is recognized

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006(b) where the left and right hand-regions overlap eachother, it was 3/20 and in all cases that was 44/71. In
addition, it was 21/24 on the on-line processing andit was 23/47 with template matching. The main reasons of poor hand tracing were as follows; (1) overlapof both hands and head (30 %), (2) poor separationof both hands (26 %), (3) poor template matching forsearching hand on the head region (22 %), (4) fastright-hand movement in the left direction just after
starting the sign language (11 %), (5) others (11 %).
The raio of right recognition for sign language was
67/94 (71 %). It took 500 to 600 msec per one word
to recognize sign language with data of hand-trace obtained by our method.

as the sign language, which has the least sum of di erence to coordinate of testing data among all training
sign languages.

4 Experiments and discussions
In this experiment, a personal computer, DELL
OPTIPLEX GX260（CPU : Pentium 2.53GHz，
main memory：512 MB, OS : Windows XP）, wasused. For programming, Microsoft Visual C++ 6.0
and WIN32 API were used.
At rst, 71 sign languages were selected for preparing training data, according to the following necessary
conditions; noun including neither meaningful movement on head nor that in the direction to listener orcamera, big motion observed by listener or camera, nocross of hands, no need of initial pose.
These sign languages were three times stored on 8mm video. The rst series of theses sign languages
were used for making training data that consisted ofsuccessful hand tracing for each sign language. The
second and third sign-languages corresponding to therst ones whose hand tracings were successfully performed by our method were used for making testing
data. The testing sign language was recognized with
our method.
The speed of image input was 14 to 15 fps for on-line
processing, and 25 to 27 fps in case of starting templatematching and performing the forthcoming processing
after inputting all input-frames of the sign language.
The hand-tracing of 47 sign-languages among thetried 71 ones was successful. For these 47 signlanguages, the time sequential data on traces of left
and right hands were obtained and used as training
data. For the second and third series of sign-languages,
the 47 video-images of sign-languages, which had thesame meanings as 47 ones of training data respectively,
were used for evaluation of our method.
It took 58 to 64 msec per frame on the on-line processing of our method. To trace hands for the rst series of sign-languages, input of image was three times
started from the di erent time of the 8-mm video. It
depends on the start-time of image input whether thehand-tracing was successfully performed. While thenumber of sign language was 44 in case that handtracing was successfully performed more than twice,that was 47 in case that more than once.
Under the criterion that the hand tracing was called
as successful one when it was successfully performed
more than twice, in the case of (a) where the left
and/or right hand-region overlaps face region, the ratio of successful hand tracing was 23/47, in the case of©ISAROB 2006

5 Conclusions
A basic method for recognizing sign language has
been developed with thermal image processing. The
Japanese words expressed by sign language were recognized with good accuracy. To shorten the time forhand-tracing and to overcome some factors causing
poor hand-tracing are next targets.

References[1] Imagawa K, Lu S, Igi S, Matsuo H (1998), Real-timetracking of human hands from a sign-language image
sequence in consideration of disappearance by skin regions (in Japanese),Trans. of IEICE, Vol.J81-D-II(8),pp.1787-1795.
[2] Inada M, Yoshitomi Y, Tanaka S (2000), Analysis ofnger characters in sign language using thermal image
processing (in Japanese), Proc. of 19th SICE Kyushu
Branch Annual Conference, pp.129-132.
[3] Yoshitomi Y, Nagayama S, Sugiyama M (2002), Sign
language recognition using thermal image processing(in Japanese), Information Technology Letters, Vol.1,
pp.191-192.
[4] Yoshitomi Y, Nagayama S, Sugiyama M (2002), Hand
trajectory extraction and sign language recognition using thermal image processing (in Japanese), Technicalreport of IEICE, Vol.WIT2002-52, pp.21-26.
[5] Kuno H (1994), Sekigaisen Kougaku (in Japanese), IEICE, Tokyo.
[6] Yoshitomi Y, Kimura S, Hira E, Tomita S (1996), Facial expression recognition using Infrared rays image
processing, Proc. of the Annual Convention IPS Japan,
No.2, pp.339-340.

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Hierarchies Based on the Number of Counters or the Space Allowed by
Four-Dimensional Multicounter Automata
Makoto Saito1 , Makoto Sakamoto1 , Kouichi Iihoshi1 , Takao Ito1 ,Hiroshi Furutani1 , Michio Kono1 , and Katsushi Inoue2
1
2

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN
Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract

new four-dimensional computational models.
An
eight-way four-dimensional k-counter automaton (4kCA), which consists of a finite control, k counters,a read-only four-dimensional input tape, k counterheads, and an input head which can move in eight
directions — north, east, south, west, up, down, future or past. In general, when we must think about
the algorithm of four-dimensional pattern processing
by using the restricted computational resources, if thealgorithm is fine in spite of its restricted computational
resources, it will be valued highly. It is the same with
automata theory. So we next introduce and investigate a seven-way four-dimensional k-counter automaton (SV 4-kCA) which is a restricted type of 4-kCA.
SV 4-kCA is a 4-kCA whose input head can move inseven directions — north, east, south, west, up, down,
or future. In this paper, we let each sidelength of each
input tape of these automata be equivalent in order toincrease the theoretical interest.
Let Σ be a finite set of symbols. A four-dimensional
tape over Σ is a four-dimensional rectangular array ofelements of Σ. The set of all the four-dimensional
tapes over Σ is denoted by Σ(4) .
Given a tape x ∈ Σ(4) , for each j(1 ≤ j ≤ 4), we letlj (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 ) . When1 ≤ ji ≤ 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 definex[(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 tape y satisfying the following(i) and (ii) :(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).
Four-dimensional tape is the sequence of threedimensional rectangular arrays along the time axis. By
Cubex (i) (i ≤ 1), we denote the ith three-dimensional
rectangular array along the time axis in a tape x ∈Σ(4) which each sidelength is equivalent.
We now introduce a seven- or eight-way fourdimensional multicounter automaton. An eight-way

Due to the advances in many application areas suchas computer animation, motion image processing, virtual reality systems, robotics, and so forth, it has become increasingly apparent that the study of threedimensional pattern processing along the time axis has
been of crucial importance. Thus, we think that the
research of four-dimensional automata as a computational model of three-dimensional pattern processing
with the time axis has been meaningful, and we introduced four-dimensional multicounter (= k-counter)automata in [6]. This paper continues the investigation about this model, and shows the hierarchies basedon the number of counters or the space allowed byfour-dimensional multicounter automata.
Key Words : computational complexity, four-dimensional automaton, multicounter, nondeterminism.

1

Introduction and Preliminaries

Inoue et al. [4] introduced a two-dimensional multicounter automaton and investigated its basic properties. Szepietowski also investigated some of its properties [10]. A two-dimensional k-counter automaton
M is a two-dimensional finite automaton [1] that has kcounters. The action of M is similar to that of the onedimensional off-line k-counter machine [3], except thatthe input head of M can move up, down, right, or left
on a two-dimensional input tape. In [7], Sakamoto
et al. introduced multicounter automata on threedimensional input tapes.
By the way, during the past about forty years, several automata on two- or three-dimensional tapes havebeen proposed and many properties of them have beenobtained [5,8]. On the other hand, recently, due tothe advances in computer animation, motion image
processing, and so on, the study of four-dimensional
information processing has been of great importance.
Thus, we think that the study of four-dimensional
automata has been meaningful as the computationalmodel of four-dimensional information processing [9].
In this paper, we introduce and investigate about
eight-way four-dimensional multicounter automata as©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

tions — east, west, south, north, up, down, or future(see Fig.1).
Let L(m) : N 7→ N (where N is the set of all the
positive integers) be a function with one variable m.
A 4-kCA (SV 4-kCA) M is said to be L(m) counterbounded if for each m ≥ 1 and each input tape x (accepted by M) with l1 (x) = l2 (x) = l3 (x) = l4 (x) = m,
the length of each counter of M is bounded by L(m).
As usual, we define nondeterministic and deterministic 4-kCA’s (SV 4-kCA’s). By N 4-kCA(L(m)) (respectively, D4-kCA(L(m)), N SV 4-kCA(L(m)), andDSV 4-kCA(L(m))), we denote a nondeterministic 4kCA (respectively, deterministic 4-kCA, nondeterministic SV 4-kCA, and deterministic SV 4-kCA )whose each sidelength of each input tape is equivalent
and which is L(m) counter-bounded. Let L[N 4-kCA(
L(m))] = {T | T = T (M ) for some N 4-kCA(L(m))
M}. L[N 4-kCA(L(m))], L[N SV 4-kCA(L(m))], and
L[DSV 4-kCA(L(m))] have similar meanings.

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Fig. 1: Four-dimensional k-counter automaton.

This section investigates how the number of counters or the space allowed (of seven-way four-dimensional multicounter automata whose each sidelength ofeach input tape is equivalent) affects the accepting
power.
To do this, we need to consider the following sets.
For each j ≥ 1, let A(j) be the set of all the input
tapes x ∈ {0, 1}(4) such that :four-dimensional k-counter automaton (4-kCA) M,
k ≥ 1, has a read-only four-dimensional input tapesurrounded by boundary symbols ]’s and k counters.
(Of course, M has a finite control, an input head, andk counter heads.) The action of M is similar to that of
the two- or three-dimensional multicounter automaton[4, 7, 10], except that the input head of M can move ineight directions — east, west, south, north, up, down,
future, or past. That is, when an input tape x ∈ Σ(4)(where Σ is the set of input symbols of M and theboundary symbols ]’s is not in Σ) is presented to M,
M determines the next state of the finite control, themove direction (east, west, south, north, up, down, future, past, or no move) of the input head, and the movedirection (right, left, or no move) of each counter head,depending on the present state of the finite control, thesymbol read by the input head, and whether or not thecontents of each counter is zero (i.e., whether or noteach counter head is on the bottom symbol Z0 of thecounter). If the input head falls off the tape x withboundary symbols, M can make no further move. M
starts in its initial state, with the input head on position (1,1,1,1) of the tape x, and with the contents ofeach counter zero (i.e., with each counter on the bottom symbol Z0 of the counter). We say that M accepts
the tape x if M eventually halts in a specified state(accepting state) on the bottom boundary symbol ]of the input. We denote by T (M ) the set of all the
four-dimensional tapes accepted by M. A seven-way
four-dimensional k-counter automaton (SV 4-kCA) is
a 4-kCA whose input head can move in seven direc-©ISAROB 2006

Hierarchies Based on the Number ofCounters or the Space Allowed(a) l1 (x) = l2 (x) = l3 (x) = l4 (x) ≥ j.
(b) There are exactly j 1’s in the first (1-2) plane of
the first cube.
(c) All the (1-2) planes from the second to the last in
the first cube contain only 0.
(d) All the cubes from the second to the last but one
contain only 0.
(e) The last cube is equal to the first.
[Lemma 2.1] For each k ≥ 2 and r ≥ 1, A(j) can be
accepted by a DSV 4-kCA(m2r ) if j ≤ (k−1)r+1.
(Proof) We show how A(j) can be accepted by a
DSV 4-kCA(m2r ) M if j = (k−1)r+1. The case whenj < (k −1)r +1 can be proved similarly. Suppose thatan input tape x with l1 (x) = l2 (x) = l3 (x) = l4 (x) = m
is presented to M. First M checks whether x satisfies conditions (a),(b), and (c) above (in the definition of A(j)). Let d(i) denote the position of the ith 1 in the first (1-2) plane of the first cube. Letd(i) = (dR (i), dC (i)), where dR (i) is the row number ofthe poisition of the i-th 1 of the first (1-2) plane of thefirst cube, and dC (i) is the column number. All d(i)’s,

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for 1 ≤ i ≤ j−1 = (k−1)r, are stored in k−1 counters ingroups of r in each counter. The first r numbers fromd(1) to d(r) are stored asr noXdR (i)(m + 1)2(r−i) + dC (i)(m + 1)2(r−i)+1

while visiting x(1, 1, 1, 2) in the accepting computations on x. For any two different x, y ∈ A(m, j),conf (x) ∩ conf (y) = φ. Otherwise, replacing the last
cube in x by the last cube of y, we obtain the tapethat is not in A(j) but is accepted by M.
Clearly,|A(m, j)| = m Cj .

i=1

in the first counter First M stores d(1) on the first
counter, and then, using the k-th counter. M storesdR (1) and going from one end of the first row to another, multiplies the first counter by (m + 1), thenadds dC (i). Next, M multiplies the first counter by(m + 1), then adds d(2), multiplies again, and so on.
Similarly, for each l (2 ≤ l ≤ k−1), the r numbers fromd((l − 1)r + 1) to d(lr) are stored in the l-th counter.
Then M stores on the k-th counter the position of thej-th 1 and checks whether all cubes from the second
to the last but one contain only 0.
M assumes that the cube that contains the first 1
below the first cube is the last cube (M will reject
the input if it finds another cube below). Next, M
checks whether there are exactly j 1’s in the first (12) plane of the last cube, and all the planes from thesecond to the last in the last cube contain only 0. After
that, unloading the k-th counter M checks whether thelast 1 in the first plane of the last cube stands on thed(j) position and then, using the empty k-th counter,
unloads one by one the numbers d(i)’s, 1 ≤ i ≤ j − 1,and checks whether there is 1 in the position d(i) of thefirst plane. Unloading is done in the following way : If
a number 2s(m + 1) + d(i), with d(i) ≤ m is stored ona counter (say the g-th), where d(i) = (m + 1) + dC (i),then M goes from the first cell of the first row to thesymbol ] standing on the other end, decreasing the gth counter by 1, comes back to the beginning of therow, after each two steps, and after reaching ] it adds
1 to the k-th counter, comes back to the beginning of
the row, and repeats the process until the g-th counter
is empty. At this moment M stands on the positionx(dR (i), dC (i)) of the first plane and keeps s on thek-th counter. It is obvious that in this way all thenumbers d(i)’s can be unloaded (in reverse order inwhich they were loaded) and the positions of all 1’s in
the first plane checked.
¤

Let c(m) be the number of possible configurations of M
while visiting x(1, 1, 1, 2). Then c(m) ≤ smkr , wheres is the number of states of the finite control. Sincej > kr, there exists m such that |A(m, j)| > c(m), andthere must be two different x, y ∈ A(m, j) such thatconf (x) ∩ conf (y) 6= φ. This contradicts the above. ¤We are now ready to prove the following theorems.
First we show that for every r ≥ 1 there exists an
infinite hierarchy, with respect to the number of counters, of languages accepted by m2r counter-bounded(deterministic or nondeterministic) seven-way fourdimensional k-counter automata.
[Theorem 2.1] For each r ≥ 1, k ≥ 1, and X∈{D, N },L[XSV 4-kCA(m2r )] ( L[XSV 4-(k + 1)CA(m2r )].
(Proof) From Lemmas 2.1 and 2.2, it follows that for
any r ≥ 1, k ≥ 1, and X ∈ {D, N },A(kr + 1) ∈ L[XSV 4-(k + 1)CA(m2r )]and A(kr + 1) ∈/ L[XSV 4-kCA(m2r )].
¤Next, we show that for every k ≥ 2 and r < k there
is an infinite hierarchy, with respect to the amount of
space allowed, of the powers of nondeterministic sevenway four-dimensional k-counter automata.
[Theorem 2.2] For each k ≥ 2 and r < k,
L[N SV 4-kCA(m2r )] ( L[N SV 4-kCA(m2(r+1) )].
(Proof) From Lemma 2.1, we haveA((r + 1)(k − 1) + 1) ∈ L[N SV 4-kCA(m2(r+1) )].
On the other hand, if r < k, then (r+1)(k−1)+1 > kr,
and thus, by Lemma 2.2, we haveA((r + 1)(k − 1) + 1) ∈/ L[N SV 4-kCA(m2r )]. ¤We finally show that for each k ≥ 2 and r ≥ 1,
L[DSV 4-kCA(m2r )] ⊆ L[DSV 4-kCA(m2(r+1) )]. To
do this, we consider the following sets. For each j ≥ 1,
let B(j) be the set of all the input tapes x ∈ {0, 1}(4)such that :[Lemma 2.2] A(j) cannot be accepted by any N SV 4kCA(m2r ) if j > kr.

(a) l1 (x) = l2 (x) = l3 (x) = l4 (x) ≥ j,(b) there are exactly j 1’s in the first (1-2) plane of
the first cube,(c) there is exactly one 1 in the (1-2) planes from thesecond to the last of the first cube (thus, there areexactly j + 1 1’s on the first cube),(d) all the cubes from the second to the last but one
contain only 0, and(e) the last cube is equal to the first cube.

(Proof) Suppose that there is an N SV 4-kCA(m2r )M accepting A(j) and j > kr. For each m ≥ j, letA(m, j) = {x ∈ A(j) | l1 (x) = l2 (x) = l3 (x) = l4 (x) = m}.
Any accepting computation of M reading any
x ∈ A(m, j) has to visit x(1, 1, 1, 2), x(m, 1, 1, 2),x(1, m, 1, 2), and x(1, 1, m, 2) in the second cube. Otherwise, if there is an x ∈ A(m, j) accepted without
visiting x(1, 1, 1, 2), then, putting x(1, 1, 1, 2) = 1, we
obtain the tape that is not in A(j) but is acceptedby M. Let conf (x) be the set of configurations of M

©ISAROB 2006

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

[Theorem 2.3] For each k ≥ 2 and r ≥ 1, L[DSV 4-k
CA(m2r )] ( L[DSV 4-kCA(m2(r+1) )].

proof of (1) above, we can easily show that B3 (m) ∈/L[DSV 4-1CA(m)]. This completes the proof of (3).
(4) of the theorem is easily proved by using a standard technique of eliminating a loop. That is, if
DSV 4-1CA M enters a loop while moving on a fourdimensional tape, at most c·|Q|·(m+2)4 ·dlog(|Q|·(m+2)4 )e cells, for some c ∈ R, are sufficient for M to use
the counter, where Q is a finite set of states, |A| is the
cardinality of a set A, and dxe is the smallest integer
greater than or equal to x. So M counts the numberof move steps of M on the four-dimensional tape byusing the counter, and find out, if the number of movesteps is greater than c·|Q|·(m+2)3 ·dlog(|Q|·(m+2)3 )e,that M will enter a loop. It will be obvious that
L[DSV 4-1CAc (m4 )] = L[DSV 4-1CA(L(m))] for any
L(m) ≥ m4 .
¤(Proof) For each j ≥ 1, let B(j) be the set describedabove. Let j = (k − 1)(r + 1). By using the sameideas as in the proof of Lemmas 2.1 and 2.2, we canshow that B(j) is accepted by DSV 4-kCA(m2(r+1) ),but not accepted by any DSV 4-kCA(m2r ).
¤[Theorem 2.4]
(1) L[DSV 4-1CA(m3 )] ( L[DSV 4-1CA(m4 )],(2) L[DSV 4-1CA(m2 )] ( L[DSV 4-1CA(m3 )],(3) L[DSV 4-1CA(m)] ( L[DSV 4-1CA(m2 )], and(4) L[DSV 4-1CA(m4 )] = L[DSV 4-1CA(L(m))]
for any L(m) ≥ m4 .
(Proof) First, we prove (1). Let B1 be the set of all
the cubic tapes x ∈ {0, 1, 2}(4) such that(1) l1 (x) = l2 (x) = l3 (x) = l4 (x) ≥ 4,(2) for one and only one i (2 ≤ i ≤ m−1), the i-th cube
contains only 2, where m = l1 (x) = l2 (x) = l3 (x) = l4 (x),(3) x[(1, 1, 1, 1), (m, m, m, i−1)] and x[(1, 1, 1, i+1),(m, m, m, m)] each contain exactly one 1, and(4) the Euclidean distance (from x(1, 1, 1, 1)) to theposition of 1 in x[(1, 1, 1, 1), (m, m, m, i−1)] is equal to
the Euclidean distance from the position of x(1, 1, 1, i+1) to the position of 1 in x[(1, 1, 1, i+1), (m, m, m, m)].
It is an easy exercise to show that B1 is accepted bya DSV 4-1CA(m4 ). Thus, B1 ∈ L[DSV 4-1CA(m4 )].
On the other hand, by using a technique of counting
argment, we can show that B1 6∈ L[DSV 4-1CA(m3 )].
Next, we prove (2). Let B2 be the set of all the
cubic tapes x ∈ {0, 1, 2}(3) such that(1) l1 (x) = l2 (x) = l3 (x) = l4 (x) ≥ 3,(2) for one and only one i (2 ≤ i ≤ m−1), the i-th cube
contains only 2, where m = l1 (x) = l2 (x) = l3 (x) = l4 (x),(3) x[(1, 1, 1, 1), (m, m, m, i−1)] and x[(1, 1, 1, i+1),(m, m, m, m)] each contain exactly one 1, and(4) the Euclidean distance (from x(1, 1, 1, 1)) to theposition of 1 in x[(1, 1, 1, 1), (m, m, m, i−1)] is equal to
the Euclidean distance from the position of x(1, 1, 1, i+1) to the position of 1 in x[(1, 1, 1, i+1), (m, m, m, m)].
It is easy to see that B2 ∈ L[DSV 4-1CA(m3 )]. On
the other hand, by using the same idea as in the
proof of (1) above, we can easily show that B2 (m) ∈/L[DSV 4-1CA(m2 )]. This completes the proof of (2).
Next, we prove (3). Let B3 be the set of all the
cubic tapes x ∈ {0, 1}(3) such that(1) l1 (x) = l2 (x) = l3 (x) = l4 (x) ≥ 3,(2) there is exactly one 1 in the first cube,(3) all the cubes from the second to the last but one
contain only 0, and(4) the last cube is equal to the first.
It is easy to see that B3 ∈ L[DSV 4-1CA(m2 )]. On
the other hand, by using the same idea as in the©ISAROB 2006

3

Conclusion

In this paper, we investigated the accepting powers of counter-bounded seven-way and eight-way fourdimensional multicounter automata. Then, we investigated a relationship between determinism and nondeterminism. In these subjects, we stated only for
four-dimensional input tape which each sidelength isequivalent. It will be also interesting to investigate
the accepting powers of ‘alternating’ four-dimensional
multicounter automata (see [2] for the concept of ‘alternation’).

References[1] M.Blum et al., IEEE Symp. Switching & Automata Theory : 155–160 (1967).
[2] A.K.Chandra et al., J.ACM 28(1) : 114–133(1981).
[3] S.A.Greibach, Theoret. Comput. Sci. 1 : 269–288(1976).
[4] K.Inoue et al., Inform. Sci. 19 : 1–20 (1979).
[5] K.Inoue et al., Inform. Sci. 55 : 99–121 (1991).
[6] M.Saito et al., AROB2005 : CD-ROM (2005).
[7] M.Sakamoto et al., IWPIPA1999 : 267–280(1999).
[8] M.Sakamoto, Ph.D. Thesis (1999).
[9] M.Sakamoto et al., WSEAS Trans. on Comput.
5(3) : 1651–1656 (2004).
[10] A.Szepietowski, Inform. Sci. 55 : 35–47 (1991).

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Iterative use of a pair of the Self-organizing Maps
looking at data in two different ways
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

the study of biological systems and the study of robots.
Consider the situation in which a robot is moving around ina room. The input from a vision sensor, such as an image,is a function of the position and the direction of the robot.
In order to acquire the position and direction of the robot
from only the vision information, it is necessary to separate
two kinds of information, i.e., the position in the room (2D
information) and the direction (1D information), from thehigher-dimensional information, i.e., the image.
The Self-Organizing Map of Kohonen can be used tosummarize such high-dimensional information and to extract important variables. SOM consisting of a two dimensional (2D) array of units is often used; it is a mathematical model that describes the formation of the functional
map in the visual cortex in the the brain in terms of a certain learning algorithm [1]. The point is to summarize thehigher-dimensional data and to extract only the two mostimportant variables. A 2D array is not essential: a 1D or
3D array can be used whenever necessary. Each unit retains an n-dimensional vector called the reference vector.
Below, the reference vector of the i-th unit is written as
mi . The dimension of mi is the same as that of the input
signal x given to the SOM. The learning algorithm is asfollows.

This paper describes a model neural network that canfind a meaningful structure hidden in high-dimensional
data. Suppose there are n input vectors, each of which is anm-dimensional vector and represents features; A set of inputs is represented as a matrix {s ji }; i = 1; ::; n; j = 1; ::; m.
Conventional Self-organizing Maps (SOM) use each s·i asinput, and the method such as random-projection is used toconvert a set of inputs into much-lower-dimensional vectors when each input is impracticably high-dimensional.
We have developed a model which has two independent
SOMs looking at data in two different ways, s·i and s j· Inlearning, each SOM influences the other in which, insteadof random-projection, representation of the other SOM isused to reduce the dimensionality of data vectors. We
demonstrate that the model is powerful enough to find ameaningful structure, and obtain an appropriate mappingin a much-lower dimensional space than the conventional
method.
Keywords: self-organizing maps, dimensionality reduction

1 Introduction
The purpose of this paper is to describe the non-linear
mapping algorithm whose purpose is to discover regularities in a high-dimensional environment. The presentation
will be nontechnical and by example. Throughout this paper, we describe our method with its application to a dataset (animal names and feature shown in Table 1), which is afamous one used for a demonstration of the Self-organizing
Map (SOM [2]). By this, SOM and our method is compared with the same data set. The idea of our method isapplied to any methods such as the Multidimensional Scaling (MDS) whose input data set is represented as a matrix.

(SOM1) Initial values of the reference vectors mi ; i =1; · · · ; n are randomly generated, in which n is the number of units in the SOM.
(SOM2) An input vector x is generated.
(SOM3) The unit which has the reference vector closest
to the input (called the winner) is determined:
c

(1)

i

(SOM4) Learning of the reference vectors is performedby the following formulas:

2 Self-organizing Map∆mi

The mechanism to acquire the structure of the external
world by self-organization is interesting in connection with©ISAROB 2006

= argmin||mi ¡ x||

hci

775

= α hci (x ¡ mi )=||rc ¡ ri ||2
exp ¡
2σ 2

(2)
(3)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

SOM0X. t = 2000, σ=0.5

hawk
eagle

fox
dog

wolf
cat
tiger

lion
horse
cow

small
mediumbig
2 legs4 legshas hooves
mane
hunt
likes
run
to
fly
swim
is

dove
hen
duck
goose

Table 1: Animal names and their attributes

1
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hawk

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8

dove

6

goose

4

duck

2

hen

cow

eagle

horse

tiger

ui2

lion

wolf
cat

0

0

fox

2

ui1

4

dog
6

8

Figure 1: Self-organizing feature map obtained by single SOM

after 2,000 learning trials. α = 0:1，σ (t) = σi (σ f /σi )t/tmax ; σi =4; σ f = 0:5;tmax = 2000. SOM of 10 × 10 was used.

Here, ri is the position of the i-th unit in the array (neuralfield), and α is a positive constant representing the rateof learning.
(SOM5) The procedure goes back to (SOM2) and is repeated.

coordinates y j = s j· = (s j1 ; · · · ; s jn ). Dimension reduction
on features means representing features by smaller number of coordinates, say, b j1 ; · · · ; b jR with R smaller than n,which in some sense retain approximately the same information as the original coordinates.
As an example of dimension reduction, consider dataof Ritter and Kohonen (1989) [2] on 14 animals Xi ; i =1; · · · ; n = 14 covering 3 size variables, 4 body variables,and 4 characteristic variables, for total of 11 variables Y j .
Thus these data may be regarded as 14 rows or points, eachhaving 11 coordinates, that is 14 points in 11 dimensional
space. Figure 1 represent the 2D configuration of animals
which was obtained by 2D (10 × 10) SOM. Animals whichshare the similar characteristics are located in the nearest
region. However, in the Fig. 1, the grass-eating animals
and meat-eating animals are not separated correctly.
As mentioned in the appendix of the Ritter and Kohonen (1989)[2], if the dimensionality of features grows,
the attribute vectors soon become impracticably highdimensional. It is difficult to study such a high-dimensional
space array of data directly. The dimension of the input,
therefore, should be reduced while preserving the metric
relations among inputs. Ritter and Kohonen (1989)[2] also
give a solution called random-projection method where
each input xi is represented as a weighted sum of vectors
r j ; j = 1; 2; · · · ; m = 11

Learning occurs near the winner. It is called neighborhood
learning, and hci is called the neighborhood function. The
neighborhood function in the above formula is a Gaussianfunction, and the positive constant σ determines the spreadof the neighborhood. The norm in Eq. (1) is the Euclidean
distance in the input signal space, and the norm in Eq. (3)is the Euclidean distance in the unit array.
Let us consider a set of input data. Table 1 shows theanimal names and their attributes, which is from Ritter andKohonen (1989) [2] and modified slightly where there are
14 animals and 11 features. In this example, an input vector
is xi ; i = 1; · · · ; 14, each of which is 11-dimensional column vectors. We can look at this set of inputs as a matrix
S:ss·2 · · · s·14

 ·1
s1 ·
s1 1 s1 2 · · · s1 14

s2 · 
 s2 1 s2 2 · · · s2 14 
(4)

.. 

. 
s11· s11 1 s11 2 · · · s11 14

3 Dimension reduction for SOM by random
projectionx̃i

In the given data (similarity or dissimilarity) matrix
S, each object xi is represented by m = 11 coordinates,
xi = s·i = (s1i ; s2i ; · · · ; smi ). Dimension reduction on objects means representing objects by a smaller number ofcoordinates, say ai1 ; · · · ; aiR with R smaller than m, which
in some sense retain approximately the same information
as the original coordinates. Similarly in the given data matrix S, each variable or feature y j is represented by n = 14

©ISAROB 2006m=

∑ s ji r j :

(5)

j=1

Note that if we take the following m-dimensional vectors:
 
 
 
1
0
0
0
1
0
 
 
 
r1 =  .  ; r2 =  .  ; rm =  .  ; .. 
 .. 
 .. 
0

776

0

1

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the projection produces the original data, x̃i = xi . In the
random-projection method, we take r j whose dimensionality is much smaller than that of xi and each componentof r j is determined randomly, In this case, if the dimensionality of r j is still large enough, the relation distortionin general will be small.

Note that the random-projection method does not reflect
dependencies among features but our method reflects thedependencies.
Since v j = (v j1 ; v j2 ); j = 1; · · · ; m is obtained by SOM
which receives inputs of y j = (s j1 ; · · · ; s jn ). If n is large,
the same problem occurs. We therefore consider to represent y in much-smaller dimensional space as

4 Iterative use of a pair of SOMsỹ j

4 legs

8

run
6

ỹ j

vi2
4

manc

=

fly

2

(7)

where ui ; i = 1; · · · ; n is considered to be the 2-dimensional
coordinate which is the location of the winner unit of the
SOM trained by a set of inputs x̃i ; i = 1; · · · ; n.
As described above, our model updates and refines x̃iand ỹ j iteratively. In the remaining part of this article, we
see whether this idea works.
The problem of determining the initial states of themodel still remains. The simplest solution is this: we firstuse a conventional SOM to obtain the 2D configuration ofanimals ui ; i = 1; · · · ; 14. We call this SOM as SOM0 , andthis obtained ui as ui (0) for convenience. When dimensionality of xi is large, we can use ui where each components are randomly generated. With this ui (0), a set of
input vectors to the SOMY , which is a new SOM, is generated as

SOM0Y. t = 2000, σ=0.5
hunt∑ s ji ui :i=1

In the random-projection method, a set of inputs is reencoded in much smaller dimensional space. In the present
paper, we look for a coding strategy other than the randomprojection. In the random-projection method, independent
random codes are assigned to features. For instance, features small and 2-legs are highly correlated since only 2 of
14 features are different as shown in Table 1, but assigned
codes independently and randomly. Here is our basic ideathat this coding strategy must be improved so that we assign correlated code to the correlated features. To fix thisidea, here we give an example.

mediumn=

1 n∑ s ji ui
k i=1

(8)

n

2 legswhere k = ∑ s ji . After trained this SOMY with these in-smallputs, we obtain the location of winner units v j for each j.
We call these v j ’s as v j (1) where 1 means at time 1. With
these v j , a set of input vectors to the SOMX , which is another new SOM, is generated as

i=1

0

big

hooves

0

2

swim
4

vi1

6

8

Figure 2: Self-organizing feature map obtained by single SOM

after 2,000 learning trials. α = 0:1，σ (t) = σi (σ f /σi )t/tmax ; σi =4; σ f = 0:5;tmax = 2000. SOM of 10 × 10 was used.

x̃i∑ s ji v j :With this inputs, we train the

SOMX and obtains the 2D configuration of animal names.
The 2D coordinates thus obtained arecalled ui (2); i = 1; · · · ; 14.
Similarly, we obtainv j (3); ui (4); v j (5); ui (6); v j (7); ui (8); · · · .
There are three 2D SOM’s. SOM0 receives highdimensional inputs, s·i ; i = 1; · · · ; n = 14. SOMX andSOMY receive 2-dimensional inputs, x·i ; i = 1; · · · ; n =14; y· j ; j = 1; · · · ; m = 11; respectively. In short, the learning procedure is as follows:(6)(P1) We train the SOM0 and obtain ui .

j=1

©ISAROB 2006

(9)

j=1

m

=

1 m
∑ s ji v j
k j=1

m

where k = ∑ s ji .

A configuration obtained by 2D-SOM in Fig. 2 is a
2 dimensional representation of features for y j = s j ; j =1; · · · ; m = 11 where inputs to the SOM were not column vectors of the matrix S but 14-dimensional raw vectors s j· ; j = 1; · · · ; m. The representation shows lowdimensional base vectors v j = (v j1 ; v j2 ) of features wherev j ; j = 1; · · · ; m is the 2-dimensional coordinate which is
the location of the winner unit of the trained SOM. Using
this results, we can represent xi in much lower-dimensional(2D) space
x̃i

=

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

SOM. t = 2000, σ=0.5, Input: random projection (2D)

(P2) We use ỹ j as inputs to the SOMY where ỹ j is represented as a weighted sum of ui ; i = 1; · · · ; n. We train
the SOMY and obtain v j .
(P3) We use x̃i as inputs to the SOMX where x̃i is represented as a weighted sum of v j ; j = 1; · · · ; m. We train
SOMX and obtain u j .
(P4) The procedure goes back to (P2) and is repeated.

cow

eagle

hawk

8

horse
6

lion

4

tiger

ui2

goose
duck
cat

fox

2

hen
0

Iterated SOM X. t = 10000
duck

wolf
0

hen

2

dove
4

6

ui1

8

SOM. t = 2000, σ=0.5, Input: random projection (5D)

cow

8

eagle

goose

dog

horse

hawk

goose

dove
duck

8

hen

6

dove

ui2

cat

lion

6

4

hawk

ui2

tiger

fox
lion

dog

4

wolf

2
2

0

eagle

fox

dog

cat

wolf
0

0

2

4

ui1

6

cow

horse

tiger

8
0

2

4

ui1

6

8

Iterated SOM Y. t = 10000
small

swim

Figure 4: Comparison to the other methods. Dimensionality of ahoovesset of inputs were reduced by the random projection method[2] to
2D (upper) and 5D (lower panel) before applied to the SOM. The
grass-eating animals and meat-eating animals are not separated
correctly. Compare the results to those shown in Fig.3.

82 legs
big

6

fly

vi2

4 legs

4

hunt
manc

2

0

medium
0

5

run
2

4

vi1

6

8

Figure 3: A pair of 2D SOM was used to obtain this map. We
trained SOMY 1000 times for each input (batch learning), thenwith that result, SOMX was trained 1000 times for each input(also batch learning). This figures show the result after this procedure repeated 10 times. Both SOMX and SOMY consist of
10 × 10 units, and the learning coefficients we used were α = 0:1，
σ (t) = σi (σ f /σi )t/tmax ; σi = 4; σ f = 0:5;tmax = 1000. Reference
vectors are initialized in each repetition.

Acknowledgments
This works was partly supported by Grant-in-Aid (16700232)
from the Japanese Ministry of Education, Culture, Sports, Scienceand Technology (MEXT).

We have done the computer simulation of animal map
formation by the present method (Fig. 3) and, for comparison, by the random-projection method [2] in which thedimension of a set of inputs were projected to the 2D and
5D (Fig.4) before applied to the SOM. We can see thatthe grass-eating animals and meat-eating animals are separated correctly in Fig.3 (our method) but not in Fig.4 (therandom-projection method).

©ISAROB 2006

Summary

We have demonstrated a method which self-organizes a
map by using a pair of SOMs to which the same set of input
data was applied but in two different ways. Although we
could not justified our method here, we have already confirmed that it works in the other problem. As we have mentioned before, our method can be applied to any method
such as MDS where a set of inputs is represented as a matrix.

References[1] T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics, vol. 43, pp. 5969, 1982.
[2] H. Ritter and T. Kohonen, “Self-organizing semantic maps,”
Biological Cybernetics, vol. 61, pp. 241–254, 1989.

778

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Orbit Control for the KEK 12GeV PS-MR by using NN algorithm

Hitaka Y , Shirakata M

, Sato H, Yokomi hi M, Kono MDepartment of Control and Information System Engineering,
Kitakyushu National College of Te hnology, Japan

Proton Syn hrotron A

elerator Division, KEK, Japan  Department of Computer S ien e and Systems Engineering, Miyazaki Univ., JapanTo experimental Laboratory

Abstra t

At KEK 12GeV Main Ring (PS-MR), when theleast square method is applied to orre t whole beam
orbit all at on e, it remains una eptable beam loss
and it is ne essary to adjust the lo al positions of thebeam orbit by hands with the beam loss monitors until
the beam loss is suppressed under an a eptable level.
However, the orre ted orbit by this way doesn't always satisfy the minimum-loss ondition. In this paper, a new method is proposed. It fo uses a fa t thatthe beam loss distribution depends on the shape ofthe beam orbit, and formulates this relationship to a
fun tional approximation by using a neural network
algorithm, Then, solving an optimization problem forgenerated network system, data of the beam shapewhi h is more suitable for the beam loss of the a elerator an be obtained. The des ription of the system
onstru tion and experimental results are presented.
1

Main Ring(12GeV)

Booster (500MeV)
Linac (40MeV)

Preinjector (750keV)

Figure 1: KEK 12GeV PS

Booster and main ring(MR), respe tively. The beam
is a elerated step by step from the preinje tor to themain ring. At the main ring, the beam is a elerated
with orbiting until the beam is given desired energy
and extra ted to any target. The main ring is designed for the orbiting beam to tra ks the desired orbit. However, due to the measurement errors of thebeam position, the setting errors of the internal devi e and the magneti eld error, the beam distorts
and strays from the desired orbit and tou hes the wallof the beam pipes in the MR and losses. Thus, it isne essary to orre t the beam orbit to de rease thebeam loss until an a eptable level.
It is known that the least square method is e e tive to orre t the orbit distortion whi h is aused by
the magneti eld error[1℄. Be ause this relationshipan be des ribed by the algebrai equations. This distortion is alled COD (Closed Orbit Distortion). However, for the other disturban e, it is de ient to orre t

Introdu tion

The a elerator is a devi e whi h a elerates ele tron, proton and many kinds of nu leus to provide highenergy. The a elerated parti les are alled "beam"
and it is a elerated lose to the light and extra ted
to some kind of target. The a elerator an be used forphysi al experiments about atomi nu leus or elemental parti les, medi al purposes (ex. high-energy radiotherapy), stru tural analysis of the polymer ma romole ule (Protein or DNA) and so on. In this paper, we onsider an beam orbit ontrol problem for ana elerator alled "Proton Syn hrotron (PS)" whi h
is in High Energy A elerator Resear h Organization,
alled KEK (Kou Enerugi kasokuki Kenkyuu kikou).
Figure 1 shows overall view of KEK 12 Giga ele tron Volt Proton Syn hrotron (KEK 12GeV PS). As
this gure indi ates, this a elerator onsists of foura elerators. These are alled Preinje tor, Linia ,©ISAROB 2006

779

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Figure 2 shows the stru ture of the beam orbit ontrol system in KEK PS-MR. At PS-MR, there are 56
beam position monitors, 56 beam loss monitors, 28
beam steering magnets for the lateral dire tion and 28
beam steering mangets for the verti al dire tion. The
measured data by those monitors are a quired to CCR(Central Control Room) and displayed on CRT monitors or some os illos opes. In urrent orbit orre tion,
operators has orre ted the beam orbit at ea h measured point of the beam position by using the lo albump method until the beam loss is suppressed under
an a eptable level. This method gives a lo al bumpto the beam orbit at any measured point with orresponding three beam steering magnets. Above pro edure takes so mu h time and the beam orbit tunedby this way doesn't always satisfy the minimum-loss
ondition for this a elerator.

the COD (Closed Orbit Distortion) by using the leastsquare method. Thus, it is ne essary from the pointof view of the beam loss monitors in order to minimize
the beam loss.
At the PS-MR, the beam orbit is adjusted by the
lo al bump method. The operators orre t the beam
orbit with the beam loss monitors until the beam loss
is suppressed under an a eptable level. However, thisstrategy depends on the traditional history of the orbit tuning and operator's experien e. Besides, thebeam orbit tuned by this way doesn't always satisfy
the minimum-loss ondition for this a elerator.
Note a fa t that the beam loss distribution depends
on the shape of the beam orbit. If this relationshipan be formulated in some way, it is expe ted that theminimum loss ondition an be obtained by using somekind of the optimization methods. In this paper, a
new strategy is proposed for the beam orbit tuning. It
formulates the relationship between the ontrol inputof the beam steering magnets whi h de ide the shapeof the beam orbit and the values of the beam loss
monitors by using the Neural Network algorithm. And
then, for the trained network, adjusting the network
inputs (inputs of the steering magnets) to redu e the
network outputs ( orresponding to the beam loss), we
will obtain the steering input data whi h will minimize
the beam loss of the a elerator. This method will be
more eÆ ient than the urrent orbit ontrol method.
This paper is onstru ted by following se tions.
This se tion is introdu tion. Next se tion, the detail of
new orbit ontrol method proposed by us is des ribed.
Se tion 3, we represent about an experiment and a
result for the proposed method. The Final se tion is
Summary.

For these problem, we propose a new orbit orre tion method by using NN algorithm. Giving somedata set of the orbit shape and orresponding beamloss distribution, the NN an be identify both relationship. And then, for the trained network, we set
a performan e riterion for the output of the trained
network and adjust the network input by some kindof optimization method to minimiize the performan eriterion. Figure 3 is the idea of new orbit ontrol
method. Figure 3 denotes that the ontrol input for
Beam Position Data
Generate Control Input of
the Beam Steering Magnets

PS-MR
Beam Loss Data

56 Beam Position
Monitors

56 Beam Position
Monitors

NN+
Update the Network
Parameter

2

Constru tion a New Orbit Control

Trainde NN

System based NN Algorithm

PSfrag repla ements

Steering magnets controllers

Optimization

VME

MonitorMemory shared
HORIZONTAL

VME

MAP

VERTICAL

VME

Figure 3: The idea of the Orbit Control baed on NN.

VME

MAP

VME

Unix

Data Base &Control terminal

Correct
beam orbit

Operator

Multiplexer

VME

Beam
positionsignalControl beam steering magnets&
Acquire beam position data

the beam steering magnets is used as the NN input
for training and optimization. Be ause for the urrent
data a quisition system in PS-MR, mu h time is ne essary to a quire the beam position data. It is known
that a steering input pattern spe i es a shape of thebeam orbit. Thus we use it as input for the NN.

Beam pisition information

PS-MR
Beam loss
information
Oscilloscope

Beam loss signal

Figure 2: The beam orbit ontrol system.

©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
2.1

A pre ise orbit tuning is not ne essary for lateral dire tion, be ause the dimensions of the beam pipes arelands ape and there are enough learan e for the beamand the pipes wall for lateral dire tion. Thus, the NN
is trained for a relationship between the ontorl input
for the beam steering magnets whi h de ide the shapeof the orbit for the verti al dire tion and orresponding beam loss data.
From 56 measured points of the beam position andbeam loss (1-1F,1-1D,1-2F,...,4-7D; F means fo using,and D means defo using), 9 steering magnets whi hgive the bumps to the beam orbit for the verti al dire tion are hosen at the points of quarter range (2-1F!A2-7D) to a quire the NN training data. Be ause
the apa ity of the data transportation of the orbit
ontrol system is not satis ed to a quire the training data with suÆ ient quantities in the limited experiment time (about 1 hour). At this range, sometraining data set is a quired by de iding the shape ofthe beam orbit at random (maximum 4.0 mm fromposition zero, step 1.0 mm). The restri tion of the
maximum range 4.0 mm is to avoid huge beam loss
and the radioa tivation of the internal devi es. At theexperiment in June14, 2004, thirty sets of the training
data ould be obtained for 1 about hour.

NN Algorithm

There are various types of neural network algorithms. In this paper, the Ba kPropagation (BP)method [2℄ is used to formulate the relationship between the inputs of the beam steering magnets andthe beam loss distribution. BP method has generated a lot of good results for many a tual problems
whose relationships between their inputs and outputs
are nonlinear or high order orre tion.
2.2

The Te hnique of Optimization

Though the optimization problem about the inputs of the beam steering magnets realizes the minimum beam loss, it has many lo al minimum solutions.
Avoiding these lo al minima and deriving a global optimal solution, we take the Simulated annealing (SA)whi h is one of the heuristi optimization methods.
The detail of SA algorithm is des ribed in [3℄.
3Experiment and Result

In this se tion, an experiment and a result for ourapproa h are des ribed. For this experiment, a datalogger system whi h logs the beam loss data from allloss monitors and a alu ulator for the neural omputing and the network optimization are installed. Figure
4 shows an updated orbit ontrol system for this experiment. In this experiment, the network training

3.2

The NN for this experiment is onstru ted from 7
input units and 56 output units. The amount of middle layer units is de ided 56. The NN training nished about 50 minutes, where, the error toleran e ofthe network outputs for training data was on gured
0.002.

MonitorHORIZONTALVERTICAL

Data Base & control
terminal

Unix

VME

Training

Control steering magnets

PS-MR
Beam position data
Beam loss signal

3.3

Data transmission
methodChange orbit shapeto create sample data

Ithernet

For the optimization of the trained network, thebest input pattern is sear hed by using normal random
numbers (average 0, dispersion 0.2). The performan eriterion for the evaluation of the solution was 1-normof the network output ve tor.
Figure 5 and Figure 6 are the optimization results.
Figure 5 indi ates an ordinal (unadjusted) beam orbit
and an optimal orbit whi h will minimize the beamloss. Left gure in Figure 6 indi ates the beam loss forthe ordinal orbit and right gure is the expe tation of
the beam loss orresponding to the optimal orbit.
Using the ontrol input of the steering magnets derived by NN optimization, the beam orbit of PS-MR
will be orre ted to minimize the beam loss as the rightgure in Figure 6.

Logs of changed orbit shape(for input of the training data set)
Calculation result

Caluculator
for numerical computing

Beam loss data(for output of the training data set)

Data logger for
beam loss signal

Figure 4: Corre tion hart of the system devi es.
and optimization is operated o -line.
3.1

Data A quisition

For KEK PS-MR, it is possible to orre t the orbit shape for lateral dire tion and verti al dire tion.

©ISAROB 2006

Optimization

MAP
Operator

781

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006
mm

mm

5
Ordinal orbit

4

Ordinal orbit in June 14,2004
4

Optimization
resultOrdinal orbit in June 22,2004
Corrected orbit by optimization result

3
2

2

1
0

0

-1
-2

-2

-3
-4

2-1D

1-1D

3-1D

4-1D

-4

1-1D

Figure 5: Expe tation about the beam position.
Volt

2-1D

3-1D

4-1D

Figure 7: Experimental result about beam position.

Volt

2

2

Volt

Volt

Ordinal beam loss

2

2

Optimization

Beam loss about experimental result

Ordinal beam loss data in June 22,2004

1.5

1.5
1.5

1.5

1

1
1

1

0.5

0.5

0.5

0.5

0

0

1-1F

2-1F

3-1F

4-1F

Number of beam loss monitor

1-1F

2-1F

3-1F

4-1F

0

0

Number of beam loss monitor

Figure 6: Expe tation about the beam loss.

3.4

3-1F

4-1F

1-1F

2-1F

3-1F

4-1F

When the beam orbit is orre ted by the omparatableamounts of the ontrol inputs whi h are de ided at
random, the beam loss must be una eptable level, orthe beam will be vanished in the a elerator. Thus, it
is said that the relationship between the shape of thebeam orbit and the beam loss an be formulated to a
fun tional approximation by the NN algorithm.
Beam orbit tuning by hand is not e e tive that itneeds quite a bit of time to redu e the beam loss under
the a eptable level. Then, if the NN training withhigh-speed and high-pre ision an be a hieved, ourmethod will be ome an e e tive beam tuning method
for many types of the a elerators.

ResultReferen es[1℄ Kamei T, Kihara M (1993), The s ien e of a elerator, Maruzen.
[2℄ Philip D. Wasserman (1993), ADVANCEDMETHODS IN NEURAL COMPUTING, Van
Nostrand Reinhold.

Summary[3℄ Colin R. Reeves (1993), Modern Heuristi Te hniques for Combinational Problems, Bla kwell S ienti Press.

The result of the experiment shows that the beamloss an be de reased slightly by using our method.

©ISAROB 2006

2-1F

Figure 8: Experimental result about beam loss.

Figure 7 and Figure 8 show a tual results in June21, 2004. Figure 7 indi ates the ordinal orbit inJune14, 2004, an ordinal orbit in June 22, 2004 anda orre ted orbit by using above optimization result.
Left gure in Figure 8 shows the beam loss for the ordinal orbit in June 22, 2004 and right gure orresponds
to the orre ted orbit. Figure 8 shows the total beamloss de rease slightly. However, expe ted result from
optimization for the trained networkwas not obtained.
For this result, two reasons are onsidered. First, wedidn't have enough time to obtain appropriate amountof the training data. It is a solution that ea h devi esof the orbit ontrol system is update to enable morehigh-speed data a quisition. The se ond reason, as
shown in Figure 4, the shape of the ordinal orbit ishanged with the passage of time. For this problem,
online training will be ne essary to adapt the hangeof the a elerator's property.
4number of beam loss monitor

number of beam loss monitor
1-1F

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4.2

Simulation for holding the object

The case using the hand of tray type is as follows.
Figure 7 shows the hand of the shape of sector (λisangle in vertical plane). F is resultant force about
gravity and inertia force , and the components of theforce are Fx and Fy. In order to prevent the object
falling from the tray, angle of the tray ( θ 3 ) is

h

n

O3

joint

Fx

λ

link3

−F y

hand of tray-type

h′
F

link4

Fig.7 Inertia force about hand of tray-type100¨
θ (rad/s2)

50
0
-50

¨
θ1
¨
θ2
0.1

0.2

-100

F (N)

0.5
0
-0.5

(9).

( j =1,2 )x3
objectcontrolled by the motor 3 on the link 3, as follows,F  π.
(8)θ 3 = tan −1  y  + − θ 2 Fx  2
Figure 8(a), which looks like the stairs, shows theangular acceleration calculated from the angularvelocity(as shown in Fig.4). And, response of resultant
force which acts on the object is influenced by the
angular acceleration, as shown in Fig.8(b). Then, theangle changes as the shape which looks like the stairs, as
shown in Fig.8(c). In Fig.8(d), bold lines show theposture of the tray.
In order to make the response of angle about the traysmooth, we have substituted the angular displacementsof the motors 1 and 2 by the function of time as follows,θ j = ∑a jk ⋅t k

y3

θ3

0.2

θ3 (rad)where, a jk is constant values which derived under thecondition that consumed energy is nearly equal to thecase of Fig.4.

0.5

0.6
sec

0.3

0.4

0.5 sec 0.6

0.4

0.5

(b)

4

k =0

0.4

(a)

Fx
Fy
0.1

0.3

2

0

0.1

0.2

Release

0.3

(c)

sec

0.6

ReleaseC

InitialT=0.2s

T=0.4s

T=0.6s

B

(a)

E (J)

Initial(d)Fig.8 Response of manipulator

8
6

Figure 9 (a) shows the modified result which isderived using Eq.9 (solid line) and the same one as Fig.8(a) (broken line). In Fig. 9 (b) and (c), angulardisplacement of motor 3 become smooth by using Eq.9,
whereas the difference about angular velocity of motor 1
is small. Figure 10 shows the response of applied
voltage of motor 3. The consumed energy of motor 3
becomes less than 1/4 by modification, and it is clearthat proposed method is effective.

4
0

0.2

0.4
0.6
0.8
working time (sec)

1

Fig.5

Relation between working timeand consumed energy

(b)

yr

40 J
35 J
30 J
25 J
20 J
15 J
13 J
10 J

0.1

0

0.1

5

Experimental results

In this section, experimental results shown under
the condition that the motion space of the manipulator issmall. Figure 11 shows an experimental apparatus,
which is two degree of freedom model with fixed tray
for holding the object. The parameters of this system are
the same as given in Table 1. The motors 1 and 2(rated 24 V, 60 W) are on the frame. We shall measurexr

Fig.6 Relation between consumed energy and release point©ISAROB 2006

A

785

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

the angular displacement of motors by rotary encoders.
And, we shall measure the electric current as a potential
difference of the resistor ( rated 1.0 Ω, 10W).
Figure 12 shows the experimental response of themanipulator from initial position ( θ 1 = π / 4, θ 2 = 0 ) to
the position of release( θ 1 = 2π / 3, θ 2 = π / 4 ) , under
the condition that the distance from origin to the point ofarrival is x=−0.4 [m], release angle is φ = 3π / 4 , and theworking time is T=0.2 [s]. Sampling time of thecontrol is 0.002 [s], and feedback gain for angulardisplacement is 50 [V/rad], feedback gain for angularvelocity is 0.5 [Vs/rad]. Experimental result of angularvelocity (solid line), which is calculated by angulardisplacement, agree with the theoretical result (broken line).
Figure 13 shows the consumed energy of the motors 1
and 2. The difference between the experimental result (solidline) and the theoretical result (broken line) are less than
6 %. From these results, it is confirmed that modeling iseffective.

6

7

References[1] H.Arisumi, K.Yokoi, T.Kotoku et al(2002), Casting
Manipulation(Experiments of Swing and Gripper
Throwing Control), International Journal of JSME,
Vol. 45, No. 1C, pp. 267-274.
[2] N.Kato, and T.Nakamura (1997), Throwing Control
for 2-DOF Robot(in Japanese), JSME, Vol. 63,
No. 614C, pp.3571-3576.
[3] A.Sato，O.Sato, M.Kono et al(2003)，Trajectory for
Saving Energy of Direct-Drive Manipulator with Two
Degree of Freedom in PTP Motion under Gravity(in Japanese), Journal of JSPE ， Vol. 69, No. 9,
pp.1281-1285.
[4] O.Sato ， H.Shimojima, and H.Yoinara(1985),
Minimum-energy Control of a Manipulator with
Two-degree of Freedom, Bulletin of JSME, Vol. 29,
No. 248, pp.573-579.
[5] T.Izumi(2000), Path Planning for Saving Energy of a
Manipulator in PTP Motion(in Japanese), Journal of
the Robotics Society of Japan, Vol. 18 , No.7，
pp.972-978.
[6] Boltyanski,V.G(1967),(translated into Japanese by
Sakamoto.M), Mathematical Method of optimal
control (in Japanese), sougo-tosho.

Conclusions

The results obtained in this paper are summarized asfollows.
(1) For throwing motion, it has been clarified that
trajectory for saving energy calculated by proposed
iteration method is effective.
(2) In order to hold the object, it is considered that
proposed method to control the hand of tray type canbe used.

Release

0

0.1

0.2

0.5

0.6
t (sec)(a)20

θ1 (using the results of Eq.9)

Fig. 11

10

0
-10

0.1

0.2

0.3

0.4

0.5
0.6
t (sec)(b)4

θ3 (rad)

0.4

・

.

0.3

θ 1 (rad/s)

θ1(rad/s)-100

Eq.8 (using the results of Eq.9)

2

0

0.1

0.2

0.3

0.4

0.5
0.6
t (sec)(c)
Fig.9 Response of manipulator (after modification)©ISAROB 2006

Experimental
Theoretical

10
0

0.1

0.2

0.3

0.4

t (sec)0.2

0.1

t (sec)

0.2

0

4

0.5
0.6
t (sec)0.1

5Fig.12

E1+E2 (J)e3 (V)

-10

20

-5

e3(using the results of Eq.9)
e3(using the results of Fig.4)

50-5Experimental apparatus10

e 1 (V)2θ¨
1 (rad/s )θ1 (using the results of Eq.9)
100

Experimental result

Experimental
Theoretical

20

t (sec) 0.2
Fig.13 Experimental result (consumed energy)
0.1

Fig.10 Voltage of motor 3

786

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

Underwater Moving Object Tracking and Graspingwith Telerobotic System
Ping Zhang, Etsuro Shimizu and Masanori Ito
Department of Marine Electronics and Mechanical Engineering
Tokyo University of Marine Science and Technology
sites, for example, in deep ocean, a manipulator arm
mounted on Kaiko, well known as deepest-dive ROV(Remote Operated Vehicle) of the world, can achievetasks like collecting specimens and settling experimental
equipments [4]. With the increase of ocean research, suchas investigating a sunken ship, the seabed environmentand resource, there will be more and more underwater
robots be put into practical use.
Basing on this background, we developed theteleoperating system for an underwater manipulator,
which can be equipped on an Autonomous Underwater
Vehicle.

Abstract
This paper introduces an underwater robot
teleoperation system based on Internet and virtual reality
model. The operator can control the action of the remoterobot through the Internet and monitor the states of therobot displayed in a 3D-Graphics simulator. First, thetechnique and method implementing interactive robot 3D
simulation based on the Java platform will be described.
Since this real time teleoperation system is connected via
a network communication, which is inevitably effectedfrom the time delay. The system will be unstable due tothe random time delays of the Internet. To overcome thisproblem, the research combines the event-triggered
autonomous control and prediction method to achieve amoving target tracking and grasping teleoperating task.
Another problem we have to face is losing track of thetarget. Because the teleoperation system uses visualfeedback, cameras probably fail to collect image for a few
samples when the target is out of view or be obstructedby the robot. This problem was solved with the help of
our predictor. Experiments are conducted to examine theperformance of the moving object tracking and grasping
utilizing this teleoperation system and experimental
results have verified the theoretical development and thevalidity of this proposal.

2.Teleoperation System Overview
The teleoperation system for the underwater arm robot
consists of four hardware components: a robot control
unit, a tele-operating console, an image-processing unit,
and an underwater robot. The underwater robot has twoarms with 7 links, built by Mitsubishi Heavy Industries
Ltd Japan. The image-processing unit consists of two
CCD cameras, a PC for processing images and anotherfor transmitting position data to the operator. The teleoperating console consists of a monitor and the robot
control GUI. In this system, a three dimensional model ofthe robot and its environment was created using Java 3 D.
This allows the operator to command the robot fromremote site and observe the state of the robot via such areal time updated GUI. Figure1 shows the hardware
architecture of the teleoperation system.

Keywords: 3D monitor, underwater manipulator
teleoperation, moving target tracking

1. Introduction
There are some places which are not suitable forhuman presences, such as deep ocean, out space, nuclear
site, etc. However, we still have to perform many tasks inthese places such as maintenance and construction of oil
drilling structures in deep sea, cleaning of sites withradioactive spill and disposal of radioactive material in
nuclear sites, construction of structures like the Space
Station and retrieval of satellites in outer space. Working
in such places, not only need humans face life threatening
in every moment, but also cost too much to equip thehuman in order to survive in these crucial environments.
As a solution, in many cases, these tasks are performed
with a robot instead of a human, and the robot will doworks according to the commands sending by a humanoperator from some remote place [1,2,3]. Such remotely
operated robots have been used in many remote working©ISAROB 2006

Figure 1. Hardware Architecture of the system

787

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

4. Solution for time delay

3. Programming Development
The project has developed a system, which use Java 2
to connect the operator site with robot site though
network. The software architecture of whole system isshown in figure 2. It is composed of three main modules.
The main module for the operator site is known as Vessel,
which sets up the virtual model and calls Controller1class. Controller1 class deals with robot unit and imageprocessing unit. Since the image processing and robot
control algorithms are developed in C programming, a
programming interface is necessary to make the Javamethods to call the C functions. We created PlinkRecv.dll
and ParmController.dll and then through the Java Native
method to call them. The virtual model is coded by using
Java 3D API. The graphic parts of robot and its
environment are created by CAD and transferred to *.obj
format. These object files can be loaded by Java
programming and reunited into Java 3D coordinate torepresent the virtual model. Figure3 is a screenshot of the
3D model.

Our real time teleoperation system utilizes a general
network to transmit data. That will be inevitably affectedfrom the time delay. Besides, considering that the
teleoperation system uses visual feedback, cameras
probably fail to collect image for a few samples when thetarget is out of view or be obstructed. As a solution forsuch these problems, the research combines the eventtriggered autonomous control and optimum filter based
prediction method to achieve a moving target tracking
and grasping teleoperating task. The target positions are
estimated through a varying gain Kalman Filter, whichuses the past value of target trajectory to give a best
predict about the target future position.

4.1 Kalman Filter Overview
According to R. E. Kalman’s theory [5], a systemmodel is required. Here we suppose the system is discrete
linear, so the state equation can be presented as
X ( k +1) = AX ( k ) + BU ( k ) + W( k ) ………………….(4.1)
The measurement output equation isZ ( k ) = CX ( k ) + V( k ) ………………………………(4.2)
In the above equations A, B, C are matrix; k is the timeindex; X is called the state of system; U is known input of
the system; Z is the measurement value; w and v aresystem noise and measurement noise respectively. Eachof these quantities is vectors and contains more than one
element.
Although the vector X includes all of the information
about the present state of the system, unfortunately it
could not be measured directly. However we can use
available measurement Z to estimate the true state X.
Then, what criteria should our estimator satisfy? Two
obvious requirements come to mind. First, we want the
average value of our state estimate to be equal to theaverage value of the true state. Mathematically, we would
say that the expected value of the estimate should beequal to the expected value of the state. Second, we wanta state estimate that varies from the true state as little aspossible. That is, not only do we want the average of the

Figure3. Java 3D Model and Teleoperation Controller©ISAROB 2006

788

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

state estimate to be equal to the average of the true state,
but we also want an estimator that results in the smallest
possible variation of the state estimate. Mathematically,
we would say that we want to find the estimator with thesmallest possible error variance. It so happens that the
Kalman filter is the estimator that satisfies these two
criteria. But the Kalman filter solution does not apply
unless we can satisfy certain assumptions about the noisethat affects our system. Remember from our systemmodel that W is the process noise and V is themeasurement noise. We have to assume that the averagevalue of W is zero and the average value of V is zero. We
have to further assume that no correlation exists between
W and V. That is, at any time k, Wk, and Vk are
independent random variables. Then the noise covariancematrices Sw and Sz are defined as:
Process noise covariance[S w = E WkWk

T

4.2 Application of Kalman Filter Predictor inMoving Target Tracking and Grasping
Now consider our moving target tracking system. We
assumed the trajectory be liner and the object moving in a
two-dimensional plane with constant velocity. Foursignals are to be estimated: the position coordinates xk ,y k and the velocity vector x& k , y& k . The state vector of
the system, X k , can be expressed as
X k = [x k]…………………………………(4.3)

x& k +1 = x& k ………………………………...……..(4.10)]

y k +1 = y k + Ty& k + w yk …………………..……..(4.11)

T

where W and VT indicate the transpose of the W and V
random noise vectors, and E(•) means the expected value.
Here we will not retrace the complete Kalman’sderivation anymore.
The three final resulting equations are given as follows:[

K k = APk C T CPk C T + S v

y& k +1 = y& k ………………………………...……..(4.12)
Where T is the sampling interval. The noise term
wk accounts for deviation from the assumed constant] …………………..(4.5)−1velocity trajectory. We can rewrite the linear systemequations in (4.1)(4.2) as following:Xˆ k +1 = ( AXˆ k + BU k ) + K k ( Z k +1 − CXˆ k) …….(4.6)

0 0⎤
0 0 ⎥⎥
X k + Wk ………….... …(4.13)
1 T⎥
⎥
0 1⎦

⎡1 T
⎢0 1
X k +1 = ⎢
⎢0 0
⎢
⎣0 0

Pk +1 = APk AT + S w − APk C T [CPk C T + S v ]−1 CPk AT…………………………………………………...(4.7)
In the above equations, a “-1”superscript indicates matrix
inversion and a ”T” superscript indicates matrix
transposition. The K matrix is called the Kalman gain,
and the P matrix is called the estimation error covariance.

⎡1
Zk = ⎢⎣0

The state estimate X̂ equation (4.6) consists of tow parts.
The first term used to derive the state estimate at time k +1 is just A times the state estimate at time k, plus B times
the known input at time k. This would be the state
estimate if we didn't have a measurement. The second

Here

0

0

0

1

we

stationary

the
T

0]

processvariance σ , σvariable2y respectively.

noise

is a zero-mean, white,random2x

Vk =[ v xk0⎤
X k + V k .…..……(4.14)
0 ⎥⎦suppose0 w ykWk =[ wxk

term in the X̂ equation is called the correction term andit represents the amount by which to correct the
propagated state estimate due to our measurement.
Inspection of the K equation (4.5) shows that if themeasurement noise is large, Sv will be large, so K will besmall and we won't give much credibility to thewith

The measurement noisev yk ] also follows the same assumption given

as variable with variance σ xz , σ yz . Hence the Process22

noise covariance can be simplified as

⎡σ x2
⎢
T
S w = E WkWk = ⎢ 0
⎢0
⎢
⎣⎢ 0

[

measurement Z when computing the next X̂ . On theother hand, if the measurement noise is small, Sv will besmall, so K will be large and we will give a lot ofcredibility to the measurement when computing the nextX̂ .
©ISAROB 2006

Ty& k ] ……………….…….(4.8)

xk +1 = xk + Tx& k + wxk …………………..……….(4.9)

S v = E VkVk ……………………….…………..(4.4)
Tyk

Under the assumption that the target moves in constantvelocity, the input acceleration is 0, and then the state
equations of the signal model are:Measurement noise covariance

[

x& x

789

]

0
0

0
0

0 σ y20

0

0⎤⎥
0⎥ ………....(4.15)
0⎥
⎥
0⎦⎥

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

And the measurement noise covariance become

[

]

⎡σ 2
T
S v = E VkVk = ⎢ xz⎢⎣ 0

Suppose that the signal estimate at time interval k=2, X̂ 2 ,is computed from the first two measurements Z1 and Z 2 .

0 ⎤⎥ …………………(4.14)
σ yz2 ⎥⎦The two components of signal X 2 can be estimated byxˆ 2 = Z x 2 ……………………….……..…….. …(4.18)

With the above signal model and measurement model, we
can make the three general Kalman Filter equations(4.5)(4.6)(4.7) specialize to our practical system. The
expression as follows:x&ˆ 2 = ( Z x 2 − Z x1 ) / T ………………………..…(4.19)

yˆ 2 = Z y 2 ……………………………………….(4.20)

T 0 0⎤
T
1 0 0 ⎥⎥ P ⎡1 0 0 0⎤k⎢⎥
0 1 T ⎥ ⎣0 0 1 0 ⎦⎥0 0 1⎦⎡1⎢
K k +1 = ⎢0⎢0⎢⎣0y&ˆ 2 = ( Z y 2 − Z y1 ) / T ……………………...…..(4.21)
The error on this estimate is calculated to be−1⎡⎡1 0 0 0⎤ ⎡1 0 0 0⎤T ⎡σxz2 0 ⎤⎤
⎥
⎢⎢
⎥Pk ⎢
⎥ +⎢
2⎥
⎢⎣⎣0 0 1 0⎦ ⎣0 0 1 0⎦ ⎢⎣ 0 σ yz⎥⎦⎥⎦x2 − Z x2⎤⎡⎢ x& − ( Z − Z ) / T ⎥2x2
x1⎥ ……...…..(4.22)
X 2 − Xˆ 2 = ⎢⎥⎢y2 − Z y2⎥⎢⎢⎣ y& 2 − ( Z y 2 − Z y1 ) / T ⎦⎥…………...……….………………..(4.15)⎡1⎢0
Xˆ k +1 = ⎢
⎢0
⎢
⎣0

T

0

1

0

0

1

0

0

0⎤
⎡
0 ⎥⎥ ˆ
⎡1
X k + K k ⎢Z k − ⎢
T⎥
⎣0
⎣
⎥
1⎦

0

0

0

1

0⎤ ˆ ⎤Xk⎥
0 ⎥⎦⎦Due to the velocity is constant, we havex& 2 = x&1 = [ x 2 − x1 − wx1 ] / T ………….……..(4.23)…………………..……………...…(4.16)
⎡1 T
⎢0 1
Pk +1 = ⎢
⎢0 0
⎢
⎣0 0

⎡1
⎢
- ⎢0
⎢0
⎢
⎣0

0 ⎤ ⎡1 T
0 ⎥⎥ ⎢⎢0 1
Pk
1 T ⎥ ⎢0 0
⎥ ⎢0 1 ⎦ ⎣0 0
0
0

T

0⎤
⎡σ
⎢
⎥
0 0⎥
0
+⎢
⎢0
1 T⎥
⎢
⎥
0 1⎦
⎢⎣ 0
0

0 0⎤
⎡1
1 0 0 ⎥⎥ Pk ⎢⎣0
0 1 T⎥⎥
0 0 1⎦

T

2
x

0
0

0
0

0
0

0 σ
0 0

2
y

0
1

0⎤
0 ⎥⎦

0⎤
⎥
0⎥
0⎥
⎥
0⎦⎥

⎡1 T

Considering the measurement noise the measurement
should be

Z x 2 = x 2 + v x 2 ……………….……………….(4.25)
Z y 2 = y 2 + v y 2 ………………..………………(4.26)

T⎡⎡1 0 0 0⎤ ⎡1 0 0 0⎤⎡σ xz2
0 ⎤⎤
P
+
⎥
⎢⎢
⎢
⎥ k⎢
⎥
2 ⎥
⎢⎣⎣0 0 1 0⎦ ⎣0 0 1 0⎦⎣⎢ 0 σ yz ⎥⎦ ⎥⎦Ty& 2 = y&1 = [ y 2 − y1 − w y1 ] / T ……………….(4.24)

So the equation 4.22 can be presented as

− vx2
⎤
⎡
⎢[ v − v − w ] / T ⎥x1
x2
x1⎥ ………….(4.27)
X 2 − Xˆ 2 = ⎢⎥⎢− v y2⎥⎢⎢⎣[ v y 2 − v y1 − w y1 ] / T ⎥⎦

−1

0 0⎤

⎢0 1 0 0 ⎥⎡1 0 0 0 ⎤⎥ …………….(4.17)⎢0 0 1 0⎥ Pk ⎢⎢0 0 1 T ⎥⎣⎦The covariance matrix of the estimate error P2 can begotten as⎥⎢⎣0 0 0 1 ⎦[values of the filter’s initial state.

©ISAROB 2006

][

]

T
P2 = E ⎡ X 2 − Xˆ 2 X 2 − Xˆ 2 ⎤⎥⎦⎢⎣Now it remains only to specify the actual values taken by
the two noises covariance S w , S v and to specify the790

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

− vx2
⎤
− vx2
⎡
⎤⎡
⎢[v − v − w ] / T ⎥ ⎢[v − v − w ] / T ⎥
x2
x1
x2
x1
⎥
= ⎢ x1⎥ ⎢ x1⎥⎢⎥⎢− vy2− v y2⎥⎢⎥⎢⎣⎢[v y 2 − v y1 − w y1 ] / T ⎦⎥ ⎣⎢[v y 2 − v y1 − w y1 ] / T ⎦⎥T

6. Conclusion
In this research, a virtual model based teleoperation
system is developed. This experimental system includes a
GUI in the operator side, which allows operator interact
with remote robot by sending the control commandthrough the virtual model input panel and view the robot
state from the real time updated Java 3D model.
An on-line state estimate system is also explored. This
paper presents a real time visual feedback tracking system
to follow a moving target, predict its future position, andcatch it. By using an optimum Kalman Filter based
prediction algorithm, we successfully overcome theunavoidable time delay and achieve the task.

⎤⎡ vx22vx22 / T
0
0
⎥
⎢2
2
2
2
2
v / T [vx1 + vx2 + wx1]/ T00⎥= ⎢ x2⎥⎢ 0
0v2y2
vv22 / T⎥⎢0
vv22 / T [vy21 + vy22 + wy21 ]/ T 2 ⎦⎥⎣⎢ 0

7. Reference⎤⎡ σxz2σxz2 /T
0
0
⎥
⎢ 2
2
2
2
σ /T [2σxz +σx ]/T00⎥ ...(4.28)= ⎢ xz⎢ 00
σ yz2
σvz2 /T ⎥⎥⎢0
σvz2 /T [2σ yz2 +σ y2]/T2 ⎦⎥⎣⎢ 0[1] Mitsuishi, T. Hori, and T. Nagao, “Predictive
information display for telehanding/machining
system”. IEEE/RSJ/GI International Conference onIntelligent Robots and Systems 1994,pp.260-267.
[2] Tachi. S, ”Real-time remote robotics- toward
networked telexistence”,IEEE Computer Graphics
and Applications 1998,18(6),pp.6-9.
[3] M. Minsk, O. Y. Ming, O. Steele, F. P. Brooks, and
M. Behensky, “Felling and Seeing: issues in force
display”. ACM SIGGRAPH Computer Graphics
1990,24(2), pp. 235-243.
[4] Kyo, M., E. Miyazaki, S. Tsukioka, H. Ochi, Y.
Amitani, T. Tsuchiya, T. Aoki, and S. Takagawa.
1995. “The sea trial of "KAIKO", the full ocean
depth research ROV ”. OCEANS'95 3:1991-1996[5]
R. E. Kalman. “A New Approach to Linear
Filtering and Prediction Problems”. Transactions of
the ASME-Journal of Basic Engineering, 1960,
82(Series D), pp.35-45.

The initial value P3 can now be calculated using
equation (4.17). The predicted signal X̂ 3 is obtained
from equation (4.16)(4.18). Using equation (4.15),
Kalman gain K 4 can be obtained. Repeating theseprocedures, the successive predictions are obtained.

5. Experiment Investigations and Result
To demonstrate the maximum permissibility of theteleoperating system, a time-limited task is chosen. A
small ball is used as a target. For verifying the effectivityof the prediction system, we make the target move along
X-axis with a constant velocity. So the Kalman Filter
prediction system is simplified from 4 states to 2 states,
they are position x k and velocity x& k . Before our Kalman
Filter predictor can be implemented, specific values must
be assigned to the noise variances σ x , σ xz . According22

to a series of tests in using different value of σ x , σ xz ,finally we choose σ x =0.2, σ xz =10. The experimentresult is showed in following Figure.

-X Coordinate(mm)

500
450

Actual Value

400

Predicted Value

350
300
250
200
150
100
50
0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8Time(Second)

Figure 3. Target Position and Predicted Position©ISAROB 2006

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

J (η )：Velocity transformation matrix

able to design a controller using LMI for the mathematicalmodel of which includes non-linear components. However, bothLinear Quadratic (LQ) optimal control and LMI cannot solve
non-linear model. So in order to design a controller using these
design method, we have to linearize the mathematical model.
Secondly, the aim of this research is long-distance cruising forsurge direction. However, moving toward vertical andhorizontal direction is not the aim but to keep the condition oforigin vicinity. In short, the role of side and vertical thrusters is to
reduce the deviation that occurs by moving toward surge
direction. So we considered that it is sufficiently to be linearlized
under first-order approximation. And the last reason is that if
many parameters are existed, “Simultaneous LMIs that depends
on parameters” can be high-dimensions, and it is difficult tosolve. As above, we linearized the mathematical model of‘MR-X1’ except the speed parameter of surge direction ‘u’.
Linearized model is represented as follows.

2.2 Dynamics [5],[9]◯Inertial matrix

In this section, we derive mathematical model of ‘MR-X1’.
Following matrices are derived by numeric calculation or
experiment. This model has been derived as faithful to ‘MR-X1’
as we could, but it isn’t strictly. However, robust control theory
has a property that even if the model is different or change fromreal character a little, the system will be able to adopt them.
Therefore, it is considered that this theory can be covered theseerrors.
The motion of underwater vehicle is represented as the 6DOF
nonlinear equation. In general, its motion can be treated as the
motion of Rigid-body. We derived the nonlinear equation of‘MR-X1’ following the method, which is suggested by Thor I.
Fossen. The nonlinear equation is represented as follows.

0m − A11
 0
−
m
A33

 mzG
− mxG
M =
0
 0
 0
0

0
 0

In this paper, we use two coordinate systems. One is the
Earth-fixed coordinate system and the other is the Body-fixed
coordinate system. Fig.2 shows the relation between eachcoordinate system. In general, linear and angular velocities are
represented by using Body-fixed coordinate system, but thetransformation to the Earth-fixed coordinate system is suitable toobserve the motion of ‘MR-X1’.
•

(2.1)

η = J (η )ν

η = [ X , Z ,ϑ , Y , φ , ϕ ]T ：Position and angle vector with the

Earth-fixed coordinate systemν = [u, w, q, v, p, r ] ：Linear and angular velocity vector withT

the Body-fixed coordinate system
X , Y , Z ：Position
φ , ϑ , ϕ ：Attitude
u , v, w：Linear velocity
p , q , r：Angular velocity•

M ν + C (ν )ν + D(ν )ν + g (η ) = τ

0
0

mzG
− mxG

0
0

I yy − A55

0

0

0
0

m − A22
− mzG

− mzG
I xx − A44

0

mxG

0

0
0




0 

mxG 
0 

I zz − A66 (2.3)◯Matrix of coriolis and centripetal terms
0
0
0
0
0
0
− (m − A11 )u0
0 (m − A11 )u00
C (u ) L = 
0
0
0
0

0
0
0
0

0
0
− (m − A11 )u
0

0 0
0 Z
w

0 M w
DL = − 
0 0
0 0

0 0

M ：Inertial matrix
C (ν ) ：Matrix of Coriolis and Centripetal
D (ν )：Damping matrix
g (η )：Matrix of restoring forces and momentsτ ：Thrusts

0
Zq

0

0
0

0
0






0 (m − A11 )u 

0
0

0
0


(2.4)

◯Damping matrix

(2.2)

0

0
0

0
0

Mq

0

0

0
0

Yv
Kv

0
0

0

Nv

0

0
0 
0

Yr 
Kr 

N r 

(2.5)◯Matrix of restoring forces and moments

2.3 Parameter-dependent (PD) Linearization

0
0

0
gL = 
000In this section, we treated the cruising speed for surge
direction as a parameter, and under this condition, themathematical model of ‘MR-X1’ is linearized. There are threereasons. First, in this research, we’d like to confirm that we’d be©ISAROB 2006

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0
0
0
0
0 ( zGW − z B B )
0
0
0
0

0
0
0
0
0
0
0
0
0 ( zGW − z B B )

0

0

0

0

0
0
0

0
0

0

(2.6)

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

PD linearized system is represented including only the speed
parameter of surge direction ‘u’. Thus, the condition, which
depends on only ‘u’, is constructed. When we make controllable
matrix using state equation (2.8) to (2.10), it becomes full rank.
So it is obvious that this system is controllable. Therefore, we‘llbe able to solve Riccati equation or inequality, and derive
positivistic symmetric definite.

◯Matrix that transform thrusts into rotational speed
 1
 0

− zTHMτ ROT = 
 0
 0

 0

0

0

0

0

0

cosα

0





xTVR cosα
sinα
zTVR sinα + yTVR cosα − xTVR sinα

cosα

0

0

xTVL cosα−1− xTHF−1zTHR− sinα− zTVL sinα − yTVL cosα

− xTHFxTHR

xTVL sinα

0
0
0
0 
9.3043
 0
0
.
84628
0
0
0 

• 0
0
0.98582
0
0 


0
0
0.95576
0 
 0
 0
0
0
0
0.96669

(2.7)

3.1 Controller Design Strategy using “Simultaneous
LMIs that depends on parameters”
In this section, the control system is designed to control‘MR-X1’ moving toward the objective point and stopping atthat point. If we treat this aim as regulator problem, we candesign a control system that following the theory of ‘origin
convergence’. So we design a controller for ‘MR-X1’ that main
thruster is used for only straight cruising and two side thrusters
and two vertical thrusters are used for moving to makedeviations zero from horizontal and vertical direction.
The control system is firstly designed using the LQ optimal
control. Generally, in order to design the LQ optimal control
system for linear systems, it is necessary to solve the Algebraic
Riccati equation by using constant matrices A and B in the state
equation. Since the Riccati equation is solved easily by using
some computer software, the state feedback control system canbe obtained easily. In order to apply the LQ control theory for‘MR-X1’, we have to fix the cruising speed u. Therefore theoptimality of the obtained control system is only compensatedabout the one speed. However, the obtained control system does
not compensate the stability in other speed situations.
In order to solve this problem, we propose a new design
method by using LMI, which correspond to the Riccati
inequality. The algorithm to solve LMI has a property that cansolve some number of LMIs simultaneously. By applying thisproperty, it is considered that we can obtain the solution thatsatisfies the LMI condition under several speeds.
The LMI that corresponds to Riccati inequality in LQ optimal
control on state equation is represented as (3.1).

2.4 State Space Representation
State equation of ‘MR-X1’ is represented as follow.
•

x = A (u ) x + B τ(2.8)

x = [ν ,η ] = [u, w, q, v, p, r , X , Z ,ϑ , Y , φ , ϕ ]TTτ = [nTHM , nTHF , nTHR , nTVL , nTVR ]T − M −1 (C (u ) L + DL ) − M −1 g L 
A=
JL
0 6×6 

 M −1τ ROT 
B= 06×6 (2.9)(2.10)

3 Controller Design for PD Linearized System
The aim of developing ‘MR-X1’ is to construct the AUV thatit not only follows the given path but also stops at the objective
point and keeps the point to investigate, observe and operate. In
order to realize them, five thrusters, which equipped on‘MR-X1’, have to be controlled to be appropriate rotational
speeds. In order to check the dynamic characteristic of ‘MR-X1’,
the main thruster was only rotated firstly. Since the displacement
toward to the heave direction occurred, to keep constant altitude,
two vertical thrusters have to be rotated. However, verticalthrusters set up by heaving the inclination from theperpendicular. If these thrusters are rotated, ‘MR-X1’ not only
moves the vertical direction but also changes the horizontal
direction. Therefore, in the case of cruising ‘MR-X1’ with theconstant altitude, these five thrusters have to be controlled
appropriately. The present paper considers the controller design
problem for ‘MR-X1’ to make it go straight on surge direction,and stop at the objective point with the constant altitude. And wedesign a control system to adopt the variation of the speed forthe surge direction.

©ISAROB 2006

 A(u i ) X + XA(u i ) T − BB T

CX

X >0

τ = − Kx = − B T X −1 x

XC T 
<0−I (i = 0, ・・・ m)

(3.1)
(3.2)

Speed parameter ‘u’ is given appropriately. By solving“Simultaneous LMIs that depends on parameters”, we can

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The movement from the initial position to the objective point
occurs the velocity consequently, so the control system of 0[m/s]cannot compensate the stability at the speed occurring, and‘MR-X1’ tends to diverge like Fig.3 and Fig.4.

obtain one solution. The control input is represented as (3.2).
The solution X satisfies LMI conditions on all speed parameters.
This means that the obtained control system compensates thestability for all speed situations, which substitute for parameters.
For LQ optimal control systems, two types of controllers were
designed. One is the optimal gain for the surge speed 0[m/s](Case1), and another is for 0.3[m/s] (Case2). The speed of
0[m/s] corresponds to keeping the objective point. The speed
0.3[m/s] is the middle speed of ‘MR-X1’ since the maximum
speed of ‘MR-X1’ is about 0.6[m/s]. On the other hand, for“Simultaneous LMIs that depends on parameters”, we set therange of speed parameter between 0[m/s] and 0.7[m/s] (Case3).
0.7[m/s] is covered the maximum speed of ‘MR-X1’.

○Case 2

3.2 Numerical Examples
Table 2 Requirements of simulations
Simulation time

300[sec]

Initial position[-100(m), 10(m), 10(m)]Target point[0(m), 0(m), 0(m)]Fig.5 Variation in linear velocity with transition of the time

○Case 1Ending point

Starting point
Target point

Fig.6 Displacement of ‘MR-X1’ in 3-D visualization

Fig.3 Variation in linear velocity with transition of the time

‘MR-X1’ does not always cruise with the constant speed.
Therefore, when speed changing occurs, the optimal gain willfluctuate in each case, and if we use the control system thatderived under one fixed speed like this simulation, the obtained
control system cannot compensate the stability at other speed
situations, ‘MR-X1’ tends to diverge like Fig.5 and Fig.6.

Ending point
Starting point
Target point

Fig.4 Displacement of ‘MR-X1’ in 3-D visualization©ISAROB 2006

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○Case 3

controller designing method “Simultaneous LMIs that depends
on parameters”. As the result, this new method made it possible
to design a control system that corresponds to speed changing.
This method has more effective performance than LQ optimal
control system.
This research is the one that it proves the breadth ofrobustness in the case of using LMI method about AUV. Thistime, the aim was set as position control, and on that basis, thereality about this aim was suggested. Furthermore, we’ll be able
to confirmed sufficient controllability even if we linearized themodel extremely, so it is considered that the system will be able
to absorb little model error.

Fig.7 Variation in linear velocity with transition of the time

REFERANCES[1] Hiroshi Yoshida: A Working AUV for Scientific Research,
OCEAN ’04 MTS／IEEE, TECHNO-OCEAN ’04,
Nov.9-12, 2004, Kobe, Japan[2] P.Apkarian, G.Becker, P. Gahinet, H.Kajiwara: LMI
Techniques in Control Engineering from Theory to Practice,IEEE CDC ’96 Workshop, 1996
[3] Ikuo Yamamoto: Marine Control System, International
Journal of Robust and Nonlinear Control, Vol.11, No.13,
2001[4] Tetsuya Iwasaki: LMI and Control, Shokodo, 1997
[5] Thor I.Fossen: Guidance and Control of Ocean Vehicles,
1994[6] Okutani, T. and N. Iwasaki (2003) Noteworthy abyssal
mollusks (excluding vesicomyid bivalves) collected fromthe Nankai Trough off Shikoku by the ROV Kaiko of theJapan Marine Science & Technology Center. Venus[7] Yuichi Nishimura: The 2002-2003 seismic activity at Suiyo
Seamount, Izu-Ogasawara (Bonin) Arc, as revealed by OBS
observations[8] Taro Aoki, Toshiaki Nakamura, Satoshi Tsukioka, Takashi
Murashima, Hiroshi Ochi, Hidehiko Nakajo, Tadahiko Ida,
Tadahiro Hyakudome: THE DEVELOPMENT OF THE
AUV -URASHIMA-, OCEANS 2000 MTS／IEEE, 2000
[9] Yohei Nasuno, Etsuro Shimizu, Taro Aoki, Ikuo Yamamoto,
Tadahiro Hyakudome, Satoshi Tsukioka, Hiroshi Yoshida,
Shojiro Ishibashi, Masanori Ito: The design method of thecontrol system for Autonomous Underwater Vehicle (AUV)
using Linear Matrix Inequality (LMI), International
Conference on Control, Automation, and Systems (ICCAS2005), June 2-5, 2005, KINTEX, Gyeonggi-Do, KOREA

Starting point
Ending point=Target point

Fig.8 Displacement of ‘MR-X1’ in 3-D visualization

While ‘MR-X1’ cruises straight toward surge direction, initialdeflections of Y-axis and Z-axis converge to the desired path on
X-axis. The speed of ‘MR-X1’ goes up to the maximum speed,
and when ‘MR-X1’ comes close to the target point, the speed
reduced and became 0[m/s] at the origin. Furthermore, thissystem can control ‘MR-X1’ even if the initial position has moredistance from target point. So this new controller design method
has effective property for designing controller of AUV.

4 Conclusions
In this paper, the mathematical model of Autonomous
Underwater Vehicle named ‘MR-X1’, which is developing at
JAMSTEC, was derived. And about the dynamic characteristicof ‘MR-X1’, which has nature of non-linear, we linearized themathematical model in order to design a controller. And we set
the aim on autonomous tracking given path and stopping at theobjective point, and for this aim, we construct a control system,
which was applied LQ optimal control system and new©ISAROB 2006

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Three-Dimensional Synchronized Alternating Turing Machines
Takao ito1 , Makoto Sakamoto1 , Makoto Saito1 , Kouichi Iihoshi1 ,Hiroshi Furutani1 , Michio Kono1 , and Katsushi Inoue2
1
2

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN
Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract

introduced three-dimensional alternating Turing machine [5].
In this paper, we continue the investigations about
three-dimensional alternating Turing machines, introduce a three-dimensional synchronized alternating
Turing machine (3-SAT M ), and investigate fundamental properties of 3-SAT M 0 s whose input tapes are
restricted to cubic ones.
Let Σ be a finite set of symbols.
A threedimensional input tape over Σ is a three-dimensional
rectangular array of elements of Σ. The set of all thethree-dimensional input tapes over Σ is denoted by
Σ(3) . Given an input tape x ∈ Σ(3) , for each j (1 ≤ j ≤3), we let lj (x) be the length of x along the jth axis.
The set of all x ∈ Σ(3) with l1 (x) = m1 ,l2 (x) = m2 andl3 (x)=m3 is denoted by Σ(m1 ,m2 ,m3 ) . If 1 ≤ ij ≤ lj (x)for each j (1 ≤ j ≤ 3), let x(i1 , i2 , i3 ) denote the symbol in x with coordinates (i1 , i2 , i3 ). Furthermore, we
define x[(i1 , i2 , i3 ),(i01 , i02 , i03 )], when 1 ≤ ij ≤ i0j ≤ lj (x)for each integer j (1 ≤ j ≤ 3), as the three-dimensional
input tape y satisfying the following;(1) for each j (1 ≤ j ≤ 3), lj (y) = i0j − ij + 1;
(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.)
We now introduce a three-dimensional synchronizedalternating Turing machine. A three-dimensional synchronized alternating Turing machine (denoted by
3-SAT M ) is a 10-tuple M = (Q, q0 , U , E, S, F , Σ,Π, Γ, δ), where(1) Q = U ∪ E ∪ S is a finite set of states,(2) q0 ∈ Q is the initial state,(3) U is the set of universal states,(4) E is the set of existential states,(5) S ⊆ {(q, s) : q ∈ U ∪ E, s ∈ Π} is the set ofsynchronizing states (s-states),(6) F ⊆ Q is the set of accepting states,(7) Σ is a finite input alphabet (# ∈/ Σ is the boundary symbol ),(8) Π is a finite alphabet of synchronizing symbols,(9) Γ is a finite storage tape alphabet containing thespecial blank symbol B,(10) δ ⊆ (Q × (Σ ∪ {#}) × Γ) × (Q× (Γ −{B}) × {east,west,south,north,top,down,no move} ×{left,right,no move}) is the next move relation.

This paper introduces a three-dimensional synchronized alternating Turing machine (3-SAT M ), and investigates fundamental properties of 3-SAT M 0 s whose
input tapes are restricted to cubic ones. The main topics of this paper are: (1) a relationship between theaccepting powers of 3-SAT M 0 s and three-dimensional
alternating Turing machines with small space bounds,(2) a relationship between the accepting powers of
five-way and six-way 3-SAT M 0 s, (3) a relationship between the accepting powers of 3-SAT M 0 s and threedimensional nondeterministic Turing machines.
Key Words: three-dimensional Turing machine, alternation, synchronization

1

Introduction and preliminaries

Synchronized alternating Turing machines were introduced in [2] to study the effect of allowing processesof an alternating Turing machine to communicate via
synchronization. Informally, a synchronized alternating machine is an alternating machine with a special
subset of internal states called synchronizing states.
Each of these synchronizing states is associated witha synchronizing symbol. If, during the course of computation, some process enters a synchronizing state,then it has to wait until all other processes enter either an accepting state or a synchronizing state withthe same synchronizing symbol. When this happens,
all processes are allows to continue their computation;otherwise, the machine is said to have a deadlock. A
computation is successful if no deadlocks occur and allprocesses terminate in accepting states. It turns outthat synchronization significantly increases the computaional power of alternating Turing machines.
On the other hand, recently, due to the advances
in many application areas such as computer vision,
robotics, and so forth, it has become increasingly apparent that the study of three-dimensional patternprocessing has been of crucial importance. Thus, theresearch of three-dimensional automata as a computational model of three-dimensional pattern processing
has also been meaningful. From this viewpoint, we©ISAROB 2006

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As shown in Fig.1, M has a read-only cubic input tape with boundary symbols #’s (# ∈/ Σ) andone semi-infinite storage tape, initially filled with theblank symbols. M begins in state q0 . A position is
assigned to each cell of the input tape and the storagetape, as shown in Fig.1. A step of M consists of reading one symbol from each tape, writing a symbol on
the storage tape, moving the input and storage tapeheads in specified directions, and entering a new state,according to the next move relation δ. When a process
P enters a synchronizing state, it stops and waits until
all the parallel processes either enter the states withthe same synchronizing element or stop in accepting
states.
An instantaneous description (ID) of a 3-SAT M
M = (Q, q0 , U , E, S, F , Σ, Π, Γ, δ) is a pair of anelement of Σ(3) and an element ofFig. 1: Three-dimensional synchronized alternating
Turing machine.

CM = (N ∪ {0}3 ) × SM , SM = Q × (Γ − {B})∗ × N,
where N denotes the set of all positive integers. The
first component of an ID I = (x, ((i1 , i2 , i3 ), (q, α, k)))represents the input to M , and the first component(i1 , i2 , i3 ) of the second component of I represents the
input head position (0 ≤ i1 ≤ l1 (x) + 1, 0 ≤ i2 ≤
l2 (x)+1, 0 ≤ i3 ≤ l3 (x)+1), and the second component(q, α, k) of the second component of I represents thestate of the finite control, nonblank contents of thestorage tape, and the storage head position (1 ≤ k ≤|α| + 1). An element of CM is called a configurationof M , and an element of SM is called a storage stateof M .
An ID is universal (existential, synchronizing, accepting) depending on the type of the state of thefinite control. The initial ID of M on input x is
IM (x) = (x, ((1, 1, 1), (q0 , ², 1))), where ² is the null
word.
Suppose I1 and I2 are two ID0 s of M and I2 follows from I1 in one step according to the next move
relation δ. Then we write I1 `M I2 and say that I2 isa successor of I1 . The reflexive and transive closureof `M is denoted by `∗M .
A sequence of ID0 s of M , I0 , I1 , · · · , Im (m ≥ 0), iscalled a sequential computation of M if I0 `M I1 `M· · · `M IM . If I0 = IM (x) for some x, we call thissequence a computation path of M on x.
The full computation tree of M on an input tape x
is a (possibly infinite) labeled tree `M
x such that(1) each node v is labeled by some ID Iv of M ,(2) the root is labeled by IM (x),(3) v2 is a direct descendant of v1 iff Iv1 `M Iv2 .
(Each branch of `M
x is called a process.)
The synchronizing sequence (s-sequence) of a node
v in a full computation tree T with root v0 is the sequence of synchronizing symbols occuring in labels of
the nodes on the path from v0 to v. Two s-sequences
are compatible if one is a prefix of the other. If s1 and©ISAROB 2006

s2 are two compatible s-sequences, and s2 is longer
than s1 , then we use s2 − s1 to denote their difference.
A computation tree of M on an input x is a (possibly
infinite) subtree T 0 of the full computation tree TxMsatisfying the following conditions:(1) if u is an internal (non-leaf) node of the tree
T 0 , Iu is universal and {I | Iu `M I} = {I1 , · · · , Im },then u has exactly m children v1 , · · · , vm , such that
Ivi = Ii , 1 ≤ i ≤ m,(2) if u is an internal node of the tree and Iu isexistential, then u has exactly one chiled v such that
Iu ` Iv ,(3) For arbitrary nodes u and v of T 0 , the ssequences of u and v are compatible.
If M on input x has no computation trees, then any
subtree of TxM that satisfies the first two conditions
above must have two processes with incompatible ssequences. In this case, we say M deadlocks on x.
The two processes with incompatible s-sequences arecalled deadlock processes and the nonmatching s-statescausing the deadlock are called deadlock states.
The longest synchronizing sequence of a node in
the computation tree T is called the synchronizing sequence of the computation tree T .
An accepting computation tree of M on an input x
is a finite computation tree of M on x such that eachleaf node is labeled by an accepting ID. We say that
M accepts x if there is an accepting computation treeof M on x. Let T (M ) = {x ∈ Σ(3) | M accepts x}.
We next introduce a five-way three-dimensional
synchronized alternating Turing machine which canbe considered as a synchronized version of five-way
three-dimensional alternating Turing machine [5].
A five-way three-dimensional synchronized alternating Turing machine (denoted by F V 3-SAT M ) is a
3-SAT M M = (Q, q0 , U, E, S, F, Σ, Π, Γ, δ), such that

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B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006δ ⊆ (Q × (Σ ∪ {#}) × Γ) × (Q × Γ − {B})× {east,
west, south, north, down, no move} × {left,right, nomove}).
That is, a F V 3-SAT M is a 3-SAT M whose input
head can move east, west, south, north, or down, but
not up.
Let L(m) : N → N be a function with one variable m. With each 3-SAT M (or F V 3-SAT M ) M we
assosiate a space complexity function SPACE whichtakes ID0 s to natural numbers. That is, for each ID
I = (x, ((i1 , i2 , i3 ), (q, α, k))), let SPACE(I) be thelength of α. We say that M is “L(m) space-bounded ” iffor all m and for all x with l1 (x) = l2 (x) = l3 (x) = m,if x is accepted by M , then there is an accepting omputation tree of M on input x such that for eachnode π of the tree, SPACE (I(π)) ≤ L(m). By “3SAT M (L(m))” (“F V 3-SAT M (L(m))”) we denote an
L(m) space-bounded 3-SAT M (F V 3-SAT M ) whose
input tapes are restricted to cubic ones.
Three-dimensional alternating Turing machines(3-AT M 0 s and five-way three-dimensional alternating Turing machines (F V 3-AT M 0 s) in [4] are
3-SAT M 0 s and F V 3-SAT M 0 s, respectively, whichhave no synchronizing states. We use 3-SU T M(F V 3-SU T M , 3-U T M , F V 3-U T M ) to denote a 3SAT M (F V 3-SAT M , 3-AT M , F V 3-AT M ) which
has no existential states. By 3-AT M (L(m)) (F V 3AT M (L(m)), 3-SU T M (L(m)), F V 3-SU T M (L(m)),
3-U T M (L(m)), F V 3-U T M (L(m))), we denote an
L(m) space-bounded 3-AT M (F V 3-AT M , 3-SU T M ,F V 3-SU T M , 3-U T M , F V 3-U T M ).
A three-dimensional deterministic Turing machine(3-DT M ) (five-way three-dimensional deterministic
Turing machine (F V 3-DT M )) is a 3-AT M (F V 3AT M ) whose ID0 s each have at most one successor,
and a three-dimensional nondeterministic Turing machine (3-N T M ) (five-way three-dimensional nondeterministic Turing machine (F V 3-N T M )) is a 3-AT M
which has no universal states. We denote an L(m)space-bounded 3-DT M (3-N T M , F V 3-DT M , F V 3N T M ) by 3-DT M (L(m)) (3-N T M (L(m)), F V 3DT M (L(m)), F V 3-N T M (L(m))).
We use 3SAF A (F V 3-SAF A, 3-AF A, F V 3-AF A, 3-N F A,
F V 3-N F A, 3-DF A,F V 3-DF A) to denote a threedimensional synchronized alternating finite automaton (five-way three-dimensional synchronized alternating finite automaton, three-dimensional alternating finite automaton, five-way three-dimensional alternaing
finite automaton, three-dimensional nondeterministic
finite automaton, five-way three-dimensional nondeterministic finite automaton, three-dimensional determinnistic finite automaton, five-way three-dimensional
deterministic finite automaton). That is, a 3-SAF A(F V 3-SAF A, 3-AF A, F V 3-AF A, 3-N F A, F V 3N F A, 3-DF A, F V 3-DF A) is a 3-SAT M (F V 3SAT M , 3-AT M , F V 3-AT M , 3-N T M , F V 3-N T M ,3-DT M , F V 3-DT M ) which doesn’t have storage©ISAROB 2006

tape. Similarly, we use 3-SU F A (F V 3-SU F A, 3U F A, F V 3-U F A) to denote a 3-SU T M (F V 3SU T M , 3-U T M , F V 3-U T M ) which doesn’t have thestorage tape. Furthermore, for any integer k ≥ 1, 3SAT M (L(m))[k] is used to denote a 3-SAT M (L(m))
such that any computation tree of M on any input
x has at most k leaves. F V 3-SAT M (L(m))[k], 3SU T M (L(m))[k], · · · , 3-SAF A(L(m))[k], etc. havethe similar meaning. For any integer k ≥ 1, 3N F A(k-heads) (3-DF A(k-heads)) is used to denotea 3-NFA (3-DF A) which has k input heads. For any
machine class C, let
L [C] = { T | T = T (M ) for some M in C }Thus, for example, L[3-SAT M (L(m))] denotes theclass of sets accepted by 3-SAT M (L(m))0 s.

2

Synchronization versus non-synchronization

This section investigates a relationship between theaccepting powers of 3-AT M 0 s and 3-SAT M 0 s.
Lemma 3.1. Let T1 = {x ∈ {0, 1}2m×2m×2m | m ≥ 1& x[(1, 1, 1), (2m, 2m, m)] = x[(1, 1, m + 1), (2m, 2m,2m)]}. Then,(1) T1 ∈ L[F V 3-SU F A[2]], and(2) T1 ∈/ L[3-AT M (L(m))] for any L : N → N suchthat L(m) = o(log m).
Proof: (1) We can construct an F V 3-SU F A[2] M
accepting T1 as follows: Given x with l1 (x) = l2 (x) =l3 (x) = 2m (m ≥ 1), starting on position (1,1,1) ofx, M first splits universally into two processes p1 andp2 . Process p2 moves its head to (1, 1, m + 1) and thensynchronizes with process p1 to compare x(i1 , i2 , i3 )and x(i1 , i2 , i3 +m) for each i1 , i2 , i3 (1 ≤ i1 ≤ 2m, 1 ≤i2 ≤ 2m, 1 ≤ i3 ≤ m). M accepts x iff x(i1 , i2 , i3 ) =x(i1 , i2 , i3 + m) for each i1 , i2 , i3 (1 ≤ i1 ≤ 2m, 1 ≤i2 ≤ 2m, 1 ≤ i3 ≤ m).
(2) This proof is the same as that of Theorem 1 in
[4].
From this lemma, we have
Theorem 2.1. For any function L(m) = o(log m),(1) L[F V 3-U T M (L(m))] ( L[F V 3-SU T M (L(m))],(2) L[F V 3-AT M (L(m))] ( L[F V 3-SAT M (L(m))],(3) L[3-U T M (L(m))] ( L[3-SU T M (L(m))], and(4) L[3-AT M (L(m))] ( L[3-SAT M (L(m))]
Corollary 2.1.
(1) L[F V 3-U F A] ( L[F V 3-SU F A],(2) L[F V 3-AF A] ( L[F V 3-SAF A],(3) L[3-U F A] ( L[3-SU F A], and(4) L[3-AF A] ( L[3-SAF A]
Theorem 2.2. For any function L(m) ≥ log m,
L[3-SU T M (L(m))] = L[3-U T M (L(m))].
Proof: Given a 3-SU T M (L(m)) M where L(m) ≥log m, we construct a 3-U T M (L(m)) M 0 to accept thesame set as follows. On input x of side-length m ≥ 1,

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M 0 simulates each process of M with a process of its
own. When some process p of M enters an s-state, thecorresponding process p0 of M 0 spawns off a process cwhose worktape contains the s-symbol associated withthe s-state and the number of s-states p has enteredso far. Since each process makes at most dL(m) moves(d is a constant), and L(m) ≥ log m, there is enoughspace to store them. Process c restars the computation of M on x and verifies that the corresponding ssymbols in other processes match with the one stored
on its worktape. If a discrepancy occurs, M 0 rejects.
It is easy to see that M and M 0 accept the same set.
By using a technique similar to that in the proof of
Theorem 2.2, we have
Theorem 2.3. For any function L(m) ≥ log m,
L[F V 3-SU T M (L(m))] = L[F V 3-U T M (L(m))].

3(1) T3 ∈ L[3-DT M (log m)] (= L[3-SU T M (log m)[1]]), and(2) T3 ∈/ L[F V 3-SU T M (L(m))] for any L : N →N such that L(m) = o(m3 ).
Proof: (1) We can construct a 3-DT M (log m) M accepting T3 as follows: Given x with l1 (x) = l2 (x) =l3 (x) = 2m (m ≥ 1), starting on position (1,1,1) of
x for all i1 ,i2 ,i3 (1 ≤ i1 ≤ 2m, 1 ≤ i2 ≤ 2m, 1 ≤i3 ≤ m), M repeats the following process; M records
the input symbol x (i1 ,i2 ,i3 ) in the finite control andchecks that two symbols x(i1 , i2 .i3 ) 6= x(i1 , i2 , i3 + m).
(This can be easily done by using log m cells of thestorage tape.) If so, M and enters an accepting state.
It is clear that T (M ) = T3 .
(2) The idea is almost the same as in the proof of
Lemma 3.1 (2).
¤From Lemmas 3.1 and 3.2 we can get the following
theorem.
Theorem 3.2. Let L : N → N be a function suchthat (i) L(m) = o(m2 ), or (ii) L(m) ≥ log m andL(m) = o(m3 ). Then,

Five-way versus six-way

This section investigates a relationship between theaccepting powers of five-way and six-way synchronizedmachines.
It is shown in [3] that three-way two-dimensional
synchronized alternating Turing machine are equivalent to two-dimensional synchronized alternating Turing machines. By using the same idea as in the proofof this fact, we can easily show that the following theorem holds.
Theorem 3.1. For any function L : N → N,

L[F V 3-SU T M (L(m))] ( L[3-SU T M (L(m))].
Corollary 3.1. L[F V 3-SU F A] ( L[3-SU F A].
lt is easy to show that the following theorem holds.
Theorem 3.3. For any function L(m) ≥ m3 ,L[F V 3-SU T M (L(m))] = L[3-SU T M (L(m))].

4

L[F V 3-SAT M (L(m))] = L[3-SAT M (L(m))].
Below,we investigate a difference betwecn the accepting powers of space-bounded 3-SU T M 0 s andF V 3-SU T M 0 s.
Lemma 3.1. Let T2 = {x ∈ {0, 1}m×m×m | m ≥ 2, &x[(1, 1, 1), (m, m, 1)] 6= x[(1, 1, 2), (m, m, 2)] & x[(1, 1,
3), (m, m, 3)] ∈ {0}(3) }. Then,(1) T2 ∈ L[3-DF A](= L[3-SU T M (0)[1]]), and(2) T2 ∈/ L[F V 3-SU T M (L(m))] for any L : N →N such that L(m) = o(m2 ).
Proof: (1) We can construct a 3-DF A M accepting T2 as follows: Given x with l1 (x) = l2 (x) =l3 (x) = m (m ≥ 2), starting on position (1,1,1) ofx, M first checks that x[(1, 1, 3), (m, m, 3)] ∈ {0}(3) .
Then, M repeats the following process from j = 1 tom; M records the input symbol x[(1, 1, 1), (m, m, 1)]
in the finite control and checks that two symbols
x[(1, 1, 1), (m, m, 1)] 6= x[(1, 1, 2), (m, m, 2)]. If so, M
enters an accepting state. It is clear that T (M ) = T2 .
(2) Suppose that there exists a F V 3-SU T M (L(m))
M accepting T2 , where L(m) = o(m). By using thetechnique of counting argument[3,4], we can get thedesired result.
¤Lemma 3.2. Let T3 = {x ∈ {0, 1}2m×2m×2m | m ≥ 1& x[(1, 1, 1), (2m, 2m, m)] 6= x[(1, 1, m + 1), (2m, 2m,2m)]}. Then,©ISAROB 2006

Nondeterminism versus synchronized alternation

This section investigates a relationship between theaccepting powers of three-dimensional synchronizedalternating machines and three-dimensional nondeterministic machines.
Let f : N → N be a function. The function f is said
to be three-dimensionally fully space constructible if
there is a 3-DT M which for any input tape x withl1 (x) = l2 (x) = l3 (x) = m (m ≥ 1) makes use ofexactly f (n) cells of the storage tape and halts.
Theorem 4.1. For any function L(m) ≥ log m,
L[3-SAT M (L(m))] = Uc≥0 L[3-N T M (m3 cL(m) )].
Proof: We first show that L[3-SAT M (L(m))] ⊆Uc>0 L[3-N T M (m3 cL(m) )]. Given a 3-SAT M (L(m))
M , we can construct a 3-N T M (m3 cL(m) ) M 0 to simulate M by doing a breadth-first-like traversal of thecomputation tree of M on input x of side-length m.
Each process of M is simulated until it enters an sstate; M 0 will compare the corresponding s-states tomake sure that no deadlock occurs before continuing
the simulation. Since there are at most m3 dL(m) distinct configurations of M on an input x of side-lengthm, M 0 needs at most m3 eL(m) space, for some constants d and e, at any time to maintain the current

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ID0 s of all processes of M on x. Then on any input
x of side-length m, M uses at most L(m) space iff M 0
uses at most m3 eL(m) space.
On the other hand, by using same idea describedin Lemma 3.4 in [1], we can show thatS
L[3-N
T M (m3 cL(m) )] ⊆ L[3-SAT M (L(m))]. ¤c≥0Theorem 4.2. For any integer k ≥ 1,
(1) L[3-SAF A[k]] = L[3-N F A(k-heads)], and(2) L[F V 3-SAF A[k]] = L[3-N F A(k-heads)]
Proof: We only prove (1). Given a 3-N F A(k -heads)
M where k ≥ 1, we can construct a 3-SAF A[k] M 0 .
Let H1 , H2 , · · · , Hk denote the input heads of M .
These heads are simulated by a single input head of
3-SAF A[k] M 0 in the following way. The computationof M 0 branches from the initial configuration in a universal manner into k processes. Note that, the initialconfiguration is the only universal configuration which
occurs in the computation. The states of M 0 (in allprocesses of M 0 ) store the simulated state of M . If thestored state is an accepting (rejecting) state, then thestate of M is also an accepting (rejecting) one. In theith process, for any i (1 ≤ i ≤ k), the input head of
M 0 is at the same position as Hi .
One step of M is simulated by two steps of M 0 . Besides the state of M the symbols scanned by all headsof M have to be known to M 0 . Every process in thecomputation of M 0 has only a part of the necessary
information. The processes can share this information
via the synchronization. The synchronizing element
consists of k components and represents the symbols
scanned by the input heads of M . The ith process,for 1 ≤ i ≤ k, sets the ith component according to
the symbol scanned by the input head of M 0 . The
other components are set nondeterministically. The
synchronization is successful only in the case when every process has correctly guessed the remaining components.
The next synchronization is necessary because of
nondeterminism. One configuration of M has several potential successors. All of the processes of M 0
must agree on the next step of M (they must simulate the same successor of the currently simulated
configuration). The synchronizing element represents
the new state of M and the actions of the heads of
M . The successful synchronization means that all processes choose the same element of the next move relation of M . After that, M 0 moves its heads and enters
a new state in accordance with the synchronizing element (i.e., in the ith process (for 1 ≤ i ≤ k), M 0 moves
by its input head like M by Hi ). It will be obviousthat M 0 can simulate M .
Conversely, given a 3-SAF A[k] M , we can construct a 3-N F A(k-heads) M 0 such that T (M ) =T (M 0 ). The proof is omitted here.
¤
We next investigate a relationship betwen the accepting powers of five-way nondeterministic machines
and five-way synchronized machines with only univer-©ISAROB 2006

sal states.
Theorem 4.3.
(1) L[F V 3-SU F A[2]] − L[F V 3-N T M (o(m3 )] 6= φ,(2) L[F V 3-N F A] − L[F V 3-SU T M (o(m2 ))] 6= φ, and(3) L[F V 3-N T M (log m)]−L[F V 3-SU T M (o(m3 )] 6= φ.
Proof: (1) Let T1 be the set described in Lemma 2.1.
By using the technique of counting argument, we canshow that T1 ∈/ L[F V 3-N T M (o(m3 ))]. (1) follows
from this fact and Lemma 2.1 (1).
(2) Let T2 be the set of described in Lemma 3.1.
It is easy to see that T2 ∈ L[F V 3-N F A]. (2) follows
from this fact and Lemma 3.1 (2).
(3) Let T3 be the set of described in Lemma 3.2.
It is easy to see that T3 ∈ L[F V 3-N T M (log m)]. (3)follows from this fact and Lemma 3.2 (2).
¤Corollary 4.1. For any function L(m) = o(m3 ),L[F V 3-SU T M (L(m))] and L[F V 3-N T M (L(m))] areincomparable.

5

Conclusions

In this section, we conclude this paper by giving
two open problems.
(1) For any function L(m) ≥ log m, L[3-AT M (L(m))] ( L[3-SAT M (L(m))] ? and L[F V 3-AT M (L(m))]( L[F V 3-SAT M (L(m))] ?
(2) For any integer k ≥ 1, L[3-SU F A[k]] ( L[3-SU
F A[k + 1]] ? and L[F V 3-SU F A[k]] ( L[F V 3-SU F A[
k + 1]] ?

References[1] C.M. Ibara and N.Q. Trân, “On space-boundedsynchronizing alternating Turing machines”, Theoretical Computer Science 99, pp243-264,1992.
[2] J. Hromkovič, “How to organize the commmunication among parallel proceses in alternating computations”, manuscript, 1986.
[3] J. Hromkovič, J. Karhumäki, B. Rován and A.
Slobodová, “On the power of synchronization inparallel computations”, Tech. Report, comenius
Univ., Bratislava, Czechoslovakia, Dept. of Theoretical Cybernetics and Institute of Computer Science, 1989.
[4] M.Sakamoto, I. Sakuramoto, K. Inoue and
I.Takanami, “A space lower-bound technique fortwo-dimensional alternating Turing machines”, IEICE Trans. D-I, J74-DI(4), pp.311–314, 1991.
[5] M. Sakamoto, K.Inoue, and I.Takanami, “A noteon three-dimensional alternating Turing machineswith space smaller than log m”, Information Sciences 72, pp.225–249, 1993.

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Leaf-Size Bounded Computation for Four-Dimensional Alternating Turing Machines
Kouichi Iihoshi1 , Makoto Sakamoto1 , Takao Ito1 , Makoto Saito1 ,Hiroshi Furutani1 , Michio Kono1 , and Katsushi Inoue2 ,1

Dept. of Computer Science and Systems Engineering, Miyazaki University, Miyazaki 889-2192, JAPAN

2

Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstract

alyzing computation of four-dimensional pattern processing to explicate the properties of four-dimensional
automata. From this viewpoint, we introduced somefour-dimensional automata[5,8].
In this paper, we continue the investigations aboutfour-dimensional alternating Turing machines[5], andmainly investigate fundamental properties of leafsize bounded four-dimensional alternating Turing machines which each sidelength of each input tape isequivalent. Leaf-size bounded computation was introduced as a simple, natural new complexity measure foralternating Turing machines[4]. Basically, the ‘leafsize’ (or ‘blanching’) is the minimum number of leavesof some accepting computation trees of processors thatrun 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:

Recently, due to the advances in computer animation, motion image processing, and so on, it isvery useful for analyzing computational complexity ofmulti-dimensional information processing to explicate
the properties of four-dimensional automata. From
this point of view, we first introduced four-dimensional
alternating Turing machines 4-ATM’s in [5]. In thispaper, we continue the investigations about 4-ATM’s,and maily investigate leaf-size bounded computationof 4-ATM’s. Basically, the ‘leaf-size’ is the minimum
number of leaves of some accepting computation treesof alternating Turing machines. Leaf-size, in a sense,
reflects the minimum number of processors that run
in parallel in accepting a given input.
KeyWords : alternation, configuration, four-dimensional input tape, space bound, Turing machine, leafsize.

1

Introduction and Preliminaries

Blum et al. first proposed two-dimensional automata, and investigated their pattern recognition
abilities in 1967 [1]. Since then, many researchers inthis 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 twodimensional alternating Turing machines as a generalization of two-dimensional nondeterministic Turingmachines and as a mechanism to model parallel computation [4]. Moreover, Sakamoto et al. presented
three-dimensional alternating Turing machines in [6].
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 an-©ISAROB 2006(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).
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.

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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 ) [5].
4-AT M M is defined by the 7-tuple

䊶䊶䊶 storage tape䊶䊶䊶䊶䊶䊶 storage-tape headthe 2nd axis

M 䊶䊶䊶 finite control

M = (Q, q0 , U ,F , Σ, Γ, δ), wherewest

the 3rd axis

(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 next-moverelation.
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 cellof the read-only input tape and to each cell of thestorage tape, as shown in Fig. 1. The step of M issimilar to that of a two- or three-dimensional Turingmachine, except that the input head of M can movein eight directions. We say that M accepts the tapex if it eventually enters an accepting state. Note thatthe machine cannot write the blank symbol. If the
input head falls off the input tape, or if the storage
head falls off the storage tape (by moving left), thenthe machine M can make no further move.
A seven-way four-dimensional alternating Turingmachine (SV 4-AT M ) is a 4-AT M whose input headcan move in seven directions – east, west, south,
north, up, down, or future, and an alternating version of a seven-way four-dimensional Turing machine(SV 4-T M ).
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 (or SV 4-AT M ) M we associate a space complexity function SP ACE that takes configurations tonatural 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 with l1 (x) = l2 (x) = l3 (x) = l4 (x) =m, if x is accepted by M , then there is an accepting
computation tree of M on input x such that for eachnode v of the tree, SP ACE(L(v)) ≤ dL(m)e1 . Weupfour-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.

denote an L(m) space-bounded 4-AT M (SV 4-AT M )by 4-AT M (L(m)) [SV 4-AT M (L(m))].
A 4-AT M (0) [SV 4-AT M (0)] is called a fourdimensional alternating finite automaton (seven-way
four-dimensional alternating finite automaton), which
can be considered as an alternating version of a fourdimensional finite automaton (4-F A) (seven-way fourdimensional finite automaton (SV 4-F A)), and is denoted by 4-AF A (SV 4-AF A).
In order to distinguish among determinism, nondeterminism, and alternation, we denote a deterministic 3-T M [nondeterministic four-dimensional Turing machine (4-T M ), deterministic seven-way fourdimensional Turing machine (SV 4-T M ), nondeterministic SV 4-T M , deterministic 4-T M (L(m)), nondeterministic 4-T M (L(m)), deterministic SV 4-T M(L(m)), nondeterministic SV 4-T M (L(m)), deterministic 4-F A, nondeterministic 4-F A, deterministic SV 4-F A, nondeterministic SV 4-F A] by 4-DT M[4-N T M , SV 4-DT M , SV 4-N T M , 4-DT M (L(m)),
4-N T M (L(m)), SV 4-DT M (L(m)), SV 4-N T M(L(m)), 4-DF A, 4-N F A, SV 4-DF A, SV 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 , 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 }. £[SV 4XT M ], £[4-XT M (L(m))], £[SV 4-XT M (L(m))],£[4-XF A], and £[SV 4-XF A] also have analogous
meanings.

2

Results

Definition 2.1. Let L(m): N → R be a function.
For each tree t, let LEAF (t) denote the leaf-size of

1 dre means the smallest integer greater than or equal to r.

©ISAROB 2006

䊶䊶䊶䊶䊶䊶 input headeast

the 1st axis

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The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

t (i.e., the number of leaves of t). We say that a 4AT M M is Z(m) leaf-size bounded if for all x withl1 (x)=l2 (x)=l3 (x)=m and for each computation tree
t of M on x, LEAF (t) ≤ dZ(m)e.
By 4-AT M (L(m),Z(m)) (SV 4-AT M (L(m),Z(m))), we denote a Z(m) leaf-size bounded 4-AT M (L(m))(SV 4-AT M (L(m))). Especially, a 4-AT M (0, Z(m))(SV 4-AT M (0, Z(m))) is denoted by 4-AF A (Z(m))(SV 4-AF A (Z(m))). Define £[4-AT M (L(m), Z(m))]= {T | T = T (m) for some 4-AT M (L(m), Z(m)) M }.
£[SV 4-AT M (L(m), Z(m))] is defined similarly.

l4 (x) =m] & (there exist exactly k 1’s both in Cubex (1)and in Cubex (2)) & (the symbol (in Cubex (2)) justbelow the r-th 1(counting from the first plane to themth plane in a cube, from the first column to the mthcolumn in a plane, and from the first row to the mth
row in a column) in Cubex (1) is 1)}. It is easily seenthat for each k ≥ 1 and for each r(1 ≤ r ≤ k), Tr [k]∈ £[SV 4-DF A ]. From this and the fact that T [k]= T1 [k] ∩ · · · ∩ Tk [k], it follows that part (1) of thelemma holds.
(2) Suppose that there is an SV 4-AT M (L(m),k)M (with L(m) = o(log m)) accepting T [k + 1]. By
using the technique of counting argument [3], we canget the desired result.
¤Definition 2.2. For any integer k ≥ 1 and for any L:
N → R, let £k [SV 4-N T M (L(m))] = {A1 ∩ · · · ∩ Ak
| each Ai ∈ £[SV 4-N T M (L(m))]}. £k [SV 4-N F A],
etc., are defined similarly.

From Lemma 2.2, we can get

It seems interesting to investigate the relationship between £[SV 4-AT M (L(m),k)] and £k [SV 4N T M (L(m))]. The main purpose of this section
is to show that £k [SV 4-N T M (L(m))] ( £[SV 4AT M (L(m),k)] for any integer k ≥ 2 and any L(m):N → R such that L(m) = o(log m).
From this result, we might say that for the space
smaller than logm, a parallel machine with cooperative
processors is more powerful than a mechanism with thesame number of processors which run independently.
We first show that £k [SV 4-N T M (L(m))] (k+1£[SV 4-N T M (L(m))] for any k ≥ 1 and any L
such that L(m) = o(log m).

Theorem 2.1. For any integer k ≥ 1 and for any
L(m): N → R such that L(m) = o(log m),(1)£k [SV 4-DT M (L(m))](£k+1 [SV 4-DT M (L(m))],and(2)£k [SV 4-N T M (L(m))](£k+1 [SV 4-N T M (L(m))].

Lemma 2.1. For any integer k ≥ 1 and for any
L(m): N → R, £[SV 4-N T M (L(m))] ⊆ £[SV 4AT M (L(m),k)].
Proof: For k = 1, the lemma is obvious. For any k ≥2, let A in £k [SV 4-N T M (L(m))] be equal to A1 ∩· · · ∩ Ak , where for each i(1 ≤ i ≤ k), Ai is acceptedby some SV 4-N T M (L(m)) Mi . We consider an SV 40
AT M (L(m),k) M which acts as follows. Given an
0
input x, M first universally branches to directly sim0
ulate the actions of M1 ,. . .,Mk−1 , and Mk on x. M
enters an accepting state if and only if Mi , 1 ≤ i ≤ k,0
enters an accepting state, It is obvious that T (M ) =A.
¤

Lemma 2.3. For each X∈{D,N }, £[SV 4-XT M(L(m))] is closed under finite intersection for any that
L such that L(m) ≥ log m (m ≥ 1).
Proof : We only show that £[SV 4-DT M (log m)] isclosed under intersection. Let M1 and M2 be any two
SV 4-DT M (log m)’s. We cosider the SV 4-DT M (logm) M which acts as follows. M divides the storage
tape into ten tracks, and uses tracks 1, 2, 3, 4 and 5 tosimulate M1 , and tracks 6, 7, 8, 9 and 10 to simulate
M2 . M first stores number 1 on track 4 and begins tosimulate the action of M1 in Cubex (1) of an input tapex (starting from x(1,1,1,1)), by using tracks 1,2 and 3.
When M1 leaves Cubex (1) of x from some position, M
stores the number i1 , i2 , i3 and 2 in a binary representation on tracks 1, 2, 3 and 4, respectively, and storesthe position of the storage-tape head at that time on
track 5. Then, M stores number 1 on track 9 andbegins to simulate the action of M2 in Cubex (1) of x
(starting from x(1,1,1,1)), by using tracks 6, 7 and 8.
When M2 leaves Cubex (1) of x from some position, M000stores the numbers i1 , i2 , i3 and 2 in a binary representation on tracks 6, 7, 8 and 9, respectively, and storesthe position of the storage-tape head at that time on

Corollary 2.1. For any integer k ≥ 1,
(1)£k [SV 4-DF A](£k+1 [SV 4-DF A], and(2)£k [SV 4-N F A](£k+1 [SV 4-N F A].
As shown in the next theorem, if L(m) ≥ logm, thena situation which differs from Theorem 5.1 emerges.
We first need the following lemma.

Lemma 2.2. For any integer k ≥ 1, let T [k] = {x ∈{0, 1}(4) | ∃m ≥ k[l1 (x) = l2 (x) = l3 (x) = l4 (x) = m]
& (there exist exactly k 1’s in Cubex (1)) & Cubex (1) =Cubex (2)}. Then,(1)T [k] ∈ £k [SV 4-DF A], and(2)T [k + 1] ∈£/ k [SV 4-N T M (L(m))] for any L: N
→ R such that L(m) = o(log m).
Proof: (1) For each k ≥ 1 and each r(1 ≤ r ≤ k), let
Tr [k] = {x ∈ {0, 1}(4) | ∃m ≥ k[l1 (x) = l2 (x) = l3 (x) =©ISAROB 2006

805

The Eleventh International Symposium on Artificial Life and Robotics 2006 (AROB 11th'06),
B-con Plaza, Beppu, Oita, Japan, January 23-25, 2006

0

track 10. M next begins to simulate the action of M1
in Cubex (2) of x, by using the information stored on
tracks 1,2,3,4 and 5. After that, M continues to simulate by turns the actions of M1 and M2 in each threedimensional rectangular array of x by using the above
techniques. Then M accepts x if and only if both M1and M2 arrive at the bottom boundary symbol andaccept x. It will be obvious that T (M ) = T (M1 ) ∩T (M2 ). By using the same ideas as those in the proof
above, we can easily see that £[SV 4-N T M (log m)] isclosed under intersection.
¤m + 1, M can store each element of H(i4 ) on the0storage tape. It will be easy to see that M can generate H(i4 +1) from H(i4 ) for each 1 ≤ i4 ≤ m.
0
M enters an accepting state if and only if it finds
out that for some i4 (1 ≤ i4 ≤ m+1), H(i4 ) contains atleast one element (i1 ,i2 ,i3 ,p) such that 0 ≤ i1 ≤ m + 1,
0 ≤ i2 ≤ m + 1, 0 ≤ i3 ≤ m + 1 and p is a storage
state whose state-component is an accepting state of0
M . It will be obvious that T (M ) = T (M ).
¤From Lemmas 2.1, 2.3 and 2.4, and by using thesame idea as in the proof of Lemma 5.5 in [7], we get
the following main theorem.

From Lemma 2.3, we can get

Thoerem 2.3. For any integer k ≥ 2 and for any L :N → R such that L(m) = o(log m),

Theorem 2.2. For any integer k≥1 and for function
L(m) ≥ log m (m ≥ 1),(1)£k [SV 4-DT M (L(m))] = £1 [SV 4-DT M (L(m))] =£[SV 4-DT M (L(m))], and(2)£k [SV 4-N T M (L(m))] = £1 [SV 4-N T M (L(m))] =£[SV 4-N T M (L(m))].

£k [SV 4-N T M (L(m))] ( £[SV 4-AT M (L(m),k)].
Corollary 2.2. For any integer k ≥ 2,
£k [SV 4-N F A] ( £[SV 4-AF A(k)].
As shown in the following results, if L(m) ≥ log
m, then a situation which differs from Theorem 2.3
emerges.

We next derive the main theorem of this section.
We need the following two lemmas.
S
Lemma 2.4. £[SV 4-N T M (L(m))] ⊆ c≥0 £[SV 4DT M (m3 L(m)cL(m) )] for any function L(m): N →R.
Proof: Let M be an SV 4-N T M (L(m)), and s and r
be the numbers of states (of the finite control) andstorage tape symbols of M , respectively. We con0
struct an SV 4-DT M (m3 L(m)cL(m) ) M accepting theset T (M ), where c is a positive constant satisfying
m3 L(m)cL(m) > (m + 2)3 sL(m)cL(m) (m ≥ 1). Suppose that an input tape x with l1 (x) = l2 (x) = l3 (x)0= l4 (x) =m (m ≥ 1) is presented to M . Let C be
the set of possible storage states of M on x. Clearly,| C | = sL(m)cL(m) . For each i4 (1 ≤ i4 ≤ i4 (x)+1= m + 1), let H(i4 ) be a subset of {0,1,. . .,m + 1}
× {0,1,. . .,m + 1} × {0,1,. . .,m + 1} × C defined asfollows :H(1) = {(1,1,1, p0 )}, where p0 is the initial storage
state of M (e.g., p0 is the storage state component of
the initial configuration of M on x).
H(i4 ), i4 ≥ 2, = {(i1 ,i2 ,i3 ,p) | IM (x) `∗M (x,((i1 ,i2 ,00
i3 ,i4 -1),p )) `M (x,((i1 ,i2 ,i3 ,i4 ),p)) for some p ∈ C,
i.e., there exists a computation path of M on x leading
to the configuration (x,((i1 ,i2 ,i3 ,i4 ),p)) just after the
input head of M has reached Cubex (i4 ) of x }.
0
M simulates the action of M on x by successively generating H(1),H(2),. . .,H(i4 ), . . ., on the stor0
age tape, in such a way that for each i4 ≥ 1, M
replaces H(i4 ) with H(i4 +1) on the storange tape.
Note that, since, by assumption, m3 L(m)cL(m) >(m + 2)3 sL(m)cL(m) ≥ | H(i4 ) | for each 1 ≤ i4 ≤©ISAROB 2006

Theorem 2.4. For any integer k ≥ 1 and for any L :N → R such that L(m) ≥ log m,£k [SV 4-N T M (L(m))] = £[SV 4-AT M (L(m),k)]Corollary 2.3. For any integer k ≥ 1,
£k [SV 4-N F A] = £[SV 4-AF A(k)].

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, MA, (1979).
[4] K. Inoue et al., Theoret. Comput. Sci. 27:61-83(1983).
[5] H. Okabe et al., Proc. 7th Words Multiconf. on
SCI :241-246 (2003).
[6] M. Sakamoto et al., Inform. Sci. 72:225-249(1993).
[7] M. Sakamoto, Ph.D. Thesis (1999).
[8] M. Sakamoto et al., WSEAS Trans. on Computers, Issue 5, Vol. 3 :1651-1656 (2004).

806

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Authors’ Index
〔A〕
Abe
Ahmad
AhraryAi
Aibe
Aihara

K.
K.
A.
D.
N.
K.

Aoki
Aranishi
Arita

T.
F.
T.

504,512,520
418
205
336
755
378,386,390,394,
398,402,406,410,
414
792
739,743
319,323

〔B〕
Bae
Baek
Baek
BalasuriyaBan
Bialowas
Bode
Bos
Bubnicki
Bykova

H.
S.
K-r.
J.C.
X.
J.
K.
J.
Z.
N.M.

142
113
146,150
675,679
336
731
667
661
727
707

〔C〕
Capi
Chang
Chao
Cho
Cho
Cho
Cho
Choi
Choi
Choi
Choi
Choi
Chou

G.
Y-Z.
G-C.
B.
S-b.
S.K.
Y.I.
B.S.
H.S.
K.C.
K-J.
W.S.
C-T.

645,657,667
209
759
150
146
154
107
159
159
165
225
580
759

〔D〕
Dai
Date
DavittDelgado-Frias
Demazeau
Derakhshan
Du

F.
A.
T.J.
J.
Y.
A.
J.

59,63,67,611
775
448
532
173
173
556

〔E〕
Eaton

M.

448

〔F〕
Finkelstein
Fuchigami
Fujihara

R.
K.
Y.

532
504
59,63,67,611

Fujii
Fujinaka
FujitaFujiwara
FukudaFunabashi
Furutani

H.
T.
H.
K.
T.
M.
H.

300
91
43
402
1
398
641,771,798,803

Fushimi

H.

767

〔G〕
Goubaru
Grzyb
Guo

E.
B.
W.

75
703,731
556

〔H〕
Ha
Habib
Haga
Haga
Hamagami
Hammer
Han
Han
Han
Hase
HashimHashimoto
Hashimoto
HashimotoHatanaka
HayashiHayashiHayashida
Hazry
Heo
HigashiHigo
Hikisaka
Hioki
HiranoHirata

S-k.
M.K.
Z.
Y.
T.
F.
C-s.
S.H.
S.S.
T.
M.F.
H.
S.
M
T.
E.
K.
J.
D.
J.S.
Y.
T.
Y.
C.
A.
Y.

146
120
516
568
528
173
197
588,594154
516
284,496
12,538272
623
440
229,244,296
755
611
241
103
747
374
296
421
313
406

Hirayama
HitakaHokari
Homma
Hong
Horng
Hoshi
Hung
Hwang
Hyakudome

J.
Y.
S.
N.
D.S.
S-J.
Y.
G-T.
Y-K.
T.

〔I〕
Ito
Ibrahim
IchidaIchikawa
Ido
Iihoshi
IimuraInaba
Inabayashi
Inoue
Ishibashi
Ishibuchi
Ishida
Ishii

M.
Z.
K.
S.
H.
K.
N.
N.
S.
K.
S.
H.
Y.
S.

Ishikawa
Ishikawa
Ishitobi
Isokawa
Ito
Ito
Ito
Ito
Itoh
IwaniakIwatsubo
Izumi

576
779
637
504,520
225
759
568
209
225
792

787
444,468,472,476
691
79
95,99
771,798,803
633
390
572
771,798,803
792
370,452,546
456,460
43,51,189,221,
576,619
M.
205
S.
272,432,488,492
M.
807
T.
542
M.
787,792N.
378,382
T.(Miyazaki) 771,798,803
T.(Ube)
641
H.
35
A.
735
K.
568
K.
671,675,679,683,
687,691〔J〕
Jang
Jayawardena
Jin
Joshi

I-H.
C.
S.
A.

169
683
116,344
711〔K〕
Kabata

K.

83

K H.
a
m
a
y
a
KamedaKamikokuryo
KamiuraKanazawa
Kanev
Kanoh
Karthigayan
Kasai
Kashiwamura
Katai
Katayama
Katoh
KatoriKatuhara
Kawakami
KawasakiKenmochi
Khomenko
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
Kim
KimuraKinjo
Kinouchi
Kita
Kitagaki
Kitamura
Kitano
Kito
Kobayashi

A.
H.
N.
M.
K.
M.
M.
N.
S.
O.
S.
Y.
Y.
M.
H.
N.
N.
A.P.
B.C.
C.S.
D-H.
D-W.
H.R.
H.S.
J-w.
J-W.
S.
S.H.
S.M.
S.Y.
W.I.
Y.
Y.B.
Y-I.
Y.K.
S.
H.
Y.
Y.
K.
K.
H.
N.
K.

512

629,633
763
542
260
292
35
276,280,284,496
476
629
305
641
623
410
374
305
59
747
707
599
425
55
560
594
580,584
146
584
142
599
425
425
599
142
187,213142
588,594292
126,130,134
572
629
524
542
370
432
183

Kobayashi
Kodani
Kogawa
Kohno
Kono

J.
T.
N.
T.
M.
T.
Y.
T.
K.
T.
Y.K.

217
63
183
414
771,779,783,798,
803,80787
520
699,735
138,775
183
599

Matsumoto
Mirenkov
Mitsuoka
Miyashita
MiyauchiMiyazaki
Mizoguchi
Mokhtar
Mori
Mukai
Murao
MuraseMurata

N.
N.
J.
S.
J.
K.
H.
N.
T.
S.
H.
Y.
S.

300
532
179
309,332
436
47
244
288
51
201
615
378,382309

KosakaKoyanaka
Kubik
KurataKuremoto
Kwon
〔L〕
Lai
Lee
Lee
Lee
Lee
Lee
Lee
Lee
Lee
Lee
Lee
Lee
Li
Li
Lim
Liu
Liu
Loukianov
Lund

H-C.
C.Y.
D.H.
Ju-JangJung-Ju
J.M.
J.W.
K.S.
M.C.
M.H.
S-H.
W.S.
Q.
J-T.
S-j.
G.L.
J-Q.
A.A.
H.H.

759
112
580
18,116,120,344
103
154,159,165
554
187,213,554
165
425
580,584
588
29
252
197
687
484
707
173

〔M〕
MackinMaeda
MajimaMakinae
MakinoMamat
Manjunath

K.J.
S.
T.
H.
Y.
M.R.
T.C.

Marasinghe
Maru
MasudaMasudaMatosiuk
MatsuiMatsuiMatsumotoMatsumoto

C.A.
N.
N.
K.
B.
N.
H.
Y.
K.

39,268,360
576
374
500
500
284,496695,711,715,719,
723
679
201
410
572
703
542
421,550
244
256

〔N〕
Na
Na
Nagarajan
NagataNagayoshi
Nakai
Nakamura
Nakamura
Nakamura
Nakamura
Nakanishi
Nakashima
Nakayama
Nakazono
Nassiraei
NasunoNawataNehashiNiimi
Ninomiya
NishimuraNishimura
Noda
NoharaNojimaNojimaNolle
NomuraNomuraNomuraNunohiro

U.Z.
U.J.
R.
F.
M.
M.
A.
K.
T.
Y.
H.
T.
K.
K.
A.
Y.
T.
T.
A.
T.
O.
H.
K.
B.T.
Yaichi
Yusuke
L.
S.
T.
Y.
E.

588,594603
276,280
516
615
807
524
550
67
51,221
134
356,370,546
550
126,130
205
792
352
47
464
193
421
542
432
568
305
452
356
305
264
421
268,360〔O〕
Obara
Obayashi
Ogawa
Ogino

T.
M.
Y.
N.

500
183
323
130

OguroOhama
OhkawaOhkuboOhnishiOhnishiOhshiroOhtakeOhuchiOka
Okada
OkazakiOkimotoOkumura
Omatu
Omoto
Ono
Ono
Oohashi
OshiroOswaldR.
Y.
F.
M.
Kouhei
Koji
M.
H.
A.
T.
M.
S.
T.
H.
S.
M.
M.
O.
M.
N.
M.

217
95
217
739
126
637
268,360
313
629,633
661
300
91
743
432
87,91,99516
91
444,468,472,476
460
134,138480〔P〕
Paluszynski
Pandian
Park
Park
Park
Park
PatekarPathiranage
PetresPoszumski

W.
P.
J.
S.Y.
C-H.
S-Y.
S.A.
C.D.
Z.
P.

735
280
142
344
560
116
711
671
538
731

〔R〕
Rajamony
Ramli
Resko
Rizon

B.
R.B.
B.
M.

Rozailan

M.

429
340
538
276,280,284,429,
496
280

〔S〕
Saad
Saad
Sagara
Saito
Saito
Sakai
Sakamoto
SapatyA.R.
P.
S.
MakotoMitsutoshi
M.
M.
P.

496
284,496
233,237
771,798,803
365
504,520
641,771,798,803
532

Sasaki
Sato
Sato
Sato
Sato
Sawaragi
Schaefer
Sekikawa
Seo
Serikawa
Shakaff
Shi
Shibata
Shibata
Shibuya
Shikauchi
ShimadaShimadaShimizu
Shimohara
Shimokawa
Shin
Shin
Shinohara
ShioseShirakata
Shoji
Sim
Son
SuehiroSugawara
Sugimine
Sugisaka

S.
M.
H.
A.
O.
T.
G.
M.
S.Y.
S.
A.Y.M.
Z.
K.
T.
T.
M.
T.
A.
E.
K.
H.
C-R.
H.B.
M.
T.
M.
K.
K-B.
T.H.
T.
Y.
N.
M.

SugitaSuh
SumiyaSuzukiSuzuki

K.
J.H.
T.
Y.
H.

444
751
779
783
783
653
356,546
390
580
365
496
29
508
189,619528
619
378,382649
787,792
484,550394
584,594
588
436
305
779
436
169,560
603
524
456
386
25,71,75,79,83,
241,248,288,418,
496,532,606,699
657
187,213,554
91
328
406

〔T〕
TachibanaTaira
TajimaTakadama
Takahashi
Takahashi
Takahashi
Takahashi
Takai
Takata

K.
Y.
A.
K.
M.
N
N.
T.
H.
H.

179,193237
687
47,374
39
783
807
95
179,193352

TakeiTakemoto
Takiguchi
Tamaki
Tamei
Taniguchi
Tazaki
Terai
Teranisi
TokitaTominaga
ToyamaTrivediTsuboiTsuchinaga
Tsuji
Tsuji
Tsukioka
TsurutaTu
Tuboi
Tymkow

K.
T.
N.
H.
T.
T.
E.
H.
M.
Y.
K.
F.
D.
Y.
M.
T.
S.
S.
N.
X.
Y.
P.

520
414
313
615
189
653
39
348
87
221
328
436
711
468,472,476
305
313
414
792
649
336
444
735

〔U〕
UchidaUemuraUmeno
Umeo
Umezaki
Uosaki

N.
H.
T.
H.
K.
K.

691
492
229
256,260
71
440

〔W〕
Wang
Wang
Watanabe
Watanabe

X.
J.
S.
K.

Whang

S-H.

25
25
217
516,671,675,679,
683,687,691
169〔Y〕
Yaacob
YamadaYamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamaguchi
Yamamori
Yamamoto
Yamamoto
Yamamoto
Yamamoto

S.
T.
D.
I.
T.
Yoshiki
Yoshiko
K.
H.
I.
M.
T.

276,280,284,496
564
39
488
444
755
767
747,751
340
792
629,633
130,134,138

TamuraTan
TanakaTang
Taniguchi
Yamamoto
Yamanaka
YamasakiYanagihara
Yang
Yang
Yang
Yano
Yasunaga
Yatoh
Yazid
Yeliseev
Yin
Yokomichi
YokotaYokotaYoon
YoshidaYoshihara
Yoshihara
Yoshikawa
Yoshimoto
Yoshitomi
Yu
Yu
Yukawa

M.
J.K.
F.
L.
R.
Y.
K.
K.
T.
C.Y.
H-C.
J-S.
M.
M.
T.
H.
S.V.
Y.
M.
M.
Y.
H-k.
H.
I.
M.
M.
J.
Y.
D.
S-n.
S.

233
272,432,488,492
633
606
649
755
305
268,360
260
252
560
252
500
751
223
284,496707
336
763,779
645,657,667
546
197
792
747,751,755
747
348
221
767
248
197
378,382〔Z〕
Zanlungo
Zhang
Zhang
Zhao

F.
Y.G.
P.
H.

319
606
787
248